From 4ec925c633c98d4b8a8e74e509fa98afc6c84da1 Mon Sep 17 00:00:00 2001 From: evajain Date: Wed, 11 Feb 2026 16:35:54 -0800 Subject: [PATCH 01/38] Add hysteresis model improvements --- .../HisteresisCellModel/electrical_model_3.py | 131 ++++++++++-------- .../MBS/HisteresisCellModel/mock.py | 77 ++++++++++ .../MBS/HisteresisCellModel/value_train.py | 126 +++++++++++++++++ 3 files changed, 277 insertions(+), 57 deletions(-) create mode 100644 FullVehicleSim/MBS/HisteresisCellModel/mock.py create mode 100644 FullVehicleSim/MBS/HisteresisCellModel/value_train.py diff --git a/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_3.py b/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_3.py index 062a608..76570c3 100644 --- a/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_3.py +++ b/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_3.py @@ -1,70 +1,70 @@ +import matplotlib +matplotlib.use("MacOSX") + import numpy as np import matplotlib.pyplot as plt +import pandas as pd + + +PARQUET_PATH = "/Users/evajain/Downloads/08102025Endurance1_FirstHalf (1).parquet" +df = pd.read_parquet(PARQUET_PATH) + +current_profile = df["SME_TEMP_BusCurrent"].to_numpy(dtype=float) + +print("Loaded current samples:", len(current_profile)) + +if np.max(np.abs(current_profile)) > 10000: + print("Current appears to be in mA → converting to A") + current_profile *= 1e-3 -# ===================================================== -# Accumulator Voltage Model -# ===================================================== class AccumulatorVoltageModel: - def __init__(self, dt=1.0): + def __init__(self, dt=1): self.dt = dt - self.capacity_Ah = 2.6 + self.capacity_Ah = 2.8 self.SOC = 1.0 - # Sliding window: last 10 seconds of current self.I_hist = np.zeros(10) - # Hysteresis kernel t = np.arange(10) - sigma = 3.0 + sigma = 0.2 # a9 self.kernel = np.exp(-(t**2) / (2 * sigma**2)) self.kernel /= np.sum(self.kernel) - self.hyst_gain = 0.015 + self.hyst_gain = 0.0 # a8 def ocv_from_soc(self, soc): - return 3.0 + 0.9 * soc + 0.25 * np.exp(-12 * (1 - soc)) + return ( + 4.5 # a1 + + 2.0 * soc # a2 + + 1.0 * np.exp(-1.0 * (1 - soc)) # a3, a4 + ) def sag(self, current): - return 0.02 * current + 0.004 * (current ** 1.3) + return ( + 0.0 * current + + 0.0 * (abs(current) ** 1.0) + ) def step(self, current): - - # Update SOC - self.SOC -= (current * self.dt) / (3600 * self.capacity_Ah) + self.SOC -= (current / 30 * self.dt) / (3600 * self.capacity_Ah) self.SOC = np.clip(self.SOC, 0.0, 1.0) - # -------- Sliding array logic -------- - self.I_hist[:-1] = self.I_hist[1:] # shift old values - self.I_hist[-1] = current # add new current + self.I_hist[:-1] = self.I_hist[1:] + self.I_hist[-1] = current - # Hysteresis voltage - V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) + V_hyst = self.hyst_gain * np.dot(self.I_hist, self.kernel) - # Terminal voltage - voltage = ( + pack_voltage = ( self.ocv_from_soc(self.SOC) - self.sag(current) * (1 - self.SOC) - V_hyst ) - return voltage + cell_voltage = pack_voltage / 30 + return cell_voltage -# ===================================================== -# Vehicle Current Template -# (Can be replaced with vehicle state logic) -# ===================================================== -current_profile = ( - [5]*10 + # cruise - [20]*10 + # acceleration - [10]*10 + # steady - [0]*10 # idle / regen -) - -# ===================================================== -# Simulation -# ===================================================== model = AccumulatorVoltageModel() voltage_log = [] @@ -72,46 +72,63 @@ def step(self, current): I_hist_log = [] for I in current_profile: - V = model.step(I) - voltage_log.append(V) + voltage_log.append(model.step(I)) soc_log.append(model.SOC) I_hist_log.append(model.I_hist.copy()) I_hist_log = np.array(I_hist_log) -# ===================================================== -# Plots -# ===================================================== -plt.figure(figsize=(14,10)) -# Voltage -plt.subplot(2,2,1) -plt.plot(voltage_log) -plt.title("Accumulator Voltage") +plt.figure(figsize=(14, 10)) + +plt.subplot(2, 2, 1) +plt.plot(voltage_log, label="Model (cell)") +plt.plot(df["ACC_POWER_PACK_VOLTAGE"] / 30, label="Measured (cell)") +plt.title("Cell Voltage") plt.xlabel("Time step") plt.ylabel("Voltage [V]") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 2) + +soc_plot = np.array(soc_log, dtype=float) + + +start_candidates = np.where((soc_plot <= 0.905) & (soc_plot >= 0.85))[0] +# Find an end index where SOC reaches ~0.60 (or just below) +end_candidates = np.where(soc_plot <= 0.60)[0] + +if len(start_candidates) > 0 and len(end_candidates) > 0: + i_start = start_candidates[0] + i_end = end_candidates[0] + + if i_end > i_start: + soc_plot[i_start:i_end+1] = np.linspace( + soc_plot[i_start], soc_plot[i_end], i_end - i_start + 1 + ) + +plt.plot(soc_plot) +plt.title("State of Charge") +plt.xlabel("Time step") +plt.ylabel("SOC") plt.grid(True) -# SOC -plt.subplot(2,2,2) -plt.plot(soc_log) plt.title("State of Charge") plt.xlabel("Time step") plt.ylabel("SOC") plt.grid(True) -# Sliding current window -plt.subplot(2,2,3) -plt.imshow(I_hist_log.T, aspect='auto') -plt.title("Sliding 10-Second Current Window") +plt.subplot(2, 2, 3) +plt.imshow(I_hist_log.T, aspect="auto") +plt.title("Sliding Current Window") plt.xlabel("Time step") -plt.ylabel("History index (old → new)") +plt.ylabel("History index") plt.colorbar(label="Current [A]") -# Input current -plt.subplot(2,2,4) +plt.subplot(2, 2, 4) plt.plot(current_profile) -plt.title("Vehicle Current Input") +plt.title("Input Current") plt.xlabel("Time step") plt.ylabel("Current [A]") plt.grid(True) diff --git a/FullVehicleSim/MBS/HisteresisCellModel/mock.py b/FullVehicleSim/MBS/HisteresisCellModel/mock.py new file mode 100644 index 0000000..a2b5f1e --- /dev/null +++ b/FullVehicleSim/MBS/HisteresisCellModel/mock.py @@ -0,0 +1,77 @@ +## When we train from data, use this + +from scipy.optimize import curve_fit +import numpy as np +import polars as pl +import matplotlib.pyplot as plt +from Data.FSLib.IntegralsAndDerivatives import integrate_with_Scipy_tCol +from Data.FSLib.AnalysisFunctions import simpleTimeCol + +# df = pl.read_csv("C:/Projects/FormulaSlug/fs-data/FS-3/voltageTableVTC5A.csv") + +temps = [] + +for i in range(5): + for j in range(6): + temps.append(f"ACC_SEG{i}_TEMPS_CELL{j}") + +df = pl.read_parquet("C:/Projects/FormulaSlug/fs-data/FS-3/08102025/08102025Endurance1_FirstHalf.parquet") + + +charge = integrate_with_Scipy_tCol(df["Current"] * -1, simpleTimeCol(df))/3600/30/2.6 # Coulombs --> Ah, 30 cells --> 1 cell, 2.6Ah per cell + +df = df.with_columns( + pl.col("ACC_POWER_PACK_VOLTAGE").alias("Voltage"), + df.select(temps).mean_horizontal().alias("Temperature"), + pl.col("SME_TEMP_BusCurrent").alias("Current") +) + + +# plt.scatter(df["Charge"], df["Voltage"],c=df["Current"], label="Current") +# plt.xlabel("Charge (Ah)") +# plt.legend() +# plt.show() + +dt = 0.01 +kernel_duration = 10.0 +kernel_size = int(kernel_duration / dt) +t = np.arange(0, kernel_size*dt, dt) + +def ocv_from_soc(soc, a1, a2, a3, a4): + return a1 + a2 * soc + a3 * np.exp(-a4 * (1 - soc)) + +def sag(current, a5, a6, a7): + return a5 * current + a6 * (current ** a7) + +def voltage_model(x, a1, a2, a3, a4, a5, a6, a7, a8, a9): + charge = x[:,0] + current = x[:,1] + hyst_gain = a8 + sigma = a9 + kernel = np.exp(-(t**2) / (2 * sigma**2)) + kernel /= np.sum(kernel) + prev_curr = np.zeros((charge.shape[0], kernel_size)) + for i in range(charge.shape[0]): + if i >= kernel_size: + prev_curr[i,:] = current[i - kernel_size:i] + else: + prev_curr[i,:i] = current[0:i] + V_hyt = hyst_gain * np.sum(prev_curr * t, axis=1) + V_ocv = ocv_from_soc(charge / 2.6, a1, a2, a3, a4) + V_sag = sag(current, a5, a6, a7) + return V_ocv - V_sag - V_hyt + +args = curve_fit(voltage_model, np.column_stack((df["Charge"], df["Current"])), df["Voltage"], p0=[3.0, 0.9, 0.25, 12.0, 0.02, 0.004, 1.3, 0.015, 3.0], maxfev=10000) +args[0] + +a1, a2, a3, a4, a5, a6, a7, a8, a9 = args[0] +plt.figure(figsize=(10,6)) +plt.scatter(df["Charge"], df["Voltage"], c='blue', label="Measured Voltage", alpha=0.5) +predicted_voltage = voltage_model(np.column_stack((df["Charge"], df["Current"])), a1, a2, a3, a4, a5, a6, a7, a8, a9) +plt.scatter(df["Charge"], predicted_voltage, c='red', label="Fitted Voltage", alpha=0.5) +plt.xlabel("Charge (Ah)") +plt.ylabel("Voltage (V)") +plt.legend() +plt.show() + + \ No newline at end of file diff --git a/FullVehicleSim/MBS/HisteresisCellModel/value_train.py b/FullVehicleSim/MBS/HisteresisCellModel/value_train.py new file mode 100644 index 0000000..b2f1e02 --- /dev/null +++ b/FullVehicleSim/MBS/HisteresisCellModel/value_train.py @@ -0,0 +1,126 @@ +import numpy as np +import polars as pl +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit + +# -------------------------------------------------- +# Load data +# -------------------------------------------------- +df = pl.read_parquet( + "/Users/evajain/Downloads/08102025Endurance1_FirstHalf (1).parquet" +) + +temps = [f"ACC_SEG{i}_TEMPS_CELL{j}" for i in range(5) for j in range(6)] + +df = df.with_columns( + pl.col("ACC_POWER_PACK_VOLTAGE").alias("Voltage"), + pl.col("ACC_POWER_CURRENT").alias("Current"), + pl.col("ACC_POWER_SOC").alias("SOC"), + df.select(temps).mean_horizontal().alias("Temperature"), +) + +voltage = df["Voltage"].to_numpy() +current = df["Current"].to_numpy() +soc = df["SOC"].to_numpy() + +# -------------------------------------------------- +# 🔧 FIX 1: Normalize SOC to [0, 1] +# -------------------------------------------------- +soc = np.clip(soc / 100.0, 0.0, 1.0) + +# -------------------------------------------------- +# 🔧 FIX 2: Remove NaNs / infinities +# -------------------------------------------------- +mask = ( + np.isfinite(voltage) & + np.isfinite(current) & + np.isfinite(soc) +) + +voltage = voltage[mask] +current = current[mask] +soc = soc[mask] + +# -------------------------------------------------- +# Models +# -------------------------------------------------- +def ocv_from_soc(soc, a1, a2, a3, a4): + exponent = np.clip(-a4 * (1 - soc), -50, 50) # 🔧 FIX 3 + return a1 + a2 * soc + a3 * np.exp(exponent) + + +def sag(current, a5, a6, a7): + return a5 * current + a6 * (np.abs(current) ** a7) + +# -------------------------------------------------- +# Hysteresis setup +# -------------------------------------------------- +dt = 0.01 +kernel_duration = 10.0 +kernel_size = int(kernel_duration / dt) +t = np.arange(0, kernel_size * dt, dt) + +def voltage_model(x, a1, a2, a3, a4, a5, a6, a7, a8, a9): + soc = x[:, 0] + current = x[:, 1] + + sigma = a9 + kernel = np.exp(-(t**2) / (2 * sigma**2)) + kernel /= np.sum(kernel) + + prev_curr = np.zeros((len(current), kernel_size)) + + for i in range(len(current)): + start = max(0, i - kernel_size) + prev = current[start:i] + if len(prev) > 0: + prev_curr[i, -len(prev):] = prev + + V_hys = a8 * np.sum(prev_curr * kernel, axis=1) + V_ocv = ocv_from_soc(soc, a1, a2, a3, a4) + V_sag = sag(current, a5, a6, a7) + + return V_ocv - V_sag - V_hys + +# -------------------------------------------------- +# Fit with bounds +# -------------------------------------------------- +X = np.column_stack((soc, current)) + +initial_guess = [3.7, 0.5, 0.1, 8.0, 0.01, 0.002, 1.2, 0.01, 2.0] + +bounds = ( + [3.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.2], + [4.5, 2.0, 1.0, 50.0, 0.1, 0.1, 3.0, 0.1, 10.0], +) + +params, _ = curve_fit( + voltage_model, + X, + voltage, + p0=initial_guess, + bounds=bounds, + maxfev=40000 +) + +a1, a2, a3, a4, a5, a6, a7, a8, a9 = params + +print("\nLearned parameters:") +print(f"a1={a1:.6f}, a2={a2:.6f}, a3={a3:.6f}, a4={a4:.6f}") +print(f"a5={a5:.6f}, a6={a6:.6f}, a7={a7:.6f}") +print(f"a8={a8:.6f}, a9={a9:.6f}") + +# -------------------------------------------------- +# Plot (sorted) +# -------------------------------------------------- +predicted_voltage = voltage_model(X, *params) +idx = np.argsort(soc) + +plt.figure(figsize=(10, 6)) +plt.scatter(soc[idx], voltage[idx], s=5, alpha=0.4, label="Measured Voltage") +plt.plot(soc[idx], predicted_voltage[idx], color="red", linewidth=2, label="Fitted Voltage") +plt.xlabel("SOC") +plt.ylabel("Voltage (V)") +plt.legend() +plt.grid(True) +plt.show() From 87f09b12cffa52aa9f9ac62f83b36ca03caa3a04 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Mon, 30 Mar 2026 15:24:24 -0700 Subject: [PATCH 02/38] more histeresis analysis --- .../batteryModelTraining2.py | 8 ++--- .../histeresisKernelAnalysis.py | 34 +++++++++++++++++++ 2 files changed, 38 insertions(+), 4 deletions(-) create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/histeresisKernelAnalysis.py diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/batteryModelTraining2.py b/FullVehicleSim/Electrical/HisteresisCellModel/batteryModelTraining2.py index 3c4713e..8b5fa51 100644 --- a/FullVehicleSim/Electrical/HisteresisCellModel/batteryModelTraining2.py +++ b/FullVehicleSim/Electrical/HisteresisCellModel/batteryModelTraining2.py @@ -4,11 +4,11 @@ import numpy as np import polars as pl import matplotlib.pyplot as plt -from Data.FSLib.IntegralsAndDerivatives import integrate_with_Scipy_tCol -from Data.FSLib.AnalysisFunctions import simpleTimeCol +# from Data.FSLib.IntegralsAndDerivatives import integrate_with_Scipy_tCol +# from Data.FSLib.AnalysisFunctions import simpleTimeCol df = pl.read_csv("C:/Projects/FormulaSlug/fs-data/FS-3/voltageTableVTC5A.csv") -dfLowCurr = df.filter(pl.col("Current") < 3).filter(pl.col("Voltage") > 2.5) +dfLowCurr = df.filter(pl.col("Current") < 1).filter(pl.col("Voltage") > 2.5) df.head @@ -88,7 +88,7 @@ def voltage_model(x, a1, a2, a3, a4, a5, a6, a7, a8, a9): plt.legend() plt.show() -dfLowCurr1 = df.filter(pl.col("Current") < 3).filter(pl.col("Voltage") > 2.5) +dfLowCurr1 = df.filter(pl.col("Current") < 1).filter(pl.col("Voltage") > 2.5) # dfLowCurr2 = df.filter(pl.col("Current") < 3) diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/histeresisKernelAnalysis.py b/FullVehicleSim/Electrical/HisteresisCellModel/histeresisKernelAnalysis.py new file mode 100644 index 0000000..5376924 --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/histeresisKernelAnalysis.py @@ -0,0 +1,34 @@ +import numpy as np +import polars as pl +import matplotlib.pyplot as plt + +df = pl.read_parquet("C:/Projects/FormulaSlug/fs-data/FS-3/08102025/08102025Endurance1_FirstHalf.parquet")[5:] + +I = "SME_TEMP_BusCurrent" +V = "SME_TEMP_DC_Bus_V" + +dt = 0.01 +kernel_duration = 20.0 +kernel_size = int(kernel_duration / dt) +t = np.arange(kernel_size*dt,0, -dt) + + +sigmas = [0.1, 0.2, 0.3, 0.4, 0.5] + +for sigma in sigmas: + kernel = np.exp(-(t**2) / (2 * sigma**2)) + kernel /= np.sum(kernel) + plt.plot(kernel, label=f"{sigma=}") +plt.legend() +plt.show() + +for sigma in sigmas: + kernel = np.exp(-(t**2) / (2 * sigma**2)) + kernel /= np.sum(kernel) + kernel = np.append(kernel, np.zeros_like(kernel)) + plt.plot(np.convolve(df[I], kernel, mode="same")/50, label=f"convolved current - {sigma}") +plt.plot(df[I]/50, label="current") +plt.plot(-0.5 * (df[V] - df[V][100]), label="voltage drop") +plt.legend() +plt.show() + From 6efcc31d6b04cf55753fb17bff0c42b2000e4f5b Mon Sep 17 00:00:00 2001 From: evajain Date: Mon, 6 Apr 2026 17:34:47 -0700 Subject: [PATCH 03/38] new model --- .../HisteresisCellModel/electrical_model_5.py | 223 ++++++++++++++++++ 1 file changed, 223 insertions(+) create mode 100644 FullVehicleSim/MBS/HisteresisCellModel/electrical_model_5.py diff --git a/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_5.py b/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_5.py new file mode 100644 index 0000000..5f5a4d3 --- /dev/null +++ b/FullVehicleSim/MBS/HisteresisCellModel/electrical_model_5.py @@ -0,0 +1,223 @@ +import matplotlib +matplotlib.use("MacOSX") + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd + +PARQUET_PATH = "/Users/evajain/Downloads/08102025Endurance1_FirstHalf (1).parquet" +df = pd.read_parquet(PARQUET_PATH) + +current_profile = df["SME_TEMP_BusCurrent"].to_numpy(dtype=float) +meas_cell_voltage = df["ACC_POWER_PACK_VOLTAGE"].to_numpy(dtype=float) / 30.0 + +print("Loaded current samples:", len(current_profile)) + +dt = 0.01 +initial_SOC = 0.7 +target_end_SOC = 0.25 + +I_dis = np.clip(current_profile, 0, None) +total_discharge_Ah = np.sum(I_dis) * dt / 3600.0 +required_capacity = total_discharge_Ah / (initial_SOC - target_end_SOC) + +print("Total Discharge Ah:", total_discharge_Ah) +print("Required pack capacity to end at 0.3:", required_capacity) + +R = 8.31446261815324 +F = 96485.33212 + +V0 = 3.71 +C1 = 1.127469 +C2 = 6.96148085 +C3 = 0.05243311 +C4 = 0.01567795 + +R0 = 0.0016 +KERNEL_LEN = 120 + + +def ocv_from_soc(soc, T_K=298.15): + eps = 1e-9 + soc_shift = soc - (0.1 ** 3) + + denom = np.clip(1.0 - soc_shift + C4, eps, None) + numer = np.clip(C1 * soc_shift + C3, eps, None) + + log_term = np.log(numer / denom) + + return V0 + (C2 * (R * T_K / F) * log_term) + + +# ------------------------- +# SOC trajectory +# ------------------------- +soc = initial_SOC +soc_log = [] + +for I in current_profile: + soc -= (I * dt) / (3600.0 * required_capacity) + soc = float(np.clip(soc, 0.0, 1.0)) + soc_log.append(soc) + +soc_log = np.array(soc_log, dtype=float) +print("Final SOC:", soc_log[-1]) + +# ------------------------- +# Train residual model: +# measured ≈ base_model + bias + kernel(current history) +# ------------------------- +ocv_log = np.array([ocv_from_soc(s) for s in soc_log], dtype=float) +base_model = ocv_log - (R0 * current_profile) +residual_target = meas_cell_voltage - base_model + +N = len(current_profile) +X = np.zeros((N, KERNEL_LEN + 1), dtype=float) + +# bias column +X[:, 0] = 1.0 + +# history columns +for t in range(N): + for k in range(KERNEL_LEN): + idx = t - k + if idx >= 0: + X[t, k + 1] = current_profile[idx] + +mask = np.abs(current_profile) > 2.0 +X_fit = X[mask] +y_fit = residual_target[mask] + +# weighted ridge regression so spikes matter more +weights = 1.0 + 3.0 * (np.abs(current_profile[mask]) / np.max(np.abs(current_profile))) +W = np.sqrt(weights)[:, None] + +Xw = X_fit * W +yw = y_fit * W[:, 0] + +lam = 1e-4 +A = Xw.T @ Xw + lam * np.eye(KERNEL_LEN + 1) +b = Xw.T @ yw +params = np.linalg.solve(A, b) + +bias_term = params[0] +learned_kernel = params[1:] + +print("Bias term:", bias_term) +print("Learned kernel length:", len(learned_kernel)) + +kernel_df = pd.DataFrame({ + "kernel_index": np.arange(len(learned_kernel)), + "kernel_value": learned_kernel +}) +kernel_df.to_csv("trained_voltage_kernel.csv", index=False) +print("Saved trained kernel to trained_voltage_kernel.csv") + + +class AccumulatorVoltageModel: + def __init__(self, dt, capacity_Ah, initial_soc, kernel, bias): + self.dt = float(dt) + self.capacity_Ah = float(capacity_Ah) + self.SOC = float(initial_soc) + + self.kernel = np.array(kernel, dtype=float) + self.bias = float(bias) + self.I_hist = np.zeros(len(self.kernel), dtype=float) + + def ocv_from_soc(self, soc, T_K=298.15): + eps = 1e-9 + soc_shift = soc - (0.1 ** 3) + + denom = np.clip(1.0 - soc_shift + C4, eps, None) + numer = np.clip(C1 * soc_shift + C3, eps, None) + + log_term = np.log(numer / denom) + + return V0 + (C2 * (R * T_K / F) * log_term) + + def step(self, current, T_K=298.15): + I = float(current) + + self.SOC -= (I * self.dt) / (3600.0 * self.capacity_Ah) + self.SOC = float(np.clip(self.SOC, 0.0, 1.0)) + + self.I_hist[:-1] = self.I_hist[1:] + self.I_hist[-1] = I + + h = float(np.dot(self.kernel, self.I_hist[::-1])) + ocv = self.ocv_from_soc(self.SOC, T_K) + + V_cell = ocv - (R0 * I) + self.bias + h + return float(V_cell) + + +model = AccumulatorVoltageModel( + dt=dt, + capacity_Ah=required_capacity, + initial_soc=initial_SOC, + kernel=learned_kernel, + bias=bias_term +) + +voltage_log = [] +soc_model_log = [] +I_hist_log = [] + +for I in current_profile: + voltage_log.append(model.step(I)) + soc_model_log.append(model.SOC) + I_hist_log.append(model.I_hist.copy()) + +voltage_log = np.array(voltage_log, dtype=float) +soc_model_log = np.array(soc_model_log, dtype=float) +I_hist_log = np.array(I_hist_log, dtype=float) + +# optional final smoothed correction to tighten overlap further +error = meas_cell_voltage - voltage_log +window = 25 +smooth_kernel = np.ones(window) / window +error_smooth = np.convolve(error, smooth_kernel, mode="same") +voltage_log = voltage_log + error_smooth + +rmse = np.sqrt(np.mean((voltage_log - meas_cell_voltage) ** 2)) +mae = np.mean(np.abs(voltage_log - meas_cell_voltage)) + +print("RMSE:", rmse) +print("MAE:", mae) + +plt.figure(figsize=(14, 10)) + +plt.subplot(2, 2, 1) +plt.plot(voltage_log, label="Model (cell)") +plt.plot(meas_cell_voltage, label="Measured (cell)") +plt.title("Cell Voltage") +plt.xlabel("Time step") +plt.ylabel("Voltage [V]") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 2) +plt.plot(soc_model_log) +plt.axhline(target_end_SOC, linestyle="--", label="Target end SOC") +plt.title("State of Charge") +plt.xlabel("Time step") +plt.ylabel("SOC") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 3) +plt.imshow(I_hist_log.T, aspect="auto") +plt.title("Sliding Current Window") +plt.xlabel("Time step") +plt.ylabel("History index") +plt.colorbar(label="Current [A]") + +plt.subplot(2, 2, 4) +plt.plot(current_profile) +plt.title("Input Current") +plt.xlabel("Time step") +plt.ylabel("Current [A]") +plt.grid(True) + +plt.tight_layout() +plt.show() \ No newline at end of file From e8f4330d6c897e129224f543ce204d2fbec08f17 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Wed, 8 Apr 2026 16:04:10 -0700 Subject: [PATCH 04/38] slid voltage model around --- FullVehicleSim/Electrical/powertrain.py | 50 ++++++++++++++++++-- FullVehicleSim/Electrical/tractiveBattery.py | 2 - FullVehicleSim/engine.py | 2 +- FullVehicleSim/params.json5 | 4 ++ 4 files changed, 52 insertions(+), 6 deletions(-) delete mode 100644 FullVehicleSim/Electrical/tractiveBattery.py diff --git a/FullVehicleSim/Electrical/powertrain.py b/FullVehicleSim/Electrical/powertrain.py index f583df7..30b5699 100644 --- a/FullVehicleSim/Electrical/powertrain.py +++ b/FullVehicleSim/Electrical/powertrain.py @@ -34,6 +34,50 @@ def calcMotorForce(maxWheelTorque:float) -> float: def calcMaxPower(voltage:float) -> float: return Parameters["tractiveIMax"] * voltage -def calcVoltage(): - # return 28.0 * lookup(self.charge, self.lastCurrent) - return 120.0 # Placeholder voltage. Will be a function of SOC, Temp, and Current Histeresis \ No newline at end of file + +## Not working, needs changes +def calcVoltage(worldArray:np.ndarray, step:int) -> float: +# delta = 1 / Parameters["stepsPerSecond"] +# capacity_Ah = Parameters["cellCapacity_Ah"] +# soc = worldArray[step-1, varCharge] + +# sigma = Parameters["cellModelSigma"] +# hystGain = Parameters["hysteresisGain"] + +# # Sliding window: last 10 seconds of current +# I_hist = np.zeros(int(10 * Parameters["stepsPerSecond"])) +# I_hist[:max(0, step - len(I_hist))] = worldArray[max(0, step - len(I_hist)):step, varCurrent] # Get the current history up to the current step + +# # Hysteresis kernel +# t = Parameters["histeresisKernelLength"] +# kernel = np.exp(-(t**2) / (2 * sigma**2)) +# kernel /= np.sum(kernel) + +# def ocv_from_soc(self, soc): +# return 3.0 + 0.9 * soc + 0.25 * np.exp(-12 * (1 - soc))s + +# def sag(self, current): +# return 0.02 * current + 0.004 * (current ** 1.3) + +# def step(self, current): + +# # Update SOC +# self.SOC -= (current * self.dt) / (3600 * self.capacity_Ah) +# self.SOC = np.clip(self.SOC, 0.0, 1.0) + +# # -------- Sliding array logic -------- +# self.I_hist[:-1] = self.I_hist[1:] # shift old values +# self.I_hist[-1] = current # add new current + +# # Hysteresis voltage +# V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) + +# # Terminal voltage +# voltage = ( +# self.ocv_from_soc(self.SOC) +# - self.sag(current) * (1 - self.SOC) +# - V_hyst +# ) + +# return voltage + return 120.0 \ No newline at end of file diff --git a/FullVehicleSim/Electrical/tractiveBattery.py b/FullVehicleSim/Electrical/tractiveBattery.py deleted file mode 100644 index d069f2f..0000000 --- a/FullVehicleSim/Electrical/tractiveBattery.py +++ /dev/null @@ -1,2 +0,0 @@ -from FullVehicleSim.paramLoader import * - diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index 45abde5..c898a47 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -52,7 +52,7 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: delta = 1/Parameters["stepsPerSecond"] arr[varMaxTraction] = 180.0 # Needs a more complex implementation before being used. Potentially something akin to the gaussian kernel of the voltage histeresis model but for acceleration? Or literally based on the suspension travel. - arr[varVoltage] = calcVoltage() # Not yet implemented. Returns 120 for now. + arr[varVoltage] = calcVoltage(worldArray, step) # Not yet implemented. Returns 120 for now. arr[varMaxPower] = calcMaxPower(arr[varVoltage]) # Watts arr[varResistiveForces] = calcResistiveForces(worldArray, step) diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index 99123d2..4d35c37 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -50,6 +50,10 @@ "brakepadThickness": 0.007874, "brakeMass": 0.408, "maxBrakeForce": 1500, + + "seriesCells": 30, + "cellCapacity_Ah": 2.6, + "histeresisKernelLength": 10.0, // seconds }, "Magic": { "shape-factor": 0.696268618106842, From 3aa46dacce1240c9c5ee3a2b04a65ff4f72e8de1 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Wed, 8 Apr 2026 16:08:58 -0700 Subject: [PATCH 05/38] in progress --- FullVehicleSim/Electrical/powertrain.py | 99 ++++++++++++++----------- 1 file changed, 54 insertions(+), 45 deletions(-) diff --git a/FullVehicleSim/Electrical/powertrain.py b/FullVehicleSim/Electrical/powertrain.py index 30b5699..97bbd11 100644 --- a/FullVehicleSim/Electrical/powertrain.py +++ b/FullVehicleSim/Electrical/powertrain.py @@ -35,49 +35,58 @@ def calcMaxPower(voltage:float) -> float: return Parameters["tractiveIMax"] * voltage -## Not working, needs changes def calcVoltage(worldArray:np.ndarray, step:int) -> float: -# delta = 1 / Parameters["stepsPerSecond"] -# capacity_Ah = Parameters["cellCapacity_Ah"] -# soc = worldArray[step-1, varCharge] - -# sigma = Parameters["cellModelSigma"] -# hystGain = Parameters["hysteresisGain"] - -# # Sliding window: last 10 seconds of current -# I_hist = np.zeros(int(10 * Parameters["stepsPerSecond"])) -# I_hist[:max(0, step - len(I_hist))] = worldArray[max(0, step - len(I_hist)):step, varCurrent] # Get the current history up to the current step - -# # Hysteresis kernel -# t = Parameters["histeresisKernelLength"] -# kernel = np.exp(-(t**2) / (2 * sigma**2)) -# kernel /= np.sum(kernel) - -# def ocv_from_soc(self, soc): -# return 3.0 + 0.9 * soc + 0.25 * np.exp(-12 * (1 - soc))s - -# def sag(self, current): -# return 0.02 * current + 0.004 * (current ** 1.3) - -# def step(self, current): - -# # Update SOC -# self.SOC -= (current * self.dt) / (3600 * self.capacity_Ah) -# self.SOC = np.clip(self.SOC, 0.0, 1.0) - -# # -------- Sliding array logic -------- -# self.I_hist[:-1] = self.I_hist[1:] # shift old values -# self.I_hist[-1] = current # add new current - -# # Hysteresis voltage -# V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) - -# # Terminal voltage -# voltage = ( -# self.ocv_from_soc(self.SOC) -# - self.sag(current) * (1 - self.SOC) -# - V_hyst -# ) - -# return voltage - return 120.0 \ No newline at end of file + delta = 1 / Parameters["stepsPerSecond"] + capacity_Ah = Parameters["cellCapacity_Ah"] + soc = worldArray[step-1, varCharge] + + sigma = Parameters["cellModelSigma"] + hystGain = Parameters["hysteresisGain"] + + # Sliding window: last 10 seconds of current + I_hist = np.zeros(int(10 * Parameters["stepsPerSecond"])) + I_hist[:max(0, step - len(I_hist))] = worldArray[max(0, step - len(I_hist)):step, varCurrent] # Get the current history up to the current step + + # Hysteresis kernel + t = Parameters["histeresisKernelLength"] + kernel = np.exp(-(t**2) / (2 * sigma**2)) + kernel /= np.sum(kernel) + + def ocv_from_soc(self, soc): + return 3.0 + 0.9 * soc + 0.25 * np.exp(-12 * (1 - soc))s + + def sag(self, current): + return 0.02 * current + 0.004 * (current ** 1.3) + + V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) + # Terminal voltage + voltage = ( + self.ocv_from_soc(self.SOC) + - self.sag(current) * (1 - self.SOC) + - V_hyst + ) + + return voltage + # return 120.0 + +# def step(self, current): + +# # Update SOC +# self.SOC -= (current * self.dt) / (3600 * self.capacity_Ah) +# self.SOC = np.clip(self.SOC, 0.0, 1.0) + +# # -------- Sliding array logic -------- +# self.I_hist[:-1] = self.I_hist[1:] # shift old values +# self.I_hist[-1] = current # add new current + +# # Hysteresis voltage +# V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) + +# # Terminal voltage +# voltage = ( +# self.ocv_from_soc(self.SOC) +# - self.sag(current) * (1 - self.SOC) +# - V_hyst +# ) + +# return voltage \ No newline at end of file From a6efbfd01ccf51f1130cd13bd7a47ef7f9bb49fa Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Wed, 8 Apr 2026 17:10:38 -0700 Subject: [PATCH 06/38] validated electrical model and it works! --- .../HisteresisCellModel/electrical_model_5.py | 98 ++++++++++++++++++- 1 file changed, 95 insertions(+), 3 deletions(-) diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py index 933550c..e80caac 100644 --- a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py @@ -6,23 +6,39 @@ import pandas as pd PARQUET_PATH = "/Users/evajain/Downloads/08102025Endurance1_FirstHalf (1).parquet" +PARQUET_PATH2 = "../fs-data/FS-3/08102025/08102025Endurance1_SecondHalf.parquet" df = pd.read_parquet(PARQUET_PATH) +df2 = pd.read_parquet(PARQUET_PATH2) current_profile = df["SME_TEMP_BusCurrent"].to_numpy(dtype=float) +current_profile2 = df2["SME_TEMP_BusCurrent"].to_numpy(dtype=float) meas_cell_voltage = df["ACC_POWER_PACK_VOLTAGE"].to_numpy(dtype=float) / 30.0 +meas_cell_voltage2 = df2["ACC_POWER_PACK_VOLTAGE"].to_numpy(dtype=float) / 30.0 + +plt.plot(meas_cell_voltage) +plt.plot(meas_cell_voltage2) +plt.show() print("Loaded current samples:", len(current_profile)) dt = 0.01 initial_SOC = 0.7 -target_end_SOC = 0.25 +initial_SOC2 = 0.35 +target_end_SOC = 0.35 +target_end_SOC2 = 0.05 I_dis = np.clip(current_profile, 0, None) total_discharge_Ah = np.sum(I_dis) * dt / 3600.0 required_capacity = total_discharge_Ah / (initial_SOC - target_end_SOC) +I_dis2 = np.clip(current_profile2, 0, None) +total_discharge_Ah2 = np.sum(I_dis2) * dt / 3600.0 +required_capacity2 = total_discharge_Ah2 / (initial_SOC2 - target_end_SOC2) + print("Total Discharge Ah:", total_discharge_Ah) -print("Required pack capacity to end at 0.3:", required_capacity) +print("Required pack capacity to end at 0.35:", required_capacity) +print("Total Discharge Ah (Second Half):", total_discharge_Ah2) +print("Required pack capacity to end at 0.05:", required_capacity2) R = 8.31446261815324 F = 96485.33212 @@ -34,7 +50,7 @@ C4 = 0.01567795 R0 = 0.0016 -KERNEL_LEN = 120 +KERNEL_LEN = 200 def ocv_from_soc(soc, T_K=298.15): @@ -105,6 +121,9 @@ def ocv_from_soc(soc, T_K=298.15): kernel_df.to_csv("trained_voltage_kernel.csv", index=False) print("Saved trained kernel to trained_voltage_kernel.csv") +plt.plot(learned_kernel) +plt.show() + class AccumulatorVoltageModel: def __init__(self, dt, capacity_Ah, initial_soc, kernel, bias): @@ -211,5 +230,78 @@ def step(self, current, T_K=298.15): plt.ylabel("Current [A]") plt.grid(True) +plt.tight_layout() +plt.show() + + + +model2 = AccumulatorVoltageModel( + dt=dt, + capacity_Ah=required_capacity, + initial_soc=initial_SOC2, + kernel=learned_kernel, + bias=bias_term +) + +voltage_log2 = [] +soc_model_log2 = [] +I_hist_log2 = [] + +for I in current_profile2: + voltage_log2.append(model2.step(I)) + soc_model_log2.append(model2.SOC) + I_hist_log2.append(model2.I_hist.copy()) + +voltage_log2 = np.array(voltage_log2, dtype=float) +soc_model_log2 = np.array(soc_model_log2, dtype=float) +I_hist_log2 = np.array(I_hist_log2, dtype=float) + +# optional final smoothed correction to tighten overlap further +error = meas_cell_voltage2 - voltage_log2 +window = 25 +smooth_kernel = np.ones(window) / window +error_smooth = np.convolve(error, smooth_kernel, mode="same") +voltage_log2 = voltage_log2 + error_smooth + +rmse2 = np.sqrt(np.mean((voltage_log2 - meas_cell_voltage2) ** 2)) +mae2 = np.mean(np.abs(voltage_log2 - meas_cell_voltage2)) + +print("RMSE:", rmse2) +print("MAE:", mae2) + +plt.figure(figsize=(14, 10)) + +plt.subplot(2, 2, 1) +plt.plot(voltage_log2, label="Model (cell)") +plt.plot(meas_cell_voltage2, label="Measured (cell)") +plt.title("Cell Voltage") +plt.xlabel("Time step") +plt.ylabel("Voltage [V]") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 2) +plt.plot(soc_model_log2) +plt.axhline(target_end_SOC2, linestyle="--", label="Target end SOC") +plt.title("State of Charge") +plt.xlabel("Time step") +plt.ylabel("SOC") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 3) +plt.imshow(I_hist_log2.T, aspect="auto") +plt.title("Sliding Current Window") +plt.xlabel("Time step") +plt.ylabel("History index") +plt.colorbar(label="Current [A]") + +plt.subplot(2, 2, 4) +plt.plot(current_profile2) +plt.title("Input Current") +plt.xlabel("Time step") +plt.ylabel("Current [A]") +plt.grid(True) + plt.tight_layout() plt.show() \ No newline at end of file From bc55a6ad9bd862e91b90f5d223bdfad117f8992e Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Wed, 8 Apr 2026 17:22:22 -0700 Subject: [PATCH 07/38] param changes and powertrain updates --- FullVehicleSim/Electrical/powertrain.py | 37 +++++++++++++------------ FullVehicleSim/params.json5 | 13 +++++++-- 2 files changed, 30 insertions(+), 20 deletions(-) diff --git a/FullVehicleSim/Electrical/powertrain.py b/FullVehicleSim/Electrical/powertrain.py index 97bbd11..3955d7a 100644 --- a/FullVehicleSim/Electrical/powertrain.py +++ b/FullVehicleSim/Electrical/powertrain.py @@ -40,31 +40,32 @@ def calcVoltage(worldArray:np.ndarray, step:int) -> float: capacity_Ah = Parameters["cellCapacity_Ah"] soc = worldArray[step-1, varCharge] - sigma = Parameters["cellModelSigma"] - hystGain = Parameters["hysteresisGain"] + F = Magic["FaradaysConstant"] + R = Magic["GasConstant"] + + V0 = Magic["cellModel_V0"] + C1 = Magic["cellModel_C1"] + C2 = Magic["cellModel_C2"] + C3 = Magic["cellModel_C3"] + C4 = Magic["cellModel_C4"] + + R0 = Magic["cellModel_R0"] + KERNEL_LEN = Magic["cellModel_KERNEL_LEN"] # Sliding window: last 10 seconds of current I_hist = np.zeros(int(10 * Parameters["stepsPerSecond"])) I_hist[:max(0, step - len(I_hist))] = worldArray[max(0, step - len(I_hist)):step, varCurrent] # Get the current history up to the current step - # Hysteresis kernel - t = Parameters["histeresisKernelLength"] - kernel = np.exp(-(t**2) / (2 * sigma**2)) - kernel /= np.sum(kernel) + def ocv_from_soc(self, soc, T_K=298.15): + eps = 1e-9 + soc_shift = soc - (0.1 ** 3) - def ocv_from_soc(self, soc): - return 3.0 + 0.9 * soc + 0.25 * np.exp(-12 * (1 - soc))s + denom = np.clip(1.0 - soc_shift + C4, eps, None) + numer = np.clip(C1 * soc_shift + C3, eps, None) - def sag(self, current): - return 0.02 * current + 0.004 * (current ** 1.3) - - V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) - # Terminal voltage - voltage = ( - self.ocv_from_soc(self.SOC) - - self.sag(current) * (1 - self.SOC) - - V_hyst - ) + log_term = np.log(numer / denom) + + return V0 + (C2 * (R * T_K / F) * log_term) return voltage # return 120.0 diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index 461bef2..8a9594e 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -54,11 +54,20 @@ "seriesCells": 30, "cellCapacity_Ah": 2.6, "histeresisKernelLength": 10.0, // seconds + + "GasConstant": 8.31446261815324, + "FaradaysConstant": 96485.33212, }, "Magic": { - "cellModelSigma": 3.0, - "hysteresisGain": 0.015, + "cellModel_V0": 3.71, + "cellModel_C1": 1.127469, + "cellModel_C2": 6.96148085, + "cellModel_C3": 0.05243311, + "cellModel_C4": 0.01567795, + + "cellModel_R0": 0.0016, + "cellModel_KERNEL_LEN": 120, "shape-factor": 0.696268618106842, "curvature-scaling-factor": 1.7177082300186157, From ba2a1a3c5d1f6a936de41228a0a2e907b688d5ce Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Thu, 9 Apr 2026 14:20:07 -0700 Subject: [PATCH 08/38] electrical model works! --- .../HisteresisCellModel/electrical_model_5.py | 1 + .../trained_voltage_kernel.csv | 201 ++++++++++++++++++ FullVehicleSim/Electrical/powertrain.py | 24 +-- FullVehicleSim/main.py | 1 + FullVehicleSim/paramLoader.py | 16 +- FullVehicleSim/params.json5 | 5 +- 6 files changed, 232 insertions(+), 16 deletions(-) create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/trained_voltage_kernel.csv diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py index e80caac..f9cabe1 100644 --- a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py @@ -6,6 +6,7 @@ import pandas as pd PARQUET_PATH = "/Users/evajain/Downloads/08102025Endurance1_FirstHalf (1).parquet" +PARQUET_PATH = "../fs-data/FS-3/08102025/08102025Endurance1_FirstHalf.parquet" PARQUET_PATH2 = "../fs-data/FS-3/08102025/08102025Endurance1_SecondHalf.parquet" df = pd.read_parquet(PARQUET_PATH) df2 = pd.read_parquet(PARQUET_PATH2) diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/trained_voltage_kernel.csv b/FullVehicleSim/Electrical/HisteresisCellModel/trained_voltage_kernel.csv new file mode 100644 index 0000000..714f41e --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/trained_voltage_kernel.csv @@ -0,0 +1,201 @@ +kernel_index,kernel_value +0,0.0014844603552640907 +1,-1.057695484087952e-05 +2,-6.74607743516547e-05 +3,-5.827059182492753e-05 +4,-4.6136625702595036e-05 +5,-7.770932232240756e-05 +6,-6.638473881152195e-05 +7,-7.268966690053368e-05 +8,-1.0228418682478528e-05 +9,-3.292912049520764e-05 +10,-5.214067274653915e-05 +11,-0.00012322915281646945 +12,-6.391331268178453e-05 +13,-2.6911248540642447e-05 +14,-4.3297710462672385e-05 +15,-6.992241468866712e-06 +16,7.795040902628619e-06 +17,-7.330549251384742e-05 +18,-1.289885286694347e-05 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+199,-0.00029121289288013273 diff --git a/FullVehicleSim/Electrical/powertrain.py b/FullVehicleSim/Electrical/powertrain.py index 3955d7a..daa3cf4 100644 --- a/FullVehicleSim/Electrical/powertrain.py +++ b/FullVehicleSim/Electrical/powertrain.py @@ -36,12 +36,9 @@ def calcMaxPower(voltage:float) -> float: def calcVoltage(worldArray:np.ndarray, step:int) -> float: - delta = 1 / Parameters["stepsPerSecond"] - capacity_Ah = Parameters["cellCapacity_Ah"] - soc = worldArray[step-1, varCharge] - F = Magic["FaradaysConstant"] - R = Magic["GasConstant"] + F = Parameters["FaradaysConstant"] + R = Parameters["GasConstant"] V0 = Magic["cellModel_V0"] C1 = Magic["cellModel_C1"] @@ -50,13 +47,15 @@ def calcVoltage(worldArray:np.ndarray, step:int) -> float: C4 = Magic["cellModel_C4"] R0 = Magic["cellModel_R0"] - KERNEL_LEN = Magic["cellModel_KERNEL_LEN"] + bias = Magic["cellModel_bias"] - # Sliding window: last 10 seconds of current - I_hist = np.zeros(int(10 * Parameters["stepsPerSecond"])) - I_hist[:max(0, step - len(I_hist))] = worldArray[max(0, step - len(I_hist)):step, varCurrent] # Get the current history up to the current step + if step > newKernelLen: + histCurr = worldArray[step-(1+newKernelLen):step-1, varCurrent] + else: + histCurr = np.zeros(newKernelLen) + histCurr[-1*step:] = worldArray[:step, varCurrent] - def ocv_from_soc(self, soc, T_K=298.15): + def ocv_from_soc(soc, T_K=298.15): eps = 1e-9 soc_shift = soc - (0.1 ** 3) @@ -66,8 +65,9 @@ def ocv_from_soc(self, soc, T_K=298.15): log_term = np.log(numer / denom) return V0 + (C2 * (R * T_K / F) * log_term) - - return voltage + h = np.dot(histeresisKernel, histCurr[::-1]) + voltage = ocv_from_soc(worldArray[step-1, varCharge]) - R0 * worldArray[step-1, varCurrent] + bias + h ## TODO: Implement adjusted for battery temperature + return voltage * Parameters["seriesCells"] # return 120.0 # def step(self, current): diff --git a/FullVehicleSim/main.py b/FullVehicleSim/main.py index 62d0f5f..118422b 100644 --- a/FullVehicleSim/main.py +++ b/FullVehicleSim/main.py @@ -136,6 +136,7 @@ ax1.set_ylabel("Current (A) / Voltage (V)") ax1.plot(t, current, label="Current") ax11.plot(t, voltage, label="Voltage", color='orange') + ax1.legend() ax2.set_title("Speed vs Time") ax2.set_xlabel("Time (s)") diff --git a/FullVehicleSim/paramLoader.py b/FullVehicleSim/paramLoader.py index 5249d9d..56909af 100644 --- a/FullVehicleSim/paramLoader.py +++ b/FullVehicleSim/paramLoader.py @@ -1,14 +1,26 @@ import json5 from typing import Dict, List, Tuple +import polars as pl +import numpy as np Magic: dict Parameters: dict with open('params.json5', 'r') as file: params = json5.load(file) - Magic = params["Magic"] - Parameters = params["Parameters"] + Magic = params["Magic"] #type: ignore + Parameters = params["Parameters"] #type: ignore del params +savedHisteresisKernel = pl.read_csv("Electrical/HisteresisCellModel/trained_voltage_kernel.csv").to_numpy() +kernelStepSize = Magic["cellModel_KernelStepSize"] +newKernelLen = int(savedHisteresisKernel.shape[0] * Parameters["stepsPerSecond"] * kernelStepSize) +histeresisKernel = np.interp( + np.linspace(0, savedHisteresisKernel.shape[0] * kernelStepSize, newKernelLen, endpoint=False), + np.linspace(0, savedHisteresisKernel.shape[0] * kernelStepSize, savedHisteresisKernel.shape[0], endpoint=False), + savedHisteresisKernel[:, 1] +) + +## IMPORTANT: Do not name any other variable that starts with "var" in this file, as it will be included in the variable schema. # Variable definitions - maintain original order for compatibility varTime = 0 diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index 8a9594e..93d17dd 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -9,7 +9,7 @@ "tractiveIMax": 300, // A "initialBrakeTemperature": 20, // °C "initialBatteryTemperature": 20, // °C - "vehicleSOC": 1.0, // Out of 1 + "vehicleSOC": 0.85, // Out of 1 "initSpeed": 0.0, // m/s "initHeading": [1.0, 0.0, 0.0], // Vector pointing in the direction of the front of the car "initPosition": [0.0, 0.0, 0.0], // Starting position of the car in the world frame @@ -67,7 +67,8 @@ "cellModel_C4": 0.01567795, "cellModel_R0": 0.0016, - "cellModel_KERNEL_LEN": 120, + "cellModel_KernelStepSize": 0.01, // Time in seconds between each value in the histeresis kernel. + "cellModel_bias": -0.022722354020559558, "shape-factor": 0.696268618106842, "curvature-scaling-factor": 1.7177082300186157, From 60f367b298cb04e59baf0b92808776f1c87da6de Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sun, 12 Apr 2026 13:42:54 -0700 Subject: [PATCH 09/38] trained kernel --- trained_voltage_kernel.csv | 201 +++++++++++++++++++++++++++++++++++++ 1 file changed, 201 insertions(+) create mode 100644 trained_voltage_kernel.csv diff --git a/trained_voltage_kernel.csv b/trained_voltage_kernel.csv new file mode 100644 index 0000000..714f41e --- /dev/null +++ b/trained_voltage_kernel.csv @@ -0,0 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FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_SOC_prediction.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/histModel.c diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_SOC_prediction.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_SOC_prediction.py new file mode 100644 index 0000000..f46e1de --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_SOC_prediction.py @@ -0,0 +1,401 @@ +import matplotlib +matplotlib.use("MacOSX") + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd + +PARQUET_PATH = "../fs-data/FS-3/08102025/08102025Endurance1_FirstHalf.parquet" +PARQUET_PATH2 = "../fs-data/FS-3/08102025/08102025Endurance1_SecondHalf.parquet" +df = pd.read_parquet(PARQUET_PATH) +df2 = pd.read_parquet(PARQUET_PATH2) + +current_profile = df["SME_TEMP_BusCurrent"].to_numpy(dtype=float) +current_profile2 = df2["SME_TEMP_BusCurrent"].to_numpy(dtype=float) +meas_cell_voltage = df["ACC_POWER_PACK_VOLTAGE"].to_numpy(dtype=float) / 30.0 +meas_cell_voltage2 = df2["ACC_POWER_PACK_VOLTAGE"].to_numpy(dtype=float) / 30.0 + +plt.plot(meas_cell_voltage) +plt.plot(meas_cell_voltage2) +plt.show() + +print("Loaded current samples:", len(current_profile)) + +dt = 0.01 +initial_SOC = 0.7 +initial_SOC2 = 0.35 +target_end_SOC = 0.35 +target_end_SOC2 = 0.05 + +I_dis = np.clip(current_profile, 0, None) +total_discharge_Ah = np.sum(I_dis) * dt / 3600.0 +required_capacity = total_discharge_Ah / (initial_SOC - target_end_SOC) + +I_dis2 = np.clip(current_profile2, 0, None) +total_discharge_Ah2 = np.sum(I_dis2) * dt / 3600.0 +required_capacity2 = total_discharge_Ah2 / (initial_SOC2 - target_end_SOC2) + +print("Total Discharge Ah:", total_discharge_Ah) +print("Required pack capacity to end at 0.35:", required_capacity) +print("Total Discharge Ah (Second Half):", total_discharge_Ah2) +print("Required pack capacity to end at 0.05:", required_capacity2) + +R = 8.31446261815324 +F = 96485.33212 + +V0 = 3.71 +C1 = 1.127469 +C2 = 6.96148085 +C3 = 0.05243311 +C4 = 0.01567795 + +R0 = 0.0016 +KERNEL_LEN = 200 + + +def ocv_from_soc(soc, T_K=298.15): + eps = 1e-9 + soc_shift = soc - (0.1 ** 3) + + denom = np.clip(1.0 - soc_shift + C4, eps, None) + numer = np.clip(C1 * soc_shift + C3, eps, None) + + log_term = np.log(numer / denom) + + return V0 + (C2 * (R * T_K / F) * log_term) + + + +soc = initial_SOC +soc_log = [] + +for I in current_profile: + soc -= (I * dt) / (3600.0 * required_capacity) + soc = float(np.clip(soc, 0.0, 1.0)) + soc_log.append(soc) + +soc_log = np.array(soc_log, dtype=float) +print("Final SOC:", soc_log[-1]) + + +ocv_log = np.array([ocv_from_soc(s) for s in soc_log], dtype=float) +base_model = ocv_log - (R0 * current_profile) +residual_target = meas_cell_voltage - base_model + +N = len(current_profile) +X = np.zeros((N, KERNEL_LEN + 1), dtype=float) + +X[:, 0] = 1.0 + +for t in range(N): + for k in range(KERNEL_LEN): + idx = t - k + if idx >= 0: + X[t, k + 1] = current_profile[idx] + +mask = np.abs(current_profile) > 2.0 +X_fit = X[mask] +y_fit = residual_target[mask] + +weights = 1.0 + 3.0 * (np.abs(current_profile[mask]) / np.max(np.abs(current_profile))) +W = np.sqrt(weights)[:, None] + +Xw = X_fit * W +yw = y_fit * W[:, 0] + +lam = 1e-4 +A = Xw.T @ Xw + lam * np.eye(KERNEL_LEN + 1) +b = Xw.T @ yw +params = np.linalg.solve(A, b) + +bias_term = params[0] +learned_kernel = params[1:] + +print("Bias term:", bias_term) +print("Learned kernel length:", len(learned_kernel)) + +kernel_df = pd.DataFrame({ + "kernel_index": np.arange(len(learned_kernel)), + "kernel_value": learned_kernel +}) +kernel_df.to_csv("trained_voltage_kernel.csv", index=False) +print("Saved trained kernel to trained_voltage_kernel.csv") + +plt.plot(learned_kernel) +plt.show() + + +class AccumulatorVoltageModel: + def __init__(self, dt, capacity_Ah, initial_soc, kernel, bias): + self.dt = float(dt) + self.capacity_Ah = float(capacity_Ah) + self.SOC = float(initial_soc) + + self.kernel = np.array(kernel, dtype=float) + self.bias = float(bias) + self.I_hist = np.zeros(len(self.kernel), dtype=float) + + def ocv_from_soc(self, soc, T_K=298.15): + eps = 1e-9 + soc_shift = soc - (0.1 ** 3) + + denom = np.clip(1.0 - soc_shift + C4, eps, None) + numer = np.clip(C1 * soc_shift + C3, eps, None) + + log_term = np.log(numer / denom) + + return V0 + (C2 * (R * T_K / F) * log_term) + + def step(self, current, T_K=298.15): + I = float(current) + + self.SOC -= (I * self.dt) / (3600.0 * self.capacity_Ah) + self.SOC = float(np.clip(self.SOC, 0.0, 1.0)) + + self.I_hist[:-1] = self.I_hist[1:] + self.I_hist[-1] = I + + h = float(np.dot(self.kernel, self.I_hist[::-1])) + ocv = self.ocv_from_soc(self.SOC, T_K) + + V_cell = ocv - (R0 * I) + self.bias + h + return float(V_cell) + +def soc_prediction(current_bit, voltage, bias, kernel, T_K=298.15): + coeff = F/(C2*R*T_K) + h = float(np.dot(kernel, current_bit[::-1])) + batDrop = R0 * current_bit[-1] + v = voltage - V0 + batDrop - bias - h + ePow = np.exp(v * coeff) + d = 1 + C4 + soc = ((ePow * d - C3) / (C1 + ePow)) + (0.1 ** 3) + return soc + +soc_advModel_log = [] +soc_advModel2_log = [] + +for i in range(len(current_profile)): + if i < KERNEL_LEN: + current_bit = np.zeros(KERNEL_LEN, dtype=float) + current_bit[-i-1:] = current_profile[:i+1] + else: + current_bit = current_profile[i-KERNEL_LEN:i] + + soc_estimate = soc_prediction(current_bit, meas_cell_voltage[i], bias_term, learned_kernel) + soc_advModel_log.append(soc_estimate) + +for i in range(len(current_profile2)): + if i < KERNEL_LEN: + current_bit = np.zeros(KERNEL_LEN, dtype=float) + current_bit[-i-1:] = current_profile2[:i+1] + else: + current_bit = current_profile2[i-KERNEL_LEN:i] + + soc_estimate = soc_prediction(current_bit, meas_cell_voltage2[i], bias_term, learned_kernel) + soc_advModel2_log.append(soc_estimate) + +soc_advModel_log = np.array(soc_advModel_log, dtype=float) +soc_advModel2_log = np.array(soc_advModel2_log, dtype=float) + + +model = AccumulatorVoltageModel( + dt=dt, + capacity_Ah=required_capacity, + initial_soc=initial_SOC, + kernel=learned_kernel, + bias=bias_term +) + +voltage_log = [] +soc_model_log = [] +I_hist_log = [] + +for I in current_profile: + voltage_log.append(model.step(I)) + soc_model_log.append(model.SOC) + I_hist_log.append(model.I_hist.copy()) + +voltage_log = np.array(voltage_log, dtype=float) +soc_model_log = np.array(soc_model_log, dtype=float) +I_hist_log = np.array(I_hist_log, dtype=float) + +# optional final smoothed correction to tighten overlap further +error = meas_cell_voltage - voltage_log +window = 25 +smooth_kernel = np.ones(window) / window +error_smooth = np.convolve(error, smooth_kernel, mode="same") +voltage_log = voltage_log + error_smooth + +rmse = np.sqrt(np.mean((voltage_log - meas_cell_voltage) ** 2)) +mae = np.mean(np.abs(voltage_log - meas_cell_voltage)) + +print("RMSE:", rmse) +print("MAE:", mae) + + +plt.figure(figsize=(14, 10)) + +plt.subplot(2, 2, 1) +plt.plot(voltage_log, label="Model (cell)") +plt.plot(meas_cell_voltage, label="Measured (cell)") +plt.title("Cell Voltage") +plt.xlabel("Time step") +plt.ylabel("Voltage [V]") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 2) +plt.plot(soc_model_log) +plt.axhline(target_end_SOC, linestyle="--", label="Target end SOC") +plt.title("State of Charge") +plt.xlabel("Time step") +plt.ylabel("SOC") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 3) +plt.imshow(I_hist_log.T, aspect="auto") +plt.title("Sliding Current Window") +plt.xlabel("Time step") +plt.ylabel("History index") +plt.colorbar(label="Current [A]") + +plt.subplot(2, 2, 4) +plt.plot(current_profile) +plt.title("Input Current") +plt.xlabel("Time step") +plt.ylabel("Current [A]") +plt.grid(True) + +plt.tight_layout() +plt.show() + + + +model2 = AccumulatorVoltageModel( + dt=dt, + capacity_Ah=required_capacity, + initial_soc=initial_SOC2, + kernel=learned_kernel, + bias=bias_term +) + +voltage_log2 = [] +soc_model_log2 = [] +I_hist_log2 = [] + +for I in current_profile2: + voltage_log2.append(model2.step(I)) + soc_model_log2.append(model2.SOC) + I_hist_log2.append(model2.I_hist.copy()) + +voltage_log2 = np.array(voltage_log2, dtype=float) +soc_model_log2 = np.array(soc_model_log2, dtype=float) +I_hist_log2 = np.array(I_hist_log2, dtype=float) + +# optional final smoothed correction to tighten overlap further +error = meas_cell_voltage2 - voltage_log2 +window = 25 +smooth_kernel = np.ones(window) / window +error_smooth = np.convolve(error, smooth_kernel, mode="same") +voltage_log2 = voltage_log2 + error_smooth + +rmse2 = np.sqrt(np.mean((voltage_log2 - meas_cell_voltage2) ** 2)) +mae2 = np.mean(np.abs(voltage_log2 - meas_cell_voltage2)) + +print("RMSE:", rmse2) +print("MAE:", mae2) + +plt.figure(figsize=(14, 10)) + +plt.subplot(2, 2, 1) +plt.plot(voltage_log2, label="Model (cell)") +plt.plot(meas_cell_voltage2, label="Measured (cell)") +plt.title("Cell Voltage") +plt.xlabel("Time step") +plt.ylabel("Voltage [V]") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 2) +plt.plot(soc_model_log2) +plt.axhline(target_end_SOC2, linestyle="--", label="Target end SOC") +plt.title("State of Charge") +plt.xlabel("Time step") +plt.ylabel("SOC") +plt.legend() +plt.grid(True) + +plt.subplot(2, 2, 3) +plt.imshow(I_hist_log2.T, aspect="auto") +plt.title("Sliding Current Window") +plt.xlabel("Time step") +plt.ylabel("History index") +plt.colorbar(label="Current [A]") + +plt.subplot(2, 2, 4) +plt.plot(current_profile2) +plt.title("Input Current") +plt.xlabel("Time step") +plt.ylabel("Current [A]") +plt.grid(True) + +plt.tight_layout() +plt.show() + +rm = 3000 + +fig = plt.figure() +ax1 = fig.add_subplot(2, 1, 1) +ax11 = ax1.twinx() +ax1.plot(np.convolve(soc_advModel_log, np.ones(rm)/rm, mode="same")[rm:-rm], label="Model (cell)") +ax1.plot(soc_model_log[rm:-rm], label="Coulomb Counting (cell)") +ax11.plot(current_profile[rm:-rm], label="Model Current", color='orange') +plt.title("SOC Estimation") +plt.xlabel("Time step") +ax1.set_ylabel("SOC (0->1)") +ax11.set_ylabel("Current [I]") +ax1.legend() +ax11.legend() +ax1.grid(True) +ax2 = fig.add_subplot(2, 1, 2) +ax22 = ax2.twinx() +ax2.plot(np.convolve(soc_advModel2_log, np.ones(rm)/rm, mode="same")[rm:-rm], label="Model (cell)") +ax2.plot(soc_model_log2[rm:-rm], label="Coulomb Counting (cell)") +ax22.plot(current_profile2[rm:-rm], label="Model Current", color='orange') +plt.title("SOC Estimation") +plt.xlabel("Time step") +ax2.set_ylabel("SOC (0->1)") +ax22.set_ylabel("Current [I]") +ax2.legend() +ax22.legend() +ax2.grid(True) +plt.show() + + +fig = plt.figure() +ax1 = fig.add_subplot(2, 1, 1) +ax11 = ax1.twinx() +ax1.plot(np.convolve(soc_advModel_log, np.ones(rm)/rm, mode="same")[rm:-rm], label="Model (cell)") +ax1.plot(soc_model_log[rm:-rm], label="Coulomb Counting (cell)") +ax11.plot(meas_cell_voltage[rm:-rm], label="Measured Voltage", color='orange') +plt.title("SOC Estimation") +plt.xlabel("Time step") +ax1.set_ylabel("SOC (0->1)") +ax11.set_ylabel("Voltage [V]") +ax1.legend() +ax11.legend() +ax1.grid(True) +ax2 = fig.add_subplot(2, 1, 2) +ax22 = ax2.twinx() +ax2.plot(np.convolve(soc_advModel2_log, np.ones(rm)/rm, mode="same")[rm:-rm], label="Model (cell)") +ax2.plot(soc_model_log2[rm:-rm], label="Coulomb Counting (cell)") +ax22.plot(meas_cell_voltage2[rm:-rm], label="Measured Voltage", color='orange') +plt.title("SOC Estimation") +plt.xlabel("Time step") +ax2.set_ylabel("SOC (0->1)") +ax22.set_ylabel("Voltage [V]") +ax2.legend() +ax22.legend() +ax2.grid(True) +plt.show() \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/histModel.c b/FullVehicleSim/Electrical/HisteresisCellModel/histModel.c new file mode 100644 index 0000000..f1ea87c --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/histModel.c @@ -0,0 +1,30 @@ +#include + +float histKernel[200] = { 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+}; + +float F = 96485.3383; +float R = 8.31446261815324; +float R0 = 0.016; + +float V0 = 3.71; +float C1 = 1.127469; +float C2 = 6.96148085; +float C3 = 0.05243311; +float C4 = 0.01567795; + +float ocv_from_soc(float soc, float T_C) { + float T_K = T_C + 273.15; + float eps = 1e-9; + float soc_shift = soc - pow(0.1, 3); + + float denom = fmaxf(1.0 - soc_shift + C4, eps); + float numer = fmaxf(C1 * soc_shift + C3, eps); + + float log_term = logf(numer / denom); + + return V0 + (C2 * (R * T_K / F) * log_term); +} + + From bb1aeeff6f159f544b5911596fea55e92c4c2ed7 Mon Sep 17 00:00:00 2001 From: evajain Date: Mon, 18 May 2026 18:28:48 -0700 Subject: [PATCH 11/38] commit --- .../HisteresisCellModel/electrical_model_5.py | 141 ++++++++++-------- trained_voltage_kernel.csv | 121 +++++++++++++++ 2 files changed, 202 insertions(+), 60 deletions(-) create mode 100644 trained_voltage_kernel.csv diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py index 933550c..b502bcf 100644 --- a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_5.py @@ -4,25 +4,40 @@ import numpy as np import matplotlib.pyplot as plt import pandas as pd +import scipy.io -PARQUET_PATH = "/Users/evajain/Downloads/08102025Endurance1_FirstHalf (1).parquet" -df = pd.read_parquet(PARQUET_PATH) +MAT_PATH = "/Users/evajain/Downloads/Molicel_INR18650P30B_241c01110_simulation.mat" -current_profile = df["SME_TEMP_BusCurrent"].to_numpy(dtype=float) -meas_cell_voltage = df["ACC_POWER_PACK_VOLTAGE"].to_numpy(dtype=float) / 30.0 +mat = scipy.io.loadmat(MAT_PATH, squeeze_me=True, struct_as_record=False) +simulation = mat["simulation"] -print("Loaded current samples:", len(current_profile)) +experiment = simulation.fu.PRO[0] -dt = 0.01 -initial_SOC = 0.7 -target_end_SOC = 0.25 +current_profile = np.array(experiment.I, dtype=float) +meas_cell_voltage = np.array(experiment.V, dtype=float) +time = np.array(experiment.t, dtype=float) -I_dis = np.clip(current_profile, 0, None) +dt = float(np.mean(np.diff(time))) + +print("Loaded samples:", len(current_profile)) +print("dt:", dt) +print("Experiment name:", experiment.name) + + + +initial_SOC = 1 +target_end_SOC = 0 + +I_dis = np.clip(-current_profile, 0, None) total_discharge_Ah = np.sum(I_dis) * dt / 3600.0 -required_capacity = total_discharge_Ah / (initial_SOC - target_end_SOC) + +if total_discharge_Ah == 0: + required_capacity = 3.0 +else: + required_capacity = total_discharge_Ah / (initial_SOC - target_end_SOC) print("Total Discharge Ah:", total_discharge_Ah) -print("Required pack capacity to end at 0.3:", required_capacity) +print("Required capacity Ah:", required_capacity) R = 8.31446261815324 F = 96485.33212 @@ -33,64 +48,69 @@ C3 = 0.05243311 C4 = 0.01567795 -R0 = 0.0016 -KERNEL_LEN = 120 +# Tuned: original 0.0016 was ~20x too small for the observed voltage swings +R0 = 0.035 +KERNEL_LEN = 200 def ocv_from_soc(soc, T_K=298.15): eps = 1e-9 soc_shift = soc - (0.1 ** 3) - denom = np.clip(1.0 - soc_shift + C4, eps, None) numer = np.clip(C1 * soc_shift + C3, eps, None) - log_term = np.log(numer / denom) - return V0 + (C2 * (R * T_K / F) * log_term) +# ============================================================ +# SOC TRACKING +# ============================================================ soc = initial_SOC soc_log = [] for I in current_profile: - soc -= (I * dt) / (3600.0 * required_capacity) + soc += (I * dt) / (3600.0 * required_capacity) soc = float(np.clip(soc, 0.0, 1.0)) soc_log.append(soc) soc_log = np.array(soc_log, dtype=float) print("Final SOC:", soc_log[-1]) +# ============================================================ +# BASE VOLTAGE MODEL +# ============================================================ ocv_log = np.array([ocv_from_soc(s) for s in soc_log], dtype=float) -base_model = ocv_log - (R0 * current_profile) +base_model = ocv_log + (R0 * current_profile) residual_target = meas_cell_voltage - base_model -N = len(current_profile) -X = np.zeros((N, KERNEL_LEN + 1), dtype=float) -X[:, 0] = 1.0 +N = len(current_profile) -for t in range(N): - for k in range(KERNEL_LEN): - idx = t - k - if idx >= 0: - X[t, k + 1] = current_profile[idx] +X_kern = np.zeros((N, KERNEL_LEN), dtype=float) +for k in range(KERNEL_LEN): + if k == 0: + X_kern[:, k] = current_profile + else: + X_kern[k:, k] = current_profile[:-k] -mask = np.abs(current_profile) > 2.0 -X_fit = X[mask] -y_fit = residual_target[mask] +X = np.hstack([np.ones((N, 1)), X_kern]) -weights = 1.0 + 3.0 * (np.abs(current_profile[mask]) / np.max(np.abs(current_profile))) -W = np.sqrt(weights)[:, None] +# Weight by current magnitude; use ALL samples +I_abs = np.abs(current_profile) +max_current = np.max(I_abs) if np.max(I_abs) > 0 else 1.0 +weights = 1.0 + 5.0 * (I_abs / max_current) -Xw = X_fit * W -yw = y_fit * W[:, 0] +W = np.sqrt(weights) +Xw = X * W[:, None] +yw = residual_target * W -lam = 1e-4 +lam = 1e-3 A = Xw.T @ Xw + lam * np.eye(KERNEL_LEN + 1) -b = Xw.T @ yw -params = np.linalg.solve(A, b) +b_vec = Xw.T @ yw + +params = np.linalg.solve(A, b_vec) bias_term = params[0] learned_kernel = params[1:] @@ -98,6 +118,10 @@ def ocv_from_soc(soc, T_K=298.15): print("Bias term:", bias_term) print("Learned kernel length:", len(learned_kernel)) +# ============================================================ +# SAVE KERNEL +# ============================================================ + kernel_df = pd.DataFrame({ "kernel_index": np.arange(len(learned_kernel)), "kernel_value": learned_kernel @@ -105,44 +129,43 @@ def ocv_from_soc(soc, T_K=298.15): kernel_df.to_csv("trained_voltage_kernel.csv", index=False) print("Saved trained kernel to trained_voltage_kernel.csv") +# ============================================================ +# MODEL CLASS +# ============================================================ + class AccumulatorVoltageModel: def __init__(self, dt, capacity_Ah, initial_soc, kernel, bias): self.dt = float(dt) self.capacity_Ah = float(capacity_Ah) self.SOC = float(initial_soc) - self.kernel = np.array(kernel, dtype=float) self.bias = float(bias) self.I_hist = np.zeros(len(self.kernel), dtype=float) def ocv_from_soc(self, soc, T_K=298.15): - eps = 1e-9 - soc_shift = soc - (0.1 ** 3) - - denom = np.clip(1.0 - soc_shift + C4, eps, None) - numer = np.clip(C1 * soc_shift + C3, eps, None) - - log_term = np.log(numer / denom) - - return V0 + (C2 * (R * T_K / F) * log_term) + return ocv_from_soc(soc, T_K) def step(self, current, T_K=298.15): I = float(current) - self.SOC -= (I * self.dt) / (3600.0 * self.capacity_Ah) + self.SOC += (I * self.dt) / (3600.0 * self.capacity_Ah) self.SOC = float(np.clip(self.SOC, 0.0, 1.0)) - self.I_hist[:-1] = self.I_hist[1:] - self.I_hist[-1] = I + # Newest current at index 0 + self.I_hist = np.roll(self.I_hist, 1) + self.I_hist[0] = I - h = float(np.dot(self.kernel, self.I_hist[::-1])) + # kernel[k] * I[t-k], no reversed index needed + h = float(np.dot(self.kernel, self.I_hist)) ocv = self.ocv_from_soc(self.SOC, T_K) + V_cell = ocv + (R0 * I) + self.bias + h - V_cell = ocv - (R0 * I) + self.bias + h return float(V_cell) + + model = AccumulatorVoltageModel( dt=dt, capacity_Ah=required_capacity, @@ -164,18 +187,16 @@ def step(self, current, T_K=298.15): soc_model_log = np.array(soc_model_log, dtype=float) I_hist_log = np.array(I_hist_log, dtype=float) -# optional final smoothed correction to tighten overlap further -error = meas_cell_voltage - voltage_log -window = 25 -smooth_kernel = np.ones(window) / window -error_smooth = np.convolve(error, smooth_kernel, mode="same") -voltage_log = voltage_log + error_smooth +# ============================================================ +# ERROR METRICS +# ============================================================ rmse = np.sqrt(np.mean((voltage_log - meas_cell_voltage) ** 2)) mae = np.mean(np.abs(voltage_log - meas_cell_voltage)) -print("RMSE:", rmse) -print("MAE:", mae) +print(f"RMSE: {rmse:.4f} V") +print(f"MAE: {mae:.4f} V") + plt.figure(figsize=(14, 10)) @@ -212,4 +233,4 @@ def step(self, current, T_K=298.15): plt.grid(True) plt.tight_layout() -plt.show() \ No newline at end of file +plt.show() diff --git a/trained_voltage_kernel.csv b/trained_voltage_kernel.csv new file mode 100644 index 0000000..6dee082 --- /dev/null +++ b/trained_voltage_kernel.csv @@ -0,0 +1,121 @@ +kernel_index,kernel_value +0,0.0016021910907593028 +1,-4.0884930635222905e-06 +2,-6.543550294005817e-05 +3,-5.923088719233176e-05 +4,-7.289373880506767e-05 +5,-8.566978668828535e-05 +6,-6.639146925721663e-05 +7,-7.271308853325891e-05 +8,-2.0417099533859173e-05 +9,-3.565276812846345e-05 +10,-5.6634704554986584e-05 +11,-0.00012460932046737367 +12,-6.813575431332786e-05 +13,-3.140492087849164e-05 +14,-4.1995314335183927e-05 +15,-5.0445133731274495e-06 +16,5.559875330028438e-06 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+91,-6.201519135333833e-06 +92,1.075195591931887e-05 +93,-7.958231666394961e-06 +94,-1.566085373584499e-05 +95,-3.444274870237118e-06 +96,1.6484175637856208e-06 +97,-2.4696790526136187e-06 +98,-2.5718225865853134e-06 +99,-2.870814602155051e-06 +100,-1.2997139385802445e-05 +101,-6.140050599490011e-06 +102,-2.6482781122335405e-06 +103,1.201689609952287e-06 +104,1.5237792878081123e-05 +105,1.4758575169866904e-05 +106,1.6252014680216034e-05 +107,1.640190268335415e-05 +108,8.522252771235008e-06 +109,1.740461432691417e-05 +110,2.334211645866296e-05 +111,2.258734604837079e-05 +112,-5.011967716732702e-06 +113,-7.502420582653692e-06 +114,3.5450748168386586e-06 +115,4.045008641604303e-05 +116,-3.8241539372363195e-06 +117,-3.574013864701268e-06 +118,-1.6944039324449247e-05 +119,-0.00019550740220202864 From 11e685be0a5163e4c95d579164aee49e0c2100c9 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Wed, 20 May 2026 15:40:21 -0700 Subject: [PATCH 12/38] doc fixes --- FullVehicleSim/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FullVehicleSim/README.md b/FullVehicleSim/README.md index a9afdf1..d535718 100644 --- a/FullVehicleSim/README.md +++ b/FullVehicleSim/README.md @@ -23,7 +23,7 @@ If you want more information on the basis of some of these simulation parts, you

Simulation Parameters

-This is a json input file with a bunch of inputs for the simulation. Unfortunately JSON doesn't support comments so the unit explanations have to go here. The file is [params.json](params.json). Tweak this for all the indepth constants and higher level stuff like step size and simulation duration. +This is a json input file with a bunch of inputs for the simulation. json5 supports comments so stuff like units and other notes like options for certain fields live inside it! The file is [params.json5](params.json5). Tweak this for all the indepth constants and higher level stuff like step size and simulation duration.

Simulation Controls

From d5eb0ba4646b0e9bfee327de8c26a07475dc7797 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 14:21:02 -0700 Subject: [PATCH 13/38] remove obsolete file --- FullVehicleSim/Electrical/tractiveBattery.py | 47 -------------------- 1 file changed, 47 deletions(-) delete mode 100644 FullVehicleSim/Electrical/tractiveBattery.py diff --git a/FullVehicleSim/Electrical/tractiveBattery.py b/FullVehicleSim/Electrical/tractiveBattery.py deleted file mode 100644 index b4d5340..0000000 --- a/FullVehicleSim/Electrical/tractiveBattery.py +++ /dev/null @@ -1,47 +0,0 @@ -from paramLoader import Parameters, Magic -from state import VehicleState -import numpy as np - -def calcVoltage(prevWorld: VehicleState) -> float: - delta = 1 / Parameters["stepsPerSecond"] - capacity_Ah = Parameters["cellCapacity_Ah"] - soc = prevWorld.charge - - sigma = Parameters["cellModelSigma"] - hystGain = Parameters["hysteresisGain"] - - # Sliding window: last 10 seconds of current - I_hist = prevWorld.current_history - - # Hysteresis kernel - t = np.arange(prevWorld.current_history.shape[0]) - kernel = np.exp(-(t**2) / (2 * sigma**2)) - kernel /= np.sum(kernel) - - def ocv_from_soc(self, soc): - return 3.0 + 0.9 * soc + 0.25 * np.exp(-12 * (1 - soc)) - - def sag(self, current): - return 0.02 * current + 0.004 * (current ** 1.3) - - def step(self, current): - - # Update SOC - self.SOC -= (current * self.dt) / (3600 * self.capacity_Ah) - self.SOC = np.clip(self.SOC, 0.0, 1.0) - - # -------- Sliding array logic -------- - self.I_hist[:-1] = self.I_hist[1:] # shift old values - self.I_hist[-1] = current # add new current - - # Hysteresis voltage - V_hyst = self.hyst_gain * np.sum(self.I_hist * self.kernel) - - # Terminal voltage - voltage = ( - self.ocv_from_soc(self.SOC) - - self.sag(current) * (1 - self.SOC) - - V_hyst - ) - - return voltage \ No newline at end of file From c7f658ba9fc94a6b86eee55ccec28710310cc00c Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 14:21:19 -0700 Subject: [PATCH 14/38] ignore sim output so it doesn't get commited --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 0131c2d..bc6004c 100644 --- a/.gitignore +++ b/.gitignore @@ -212,3 +212,4 @@ Projects/.DS_Store .DS_Store .fake *.joblib +FullVehicleSim/simulation_output.parquet From 757390c4d90181810393c3e4823ed50265dab0b4 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 15:17:36 -0700 Subject: [PATCH 15/38] couple corrections to torque calculations --- FullVehicleSim/Electrical/powertrain.py | 8 ++++---- FullVehicleSim/engine.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/FullVehicleSim/Electrical/powertrain.py b/FullVehicleSim/Electrical/powertrain.py index daa3cf4..965c2a6 100644 --- a/FullVehicleSim/Electrical/powertrain.py +++ b/FullVehicleSim/Electrical/powertrain.py @@ -14,7 +14,7 @@ def calcMaxMotorTorque(worldArray:np.ndarray, step:int, resistiveForces:float, m maxPowerTorque = maxPower / worldArray[step-1, varMotorRotationsHZ] * Parameters["gearRatio"] else: ## Avoid divide by 0 error but it's just the same as the max torque that the motor can deliver (180 Nm) maxPowerTorque = 180.0 # Nm at 0 rpm - torque = min(Parameters["maxTorque"], maxPowerTorque, maxTractionTorqueAtWheel/Parameters["gearRatio"]) + torque = min(Parameters["maxTorque"], maxPowerTorque, 2*maxTractionTorqueAtWheel/Parameters["gearRatio"]) return torque def calcCurrent(power:float, voltage:float) -> float: @@ -24,12 +24,12 @@ def calcCurrent(power:float, voltage:float) -> float: def calcMaxWheelTorque(maxMotorTorque): ''' - maxMotorTorque * gear rato + maxMotorTorque * gear ratio ''' return maxMotorTorque * Parameters["gearRatio"] -def calcMotorForce(maxWheelTorque:float) -> float: - return (maxWheelTorque / Parameters["wheelRadius"]) +def calcMotorForce(motorTorque:float) -> float: + return (motorTorque * Parameters["gearRatio"] / Parameters["wheelRadius"]) def calcMaxPower(voltage:float) -> float: return Parameters["tractiveIMax"] * voltage diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index 40a915d..0f7c47a 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -51,7 +51,7 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: delta = 1/Parameters["stepsPerSecond"] arr[varMaxTraction] = 180.0 # Needs a more complex implementation before being used. Potentially something akin to the gaussian kernel of the voltage histeresis model but for acceleration? Or literally based on the suspension travel. - arr[varVoltage] = calcVoltage(worldArray, step) # Not yet implemented. Returns 120 for now. + arr[varVoltage] = calcVoltage(worldArray, step) # Volts, Voltage of the entire pack, not a single cell arr[varMaxPower] = calcMaxPower(arr[varVoltage]) # Watts arr[varResistiveForces] = calcResistiveForces(worldArray, step) @@ -61,11 +61,11 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: arr[varFrontBrakeTemperature] = worldArray[step-1, varFrontBrakeTemperature] + arr[varFrontBrakeHeating] - arr[varFrontBrakeCooling] arr[varRearBrakeTemperature] = worldArray[step-1, varRearBrakeTemperature] + arr[varRearBrakeHeating] - arr[varRearBrakeCooling] - arr[varMaxMotorTorque] = calcMaxMotorTorque(worldArray, step, arr[varResistiveForces], arr[varMaxPower], arr[varMaxTraction]) + arr[varMaxMotorTorque] = calcMaxMotorTorque(worldArray, step, arr[varResistiveForces], arr[varMaxPower], arr[varMaxTraction]) # Nm arr[varMotorTorque] = min(Parameters["maxTorque"]*arr[varThrottle], arr[varMaxMotorTorque]) # Nm arr[varPower] = arr[varMotorTorque] * worldArray[step-1, varMotorRotationsHZ] # Watts - arr[varMotorForce] = calcMotorForce(arr[varMotorTorque]) # Newtons + arr[varMotorForce] = calcMotorForce(arr[varMotorTorque]) # Newtons, Longitudinal force at the wheel from the motor arr[varNetForce] = arr[varMotorForce] + arr[varResistiveForces] # Newtons arr[varAcceleration] = arr[varNetForce] / Parameters["Mass"] # m/s^2 From d9e67ea3f0db890d877171c6eb18934748daebb5 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 15:44:58 -0700 Subject: [PATCH 16/38] brake calipers act on both sides of disk --- FullVehicleSim/Mech/braking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FullVehicleSim/Mech/braking.py b/FullVehicleSim/Mech/braking.py index 4a15070..718ec70 100644 --- a/FullVehicleSim/Mech/braking.py +++ b/FullVehicleSim/Mech/braking.py @@ -6,7 +6,7 @@ # The goal of these functions are to calculate the net force on the brakes, applied reverse to heading def brakePSI_toNewtons(psi:float) -> float: - return psi * Parameters["brakeCaliperArea"] * 4.448222 # lb force to Newtons + return psi * Parameters["brakeCaliperArea"] * 2 * 4.448222 # lb force to Newtons def calcBrakeForce(worldArray:np.ndarray, step:int) -> tuple[float,float]: """ From b0089bec6a47280f430bac1b87f770e0d077541d Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 15:45:12 -0700 Subject: [PATCH 17/38] gear ratio error for rpm calculation --- FullVehicleSim/engine.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index 0f7c47a..4180cad 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -85,8 +85,8 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: arr[varFrontSlipAngle], arr[varRearSlipAngle] = calcSlipAngle(worldArray, step) arr[varMaxWheelTorque] = calcMaxWheelTorque(arr[varMaxMotorTorque]) - arr[varWheelRotationsHZ] = arr[varSpeed] / Parameters["wheelCircumferance"] * 2.0 * np.pi - arr[varMotorRotationsHZ] = arr[varWheelRotationsHZ] / Parameters["gearRatio"] + arr[varWheelRotationsHZ] = arr[varSpeed] / Parameters["wheelRadius"] + arr[varMotorRotationsHZ] = arr[varWheelRotationsHZ] * Parameters["gearRatio"] arr[varWheelRPM] = arr[varWheelRotationsHZ] * 60.0 arr[varMotorRPM] = arr[varWheelRPM] / Parameters["gearRatio"] return arr From bfb14d827246a6d9764b1ec345e6b6d75f63c063 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 16:42:50 -0700 Subject: [PATCH 18/38] fix another gear ratio issue --- FullVehicleSim/engine.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index 4180cad..c7efb5a 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -88,7 +88,7 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: arr[varWheelRotationsHZ] = arr[varSpeed] / Parameters["wheelRadius"] arr[varMotorRotationsHZ] = arr[varWheelRotationsHZ] * Parameters["gearRatio"] arr[varWheelRPM] = arr[varWheelRotationsHZ] * 60.0 - arr[varMotorRPM] = arr[varWheelRPM] / Parameters["gearRatio"] + arr[varMotorRPM] = arr[varWheelRPM] * Parameters["gearRatio"] return arr def dynamicStepState(step:np.ndarray) -> np.ndarray: From 1ed02d1a467d30de10d9f78c9faa87782090a86d Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 16:43:05 -0700 Subject: [PATCH 19/38] fixed braking cooling calculations --- FullVehicleSim/Mech/brakeThermalTransfer.csv | 10 ++++++++ FullVehicleSim/Mech/braking.py | 24 ++++++++++++++++++-- 2 files changed, 32 insertions(+), 2 deletions(-) create mode 100644 FullVehicleSim/Mech/brakeThermalTransfer.csv diff --git a/FullVehicleSim/Mech/brakeThermalTransfer.csv b/FullVehicleSim/Mech/brakeThermalTransfer.csv new file mode 100644 index 0000000..db68835 --- /dev/null +++ b/FullVehicleSim/Mech/brakeThermalTransfer.csv @@ -0,0 +1,10 @@ +airSpeed,coeff +0,5 +10,7 +20,10 +30,15 +40,20 +50,25 +60,30 +70,35 +80,40 \ No newline at end of file diff --git a/FullVehicleSim/Mech/braking.py b/FullVehicleSim/Mech/braking.py index 718ec70..4ae58f9 100644 --- a/FullVehicleSim/Mech/braking.py +++ b/FullVehicleSim/Mech/braking.py @@ -1,10 +1,22 @@ from Mech import brakepadFrictionModel from paramLoader import * import numpy as np +import polars as pl # Docs: # https://docs.google.com/document/d/1oGsGDnY0DEKWpE3S6481A9yZ0F9qUEwWkSXJwTSz4E4/edit?tab=t.2rmbsj26c7w # The goal of these functions are to calculate the net force on the brakes, applied reverse to heading +try: + brakeConvectionData = pl.read_csv("Mech/brakeThermalTransfer.csv") +except Exception as e: + print("Error loading brake thermal transfer data from Mech/brakeThermalTransfer.csv:", e) + raise e +brakeAirSpeedData, brakeCoeffData = brakeConvectionData["airSpeed"].to_numpy(), brakeConvectionData["coeff"].to_numpy() + +def brakeTransferCoeff(speed:float) -> float: + # Interpolate the heat transfer coefficient based on air speed + return np.interp(speed, brakeAirSpeedData, brakeCoeffData)[0] + def brakePSI_toNewtons(psi:float) -> float: return psi * Parameters["brakeCaliperArea"] * 2 * 4.448222 # lb force to Newtons @@ -35,8 +47,16 @@ def calcBrakeCooling(worldArray:np.ndarray, step:int) -> tuple[float,float]: :param prevWorld: World State :return: Change in Temperature """ - frontBrakeCooling = Parameters["ambientTemperature"] + (worldArray[step-1, varFrontBrakeTemperature] - Parameters["ambientTemperature"]) * np.e ** (-1 / Parameters["stepsPerSecond"]/50.2) - rearBrakeCooling = Parameters["ambientTemperature"] + (worldArray[step-1, varRearBrakeTemperature] - Parameters["ambientTemperature"]) * np.e ** (-1 / Parameters["stepsPerSecond"]/50.2) + speed = worldArray[step-1, varSpeed] + heatTransferCoeff = brakeTransferCoeff(speed) + brakeThermalMass = Parameters["4130SpecificHeatCapacity"] * Parameters["brakeRotorMass"] + brakeCoolingArea = Parameters["brakeRotorArea"] + frontBrakeCoolingEnergy = heatTransferCoeff * brakeCoolingArea *(worldArray[step-1, varFrontBrakeTemperature] - Parameters["ambientTemperature"]) + frontBrakeCooling = frontBrakeCoolingEnergy / brakeThermalMass / Parameters["stepsPerSecond"] + + rearBrakeCoolingEnergy = heatTransferCoeff * brakeCoolingArea *(worldArray[step-1, varRearBrakeTemperature] - Parameters["ambientTemperature"]) + rearBrakeCooling = rearBrakeCoolingEnergy / brakeThermalMass / Parameters["stepsPerSecond"] + return frontBrakeCooling, rearBrakeCooling #q = (initTemperature - parameters["ambientTemperature"]) * parameters["brakeMass"] * parameters["brakeSpecificHeatCapacity"] #change = (q * parameters["brakepadThickness"])/(initTemperature * parameters["brakeThermalConductivity"] * parameters["brakeSurfaceArea"] From 1bdce345d1491b711f347068af61b8d049aac668 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 16:43:23 -0700 Subject: [PATCH 20/38] also some params for those fixes and testing --- FullVehicleSim/params.json5 | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index e01b1a5..ea6ffbd 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -1,15 +1,15 @@ // Base SI Units unless otherwise specified { "Parameters": { - "stepsPerSecond": 300, - "simulationDuration": 40.0, + "stepsPerSecond": 100, + "simulationDuration": 500.0, // Seconds "interpolationMethod": "linear", // "linear" or "cubic", this is for interpolating the simultion controls. "ambientTemperature": 20, // °C - "tractiveIMax": 300, // A + "tractiveIMax": 400, // A "initialBrakeTemperature": 20, // °C "initialBatteryTemperature": 20, // °C - "vehicleSOC": 0.85, // Out of 1 + "vehicleSOC": 1, // Out of 1 "initSpeed": 0.0, // m/s "initHeading": [1.0, 0.0, 0.0], // Vector pointing in the direction of the front of the car "initPosition": [0.0, 0.0, 0.0], // Starting position of the car in the world frame @@ -21,6 +21,10 @@ "brakeDiscRadius": 0.09525, // meters (3.75 inches) -- Brake pad is ~1 inch wide near the edge of an 8 inch diamater rotor "brakeCaliperArea": 1.23, // in^2 + "brakeRotorArea": 77, // in^2, not simply calculated because the brake disc has holes in it + "brakeRotorMass": 0.4908, // kg + "4130SpecificHeatCapacity": 540, // J/(kg*k) + "wheelCircumferance": 1.35716802635079, "wheelRadius": 0.216, "gearRatio": 3.41667, @@ -31,7 +35,7 @@ "p_0": 82000, "load_0": 300, - "Mass": 300, // kg + "Mass": 280, // kg "wheelBase": 1.65471, "a": 0.853506, "frontWeightDist": 46.46, From 7ffa1ec33d263e6cfb8113a90eb7f38aec305996 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 16:45:27 -0700 Subject: [PATCH 21/38] fixed energy change order of magnitude issue --- FullVehicleSim/Mech/braking.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FullVehicleSim/Mech/braking.py b/FullVehicleSim/Mech/braking.py index 4ae58f9..c9bd6f1 100644 --- a/FullVehicleSim/Mech/braking.py +++ b/FullVehicleSim/Mech/braking.py @@ -68,7 +68,7 @@ def calcBrakeHeating(worldArray:np.ndarray, step:int) -> tuple[float,float]: # Guess energy increase based on kinetic energy decrease of the vehicle. # Assumption is 100% of kinetic energy lost goes into brake heating. speedChange = (frontBrakeForce + rearBrakeForce) / Parameters["Mass"] / Parameters["stepsPerSecond"] # momentum impulse - energyChange = 0.5 * Parameters["Mass"] * (worldArray[step-1, varSpeed] - (worldArray[step-1, varSpeed] - speedChange)) + energyChange = 0.5 * Parameters["Mass"] * (worldArray[step-1, varSpeed]**2 - ((worldArray[step-1, varSpeed] - speedChange)**2)) tempChange = energyChange/(Parameters["brakeMass"] * Parameters["brakeSpecificHeatCapacity"]) # While this doesn't seem physically intuitive, it is based on the idea that the front and rear brakes share heat based on their contribution to total braking force. From bd0321373306364d3113e4bccf2b139998d33ae9 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sat, 30 May 2026 16:47:13 -0700 Subject: [PATCH 22/38] clean and mix max torque calculation --- FullVehicleSim/Electrical/powertrain.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/FullVehicleSim/Electrical/powertrain.py b/FullVehicleSim/Electrical/powertrain.py index 965c2a6..4487971 100644 --- a/FullVehicleSim/Electrical/powertrain.py +++ b/FullVehicleSim/Electrical/powertrain.py @@ -11,9 +11,9 @@ def calcMaxMotorTorque(worldArray:np.ndarray, step:int, resistiveForces:float, m if worldArray[step-1, varMotorRPM] > 7490: return -1 * resistiveForces * Parameters["wheelRadius"] if worldArray[step-1, varMotorRotationsHZ] != 0: ## If rolling, torque may be power limited. - maxPowerTorque = maxPower / worldArray[step-1, varMotorRotationsHZ] * Parameters["gearRatio"] + maxPowerTorque = maxPower / worldArray[step-1, varMotorRotationsHZ] else: ## Avoid divide by 0 error but it's just the same as the max torque that the motor can deliver (180 Nm) - maxPowerTorque = 180.0 # Nm at 0 rpm + maxPowerTorque = Parameters["maxTorque"] # Nm at 0 rpm torque = min(Parameters["maxTorque"], maxPowerTorque, 2*maxTractionTorqueAtWheel/Parameters["gearRatio"]) return torque From ce42ff6f5acfd811e6b635c786c0726cc71a3471 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sun, 31 May 2026 02:21:24 -0700 Subject: [PATCH 23/38] couple more fixes --- FullVehicleSim/engine.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index c7efb5a..996cc75 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -70,9 +70,9 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: arr[varAcceleration] = arr[varNetForce] / Parameters["Mass"] # m/s^2 - arr[varCurrent] = arr[varPower] / arr[varVoltage] # Amps + arr[varCurrent] = calcCurrent(arr[varPower], arr[varVoltage]) # Amps. clamps for current limit. pack current. - arr[varCharge] = worldArray[step-1, varCharge] - worldArray[step, varCurrent] * delta / 3600.0 + arr[varCharge] = worldArray[step-1, varCharge] - worldArray[step, varCurrent] * delta / 3600.0 / Parameters["parallelCells"] / Parameters["cellCapacity_Ah"] arr[varPosX:varPosZ+1] = worldArray[step-1, varPosX:varPosZ+1] + worldArray[step-1, varVelX:varVelZ+1] * delta arr[varSpeed] = max(0, worldArray[step-1, varSpeed] + arr[varAcceleration] * delta) # Sometimes braking falls a tad below 0 so we just correct that because otherwise everything breaks arr[varYawRate] = worldArray[step-1, varYawRate] @@ -87,7 +87,7 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: arr[varMaxWheelTorque] = calcMaxWheelTorque(arr[varMaxMotorTorque]) arr[varWheelRotationsHZ] = arr[varSpeed] / Parameters["wheelRadius"] arr[varMotorRotationsHZ] = arr[varWheelRotationsHZ] * Parameters["gearRatio"] - arr[varWheelRPM] = arr[varWheelRotationsHZ] * 60.0 + arr[varWheelRPM] = arr[varWheelRotationsHZ] * 60.0 / (2 * np.pi) arr[varMotorRPM] = arr[varWheelRPM] * Parameters["gearRatio"] return arr From f82a5780ebd76f844070ebc26b509f3a36dd12c8 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sun, 31 May 2026 02:21:50 -0700 Subject: [PATCH 24/38] Implemented regen and brake pedal travel --- FullVehicleSim/Mech/braking.py | 5 ++- FullVehicleSim/engine.py | 2 +- FullVehicleSim/main.py | 13 +++--- FullVehicleSim/paramLoader.py | 81 +++++++++++++++++----------------- FullVehicleSim/params.json5 | 11 +++-- 5 files changed, 60 insertions(+), 52 deletions(-) diff --git a/FullVehicleSim/Mech/braking.py b/FullVehicleSim/Mech/braking.py index c9bd6f1..0f2dea0 100644 --- a/FullVehicleSim/Mech/braking.py +++ b/FullVehicleSim/Mech/braking.py @@ -15,10 +15,10 @@ def brakeTransferCoeff(speed:float) -> float: # Interpolate the heat transfer coefficient based on air speed - return np.interp(speed, brakeAirSpeedData, brakeCoeffData)[0] + return np.interp(speed, brakeAirSpeedData, brakeCoeffData) #type: ignore Function says it returns an array but it just returns a scalar def brakePSI_toNewtons(psi:float) -> float: - return psi * Parameters["brakeCaliperArea"] * 2 * 4.448222 # lb force to Newtons + return psi * Parameters["brakeCaliperArea"] * 2 * 4.448222 # lb force to Newtons, 2 for 2 sides of a caliper per disc def calcBrakeForce(worldArray:np.ndarray, step:int) -> tuple[float,float]: """ @@ -36,6 +36,7 @@ def calcBrakeForce(worldArray:np.ndarray, step:int) -> tuple[float,float]: rearBrakeForce = brakePSI_toNewtons(rearBrakePSI) # Calculate Brake Force + # Factor of 2 for 2 brakes on front and 2 on rear frontBrakeForce:float = brakepadFrictionModel.calcFrictionCoeff(worldArray[step-1, varFrontBrakeTemperature]) * frontBrakeForce * 2 * Parameters["brakeDiscRadius"] / Parameters["wheelRadius"] rearBrakeForce:float = brakepadFrictionModel.calcFrictionCoeff(worldArray[step-1, varRearBrakeTemperature]) * rearBrakeForce * 2 * Parameters["brakeDiscRadius"] / Parameters["wheelRadius"] return frontBrakeForce, rearBrakeForce diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index 996cc75..a290a00 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -62,7 +62,7 @@ def stepState(worldArray:np.ndarray, step:int) -> np.ndarray: arr[varRearBrakeTemperature] = worldArray[step-1, varRearBrakeTemperature] + arr[varRearBrakeHeating] - arr[varRearBrakeCooling] arr[varMaxMotorTorque] = calcMaxMotorTorque(worldArray, step, arr[varResistiveForces], arr[varMaxPower], arr[varMaxTraction]) # Nm - arr[varMotorTorque] = min(Parameters["maxTorque"]*arr[varThrottle], arr[varMaxMotorTorque]) # Nm + arr[varMotorTorque] = min(Parameters["maxTorque"]*(arr[varThrottle]-arr[varBrakePedalTravel]), arr[varMaxMotorTorque]) # Nm arr[varPower] = arr[varMotorTorque] * worldArray[step-1, varMotorRotationsHZ] # Watts arr[varMotorForce] = calcMotorForce(arr[varMotorTorque]) # Newtons, Longitudinal force at the wheel from the motor diff --git a/FullVehicleSim/main.py b/FullVehicleSim/main.py index 118422b..88bcfe5 100644 --- a/FullVehicleSim/main.py +++ b/FullVehicleSim/main.py @@ -28,8 +28,8 @@ raise Exception("Please provide a valid simulation controls file path using --simulation_controls or -c") ## Double check it has the correct columns - if df_controls.columns != ['time', 'throttle', 'brakePressureFront','brakePressureRear', 'steerAngle']: - raise Exception("Simulation controls file must contain the following columns: 'time', 'throttle', 'brakePressureFront', 'brakePressureRear', 'steerAngle'") + if df_controls.columns != ['time', 'throttle', 'brakePressureFront','brakePressureRear', 'brakePedalTravel', 'steerAngle']: + raise Exception("Simulation controls file must contain the following columns: 'time', 'throttle', 'brakePressureFront', 'brakePressureRear', 'brakePedalTravel', 'steerAngle'") totalSteps = int(Parameters["stepsPerSecond"] * Parameters["simulationDuration"]) steps = np.arange(0, Parameters["simulationDuration"], 1/Parameters["stepsPerSecond"]) @@ -51,6 +51,7 @@ 'throttle': df_controls["throttle"][-1], 'brakePressureFront': df_controls["brakePressureFront"][-1], 'brakePressureRear': df_controls["brakePressureRear"][-1], + 'brakePedalTravel': df_controls["brakePedalTravel"][-1], 'steerAngle': df_controls["steerAngle"][-1]})) timeSeries = df_controls['time'] @@ -62,11 +63,12 @@ cs = CubicSpline(timeSeries, df_controls.drop('time').to_numpy()) controlInputs = cs(steps) elif Parameters["interpolationMethod"] == "linear": - controlInputs = np.zeros((len(steps), 4)) + controlInputs = np.zeros((len(steps), 5)) controlInputs[:,0] = np.interp(steps, timeSeries, df_controls['throttle']) controlInputs[:,1] = np.interp(steps, timeSeries, df_controls['brakePressureFront']) controlInputs[:,2] = np.interp(steps, timeSeries, df_controls['brakePressureRear']) - controlInputs[:,3] = np.interp(steps, timeSeries, df_controls['steerAngle']) + controlInputs[:,3] = np.interp(steps, timeSeries, df_controls['brakePedalTravel']) + controlInputs[:,4] = np.interp(steps, timeSeries, df_controls['steerAngle']) else: raise Exception("Unsupported interpolation method. Please use 'cubic' or 'linear'.") @@ -75,7 +77,8 @@ worldArray[1:, varThrottle] = controlInputs[:,0] worldArray[1:, varBrakePressureFront] = controlInputs[:,1] worldArray[1:, varBrakePressureRear] = controlInputs[:,2] - worldArray[1:, varSteerAngle] = controlInputs[:,3] + worldArray[1:, varBrakePedalTravel] = controlInputs[:,3] + worldArray[1:, varSteerAngle] = controlInputs[:,4] worldArray[0,varCharge] = Parameters["vehicleSOC"] worldArray[0,varFrontBrakeTemperature] = Parameters["initialBrakeTemperature"] worldArray[0,varRearBrakeTemperature] = Parameters["initialBrakeTemperature"] diff --git a/FullVehicleSim/paramLoader.py b/FullVehicleSim/paramLoader.py index 56909af..30cdc51 100644 --- a/FullVehicleSim/paramLoader.py +++ b/FullVehicleSim/paramLoader.py @@ -27,46 +27,47 @@ varThrottle = 1 varBrakePressureFront = 2 varBrakePressureRear = 3 -varSteerAngle = 4 -varPosX = 5 -varPosY = 6 -varPosZ = 7 -varVelX = 8 -varVelY = 9 -varVelZ = 10 -varSpeed = 11 -varHeadingX = 12 -varHeadingY = 13 -varHeadingZ = 14 -varYawRate = 15 -varFrontBrakeTemperature = 16 -varRearBrakeTemperature = 17 -varCharge = 18 -varDrag = 19 -varResistiveForces = 20 -varMotorTorque = 21 -varMotorForce = 22 -varNetForce = 23 -varMaxTraction = 24 -varWheelRotationsHZ = 25 -varMotorRPM = 26 -varMotorRotationsHZ = 27 -varCurrent = 28 -varMaxWheelTorque = 29 -varMaxPower = 30 -varPower = 31 -varVoltage = 32 -varFrontBrakeForce = 33 -varRearBrakeForce = 34 -varFrontBrakeHeating = 35 -varRearBrakeHeating = 36 -varFrontBrakeCooling = 37 -varRearBrakeCooling = 38 -varFrontSlipAngle = 39 -varRearSlipAngle = 40 -varMaxMotorTorque = 41 -varAcceleration = 42 -varWheelRPM = 43 +varBrakePedalTravel = 4 +varSteerAngle = 5 +varPosX = 6 +varPosY = 7 +varPosZ = 8 +varVelX = 9 +varVelY = 10 +varVelZ = 11 +varSpeed = 12 +varHeadingX = 13 +varHeadingY = 14 +varHeadingZ = 15 +varYawRate = 16 +varFrontBrakeTemperature = 17 +varRearBrakeTemperature = 18 +varCharge = 19 +varDrag = 20 +varResistiveForces = 21 +varMotorTorque = 22 +varMotorForce = 23 +varNetForce = 24 +varMaxTraction = 25 +varWheelRotationsHZ = 26 +varMotorRPM = 27 +varMotorRotationsHZ = 28 +varCurrent = 29 +varMaxWheelTorque = 30 +varMaxPower = 31 +varPower = 32 +varVoltage = 33 +varFrontBrakeForce = 34 +varRearBrakeForce = 35 +varFrontBrakeHeating = 36 +varRearBrakeHeating = 37 +varFrontBrakeCooling = 38 +varRearBrakeCooling = 39 +varFrontSlipAngle = 40 +varRearSlipAngle = 41 +varMaxMotorTorque = 42 +varAcceleration = 43 +varWheelRPM = 44 # Automatically generate schema from defined variables def generate_variable_schema() -> Dict[int, str]: diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index ea6ffbd..af601f6 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -24,11 +24,13 @@ "brakeRotorArea": 77, // in^2, not simply calculated because the brake disc has holes in it "brakeRotorMass": 0.4908, // kg "4130SpecificHeatCapacity": 540, // J/(kg*k) + + "regenMaxTorque": 180, // Nm, this is the maximum amount of torque the regen system can apply at the motor. "wheelCircumferance": 1.35716802635079, - "wheelRadius": 0.216, - "gearRatio": 3.41667, - "maxTorque": 180, + "wheelRadius": 0.216, // m + "gearRatio": 3.41667, // 41/12 + "maxTorque": 180, // Nm, max torque at the motor. "friction-coeff-lat": 1.7333, "friction-coeff-long": 1.7333, "unloaded-radius": 1.7333, @@ -56,7 +58,8 @@ "maxBrakeForce": 1500, "seriesCells": 30, - "cellCapacity_Ah": 2.6, + "parallelCells": 20, + "cellCapacity_Ah": 2.6, // Amp hours "histeresisKernelLength": 10.0, // seconds "GasConstant": 8.31446261815324, From 78096eb61fb9ceba051b209ebed1495807a0115f Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Fri, 5 Jun 2026 14:47:59 -0700 Subject: [PATCH 25/38] simulation analysis script --- Data/simAnalysis.py | 52 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 Data/simAnalysis.py diff --git a/Data/simAnalysis.py b/Data/simAnalysis.py new file mode 100644 index 0000000..e7fffea --- /dev/null +++ b/Data/simAnalysis.py @@ -0,0 +1,52 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt + + +dfComparison = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet") +dfComparison = dfComparison.fill_null(strategy="forward").fill_null(strategy="backward") +dfComparison = dfComparison.with_columns( + (pl.col("Time_ms")*0.001 - pl.col("Time_ms").min() * 0.001).alias("time"), + ((dfComparison["TMAIN_DATA_STEERING"]-10.5)/180*np.pi).alias("steerAngle"), + (dfComparison["ETC_STATUS_PEDAL_TRAVEL"]/100.0).alias("throttle"), + pl.Series(np.clip(dfComparison["ETC_STATUS_BRAKE_SENSE_VOLTAGE"]-330, 0, 2640)/2640*2000).alias("brakePressureFront"), + pl.Series(np.clip(dfComparison["ETC_STATUS_BRAKE_SENSE_VOLTAGE"]-330, 0, 2640)/2640*2000).alias("brakePressureRear") +) + +df = pl.read_parquet("FullVehicleSim/simulation_output.parquet") +df = df.filter(pl.col("time") < dfComparison["time"].max()) + +## Columns +# ['time', 'throttle', 'brakePressureFront', 'brakePressureRear', 'steerAngle', +# 'posX', 'posY', 'posZ', 'velX', 'velY', 'velZ', 'speed', 'headingX', 'headingY', +# 'headingZ', 'yawRate', 'frontBrakeTemperature', 'rearBrakeTemperature', 'charge', +# 'drag', 'resistiveForces', 'motorTorque', 'motorForce', 'netForce', 'maxTraction', +# 'wheelRotationsHZ', 'motorRPM', 'motorRotationsHZ', 'current', 'maxWheelTorque', +# 'maxPower', 'power', 'voltage', 'frontBrakeForce', 'rearBrakeForce', 'frontBrakeHeating', +# 'rearBrakeHeating', 'frontBrakeCooling', 'rearBrakeCooling', 'frontSlipAngle', +# 'rearSlipAngle', 'maxMotorTorque', 'acceleration', 'wheelRPM'] + +plt.plot(df["time"], df["motorRPM"], label="Sim") +plt.plot(dfComparison["time"], dfComparison["SME_TRQSPD_Speed"], label="Real") +plt.plot(df["time"], df["motorTorque"]*10, label="Motor Torque") +plt.plot(df["time"], df["frontBrakeForce"] + df["rearBrakeForce"], label="Total Brake Force") +# plt.plot(df["time"], df["brakePressureFront"]*20, label="Sim Brake Pressure Front") +plt.legend() +plt.show() + + +plt.plot(df["time"], df["voltage"]/30) +plt.plot(df["time"], df["charge"]) +plt.plot(df["time"], df["current"], label="Current") +plt.plot(df["time"], df["power"], label="Power") +plt.plot(df["time"], df["motorRPM"], label="Motor RPM") +plt.plot(df["time"], df["motorTorque"], label="Motor Torque") +plt.plot(df["time"], df["throttle"], label="Throttle") +plt.plot(df["time"], df["maxMotorTorque"], label="Max Motor Torque") +plt.plot(df["time"], df["speed"]*2.237, label="Speed") +plt.plot(df["time"], df["frontBrakeForce"], label="Front Brake Force") +plt.plot(df["time"], df["rearBrakeForce"], label="Rear Brake Force") +plt.plot(df["time"], df["frontBrakeForce"] + df["rearBrakeForce"], label="Total Brake Force") +plt.plot(df["time"], df["drag"], label="Drag Force") +plt.legend() +plt.show() \ No newline at end of file From 95e8537066a11f7fe1271df7450266357c2a50ea Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Fri, 5 Jun 2026 14:48:12 -0700 Subject: [PATCH 26/38] sim controls example --- ...onControls_Mar162026_SteeperRegenCurve.csv | 41354 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76dd48996d980673352f065d82e5376f402c4ad9 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Fri, 5 Jun 2026 14:48:46 -0700 Subject: [PATCH 27/38] script for modding data into sim input --- .../vehicleControlInputsModification.py | 65 +++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 FullVehicleSim/SimulationControlInputs/vehicleControlInputsModification.py diff --git a/FullVehicleSim/SimulationControlInputs/vehicleControlInputsModification.py b/FullVehicleSim/SimulationControlInputs/vehicleControlInputsModification.py new file mode 100644 index 0000000..1c98642 --- /dev/null +++ b/FullVehicleSim/SimulationControlInputs/vehicleControlInputsModification.py @@ -0,0 +1,65 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit + +# time,throttle,brakePressureFront,brakePressureRear,steerAngle + +df = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet") +df = df.fill_null(strategy="forward").fill_null(strategy="backward") +df = df.with_columns( + (pl.col("Time_ms")*0.001 - pl.col("Time_ms").min() * 0.001).alias("time"), + ((df["TMAIN_DATA_STEERING"]-10.5)/180*np.pi).alias("steerAngle"), + (df["ETC_STATUS_PEDAL_TRAVEL"]/100.0).alias("throttle"), + pl.Series(np.clip(df["ETC_STATUS_BRAKE_SENSE_VOLTAGE"]-330, 0, 2640)/2640*2000).alias("brakePressureFront"), + pl.Series(np.clip(df["ETC_STATUS_BRAKE_SENSE_VOLTAGE"]-330, 0, 2640)/2640*2000).alias("brakePressureRear"), + (pl.col("ETC_STATUS_BRAKE_PEDAL_TRAVEL") / 106.7).alias("brakePedalTravel") +) + +[x for x in df.columns if "SME" in x] + +plt.plot(df["time"], (df["TMAIN_DATA_STEERING"])) +plt.show() + +plt.plot(df["time"], (df["TMAIN_DATA_STEERING"]-10.5)/180*np.pi) +plt.show() + + +dfT = df.filter(pl.col("time") < 50).filter(pl.col("time") > 48) +dfT = df +plt.scatter(dfT["VDM_GPS_Latitude"], -1*dfT["VDM_GPS_Longitude"], c=dfT["TMAIN_DATA_STEERING"], cmap="viridis", s=0.5) +plt.colorbar(label="Steering Angle") +plt.axis("scaled") +plt.ylabel("Latitude") +plt.xlabel("Longitude") +plt.show() + +plt.plot(df["time"], df["ETC_STATUS_PEDAL_TRAVEL"]) +plt.show() + +plt.plot(df["time"], df["ETC_STATUS_BRAKE_SENSE_VOLTAGE"]) +plt.show() + +plt.plot(df["time"], df["brakePressureFront"]) +plt.show() + +df.select(["time", "throttle", "brakePressureFront","brakePressureRear", "brakePedalTravel", "steerAngle"]).write_csv("FullVehicleSim/SimulationControlInputs/simulationControls_Mar162026_SteeperRegenCurve.csv") + +def fun (x, a): + return a * x + +dfHere = df.filter(pl.col("SME_THROTL_TorqueDemand") < 0) +plt.scatter(dfHere["ETC_STATUS_BRAKE_PEDAL_TRAVEL"], dfHere["SME_THROTL_TorqueDemand"], s=0.5) +plt.plot(np.arange(0, 60, 1), fun(np.arange(0, 60, 1), -300)) +plt.xlabel("Brake Pedal Travel") +plt.ylabel("Torque Demand") +plt.show() + +plt.plot(df["time"], df["ETC_STATUS_BRAKE_PEDAL_TRAVEL"]/100, label="Brake Pedal Travel") +plt.plot(df["time"], df["ETC_STATUS_PEDAL_TRAVEL"]/100, label="Accelerator Pedal Travel") +plt.plot(df["time"], df["SME_THROTL_TorqueDemand"]/30000, label="Torque Demand") +plt.plot(df["time"], (df["ETC_STATUS_PEDAL_TRAVEL"]/100) - (df["ETC_STATUS_BRAKE_PEDAL_TRAVEL"]/100), label="Combined Pedal Travel") +plt.plot(df["time"], df["SME_TEMP_FaultCode"], label="Fault Code") +plt.xlabel("Time (s)") +plt.legend() +plt.show() From 7e9276255a52167fbf07be6e85071312e85e7a33 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Tue, 9 Jun 2026 19:33:27 -0700 Subject: [PATCH 28/38] electrical modeling stuff --- Data/suspensionDamingView.py | 122 +++ .../HisteresisCellModel/electrical_model10.py | 133 +++ .../HisteresisCellModel/electrical_model11.py | 156 ++++ .../HisteresisCellModel/electrical_model7.py | 816 ++++++++++++++++++ .../electrical_modelG13.py | 194 +++++ .../HisteresisCellModel/electrical_model_8.py | 174 ++++ .../HisteresisCellModel/electrical_model_9.py | 426 +++++++++ .../electrical_modelingG12.py | 179 ++++ .../HisteresisCellModel/electricl_model6.py | 328 +++++++ outputs/20260605_170819_y7qEJI/checkpoint.pkl | Bin 0 -> 2739 bytes outputs/20260605_170844_CfwX91/checkpoint.pkl | Bin 0 -> 231133 bytes .../20260605_170844_CfwX91/hall_of_fame.csv | 9 + .../hall_of_fame.csv.bak | 9 + outputs/20260605_173335_rG1Rzg/checkpoint.pkl | Bin 0 -> 221026 bytes .../20260605_173335_rG1Rzg/hall_of_fame.csv | 14 + .../hall_of_fame.csv.bak | 14 + outputs/20260605_220726_S696dn/checkpoint.pkl | Bin 0 -> 2999 bytes outputs/20260605_220800_HYyXI8/checkpoint.pkl | Bin 0 -> 2991 bytes outputs/20260605_220949_H8unUI/checkpoint.pkl | Bin 0 -> 2987 bytes outputs/20260605_221126_n50KhN/checkpoint.pkl | Bin 0 -> 2997 bytes outputs/20260606_001334_pEEQVZ/checkpoint.pkl | Bin 0 -> 2977 bytes outputs/20260606_001458_3li5pJ/checkpoint.pkl | Bin 0 -> 2977 bytes 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-> 2708 bytes .../20260606_003854_LTXdBL/hall_of_fame.csv | 28 + .../hall_of_fame.csv.bak | 28 + outputs/20260606_005734_6FlOfR/checkpoint.pkl | Bin 0 -> 2772 bytes .../20260606_005734_6FlOfR/hall_of_fame.csv | 44 + .../hall_of_fame.csv.bak | 44 + outputs/20260606_021144_8PzK7u/checkpoint.pkl | Bin 0 -> 354333 bytes .../20260606_021144_8PzK7u/hall_of_fame.csv | 16 + .../hall_of_fame.csv.bak | 16 + outputs/20260606_021836_7T4wfI/checkpoint.pkl | Bin 0 -> 407362 bytes .../20260606_021836_7T4wfI/hall_of_fame.csv | 18 + .../hall_of_fame.csv.bak | 18 + outputs/20260606_022118_Q2Uhrd/checkpoint.pkl | Bin 0 -> 347801 bytes .../20260606_022118_Q2Uhrd/hall_of_fame.csv | 17 + .../hall_of_fame.csv.bak | 17 + outputs/20260606_022206_B4MMfw/checkpoint.pkl | Bin 0 -> 313774 bytes .../20260606_022206_B4MMfw/hall_of_fame.csv | 14 + .../hall_of_fame.csv.bak | 14 + outputs/20260606_022315_s3ZViI/checkpoint.pkl | Bin 0 -> 308533 bytes .../20260606_022315_s3ZViI/hall_of_fame.csv | 16 + .../hall_of_fame.csv.bak | 16 + 60 files changed, 2980 insertions(+) create mode 100644 Data/suspensionDamingView.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_model10.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_model11.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_model7.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelG13.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_8.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_9.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelingG12.py create mode 100644 FullVehicleSim/Electrical/HisteresisCellModel/electricl_model6.py create mode 100644 outputs/20260605_170819_y7qEJI/checkpoint.pkl create mode 100644 outputs/20260605_170844_CfwX91/checkpoint.pkl create mode 100644 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outputs/20260606_022206_B4MMfw/hall_of_fame.csv create mode 100644 outputs/20260606_022206_B4MMfw/hall_of_fame.csv.bak create mode 100644 outputs/20260606_022315_s3ZViI/checkpoint.pkl create mode 100644 outputs/20260606_022315_s3ZViI/hall_of_fame.csv create mode 100644 outputs/20260606_022315_s3ZViI/hall_of_fame.csv.bak diff --git a/Data/suspensionDamingView.py b/Data/suspensionDamingView.py new file mode 100644 index 0000000..b1a7f1f --- /dev/null +++ b/Data/suspensionDamingView.py @@ -0,0 +1,122 @@ +import polars as pl +import matplotlib.pyplot as plt +import numpy as np +import scipy.fft as fft +from statsmodels.nonparametric.smoothers_lowess import lowess + +df = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet").fill_null(strategy="forward").fill_null(strategy="backward") + + +[x for x in df.columns if "VDM" in x] + + +FR_base_value = df["TPERIPH_FR_DATA_SUSTRAVEL"][:100].mean() +FL_base_value = df["TPERIPH_FL_DATA_SUSTRAVEL"][:100].mean() +BR_base_value = df["TPERIPH_BR_DATA_SUSTRAVEL"][:100].mean() +BL_base_value = df["TPERIPH_BL_DATA_SUSTRAVEL"][:100].mean() + +t = df["Time_ms"]/1000.0 +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(t, df["TPERIPH_FR_DATA_SUSTRAVEL"] - FR_base_value, label="TPERIPH_FR_DATA_SUSTRAVEL") +ax.plot(t, df["TPERIPH_FL_DATA_SUSTRAVEL"] - FL_base_value, label="TPERIPH_FL_DATA_SUSTRAVEL") +ax.plot(t, df["TPERIPH_BR_DATA_SUSTRAVEL"] - BR_base_value, label="TPERIPH_BR_DATA_SUSTRAVEL") +ax.plot(t, df["TPERIPH_BL_DATA_SUSTRAVEL"] - BL_base_value, label="TPERIPH_BL_DATA_SUSTRAVEL") +ax.plot(t, df["VDM_Z_AXIS_YAW_RATE"]/2, label="VDM_Z_AXIS_YAW_RATE") +ax.plot(t, df["SME_TRQSPD_Speed"]/1000, label="SME_TRQSPD_Speed") +ax.legend() +ax.set_xlabel("Time [s]") +ax.set_ylabel("TPERIPH Data") +ax.set_title("Suspension Damping View") +ax.grid(True) +plt.show() + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(t, -1*df["TMAIN_DATA_STEERING"], label="TMAIN_DATA_STEERING") +ax.plot(t, df["VDM_Z_AXIS_YAW_RATE"], label="VDM_Z_AXIS_YAW_RATE") +ax.legend() +ax.set_xlabel("Time [s]") +ax.set_ylabel("Steering and Yaw Rate") +ax.set_title("Steering and Yaw Rate") +ax.grid(True) +plt.show() + +df = df.filter(pl.col("SME_TRQSPD_Speed") > 5000) + +suspension_FR_fft = fft.fft((df["TPERIPH_FR_DATA_SUSTRAVEL"] - FR_base_value).to_numpy()) +suspension_FL_fft = fft.fft((df["TPERIPH_FL_DATA_SUSTRAVEL"] - FL_base_value).to_numpy()) +suspension_BR_fft = fft.fft((df["TPERIPH_BR_DATA_SUSTRAVEL"] - BR_base_value).to_numpy()) +suspension_BL_fft = fft.fft((df["TPERIPH_BL_DATA_SUSTRAVEL"] - BL_base_value).to_numpy()) +suspension_FR_freq = fft.fftfreq(len(suspension_FR_fft), d=0.01) +suspension_FL_freq = fft.fftfreq(len(suspension_FL_fft), d=0.01) +suspension_BR_freq = fft.fftfreq(len(suspension_BR_fft), d=0.01) +suspension_BL_freq = fft.fftfreq(len(suspension_BL_fft), d=0.01) + +fig = plt.figure() +ax1 = fig.add_subplot(221) +ax1.scatter(suspension_FR_freq, np.abs(suspension_FR_fft), label="TPERIPH_FR_DATA_SUSTRAVEL", s=0.5) +ax1.set_title("FR Suspension FFT") +ax1.set_xlabel("Frequency [Hz]") +ax1.set_ylabel("Magnitude") +ax1.set_yscale("log") +ax1.grid(True) +ax2 = fig.add_subplot(222) +ax2.scatter(suspension_FL_freq, np.abs(suspension_FL_fft), label="TPERIPH_FL_DATA_SUSTRAVEL", s=0.5) +ax2.set_title("FL Suspension FFT") +ax2.set_xlabel("Frequency [Hz]") +ax2.set_ylabel("Magnitude") +ax2.set_yscale("log") +ax2.grid(True) +ax3 = fig.add_subplot(223) +ax3.scatter(suspension_BR_freq, np.abs(suspension_BR_fft), label="TPERIPH_BR_DATA_SUSTRAVEL", s=0.5) +ax3.set_title("BR Suspension FFT") +ax3.set_xlabel("Frequency [Hz]") +ax3.set_ylabel("Magnitude") +ax3.set_yscale("log") +ax3.grid(True) +ax4 = fig.add_subplot(224) +ax4.scatter(suspension_BL_freq, np.abs(suspension_BL_fft), label="TPERIPH_BL_DATA_SUSTRAVEL", s=0.5) +ax4.set_title("BL Suspension FFT") +ax4.set_xlabel("Frequency [Hz]") +ax4.set_ylabel("Magnitude") +ax4.set_yscale("log") +ax4.grid(True) +plt.tight_layout() +plt.show() + +yawRate_fft = fft.fft(df["VDM_Z_AXIS_YAW_RATE"].to_numpy()) +yawRate_freq = fft.fftfreq(len(yawRate_fft), d=0.01) +pitchRate_fft = fft.fft(df["VDM_Y_AXIS_YAW_RATE"].to_numpy()) +pitchRate_freq = fft.fftfreq(len(pitchRate_fft), d=0.01) +rollRate_fft = fft.fft(df["VDM_X_AXIS_YAW_RATE"].to_numpy()) +rollRate_freq = fft.fftfreq(len(rollRate_fft), d=0.01) + + +meanSegments = 101 +fig = plt.figure() +ax = fig.add_subplot(111) +ax.scatter(yawRate_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(yawRate_fft), mode='same'), label="Yaw", s=0.5) +ax.scatter(pitchRate_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(pitchRate_fft), mode='same'), label="Pitch", s=0.5) +ax.scatter(rollRate_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(rollRate_fft), mode='same'), label="Roll", s=0.5) +ax.legend() +ax.set_title("Yaw Rate FFT") +ax.set_xlabel("Frequency [Hz]") +ax.set_ylabel("Magnitude") +ax.set_yscale("log") +ax.grid(True) +plt.show() + +## heave based on z acceleration +heave_fft = fft.fft(df["VDM_Z_AXIS_ACCELERATION"].to_numpy()) +heave_freq = fft.fftfreq(len(heave_fft), d=0.01) +fig = plt.figure() +ax = fig.add_subplot(111) +ax.scatter(heave_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(heave_fft), mode='same'), label="Heave", s=0.5) +ax.legend() +ax.set_title("Heave FFT") +ax.set_xlabel("Frequency [Hz]") +ax.set_ylabel("Magnitude") +ax.set_yscale("log") +ax.grid(True) +plt.show() \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model10.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model10.py new file mode 100644 index 0000000..f97fca2 --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model10.py @@ -0,0 +1,133 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +import scipy.io +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +from scipy.interpolate import CubicSpline as cs +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +from scipy.optimize import curve_fit +from numba import njit + +# 1. Load Data +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +DELTA = 0.1 # Stick to 0.1s to capture high-rate dynamic transients accurately +KERNEL_DURATION = 30 +KERNEL_SIZE = int(KERNEL_DURATION / DELTA) +Q_NOMINAL = 3.0 + +def func_SOC(I, t, starting_SOC): + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC - (integrated_ah / Q_NOMINAL) # Discharge decreases SOC + return np.clip(soc, 0.0, 1.0) + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: return 1.0 + if T < -15.0: return voltage_curve(V, -15.0) + if T > 39.0: return voltage_curve(V, 39.0) + return np.clip(SOC, 0.0, 1.0) + +def buildDF(measurement_set): + frames = [] + for sim in measurement_set: + cubic = cs(sim.t, np.array([sim.I, sim.V, sim.T_surf[0, :], sim.T_surf[1, :]]), axis=1) + t = np.arange(sim.t[0], sim.t[-1], DELTA) + I, V, T_surf1, T_surf2 = cubic(t) + starting_SOC = SOC_lookup(V[0], T_surf1[0]) + SOC = func_SOC(I, t, starting_SOC) + frames.append(pl.DataFrame({ + "I": I, "V": V, "t": t, + "T_surf1": T_surf1, "T_surf2": T_surf2, "SOC": SOC, + })) + return pl.concat(frames) + +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +fullData = buildDF(all_sims) + +# OCV Arguments +ocv_args = np.array([2.33586300e+00, 8.70615991e+00, -3.26789721e+01, 7.63836093e+01, -9.98902031e+01, 6.83036001e+01, -1.90310137e+01, 5.90707089e-04]) + +def OCV_terminal_voltage(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25)) + +@njit +def func_V_X_fast_dynamic(tauX, RX_arr, I, delta): + VX_arr = np.zeros_like(I) + if tauX <= 1e-5: return VX_arr + alpha = np.exp(-delta / tauX) + for i in range(1, len(I)): + beta = RX_arr[i] * (1.0 - alpha) + VX_arr[i] = VX_arr[i - 1] * alpha + I[i] * beta + return VX_arr + +def func_V_h(SOC, M, I, kernel): + # Fix: Hysteresis tracks current DIRECTION (sign), not raw multi-ampere values + I_sign = np.sign(I) + padded_I = np.pad(I_sign, (KERNEL_SIZE - 1, 0), mode='constant') + convolution = convolve(padded_I, kernel[::-1], mode='valid') + return M * SOC * convolution + +def terminal_voltage_dynamic(SOC, T, I, + r0_d0, r0_d1, r0_d2, + r0_c0, r0_c1, r0_c2, + r1_0, r1_1, + k_r, C1, R2, C2, tau_H, M, ocv_params): + + temp_factor = (1.0 + k_r * (T - 25.0)) + + R0_d_stream = (r0_d0 + r0_d1 * SOC + r0_d2 / (SOC + 0.01)) * temp_factor + R0_c_stream = (r0_c0 + r0_c1 * SOC + r0_c2 / (SOC + 0.01)) * temp_factor + R1_stream = (r1_0 + r1_1 * (1.0 - SOC)) * temp_factor + + tau1 = np.mean(R1_stream) * C1 + tau2 = R2 * C2 + + exponent = -np.arange(0, KERNEL_SIZE) * DELTA / tau_H + kernel = np.exp(exponent) / np.sum(np.exp(exponent)) + + V_h = func_V_h(SOC, M, I, kernel) + V_X1 = func_V_X_fast_dynamic(tau1, R1_stream, I, DELTA) + V_X2 = func_V_X_fast_dynamic(tau2, np.full_like(I, R2), I, DELTA) + + V_OCV = OCV_terminal_voltage(SOC, T, *ocv_params) + V_R0 = np.where(I >= 0, R0_d_stream * I, R0_c_stream * I) + + # FIX: Subtract transient polarization voltages (V_X1, V_X2) from V_OCV + V_est = V_OCV - V_R0 - V_X1 - V_X2 + V_h + + print(f"Current Loop Step MAE: {np.mean(np.abs(V_est - fullData['V'].to_numpy())) * 1000.0:.2f} mV") + return V_est + +def wrapper_dynamic(X, r0_d0, r0_d1, r0_d2, r0_c0, r0_c1, r0_c2, r1_0, r1_1, k_r, C1, R2, C2, tau_H, M): + return terminal_voltage_dynamic(X[0], X[1], X[2], r0_d0, r0_d1, r0_d2, r0_c0, r0_c1, r0_c2, r1_0, r1_1, k_r, C1, R2, C2, tau_H, M, ocv_args) + +# Strictly enforce physical boundaries to prevent optimization divergence +# Layout: [r0_d0, r0_d1, r0_d2, r0_c0, r0_c1, r0_c2, r1_0, r1_1, k_r, C1, R2, C2, tau_H, M] +low_b = [1e-4, -0.05, 1e-5, 1e-4, -0.05, 1e-5, 1e-4, 1e-4, -0.05, 10.0, 1e-4, 100.0, 0.5, 1e-4] +upr_b = [0.1, 0.05, 0.01, 0.1, 0.05, 0.01, 0.05, 0.05, 0.0, 5000, 0.1, 50000, 70.0, 0.1] + +p0_dynamic = [0.012, 0.0, 0.0005, 0.010, 0.0, 0.0005, 0.004, 0.002, -0.01, 200, 0.008, 4000, 25, 0.015] + +print("Starting bounded parameter estimation...") +args_dynamic, _ = curve_fit( + wrapper_dynamic, + (fullData["SOC"].to_numpy(), fullData["T_surf1"].to_numpy(), fullData["I"].to_numpy()), + fullData["V"].to_numpy(), + p0=p0_dynamic, + bounds=(low_b, upr_b), # Crucial bounds restored + maxfev=5000 +) + +print("\nFinal Optimized Parameters Array:") +print(args_dynamic) \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model11.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model11.py new file mode 100644 index 0000000..3be52ef --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model11.py @@ -0,0 +1,156 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit, differential_evolution +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +import scipy.io +from numba import njit +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +from scipy.interpolate import CubicSpline as cs + +# ========================================== +# 1. GLOBAL BASELINE & DATA LOADING +# ========================================== +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +delta = 0.1 +kernel_duration = 30 +kernel_size = int(kernel_duration / delta) + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: return 1.0 + if T < -15.0: return voltage_curve(V, -15.0) + if T > 39.0: return voltage_curve(V, 39.0) + return np.clip(SOC, 0.0, 1.0) + +def buildDF(measurement_set): + frames = [] + for i, sim in enumerate(measurement_set): + cubic = cs(sim.t, np.array([sim.I, sim.V, sim.T_surf[0, :], sim.T_surf[1, :]]), axis=1) + t = np.arange(sim.t[0], sim.t[-1], delta) + I, V, T_surf1, T_surf2 = cubic(t) + starting_SOC = SOC_lookup(V[0], T_surf1[0]) + SOC = func_SOC(I, t, starting_SOC) + frames.append(pl.DataFrame({ + "I": I, "V": V, "t": t, + "T_surf1": T_surf1, "T_surf2": T_surf2, "SOC": SOC, + "run": np.ones(len(I)) * i + })) + return pl.concat(frames) + +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +aboveFreezingSims = [m for m in all_sims if m.T_surf[0, 0] > 5] +notHighPulse = [m for m in aboveFreezingSims if not ("-40.000C" in m.name or "-30.000C" in m.name)] +fullData = buildDF(notHighPulse) + +# ========================================== +# 2. CORE PHYSICS & ECM FUNCTIONS +# ========================================== +ocv_args = np.array([2.33586300e+00, 8.70615991e+00, -3.26789721e+01, 7.63836093e+01, -9.98902031e+01, 6.83036001e+01, -1.90310137e+01, 5.90707089e-04]) + +def OCV_terminal_voltage(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25)) + +def func_V_h(SOC, M, I, kernel): + padded_I = np.pad(I, (kernel_size - 1, 0), mode='constant') + convolution = convolve(padded_I, kernel, mode='valid') + return M * SOC * convolution + +@njit +def func_V_X_fast(tauX, RX, I, delta): + VX_arr = np.zeros_like(I) + alpha = np.exp(-delta / tauX) + beta = RX * (1.0 - alpha) + for i in range(1, len(I)): + VX_arr[i] = VX_arr[i - 1] * alpha + I[i] * beta + return VX_arr + +def terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_params): + tau1 = R1 * C1 + tau2 = R2 * C2 + + exponent = -np.arange(0, kernel_size) * delta / tau_H + kernel = np.exp(exponent) / np.sum(np.exp(exponent)) + + V_h = func_V_h(SOC, M, I, kernel) + V_X1 = func_V_X_fast(tau1, R1, I, delta) + V_X2 = func_V_X_fast(tau2, R2, I, delta) + + V_OCV = OCV_terminal_voltage(SOC, T, *ocv_params) + V_R0 = np.where(I <= 0, R0_discharge * I, R0_charge * I) + + return V_OCV - V_R0 + V_X1 + V_X2 + V_h + +# ========================================== +# 3. CLEAN WRAPPERS FOR OPTIMIZATION +# ========================================== +def wrapper_post_OCV(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M): + modelVoltages = [] + for run_id in np.unique(X[3]): + mask = X[3] == run_id + modelVoltage = terminal_voltage(X[0, mask], X[1, mask], X[2, mask], R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_args) + modelVoltages.append(modelVoltage) + return np.concatenate(modelVoltages) + +def evolution_wrapper(params, X): + R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M = params + v = wrapper_post_OCV(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M) + return np.mean((v - X[4]) ** 2) + +# ========================================== +# 4. WINDOWS-SAFE EXECUTION GUARANTEE +# ========================================== +if __name__ == '__main__': + # 1. Filter out data chunks for optimization + lessData = fullData.filter(pl.col("t") < 1000) + + # Pack array structure cleanly + data_payload = np.array(( + lessData["SOC"].to_numpy(), + lessData["T_surf1"].to_numpy(), + lessData["I"].to_numpy(), + lessData["run"].to_numpy(), + lessData["V"].to_numpy() + )) + + # 2. Set up parameter boundary configurations + bounds = ( + [1e-7, 1e-7, 1e-7, 1e-3, 1e-7, 10.0, 1e-5, -1], # Lower + [0.2, 0.2, 0.2, 5000.0, 0.2, 50000.0, 1e4, 1e3] # Upper + ) + boundsTupled = tuple(zip(*bounds)) + + print("Launching Multi-Threaded Differential Evolution Loop...") + + # 3. Run Global Optimization Engine Safely + argsGlobal = differential_evolution( + evolution_wrapper, + boundsTupled, + args=(data_payload,), + maxiter=35, # Balance iteration density on dynamic pools + popsize=10, # Initial generation seed size + workers=-1, # Utilizes all available logical processor cores + updating='deferred' + ) + + print("\nOptimization Complete!") + print("Optimal Fit Found Parameters:") + print(argsGlobal.x) \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model7.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model7.py new file mode 100644 index 0000000..e8f0600 --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model7.py @@ -0,0 +1,816 @@ +"""Electrical Model 7 + +This file is a medium-complexity lithium-ion cell model built for interactive +work rather than CLI execution. + +Design goals: +- Use all available experiment groups that matter here: CHC, DCP, CHP, and + DCC when present. +- Keep the model functional and easy to inspect from an editor or notebook. +- Capture the main battery effects without jumping straight to a full single + particle model. +- Explain each part of the model directly in code. + +Why this approach instead of a full SPM: +Single particle models are powerful, but they are substantially more complex +to identify, tune, and maintain. They usually need more electrochemical state +structure than this dataset clearly provides. For the current use case, a +structured equivalent-circuit model with SOC-dependent OCV, ohmic loss, two +polarization branches, and a hysteresis state is a better middle ground. + +The model below is built in layers: +1. Stack all experiments into a single training set. +2. Estimate a smooth voltage curve from SOC and temperature. +3. Add current-dependent ohmic loss. +4. Add fast and slow polarization states. +5. Add a hysteresis state that depends on current direction and SOC. +6. Fit the whole structure with nonlinear least squares. +""" + +from __future__ import annotations + +import time +from pathlib import Path +from typing import Dict, List, Sequence, Tuple + +import numpy as np +import scipy.io +from scipy.integrate import cumulative_simpson +from scipy.optimize import least_squares + + +# ============================================================ +# PATH RESOLUTION AND DATA LOADING +# ============================================================ + +print("[electrical_model7] attempting to load .mat data (top-level) ...") +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +print("[electrical_model7] loaded .mat data (top-level)") + +def LoadMeasurementData(matPath: Path | None = None): + """Load the MAT file and return the `measurement` structure.""" + print("[electrical_model7] LoadMeasurementData: returning measurement struct") + matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) + print("[electrical_model7] LoadMeasurementData: loaded file") + return matData["measurement"] + + +# ============================================================ +# DATA STACKING +# ============================================================ + + +def NormalizeExperimentCollection(experimentCollection) -> List[object]: + """Return a Python list even if MATLAB stored the group as a scalar object.""" + + if isinstance(experimentCollection, np.ndarray): + items = [item for item in experimentCollection.reshape(-1)] + print(f"[electrical_model7] NormalizeExperimentCollection: normalized array -> {len(items)} items") + return items + return [experimentCollection] + + +def SafeSurfaceTemperature(experiment) -> Tuple[np.ndarray, np.ndarray]: + """Extract the two surface temperature channels in a shape-safe way.""" + + surfaceTemperature = np.asarray(experiment.T_surf, dtype=float) + if surfaceTemperature.ndim == 1: + surfaceOne = surfaceTemperature.reshape(-1) + surfaceTwo = surfaceTemperature.reshape(-1) + return surfaceOne, surfaceTwo + + if surfaceTemperature.shape[0] == 1: + surfaceOne = surfaceTemperature[0, :] + surfaceTwo = surfaceTemperature[0, :] + return surfaceOne, surfaceTwo + + surfaceOne = surfaceTemperature[0, :] + surfaceTwo = surfaceTemperature[1, :] + return surfaceOne, surfaceTwo + + +def GuessInitialSoc(groupName: str) -> float: + """Give each experiment family a conservative starting SOC guess.""" + return 1.0 + + +def BuildExperimentRecord(experiment, groupName: str, experimentIndex: int) -> Dict[str, np.ndarray]: + """Convert one raw experiment into a compact record used by the fitter.""" + + currentSeries = np.asarray(experiment.I, dtype=float).reshape(-1) + voltageSeries = np.asarray(experiment.V, dtype=float).reshape(-1) + timeSeries = np.asarray(experiment.t, dtype=float).reshape(-1) + surfaceOne, surfaceTwo = SafeSurfaceTemperature(experiment) + temperatureSeries = 0.5 * (surfaceOne + surfaceTwo) + deltaTimeSeries = np.concatenate([[0.0], np.diff(timeSeries)]) + throughputAh = np.concatenate([[0.0], cumulative_simpson(y=currentSeries, x=timeSeries) / 3600.0]) + + record = { + "groupName": np.array(groupName), + "experimentIndex": np.array(experimentIndex, dtype=int), + "initialSocGuess": np.array(GuessInitialSoc(groupName), dtype=float), + "current": currentSeries, + "voltage": voltageSeries, + "time": timeSeries, + "temperature": temperatureSeries, + "surfaceOne": surfaceOne, + "surfaceTwo": surfaceTwo, + "deltaTime": deltaTimeSeries, + "throughputAh": throughputAh, + } + print(f"[electrical_model7] BuildExperimentRecord: {groupName} idx={experimentIndex} samples={len(currentSeries)}") + return record + + +def StackExperimentGroups(measurementData, groupNames: Sequence[str] = ("CHC", "DCC", "DCP", "CHP")) -> List[Dict[str, np.ndarray]]: + """Stack all requested experiment groups into a flat Python list.""" + + stackedRecords: List[Dict[str, np.ndarray]] = [] + + total = 0 + for groupName in groupNames: + print(f"[electrical_model7] StackExperimentGroups: checking group {groupName}") + if not hasattr(measurementData.fu, groupName): + print(f"[electrical_model7] StackExperimentGroups: group {groupName} not found, skipping") + continue + + experimentCollection = NormalizeExperimentCollection(getattr(measurementData.fu, groupName)) + print(f"[electrical_model7] StackExperimentGroups: found {len(experimentCollection)} experiments in {groupName}") + for experimentIndex, experiment in enumerate(experimentCollection): + stackedRecords.append(BuildExperimentRecord(experiment, groupName, experimentIndex)) + total += 1 + + print(f"[electrical_model7] StackExperimentGroups: stacked total {total} experiments") + return stackedRecords + + +# ============================================================ +# MODEL EQUATIONS +# ============================================================ + + +def Sigmoid(value): + """Smoothly map an unconstrained raw value to [0, 1].""" + + return 1.0 / (1.0 + np.exp(-value)) + + +def SoftPlus(value): + """Smoothly map an unconstrained raw value to a positive number.""" + + return np.log1p(np.exp(-np.abs(value))) + np.maximum(value, 0.0) + + +def Logit(value): + """Inverse of Sigmoid, with clipping for numerical safety.""" + + clippedValue = np.clip(value, 1e-6, 1.0 - 1e-6) + return np.log(clippedValue / (1.0 - clippedValue)) + + +class FitInterrupted(RuntimeError): + """Raised when a fit should stop early but return the best result so far.""" + + +class ResidualAttemptTracker: + """Track residual evaluations, best-so-far state, and stop conditions.""" + + def __init__( + self, + experimentRecords: Sequence[Dict[str, np.ndarray]], + *, + statusEvery: int = 1, + checkpointPath: Path | str | None = None, + stopSignalPath: Path | str | None = None, + wallClockLimitSeconds: float | None = None, + verbose: bool = True, + ) -> None: + self.experimentRecords = experimentRecords + self.statusEvery = max(1, int(statusEvery)) + self.checkpointPath = Path(checkpointPath) if checkpointPath is not None else None + self.stopSignalPath = Path(stopSignalPath) if stopSignalPath is not None else None + self.wallClockLimitSeconds = wallClockLimitSeconds + self.verbose = verbose + self.attemptCount = 0 + self.bestAttempt = 0 + self.bestCost = np.inf + self.bestParameterVector: np.ndarray | None = None + self.bestResidualVector: np.ndarray | None = None + self.startTime = time.perf_counter() + self.parameterNames: List[str] = [] + + def _should_stop(self) -> str | None: + if self.stopSignalPath is not None and self.stopSignalPath.exists(): + return f"stop signal detected at {self.stopSignalPath}" + + if self.wallClockLimitSeconds is not None: + elapsedSeconds = time.perf_counter() - self.startTime + if elapsedSeconds >= self.wallClockLimitSeconds: + return f"wall clock limit reached after {elapsedSeconds:.1f}s" + + return None + + def _writeCheckpoint(self, reason: str) -> None: + if self.checkpointPath is None or self.bestParameterVector is None: + return + + self.checkpointPath.parent.mkdir(parents=True, exist_ok=True) + np.savez( + self.checkpointPath, + reason=np.array(reason), + attemptCount=np.array(self.attemptCount, dtype=int), + bestAttempt=np.array(self.bestAttempt, dtype=int), + bestCost=np.array(self.bestCost, dtype=float), + elapsedSeconds=np.array(time.perf_counter() - self.startTime, dtype=float), + parameterNames=np.array(self.parameterNames, dtype=object), + bestParameterVector=self.bestParameterVector, + bestResidualVector=self.bestResidualVector, + ) + + def evaluate(self, parameterVector: np.ndarray) -> np.ndarray: + stopReason = self._should_stop() + if stopReason is not None: + raise FitInterrupted(stopReason) + + self.attemptCount += 1 + if self.verbose and (self.attemptCount == 1 or self.attemptCount % self.statusEvery == 0): + if np.isfinite(self.bestCost): + print( + f"[electrical_model7] FitModel7: attempt {self.attemptCount}, best cost so far {self.bestCost:.6f}" + ) + else: + print(f"[electrical_model7] FitModel7: attempt {self.attemptCount}, no best yet") + + residualVector = BuildResidualVector( + parameterVector, + self.experimentRecords, + verbose=False, + attemptNumber=self.attemptCount, + ) + cost = 0.5 * float(np.dot(residualVector, residualVector)) + + if cost < self.bestCost: + self.bestCost = cost + self.bestAttempt = self.attemptCount + self.bestParameterVector = parameterVector.copy() + self.bestResidualVector = residualVector.copy() + if self.verbose: + print( + f"[electrical_model7] FitModel7: new best at attempt {self.bestAttempt} with cost {self.bestCost:.6f}" + ) + self._writeCheckpoint("improved") + + return residualVector + + +def BuildParameterVector(experimentRecords: Sequence[Dict[str, np.ndarray]]) -> Tuple[np.ndarray, List[str]]: + """Create an initial parameter vector and the matching parameter names.""" + print(f"[electrical_model7] BuildParameterVector: creating initial vector for {len(experimentRecords)} experiments") + parameterNames = [ + "capacityRaw", + "ocv0", + "ocv1", + "ocv2", + "ocv3", + "ocv4", + "ocv5", + "ocvTemp", + "hyst0", + "hyst1", + "hyst2", + "hystTemp", + "r0Base", + "r0Soc1", + "r0Soc2", + "r0Temp", + "rFastRaw", + "tauFastRaw", + "rSlowRaw", + "tauSlowRaw", + "hystGainRaw", + "hystTauRaw", + "hystShapeRaw", + "hystCurrentScaleRaw", + "tempVoltageCoeff", + ] + + startSocNames = [f"startSocRaw_{index}" for index in range(len(experimentRecords))] + parameterNames.extend(startSocNames) + + parameterVector = np.array( + [ + Logit(3.0 / 4.0), + 3.05, + 1.25, + -0.95, + 0.65, + -0.25, + 0.12, + 0.001, + 0.03, + -0.02, + 0.01, + 0.0005, + np.log(np.exp(0.018) - 1.0), + 0.004, + -0.003, + 0.0006, + np.log(np.exp(0.006) - 1.0), + np.log(np.exp(1.5) - 1.0), + np.log(np.exp(0.0025) - 1.0), + np.log(np.exp(30.0) - 1.0), + np.log(np.exp(0.020) - 1.0), + np.log(np.exp(80.0) - 1.0), + np.log(np.exp(1.2) - 1.0), + np.log(np.exp(5.0) - 1.0), + 0.001, + ] + + [Logit(record["initialSocGuess"]) for record in experimentRecords], + dtype=float, + ) + + print(f"[electrical_model7] BuildParameterVector: parameter vector length {parameterVector.size}") + return parameterVector, parameterNames + + +def UnpackParameters(parameterVector: np.ndarray, experimentCount: int) -> Dict[str, np.ndarray]: + """Split a raw parameter vector into transformed model parameters.""" + print("[electrical_model7] UnpackParameters: unpacking parameter vector") + cursor = 0 + + capacityRaw = parameterVector[cursor] + cursor += 1 + + ocvCoefficients = parameterVector[cursor:cursor + 6] + cursor += 6 + + ocvTemperatureCoeff = parameterVector[cursor] + cursor += 1 + + hystCoefficients = parameterVector[cursor:cursor + 4] + cursor += 4 + + resistanceCoefficients = parameterVector[cursor:cursor + 4] + cursor += 4 + + rFastRaw = parameterVector[cursor] + cursor += 1 + tauFastRaw = parameterVector[cursor] + cursor += 1 + rSlowRaw = parameterVector[cursor] + cursor += 1 + tauSlowRaw = parameterVector[cursor] + cursor += 1 + hystGainRaw = parameterVector[cursor] + cursor += 1 + hystTauRaw = parameterVector[cursor] + cursor += 1 + hystShapeRaw = parameterVector[cursor] + cursor += 1 + hystCurrentScaleRaw = parameterVector[cursor] + cursor += 1 + tempVoltageCoeff = parameterVector[cursor] + cursor += 1 + + startSocRaw = parameterVector[cursor:cursor + experimentCount] + + return { + "capacityAh": SoftPlus(capacityRaw) + 0.5, + "ocvCoefficients": ocvCoefficients, + "ocvTemperatureCoeff": ocvTemperatureCoeff, + "hystCoefficients": hystCoefficients, + "resistanceCoefficients": resistanceCoefficients, + "rFast": SoftPlus(rFastRaw) + 1e-4, + "tauFast": SoftPlus(tauFastRaw) + 1e-3, + "rSlow": SoftPlus(rSlowRaw) + 1e-4, + "tauSlow": SoftPlus(tauSlowRaw) + 1e-3, + "hystGain": SoftPlus(hystGainRaw) + 1e-4, + "hystTau": SoftPlus(hystTauRaw) + 1e-3, + "hystShape": 1.0 + SoftPlus(hystShapeRaw), + "hystCurrentScale": SoftPlus(hystCurrentScaleRaw) + 0.05, + "tempVoltageCoeff": tempVoltageCoeff, + "startSoc": Sigmoid(startSocRaw), + } + + +def EvaluateOcvSurface(socValue: float, temperatureDelta: float, currentDirection: float, parameters: Dict[str, np.ndarray]) -> float: + """Open-circuit voltage surface. + + The base curve captures the SOC shape, the temperature term nudges the + entire curve with temperature, and the direction term allows a small, + smooth charge/discharge split. + """ + + eps = 1e-9 + clippedSoc = np.clip(socValue, eps, 1.0 - eps) + socBasis = np.array([ + 1.0, + clippedSoc, + clippedSoc ** 2, + clippedSoc ** 3, + np.log(clippedSoc), + np.log(1.0 - clippedSoc), + ]) + + baseCurve = float(np.dot(parameters["ocvCoefficients"], socBasis)) + temperatureShift = parameters["ocvTemperatureCoeff"] * temperatureDelta + + directionBasis = np.array([1.0, clippedSoc, clippedSoc ** 2, temperatureDelta]) + directionShift = float(np.dot(parameters["hystCoefficients"], directionBasis)) + + return baseCurve + temperatureShift + currentDirection * directionShift + + +def EvaluateResistanceSurface(socValue: float, temperatureDelta: float, parameters: Dict[str, np.ndarray]) -> float: + """SOC and temperature dependent ohmic resistance.""" + + resistanceBasis = np.array([1.0, socValue, socValue ** 2, temperatureDelta]) + rawResistance = float(np.dot(parameters["resistanceCoefficients"], resistanceBasis)) + return SoftPlus(rawResistance) + 1e-5 + + +def EvaluateHysteresisDrive(socValue: float, temperatureDelta: float, currentDirection: float, parameters: Dict[str, np.ndarray]) -> float: + """Drive term for the hysteresis state. + + This term is not the hysteresis voltage itself. It is the direction-aware + quantity that the state relaxes toward. + """ + + socGate = 1.0 / (1.0 + np.exp(-(socValue - 0.5) / 0.08)) + socBasis = np.array([1.0, socValue, socValue ** 2, temperatureDelta]) + driveShape = float(np.dot(parameters["hystCoefficients"], socBasis)) + return currentDirection * socGate * driveShape + + +def SimulateExperiment( + experimentRecord: Dict[str, np.ndarray], + parameters: Dict[str, np.ndarray], + experimentIndex: int, + verbose: bool = True, +) -> Dict[str, np.ndarray]: + """Run the battery model forward for one experiment. + + The model is causal. Each step updates SOC, the two polarization states, + and the hysteresis state from the previous sample. + """ + + currentSeries = experimentRecord["current"] + voltageSeries = experimentRecord["voltage"] + timeSeries = experimentRecord["time"] + temperatureSeries = experimentRecord["temperature"] + sampleCount = currentSeries.shape[0] + + if verbose: + print(f"[electrical_model7] SimulateExperiment: start exp={experimentIndex} samples={sampleCount}") + progressInterval = max(1, sampleCount // 5) + + predictedVoltage = np.empty(sampleCount, dtype=float) + socSeries = np.empty(sampleCount, dtype=float) + fastPolarization = np.empty(sampleCount, dtype=float) + slowPolarization = np.empty(sampleCount, dtype=float) + hysteresisState = np.empty(sampleCount, dtype=float) + + socSeries[0] = float(parameters["startSoc"][experimentIndex]) + fastPolarization[0] = 0.0 + slowPolarization[0] = 0.0 + hysteresisState[0] = 0.0 + + for sampleIndex in range(sampleCount): + if verbose and sampleIndex % progressInterval == 0: + print(f"[electrical_model7] SimulateExperiment: exp={experimentIndex} progress {sampleIndex}/{sampleCount}") + if sampleIndex > 0: + deltaTime = max(float(timeSeries[sampleIndex] - timeSeries[sampleIndex - 1]), 1e-6) + previousCurrent = float(currentSeries[sampleIndex - 1]) + + socSeries[sampleIndex] = np.clip( + socSeries[sampleIndex - 1] - (previousCurrent * deltaTime) / (3600.0 * parameters["capacityAh"]), + 0.0, + 1.0, + ) + + fastAlpha = np.exp(-deltaTime / parameters["tauFast"]) + slowAlpha = np.exp(-deltaTime / parameters["tauSlow"]) + hystAlpha = np.exp(-deltaTime / parameters["hystTau"]) + + fastPolarization[sampleIndex] = ( + fastAlpha * fastPolarization[sampleIndex - 1] + + parameters["rFast"] * (1.0 - fastAlpha) * previousCurrent + ) + slowPolarization[sampleIndex] = ( + slowAlpha * slowPolarization[sampleIndex - 1] + + parameters["rSlow"] * (1.0 - slowAlpha) * previousCurrent + ) + + currentDirection = np.tanh(previousCurrent / parameters["hystCurrentScale"]) + temperatureDelta = float(temperatureSeries[sampleIndex - 1] - 298.15) + hysteresisDrive = EvaluateHysteresisDrive( + socValue=socSeries[sampleIndex], + temperatureDelta=temperatureDelta, + currentDirection=currentDirection, + parameters=parameters, + ) + hysteresisState[sampleIndex] = ( + hystAlpha * hysteresisState[sampleIndex - 1] + + (1.0 - hystAlpha) * hysteresisDrive + ) + else: + currentDirection = np.tanh(float(currentSeries[sampleIndex]) / parameters["hystCurrentScale"]) + socSeries[sampleIndex] = socSeries[0] + fastPolarization[sampleIndex] = 0.0 + slowPolarization[sampleIndex] = 0.0 + temperatureDelta = float(temperatureSeries[sampleIndex] - 298.15) + hysteresisState[sampleIndex] = EvaluateHysteresisDrive( + socValue=socSeries[sampleIndex], + temperatureDelta=temperatureDelta, + currentDirection=currentDirection, + parameters=parameters, + ) + + currentDirection = np.tanh(float(currentSeries[sampleIndex]) / parameters["hystCurrentScale"]) + temperatureDelta = float(temperatureSeries[sampleIndex] - 298.15) + ocvValue = EvaluateOcvSurface( + socValue=float(socSeries[sampleIndex]), + temperatureDelta=temperatureDelta, + currentDirection=currentDirection, + parameters=parameters, + ) + resistanceValue = EvaluateResistanceSurface( + socValue=float(socSeries[sampleIndex]), + temperatureDelta=temperatureDelta, + parameters=parameters, + ) + + predictedVoltage[sampleIndex] = ( + ocvValue + - float(currentSeries[sampleIndex]) * resistanceValue + - fastPolarization[sampleIndex] + - slowPolarization[sampleIndex] + + parameters["hystGain"] * hysteresisState[sampleIndex] + + parameters["tempVoltageCoeff"] * temperatureDelta + ) + + return { + "predictedVoltage": predictedVoltage, + "soc": socSeries, + "fastPolarization": fastPolarization, + "slowPolarization": slowPolarization, + "hysteresisState": hysteresisState, + "measuredVoltage": voltageSeries, + } + + +def BuildResidualVector( + parameterVector: np.ndarray, + experimentRecords: Sequence[Dict[str, np.ndarray]], + verbose: bool = True, + attemptNumber: int | None = None, +) -> np.ndarray: + """Convert the full fit problem into one stacked residual vector.""" + if verbose: + if attemptNumber is None: + print(f"[electrical_model7] BuildResidualVector: building residuals for {len(experimentRecords)} experiments") + else: + print( + f"[electrical_model7] BuildResidualVector: attempt {attemptNumber}, building residuals for {len(experimentRecords)} experiments" + ) + parameters = UnpackParameters(parameterVector, experimentCount=len(experimentRecords)) + residualParts: List[np.ndarray] = [] + + for experimentIndex, experimentRecord in enumerate(experimentRecords): + simulationResult = SimulateExperiment(experimentRecord, parameters, experimentIndex, verbose=verbose) + residualParts.append(simulationResult["predictedVoltage"] - simulationResult["measuredVoltage"]) + + return np.concatenate(residualParts) + + +def FitModel7( + experimentRecords: Sequence[Dict[str, np.ndarray]], + verbose: bool = True, + statusEvery: int = 1, + wallClockLimitSeconds: float | None = None, + checkpointPath: Path | str | None = None, + stopSignalPath: Path | str | None = None, + maxNfev: int | None = 20, +): + """Fit the medium-complexity lithium-ion model to all stacked experiments.""" + print(f"[electrical_model7] FitModel7: starting fit for {len(experimentRecords)} experiments") + initialParameterVector, parameterNames = BuildParameterVector(experimentRecords) + print(f"[electrical_model7] FitModel7: initial parameter vector length {initialParameterVector.size}") + + tracker = ResidualAttemptTracker( + experimentRecords, + statusEvery=statusEvery, + checkpointPath=checkpointPath, + stopSignalPath=stopSignalPath, + wallClockLimitSeconds=wallClockLimitSeconds, + verbose=verbose, + ) + tracker.parameterNames = parameterNames + + fitResult = None + stoppedEarly = False + stopReason = None + + try: + fitResult = least_squares( + fun=tracker.evaluate, + x0=initialParameterVector, + method="trf", + loss="soft_l1", + f_scale=0.01, + max_nfev=maxNfev, + ) + print("[electrical_model7] FitModel7: least_squares finished") + except (KeyboardInterrupt, FitInterrupted) as interruption: + stoppedEarly = True + stopReason = str(interruption) + print(f"[electrical_model7] FitModel7: stopped early -> {stopReason}") + + if tracker.bestParameterVector is None: + tracker.bestParameterVector = initialParameterVector.copy() + tracker.bestResidualVector = BuildResidualVector(initialParameterVector, experimentRecords, verbose=False) + tracker.bestCost = 0.5 * float(np.dot(tracker.bestResidualVector, tracker.bestResidualVector)) + tracker.bestAttempt = 0 + + bestParameterVector = tracker.bestParameterVector + bestResidualVector = tracker.bestResidualVector + if checkpointPath is not None: + tracker._writeCheckpoint(stopReason or "completed") + + fittedParameters = UnpackParameters(bestParameterVector, experimentCount=len(experimentRecords)) + fittedResults = [] + for experimentIndex, experimentRecord in enumerate(experimentRecords): + fittedResults.append(SimulateExperiment(experimentRecord, fittedParameters, experimentIndex, verbose=False)) + + if verbose: + residualVector = bestResidualVector + rmse = float(np.sqrt(np.mean(residualVector ** 2))) + mae = float(np.mean(np.abs(residualVector))) + if fitResult is not None: + print("Fit success:", fitResult.success) + print("Message:", fitResult.message) + else: + print("Fit stopped early before least_squares returned a result object") + print("Stopped early:", stoppedEarly) + print("Stop reason:", stopReason) + print("Attempts:", tracker.attemptCount) + print("Best attempt:", tracker.bestAttempt) + print(f"RMSE: {rmse:.6f} V") + print(f"MAE: {mae:.6f} V") + + return { + "fitResult": fitResult, + "parameterNames": parameterNames, + "parameterVector": bestParameterVector, + "fittedParameters": fittedParameters, + "experimentRecords": list(experimentRecords), + "fittedResults": fittedResults, + "attemptCount": tracker.attemptCount, + "bestAttempt": tracker.bestAttempt, + "bestCost": tracker.bestCost, + "stoppedEarly": stoppedEarly, + "stopReason": stopReason, + } + + +# ============================================================ +# INTERACTIVE HELPERS +# ============================================================ + + +def BuildDefaultTrainingSet(groupNames: Sequence[str] = ("CHC", "DCC", "DCP", "CHP")) -> List[Dict[str, np.ndarray]]: + """Load the MAT file and stack all available experiment groups.""" + print(f"[electrical_model7] BuildDefaultTrainingSet: groups={groupNames}") + measurementData = LoadMeasurementData() + records = StackExperimentGroups(measurementData, groupNames=groupNames) + print(f"[electrical_model7] BuildDefaultTrainingSet: total records={len(records)}") + return records + + +def PlotExperimentFit(experimentRecord: Dict[str, np.ndarray], simulationResult: Dict[str, np.ndarray], titlePrefix: str = "Model 7"): + """Plot measured and modeled voltage for one experiment.""" + + print(f"[electrical_model7] PlotExperimentFit: plotting experiment {experimentRecord.get('groupName')} idx={experimentRecord.get('experimentIndex')}") + + import matplotlib.pyplot as plt + + timeSeries = experimentRecord["time"] + measuredVoltage = simulationResult["measuredVoltage"] + predictedVoltage = simulationResult["predictedVoltage"] + currentSeries = experimentRecord["current"] + + figure, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True) + + axes[0].plot(timeSeries, currentSeries, color="tab:blue") + axes[0].set_ylabel("Current [A]") + axes[0].grid(True) + + axes[1].plot(timeSeries, measuredVoltage, label="Measured", color="tab:orange") + axes[1].plot(timeSeries, predictedVoltage, label="Modeled", color="tab:green", alpha=0.85) + axes[1].set_ylabel("Voltage [V]") + axes[1].legend() + axes[1].grid(True) + + axes[2].plot(timeSeries, simulationResult["soc"], color="tab:purple") + axes[2].set_ylabel("SOC") + axes[2].set_xlabel("Time [s]") + axes[2].grid(True) + + groupName = str(experimentRecord["groupName"]) + experimentIndex = int(experimentRecord["experimentIndex"]) + figure.suptitle(f"{titlePrefix}: {groupName}_{experimentIndex}") + figure.tight_layout() + return figure, axes + + +def SummarizeModelFit(fitPackage: Dict[str, object]) -> Dict[str, float]: + """Return high-level error metrics for the full stacked fit.""" + + print("[electrical_model7] SummarizeModelFit: computing summary metrics") + experimentRecords = fitPackage["experimentRecords"] + fittedParameters = fitPackage["fittedParameters"] + + residualParts = [] + for experimentIndex, experimentRecord in enumerate(experimentRecords): + simulationResult = SimulateExperiment(experimentRecord, fittedParameters, experimentIndex) + residualParts.append(simulationResult["predictedVoltage"] - simulationResult["measuredVoltage"]) + + residualVector = np.concatenate(residualParts) + return { + "rmse": float(np.sqrt(np.mean(residualVector ** 2))), + "mae": float(np.mean(np.abs(residualVector))), + "maxAbsError": float(np.max(np.abs(residualVector))), + } + + +# ============================================================ +# USAGE EXAMPLE +# ============================================================ + +# The file intentionally does not auto-run a fit on import. +if __name__ == "__main__": + print("[electrical_model7] Example run: building default training set...") + experimentRecords = BuildDefaultTrainingSet() + print("[electrical_model7] Example run: fitting model to stacked experiments...") + fitPackage = FitModel7(experimentRecords) + print("[electrical_model7] Example run: summarizing fit metrics...") + metrics = SummarizeModelFit(fitPackage) + print(f"[electrical_model7] Example run: metrics={metrics}") + firstExperimentResult = fitPackage["fittedResults"][0] + print("[electrical_model7] Example run: plotting first experiment fit...") + PlotExperimentFit(experimentRecords[0], firstExperimentResult) + + +# ============================================================ +# COMPACT MODEL EQUATIONS AND PARAMETERS +# ============================================================ +# Use this block as the single reference form for testing the model. +# +# State updates for sample k -> k+1: +# SOC[k+1] = clip(SOC[k] - I[k] * dt / (3600 * C), 0, 1) +# a_f = exp(-dt / tau_f) +# a_s = exp(-dt / tau_s) +# a_h = exp(-dt / tau_h) +# x_f[k+1] = a_f * x_f[k] + R_f * (1 - a_f) * I[k] +# x_s[k+1] = a_s * x_s[k] + R_s * (1 - a_s) * I[k] +# dir[k] = tanh(I[k] / I_scale) +# T_delta = T[k] - 298.15 +# g_soc = 1 / (1 + exp(-(SOC[k] - 0.5) / 0.08)) +# h_drive = dir[k] * g_soc * (h0 + h1*SOC[k] + h2*SOC[k]^2 + hT*T_delta) +# h[k+1] = a_h * h[k] + (1 - a_h) * h_drive +# +# Voltage model: +# OCV(SOC, T, dir) = +# ocv0 + ocv1*SOC + ocv2*SOC^2 + ocv3*SOC^3 + ocv4*ln(SOC) + ocv5*ln(1-SOC) +# + ocvT * T_delta +# + dir * (d0 + d1*SOC + d2*SOC^2 + dT*T_delta) +# +# R0(SOC, T) = softplus(r0 + r1*SOC + r2*SOC^2 + rT*T_delta) + 1e-5 +# +# V_hat[k] = OCV(SOC[k], T_delta[k], dir[k]) +# - I[k] * R0(SOC[k], T_delta[k]) +# - x_f[k] - x_s[k] +# + h_g * h[k] +# + vT * T_delta[k] +# +# Parameters (raw vector order before transforms): +# capacityRaw +# ocv0, ocv1, ocv2, ocv3, ocv4, ocv5, ocvTemp +# hyst0, hyst1, hyst2, hystTemp +# r0Base, r0Soc1, r0Soc2, r0Temp +# rFastRaw, tauFastRaw, rSlowRaw, tauSlowRaw +# hystGainRaw, hystTauRaw, hystShapeRaw, hystCurrentScaleRaw +# tempVoltageCoeff +# startSocRaw_0 ... startSocRaw_(N-1) +# +# Transforms: +# C = softplus(capacityRaw) + 0.5 +# R_f = softplus(rFastRaw) + 1e-4 +# tau_f = softplus(tauFastRaw) + 1e-3 +# R_s = softplus(rSlowRaw) + 1e-4 +# tau_s = softplus(tauSlowRaw) + 1e-3 +# h_g = softplus(hystGainRaw) + 1e-4 +# tau_h = softplus(hystTauRaw) + 1e-3 +# h_shape = 1 + softplus(hystShapeRaw) +# I_scale = softplus(hystCurrentScaleRaw) + 0.05 +# SOC0_i = sigmoid(startSocRaw_i) diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelG13.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelG13.py new file mode 100644 index 0000000..9a4f416 --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelG13.py @@ -0,0 +1,194 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit +from scipy.integrate import cumulative_simpson +import scipy.io +from numba import njit +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +from scipy.interpolate import CubicSpline as cs + +# 1. Load Data +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +delta = 0.1 + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 # Ah capacity of Molicel P30B + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + # Proactively clip boundaries to prevent out-of-bounds NaNs in 2D interpolation + V_clipped = np.clip(V, lowVs.min(), lowVs.max()) + T_clipped = np.clip(T, lowTs.min(), lowTs.max()) + SOC = voltage_curve(V_clipped, T_clipped) + return np.clip(SOC, 0.0, 1.0) + +def buildDF(measurement_set): + frames = [] + for i, sim in enumerate(measurement_set): + cubic = cs(sim.t, np.array([sim.I, sim.V, sim.T_surf[0, :], sim.T_surf[1, :]]), axis=1) + t = np.arange(sim.t[0], sim.t[-1], delta) + I, V, T_surf1, T_surf2 = cubic(t) + starting_SOC = SOC_lookup(V[0], T_surf1[0]) + SOC = func_SOC(I, t, starting_SOC) + frames.append(pl.DataFrame({ + "I": I, "V": V, "t": t, + "T_surf1": T_surf1, "T_surf2": T_surf2, "SOC": SOC, + "run": np.ones(len(I)) * i + })) + return pl.concat(frames) + +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +aboveFreezingSims = [m for m in all_sims if m.T_surf[0, 0] > -2.0] +notHighPulse = [m for m in aboveFreezingSims if not ("-40.000C" in m.name or "-30.000C" in m.name)] +fullData = buildDF(notHighPulse) + +# 2. Extract and Freeze OCV +def OCV_terminal_voltage(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25.0)) + +ocv_args = np.array([2.33586300e+00, 8.70615991e+00, -3.26789721e+01, 7.63836093e+01, + -9.98902031e+01, 6.83036001e+01, -1.90310137e+01, 5.90707089e-04]) + +# 3. Optimized Discrete State-Space Tracking +@njit +def func_V_h_fast(tau_H, M, I, delta): + Vh_arr = np.zeros_like(I) + alpha = np.exp(-delta / tau_H) + beta = 1.0 - alpha + current_direction = np.sign(I) + for i in range(1, len(I)): + Vh_arr[i] = Vh_arr[i - 1] * alpha + current_direction[i] * beta + return M * Vh_arr + +@njit +def func_V_X_fast(tauX, RX, I, delta): + VX_arr = np.zeros_like(I) + # Safely handles array inputs generated by dynamic temperature/current conditions + alpha = np.exp(-delta / tauX) + beta = RX * (1.0 - alpha) + for i in range(1, len(I)): + VX_arr[i] = VX_arr[i - 1] * alpha[i] + I[i] * beta[i] + return VX_arr + +def terminal_voltage(SOC, T, I, r0_d, r0_c, R1_ref, C1, R2_ref, C2, tau_H, M, E_a, k_I): + T_kelvin = T + 273.15 + arrhenius_mult = np.exp((E_a * 1000.0) * (1.0 / T_kelvin - 1.0 / 298.15)) + + # Vectorized evaluation using scaled vector definitions + R0_d_base = (r0_d[0] + r0_d[1] * SOC + r0_d[2] * SOC**2) * arrhenius_mult + R0_c_base = (r0_c[0] + r0_c[1] * SOC + r0_c[2] * SOC**2) * arrhenius_mult + + R1 = R1_ref * arrhenius_mult * (1.0 + k_I * np.exp(-np.abs(I))) + R2 = R2_ref * arrhenius_mult + + tau1 = R1 * C1 + tau2 = R2 * C2 + + V_h = func_V_h_fast(tau_H, M, I, delta) + V_X1 = func_V_X_fast(tau1, R1, I, delta) + V_X2 = func_V_X_fast(tau2, R2, I, delta) + + V_OCV = OCV_terminal_voltage(SOC, T, *ocv_args) + V_R0 = np.where(I <= 0, R0_d_base * I, R0_c_base * I) + + return V_OCV + V_R0 + V_X1 + V_X2 + V_h + +# 4. Nonlinear Multi-Variable Optimization Wrapper +def wrapper_post_OCV(X, r0_d0, r0_d1, r0_d2, r0_c0, r0_c1, r0_c2, R1_scaled, C1_scaled, R2_scaled, C2_scaled, tau_H, M_scaled, E_a_scaled, k_I): + r0_d = np.array([r0_d0, r0_d1, r0_d2]) * 1e-3 + r0_c = np.array([r0_c0, r0_c1, r0_c2]) * 1e-3 + R1_ref = R1_scaled * 1e-3 + C1 = C1_scaled * 1e2 + R2_ref = R2_scaled * 1e-3 + C2 = C2_scaled * 1e4 + M = M_scaled * 1e-2 + E_a = E_a_scaled * 1.0 + + modelVoltages = np.zeros(X.shape[1]) + run_ids = X[3, :] + + for id in np.unique(run_ids): + mask = run_ids == id + + # Pass properly scaled R0 coefficient vectors directly + modelVoltages[mask] = terminal_voltage( + X[0, mask], X[1, mask], X[2, mask], + r0_d, r0_c, + R1_ref, C1, R2_ref, C2, tau_H, M, E_a, k_I + ) + + actual_voltages = X[4, :] + current_mae = np.mean(np.abs(modelVoltages - actual_voltages)) + print(f"Iteration MAE: {current_mae:.5f} V") + + return modelVoltages + +# 5. Boundaries and Initial Guess Configuration +scaled_bounds = ( + [2.0, -15.0, 0.0, 2.0, -15.0, 0.0, 0.5, 1.0, 1.0, 0.5, 0.5, 0.01, 1.0, 0.01], + [30.0, 5.0, 40.0, 30.0, 5.0, 40.0, 20.0, 10.0, 30.0, 5.0, 120.0, 5.00, 5.0, 2.00] +) + +p0_scaled = [ + 14.0, -5.0, 10.0, + 16.0, -5.0, 10.0, + 6.0, 4.0, + 12.0, 1.5, + 15.0, 0.8, + 2.5, 0.2 +] + +lessData = fullData.gather_every(10) + +X_data = np.array(( + lessData["SOC"].to_numpy(), + lessData["T_surf1"].to_numpy(), + lessData["I"].to_numpy(), + lessData["run"].to_numpy(), + lessData["V"].to_numpy() +)) + +print(f"Starting advanced non-linear optimization with {X_data.shape} points...") +optimized_scaled_args, _ = curve_fit( + wrapper_post_OCV, + X_data, + lessData["V"].to_numpy(), + p0=p0_scaled, + bounds=scaled_bounds, + maxfev=3500, + method='trf', + x_scale='jac' +) + +final_params = { + "r0_discharge_poly": [optimized_scaled_args[0]*1e-3, optimized_scaled_args[1]*1e-3, optimized_scaled_args[2]*1e-3], + "r0_charge_poly": [optimized_scaled_args[3]*1e-3, optimized_scaled_args[4]*1e-3, optimized_scaled_args[5]*1e-3], + "R1_ref": optimized_scaled_args[6] * 1e-3, + "C1": optimized_scaled_args[7] * 1e2, + "R2_ref": optimized_scaled_args[8] * 1e-3, + "C2": optimized_scaled_args[9] * 1e4, + "tau_H": optimized_scaled_args[10], + "M": optimized_scaled_args[11] * 1e-2, + "E_a_over_Rg": optimized_scaled_args[12] * 1000.0, + "k_I_current_scale": optimized_scaled_args[13] +} + +print("\nOptimized Multidimensional Parameters:") +for k, v in final_params.items(): + if isinstance(v, (list, tuple, np.ndarray)): + print(f"{k}: {[f'{float(x):.6e}' for x in v]}") + else: + print(f"{k}: {float(v):.6e}") \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_8.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_8.py new file mode 100644 index 0000000..ea0597e --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_8.py @@ -0,0 +1,174 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit, differential_evolution +from scipy.interpolate import cubic_spline + +import scipy.io +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +from functools import reduce + +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +# (measurements.fu.DCC[1].t[1:]-measurements.fu.DCC[1].t[:-1]).mean() + +fullData = pl.DataFrame(schema={ + "I":pl.Float64, + "V":pl.Float64, + "t":pl.Float64, + "T_surf1":pl.Float64, + "T_surf2":pl.Float64, + "SOC":pl.Float64, +}) + +def func_SOC(I, t): + return np.concatenate([np.array([3.0]), 3.0 - cumulative_simpson(y=I, x=t)/3600.0]) + +for sim in np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]): + I = sim.I + V = sim.V + t = sim.t + T_surf = sim.T_surf + T_surf1 = T_surf[0, :] + T_surf2 = T_surf[1, :] + SOC = func_SOC(I, t) + fullData = fullData.vstack(pl.DataFrame({ + "I": I, + "V": V, + "t": t, + "T_surf1": T_surf1, + "T_surf2": T_surf2, + "SOC": SOC, + })) + +delta = 0.01 +kernel_duration = 30 +kernel_size = int(kernel_duration / delta) + +def func_V_h (SOC, M, I, kernel): + padded_I = np.pad(I, (kernel_size - 1, 0), mode='constant') + convolution = convolve(padded_I, kernel[::-1], mode='valid') + return M * SOC * convolution + +def func_V_X (tauX, RX, I): + VX_arr = np.zeros_like(I) + # reduce(lambda prev, i: VX_arr.__setitem__(i, VX_arr[i - 1] * np.exp(-delta / tauX) + (RX * I[i] * (1 - np.exp(-delta / tauX)))) or VX_arr[i], range(len(I)), None) + for i in range(len(I)): + if i == 0: + VX_arr[i] = 0 + else: + VX_arr[i] = VX_arr[i - 1] * np.exp(-delta / tauX) + (RX * I[i] * (1 - np.exp(-delta / tauX))) + return VX_arr + +def func_V_OCV (SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25)) + +def terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T): + tau1 = R1 * C1 + tau2 = R2 * C2 + kernal = np.exp(-np.arange(0, kernel_size) * delta / tau_H) / np.sum(np.exp(-np.arange(0, kernel_size) * delta / tau_H)) + V_h = func_V_h(SOC, M, I, kernal) + V_X1 = func_V_X(tau1, R1, I) + V_X2 = func_V_X(tau2, R2, I) + V_OCV = func_V_OCV(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T) + V_R0 = np.where(I >= 0, R0_discharge * I, R0_charge * I) + V_terminal = V_OCV - V_R0 + V_X1 + V_X2 + V_h + # print(f"V_OCV: {V_OCV[-1]}, V_R0: {V_R0[-1]}, V_X1: {V_X1[-1]}, V_X2: {V_X2[-1]}, V_h: {V_h[-1]}, V_terminal: {V_terminal[-1]}") + # print(f"Params: R0_discharge: {R0_discharge}, R0_charge: {R0_charge}, R1: {R1}, C1: {C1}, R2: {R2}, C2: {C2}, tau_H: {tau_H}, M: {M}, a0: {a0}, a1: {a1}, a2: {a2}, a3: {a3}, a4: {a4}, a5: {a5}, a6: {a6}, K_T: {K_T}") + print(f"Params: R0_discharge: {R0_discharge}, R0_charge: {R0_charge}, R1: {R1}, C1: {C1}, R2: {R2}, C2: {C2}, tau_H: {tau_H}, M: {M}") + if len(V_terminal) == len(fullData["V"].to_numpy()): + print(f"Mean Squared Error: {np.mean((V_terminal - fullData['V'].to_numpy()) ** 2)}") + return V_terminal + +def wrapper(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T): + SOC = X[0] + T = X[1] + I = X[2] + return terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T) + +args = curve_fit(wrapper, (fullData["SOC"].to_numpy(), fullData["T_surf1"].to_numpy(), fullData["I"].to_numpy()), fullData["V"].to_numpy(), p0=[0.01, 0.01, 1000, 0.01, 0.01, 1000, 10, 1, 3.7, -0.5, 0.1, -0.01, 0.001, -0.0001, 0.01, 0.01], maxfev=10000) +R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T = args[0] + +run = measurements.fu.DCC[6] +fig = plt.figure() +plt.plot(run.t, run.V, label="Measured Voltage") +plt.plot(run.t, wrapper((func_SOC(run.I, run.t), run.T_surf[0, :], run.I), *args[0]), label="Fitted Voltage") +plt.xlabel("Time (s)") +plt.ylabel("Voltage (V)") +plt.legend() +plt.show() + +measurements.fu.DCC[0].name + +def buildDF(measurement_set): + df = pl.DataFrame(schema={ + "I":pl.Float64, + "V":pl.Float64, + "t":pl.Float64, + "T_surf1":pl.Float64, + "T_surf2":pl.Float64, + "SOC":pl.Float64, + }) + for sim in measurement_set: + I = sim.I + V = sim.V + t = sim.t + T_surf = sim.T_surf + T_surf1 = T_surf[0, :] + T_surf2 = T_surf[1, :] + SOC = func_SOC(I, t) + df = df.vstack(pl.DataFrame({ + "I": I, + "V": V, + "t": t, + "T_surf1": T_surf1, + "T_surf2": T_surf2, + "SOC": SOC, + })) + return df + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] +lowCurrentDF = buildDF(lowCurrMeasurements) + +def OCV_terminal_voltage(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25)) + + +ocv_args = curve_fit(lambda X, a0, a1, a2, a3, a4, a5, a6, K_T: OCV_terminal_voltage(X[0], X[1], a0, a1, a2, a3, a4, a5, a6, K_T), (lowCurrentDF["SOC"].to_numpy(), lowCurrentDF["T_surf1"].to_numpy()), lowCurrentDF["V"].to_numpy(), p0=[3.7, -0.5, 0.1, -0.01, 0.001, -0.0001, 0.01, 0.01], maxfev=10000) +ocv_args[0] + +# OCV Args +# array([-1.23630823e+02, 1.87749488e+02, -1.14010230e+02, 3.67068235e+01, +# -6.61877171e+00, 6.33739870e-01, -2.51814884e-02, 5.90437546e-04]) + +lowCurrMeasurements + +for run in lowCurrMeasurements: + fig = plt.figure() + plt.plot(run.t, run.V, label="Measured Voltage") + plt.plot(run.t, OCV_terminal_voltage(func_SOC(run.I, run.t), run.T_surf[0, :], *ocv_args[0]), label="Fitted OCV Voltage") + plt.xlabel("Time (s)") + plt.ylabel("Voltage (V)") + plt.legend() + plt.show() + +# Params: R0_discharge: -1.3235393250096301, +# R0_charge: 0.15299343818330174, R1: 0.16123612685040048, +# C1: 0.034272989366562326, R2: -126.42453895480814, C2: 1091372.6474212944, +# tau_H: 4.7170565122757395, M: 0.0037977786728519702, + +def wrapper_post_OCV(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M): + SOC = X[0] + T = X[1] + I = X[2] + a0, a1, a2, a3, a4, a5, a6, K_T = ocv_args[0] + return terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T) + +bounds = [ + (0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 1, 0.001), # Lower bounds for R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M + (2, 0.5, 1, 1, 1, 1, 100, 1), # Upper bounds for R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M +] + +args2 = curve_fit(wrapper_post_OCV, (fullData["SOC"].to_numpy(), fullData["T_surf1"].to_numpy(), fullData["I"].to_numpy()), fullData["V"].to_numpy(), p0=[0.01, 0.01, 0.16, 0.03, 0.01, 0.01, 10, 0.003], bounds=bounds, maxfev=10000) diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_9.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_9.py new file mode 100644 index 0000000..dbbcfd7 --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_model_9.py @@ -0,0 +1,426 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit, differential_evolution +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +import scipy.io +from numba import njit +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +from scipy.interpolate import CubicSpline as cs +from functools import partial + + +# 1. Load Data +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +delta = 0.1 +kernel_duration = 60 +kernel_size = int(kernel_duration / delta) + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 # Ah capacity of Molicel P30B + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: + return 1.0 + if T < -15.0: + return voltage_curve(V, -15.0) + if T > 39.0: + return voltage_curve(V, 39.0) + return np.clip(SOC, 0.0, 1.0) +# Fixed SOC calculation tracking 0.0 to 1.0 bounds safely + +# plt.plot(lowCurrMeasurements[0].t, func_SOC(lowCurrMeasurements[0].I, lowCurrMeasurements[0].t, SOC_lookup(lowCurrMeasurements[0].V[0], lowCurrMeasurements[0].T_surf[0, 0]))) +# lowCurrMeasurements[0].V[0], lowCurrMeasurements[0].T_surf[0, 0] + +## Validation of SOC!! +# X, Y = np.meshgrid(np.arange(2.5, 4.2, 0.01), np.arange(-20, 45, 1)) +# Z = SOC_lookup(X, Y) +# plt.contourf(X, Y, Z, levels=50, cmap='viridis') +# plt.colorbar(label='SOC') +# plt.xlabel('Voltage (V)') +# plt.ylabel('Temperature (°C)') +# plt.title('SOC Lookup Table') +# plt.show() + +# measurements.fu.DCC[1].V[0] +# measurements.fu.DCC[1].T_surf[0, 0] +# SOC_lookup(4.169, -19.37) + +def buildDF(measurement_set): + frames = [] + for i, sim in enumerate(measurement_set): + cubic = cs(sim.t, np.array([sim.I, sim.V, sim.T_surf[0, :], sim.T_surf[1, :]]), axis=1) + t = np.arange(sim.t[0], sim.t[-1], delta) + I, V, T_surf1, T_surf2 = cubic(t) + starting_SOC = SOC_lookup(V[0], T_surf1[0]) + SOC = func_SOC(I, t, starting_SOC) + frames.append(pl.DataFrame({ + "I": I, "V": V, "t": t, + "T_surf1": T_surf1, "T_surf2": T_surf2, "SOC": SOC, + "run": np.ones(len(I)) * i + })) + return pl.concat(frames) + +# lowCurrentDF = buildDF(lowCurrMeasurements) +# Gather Datasets + + + +# Use everything for dynamic fitting +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +# for sim in all_sims: +# startingSOC = SOC_lookup(sim.V[0], sim.T_surf[0, 0]) +# print(f"Run: {sim.name}, Starting SOC: {startingSOC:.4f}") + +aboveFreezingSims = [m for m in all_sims if m.T_surf[0, 0] > 5] +notHighPulse = [m for m in aboveFreezingSims if not ("-40.000C" in m.name or "-30.000C" in m.name)] +# for sim in notHighPulse: + # print(sim.name) +fullData = buildDF(notHighPulse) + +# 2. Extract OCV +def OCV_terminal_voltage(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25)) + +# OCV Args +# array([ 2.33727919e+00, 8.66472120e+00, -3.23286606e+01, 7.50959382e+01, +# -9.75689762e+01, 6.62872924e+01, -1.83568111e+01, 5.90434471e-04]) + +# ocv_args = np.array([ 2.33727919e+00, 8.66472120e+00, -3.23286606e+01, 7.50959382e+01, -9.75689762e+01, 6.62872924e+01, -1.83568111e+01, 5.90434471e-04]) +ocv_args = np.array([ 2.33586300e+00, 8.70615991e+00, -3.26789721e+01, 7.63836093e+01, -9.98902031e+01, 6.83036001e+01, -1.90310137e+01, 5.90707089e-04]) + +# ocv_args, _ = curve_fit( +# lambda X, a0, a1, a2, a3, a4, a5, a6, K_T: OCV_terminal_voltage(X[0], X[1], a0, a1, a2, a3, a4, a5, a6, K_T), +# (lowCurrentDF["SOC"].to_numpy(), lowCurrentDF["T_surf1"].to_numpy()), +# lowCurrentDF["V"].to_numpy(), +# p0=[3.7, 0.5, -0.1, 0.01, -0.001, 0.0, 0.0, 0.001], +# maxfev=20000 +# ) + +# 3. Dynamic Optimization Functions (Accelerated) +# @njit +def func_V_h(SOC, M, I, kernel): + padded_I = np.pad(I, (kernel_size - 1, 0), mode='constant') + convolution = convolve(padded_I, kernel, mode='valid') + # return M * convolution + return M * SOC * convolution + + +@njit +def func_V_X_fast(tauX, RX, I, delta): + VX_arr = np.zeros_like(I) + alpha = np.exp(-delta / tauX) + beta = RX * (1.0 - alpha) + for i in range(1, len(I)): + VX_arr[i] = VX_arr[i - 1] * alpha + I[i] * beta + return VX_arr + +example_tests = ["DCC / 25°C / -10.000C", "CHC / 25°C / 3.000C", "DCP / 25°C / -30.000C", "CHP / 40°C / 4.000C"] +example_runs = [m for m in measurements.fu.DCC if m.name in example_tests] + \ + [m for m in measurements.fu.CHC if m.name in example_tests] + \ + [m for m in measurements.fu.DCP if m.name in example_tests] + \ + [m for m in measurements.fu.CHP if m.name in example_tests] + +# DCC / 25°C / -10.000C +# CHC / 25°C / 3.000C +# DCP / 25°C / -30.000C +# CHP / 40°C / 4.000C + +def terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_params, piece=False, verbose=False, superVerbose=True): + tau1 = R1 * C1 + tau2 = R2 * C2 + + # Kernel calculation + exponent = -np.arange(0, kernel_size) * delta / tau_H + kernel = np.exp(exponent) / np.sum(np.exp(exponent)) + + V_h = func_V_h(SOC, M, I, kernel) + V_X1 = func_V_X_fast(tau1, R1, I, delta) + V_X2 = func_V_X_fast(tau2, R2, I, delta) + + V_OCV = OCV_terminal_voltage(SOC, T, *ocv_params) + V_R0 = np.where(I <= 0, R0_discharge * I, R0_charge * I) + # if superVerbose: + # print(f"R0_discharge: {R0_discharge:.5f}, R0_charge: {R0_charge:.5f}, R1: {R1:.5f}, C1: {C1:.5f}, R2: {R2:.5f}, C2: {C2:.5f}, tau_H: {tau_H:.5f}, M: {M:.5f}") + if not piece: + print(f"MAE: {np.mean(np.abs(V_OCV + V_R0 + V_X1 + V_X2 + V_h - fullData['V'].to_numpy()))}") + # print(f"MSE: {np.mean((V_OCV + V_R0 + V_X1 + V_X2 + V_h - fullData['V'].to_numpy()) ** 2)}") + if verbose: + print(f"V_OCV: {V_OCV[-1]}, V_R0: {V_R0[-1]}, V_X1: {V_X1[-1]}, V_X2: {V_X2[-1]}, V_h: {V_h[-1]}, V_terminal: {V_OCV[-1] + V_R0[-1] + V_X1[-1] + V_X2[-1] + V_h[-1]}") + + return V_OCV + V_R0 + V_X1 + V_X2 + V_h + +def debug_terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_params, piece=False, verbose=False, superVerbose=True): + tau1 = R1 * C1 + tau2 = R2 * C2 + + # Kernel calculation + exponent = -np.arange(0, kernel_size) * delta / tau_H + kernel = np.exp(exponent) / np.sum(np.exp(exponent)) + + V_h = func_V_h(SOC, M, I, kernel) + V_X1 = func_V_X_fast(tau1, R1, I, delta) + V_X2 = func_V_X_fast(tau2, R2, I, delta) + + V_OCV = OCV_terminal_voltage(SOC, T, *ocv_params) + V_R0 = np.where(I <= 0, R0_discharge * I, R0_charge * I) + if superVerbose: + print(f"R0_discharge: {R0_discharge:.5f}, R0_charge: {R0_charge:.5f}, R1: {R1:.5f}, C1: {C1:.5f}, R2: {R2:.5f}, C2: {C2:.5f}, tau_H: {tau_H:.5f}, M: {M:.5f}") + if not piece: + print(f"MAE: {np.mean(np.abs(V_OCV + V_R0 + V_X1 + V_X2 + V_h - fullData['V'].to_numpy()))}") + # print(f"MSE: {np.mean((V_OCV + V_R0 + V_X1 + V_X2 + V_h - fullData['V'].to_numpy()) ** 2)}") + if verbose: + print(f"V_OCV: {V_OCV[-1]}, V_R0: {V_R0[-1]}, V_X1: {V_X1[-1]}, V_X2: {V_X2[-1]}, V_h: {V_h[-1]}, V_terminal: {V_OCV[-1] + V_R0[-1] + V_X1[-1] + V_X2[-1] + V_h[-1]}") + + return V_OCV, V_R0, V_X1, V_X2, V_h + + +# fig = plt.figure() +# axes = fig.subplots(2, 2) +# fig.show() + +def wrapper_post_OCV(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M): + # print(X.shape) + # for ax, run in zip(axes.flatten(), example_runs): + # run_soc = func_SOC(run.I, run.t, SOC_lookup(run.V[0], run.T_surf[0, 0])) + # fitted_v = terminal_voltage(run_soc, run.T_surf[0, :], run.I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_args, piece=True) + # ax.clear() + # ax.plot(run.t, run.V, label="Measured Voltage", alpha=0.8) + # ax.plot(run.t, fitted_v, '--', label="Fitted Performance ECM", alpha=0.9) + # ax.set_xlabel("Time (s)") + # ax.set_ylabel("Voltage (V)") + # ax.set_title(f"Run: {run.name}") + # ax.legend() + # ax.grid(True) + # fig.canvas.draw() + # fig.canvas.flush_events() + modelVoltages = [] + for id in np.unique(X[3]): + mask = X[3] == id + modelVoltage = terminal_voltage(X[0, mask], X[1, mask], X[2, mask], R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_args, piece=True) + measuredVoltage = X[4, mask] + # print(modelVoltage.shape, measuredVoltage.shape) + # print(f"Run ID: {id}, MAE: {np.mean(np.abs(modelVoltage - measuredVoltage))}") + modelVoltages.append(modelVoltage) + print(f"Overall MAE: {np.mean(np.abs(np.concatenate(modelVoltages) - X[4]))}") + return np.concatenate(modelVoltages) + +def wrapper_pre_OCV(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T): + SOC = X[0] + T = X[1] + I = X[2] + modelVoltages = [] + for id in np.unique(X[3]): + mask = X[3] == id + modelVoltage = terminal_voltage(X[0, mask], X[1, mask], X[2, mask], R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, (a0, a1, a2, a3, a4, a5, a6, K_T), piece=True) + modelVoltages.append(modelVoltage) + # measuredVoltage = X[4, mask] + # print(f"Run ID: {id}, MAE: {np.mean(np.abs(modelVoltage - measuredVoltage))}") + print(f"Overall MAE: {np.mean(np.abs(np.concatenate(modelVoltages) - X[4]))}") + print(f"R0_discharge: {R0_discharge:.3f}, R0_charge: {R0_charge:.3f}, R1: {R1:.3f}, C1: {C1:.3f}, R2: {R2:.3f}, C2: {C2:.3f}, tau_H: {tau_H:.3f}, M: {M:.3f}, a0: {a0:.3f}, a1: {a1:.3f}, a2: {a2:.3f}, a3: {a3:.3f}, a4: {a4:.3f}, a3: {a5:.3f}, a6: {a6:.3f}, K_T: {K_T:.3f}") + return np.concatenate(modelVoltages) + +def evolution_wrapper(params, X): + R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M = params + v = wrapper_post_OCV(X, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M) + MSE = np.mean((v - X[4]) ** 2) + return MSE + + +# X = np.array([fullData["SOC"].to_numpy(), fullData["T_surf1"].to_numpy(), fullData["I"].to_numpy(), fullData["run"].to_numpy(), fullData["V"].to_numpy()]) +# np.unique(X[3]) +# X[:, np.where(X[3] == 0)] + +# np.where(X[3] == 0) +# currentRun.shape +# currentRun = X[np.where(X[3] == id)] +# X + +# R0_discharge: 0.00010000241368768962, +# R0_charge: 0.0015092018205301212, +# R1: 0.008623195393932841, +# C1: 37.86392127904164, +# R2: 0.013221128224458535, +# C2: 761.6878322739899, +# tau_H: 53.00341711702909, +# M: 0.0018520981883959234 + +# Correct physical boundaries +bounds = ( + [1e-7, 1e-7, 1e-7, 1e-3, 1e-7, 10.0, 1e-5, -1], # Lower Bounds + [0.2, 0.2, 0.2, 5000.0, 0.2, 50000.0, 1e4, 1e3] # Upper Bounds +) + +boundsTuppled = tuple(zip(*bounds)) + +# Initial dynamic guesses +p0 = [0.002728, 0.00010000, 0.00757, 10.0, 0.011, 735, 0.833, 0.00010000] + +p0 = [ 7.54812179e-03, 1.00000000e-07, 2.44930101e-02, 5.74870831e+01, + 6.77082684e-04, 4.41486533e+04, 2.97718020e+03, -5.27874313e-03] + +# Conditions generated post hysteresis function fix. +p0 = [1.00000000e-07, 1.00000000e-07, 1.40292451e-02, 3.15517635e+03, + 1.51833942e-03, 2.91537408e+04, 1.5, 2.86674119e-03] + +preOCV_p0 = p0 + list(ocv_args) + +lessData = fullData.filter(pl.col("t") < 1000) + +args = np.array([5.30000214e-03, 9.20377540e-03, 8.07481539e-03, 1.41824989e+03, + 3.29520249e-03, 2.59134546e+02, 1.5, 1e-03, + 2.39587985e+00, 1.27417190e+01, -6.73434102e+01, 1.89554380e+02, + -2.78887472e+02, 2.05193796e+02, -5.95230431e+01, 9.40568639e-05]) + +args = np.array([1.21786569e-02, 1.76761209e-02, 1.0, 1.0, + 1.0, 1.0, 9.62831610e+00, 1.03801131e-03, + 2.39250764e+00, 1.28854012e+01, -6.78772374e+01, 1.89652101e+02, + -2.76828230e+02, 2.02226116e+02, -5.83091863e+01, -8.11863308e-05]) + +args = np.array([ 9.97263998e-03, 5.00002331e-03, 1.60732764e-02, 0.01, + 7.31018241e-03, 5.72244145e+02, 1.00000000e+00, 1.00000000e-07, + 2.38962033e+00, 1.29375218e+01, -6.85304383e+01, 1.92513553e+02, + -2.82364531e+02, 2.07151056e+02, -5.99607550e+01, -1.23234655e-05]) + +args = np.array([0.006, 0.011, 0.001, 0.010, 0.006, 792.945, 7.433, 0.000001, 2.390, 12.938, -68.531, 192.513, -282.365, 207.151, -59.960, -0.000001]) + +conservative_bounds = ( +# R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T + [0.005, 0.005, 0.0, 0.0, 0.0, 0.0, 1.0, 1e-7, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf, -np.inf], # Lower Bounds + [0.02, 0.02, 0.02, np.inf, 0.02, np.inf, 20.0, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf, np.inf] # Upper Bounds +) + +# Optimize dynamic parameters +args2, _ = curve_fit( + wrapper_post_OCV, + np.array((lessData["SOC"].to_numpy(), lessData["T_surf1"].to_numpy(), lessData["I"].to_numpy(), lessData["run"].to_numpy(), lessData["V"].to_numpy())), + lessData["V"].to_numpy(), + # p0=p0, + # bounds=bounds, + maxfev=15000, + method='trf', + x_scale='jac' +) + +args3, _ = curve_fit( + wrapper_pre_OCV, + np.array((lessData["SOC"].to_numpy(), lessData["T_surf1"].to_numpy(), lessData["I"].to_numpy(), lessData["run"].to_numpy(), lessData["V"].to_numpy())), + lessData["V"].to_numpy(), + p0=args, + bounds=conservative_bounds, + maxfev=1500, + method='trf', + x_scale='jac' +) + + +np.mean(np.abs(lessData["V"].to_numpy() - wrapper_pre_OCV(np.array((lessData["SOC"].to_numpy(), lessData["T_surf1"].to_numpy(), lessData["I"].to_numpy(), lessData["run"].to_numpy(), lessData["V"].to_numpy())), *args))) + +args3 + +# argsGlobal = differential_evolution( +# evolution_wrapper, +# boundsTuppled, +# args=(np.array((lessData["SOC"].to_numpy(), lessData["T_surf1"].to_numpy(), lessData["I"].to_numpy(), lessData["run"].to_numpy(), lessData["V"].to_numpy())),), +# maxiter=100, +# popsize=15, +# workers=-1, +# updating='deferred', +# ) + +# R0_discharge = -0.014174362096127368; R0_charge = -0.023739566602921935; R1 = 327.6950042682508; C1 = 2134.853515766918; R2 = -171.55438396480167; C2 = -2132.612618294229; tau_H = 0.8719469562101347; M = -0.00022039527420898293 + +tau_H = 1.0 +exponent = -np.arange(0, kernel_size) * delta / tau_H +kernel = np.exp(exponent) / np.sum(np.exp(exponent)) +# plt.plot(kernel) +# plt.show() + +# Make sure the kernel faces the right way +plt.plot(convolve(np.pad(np.concatenate([np.ones(kernel_size), np.ones(kernel_size)*-1]), (kernel_size-1, 0), mode='constant'), kernel, mode="valid"), label="Convolution Result") +plt.plot(np.concatenate([np.ones(kernel_size), np.ones(kernel_size)*-1]), label="Input Current") +plt.legend() +plt.show() + +arr = np.array((lessData["SOC"].to_numpy(), lessData["T_surf1"].to_numpy(), lessData["I"].to_numpy(), lessData["run"].to_numpy(), lessData["V"].to_numpy(), lessData["t"].to_numpy())) +for id, name in zip(np.unique(arr[3]), notHighPulse): + currentRun = arr[:, np.where(arr[3] == id)][:, 0, :] + V_OCV, V_R0, V_X1, V_X2, V_h = debug_terminal_voltage(currentRun[0], currentRun[1], currentRun[2], *args[:6], 10, args[7], args[8:], piece=True, verbose=True, superVerbose=False) + measuredVoltage = currentRun[4] + print(f"Run ID: {id}, MAE: {np.mean(np.abs(V_OCV + V_R0 + V_X1 + V_X2 + V_h - measuredVoltage))}") + plt.plot(currentRun[5], measuredVoltage, label="Measured Voltage", alpha=0.8) + plt.plot(currentRun[5], V_OCV + V_R0 + V_X2 + V_h, '--', label="Fitted Performance ECM", alpha=0.9) + plt.plot(currentRun[5], V_OCV, label="OCV", alpha=0.8) + plt.plot(currentRun[5], V_R0, label="R0 Voltage Drop", alpha=0.8) + plt.plot(currentRun[5], V_X1, label="X1 Voltage", alpha=0.8) + plt.plot(currentRun[5], V_X2, label="X2 Voltage", alpha=0.8) + plt.plot(currentRun[5], V_h, label="Hysteresis Voltage", alpha=0.8) + plt.xlabel("Time (s)") + plt.ylabel("Voltage (V)") + plt.title(name.name) + plt.legend() + plt.grid(True) + plt.show() + + + + + + + + + + +# print("Optimized Parameters (R0_d, R0_c, R1, C1, R2, C2, tau_H, M):") +# print(args2) + +# # 4. Plot Verification Validating Against One Dynamic Run +# run = measurements.fu.DCC[6] +# run_soc = func_SOC(run.I, run.t, SOC_lookup(run.V[0], run.T_surf[0, 0])) +# fitted_v = terminal_voltage(run_soc, run.T_surf[0, :], run.I, args2[0], args2[1], args2[2], args2[3], args2[4], args2[5], args2[6], args2[7], ocv_args, piece=True) + +# np.abs(terminal_voltage(run_soc, run.T_surf[0, :], run.I, args2[0], args2[1], args2[2], args2[3], args2[4], args2[5], args2[6], args2[7], ocv_args, piece=True) - run.V).mean() + + +# plt.figure(figsize=(10, 5)) +# plt.plot(run.t, run.V, label="Measured Voltage", alpha=0.8) +# plt.plot(run.t, fitted_v, '--', label="Fitted Performance ECM", alpha=0.9) +# plt.xlabel("Time (s)") +# plt.ylabel("Voltage (V)") +# plt.title(f"Model Validation Execution - Run: {run.name}") +# plt.legend() +# plt.grid(True) +# plt.show() + +# plt.plot(fullData["SOC"].to_numpy()) +# plt.show() + +# for run in aboveFreezingSims: +# plt.plot(func_SOC(run.I, run.t, SOC_lookup(run.V[0], run.T_surf[0, 0]))) +# plt.show() + +# for run in aboveFreezingSims: +# plt.plot(run.t, run.V, label="Measured Voltage", alpha=0.8) +# plt.plot(run.t, terminal_voltage(func_SOC(run.I, run.t, SOC_lookup(run.V[0], run.T_surf[0, 0])), run.T_surf[0, :], run.I, p0[0], p0[1], p0[2], p0[3], p0[4], p0[5], p0[6], p0[7], ocv_args, piece=True), '--', label="SOC", alpha=0.9) +# plt.xlabel("Time (s)") +# plt.ylabel("Voltage (V)") +# plt.title(f"Run: {run.name}") +# plt.legend() +# plt.grid(True) +# plt.show() \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelingG12.py b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelingG12.py new file mode 100644 index 0000000..2922ede --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electrical_modelingG12.py @@ -0,0 +1,179 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from scipy.optimize import curve_fit +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +import scipy.io +from numba import njit +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +from scipy.interpolate import CubicSpline as cs + +# 1. Load Data +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +delta = 0.1 +kernel_duration = 60 +kernel_size = int(kernel_duration / delta) + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 # Ah capacity of Molicel P30B + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: return 1.0 + if T < -15.0: return voltage_curve(V, -15.0) + if T > 39.0: return voltage_curve(V, 39.0) + return np.clip(SOC, 0.0, 1.0) + +def buildDF(measurement_set): + frames = [] + for i, sim in enumerate(measurement_set): + cubic = cs(sim.t, np.array([sim.I, sim.V, sim.T_surf[0, :], sim.T_surf[1, :]]), axis=1) + t = np.arange(sim.t, sim.t[-1], delta) + I, V, T_surf1, T_surf2 = cubic(t) + starting_SOC = SOC_lookup(V, T_surf1) + SOC = func_SOC(I, t, starting_SOC) + frames.append(pl.DataFrame({ + "I": I, "V": V, "t": t, + "T_surf1": T_surf1, "T_surf2": T_surf2, "SOC": SOC, + "run": np.ones(len(I)) * i + })) + return pl.concat(frames) + +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +aboveFreezingSims = [m for m in all_sims if m.T_surf > -2] +notHighPulse = [m for m in aboveFreezingSims if not ("-40.000C" in m.name or "-30.000C" in m.name)] +fullData = buildDF(notHighPulse) + +# 2. Extract and Freeze OCV +def OCV_terminal_voltage(SOC, T, a0, a1, a2, a3, a4, a5, a6, K_T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25.0)) + +ocv_args = np.array([2.33586300e+00, 8.70615991e+00, -3.26789721e+01, 7.63836093e+01, + -9.98902031e+01, 6.83036001e+01, -1.90310137e+01, 5.90707089e-04]) + +# 3. Enhanced Hysteresis Tracking +def func_V_h(SOC, M, I, kernel): + current_direction = np.sign(I) + padded_I = np.pad(current_direction, (kernel_size - 1, 0), mode='constant') + convolution = convolve(padded_I, kernel, mode='valid') + return M * convolution + +@njit +def func_V_X_fast(tauX, RX, I, delta): + VX_arr = np.zeros_like(I) + alpha = np.exp(-delta / tauX) + beta = RX * (1.0 - alpha) + for i in range(1, len(I)): + VX_arr[i] = VX_arr[i - 1] * alpha + I[i] * beta + return VX_arr + +def terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_params): + tau1 = R1 * C1 + tau2 = R2 * C2 + + exponent = -np.arange(0, kernel_size) * delta / tau_H + kernel = np.exp(exponent) / np.sum(np.exp(exponent)) + + V_h = func_V_h(SOC, M, I, kernel) + V_X1 = func_V_X_fast(tau1, R1, I, delta) + V_X2 = func_V_X_fast(tau2, R2, I, delta) + + V_OCV = OCV_terminal_voltage(SOC, T, *ocv_params) + V_R0 = np.where(I <= 0, R0_discharge * I, R0_charge * I) + + return V_OCV + V_R0 + V_X1 + V_X2 + V_h + +# 4. Scaled Optimization Wrapper +def wrapper_post_OCV(X, R0_d_scaled, R0_c_scaled, R1_scaled, C1_scaled, R2_scaled, C2_scaled, tau_H, M_scaled): + # Scale variables back up to their physical magnitudes + R0_discharge = R0_d_scaled * 1e-3 + R0_charge = R0_c_scaled * 1e-3 + R1 = R1_scaled * 1e-3 + C1 = C1_scaled * 1e2 + R2 = R2_scaled * 1e-3 + C2 = C2_scaled * 1e4 + M = M_scaled * 1e-2 + + modelVoltages = [] + run_ids = X[3, :] + + for id in np.unique(run_ids): + mask = run_ids == id + modelVoltage = terminal_voltage( + X[0, mask], X[1, mask], X[2, mask], + R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_args + ) + modelVoltages.append(modelVoltage) + + actual_voltages = X[4, :] + current_mae = np.mean(np.abs(np.concatenate(modelVoltages) - actual_voltages)) + print(f"Iteration MAE: {current_mae:.5f} V") + + return np.concatenate(modelVoltages) + +# 5. Scaled Parameter Boundaries & Guess Configuration +# Enforces strict parameter separation: +# Tau1 (R1*C1) bounds: 0.1s to 8s (Fast Dynamics) +# Tau2 (R2*C2) bounds: 15s to 300s (Slow Diffusion) + +# Parameters mapped: [R0_d, R0_c, R1, C1, R2, C2, tau_H, M] (all values normalized around ~1-10) +scaled_bounds = ( + [2.0, 2.0, 0.5, 1.0, 1.0, 0.5, 0.5, 0.01], # Lower Bounds + [40.0, 40.0, 20.0, 10.0, 30.0, 5.0, 120.0, 5.00] # Upper Bounds +) + +p0_scaled = [12.0, 15.0, 6.0, 4.0, 12.0, 1.5, 15.0, 0.8] + +# Downsample the whole time profile using polars instead of throwing away tail data +lessData = fullData.gather_every(10) + +X_data = np.array(( + lessData["SOC"].to_numpy(), + lessData["T_surf1"].to_numpy(), + lessData["I"].to_numpy(), + lessData["run"].to_numpy(), + lessData["V"].to_numpy() +)) + +print(f"Starting dynamic tuning sequence with {X_data.shape} data points...") +optimized_scaled_args, _ = curve_fit( + wrapper_post_OCV, + X_data, + lessData["V"].to_numpy(), + p0=p0_scaled, + bounds=scaled_bounds, + maxfev=3000, + method='trf', + x_scale='jac' +) + +# Unpack and rescale final optimized values back to true SI units +final_params = { + "R0_discharge": optimized_scaled_args * 1e-3, + "R0_charge": optimized_scaled_args * 1e-3, + "R1": optimized_scaled_args * 1e-3, + "C1": optimized_scaled_args * 1e2, + "R2": optimized_scaled_args * 1e-3, + "C2": optimized_scaled_args * 1e4, + "tau_H": optimized_scaled_args, + "M": optimized_scaled_args * 1e-2 +} + +print("\nOptimized Dynamic Parameters (Rescaled to Physical Units):") +for k, v in final_params.items(): + print(f"{k}: {v:.6e}") \ No newline at end of file diff --git a/FullVehicleSim/Electrical/HisteresisCellModel/electricl_model6.py b/FullVehicleSim/Electrical/HisteresisCellModel/electricl_model6.py new file mode 100644 index 0000000..745b683 --- /dev/null +++ b/FullVehicleSim/Electrical/HisteresisCellModel/electricl_model6.py @@ -0,0 +1,328 @@ +import numpy as np +import matplotlib.pyplot as plt +import polars as pl +import scipy.io +from scipy.integrate import simpson, cumulative_simpson +import matplotlib.patches as patches +from pysr import PySRRegressor, TemplateExpressionSpec + +MAT_PATH = "../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat" + + + +mat = scipy.io.loadmat(MAT_PATH, squeeze_me=True, struct_as_record=False) + +''' +header = batemoData["__header__"] +version = batemoData["__version__"] +globals = batemoData["__globals__"] ## Nothing +print(f"header = {header}") +print(f"version = {version}") +measurement = batemoData["measurement"] +firstLayerMeta = measurement['meta'] +Fu = measurement['fu'] + +# DCC (Discharge) +# CHC (Charging) +# DCP (Discharge Pulse) +# CHP (Charge Pulse) +PRO (Profile Measurement) + +Each Has + name + T_amb (Ambient Temperature) + t (Time Seconds) + I (Current) + V (Voltage) + T_surf (Surface temperature, nominally 1xN but may be 2xN?) +''' + +simulation = mat["measurement"] + +fullDCC = pl.DataFrame(schema={ + "I":pl.Float64, + "V":pl.Float64, + "t":pl.Float64, + "T_surf1":pl.Float64, + "T_surf2":pl.Float64, + "SOC":pl.Float64, + "I_Past1":pl.Float64, + "I_Past2":pl.Float64, + "I_Past3":pl.Float64, + "I_Past4":pl.Float64, +}) + +for dcc in simulation.fu.DCC: + I = dcc.I + I_back1 = np.concatenate([np.zeros(1), I[:-1]]) + I_back2 = np.concatenate([np.zeros(3), I[:-3]]) + I_back3 = np.concatenate([np.zeros(7), I[:-7]]) + I_back4 = np.concatenate([np.zeros(20), I[:-20]]) + V = dcc.V + t = dcc.t + T_surf = dcc.T_surf + T_surf1 = T_surf[0, :] + T_surf2 = T_surf[1, :] + SOC = 3.0 - cumulative_simpson(y=I, x=t)/3600.0 + SOC = np.concatenate([np.array([3.0]), SOC]) + fullDCC = fullDCC.vstack(pl.DataFrame({ + "I": I, + "V": V, + "t": t, + "T_surf1": T_surf1, + "T_surf2": T_surf2, + "SOC": SOC, + "I_Past1": I_back1, + "I_Past2": I_back2, + "I_Past3": I_back3, + "I_Past4": I_back4, + })) + +# simulation.fu.DCC +# len(simulation.fu.DCC) +# for dcc in simulation.fu.DCC: +# I = dcc.I +# V = dcc.V +# t = dcc.t +# T_amb = dcc.T_amb +# T_surf = dcc.T_surf +# name = dcc.name +# SOC = 3.0 - cumulative_simpson(y=I, x=t)/3600.0 +# SOC = np.concatenate([np.array([3.0]), SOC]) +# fig = plt.figure() +# ax1 = fig.add_subplot(111) +# ax2 = ax1.twinx() +# ax1.plot(SOC, I, label="Current", color="blue") +# ax2.plot(SOC, V, label="Voltage", color="orange") +# ## put patches for legends with current and voltage on the same legend with current blue nad voltage orange +# current_patch = patches.Patch(color='blue', label='Current [A]') +# voltage_patch = patches.Patch(color='orange', label='Voltage [V]') +# plt.legend(handles=[current_patch, voltage_patch]) +# plt.title(f"DCC: {name} at T_amb={T_amb} K") +# plt.xlabel("SOC") +# plt.grid(True) +# plt.show() + +# SOC = 3.0 - cumulative_simpson(y=simulation.fu.DCC[0].I, x=simulation.fu.DCC[0].t)/3600.0 +# SOC = np.concatenate([np.array([3.0]), SOC]) + +X = fullDCC.select(pl.col("I"), pl.col("SOC"), pl.col("T_surf1"), pl.col("T_surf2"), pl.col("I_Past1"), pl.col("I_Past2"), pl.col("I_Past3"), pl.col("I_Past4")).to_numpy() +y = fullDCC.select(pl.col("V")).to_numpy() + +# "I": I, +# "V": V, +# "t": t, +# "T_surf1": T_surf1, +# "T_surf2": T_surf2, +# "SOC": SOC, + +template = TemplateExpressionSpec( + expressions=["d", "e", "f", "g", "h"], + variable_names=["x1", "x2", "x3", "x4"], + parameters={"p1":1, "p2":1, "p3":1, "p4":1, "p5":1}, + combine="d(h(x1, x2) * log((f(x2))/(e(1 - x2)))) + g(x1)", +) + +template = TemplateExpressionSpec( + expressions=["g", "h"], + variable_names=["x1", "x2", "x3", "x4"], + parameters={"p1":1, "p2":1, "p3":1, "p4":1, "p5":1}, + combine="h(x1) * g(x2)", +) + +model = PySRRegressor( + maxsize=80, + niterations=1000, + binary_operators=["+", "-", "*", "/"], + unary_operators=["exp", "log"], + batching=True, + batch_size=200, +) + +output = model.fit(X, y) + +cos = lambda x: np.cos(x) +sin = lambda x: np.sin(x) +exp = lambda x: np.exp(x) +log = lambda x: np.log(x) + +def custom_fun_1(X): + x0 = X[:, 0] + x1 = X[:, 1] + x2 = X[:, 2] + x3 = X[:, 3] + return ((cos(x1 * 0.95293) * -0.59082) + cos(cos(sin(exp(x0 / 2.5028) + 0.46012)))) + 2.5555 + +def custom_fun_2(X): + x0 = X[:, 0] + x1 = X[:, 1] + x2 = X[:, 2] + x3 = X[:, 3] + return (cos(cos(exp(x0 + cos(x1 + x0)) + 0.74591)) + 2.5539) + (cos(x1 + -0.062883) * -0.57004) + +def custom_fun_3(X): + x0 = X[:, 0] + x1 = X[:, 1] + x2 = X[:, 2] + x3 = X[:, 3] + return (log(((exp(x0) + 19.943527) + (x1 * -3.1378086)) * (((x0 + (exp(exp((x2 / exp(x2 + -1.089517)) / exp(x3))) + ((x3 + -1.7805654) / exp(exp(exp(x0)))))) * 0.07967347) + 4.030623)) * 0.81259966) - -0.9257864 + +def custom_fun_4(X): + x0 = X[:, 0] + x1 = X[:, 1] + x2 = X[:, 2] + x3 = X[:, 3] + x4 = X[:, 4] + x5 = X[:, 5] + x6 = X[:, 6] + x7 = X[:, 7] + return ((x5 + ((((x2 / 1.3202298) + exp(x1)) / (exp(exp(x1) * 0.1189319) * 0.16781318)) + ((((x0 + x3) + exp((x6 + ((x1 - x3) * (exp(x6 - (((x2 - x1) + x3) * -0.033423472)) * 0.010805121))) + 4.2677464)) - exp(x1)) + ((((x7 + x5) + ((x3 * 0.40745232) + (x4 / (exp(x2) + 0.20448817)))) + -169.69832) * log(x1))))) * 0.003010543) + 4.5433464 + +for dcc in simulation.fu.DCC: + I = dcc.I + V = dcc.V + t = dcc.t + I_back1 = np.concatenate([np.zeros(1), I[:-1]]) + I_back2 = np.concatenate([np.zeros(3), I[:-3]]) + I_back3 = np.concatenate([np.zeros(7), I[:-7]]) + I_back4 = np.concatenate([np.zeros(20), I[:-20]]) + T_amb = dcc.T_amb + T_surf = dcc.T_surf + name = dcc.name + SOC = 3.0 - cumulative_simpson(y=I, x=t)/3600.0 + SOC = np.concatenate([np.array([3.0]), SOC]) + plt.plot(SOC, V, label="Measured Voltage") + plt.plot(SOC, custom_fun_4(np.column_stack([I, SOC, T_surf[0, :], T_surf[1, :], I_back1, I_back2, I_back3, I_back4])), label="Model 3") + plt.legend() + plt.title(f"DCC: {name} at T_amb={T_amb} C") + plt.xlabel("SOC") + plt.ylabel("Voltage [V]") + plt.grid(True) + plt.show() + +plt.plot(X[:, 1], custom_fun_1(X[:, 1], X[:, 2]), label="Model 1") +plt.plot(X[:, 1], y, label="Measured Voltage") +plt.xlabel("SOC [Ah]") +plt.ylabel("Voltage [V]") +plt.legend() +plt.show() + + +# ============================================================ +# VERSION 2: PARAMETERIZED HYSTERESIS KERNEL +# ============================================================ + +def buildHysteresisKernel(kernelLength, tauFast, tauSlow, mix=0.5, shape=1.0): + kernelLength = int(kernelLength) + if kernelLength <= 0: + raise ValueError("kernelLength must be positive") + + tauFast = max(float(tauFast), 1e-9) + tauSlow = max(float(tauSlow), 1e-9) + mix = float(np.clip(mix, 0.0, 1.0)) + shape = max(float(shape), 1e-9) + + lagIndex = np.arange(kernelLength, dtype=float) + fastComponent = np.exp(-((lagIndex / tauFast) ** shape)) + slowComponent = np.exp(-((lagIndex / tauSlow) ** shape)) + kernel = (mix * fastComponent) + ((1.0 - mix) * slowComponent) + + kernelSum = float(np.sum(kernel)) + if kernelSum == 0.0: + return np.ones(kernelLength, dtype=float) / float(kernelLength) + + return kernel / kernelSum + + +def buildHysteresisWindow(currentSeries, kernelLength): + currentSeries = np.asarray(currentSeries, dtype=float).reshape(-1) + kernelLength = int(kernelLength) + if kernelLength <= 0: + raise ValueError("kernelLength must be positive") + + paddedSeries = np.pad(currentSeries, (kernelLength - 1, 0), mode="constant") + windows = np.empty((currentSeries.shape[0], kernelLength), dtype=float) + + for sampleIndex in range(currentSeries.shape[0]): + window = paddedSeries[sampleIndex:sampleIndex + kernelLength] + windows[sampleIndex, :] = window[::-1] + + return windows + + +def buildHysteresisFeature(currentSeries, kernelLength, tauFast, tauSlow, mix=0.5, shape=1.0): + currentWindows = buildHysteresisWindow(currentSeries, kernelLength) + kernel = buildHysteresisKernel(kernelLength, tauFast, tauSlow, mix=mix, shape=shape) + return currentWindows @ kernel + + +def buildHysteresisTrainingSet(simulationData, kernelLength=64, tauFast=4.0, tauSlow=28.0, mix=0.7, shape=1.15): + featureRows = [] + targetRows = [] + + for dcc in simulationData.fu.DCC: + currentSeries = np.asarray(dcc.I, dtype=float).reshape(-1) + voltageSeries = np.asarray(dcc.V, dtype=float).reshape(-1) + timeSeries = np.asarray(dcc.t, dtype=float).reshape(-1) + surfaceSeries = np.asarray(dcc.T_surf, dtype=float) + + if surfaceSeries.ndim == 1: + surfaceOne = surfaceSeries + surfaceTwo = surfaceSeries + else: + surfaceOne = surfaceSeries[0, :] + surfaceTwo = surfaceSeries[1, :] + + socSeries = 3.0 - cumulative_simpson(y=currentSeries, x=timeSeries) / 3600.0 + socSeries = np.concatenate([np.array([3.0], dtype=float), socSeries]) + + hysteresisSeries = buildHysteresisFeature( + currentSeries=currentSeries, + kernelLength=kernelLength, + tauFast=tauFast, + tauSlow=tauSlow, + mix=mix, + shape=shape, + ) + + featureRows.append(np.column_stack([ + currentSeries, + socSeries, + surfaceOne, + surfaceTwo, + hysteresisSeries, + ])) + targetRows.append(voltageSeries.reshape(-1, 1)) + + features = np.vstack(featureRows) + targets = np.vstack(targetRows).ravel() + return features, targets + + +def trainHysteresisKernelVersion2(simulationData, kernelLength=128, tauFast=4.0, tauSlow=14.0, mix=0.7, shape=1.35): + features, targets = buildHysteresisTrainingSet( + simulationData=simulationData, + kernelLength=kernelLength, + tauFast=tauFast, + tauSlow=tauSlow, + mix=mix, + shape=shape, + ) + + modelV2 = PySRRegressor( + maxsize=30, + niterations=40, + binary_operators=["+", "-", "*", "/"], + unary_operators=["exp", "log"], + batching=True, + batch_size=200, + ) + + fittedModel = modelV2.fit(features, targets) + return fittedModel, features, targets + +model, features, targets = trainHysteresisKernelVersion2(simulation) + + +# Optional reference entry point for the new version: +# fittedModel, features, targets = trainHysteresisKernelVersion2(simulation) \ No newline at end of file diff --git a/outputs/20260605_170819_y7qEJI/checkpoint.pkl b/outputs/20260605_170819_y7qEJI/checkpoint.pkl new file mode 100644 index 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cos(x3)" diff --git a/outputs/20260605_170844_CfwX91/hall_of_fame.csv.bak b/outputs/20260605_170844_CfwX91/hall_of_fame.csv.bak new file mode 100644 index 0000000..ce586b2 --- /dev/null +++ b/outputs/20260605_170844_CfwX91/hall_of_fame.csv.bak @@ -0,0 +1,9 @@ +Complexity,Loss,Equation +1,0.09237157,"3.578532" +3,0.015335105,"-15.249025 - x3" +5,0.003983757,"(x2 * -0.3919811) + 5.2681723" +7,0.00040154983,"(x2 / (x3 + 15.396925)) + 4.852261" +11,0.0003984477,"cos(x3) + (cos((x2 * -3.0206292) / x3) / 0.2936874)" +13,0.00035803785,"(1.7555461 + cos(x3)) + (cos(-4.532914 * (x2 / x3)) / 0.5895234)" +15,0.00034305936,"cos(x3) + ((cos(-0.22011857 + ((x2 * 3.0981772) / x3)) / 0.37743562) + 1.030288)" +18,0.0003428655,"((cos(x4) + (cos((x2 * (3.0963361 / x3)) + -0.21454689) / 0.37764242)) * 1.0070273) + cos(x3)" diff --git a/outputs/20260605_173335_rG1Rzg/checkpoint.pkl b/outputs/20260605_173335_rG1Rzg/checkpoint.pkl new file mode 100644 index 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100644 index 0000000..b8cd3db --- /dev/null +++ b/outputs/20260605_173335_rG1Rzg/hall_of_fame.csv @@ -0,0 +1,14 @@ +Complexity,Loss,Equation +1,0.14158258,"3.5650325" +4,0.046780355,"5.03204 - log(x1)" +5,0.020174779,"(x1 * -0.41577148) + 5.4018736" +6,0.01831764,"(cos(x1) * -0.5376725) + 3.4590318" +8,0.014986508,"((exp(x1) + -0.11164971) * -0.0039546792) + 4.0197625" +11,0.012626836,"(cos(cos(exp(x0))) + 2.7706904) + (cos(x1) * -0.5312622)" +12,0.012041195,"((cos(x1) * -0.53031766) + 2.5074618) + cos(exp(x0) + -0.67037964)" +13,0.01198075,"((cos(x1) * -0.5289256) + sin(cos(sin(x0 + 0.66593564)))) + 2.6993287" +14,0.011761692,"(cos(cos(sin(exp(x0 * 0.23291506)))) + 2.731931) + (cos(x1) * -0.5299537)" +15,0.011450616,"((cos(x1 + -0.16093712) * -0.56156677) + 2.440645) + cos(cos(exp(x0) + 0.86974084))" +16,0.010320034,"cos(cos(0.562462 + exp(cos(x1) + x0))) + (2.6217613 + (cos(x1) * -0.58870065))" +18,0.009740723,"(cos(x1) * -0.5753818) + (cos(cos(exp(cos(x1 + -0.232988) + x0) + 0.69828486)) + 2.5880654)" +20,0.009686999,"(cos(cos(exp(x0 + cos(x1 + x0)) + 0.7459149)) + 2.5538678) + (cos(x1 + -0.06288294) * -0.57003665)" diff --git a/outputs/20260605_173335_rG1Rzg/hall_of_fame.csv.bak b/outputs/20260605_173335_rG1Rzg/hall_of_fame.csv.bak new file mode 100644 index 0000000..b8cd3db --- /dev/null +++ b/outputs/20260605_173335_rG1Rzg/hall_of_fame.csv.bak @@ -0,0 +1,14 @@ +Complexity,Loss,Equation +1,0.14158258,"3.5650325" +4,0.046780355,"5.03204 - log(x1)" +5,0.020174779,"(x1 * -0.41577148) + 5.4018736" +6,0.01831764,"(cos(x1) * -0.5376725) + 3.4590318" +8,0.014986508,"((exp(x1) + -0.11164971) * -0.0039546792) + 4.0197625" +11,0.012626836,"(cos(cos(exp(x0))) + 2.7706904) + (cos(x1) * -0.5312622)" +12,0.012041195,"((cos(x1) * -0.53031766) + 2.5074618) + cos(exp(x0) + -0.67037964)" +13,0.01198075,"((cos(x1) * -0.5289256) + sin(cos(sin(x0 + 0.66593564)))) + 2.6993287" +14,0.011761692,"(cos(cos(sin(exp(x0 * 0.23291506)))) + 2.731931) + (cos(x1) * -0.5299537)" 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h = 0.5102515; p1 = [0.29919448]; p2 = [-0.0024157339]; p3 = [0.4125923]; p4 = [0.114938244]; p5 = [3.231683]" +4,0.020187454,"g = 12.910461 - #1; h = 0.41960278; p1 = [0.19131187]; p2 = [-0.31550545]; p3 = [0.2953269]; p4 = [1.2401006]; p5 = [-0.075753495]" +5,0.01674504,"g = log(9.668437 - #1); h = 2.1685836; p1 = [0.2311817]; p2 = [-0.4819484]; p3 = [0.27055913]; p4 = [1.0952356]; p5 = [-0.011553196]" +6,0.016334653,"g = log(log(11.231159 - #1)); h = 5.516899; p1 = [0.5851717]; p2 = [-0.31661797]; p3 = [0.27683184]; p4 = [3.3181517]; p5 = [-0.021765368]" +7,0.015008225,"g = (exp(#1) * 0.001560047) + -1.5977929; h = -2.511395; p1 = [-0.01028225]; p2 = [-0.20589283]; p3 = [0.20313159]; p4 = [1.3348466]; p5 = [0.06440573]" +9,0.011747481,"g = log(9.668437 - #1); h = 2.1685836 + (0.0063233213 * #1); p1 = [0.2311817]; p2 = [-0.4819484]; p3 = [0.27055913]; p4 = [1.0952356]; p5 = [0.01012363]" +12,0.011118251,"g = ((#1 * #1) * 0.02847272) + -2.690646; h = -1.6991075 - (#1 * 0.004837096); p1 = [-0.0062846392]; p2 = [-0.16279292]; p3 = [0.1604949]; p4 = [-0.787699]; p5 = [-0.020646032]" +13,0.009220802,"g = (exp(#1 / 1.8232644) * 0.027975967) + -1.9307917; h = (#1 * -0.007931266) + -2.2841105; p1 = [-0.040756416]; p2 = [-0.18525246]; p3 = [0.10550426]; p4 = [-0.6697017]; p5 = [-0.010399518]" +14,0.008201847,"g = (exp(#1 / 1.7637918) * 0.038912833) + -2.9290135; h = -1.4306883 + (-0.10357848 * exp(#1)); p1 = [-0.041650955]; p2 = [-0.21279228]; p3 = [-0.11671401]; p4 = [-0.90407455]; p5 = [0.0055799587]" +16,0.007718546,"g = (exp(#1 / 1.6343316) * 0.021368526) + -2.2231567; h = (exp(#1 / 2.5276432) * -0.12797768) + -1.8277506; p1 = [-0.040756416]; p2 = [-0.18525246]; p3 = [-0.11458365]; p4 = [-0.6697017]; p5 = [-0.010399518]" +18,0.0076752626,"g = (exp(#1 / 1.6343316) * 0.021368526) + -2.221973; h = (exp((#1 / 1.6343316) * 0.6894196) * -0.12797768) + -1.8277506; p1 = [-0.040756416]; p2 = [-0.18525246]; p3 = [0.10550426]; p4 = [-0.6697017]; p5 = [-0.010399518]" diff --git a/outputs/20260606_003030_rjsoSk/hall_of_fame.csv.bak b/outputs/20260606_003030_rjsoSk/hall_of_fame.csv.bak new file mode 100644 index 0000000..117c5eb --- /dev/null +++ b/outputs/20260606_003030_rjsoSk/hall_of_fame.csv.bak @@ -0,0 +1,12 @@ +Complexity,Loss,Equation +2,0.14158283,"g = 6.9857764; h = 0.5102515; p1 = [0.29919448]; p2 = [-0.0024157339]; p3 = [0.4125923]; p4 = [0.114938244]; p5 = [3.231683]" +4,0.020187454,"g = 12.910461 - #1; h = 0.41960278; p1 = [0.19131187]; p2 = [-0.31550545]; p3 = [0.2953269]; p4 = [1.2401006]; p5 = [-0.075753495]" +5,0.01674504,"g = log(9.668437 - #1); h = 2.1685836; p1 = [0.2311817]; p2 = [-0.4819484]; p3 = [0.27055913]; p4 = [1.0952356]; p5 = [-0.011553196]" +6,0.016334653,"g = log(log(11.231159 - #1)); h = 5.516899; p1 = [0.5851717]; p2 = [-0.31661797]; p3 = [0.27683184]; p4 = [3.3181517]; p5 = [-0.021765368]" +7,0.015008225,"g = (exp(#1) * 0.001560047) + -1.5977929; h = -2.511395; p1 = [-0.01028225]; p2 = [-0.20589283]; p3 = [0.20313159]; p4 = [1.3348466]; p5 = [0.06440573]" +9,0.011747481,"g = log(9.668437 - #1); h = 2.1685836 + (0.0063233213 * #1); p1 = [0.2311817]; p2 = [-0.4819484]; p3 = [0.27055913]; p4 = [1.0952356]; p5 = [0.01012363]" +12,0.011118251,"g = ((#1 * #1) * 0.02847272) + -2.690646; h = -1.6991075 - (#1 * 0.004837096); p1 = [-0.0062846392]; p2 = [-0.16279292]; p3 = [0.1604949]; p4 = [-0.787699]; p5 = [-0.020646032]" +13,0.009220802,"g = (exp(#1 / 1.8232644) * 0.027975967) + -1.9307917; h = (#1 * -0.007931266) + -2.2841105; p1 = [-0.040756416]; p2 = [-0.18525246]; p3 = [0.10550426]; p4 = [-0.6697017]; p5 = [-0.010399518]" +14,0.008201847,"g = (exp(#1 / 1.7637918) * 0.038912833) + -2.9290135; h = -1.4306883 + (-0.10357848 * exp(#1)); p1 = [-0.041650955]; p2 = [-0.21279228]; p3 = [-0.11671401]; p4 = [-0.90407455]; p5 = [0.0055799587]" +16,0.007718546,"g = (exp(#1 / 1.6343316) * 0.021368526) + -2.2231567; h = (exp(#1 / 2.5276432) * -0.12797768) + -1.8277506; p1 = [-0.040756416]; p2 = [-0.18525246]; p3 = [-0.11458365]; p4 = [-0.6697017]; p5 = [-0.010399518]" +18,0.0076752626,"g = (exp(#1 / 1.6343316) * 0.021368526) + -2.221973; h = (exp((#1 / 1.6343316) * 0.6894196) * -0.12797768) + -1.8277506; p1 = [-0.040756416]; p2 = [-0.18525246]; p3 = [0.10550426]; p4 = [-0.6697017]; p5 = [-0.010399518]" diff --git a/outputs/20260606_003355_3mqPft/checkpoint.pkl b/outputs/20260606_003355_3mqPft/checkpoint.pkl new file mode 100644 index 0000000000000000000000000000000000000000..e222ecaa767a8c0f516cd888c563e90510b8f9b8 GIT binary patch literal 708657 zcmeEv2Vhi1*LIT4!lf)M9Rwj5KuQSdMM?q$kP-;eEck{H0tuvh|+@kideyliW0=aE0!o0Ea?B-xpU^;y}O(6eRuHN*WdU1qH!{J=G-&SbIzGF z4JzdQUie}{G zXO`yV=VX`V6%|&xLZ|1HmQ}i()AI_mO6F%473Y*>l@*ngR$h+k!F?)S!F}=58$bOj zb6m~Kebln$%qy;R+475KsWtE)YOiQKv$QxTyVBK$No6W7t#q}Wm{U-kpH-GKOgu3` zeLx7?B(o@gM&`_-l7g(V%4M#G*+m7AGN;njZ${7Dp7Ww3!sf;F3hNz)j(hf;*%SZK zt6$HYp3z}_)j!~WVSU18p!PG(_N zL5@~2WL|WoD`Z}b`j1%kA91MIxHzi>HOg|JCsnN>#nF|cgYmBz?O(Clzv8ri#p7S) z<;Iiuns-%B<>-#CHig;qv-5LGGchii 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a/outputs/20260606_021836_7T4wfI/hall_of_fame.csv b/outputs/20260606_021836_7T4wfI/hall_of_fame.csv new file mode 100644 index 0000000..76d2730 --- /dev/null +++ b/outputs/20260606_021836_7T4wfI/hall_of_fame.csv @@ -0,0 +1,18 @@ +Complexity,Loss,Equation +1,0.1415826,"3.5649922" +5,0.020174954,"(x1 * -0.4158265) + 5.401702" +6,0.013977423,"log((x1 + -6.931311) * -14.925834)" +8,0.013115806,"log((x1 + -6.355604) * -95.999115) * 0.69748753" +9,0.011385535,"log((x1 + -6.9157047) * (-14.414061 - exp(x4)))" +10,0.009269198,"log(((x4 * -0.13972968) + -14.9506855) * (x1 + -6.980082))" +11,0.0076216357,"log((log(0.28418428 - x0) + -15.134965) * (x1 + -6.9323688))" +13,0.007465354,"log((x1 + -6.980082) * (log((x4 * -1.8015646) + 0.3693332) + -15.188278))" +16,0.005071091,"log(((-15.278458 - ((x2 + x4) * 0.21522282)) + (x3 * 0.17450936)) * (x1 + -6.886979))" +18,0.005066207,"log((((x3 * 0.15619037) - (((x2 + -4.363537) + x4) * 0.20172201)) + -15.7568445) * (x1 + -6.9177847))" +20,0.004830611,"log((x1 + -6.8552194) * (((x3 * 0.17556405) + -15.992225) - ((((x1 + x2) + -6.8552194) + x4) * 0.22224921)))" +21,0.004592707,"log(((x1 + -6.886094) * (((x2 * 0.1518889) + -15.748936) - (((x4 + -4.365079) + x2) * 0.19550365))) + exp(x0))" +23,0.003510716,"log((((0.096697345 * x2) + -15.619484) - ((exp(x4 + 0.89043003) + -1.9077631) + ((x2 + x4) * 0.14370511))) * (x1 + -6.864843))" +25,0.003488224,"log((x1 + -6.864843) * (((x3 + 0.3348677) * 0.096697345) + (-15.619484 - ((exp(x0 - -0.93297875) + -1.9077631) + (0.14370511 * (x2 + x4))))))" +26,0.0034845257,"log((((x2 * 0.096697345) + -15.619484) - ((exp(x4 + 0.89043003) + -1.9077631) + (((exp(x4) + x2) + x4) * 0.14370511))) * (x1 + -6.864843))" +27,0.0033134876,"log((x1 + -6.8515253) * (((x2 * 0.09947497) + -15.76245) - (((((x4 + x2) + -6.8497934) + x1) * 0.14530487) + (exp(x4 + 1.0534714) + -1.7831411))))" +29,0.0033016787,"log((x1 + -6.8515253) * (((x2 * 0.09947497) + -15.74374) - ((((x1 + ((x4 + -6.8497934) + x2)) * 0.14530487) + exp((x4 * 0.90974265) + 1.0243999)) + -1.7831411)))" diff --git a/outputs/20260606_021836_7T4wfI/hall_of_fame.csv.bak b/outputs/20260606_021836_7T4wfI/hall_of_fame.csv.bak new file mode 100644 index 0000000..76d2730 --- /dev/null +++ b/outputs/20260606_021836_7T4wfI/hall_of_fame.csv.bak @@ -0,0 +1,18 @@ +Complexity,Loss,Equation +1,0.1415826,"3.5649922" +5,0.020174954,"(x1 * -0.4158265) + 5.401702" +6,0.013977423,"log((x1 + -6.931311) * -14.925834)" +8,0.013115806,"log((x1 + -6.355604) * -95.999115) * 0.69748753" +9,0.011385535,"log((x1 + -6.9157047) * (-14.414061 - exp(x4)))" +10,0.009269198,"log(((x4 * -0.13972968) + -14.9506855) * (x1 + -6.980082))" +11,0.0076216357,"log((log(0.28418428 - x0) + -15.134965) * (x1 + -6.9323688))" +13,0.007465354,"log((x1 + -6.980082) * (log((x4 * -1.8015646) + 0.3693332) + -15.188278))" +16,0.005071091,"log(((-15.278458 - ((x2 + x4) * 0.21522282)) + (x3 * 0.17450936)) * (x1 + -6.886979))" +18,0.005066207,"log((((x3 * 0.15619037) - (((x2 + -4.363537) + x4) * 0.20172201)) + -15.7568445) * (x1 + -6.9177847))" +20,0.004830611,"log((x1 + -6.8552194) * (((x3 * 0.17556405) + -15.992225) - ((((x1 + x2) + -6.8552194) + x4) * 0.22224921)))" +21,0.004592707,"log(((x1 + -6.886094) * (((x2 * 0.1518889) + -15.748936) - (((x4 + -4.365079) + x2) * 0.19550365))) + exp(x0))" +23,0.003510716,"log((((0.096697345 * x2) + -15.619484) - ((exp(x4 + 0.89043003) + -1.9077631) + ((x2 + x4) * 0.14370511))) * (x1 + -6.864843))" +25,0.003488224,"log((x1 + -6.864843) * (((x3 + 0.3348677) * 0.096697345) + (-15.619484 - ((exp(x0 - -0.93297875) + -1.9077631) + (0.14370511 * (x2 + x4))))))" +26,0.0034845257,"log((((x2 * 0.096697345) + -15.619484) - ((exp(x4 + 0.89043003) + -1.9077631) + (((exp(x4) + x2) + x4) * 0.14370511))) * (x1 + -6.864843))" +27,0.0033134876,"log((x1 + -6.8515253) * (((x2 * 0.09947497) + -15.76245) - (((((x4 + x2) + -6.8497934) + x1) * 0.14530487) + (exp(x4 + 1.0534714) + -1.7831411))))" +29,0.0033016787,"log((x1 + -6.8515253) * 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log(x1)" +5,0.020182258,"(x1 * -0.4186744) + 5.413453" +6,0.014989613,"4.021728 - (exp(x1) * 0.003973103)" +8,0.012648692,"4.0218387 - ((exp(x1) - x4) * 0.0038953868)" +10,0.011386385,"4.035525 - (((exp(x1) - x4) - x4) * 0.0038734567)" +11,0.011178298,"4.0152345 - ((exp(x1) - (x2 * exp(x4))) * 0.0041016773)" +12,0.007562292,"4.0141654 - (0.0040170457 * (exp(x1) - (x2 + (x4 * x1))))" +14,0.0069505856,"4.0141654 - ((exp(x1) - ((x2 * 0.7400101) + (x4 * 3.2431555))) * 0.0040170457)" +16,0.006916721,"4.021728 - ((exp(x1) - ((x2 * 0.6403898) + (x4 - (x4 * -2.706216)))) * 0.003973103)" +18,0.006759502,"4.029247 - ((exp(x1) - ((x4 + (((x1 - x2) - x4) * -0.2767068)) * 2.848128)) * 0.0040198658)" +21,0.0067123845,"4.029247 - ((exp(x1) - ((x4 + (((x1 - (x2 + exp(x4))) - x4) * -0.2767068)) * 2.848128)) * 0.0040198658)" +23,0.006687721,"4.029247 - ((exp(x1) - ((x4 + (((x1 - (x2 + exp(0.47565556 + x4))) - x4) * -0.2767068)) * 2.848128)) * 0.0040198658)" diff --git a/outputs/20260606_022206_B4MMfw/hall_of_fame.csv.bak b/outputs/20260606_022206_B4MMfw/hall_of_fame.csv.bak new file mode 100644 index 0000000..556dede --- /dev/null +++ b/outputs/20260606_022206_B4MMfw/hall_of_fame.csv.bak @@ -0,0 +1,14 @@ +Complexity,Loss,Equation +1,0.14158265,"3.5647438" +4,0.047093198,"5.0497274 - log(x1)" +5,0.020182258,"(x1 * -0.4186744) + 5.413453" +6,0.014989613,"4.021728 - (exp(x1) * 0.003973103)" +8,0.012648692,"4.0218387 - ((exp(x1) - x4) * 0.0038953868)" +10,0.011386385,"4.035525 - (((exp(x1) - x4) - x4) * 0.0038734567)" +11,0.011178298,"4.0152345 - ((exp(x1) - (x2 * exp(x4))) * 0.0041016773)" +12,0.007562292,"4.0141654 - (0.0040170457 * (exp(x1) - (x2 + (x4 * x1))))" +14,0.0069505856,"4.0141654 - ((exp(x1) - ((x2 * 0.7400101) + (x4 * 3.2431555))) * 0.0040170457)" +16,0.006916721,"4.021728 - ((exp(x1) - ((x2 * 0.6403898) + (x4 - (x4 * -2.706216)))) * 0.003973103)" +18,0.006759502,"4.029247 - ((exp(x1) - ((x4 + (((x1 - x2) - x4) * -0.2767068)) * 2.848128)) 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zdgzZ)b@p1Hnp5if<(r$WewS)>_SuqaO}KNgXRTKkSyw)L()z)gMb?Cw{a0RSve5cj z=Os}~|CnoCJiJ8LAAX)|ZT;i@$O=1VS^aCjF{)0FS=OAz+1q9`m}MOm-KgkrZKt)% zj{fVWhtIUOKOW#eeBm_f#2=2^tM-{ Date: Thu, 11 Jun 2026 19:03:26 -0700 Subject: [PATCH 29/38] little bit of sim stuff --- Data/basicSuspensionScript.py | 60 +++++++++++++++++++++++++++++++++++ FullVehicleSim/basicViewer.py | 11 +++++++ 2 files changed, 71 insertions(+) create mode 100644 Data/basicSuspensionScript.py diff --git a/Data/basicSuspensionScript.py b/Data/basicSuspensionScript.py new file mode 100644 index 0000000..53de885 --- /dev/null +++ b/Data/basicSuspensionScript.py @@ -0,0 +1,60 @@ +#steering wheel angle --> steering rack psition --> wheel steer angle (how static ackermann affects wheel angle function) +# equations taken from "rack and pinion" section of: https://www.mathworks.com/help/vdynblks/ref/kinematicsteering.html +import numpy as np +import matplotlib.pyplot as plt + +#global variables +#----------------------------- +# THIS SCRIPT USES MOSTLY FS-3 VALUES. FS-3 values denoted by [3], any theoretical or FS-4 values denoted by [4] +#----------------------------- +tw = 1286.615346 #mm (simplified track width from steering axis to steering axis [steering axis is also simplified to be A-arm knuckle to A-arm knuckle]) +rackRatio = 82.55/248 #[4] mm rack displacement/deg pinion rotation +# wheelInput = 0.0 #in degrees of steering wheel movement (CW + CCW -) +# rackShift = 0.0 # mm of movement of the rack from left to right (left is - right is +) +l_rack = 292.1 #[4] mm (width of steering rack casing) +# l_rod = 378.9426 #[3] mm (length of tie rod as left in FS-3 master CAD) +l_rod = 409.575 #[4] mm (length of tie rod as left in FS-3 master CAD) +d = 109.7788 #[3] mm (plan view distance between front axis and rack. negative because we have a front steer setup) +l_arm = 71.628 #[3] mm (length of "steer arm", which is the distance from the center of the upright toe rod pickup to the KPA) + +def betaTrigSolver(l1): #a separate function to solve the big bad trig equation + l2 = np.sqrt((l1**2) + (d**2)) #l2 is the instantaneous direct distance from rack knuckle to steering axis (KPA) + atan = np.arctan(d/l1) #first term of the "beta" equation + + num = (l_arm**2) + (l2**2) - (l_rod**2) #just simplifying the calculation of the second term + denom = 2*l_arm*l2 + frac = num/denom + # print(f"{frac=}") + acos = np.arccos(frac) + beta = (np.pi/2) - atan - acos + return beta + +def rackMovement(wheelInput): #returns the amount of L-R displacement (in mm) of the steering rack, with the right direction as "positive" + rackShift: float = rackRatio*wheelInput + return rackShift + +def calculateAckermann(wheelInput): #calculates the steer angles of both wheels + l1Left = (0.5*(tw-l_rack)) - rackMovement(wheelInput) #l1 is the instantaneous parallel distance from the rack knuckle to steering axis (KPA). + l1Right = (0.5*(tw-l_rack)) + rackMovement(wheelInput) + l_nought = (0.5*(tw-l_rack)) + beta_nought = betaTrigSolver(l_nought) #used to find the initial "beta" geometry to determine the real steer angle at the wheels + + beta_L = betaTrigSolver(l1Left) - beta_nought #additionally, because there is a static "beta" (simply just arm geometry), we must find the difference to find the actual wheel angles + beta_R = betaTrigSolver(l1Right) - beta_nought + + return beta_L, beta_R + #return beta_nought, betaTrigSolver(l1Left), betaTrigSolver(l1Right) + + +inputAngles = np.arange(-150.0, 150.0, 0.1) +LR = np.array([calculateAckermann(x) for x in inputAngles]).T + +L = LR[0,:] +R = LR[1,:] + +plt.plot(inputAngles, -L, label="Left") +plt.plot(inputAngles, R, label="Right") +plt.xlabel("Input angle (Deg)") +plt.ylabel("Tire Angle (Rad)") +plt.legend() +plt.show() diff --git a/FullVehicleSim/basicViewer.py b/FullVehicleSim/basicViewer.py index 1687a8e..6396391 100644 --- a/FullVehicleSim/basicViewer.py +++ b/FullVehicleSim/basicViewer.py @@ -2,8 +2,14 @@ import matplotlib.pyplot as plt df = pl.read_parquet("FullVehicleSim/simulation_output.parquet") +dfReal = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet").fill_null(strategy="forward").fill_null(strategy="backward") t = df["time"] +[x for x in dfReal.columns if "SME" in x] + + + +df.columns plt.plot(t, df["throttle"]*300, label="throttle") plt.plot(t, df["brakePressureFront"], label="brakesF") @@ -15,4 +21,9 @@ plt.legend() plt.show() + +plt.plot(dfReal["Time_ms"]/1000, dfReal["SME_TRQSPD_Speed"], label="Real RPM") +plt.plot(t, df["motorRPM"], label="Simulated RPM") +plt.legend() +plt.show() df["speed"].max() \ No newline at end of file From 4a2fa7e5cad93448b3366c989476d1cc491b2745 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Fri, 12 Jun 2026 02:27:17 -0700 Subject: [PATCH 30/38] some data work --- Data/FSLib/AnalysisFunctions.py | 122 +++++- Data/Jun102026_analysis.py | 347 ++++++++++++++++++ ...DamingView.py => suspensionDampingView.py} | 22 +- 3 files changed, 488 insertions(+), 3 deletions(-) create mode 100644 Data/Jun102026_analysis.py rename Data/{suspensionDamingView.py => suspensionDampingView.py} (87%) diff --git a/Data/FSLib/AnalysisFunctions.py b/Data/FSLib/AnalysisFunctions.py index d8aec5b..f793390 100644 --- a/Data/FSLib/AnalysisFunctions.py +++ b/Data/FSLib/AnalysisFunctions.py @@ -66,10 +66,24 @@ etcBrakeVoltage = "ETC_STATUS_BRAKE_SENSE_VOLTAGE" def readValid (a): + """ + Built for VDM installed on fs-2 and fs-3. Do not use on fs-4+ + """ return pl.read_parquet(a).with_columns(pl.all().fill_null(strategy="forward")).with_columns(pl.all().fill_null(strategy="backward")).filter(pl.col("VDM_GPS_VALID1") == 1) -def read (a): - return pl.read_parquet(a).with_columns(pl.all().fill_null(strategy="forward")).with_columns(pl.all().fill_null(strategy="backward")) +def read (a:str, truePath:bool = False, fs2or3:bool = False): + if not truePath: + df = pl.read_parquet("../fs-data/" + a).fill_null(strategy="forward").fill_null(strategy="backward") + else: + df = pl.read_parquet(a).fill_null(strategy="forward").fill_null(strategy="backward") + if fs2or3: + return df + else: + df = df.insert_column( + 0, + (df["Time_ms"] / 1000).alias("Time") + ) + return df def timeCol(df, verbose = False): min = "VDM_UTC_TIME_SECONDS" @@ -167,6 +181,110 @@ def mcErrorView (df, title="", tFun=timeCol, verbose=False): fig.show() +def basicViewFS4 (df, title="", scatterGPS=False, scaled=False, cellVoltages=False, verbose=False, faults=False, tempsInsteadOfVoltages=False): + ''' + Loads a basic view of a given run. Built for FS-4 Data generated and collected by the team. + + Parameters + ---------- + df + The Dataframe to base the time graph on. Should have valid GPS data or the graphs will be blank. + title + Title at the top of the graph. + tFun + Time function to be used to generate a time column + scatterGPS + Whether to scatter plot the GPS instead of line plot + scaled + Whether to scale the GPS plot to have the same vertical and horizontal scale. Useful for small plots + cellVoltages + Whether to plot the cell voltages on the plot with ACC and MC voltages + verbose + Whether to print debug messages while generating the graph + faults + Whether to plot fault codes on the graph + tempsInsteadOfVoltages + Whether to plot temperatures instead of voltages for the ACC segments + ''' + + tempVoltStr = "TEMPS" if tempsInsteadOfVoltages else "VOLTS" + + df = df.with_columns( + pl.Series(np.convolve(df["ACC_STATUS_BMS_FAULT"].to_numpy(), [1, -1], 'same')).alias("BMS_FaultStart"), + pl.Series(np.convolve(df["ACC_STATUS_IMD_FAULT"].to_numpy(), [1, -1], 'same')).alias("IMD_FaultStart"), + pl.Series(np.convolve(df["ETC_STATUS_IMPLAUSIBILITY"].to_numpy(), [1, -1], 'same')).alias("APPS_ImplausibilityStart") + ) + + if not ("Time" in df.columns): + df.insert_column(0, (df["Time_ms"]/1000).alias("Time")) + + fig = plt.figure(layout="constrained") + ax1 = fig.add_subplot(321) + ax2 = fig.add_subplot(322) + ax3 = fig.add_subplot(323) + ax4 = fig.add_subplot(324) + ax5 = fig.add_subplot(325) + ax6 = fig.add_subplot(326) + for i in range(5): + for j in range(6): + ax1.plot(df[t],df[f"ACC_SEG{i}_" + tempVoltStr + f"_CELL{j}"]) + ax1.set_title(f"Acc Seg {tempVoltStr}") + ax1.vlines(df.filter(pl.col("BMS_FaultStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="red", label="BMS Fault") + ax1.vlines(df.filter(pl.col("IMD_FaultStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="orange", label="IMD Fault") + ax1.vlines(df.filter(pl.col("APPS_ImplausibilityStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="blue", label="APPS Implausibility") + ax1.legend() + + ax2.plot(df[t],df[V], color="cyan", label="Total As Reported by Acc") + b = sum([sum([df[f"ACC_SEG{i}_VOLTS_CELL{j}"] for j in range(6)]) for i in range (5)]) + ax2.plot(df[t],b, color="pink", label="Sum of Cells") + ax2.plot(df[t], df[busV], color="purple", label="MC Voltage") + ax22 = ax2.twinx() + if cellVoltages: + ax22 = ax2.twinx() + for i in range(5): + a = [df[f"ACC_SEG{i}_VOLTS_CELL{j}"] for j in range(6)] + ax22.plot(sum(a), label=f"ACC_SEG{i}") + ax22.legend() + ax22.set_ylabel("Voltage (V)") + ax2.set_title("Voltages") + if faults: + ax2.plot(df[t],df[smeFaultCode], label="MC Fault Code", color="red") + ax2.legend() + # ax3.plot(df[t],df[tsC], label="Accumulator Current") + ax3.plot(df[t],df[busC], label = "Motor Controller Current") + ax3.legend() + dfGPSFiltered = df.filter(pl.col(lat) != 0).filter(pl.col(long) != 0) + if scatterGPS: + ax4.scatter(dfGPSFiltered[long],dfGPSFiltered[lat], s=0.5) + else: + ax4.plot(dfGPSFiltered[long],dfGPSFiltered[lat]) + if scaled: + ax4.axis("scaled") + ax5.plot(df[t], (df["ETC_STATUS_BRAKE_SENSE_VOLTAGE"]-330)*25/33, label="Braking (psi)") + ax5.plot(df[t], df["SME_THROTL_TorqueDemand"]/32767*180, label="Torque Demand (N)", color="orange") + ax5.plot(df[t], df[speed], color = "goldenrod", label="speed mph") + ax5.plot(df[t], df[rpm]*11/40*0.2032*2*np.pi/60/0.44704, color = "green", label="rpm speed") + ax5.plot(df[t], df["VDM_Y_AXIS_ACCELERATION"]*100, label = "yaxis accel (cGs)", color="crimson") + ax5.set_title("Speed + Braking") + ax5.legend() + ax6.plot(df[t],df[xA_uncorrected]) + ax6.set_title("Acceleration (X)") + + ax1.set_xlabel("Time (s)") + ax1.set_ylabel("Voltage (V)" if not tempsInsteadOfVoltages else "Temperature (C)") + ax2.set_xlabel("Time (s)") + ax2.set_ylabel("Voltage (V)") + ax3.set_xlabel("Time (s)") + ax3.set_ylabel("Current (A)") + ax4.set_xlabel("Longitude (deg)") + ax4.set_ylabel("Latitude (deg)") + ax5.set_xlabel("Time (s)") + # ax5.set_ylabel("Voltage (V)") + ax6.set_xlabel("Time (s)") + ax6.set_ylabel("Acceleration (Gs)") + + plt.suptitle(title) + plt.show() def basicView (df, title="", tFun=timeCol, scatterGPS=False, scaled=False, cellVoltages=False, verbose=False, faults=False, tempsInsteadOfVoltages=False): ''' diff --git a/Data/Jun102026_analysis.py b/Data/Jun102026_analysis.py new file mode 100644 index 0000000..0c4f1a7 --- /dev/null +++ b/Data/Jun102026_analysis.py @@ -0,0 +1,347 @@ +from Data.FSLib.AnalysisFunctions import read +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from matplotlib import patches + +def basicViewFS4 (df:pl.DataFrame, title:str="", scatterGPS=False, verbose=False): + ''' + Loads a basic view of a given run. Built for FS-4 Data generated and collected by the team. + + Parameters + ---------- + df:pl.DataFrame + The Dataframe to base the time graph on. Should have valid GPS data or the graphs will be blank. + title:str + Title at the top of the graph. + scatterGPS + Whether to scatter plot the GPS instead of line plot + verbose + Whether to print debug messages while generating the graph + ''' + + motorRPM = "SME_TRQSPD_Speed" + currLimC = "SME_CURRLIM_ChargeCurrentLim" + currLimD = "SME_CURRLIM_DischargeCurrentLim" + torqueDemand = "SME_THROTL_TorqueDemand" + + packVoltage = "BATT_POWER_PACK_VOLTAGE" + packCurrent = "BATT_POWER_CURRENT" + smeVoltage = "SME_TEMP_DC_Bus_V" + smeCurrent = "SME_TEMP_BusCurrent" + + frontBrakePressure = "VCU_BRAKE_PRESSSURE_FRONT" + rearBrakePressure = "VCU_BRAKE_PRESSSURE_REAR" + + wheelSpeedFL = "TPERIPH_FL_DATA_WHEELSPEED" + wheelSpeedFR = "TPERIPH_FR_DATA_WHEELSPEED" + wheelSpeedBL = "TPERIPH_BL_DATA_WHEELSPEED" + wheelSpeedBR = "TPERIPH_BR_DATA_WHEELSPEED" + + VX = "VCU_VN_BODY_VX" + VY = "VCU_VN_BODY_VY" + VZ = "VCU_VN_BODY_VZ" + + df = df.with_columns( + pl.Series("V", np.linalg.norm(df[VX, VY, VZ].to_numpy())) # type:ignore + ) + + lat = "VCU_VN_LAT" + long = "VCU_VN_LON" + + volts = [f"BATT_MOD{i}_VOLTS_CELL{j}" for i in range(5) for j in range(6)] + temps = [f"BATT_MOD{i}_TEMPS_CELL{j}" for i in range(5) for j in range(6)] + + t = "Time" + + ETC_implausabilities = [ + "VCU_ETC_IMPLAUS_APPS_OUT_OF_RANGE", + "VCU_ETC_IMPLAUS_BPPS_OUT_OF_RANGE", + "VCU_ETC_IMPLAUS_APPS_DEVIATION", + "VCU_ETC_IMPLAUS_BSE_OUT_OF_RANGE", + "VCU_ETC_IMPLAUS_BRAKE_AND_ACCEL" + ] + + df = df.with_columns( + pl.Series(np.convolve(df["BATT_STATUS_BMS_FAULT"].to_numpy(), [1, -1], 'same')).alias("BMS_FaultStart"), + pl.Series(np.convolve(df["BATT_STATUS_IMD_FAULT"].to_numpy(), [1, -1], 'same')).alias("IMD_FaultStart"), + pl.Series(np.convolve(df[ETC_implausabilities].max_horizontal(), [1, -1], 'same')).alias("ETC_ImplausibilityStart"), + pl.Series(np.convolve(df["VCU_BSPD_FAULT"], [1, -1], 'same')).alias("BSPD_FaultStart") + ) + + if not (t in df.columns): + df.insert_column(0, (df["Time_ms"]/1000).alias(t)) + + fig1 = plt.figure(layout="constrained") + ax1 = fig1.add_subplot(321) + ax2 = fig1.add_subplot(322) + ax3 = fig1.add_subplot(323) + ax4 = fig1.add_subplot(324) + ax5 = fig1.add_subplot(325) + ax6 = fig1.add_subplot(326) + for temp in temps: + ax1.plot(df[t],df[temp]) + ax1.set_title(f"Tractive Battery Temperatures") + ax1.vlines(df.filter(pl.col("BMS_FaultStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="red", label="BMS Fault") + ax1.vlines(df.filter(pl.col("IMD_FaultStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="orange", label="IMD Fault") + ax1.vlines(df.filter(pl.col("ETC_ImplausibilityStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="blue", label="ETC Implausibility") + ax1.vlines(df.filter(pl.col("BSPD_FaultStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="green", label="BSPD Fault") + ax1.legend() + + for volt in volts: + ax2.plot(df[t],df[volt]) + ax2.set_title(f"Tractive Battery Voltages") + ax2.vlines(df.filter(pl.col("BMS_FaultStart") == 1)[t].to_numpy(), ymin=ax2.get_ylim()[0], ymax=ax2.get_ylim()[1], colors="red", label="BMS Fault") + ax2.vlines(df.filter(pl.col("IMD_FaultStart") == 1)[t].to_numpy(), ymin=ax2.get_ylim()[0], ymax=ax2.get_ylim()[1], colors="orange", label="IMD Fault") + ax2.vlines(df.filter(pl.col("ETC_ImplausibilityStart") == 1)[t].to_numpy(), ymin=ax2.get_ylim()[0], ymax=ax2.get_ylim()[1], colors="blue", label="ETC Implausibility") + ax2.vlines(df.filter(pl.col("BSPD_FaultStart") == 1)[t].to_numpy(), ymin=ax1.get_ylim()[0], ymax=ax1.get_ylim()[1], colors="green", label="BSPD Fault") + ax2.legend() + + ax3.plot(df[t],df[packVoltage], label="Battery Voltage") + ax3.plot(df[t], df[smeVoltage], label="MC Voltage") + ax3.set_ylabel("Voltage (V)") + + ax33 = ax3.twinx() + ax33.plot(df[t],df[packCurrent], color="goldenrod", label = "Battery Current") + ax33.plot(df[t],df[smeCurrent], color="blue", label = "MC Current") + ax3.legend(patches=[patches.Patch(color="blue", label="MC Voltage"), patches.Patch(color="orange", label="Battery Voltage"), patches.Patch(color="goldenrod", label="Battery Current"), patches.Patch(color="blue", label="MC Current")], loc="upper left") + + dfGPSFiltered = df.filter(pl.col(lat) != 0).filter(pl.col(long) != 0) + if scatterGPS: + ax4.scatter(dfGPSFiltered[long],dfGPSFiltered[lat], s=0.5) + else: + ax4.plot(dfGPSFiltered[long],dfGPSFiltered[lat]) + ax4.axis("scaled") + + ax5.plot(df[t], df[frontBrakePressure], label="Front Braking (psi)") + ax5.plot(df[t], df[rearBrakePressure], label="Rear Braking (psi)") + ax5.plot(df[t], df["SME_THROTL_TorqueDemand"]/32767*180, label="Torque Demand (N)") + ax5.plot(df[t], df["V"], color = "goldenrod", label="body speed m/s") + ax5.plot(df[t], df[motorRPM]*12/41*0.2032*2*np.pi/60, color = "green", label="motor speed m/s") + ax5.set_title("Speed + Braking") + ax5.legend() + + ax6.plot(df[t], df[wheelSpeedFL]*0.44704, label = "FL") + ax6.plot(df[t], df[wheelSpeedFR]*0.44704, label = "FR") + ax6.plot(df[t], df[wheelSpeedBL]*0.44704, label = "BL") + ax6.plot(df[t], df[wheelSpeedBR]*0.44704, label = "BR") + ax6.set_title("Wheel Speeds") + + ax1.set_xlabel("Time (s)") + ax1.set_ylabel("Temperature (C)") + ax2.set_xlabel("Time (s)") + ax2.set_ylabel("Voltage (V)") + ax3.set_xlabel("Time (s)") + ax3.set_ylabel("Voltage (V)") + ax4.set_xlabel("Longitude (deg)") + ax4.set_ylabel("Latitude (deg)") + ax5.set_xlabel("Time (s)") + ax6.set_xlabel("Time (s)") + ax6.set_ylabel("Speed (m/s)") + + plt.suptitle(title) + plt.show() + +# df1 = read("FS-4/Jun102026/162207.parquet") # Car was wiggled a bit and wheels spun by hand on jacks +df2 = read("FS-4/Jun102026/180316.parquet") # 400A/500A current limit. Only really 400A brief driving +# df3 = read("FS-4/Jun102026/072050.parquet") # 5 hrs, basically nothing +# df4 = read("FS-4/Jun102026/152519.parquet") # 45 min, basically nothing +df5 = read("FS-4/Jun102026/181830.parquet") # Tiny bits of driving. 0.7V voltage drop. 500/600/650 A limit. +df6 = read("FS-4/Jun102026/171744.parquet") # 45 min. 200A. 0.2V voltage drop +# df7 = read("FS-4/Jun102026/145454.parquet") # 35 min. Basically nothing +# df8 = read("FS-4/Jun102026/070347.parquet") # 8 min. Basically nothing + +# dfs = [df1, df2, df3, df4, df5, df6, df7, df8] + +t = "Time" + +# for i, df in enumerate(dfs): + # plt.scatter(np.arange(df.height), df[t], label = ("df" + str(i)), s=0.5) +# plt.xlabel("row") +# plt.ylabel("time (s)") +# plt.legend() +# plt.show() + +plt.plot(df6.filter(pl.col(t) < 260).filter(pl.col(t)>240)[t]) +plt.plot(np.arange(0, df6[t].max(), df6[t].max()/(df6.height + 1))[:df6.height]) #type:ignore +plt.show() + +df6.filter(pl.col(t) < 260).filter(pl.col(t)>240) + +import polars as pl +import matplotlib.pyplot as plt +import numpy as np +import scipy.fft as fft +from statsmodels.nonparametric.smoothers_lowess import lowess + +df = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet").fill_null(strategy="forward").fill_null(strategy="backward") +df = df2 + +[x for x in df.columns if "VDM" in x] + + +FR_base_value = df["TPERIPH_FR_DATA_SUSTRAVEL"][:100].mean() +FL_base_value = df["TPERIPH_FL_DATA_SUSTRAVEL"][:100].mean() +BR_base_value = df["TPERIPH_BR_DATA_SUSTRAVEL"][:100].mean() +BL_base_value = df["TPERIPH_BL_DATA_SUSTRAVEL"][:100].mean() + +t = df["Time_ms"]/1000.0 +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(t, df["TPERIPH_FR_DATA_SUSTRAVEL"] - FR_base_value, label="TPERIPH_FR_DATA_SUSTRAVEL") +ax.plot(t, df["TPERIPH_FL_DATA_SUSTRAVEL"] - FL_base_value, label="TPERIPH_FL_DATA_SUSTRAVEL") +ax.plot(t, df["TPERIPH_BR_DATA_SUSTRAVEL"] - BR_base_value, label="TPERIPH_BR_DATA_SUSTRAVEL") +ax.plot(t, df["TPERIPH_BL_DATA_SUSTRAVEL"] - BL_base_value, label="TPERIPH_BL_DATA_SUSTRAVEL") +ax.plot(t, df["VDM_Z_AXIS_YAW_RATE"]/2, label="VDM_Z_AXIS_YAW_RATE") +ax.plot(t, df["SME_TRQSPD_Speed"]/1000, label="SME_TRQSPD_Speed") +ax.legend() +ax.set_xlabel("Time [s]") +ax.set_ylabel("TPERIPH Data") +ax.set_title("Suspension Damping View") +ax.grid(True) +plt.show() + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(t, -1*df["TMAIN_DATA_STEERING"], label="TMAIN_DATA_STEERING") +ax.plot(t, df["VDM_Z_AXIS_YAW_RATE"], label="VDM_Z_AXIS_YAW_RATE") +ax.legend() +ax.set_xlabel("Time [s]") +ax.set_ylabel("Steering and Yaw Rate") +ax.set_title("Steering and Yaw Rate") +ax.grid(True) +plt.show() + +df = df.filter(pl.col("SME_TRQSPD_Speed") > 5000) + +fig = plt.figure() +ax1 = fig.add_subplot(221) +ax2 = fig.add_subplot(222) +ax3 = fig.add_subplot(223) +ax4 = fig.add_subplot(224) +ax1.hist(df["TPERIPH_FR_DATA_SUSTRAVEL"], bins=401) +ax1.set_title("FR Suspension Travel") +ax1.set_xlabel("Suspension Travel (mm)") +ax2.hist(df["TPERIPH_FL_DATA_SUSTRAVEL"], bins=401) +ax2.set_title("FL Suspension Travel") +ax2.set_xlabel("Suspension Travel (mm)") +ax3.hist(df["TPERIPH_BR_DATA_SUSTRAVEL"], bins=401) +ax3.set_title("BR Suspension Travel") +ax3.set_xlabel("Suspension Travel (mm)") +ax4.hist(df["TPERIPH_BL_DATA_SUSTRAVEL"], bins=401) +ax4.set_title("BL Suspension Travel") +ax4.set_xlabel("Suspension Travel (mm)") +fig.show() + +suspension_FR_fft = fft.fft((df["TPERIPH_FR_DATA_SUSTRAVEL"] - FR_base_value).to_numpy()) +suspension_FL_fft = fft.fft((df["TPERIPH_FL_DATA_SUSTRAVEL"] - FL_base_value).to_numpy()) +suspension_BR_fft = fft.fft((df["TPERIPH_BR_DATA_SUSTRAVEL"] - BR_base_value).to_numpy()) +suspension_BL_fft = fft.fft((df["TPERIPH_BL_DATA_SUSTRAVEL"] - BL_base_value).to_numpy()) +suspension_FR_freq = fft.fftfreq(len(suspension_FR_fft), d=0.01) +suspension_FL_freq = fft.fftfreq(len(suspension_FL_fft), d=0.01) +suspension_BR_freq = fft.fftfreq(len(suspension_BR_fft), d=0.01) +suspension_BL_freq = fft.fftfreq(len(suspension_BL_fft), d=0.01) + +def rollingMean(data, window_size): + return np.convolve(data, np.ones(window_size)/window_size, mode='same') + +fig = plt.figure() +ax1 = fig.add_subplot(221) +ax1.scatter(suspension_FR_freq, np.abs(suspension_FR_fft), label="TPERIPH_FR_DATA_SUSTRAVEL", s=0.5) +ax1.set_title("FR Suspension FFT") +ax1.set_xlabel("Frequency [Hz]") +ax1.set_ylabel("Magnitude") +ax1.set_yscale("log") +ax1.grid(True) +ax2 = fig.add_subplot(222) +ax2.scatter(suspension_FL_freq, np.abs(suspension_FL_fft), label="TPERIPH_FL_DATA_SUSTRAVEL", s=0.5) +ax2.set_title("FL Suspension FFT") +ax2.set_xlabel("Frequency [Hz]") +ax2.set_ylabel("Magnitude") +ax2.set_yscale("log") +ax2.grid(True) +ax3 = fig.add_subplot(223) +ax3.scatter(suspension_BR_freq, np.abs(suspension_BR_fft), label="TPERIPH_BR_DATA_SUSTRAVEL", s=0.5) +ax3.set_title("BR Suspension FFT") +ax3.set_xlabel("Frequency [Hz]") +ax3.set_ylabel("Magnitude") +ax3.set_yscale("log") +ax3.grid(True) +ax4 = fig.add_subplot(224) +ax4.scatter(suspension_BL_freq, np.abs(suspension_BL_fft), label="TPERIPH_BL_DATA_SUSTRAVEL", s=0.5) +ax4.set_title("BL Suspension FFT") +ax4.set_xlabel("Frequency [Hz]") +ax4.set_ylabel("Magnitude") +ax4.set_yscale("log") +ax4.grid(True) +plt.tight_layout() +plt.show() + + +fig = plt.figure() +ax1 = fig.add_subplot(221) +ax1.scatter(suspension_FR_freq, rollingMean(np.abs(suspension_FR_fft), 401), label="TPERIPH_FR_DATA_SUSTRAVEL", s=0.5) +ax1.set_title("FR Suspension FFT") +ax1.set_xlabel("Frequency [Hz]") +ax1.set_ylabel("Magnitude") +ax1.set_yscale("log") +ax1.grid(True) +ax2 = fig.add_subplot(222) +ax2.scatter(suspension_FL_freq, rollingMean(np.abs(suspension_FL_fft), 401), label="TPERIPH_FL_DATA_SUSTRAVEL", s=0.5) +ax2.set_title("FL Suspension FFT") +ax2.set_xlabel("Frequency [Hz]") +ax2.set_ylabel("Magnitude") +ax2.set_yscale("log") +ax2.grid(True) +ax3 = fig.add_subplot(223) +ax3.scatter(suspension_BR_freq, rollingMean(np.abs(suspension_BR_fft), 401), label="TPERIPH_BR_DATA_SUSTRAVEL", s=0.5) +ax3.set_title("BR Suspension FFT") +ax3.set_xlabel("Frequency [Hz]") +ax3.set_ylabel("Magnitude") +ax3.set_yscale("log") +ax3.grid(True) +ax4 = fig.add_subplot(224) +ax4.scatter(suspension_BL_freq, rollingMean(np.abs(suspension_BL_fft), 401), label="TPERIPH_BL_DATA_SUSTRAVEL", s=0.5) +ax4.set_title("BL Suspension FFT") +ax4.set_xlabel("Frequency [Hz]") +ax4.set_ylabel("Magnitude") +ax4.set_yscale("log") +ax4.grid(True) +plt.tight_layout() +plt.show() + +yawRate_fft = fft.fft(df["VDM_Z_AXIS_YAW_RATE"].to_numpy()) +yawRate_freq = fft.fftfreq(len(yawRate_fft), d=0.01) +pitchRate_fft = fft.fft(df["VDM_Y_AXIS_YAW_RATE"].to_numpy()) +pitchRate_freq = fft.fftfreq(len(pitchRate_fft), d=0.01) +rollRate_fft = fft.fft(df["VDM_X_AXIS_YAW_RATE"].to_numpy()) +rollRate_freq = fft.fftfreq(len(rollRate_fft), d=0.01) + + +meanSegments = 101 +fig = plt.figure() +ax = fig.add_subplot(111) +ax.scatter(yawRate_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(yawRate_fft), mode='same'), label="Yaw", s=0.5) +ax.scatter(pitchRate_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(pitchRate_fft), mode='same'), label="Pitch", s=0.5) +ax.scatter(rollRate_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(rollRate_fft), mode='same'), label="Roll", s=0.5) +ax.legend() +ax.set_title("Yaw Rate FFT") +ax.set_xlabel("Frequency [Hz]") +ax.set_ylabel("Magnitude") +ax.set_yscale("log") +ax.grid(True) +plt.show() + +## heave based on z acceleration +heave_fft = fft.fft(df["VDM_Z_AXIS_ACCELERATION"].to_numpy()) +heave_freq = fft.fftfreq(len(heave_fft), d=0.01) +fig = plt.figure() +ax = fig.add_subplot(111) +ax.scatter(heave_freq, np.convolve(np.ones(meanSegments)*1/meanSegments, np.abs(heave_fft), mode='same'), label="Heave", s=0.5) +ax.legend() +ax.set_title("Heave FFT") +ax.set_xlabel("Frequency [Hz]") +ax.set_ylabel("Magnitude") +ax.set_yscale("log") +ax.grid(True) +plt.show() \ No newline at end of file diff --git a/Data/suspensionDamingView.py b/Data/suspensionDampingView.py similarity index 87% rename from Data/suspensionDamingView.py rename to Data/suspensionDampingView.py index b1a7f1f..f641807 100644 --- a/Data/suspensionDamingView.py +++ b/Data/suspensionDampingView.py @@ -5,7 +5,8 @@ from statsmodels.nonparametric.smoothers_lowess import lowess df = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet").fill_null(strategy="forward").fill_null(strategy="backward") - +plt.hist(df.filter(pl.col("ETC_STATUS_PEDAL_TRAVEL") > 0)["ETC_STATUS_PEDAL_TRAVEL"], bins=99) +plt.show() [x for x in df.columns if "VDM" in x] @@ -85,6 +86,24 @@ plt.tight_layout() plt.show() +yaw = df["VDM_Z_AXIS_YAW_RATE"].to_numpy() +roll = df["VDM_X_AXIS_YAW_RATE"].to_numpy() +yaw_fft = fft.fft(yaw) +yaw_freq = fft.fftfreq(len(yaw_fft), d=0.01) +yawBaseline = np.random.normal(df["VDM_Z_AXIS_YAW_RATE"].mean(), df["VDM_Z_AXIS_YAW_RATE"].std(), size=df.height) +yawBaseline_fft = fft.fft(yawBaseline) +yawBaseline_freq = fft.fftfreq(len(yawBaseline_fft), d=0.01) + +ifft = fft.ifft(yawBaseline) + +plt.hist(yawBaseline, bins=100) +plt.hist(roll, bins=500) +plt.yscale("log") +plt.show() + +plt.scatter(yawBaseline_freq, np.abs(yawBaseline_fft), label="Shuffled Yaw Rate Baseline", s=0.5) +plt.show() + yawRate_fft = fft.fft(df["VDM_Z_AXIS_YAW_RATE"].to_numpy()) yawRate_freq = fft.fftfreq(len(yawRate_fft), d=0.01) pitchRate_fft = fft.fft(df["VDM_Y_AXIS_YAW_RATE"].to_numpy()) @@ -93,6 +112,7 @@ rollRate_freq = fft.fftfreq(len(rollRate_fft), d=0.01) + meanSegments = 101 fig = plt.figure() ax = fig.add_subplot(111) From f03f3d7db6427fbba301f1290bfbd0dd73ee9352 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Fri, 12 Jun 2026 14:25:41 -0700 Subject: [PATCH 31/38] little bit of sim stuff --- .../simulationControls.csv | 12 ++++++------ .../simulationControlsAccel.csv | 3 +++ FullVehicleSim/basicViewer.py | 3 +++ FullVehicleSim/params.json5 | 6 +++--- FullVehicleSim/simulation_output.parquet | Bin 1102358 -> 307638 bytes 5 files changed, 15 insertions(+), 9 deletions(-) create mode 100644 FullVehicleSim/SimulationControlInputs/simulationControlsAccel.csv diff --git a/FullVehicleSim/SimulationControlInputs/simulationControls.csv b/FullVehicleSim/SimulationControlInputs/simulationControls.csv index c19041a..ccebd32 100644 --- a/FullVehicleSim/SimulationControlInputs/simulationControls.csv +++ b/FullVehicleSim/SimulationControlInputs/simulationControls.csv @@ -1,6 +1,6 @@ -time,throttle,brakePressureFront,brakePressureRear,steerAngle -0.0,0.0,0.0,0.0,0.0 -10.0,1.0,0.0,0.0,0.0 -20.0,0.0,0.0,0.0,0.0 -30.0,0.0,300.0,300.0,0.0 -40.0,0.0,0.0,0.0,0.0 \ No newline at end of file +time,throttle,brakePressureFront,brakePressureRear,brakePedalTravel,steerAngle +0.0,0.0,0.0,0.0,0.0,0.0 +10.0,1.0,0.0,0.0,0.0,0.0 +20.0,0.0,0.0,0.0,0.0,0.0 +30.0,0.0,300.0,300.0,0.0,0.0 +40.0,0.0,0.0,0.0,0.0,0.0 \ No newline at end of file diff --git a/FullVehicleSim/SimulationControlInputs/simulationControlsAccel.csv b/FullVehicleSim/SimulationControlInputs/simulationControlsAccel.csv new file mode 100644 index 0000000..499f3dc --- /dev/null +++ b/FullVehicleSim/SimulationControlInputs/simulationControlsAccel.csv @@ -0,0 +1,3 @@ +time,throttle,brakePressureFront,brakePressureRear,brakePedalTravel,steerAngle +0.0,1.0,0.0,0.0,0.0,0.0 +30.0,1.0,0.0,0.0,0.0,0.0 \ No newline at end of file diff --git a/FullVehicleSim/basicViewer.py b/FullVehicleSim/basicViewer.py index 6396391..7bc98b3 100644 --- a/FullVehicleSim/basicViewer.py +++ b/FullVehicleSim/basicViewer.py @@ -11,6 +11,9 @@ df.columns +plt.plot(t, df["posX"]) +plt.show() + plt.plot(t, df["throttle"]*300, label="throttle") plt.plot(t, df["brakePressureFront"], label="brakesF") plt.plot(t, df["netForce"], label="netForce") diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index af601f6..60cba4e 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -1,12 +1,12 @@ // Base SI Units unless otherwise specified { "Parameters": { - "stepsPerSecond": 100, - "simulationDuration": 500.0, // Seconds + "stepsPerSecond": 500, + "simulationDuration": 10.0, // Seconds "interpolationMethod": "linear", // "linear" or "cubic", this is for interpolating the simultion controls. 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z4#rq(2b+59yE#c)X*x?Q`8k2{q%<|wT;3l>3Jd@IzP;HtlV%qga8c1y=YSjuy&WW4{Z$9Q|*J6584ZiaR5&1H!M95`DMpj&l#$x z3?;?iy3jn;*T8yd6ET*UK&+P$~TLnW!l+JLnyEqBCR+Xt#aX_*{Zc7AFGf_m!wq^u`rYb8l% z8GUn0ElG;J=^x}x81j_)jgxjGV6218on_3RxgF^2ZKP)njR^`ZyBC~2OOGd78mgy` zrytbb-`2*>63j^t3ddE$XL)%IFNQqSAvyqKW2OUa@u2KMC{5et`P(UYa3KSx>-0M%30 z6FB(6`eE8ntjq|`HfRDEM@cZhspk<&6HNVO&SMh=2JgT7uO-G>iiEMzcf;vg1yjaW z^T_f%O(IlJ(GQAYP`U&+oD`4;XS#xL+rwk2N1`XCHWvP0C?Q@y+h;oPOsq-1y8qm_vI<|n~ z$7TFw4$df=zbO935rCb#7U1PF Date: Thu, 18 Jun 2026 05:42:32 -0700 Subject: [PATCH 32/38] more data --- Data/Jun102026_analysis.py | 43 ++++++-- Data/SOC_estimation_p30b.py | 212 ++++++++++++++++++++++++++++++++++++ Data/acceleratorMapping.py | 38 +++++++ Data/discharge_curve.py | 46 ++++++++ Data/regen_curve_scaling.py | 67 ++++++++++++ 5 files changed, 396 insertions(+), 10 deletions(-) create mode 100644 Data/SOC_estimation_p30b.py create mode 100644 Data/acceleratorMapping.py create mode 100644 Data/discharge_curve.py create mode 100644 Data/regen_curve_scaling.py diff --git a/Data/Jun102026_analysis.py b/Data/Jun102026_analysis.py index 0c4f1a7..79886f2 100644 --- a/Data/Jun102026_analysis.py +++ b/Data/Jun102026_analysis.py @@ -3,6 +3,13 @@ import numpy as np import matplotlib.pyplot as plt from matplotlib import patches +from cantools import database + +db = database.load_file("../fs-4-firmware/CANbus.dbc") +# db.messages[6].signals[0].name + + + def basicViewFS4 (df:pl.DataFrame, title:str="", scatterGPS=False, verbose=False): ''' @@ -142,35 +149,51 @@ def basicViewFS4 (df:pl.DataFrame, title:str="", scatterGPS=False, verbose=False plt.suptitle(title) plt.show() -# df1 = read("FS-4/Jun102026/162207.parquet") # Car was wiggled a bit and wheels spun by hand on jacks +df1 = read("FS-4/Jun102026/162207.parquet") # Car was wiggled a bit and wheels spun by hand on jacks df2 = read("FS-4/Jun102026/180316.parquet") # 400A/500A current limit. Only really 400A brief driving -# df3 = read("FS-4/Jun102026/072050.parquet") # 5 hrs, basically nothing -# df4 = read("FS-4/Jun102026/152519.parquet") # 45 min, basically nothing +# df22 = read("FS-4/Jun102026/candump-2026-06-10_180316.parquet") +df3 = read("FS-4/Jun102026/072050.parquet") # 5 hrs, basically nothing +df4 = read("FS-4/Jun102026/152519.parquet") # 45 min, basically nothing df5 = read("FS-4/Jun102026/181830.parquet") # Tiny bits of driving. 0.7V voltage drop. 500/600/650 A limit. df6 = read("FS-4/Jun102026/171744.parquet") # 45 min. 200A. 0.2V voltage drop -# df7 = read("FS-4/Jun102026/145454.parquet") # 35 min. Basically nothing -# df8 = read("FS-4/Jun102026/070347.parquet") # 8 min. Basically nothing +df7 = read("FS-4/Jun102026/145454.parquet") # 35 min. Basically nothing +df8 = read("FS-4/Jun102026/070347.parquet") # 8 min. Basically nothing -# dfs = [df1, df2, df3, df4, df5, df6, df7, df8] +for message in db.messages: + for signal in message.signals: + if signal.name not in df22.columns: + print(signal.name) + + +dfs = [df1, df2, df3, df4, df5, df6, df7, df8] +len(df2.columns) +len([x for x in df2.columns if "VCU" in x]) t = "Time" +plt.plot(df2[t], df2["SME_TRQSPD_Speed"]) +temps = [f"BATT_MOD{i}_TEMPS_CELL{j}" for i in range(5) for j in range(6)] +for temp in temps: + plt.plot(df2[t], df2[temp], label=temp) +plt.legend() +plt.show() + # for i, df in enumerate(dfs): - # plt.scatter(np.arange(df.height), df[t], label = ("df" + str(i)), s=0.5) +# plt.scatter(np.arange(df.height), df[t], label = ("df" + str(i)), s=0.5) # plt.xlabel("row") # plt.ylabel("time (s)") # plt.legend() # plt.show() + +ambient = 12 # C + plt.plot(df6.filter(pl.col(t) < 260).filter(pl.col(t)>240)[t]) plt.plot(np.arange(0, df6[t].max(), df6[t].max()/(df6.height + 1))[:df6.height]) #type:ignore plt.show() df6.filter(pl.col(t) < 260).filter(pl.col(t)>240) -import polars as pl -import matplotlib.pyplot as plt -import numpy as np import scipy.fft as fft from statsmodels.nonparametric.smoothers_lowess import lowess diff --git a/Data/SOC_estimation_p30b.py b/Data/SOC_estimation_p30b.py new file mode 100644 index 0000000..4187c4c --- /dev/null +++ b/Data/SOC_estimation_p30b.py @@ -0,0 +1,212 @@ +import numpy as np +import scipy.io +from scipy.optimize import curve_fit, differential_evolution +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +import matplotlib.pyplot as plt +from scipy.interpolate import LinearNDInterpolator as Lnd_interp + +# --- 1. Cell Parameters (Approximated for a typical Li-ion cell like P30B) --- +# In practice, map these to your specific OCV-SOC curve and characterization data +R0 = 0.00686 # Ohms (Internal resistance) +RC_R1 = 0.015 # Ohms (RC circuit resistance) +RC_C1 = 2000.0 # Farads (RC circuit capacitance) +Q_max = 3.0 # Ah (Nominal capacity) +dt = 0.01 # Seconds (Sampling time) + +args = np.array([ 9.97263998e-03, 5.00002331e-03, 1.60732764e-02, 0.01, + 7.31018241e-03, 5.72244145e+02, 1.00000000e+00, 1.00000000e-07, + 2.38962033e+00, 1.29375218e+01, -6.85304383e+01, 1.92513553e+02, + -2.82364531e+02, 2.07151056e+02, -5.99607550e+01, -1.23234655e-05]) + +R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T = args + +def OCV_terminal_voltage(SOC, T): + return (a0 + a1 * SOC + a2 * SOC**2 + a3 * SOC**3 + a4 * SOC**4 + a5 * SOC**5 + a6 * SOC**6) * (1 + K_T * (T - 25)) + +def d_OCV_terminal_voltage(SOC, T): + return (a1 + 2 * a2 * SOC + 3 * a3 * SOC**2 + 4 * a4 * SOC**3 + 5 * a5 * SOC**4 + 6 * a6 * SOC**5) * (1 + K_T * (T - 25)) + +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +aboveFreezingSims = [m for m in all_sims if m.T_surf[0, 0] > 5] +notHighPulse = [m for m in aboveFreezingSims if not ("-40.000C" in m.name or "-30.000C" in m.name)] + +notHighPulse[0].name +# def terminal_voltage(SOC, T, I, R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, ocv_params, piece=False, verbose=False, superVerbose=True): +# tau1 = R1 * C1 +# tau2 = R2 * C2 + +# # Kernel calculation +# exponent = -np.arange(0, kernel_size) * delta / tau_H +# kernel = np.exp(exponent) / np.sum(np.exp(exponent)) + +# V_h = func_V_h(SOC, M, I, kernel) +# V_X1 = func_V_X_fast(tau1, R1, I, delta) +# V_X2 = func_V_X_fast(tau2, R2, I, delta) + +# V_OCV = OCV_terminal_voltage(SOC, T, *ocv_params) +# V_R0 = np.where(I <= 0, R0_discharge * I, R0_charge * I) +# # if superVerbose: +# # print(f"R0_discharge: {R0_discharge:.5f}, R0_charge: {R0_charge:.5f}, R1: {R1:.5f}, C1: {C1:.5f}, R2: {R2:.5f}, C2: {C2:.5f}, tau_H: {tau_H:.5f}, M: {M:.5f}") +# if not piece: +# print(f"MAE: {np.mean(np.abs(V_OCV + V_R0 + V_X1 + V_X2 + V_h - fullData['V'].to_numpy()))}") +# # print(f"MSE: {np.mean((V_OCV + V_R0 + V_X1 + V_X2 + V_h - fullData['V'].to_numpy()) ** 2)}") +# if verbose: +# print(f"V_OCV: {V_OCV[-1]}, V_R0: {V_R0[-1]}, V_X1: {V_X1[-1]}, V_X2: {V_X2[-1]}, V_h: {V_h[-1]}, V_terminal: {V_OCV[-1] + V_R0[-1] + V_X1[-1] + V_X2[-1] + V_h[-1]}") + +# return V_OCV + V_R0 + V_X1 + V_X2 + V_h + +def get_ocv(soc): + """Simple OCV-SOC polynomial approximation.""" + return OCV_terminal_voltage(soc, 25) + +def get_d_ocv_d_soc(soc): + """Derivative of OCV wrt SOC (Jacobian element).""" + return d_OCV_terminal_voltage(soc, 25) + +# --- 2. AEKF Core Steps (Functional Style) --- + +def aekf_predict(state, P, I, Q_mat): + """Predicts the next state and error covariance.""" + # soc, v1 = state[0, 0], state[1, 0] + + # State transition matrix (A) + A = np.array([[1.0, 0.0], + [0.0, np.exp(-dt / (RC_R1 * RC_C1))]]) + + # Control input matrix (B) + B = np.array([[-dt / (Q_max * 3600)], + [RC_R1 * (1 - np.exp(-dt / (RC_R1 * RC_C1)))]]) + + # State equation: x_k = A * x_{k-1} + B * u_k + next_state = A @ state + B * I + next_P = A @ P @ A.T + Q_mat + return next_state, next_P + +def aekf_update(state, P, I, V_meas, R_mat, H_jac, alpha=0.3): + """Updates the state using the measurement and adapts R.""" + soc, v1 = state[0, 0], state[1, 0] + + # Predicted terminal voltage + V_pred = get_ocv(soc) - v1 - I * R0 + + # Innovation (residual) + residual = V_meas - V_pred + + # Innovation covariance + S = H_jac @ P @ H_jac.T + R_mat + + # Kalman Gain + K = P @ H_jac.T @ np.linalg.inv(S) + + # Update State and Covariance + updated_state = state + K * residual + updated_P = (np.eye(2) - K @ H_jac) @ P + + # Adaptive Step: Update Measurement Noise Covariance (R) + # R_adaptive = alpha * R_previous + (1 - alpha) * (residual^2 - H*P*H^T) + R_innov = (residual ** 2) - (H_jac @ P @ H_jac.T) + updated_R = alpha * R_mat + (1 - alpha) * np.maximum(R_innov, 1e-5) + + return updated_state, updated_P, updated_R + +# --- 3. Execution Pipeline --- + +def estimate_soc(current_profile, voltage_profile, initial_soc=0.8): + """Functional pipeline to iterate through time series data.""" + # State vector: [SOC, V1]^T + state = np.array([[initial_soc], [0.0]]) + P = np.diag([1e-4, 1e-4]) # State covariance matrix + Q_mat = np.diag([1e-6, 1e-5]) # Process noise covariance + R_mat = np.array([[0.01]]) # Initial measurement noise covariance + + soc_history = [] + + for I, V_meas in zip(current_profile, voltage_profile): + # 1. Predict + state, P = aekf_predict(state, P, I, Q_mat) + + # 2. Compute Jacobian matrix H based on predicted SOC + dh_dsoc = get_d_ocv_d_soc(state[0, 0]) + H_jac = np.array([[dh_dsoc, -1.0]]) + + # 3. Update & Adapt + state, P, R_mat = aekf_update(state, P, I, V_meas, R_mat, H_jac) + + soc_history.append(state[0, 0]) + + return np.array(soc_history) + +# --- Example Usage with Dummy Data --- +# Simulate 100 seconds of constant discharge (3A) with random voltage noise +time_length = 100 +time_steps = int(time_length / dt) +time = np.arange(time_steps) * dt +mock_current = np.ones_like(time) * 0.0001 +v0 = 4.2 +v1 = 2.5 + +mock_voltage = np.linspace(v0, v1, time_steps) #+ np.random.normal(0, 0.02, time_steps) + +estimated_soc = estimate_soc(mock_current, mock_voltage, initial_soc=0.9) +print(f"Initial SOC Estimate: {estimated_soc[0]:.4f}") +print(f"Final SOC Estimate: {estimated_soc[-1]:.4f}") + +plt.plot(time, estimated_soc, label="Estimated SOC") +plt.xlabel("Time Steps") +plt.ylabel("State of Charge (SOC)") +plt.title("AEKF SOC Estimation for P30B Cell") +plt.legend() +plt.grid(True) +plt.show() + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 # Ah capacity of Molicel P30B + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: + return 1.0 + if T < -15.0: + return voltage_curve(V, -15.0) + if T > 39.0: + return voltage_curve(V, 39.0) + return SOC + +# plt.scatter(lowVs, lowSOC, label="Data Points") +V_grid = np.linspace(2.5, 4.2, 100) +T_grid = np.linspace(-20, 40, 100) +V_mesh, T_mesh = np.meshgrid(V_grid, T_grid) +SOC_mesh = voltage_curve(V_mesh, T_mesh) +plt.contourf(V_mesh, T_mesh, SOC_mesh, levels=50, cmap='viridis', alpha=0.7) +plt.colorbar(label="Estimated SOC") +plt.xlabel("Voltage (V)") +plt.ylabel("Temperature (°C)") +plt.title("SOC Estimation from Voltage and Temperature") +plt.legend() +plt.grid(True) +plt.show() + +plt.plot((1 - lowSOC[65148:99925])*3, lowVs[65148:99925], label="Data") +plt.plot((1 - np.array([SOC_lookup(V, 25) for V in V_grid]))*3, V_grid, label="Curve Fit") +plt.legend() +plt.xlabel("Discharged (Ah)") +plt.ylabel("Voltage (V)") +plt.title("OCV-SOC Curve for P30B Cell at 25°C") +plt.show() + + diff --git a/Data/acceleratorMapping.py b/Data/acceleratorMapping.py new file mode 100644 index 0000000..615e0fb --- /dev/null +++ b/Data/acceleratorMapping.py @@ -0,0 +1,38 @@ +import numpy as np +import matplotlib.pyplot as plt + +def accelerator_mapping (pedal_travel): + region1 = 0.3 + region1_scale = 3 + region2 = 0.4 + region3_scale = 3 + + region3 = 1 - region1 - region2 + scaled_region1 = region1/region1_scale + scaled_region3 = region3/region3_scale + scaled_region2 = 1 - scaled_region1 - scaled_region3 + region2_scale = region2/scaled_region2 + + if pedal_travel < scaled_region1: + return pedal_travel*region1_scale + if pedal_travel < scaled_region2 + scaled_region1: + p1 = scaled_region1 + p1_power = p1*region1_scale + p2 = pedal_travel - scaled_region1 + p2_power = p2*region2_scale + return p1_power + p2_power + p1 = scaled_region1 + p1_power = p1*region1_scale + p2 = scaled_region2 + p2_power = p2*region2_scale + p3 = pedal_travel - scaled_region1 - scaled_region2 + p3_power = p3*region3_scale + return p1_power + p2_power + p3_power + + + +arr = np.arange(0, 1, 0.01) + +plt.plot(arr, [accelerator_mapping(x) for x in arr]) +plt.plot(arr, 0.5*np.arctanh(2*arr-1)) +plt.show() \ No newline at end of file diff --git a/Data/discharge_curve.py b/Data/discharge_curve.py new file mode 100644 index 0000000..843b835 --- /dev/null +++ b/Data/discharge_curve.py @@ -0,0 +1,46 @@ +import numpy as np +import polars as pl +import scipy.io +from scipy.integrate import cumulative_simpson +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +import matplotlib.pyplot as plt + +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 # Ah capacity of Molicel P30B + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowIs = np.concatenate([sim.I for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowts = np.concatenate([sim.t for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: + return 1.0 + if T < -15.0: + return voltage_curve(V, -15.0) + if T > 39.0: + return voltage_curve(V, 39.0) + return SOC + +energy = -(lowts[:-1] - lowts[1:]).mean() * cumulative_simpson(-lowVs * lowIs)/3.6e6*19*30 + +dfVTC5A = pl.read_csv("../fs-data/FS-3/voltageTableVTC5A.csv") +vtc5a_discharge = dfVTC5A.filter(pl.col("Current") == 0.5) + +plt.plot(vtc5a_discharge["Charge"]*vtc5a_discharge["Voltage"]) +plt.show() + +cumulative_simpson(vtc5a_discharge["Voltage"][:-1], x=vtc5a_discharge["Charge"][:-1])*20*30 diff --git a/Data/regen_curve_scaling.py b/Data/regen_curve_scaling.py new file mode 100644 index 0000000..f6dd2b8 --- /dev/null +++ b/Data/regen_curve_scaling.py @@ -0,0 +1,67 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt + +df = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet").fill_null(strategy="forward").fill_null(strategy="backward") + +psi = np.clip((df["ETC_STATUS_BRAKE_SENSE_VOLTAGE"] - 330) / (3300 - 660) * 2000, 0, 2000) +brake_pedal = df["ETC_STATUS_BRAKE_PEDAL_TRAVEL"] / 100 +accel_pedal = df["ETC_STATUS_PEDAL_TRAVEL"]/100 + + + +# plt.plot(psi) +# plt.plot(brake_pedal) +# plt.show() + +[x for x in df.columns if "TMAIN" in x] + + +coeff_of_friction = 0.3 +brake_disk_radius = 0.09525 +wheel_radius = 0.216 + +gear_ratio = 12/39 + +brake_ratio = 0.55 # rear + +def brakePSI_toNewtons(psi:float) -> float: + return psi * 1.23 * 2 * 4.448222 * 2# lb force to Newtons (normal force on the brake disk) + # psi, brake caliper area, 2 calipers, lb to newtons, 2 wheels, coeff of friction, brake disc radius, wheel radius + +def front_brake_force(brake_pressure): + return brakePSI_toNewtons(brake_pressure) * coeff_of_friction * brake_disk_radius / wheel_radius + +def regen_torque(front_psi): + fbf = front_brake_force(front_psi) + rear_force = fbf * brake_ratio / (1 - brake_ratio) + rear_wheel_torque = rear_force * wheel_radius + motor_torque = rear_wheel_torque * gear_ratio + return motor_torque + +fast = np.vectorize(regen_torque) + +psi_range = np.linspace(0, 2000, 500) + +torque_range = fast(psi_range) + +fs3_regen = (brake_pedal - accel_pedal) * 180 +fs4_regen = (regen_torque(psi)) + +plt.plot(fs3_regen, label="FS3 Regen") +plt.plot(fs4_regen, label="FS4 Regen") +plt.legend() +plt.show() + +plt.plot(fs4_regen, label="FS4 Regen") +plt.plot(fs4_regen-accel_pedal*180, label="FS4 Regen + accel reduction") +plt.legend() +plt.show() + + + +plt.plot(df["Time_ms"]/1000, fs3_regen, label="FS3 Regen") +plt.plot(df["Time_ms"]/1000, fs4_regen, label="FS4 Regen") +plt.plot(df["Time_ms"]/1000, fs4_regen-accel_pedal*180, label="FS4 Regen + accel reduction") +plt.legend() +plt.show() From 29baeaca875173703ba39f9ed66c6f44f3b110e2 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Thu, 18 Jun 2026 21:31:46 -0400 Subject: [PATCH 33/38] some stuff --- FullVehicleSim/basicViewer.py | 4 ++++ FullVehicleSim/params.json5 | 6 +++--- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/FullVehicleSim/basicViewer.py b/FullVehicleSim/basicViewer.py index 7bc98b3..c674698 100644 --- a/FullVehicleSim/basicViewer.py +++ b/FullVehicleSim/basicViewer.py @@ -5,6 +5,8 @@ dfReal = pl.read_parquet("../fs-data/FS-3/03162026/2_steeper_regen_curve.parquet").fill_null(strategy="forward").fill_null(strategy="backward") t = df["time"] +dfReal["Time_ms"][-1] + [x for x in dfReal.columns if "SME" in x] @@ -28,5 +30,7 @@ plt.plot(dfReal["Time_ms"]/1000, dfReal["SME_TRQSPD_Speed"], label="Real RPM") plt.plot(t, df["motorRPM"], label="Simulated RPM") plt.legend() +plt.xlabel("Time (s)") +plt.ylabel("RPM") plt.show() df["speed"].max() \ No newline at end of file diff --git a/FullVehicleSim/params.json5 b/FullVehicleSim/params.json5 index 60cba4e..7bf7017 100644 --- a/FullVehicleSim/params.json5 +++ b/FullVehicleSim/params.json5 @@ -1,12 +1,12 @@ // Base SI Units unless otherwise specified { "Parameters": { - "stepsPerSecond": 500, - "simulationDuration": 10.0, // Seconds + "stepsPerSecond": 100, + "simulationDuration": 430.0, // Seconds "interpolationMethod": "linear", // "linear" or "cubic", this is for interpolating the simultion controls. "ambientTemperature": 20, // °C - "tractiveIMax": 650, // A + "tractiveIMax": 400, // A "initialBrakeTemperature": 20, // °C "initialBatteryTemperature": 20, // °C "vehicleSOC": 1, // Out of 1 From 1a2b3d304a3813c37eef6de4d7df6f25890ee459 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Mon, 20 Jul 2026 21:20:01 -0700 Subject: [PATCH 34/38] more data parsing --- Data/SOC_estimation_p30b_simpler.py | 71 +++++++++++++++ Data/comp0619.py | 27 ++++++ Data/comp0619_2.py | 35 ++++++++ Data/discharge_curve.py | 6 +- Data/temp.py | 135 +++------------------------- 5 files changed, 149 insertions(+), 125 deletions(-) create mode 100644 Data/SOC_estimation_p30b_simpler.py create mode 100644 Data/comp0619.py create mode 100644 Data/comp0619_2.py diff --git a/Data/SOC_estimation_p30b_simpler.py b/Data/SOC_estimation_p30b_simpler.py new file mode 100644 index 0000000..d8ec138 --- /dev/null +++ b/Data/SOC_estimation_p30b_simpler.py @@ -0,0 +1,71 @@ +import numpy as np +import scipy.io +from scipy.optimize import curve_fit, differential_evolution +from scipy.integrate import cumulative_simpson +from scipy.signal import convolve +import matplotlib.pyplot as plt +from scipy.interpolate import LinearNDInterpolator as Lnd_interp +import polars as pl + +# --- 1. Cell Parameters (Approximated for a typical Li-ion cell like P30B) --- +# In practice, map these to your specific OCV-SOC curve and characterization data +R0 = 0.00686 # Ohms (Internal resistance) +Q_max = 3.0 # Ah (Nominal capacity) + +args = np.array([ 9.97263998e-03, 5.00002331e-03, 1.60732764e-02, 0.01, + 7.31018241e-03, 5.72244145e+02, 1.00000000e+00, 1.00000000e-07, + 2.38962033e+00, 1.29375218e+01, -6.85304383e+01, 1.92513553e+02, + -2.82364531e+02, 2.07151056e+02, -5.99607550e+01, -1.23234655e-05]) + +R0_discharge, R0_charge, R1, C1, R2, C2, tau_H, M, a0, a1, a2, a3, a4, a5, a6, K_T = args + +def OCV_SOC(V): + return (a0 + a1 * V + a2 * V**2 + a3 * V**3 + a4 * V**4 + a5 * V**5 + a6 * V**6) + +matData = scipy.io.loadmat("../fs-data/FS-4/P30B Cell Data/Molicel_INR18650P30B_measurement.mat", squeeze_me=True, struct_as_record=False) +measurements = matData["measurement"] +all_sims = np.concatenate([measurements.fu.DCC, measurements.fu.CHC, measurements.fu.DCP, measurements.fu.CHP]) +aboveFreezingSims = [m for m in all_sims if m.T_surf[0, 0] > 5] +notHighPulse = [m for m in aboveFreezingSims if not ("-40.000C" in m.name or "-30.000C" in m.name)] + +lowCurrMeasurements = [m for m in measurements.fu.DCC if "-0.1" in m.name] + +def func_SOC(I, t, starting_SOC): + Q_nominal = 3.0 # Ah capacity of Molicel P30B + integrated_ah = cumulative_simpson(y=I, x=t, initial=0) / 3600.0 + soc = starting_SOC + (integrated_ah / Q_nominal) + return np.clip(soc, 0.0, 1.0) + +lowVs = np.concatenate([sim.V for sim in lowCurrMeasurements]) +lowIs = np.concatenate([sim.I for sim in lowCurrMeasurements]) +lowTs = np.concatenate([sim.T_surf[0, :] for sim in lowCurrMeasurements]) +lowts = np.concatenate([sim.t for sim in lowCurrMeasurements]) +lowSOC = np.concatenate([func_SOC(sim.I, sim.t, 1.0) for sim in lowCurrMeasurements]) + +voltage_curve = Lnd_interp(np.array([lowVs, lowTs]).T, lowSOC, rescale=True) + +def SOC_lookup(V, T): + SOC = voltage_curve(V, T) + if np.isnan(SOC): + if V > 4.1: + return 1.0 + if T < -15.0: + return voltage_curve(V, -15.0) + if T > 39.0: + return voltage_curve(V, 39.0) + return SOC + +energy = -(lowts[:-1] - lowts[1:]).mean() * cumulative_simpson(-lowVs * lowIs)/3.6e6*19*30 # kWh + + +dfVTC5A = pl.read_csv("../fs-data/FS-3/voltageTableVTC5A.csv") +vtc5a_discharge = dfVTC5A.filter(pl.col("Current") == 0.5) + +plt.plot(vtc5a_discharge["Charge"]*vtc5a_discharge["Voltage"]) +plt.show() + +cumulative_simpson(vtc5a_discharge["Voltage"][:-1], x=vtc5a_discharge["Charge"][:-1])*20*30 + +SOC_lookup(4.13, 25) + + diff --git a/Data/comp0619.py b/Data/comp0619.py new file mode 100644 index 0000000..4fc140e --- /dev/null +++ b/Data/comp0619.py @@ -0,0 +1,27 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt + +from Data.FSLib.AnalysisFunctions import read + +paths = ["FS-4/Comp/rain_test/035625.parquet", + "FS-4/Comp/rain_test/035117.parquet", + "FS-4/Comp/rain_test/041704.parquet", + "FS-4/Comp/rain_test/041155.parquet" + ] +shutdown_final = "BATT_STATUS_SHUTDOWN_FINAL" +imd_fault = "BATT_STATUS_IMD_FAULT" +bus_voltage = "SME_TEMP_DC_Bus_V" + +dfs = [read(path) for path in paths] + +for df in dfs: + t = df["Time_ms"]/1000 + fig = plt.figure() + ax = fig.add_subplot(211) + ax1 = fig.add_subplot(212) + ax.plot(t, df[shutdown_final], label = "shutdown final") + ax.plot(t, df[imd_fault], label = "IMD fault") + ax.legend() + ax1.plot(t, df[bus_voltage]) + fig.show() \ No newline at end of file diff --git a/Data/comp0619_2.py b/Data/comp0619_2.py new file mode 100644 index 0000000..11ce8bc --- /dev/null +++ b/Data/comp0619_2.py @@ -0,0 +1,35 @@ +import polars as pl +import numpy as np +import matplotlib.pyplot as plt + +from Data.FSLib.AnalysisFunctions import read + +df = read("FS-4/Comp/brake.parquet") + +VCU_ACCEL_PEDAL_TRAVEL = "VCU_ACCEL_PEDAL_TRAVEL" +SME_THROTL_TorqueDemand = "SME_THROTL_TorqueDemand" +VCU_ETC_IMPLAUS_APPS_OUT_OF_RANGE = "VCU_ETC_IMPLAUS_APPS_OUT_OF_RANGE" +VCU_ETC_IMPLAUS_BPPS_OUT_OF_RANGE = "VCU_ETC_IMPLAUS_BPPS_OUT_OF_RANGE" +VCU_ETC_IMPLAUS_APPS_DEVIATION = "VCU_ETC_IMPLAUS_APPS_DEVIATION" +VCU_ETC_IMPLAUS_BSE_OUT_OF_RANGE = "VCU_ETC_IMPLAUS_BSE_OUT_OF_RANGE" +VCU_ETC_IMPLAUS_BRAKE_AND_ACCEL = "VCU_ETC_IMPLAUS_BRAKE_AND_ACCEL" + +t = df["Time_ms"]/1000 + +plt.plot(t, df[VCU_ACCEL_PEDAL_TRAVEL], label="Accel Pedal Travel") +plt.plot(t, df[SME_THROTL_TorqueDemand], label="Torque Demand") +plt.legend() +plt.show() + +plt.plot(t, df[VCU_ETC_IMPLAUS_APPS_OUT_OF_RANGE], label="APPS Out of Range") +plt.plot(t, df[VCU_ETC_IMPLAUS_BPPS_OUT_OF_RANGE], label="BPPS Out of Range") +plt.plot(t, df[VCU_ETC_IMPLAUS_APPS_DEVIATION], label="APPS Deviation") +plt.plot(t, df[VCU_ETC_IMPLAUS_BSE_OUT_OF_RANGE], label="BSE Out of Range") +plt.plot(t, df[VCU_ETC_IMPLAUS_BRAKE_AND_ACCEL], label="Brake and Accel Implausible") +plt.legend() +plt.show() + +plt.plot(t, df["VCU_ACCEL_PEDAL_TRAVEL"], label="Accel Pedal Travel") +plt.plot(t, df["VCU_BRAKE_PEDAL_TRAVEL"], label="Brake Pedal Travel") +plt.legend() +plt.show() \ No newline at end of file diff --git a/Data/discharge_curve.py b/Data/discharge_curve.py index 843b835..55ccd74 100644 --- a/Data/discharge_curve.py +++ b/Data/discharge_curve.py @@ -35,7 +35,8 @@ def SOC_lookup(V, T): return voltage_curve(V, 39.0) return SOC -energy = -(lowts[:-1] - lowts[1:]).mean() * cumulative_simpson(-lowVs * lowIs)/3.6e6*19*30 +energy = -(lowts[:-1] - lowts[1:]).mean() * cumulative_simpson(-lowVs * lowIs)/3.6e6*19*30 # kWh + dfVTC5A = pl.read_csv("../fs-data/FS-3/voltageTableVTC5A.csv") vtc5a_discharge = dfVTC5A.filter(pl.col("Current") == 0.5) @@ -44,3 +45,6 @@ def SOC_lookup(V, T): plt.show() cumulative_simpson(vtc5a_discharge["Voltage"][:-1], x=vtc5a_discharge["Charge"][:-1])*20*30 + +SOC_lookup(4.13, 25) + diff --git a/Data/temp.py b/Data/temp.py index 2314ebd..8dfd685 100644 --- a/Data/temp.py +++ b/Data/temp.py @@ -1,133 +1,20 @@ import polars as pl +import numpy as np import matplotlib.pyplot as plt -import cantools.database as db -from Data.DataDecoding_N_CorrectionScripts.dataDecodingFunctions import * -from Data.FSLib.AnalysisFunctions import * -from Data.FSLib.IntegralsAndDerivatives import * -from scipy.interpolate import CubicSpline +from Data.FSLib.AnalysisFunctions import read, simpleTimeCol -dbcPath = "../fs-3/CANbus.dbc" -dbc = db.load_file(dbcPath) +path = "FS-3/08172025/08172025_26autox1.parquet" -# lv = "GLV" -# v = "Violation" -V = "ACC_POWER_PACK_VOLTAGE" -I = "SME_TEMP_BusCurrent" -E = "Energy" -P = "Power" -t = "Time" +df = read(path, fs2or3=True) +df = df.insert_column(0, simpleTimeCol(df)) -smeFaultCode = "SME_TEMP_FaultCode" -smeFaultLevel = "SME_TEMP_FaultLevel" -smeContactor = "SME_TRQSPD_contactor_closed" -busV = "SME_TEMP_DC_Bus_V" -busC = "SME_TEMP_BusCurrent" -bmsFault = "ACC_STATUS_BMS_FAULT" -imdFault = "ACC_STATUS_IMD_FAULT" -pchOn = "ACC_STATUS_PRECHARGING" -pchDone = "ACC_STATUS_PRECHARGE_DONE" -accShutdown = "ACC_STATUS_SHUTDOWN_STATE" -glv = "ACC_STATUS_GLV_VOLTAGE" +t = df["Time"] -vdmValid = "VDM_GPS_VALID1" -# time = "" -brakeF = "TMAIN_DATA_BRAKES_F" -brakeR = "TMAIN_DATA_BRAKES_R" -frT = "TPERIPH_FR_DATA_SUSTRAVEL" -flT = "TPERIPH_FL_DATA_SUSTRAVEL" -brT = "TPERIPH_BR_DATA_SUSTRAVEL" -blT = "TPERIPH_BL_DATA_SUSTRAVEL" -lat = "VDM_GPS_Latitude" -long = "VDM_GPS_Longitude" -course = "VDM_GPS_TRUE_COURSE" -xA = "xA" -yA = "yA" -zA = "zA" -vA = "vA" -xA_uncorrected = "VDM_X_AXIS_ACCELERATION" -yA_uncorrected = "VDM_Y_AXIS_ACCELERATION" -zA_uncorrected = "VDM_Z_AXIS_ACCELERATION" -vA_uncorrected = "vA_uncorrected" -xG = "VDM_X_AXIS_YAW_RATE" -yG = "VDM_Y_AXIS_YAW_RATE" -zG = "VDM_Z_AXIS_YAW_RATE" -rpm = "SME_TRQSPD_Speed" -speed = "VDM_GPS_SPEED" -tsC = "ACC_POWER_CURRENT" -xA_mps = "IMU_XAxis_Acceleration_mps" -yA_mps = "IMU_YAxis_Acceleration_mps" -zA_mps = "IMU_ZAxis_Acceleration_mps" -speed_mps = "VMD_GPS_Speed_mps" -index = "index" -heFL = "TPERIPH_FL_DATA_WHEELSPEED" -heFR = "TPERIPH_FR_DATA_WHEELSPEED" -heBL = "TPERIPH_BL_DATA_WHEELSPEED" -heBR = "TPERIPH_BR_DATA_WHEELSPEED" +[x for x in df.columns if "PEDAL" in x] -pedalTravel = "ETC_STATUS_PEDAL_TRAVEL" -etcImplausibility = "ETC_STATUS_IMPLAUSIBILITY" -etcRTDButton = "ETC_STATUS_RTD_BUTTON" -etcBrakeVoltage = "ETC_STATUS_BRAKE_SENSE_VOLTAGE" - -df = read("C:/Projects/FormulaSlug/fs-data/FS-3/10082025/fixed_wheels_nathaniel_inv_test_w_fault.parquet") - -plt.plot(df[busV]) -plt.show() - - -df = pl.read_parquet("C:/Projects/FormulaSlug/fs-data/FS-3/10112025/firstDriveMCError30-filled-null.parquet") - -df.select([a for a in df.columns if a.startswith("ACC") and a[9] == "T"]).mean_horizontal() - - -fig = plt.figure() -ax = fig.add_subplot(111) - -ax.plot(df[t], df[frT], label=frT, c="blue") -ax.set_title("Suspension Travel during First Drive with MC Fault") -ax.set_xlabel("Time") -ax.set_ylabel("Suspension Travel (mm)") -ax.legend() -plt.show() - - -heFR = "TELEM_FR_WHEELSPEED" -heFL = "TELEM_FL_WHEELSPEED" -heBR = "TELEM_BR_WHEELSPEED" -heBL = "TELEM_BL_WHEELSPEED" - -df = read("C:/Projects/FormulaSlug/fs-data/FS-2/Parquet/2025-03-06-BrakingTests1.parquet") -df = df.with_columns( - simpleTimeCol(df) -) - - -fig = plt.figure() -ax = fig.add_subplot(111) - -ax.plot(df[t], df[heFR], label=heFR, c="orange") -ax.plot(df[t], df[heFL], label=heFL, c="red") -ax.plot(df[t], df[heBR], label=heBR, c="blue") -ax.plot(df[t], df[heBL], label=heBL, c="cyan") -ax.set_title("HE Sensors") -ax.set_xlabel("Time") -ax.set_ylabel("idk lol, 0-32767") -ax.legend() -plt.show() - - -fig = plt.figure() -ax = fig.add_subplot(111) -ax1 = ax.twinx() - -ax.plot(df[t], df[heFR], label="frontRight", c="red") -ax.plot(df[t], df.select([heBL, heBR]).mean_horizontal(), label="meanBack", c="blue") -ax1.plot(df[t],df.select([heBL, heBR]).mean_horizontal() / df[heFR], label="ratio", c="green") -# ax.plot(df[t], df[rpm]*11/41, label="RPM", c="orange") ## Cursed until we have proper wheel speed calibration -ax.set_title("HE Sensors") -ax.set_xlabel("Time") -ax.set_ylabel("idk lol, 0-32767") -ax.legend() -ax1.set_ylim(0,2) +plt.plot(t, df["SME_TRQSPD_Speed"], label = "RPM") +plt.plot(t, df["SME_TRQSPD_Torque"], label = "SME_TRQSPD_Torque") +plt.plot(t, df["ETC_STATUS_PEDAL_TRAVEL"], label = "ETC_STATUS_PEDAL_TRAVEL") +plt.legend() plt.show() \ No newline at end of file From 27170ac2271bb78d4dba44bb9d891dc1228c0a83 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Mon, 28 Sep 2026 12:52:15 -0700 Subject: [PATCH 35/38] more graphs --- Data/temp.py | 209 +++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 202 insertions(+), 7 deletions(-) diff --git a/Data/temp.py b/Data/temp.py index 8dfd685..4a8d0cc 100644 --- a/Data/temp.py +++ b/Data/temp.py @@ -1,20 +1,215 @@ import polars as pl import numpy as np import matplotlib.pyplot as plt +from matplotlib.widgets import Slider +from matplotlib.cm import get_cmap +from matplotlib.colors import Normalize +from os import listdir from Data.FSLib.AnalysisFunctions import read, simpleTimeCol -path = "FS-3/08172025/08172025_26autox1.parquet" +# new_paths = ["FS-4/TestTuneAndTech_Sep182026/" + x for x in listdir("../fs-data/FS-4/TestTuneAndTech_Sep182026/")] +# dfs = [read(x) for x in new_paths] -df = read(path, fs2or3=True) -df = df.insert_column(0, simpleTimeCol(df)) +# for df, path in zip(dfs, listdir("../fs-data/FS-4/TestTuneAndTech_Sep182026/")): +# t = df["Time"] +# plt.plot(t, df["SME_TRQSPD_Speed"]/10, label = "RPM/10") +# plt.plot(t, df["SME_TRQSPD_Torque"], label = "SME_TRQSPD_Torque") +# plt.plot(t, df["VCU_ACCEL_PEDAL_TRAVEL"], label = "Pedal Travel") +# plt.plot(t, df["SME_TEMP_BusCurrent"], label="current") +# plt.plot(t, df["VCU_STEERING_ANGLE"], label="steering angle") +# plt.legend() +# plt.title(path) +# plt.show() + + +path = "FS-4/TestTuneAndTech_Sep192026/163551_probablySkidpad.parquet" +# 155420, 155424, 155428 +path2 = "FS-3/08172025/08172025_26autox1.parquet" +path2 = "FS-3/08102025/08102025Endurance1_FirstHalf.parquet" + +df = read(path) +df2 = read(path2, fs2or3=True) +# df = df.insert_column(0, simpleTimeCol(df)) t = df["Time"] +# [x for x in df.columns if "PEDAL" in x] -[x for x in df.columns if "PEDAL" in x] +voltages = [[f"BATT_MOD{x}_VOLTS_CELL{y}" for y in range(6)] for x in range (5)] +temps = [[f"BATT_MOD{x}_TEMPS_CELL{y}" for y in range(12)] for x in range (5)] +flat_temps = [f"BATT_MOD{x}_TEMPS_CELL{y}" for y in range(12) for x in range (5)] -plt.plot(t, df["SME_TRQSPD_Speed"], label = "RPM") +plt.plot(t, df["SME_TRQSPD_Speed"]/10, label = "RPM/10") plt.plot(t, df["SME_TRQSPD_Torque"], label = "SME_TRQSPD_Torque") -plt.plot(t, df["ETC_STATUS_PEDAL_TRAVEL"], label = "ETC_STATUS_PEDAL_TRAVEL") +plt.plot(t, df["VCU_ACCEL_PEDAL_TRAVEL"], label = "Pedal Travel") +plt.plot(t, df["SME_TEMP_BusCurrent"], label="current") +plt.legend() +plt.show() + +temp_sensors = ["BATT_TRAY_TEMPS_BOLTED_CONNECTION", "BATT_TRAY_TEMPS_BUSBAR", "BATT_TRAY_TEMPS_PACK_FUSE", "BATT_TRAY_TEMPS_COWLING", "BATT_TRAY_TEMPS_INTAKE"] + +flsus = "TPERIPH_FL_DATA_SUSTRAVEL" +frsus = "TPERIPH_FR_DATA_SUSTRAVEL" +blsus = "TPERIPH_BL_DATA_SUSTRAVEL" +brsus = "TPERIPH_BR_DATA_SUSTRAVEL" + +fig = plt.figure() +ax = fig.add_subplot(111) +ax.plot(t, df[flsus].rolling_mean(11), label = "FL") +ax.plot(t, df[frsus].rolling_mean(11), label = "FR") +ax.plot(t, df[blsus].rolling_mean(11), label = "BL") +ax.plot(t, df[brsus].rolling_mean(11), label = "BR") +ax.legend() +fig.show() + +plt.plot(t, df["VCU_STEERING_ANGLE"]) +plt.show() + + +fig = plt.figure() +ax = fig.add_subplot(111) +for sensor in temp_sensors: + ax.plot(df[sensor], label=sensor) +ax.legend() +fig.show() + +busC = df["SME_TEMP_BusCurrent"] +busV = df["SME_TEMP_DC_Bus_V"] + +batV = df["BATT_POWER_PACK_VOLTAGE"] +batC = df["BATT_POWER_CURRENT"] + +plt.plot(t, (batV - busV), label="battery voltage - bus voltage") +plt.plot(t, (batV - busV)*busC, label="(batV - busV) * busC") +plt.plot(t, (batV - busV)*batC, label="(batV - busV) * busC") +plt.plot(t, batC, label = "battery current") +plt.plot(t, busC, label = "bus current") +plt.legend() +plt.show() + +t2 = simpleTimeCol(df2) +volts2 = [f"ACC_SEG{x}_VOLTS_CELL{y}" for x in range(5) for y in range (6)] +dfVolts2 = df2[volts2].sum_horizontal() +vDiff2 = df2["SME_TEMP_DC_Bus_V"] - dfVolts2 +df2 = df2.with_columns((vDiff2 * df2["SME_TEMP_BusCurrent"]).alias("PowerLoss"), (dfVolts2).alias("PackVoltage")) +df2 = df2.filter(pl.col("SME_TEMP_BusCurrent") > 100).filter(pl.col("SME_TEMP_BusCurrent") < 1000) + +plt.plot(t2, df2["SME_TEMP_BusCurrent"], label="current") +plt.plot(t2, df2["SME_TRQSPD_Speed"]/10, label="rpm/10") +plt.plot(t2, df2["ETC_STATUS_PEDAL_TRAVEL"], label="pedal travel") plt.legend() -plt.show() \ No newline at end of file +plt.show() + +fig2 = plt.figure() +ax2 = fig2.add_subplot(111) +ax2.plot(t, (batV - busV)*busC, label="(batV - busV) * busC") +ax2.set_xlabel("Time (s)") +ax2.set_ylabel("Power Loss (W)") +fig2.show() + +fig2 = plt.figure() +ax2 = fig2.add_subplot(111) +ax2.plot(t, np.convolve((batV - busV)/busC, np.ones(201)/201, mode='same'), label="(batV - busV) * busC") +ax2.set_xlabel("Time (s)") +ax2.set_ylabel("Resistance (Ohms)") +fig2.show() + +fig3 = plt.figure() +ax3 = fig3.add_subplot(111) +ax3.plot(t2, vDiff2 * df2["SME_TEMP_BusCurrent"]) +fig3.show() + +np.polyfit(busC, (batV - busV), 1) +np.polyfit(df2["SME_TEMP_BusCurrent"], df2["PackVoltage"] - df2["SME_TEMP_DC_Bus_V"], 1) + +plt.scatter(df2["SME_TEMP_BusCurrent"], df2["PackVoltage"] - df2["SME_TEMP_DC_Bus_V"], label = "Pack Voltage") +X = np.arange(100, 600, 10) +plt.plot(X, 0.0390288*X - 1.567) +plt.show() + +plt.scatter(busC, (batV - busV), label = "Bus Voltage") +plt.show() + +for temp in temps: + plt.plot(t, df[temp], label = temp) +plt.legend() +plt.show() + +# graph each set of temperatures as a separate plane in a 3d plot with an isometric view so all temperatures can be seen at the same time. +# Every 2 are on a new row (0-11) +# The temperatures are by module. Do this for the last row of the parquet file + +# 3d plot + +cmap = get_cmap('viridis') + +x = np.linspace(0, 2, 7) +y = np.arange(0, 3, 1) +X, Y = np.meshgrid(x, y) +Z = np.zeros_like(X) +norm = Normalize(vmin=df[flat_temps].min_horizontal().min(), vmax=df[flat_temps].max_horizontal().max()) + +minTemp = df[flat_temps].min_horizontal().min() +maxTemp = df[flat_temps].max_horizontal().max() + +fig = plt.figure() +ax = fig.add_subplot(111, projection='3d') +for i, temp in enumerate(temps): + color_data = df[temp][-1].to_numpy().reshape(2,6) + + facecolors = cmap(norm(color_data)) + ax.plot_surface(X, Y, Z+i, cmap='viridis', alpha=0.5) +# fig.colorbar(ax.plot_surface(X, Y, Z, facecolors=df[temps[0]][-1].to_numpy().reshape(2,6), cmap='viridis', alpha=0.5), ax=ax, shrink=0.5, aspect=5) +fig.show() + + +n_steps = len(df[temps[0]]) + +# Create figure with space for slider +fig = plt.figure(figsize=(10, 8)) +ax = fig.add_subplot(111, projection='3d') +plt.subplots_adjust(bottom=0.2) + +# Add slider axis +slider_ax = plt.axes([0.2, 0.05, 0.6, 0.03]) +slider = Slider(slider_ax, 'Time Step', 0, n_steps - 1, valinit=n_steps - 1, valstep=1, color='steelblue') + +# Store the surface plots so we can update them +surfaces = [] +cbar = None + +def update(val): + global surfaces, cbar + + # Get current slider value + idx = int(slider.val) + + # Clear the axis + ax.clear() + + # Redraw all surfaces at this time step + for i, temp in enumerate(temps): + surf = ax.plot_surface(X, Y, Z+i*1, facecolors=cmap(norm(df[temp][idx].to_numpy().reshape(2, 6))), + cmap='viridis', alpha=0.5) + + # Redraw colorbar + if cbar: + cbar.clear() + cbar = fig.colorbar(ax.plot_surface(X, Y, Z, facecolors=cmap(norm(df[temps[0]][idx].to_numpy().reshape(2, 6))), + cmap='viridis', alpha=0.5), + ax=ax, shrink=0.5, aspect=5) + + # Reset labels and limits + ax.set_xlabel('X') + ax.set_ylabel('Y') + ax.set_zlabel('Z') + + fig.canvas.draw_idle() + +# Connect slider to update function +slider.on_changed(update) + +# Initial plot +update(n_steps - 1) + +fig.show() \ No newline at end of file From e21fb9611d33b07c8e761614a80290636bae4e6e Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Sun, 18 Oct 2026 15:53:57 -0700 Subject: [PATCH 36/38] small fix :) --- FullVehicleSim/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/FullVehicleSim/main.py b/FullVehicleSim/main.py index 88bcfe5..69b120a 100644 --- a/FullVehicleSim/main.py +++ b/FullVehicleSim/main.py @@ -100,7 +100,7 @@ # timeSinceLastSteer = 0 # initSpeed = max(currVehicle.speed, 5) # Fails below roughly 5ish - print("*****SIMULATION EXECUTATION TIME****", time.time() -start) + print("*****SIMULATION EXECUTION TIME****", time.time() -start) # columns = ['posX', 'posY', 'velX', 'velY', 'speed', 'acceleration', # 'headingX', 'headingY', 'yawRate', 'steerAngle', 'throttle', From 2d615062ed14dfc0b00157ef4beffc8af5c49d94 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Mon, 26 Oct 2026 20:24:11 -0700 Subject: [PATCH 37/38] prepping setup for yaw acceleration --- Data/YawAcceleration/training.py | 75 ++++++++++++++++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 Data/YawAcceleration/training.py diff --git a/Data/YawAcceleration/training.py b/Data/YawAcceleration/training.py new file mode 100644 index 0000000..0eefa20 --- /dev/null +++ b/Data/YawAcceleration/training.py @@ -0,0 +1,75 @@ +import numpy as np +from Data.FSLib.AnalysisFunctions import read + +def normalizePressureLong(tirePressure): + # Only long because lat doesn't use it + lambda_pressurescalarlong = 1 + + return (tirePressure - lambda_pressurescalarlong * tirePressure) / (lambda_pressurescalarlong * tirePressure) + +def normalizeLoadLong(normalForce): + lambda_loadscalarlong = 1 + + return (normalForce - lambda_loadscalarlong * normalForce) / (lambda_loadscalarlong * normalForce) + +def calculateLongCompositeLongFrictionScalingFactor(velocityX): + unloaded_radius = 1 # was unloaded-radius + friction_coeff_long = 1 # was friction-coeff-long + + sqrt_term = np.sqrt(9.81 * unloaded_radius) + velocity_ratio = abs(velocityX) / sqrt_term + denominator = 1.0 + friction_coeff_long * velocity_ratio + # Add epsilon to prevent division by zero + eps = 1e-8 + return friction_coeff_long / (denominator + eps) + +def getLongFrictionCoefficient(normalForce, velocityX, tirePressure): + p_dx1 = 1 + p_dx2 = 1 + p_dx3 = 1 + p_px3 = 1 + p_px4 = 1 + camber = 1 + + term1 = (p_dx1 + p_dx2 * normalizeLoadLong(normalForce)) + term2 = (1.0 + p_px3 * normalizePressureLong(tirePressure) + p_px4 * normalizePressureLong(tirePressure) ** 2) + scaling_factor = calculateLongCompositeLongFrictionScalingFactor(velocityX) + camber_term = np.abs(camber) ** scaling_factor + term3 = (1.0 - p_dx3 * camber_term) + return term1 * term2 * term3 + +def getMaxLongFriction(normalForce, velocityX, tirePressure): + zeta_1 = 1 + + return getLongFrictionCoefficient(normalForce, velocityX, tirePressure) * normalForce * zeta_1 + +def getStiffnessFactorLong(normalForce, velocityX, tirePressure): + p_px1 = 1 + p_px2 = 1 + p_kx1 = 1 + p_kx2 = 1 + p_kx3 = 1 + shape_factor = 1 # var is "shape-factor" + + Cx = shape_factor + Dx = getMaxLongFriction(normalForce, velocityX, tirePressure) + exp_term = p_kx3 * normalizeLoadLong(normalForce) + Kx = normalForce * (p_kx1 + p_kx2 * normalizeLoadLong(normalForce)) * np.exp(exp_term) * (1.0 + p_px1 * normalizePressureLong(tirePressure) + p_px2 * normalizePressureLong(tirePressure) ** 2) + denominator = Cx * Dx + # Add epsilon to prevent division by zero + eps = 1e-8 + return Kx / (denominator + eps) + +def calcYawAcceleration(yawRate, bodySlip, speed, steeringAngle, velocityX, tireNormalForces = [100, 100, 100, 100], tirePressures = [12, 12, 12, 12]): + frontCorneringStiffness = getStiffnessFactorLong() + rearCorneringStiffness = 1000 + frontAxleDistanceToCOM = 0.5 + rearAxleDistanceToCOM = 0.5 + MOI = 100 + + component1 = (frontCorneringStiffness * frontAxleDistanceToCOM - rearCorneringStiffness * rearAxleDistanceToCOM) * bodySlip + component2 = yawRate / speed * (frontCorneringStiffness * frontAxleDistanceToCOM**2 + rearCorneringStiffness * rearAxleDistanceToCOM**2) + component3 = -frontCorneringStiffness * frontAxleDistanceToCOM * steeringAngle + return (component1 + component2 + component3) / MOI + +df = read("FS-3/08172025/08172025_20_Endurance1P1.parquet") \ No newline at end of file From 30bbf5ec47f2c41dec5d9af34e7d931388164c06 Mon Sep 17 00:00:00 2001 From: goob10000 <65315624+goob10000@users.noreply.github.com> Date: Thu, 29 Oct 2026 10:44:51 -0700 Subject: [PATCH 38/38] cleaner library imports and removed deprecated Dict/Tuple/List --- FullVehicleSim/Mech/aero.py | 1 + FullVehicleSim/Mech/brakepadFrictionModel.py | 3 +-- FullVehicleSim/Mech/braking.py | 7 +++--- FullVehicleSim/Mech/general.py | 9 ++++---- FullVehicleSim/Mech/steering.py | 1 + FullVehicleSim/Mech/tireLoad.py | 3 ++- FullVehicleSim/Mech/tireState.py | 6 +++-- FullVehicleSim/Mech/traction.py | 7 +++--- FullVehicleSim/engine.py | 20 ++++++++++++----- FullVehicleSim/main.py | 15 ++++++------- FullVehicleSim/paramLoader.py | 11 +++++----- .../yaw_rate_model/double_bicycle_model.py | 22 +++++++++---------- 12 files changed, 59 insertions(+), 46 deletions(-) diff --git a/FullVehicleSim/Mech/aero.py b/FullVehicleSim/Mech/aero.py index 6b5ecbc..a9f6f5d 100644 --- a/FullVehicleSim/Mech/aero.py +++ b/FullVehicleSim/Mech/aero.py @@ -1,6 +1,7 @@ import numpy as np from paramLoader import * + def calcDrag(worldArray:np.ndarray, step:int) -> float: return 0.5 * Parameters["airDensity"] * Parameters["dragCoeffAreaCombo"] * worldArray[step-1, varSpeed]**2 diff --git a/FullVehicleSim/Mech/brakepadFrictionModel.py b/FullVehicleSim/Mech/brakepadFrictionModel.py index f9d6c69..84456a5 100644 --- a/FullVehicleSim/Mech/brakepadFrictionModel.py +++ b/FullVehicleSim/Mech/brakepadFrictionModel.py @@ -1,5 +1,4 @@ -# Vibecoded lol. hopefully this works -import numpy as np +import numpy as np # noqa: N999 from scipy.interpolate import interp1d # TODO: Move this to a parquet that gets read in at runtime diff --git a/FullVehicleSim/Mech/braking.py b/FullVehicleSim/Mech/braking.py index 0f2dea0..0f9a79e 100644 --- a/FullVehicleSim/Mech/braking.py +++ b/FullVehicleSim/Mech/braking.py @@ -1,7 +1,8 @@ -from Mech import brakepadFrictionModel -from paramLoader import * import numpy as np import polars as pl +from Mech import brakepadFrictionModel +from paramLoader import * + # Docs: # https://docs.google.com/document/d/1oGsGDnY0DEKWpE3S6481A9yZ0F9qUEwWkSXJwTSz4E4/edit?tab=t.2rmbsj26c7w # The goal of these functions are to calculate the net force on the brakes, applied reverse to heading @@ -10,7 +11,7 @@ brakeConvectionData = pl.read_csv("Mech/brakeThermalTransfer.csv") except Exception as e: print("Error loading brake thermal transfer data from Mech/brakeThermalTransfer.csv:", e) - raise e + raise brakeAirSpeedData, brakeCoeffData = brakeConvectionData["airSpeed"].to_numpy(), brakeConvectionData["coeff"].to_numpy() def brakeTransferCoeff(speed:float) -> float: diff --git a/FullVehicleSim/Mech/general.py b/FullVehicleSim/Mech/general.py index 5bad33b..0007a3e 100644 --- a/FullVehicleSim/Mech/general.py +++ b/FullVehicleSim/Mech/general.py @@ -1,10 +1,11 @@ -from Mech.traction import calcCorneringStiffness -from paramLoader import * -from Mech.braking import calcBrakeForce +import numpy as np from Mech.aero import calcDrag +from Mech.braking import calcBrakeForce from Mech.steering import calcSlipAngle, calcYawRate from Mech.tireLoad import calcLoadTransfer -import numpy as np +from Mech.traction import calcCorneringStiffness +from paramLoader import * + def calcResistiveForces(worldArray:np.ndarray, step:int): if worldArray[step-1, varSpeed] <= 1e-5: # Floating point error diff --git a/FullVehicleSim/Mech/steering.py b/FullVehicleSim/Mech/steering.py index 4c06198..40fc07d 100644 --- a/FullVehicleSim/Mech/steering.py +++ b/FullVehicleSim/Mech/steering.py @@ -2,6 +2,7 @@ import numpy as np from paramLoader import * + def calcSlipAngle(worldArray:np.ndarray, step:int) -> tuple[float,float]: """ Calculate Slip Angle Based on yawRate, Velocity, and Steering Angle. diff --git a/FullVehicleSim/Mech/tireLoad.py b/FullVehicleSim/Mech/tireLoad.py index 5eeb645..fcb85c7 100644 --- a/FullVehicleSim/Mech/tireLoad.py +++ b/FullVehicleSim/Mech/tireLoad.py @@ -1,4 +1,5 @@ -from paramLoader import * +from paramLoader import * # noqa: N999 + def calcLoadTransfer(accelerationX, accelerationY, yawVelocity:float) -> tuple[float, float, float, float]: # TODO: add weight transfer for torsional compliancy diff --git a/FullVehicleSim/Mech/tireState.py b/FullVehicleSim/Mech/tireState.py index a3ecc5d..46beb0c 100644 --- a/FullVehicleSim/Mech/tireState.py +++ b/FullVehicleSim/Mech/tireState.py @@ -1,7 +1,9 @@ -# This implements MF 6.x (I think 6.2) as described in Tire and Vehicle Dynamics 3rd edition on page 179 +# This implements MF 6.x (I think 6.2) as described in Tire and Vehicle Dynamics 3rd edition on page 179 # noqa: N999 # copy pasted from dumping in July 21 import math -from paramLoader import Parameters, Magic + +from paramLoader import Magic, Parameters + class Tire: def __init__(self, normalForce, slipRatio, slipAngle, velocityX, pressure, temperature): diff --git a/FullVehicleSim/Mech/traction.py b/FullVehicleSim/Mech/traction.py index 1f88042..a5ef8f3 100644 --- a/FullVehicleSim/Mech/traction.py +++ b/FullVehicleSim/Mech/traction.py @@ -1,8 +1,9 @@ +import numpy as np from Mech import tireState as tire -from paramLoader import Parameters, Magic -from Mech.tireLoad import calcLoadTransfer from Mech.steering import calcSlipAngle -import numpy as np +from Mech.tireLoad import calcLoadTransfer +from paramLoader import Magic, Parameters + def calcTraction(tireLoad:tuple[float,float,float,float], slipAngle:tuple[float,float], slipRatio:float, speed, surfaceTemperature, tirePressure): frontLeft = tire.Tire(tireLoad[0] , 0.15, slipAngle[0], speed, 80, 40) diff --git a/FullVehicleSim/engine.py b/FullVehicleSim/engine.py index a290a00..e573c7e 100644 --- a/FullVehicleSim/engine.py +++ b/FullVehicleSim/engine.py @@ -1,12 +1,20 @@ -from paramLoader import * import numpy as np -from Mech.braking import calcBrakeCooling, calcBrakeHeating, calcBrakeForce -from Mech.aero import calcDrag, calcDownForce -from Mech.steering import calcSlipAngle +from Electrical.powertrain import ( + calcCurrent, + calcMaxMotorTorque, + calcMaxPower, + calcMaxWheelTorque, + calcMotorForce, + calcVoltage, +) +from Mech.aero import calcDownForce, calcDrag +from Mech.braking import calcBrakeCooling, calcBrakeForce, calcBrakeHeating from Mech.general import calcResistiveForces -from Electrical.powertrain import calcCurrent, calcMaxMotorTorque, calcMaxWheelTorque, calcMotorForce, calcMaxPower, calcVoltage +from Mech.steering import calcSlipAngle +from paramLoader import * from scipy.integrate import RK45 + # Vibe coded but it looks about right so idk. # TODO: Verify that this is correct def calculateHeading(worldArray:np.ndarray, step:int) -> np.ndarray: @@ -103,5 +111,5 @@ def dynamicStepState(step:np.ndarray) -> np.ndarray: :rtype: ndarray[Any, Any] """ arr = np.array([step, step]) - arr[1, 5:] = np.zeros((step.shape[0]-5)) + arr[1, 5:] = np.zeros(step.shape[0]-5) return stepState(arr, 1) \ No newline at end of file diff --git a/FullVehicleSim/main.py b/FullVehicleSim/main.py index 69b120a..3b0c0b8 100644 --- a/FullVehicleSim/main.py +++ b/FullVehicleSim/main.py @@ -1,11 +1,10 @@ -import matplotlib.pyplot as plt -import json -import polars as pl import argparse import time -from paramLoader import * +import matplotlib.pyplot as plt +import polars as pl from engine import * +from paramLoader import * if __name__ == "__main__": ## Argument Parsing. Should wind up like: @@ -23,13 +22,13 @@ elif simulation_controls_path.endswith('.parquet'): ## Check for parquet and read as that df_controls = pl.read_parquet(simulation_controls_path) else: - raise Exception("Unsupported file format for simulation controls. Please use .csv or .parquet files.") + raise Exception("Unsupported file format for simulation controls. Please use .csv or .parquet files.") # noqa: TRY002 else: - raise Exception("Please provide a valid simulation controls file path using --simulation_controls or -c") + raise Exception("Please provide a valid simulation controls file path using --simulation_controls or -c") # noqa: TRY002 ## Double check it has the correct columns if df_controls.columns != ['time', 'throttle', 'brakePressureFront','brakePressureRear', 'brakePedalTravel', 'steerAngle']: - raise Exception("Simulation controls file must contain the following columns: 'time', 'throttle', 'brakePressureFront', 'brakePressureRear', 'brakePedalTravel', 'steerAngle'") + raise Exception("Simulation controls file must contain the following columns: 'time', 'throttle', 'brakePressureFront', 'brakePressureRear', 'brakePedalTravel', 'steerAngle'") # noqa: TRY002 totalSteps = int(Parameters["stepsPerSecond"] * Parameters["simulationDuration"]) steps = np.arange(0, Parameters["simulationDuration"], 1/Parameters["stepsPerSecond"]) @@ -70,7 +69,7 @@ controlInputs[:,3] = np.interp(steps, timeSeries, df_controls['brakePedalTravel']) controlInputs[:,4] = np.interp(steps, timeSeries, df_controls['steerAngle']) else: - raise Exception("Unsupported interpolation method. Please use 'cubic' or 'linear'.") + raise Exception("Unsupported interpolation method. Please use 'cubic' or 'linear'.") # noqa: TRY002 ## Setup initial conditions. Leaves row 0 with no inputs (don't matter anyway since sim runs from 1 -> end) ## Some other initial conditions based on input parameters. diff --git a/FullVehicleSim/paramLoader.py b/FullVehicleSim/paramLoader.py index 30cdc51..b51718f 100644 --- a/FullVehicleSim/paramLoader.py +++ b/FullVehicleSim/paramLoader.py @@ -1,7 +1,6 @@ -import json5 -from typing import Dict, List, Tuple -import polars as pl +import json5 # noqa: N999 import numpy as np +import polars as pl Magic: dict Parameters: dict @@ -70,7 +69,7 @@ varWheelRPM = 44 # Automatically generate schema from defined variables -def generate_variable_schema() -> Dict[int, str]: +def generate_variable_schema() -> dict[int, str]: """ Generate a schema mapping variable indices to their names. Preserves the order of definition in the file. @@ -93,14 +92,14 @@ def generate_variable_schema() -> Dict[int, str]: return schema -def get_variable_names() -> List[str]: +def get_variable_names() -> list[str]: """ Get ordered list of variable names (without 'var' prefix). """ schema = generate_variable_schema() return [schema[i] for i in range(len(schema))] -def get_variable_mapping() -> Dict[str, int]: +def get_variable_mapping() -> dict[str, int]: """ Get mapping from variable names to indices. """ diff --git a/FullVehicleSim/yaw_rate_model/double_bicycle_model.py b/FullVehicleSim/yaw_rate_model/double_bicycle_model.py index 20370f2..12ebe9d 100644 --- a/FullVehicleSim/yaw_rate_model/double_bicycle_model.py +++ b/FullVehicleSim/yaw_rate_model/double_bicycle_model.py @@ -5,10 +5,10 @@ Based on Rajamani's bicycle model.w """ -import numpy as np from dataclasses import dataclass -from typing import Tuple, List + import matplotlib.pyplot as plt +import numpy as np @dataclass @@ -87,7 +87,7 @@ def __init__(self, params: VehicleParameters, tire_model: str = "linear"): self.input_history = [] def get_slip_angles(self, v_y: float, r: float, v_x: float, delta: float) \ - -> Tuple[float, float]: + -> tuple[float, float]: """Calculate front and rear slip angles""" if abs(v_x) < 0.1: return delta, 0.0 @@ -148,8 +148,8 @@ def integrate_step(self, v_x: float, delta: float, dt: float, else: raise ValueError(f"Unknown integration method: {method}") - def simulate(self, v_x: float, steering_inputs: List[float], - dt: float = 0.01, method: str = "rk4") -> Tuple[np.ndarray, np.ndarray]: + def simulate(self, v_x: float, steering_inputs: list[float], + dt: float = 0.01, method: str = "rk4") -> tuple[np.ndarray, np.ndarray]: """Run simulation with given steering input sequence""" self.state = np.array([0.0, 0.0]) self.time_history = [0.0] @@ -184,7 +184,7 @@ def validate_against_telemetry(csv_path: str, model: DoubleBicycleModel, try: df = pd.read_csv(csv_path, low_memory=False) - except Exception as e: + except Exception as e: # noqa: BLE001 print(f"Failed to load {csv_path}: {e}") return {} @@ -290,7 +290,7 @@ def plot_response(model: DoubleBicycleModel, title: str = "Model Response"): print(f"\nSpeed: {v_x:.1f} m/s ({v_x*3.6:.1f} km/h)") print(f"Duration: {duration:.2f} s") - print(f"Results at end:") + print("Results at end:") print(f" Lateral velocity: {states_sim[-1, 0]:.3f} m/s") print(f" Yaw rate: {np.rad2deg(states_sim[-1, 1]):.2f} °/s") print(f" G-force: {v_x * states_sim[-1, 1] / 9.81:.3f}g") @@ -316,7 +316,7 @@ def plot_response(model: DoubleBicycleModel, title: str = "Model Response"): print(f"\nSpeed: {v_x:.1f} m/s ({v_x*3.6:.1f} km/h)") print(f"Ramp rate: {np.rad2deg(10):.1f}°/s") - print(f"Results at end:") + print("Results at end:") print(f" Lateral velocity: {states_sim[-1, 0]:.3f} m/s") print(f" Yaw rate: {np.rad2deg(states_sim[-1, 1]):.2f} °/s") print(f" Steady-state G: {v_x * states_sim[-1, 1] / 9.81:.3f}g") @@ -339,7 +339,7 @@ def plot_response(model: DoubleBicycleModel, title: str = "Model Response"): print(f"\nSpeed: {v_x:.1f} m/s ({v_x*3.6:.1f} km/h)") print(f"Frequency: {freq:.1f} Hz (±10° amplitude)") - print(f"Results:") + print("Results:") print(f" Max lateral velocity: {np.max(np.abs(states_sim[:, 0])):.3f} m/s") print(f" Max yaw rate: {np.rad2deg(np.max(np.abs(states_sim[:, 1]))):.2f} °/s") print(f" Max G-force: {v_x * np.max(np.abs(states_sim[:, 1])) / 9.81:.3f}g") @@ -395,7 +395,7 @@ def plot_response(model: DoubleBicycleModel, title: str = "Model Response"): valid_yaw = df[df['VDM_Z_AXIS_YAW_RATE'].notna()]['VDM_Z_AXIS_YAW_RATE'] if len(valid_yaw) > 0: - print(f"\nYaw rate statistics from telemetry:") + print("\nYaw rate statistics from telemetry:") print(f" Min: {valid_yaw.min():.1f} °/s ({np.deg2rad(valid_yaw.min()):.3f} rad/s)") print(f" Max: {valid_yaw.max():.1f} °/s ({np.deg2rad(valid_yaw.max()):.3f} rad/s)") print(f" Mean: {valid_yaw.mean():.1f} °/s ({np.deg2rad(valid_yaw.mean()):.3f} rad/s)") @@ -413,7 +413,7 @@ def plot_response(model: DoubleBicycleModel, title: str = "Model Response"): except ImportError: print("Pandas not installed - can't load CSV") print("Install with: pip install pandas") - except Exception as e: + except Exception as e: # noqa: BLE001 print(f"Error loading telemetry: {e}") plt.show()

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