Model producing unexpected capacity output.

[Rahate88]

Hello Good People,
Thank you so much for your willingness to read my question. I have designed a pouch cell model using "Marquis2019" parameters. I adjusted my pouch cell's physical parameters in the model (changed electrode dimensions, thickness, volume, surface are and etc.). Anode and Cathode assumed as graphite and NMC (simulation at -10 Degree C). The simulation results shows at 0.2 C-rate is pretty normal but for high C-rate (3C) the model is producing abnormal capacity. Also, I am not getting voltage cutoff at high C-rate, like at a 3 C-rate the voltage should reach its cutoff at around 60-70% of charging capacity. But I am getting the voltage cutoff at 95% of its total capacity (too high at -10 deg C at 3 C-rate).

Here is the code:

import pybamm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Load parameter values
parameter_values = pybamm.ParameterValues("Marquis2019")

# Update parameter values
parameter_values.update({
    "Initial concentration in negative electrode [mol.m-3]": 500,
    "Initial concentration in positive electrode [mol.m-3]": 47500,
    "Maximum concentration in negative electrode [mol.m-3]": 31507,
    "Current function [A]": 1,
    "Nominal cell capacity [A.h]": 2.1
})

parameter_values.update({
    "Cell cooling surface area [m2]": 0.004921,
    "Cell volume [m3]": 0.000014112,
    "Electrode height [m]": 0.08,
    "Electrode width [m]": 0.056,
    "Lower voltage cut-off [V]": 3.2,
    "Open-circuit voltage at 0% SOC [V]": 3.2,
    "Open-circuit voltage at 100% SOC [V]": 4.2,
    "Upper voltage cut-off [V]": 4.2,
    "Negative electrode thickness [m]": 0.000060,
    "Positive electrode thickness [m]": 0.000080,
    "Positive current collector thickness [m]": 0.00001,
    "Negative current collector thickness [m]": 0.00001,
    "Positive particle radius [m]": 0.000005,
    "Number of electrodes connected in parallel to make a cell": 18.0,
  #  "Negative tab width [m]": 0.01,
  #  "Positive tab width [m]": 0.01,
    
})
parameter_values.search('voltage')
parameter_values.search('particle')

parameter_values.update({"Negative particle radius [m]": 15e-6})
parameter_values.update({"Positive particle radius [m]": 5e-6})
parameter_values.search('particle')

parameter_values.update({"Ambient temperature [K]": 263.15})
parameter_values.update({"Initial temperature [K]": 263.15})
parameter_values.update({"Reference temperature [K]": 298.15})

experiment_steps = [
#    "Charge at 0.1C for 1 minutes",
 #   "Charge at 0.2C for 2 minutes",
]
# Define the repeated part of the experiment using list comprehension
repeated_steps = (
     [
    # "Charge at 0.1C for 100 seconds",   
     "Charge at 4C until 4.2V"
   ]    
                 )

#Combine the initial steps with the repeated steps

experiment_steps.extend(repeated_steps)

# Define the experiment in PyBaMM
experiment = pybamm.Experiment(experiment_steps)


# Create the model
model = pybamm.lithium_ion.DFN({"thermal": "x-full"}, name="full thermal model")


solver = pybamm.CasadiSolver(dt_max=60)  # Reduce from 600 to 60 seconds (or even smaller)
sim = pybamm.Simulation(model, experiment=experiment, parameter_values=parameter_values, solver=solver)
solution = sim.solve()
sim.plot()
# Create and run the simulation

# Extract the solution data
time = solution["Time [s]"].entries
current1 = solution["Current [A]"].entries
voltage = solution["Terminal voltage [V]"].entries

main_current = np.array(current1)
current = -main_current

# Integrate current over time to get capacity (Ah)
dt = np.diff(time)
incremental_capacity = current[:-1] * dt
capacity = np.cumsum((incremental_capacity)) / 3600  # Convert to Ah

         
#Adjust length of voltage to match capacity
voltage = voltage[:-1]

#-------------------------------------------------------------------------------------------------
# Voltage vs capacity 

data = np.column_stack((capacity, voltage))
np.savetxt("Voltage_vs_capacity_data.csv", data, delimiter=",", header="Capacity [Ah],Voltage [V]", comments="")

# Save using pandas
df = pd.DataFrame({"Capacity [Ah]": capacity, "Voltage [V]": voltage})
df.to_csv("Voltage_vs_capacity_data.csv", index=False)

# Plot the charging capacity vs voltage
plt.figure(figsize=(10, 6))
plt.plot(capacity, voltage, label="Charging")
plt.xlabel("Capacity [Ah]")
plt.ylabel("Voltage [V]")
plt.title("Charging Capacity vs Voltage")
plt.legend()
plt.grid(True)
plt.show()

-326de2e2e1e6)