Questions about combining model options

[YouHyin]

Hi. I'm simulating an experiment that's long enough to have a cycle of over 100. (https://github.com/pybamm-team/PyBaMM/blob/develop/examples/notebooks/simulating-long-experiments.ipynb)

I want to combine thermal option({"thermal : x-lumped"}) and aging option( {"SEI": "ec reaction limited"}) together.
However, looking at the plot, the battery temperature does not change.

I want to change the temperature of the battery, but what should I do?
I'd really appreciate your help.

Here's my code.

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

parameter_values = pybamm.ParameterValues("Mohtat2020")
parameter_values.update({"SEI kinetic rate constant [m.s-1]": 1e-14})

spm = pybamm.lithium_ion.SPM({"SEI": "ec reaction limited"}, {"thermal : x-lumped"})

Calculate stoichiometries at 100% SOC

print(parameter_values)
param = spm.param
esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(parameter_values, param)

Vmin = 3.0
Vmax = 4.2
Cn = parameter_values.evaluate(param.n.cap_init)
Cp = parameter_values.evaluate(param.p.cap_init)
n_Li_init = parameter_values.evaluate(param.n_Li_particles_init)

inputs={ "V_min": Vmin, "V_max": Vmax, "C_n": Cn, "C_p": Cp, "n_Li": n_Li_init}
esoh_sol = esoh_solver.solve(inputs)

print(f"Initial negative electrode SOC: {esoh_sol['x_100'].data[0]:.3f}")
print(f"Initial positive electrode SOC: {esoh_sol['y_100'].data[0]:.3f}")

Update parameter values with initial conditions

c_n_max = parameter_values.evaluate(param.n.prim.c_max)
c_p_max = parameter_values.evaluate(param.p.prim.c_max)
parameter_values.update(
{
"Initial concentration in negative electrode [mol.m-3]": esoh_sol["x_100"].data[0] * c_n_max,
"Initial concentration in positive electrode [mol.m-3]": esoh_sol["y_100"].data[0] * c_p_max,
}
)
pybamm.set_logging_level("NOTICE")

experiment = pybamm.Experiment([
(f"Discharge at 4C until 3.0 V",
#"Hold at 3.0 V until C/50",
"Rest for 1 hour",
f"Charge at 3 C until 4.1 V",
f"Hold at 4.1 V until C/50")
]*800 ,
termination="20% capacity"
)
sim = pybamm.Simulation(spm, experiment=experiment, parameter_values=parameter_values)
sol = sim.solve()
#sol.plot(["Current [A]", "Terminal voltage [V]"])
pybamm.plot_summary_variables(sol)
sol_list = sim.solve(save_at_cycles=[30,45,55])
sol_list.cycles[44].plot(["Current [A]","Terminal voltage [V]","X-averaged cell temperature [K]","Discharge capacity [A.h]"])

[valentinsulzer]

pybamm.lithium_ion.SPM({"SEI": "ec reaction limited", "thermal : x-lumped"})

[Thiyaga2025]

@valentinsulzer and @YouHyin,
i have a question regarding below part of the above mentioned code,

won't this be done by PyBamm itself, should we manually feed this information. when i tested with and without this particular step there is some minor difference in initial concentration. please help me here. thanks.

[valentinsulzer]

This code is updating the initial SOC by changing the initial concentrations. You can also make pybamm do this by passing "initial_soc=x" to sim.solve(). (x should be a float between 0 and 1).

[Thiyaga2025]

hi @valentinsulzer , thanks a lot for the clarification. i guess, in that case it is equivalent to setting initial_soc=1. in sim.solve() and the user can do it in either way(in initial_soc or through the above mentioned process). what's the significance of doing this using eSOH solver since it is very easy to set "initial_soc" value in sim.solve(). also, how to set an initial_soc value of 0.5(or any value other than 1 by eSOH solver approach. can i simply multiply the 100% concentration with the fraction value. regards.

[valentinsulzer]

There is no special significance, but sometimes people like to know what's happening inside a function.
You are correct about initial_soc=1. To set a different initial SOC, you need to do x = x_0 + (x_100 - x_0) * soc, and the same thing for y (note that y_0 > y_100 but the same equation works)

[Thiyaga2025]

thanks @valentinsulzer , this helps.