Initial SOC

[HamidHamed1990]

How can I chnage the initial state of charge of the postivite/negative electrodes in the DFN model?

[brosaplanella]

When you solve a simulation you can use the argument initial_soc, see https://docs.pybamm.org/en/latest/source/api/simulation.html#pybamm.Simulation.solve

[brosaplanella]

The SoC only makes sense as a global quantity for the battery. If you want to adjust the initial stoichiometry of each electrode separately, you can update the initial concentrations in the parameter values.

[HamidHamed1990]

the full cell SOC relates to both cathode and Anode SOCs, so by defining one global SoC of battery, the individual electrodes' SOC cannot be determined.
Thanks for the tip on 'initial concentrations'.

[Abhinav-Kshirsagar]

Dear @brosaplanella ,

This insight about stiochiometry being related to initial concentrations is really useful.
I was trying to figure out how could I directly vary the stoichiometry but this reply seems to be useful.

But i still fail to understand the corelation between the stoichiometry and initial concentration. When I set
"Initial concentration in negative electrode [mol.m-3]": 37500,
"Initial concentration in positive electrode [mol.m-3]": 7750,

What's the relation between these values and stoichiometries of 0 and 1? or rather how to establish a relation?
Could you please guide me in this case?

Thanks in advance for your help.

[brosaplanella]

PyBaMM uses an eSOH model (see https://github.com/pybamm-team/PyBaMM/blob/develop/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py) to compute the SoC from the concentrations and the maximum and minimum voltages. The approach is explained in Section 3.2.1 of Mohtat et al (2019).

[Abhinav-Kshirsagar]

Noted. Thanks!

[HamidHamed1990]

Dear Ferran,
I am also trying to introduce my own OCV data which is in the form of a csv file. I am using the following code:

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


file_path = (
    "C:\\Users\\lucp9593\\Anaconda3\\Lib\\site-packages\\pybamm\\input\\parameters\\"
    "lithium_ion\\data/nmc_LGM50_ocp_Chen2020.csv"
)

model = pybamm.lithium_ion.DFN()
params = pybamm.ParameterValues("Chen2020")
ocp_NMC = pd.read_csv(file_path, comment="#", header=None).to_numpy()
ocp_NMC_interpol = pybamm.Interpolant(ocp_NMC[:, 0], ocp_NMC[:, 1], pybamm.t)
params["Positive electrode OCP [V]"] = ocp_NMC_interpol
solver = pybamm.CasadiSolver(mode="fast")
sim = pybamm.Simulation(model, parameter_values=params, solver=solver)
sim.solve([0, 3600])
sim.plot()

But I get this error: Solver failed because the following interpolation bounds were exceeded: ["Interpolant 'interpolating_function' lower bound"]. You may need to provide additional interpolation points outside these bounds.
May I ask how to solve this issue?

[brosaplanella]

Note that for the OCP, the interpolant variable should not be time but stoichiometry. You can find how to do that in OKane2022.py:

PyBaMM/pybamm/input/parameters/lithium_ion/OKane2022.py

Lines 495 to 497 in 5e28bb9

	def graphite_LGM50_ocp_Chen2020(sto):
	name, (x, y) = graphite_LGM50_ocp_Chen2020_data
	return pybamm.Interpolant(x, y, sto, name=name, interpolator="cubic")

[HamidHamed1990]

Thanks again.