What is causing this rapid aging?

[wass-cloud]

I have an experiment that is killing my cell extremely fast, in around 80 cycles my cell is already past the aging knee. This seems incorrect since the cell is rated much more than 100 cycles. Attached is the code. Most of the parameters I had to guess on my own. Any help is much appreciated.

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



model = pybamm.lithium_ion.SPMe(options={'calculate discharge energy': 'false',
'cell geometry': 'pouch',
'lithium plating': 'irreversible',
'lithium plating porosity change':'true',
'loss of active material': 'none',
'particle': 'Fickian diffusion', #sets to 1/r^2 PDE, see source
'SEI': "ec reaction limited",
'SEI film resistance': 'distributed',
'SEI porosity change': 'true',
'thermal':'x-full'
},
name='default')


param = pybamm.ParameterValues("Mohtat2020") #use chen b/c of NMC properties
# if we end up with the error Typical Lithium Conc etc. use Mohtat.
param['Ambient temperature [K]'] = 25 + 273.15 #setting the temperature to desired temp
param['Nominal cell capacity [A.h]'] = 60 #set to the cells nominal capacity


param['Maximum concentration in negative electrode [mol.m-3]'] = 11*28746.0
param['Maximum concentration in positive electrode [mol.m-3]'] = 11*35380.0
param['Negative electrode thickness [m]'] = 5.8*6e-05
param['Positive electrode thickness [m]'] = 5.8*7e-05
param['Negative electrode active material volume fraction'] = 0.89
param['Positive electrode active material volume fraction'] = 0.91


param['Negative electrode porosity'] = 0.95
param['Positive electrode porosity'] = 0.95
param['Separator porosity'] = 0.98










print(model.default_var_pts)
var_pts = {"x_n": 15, #setting the mesh, coarse mesh through the electrolyte and tabs
"x_s": 10,
"x_p": 15, #finer mesh through the electrode plates
"r_n": 5,
"r_p": 5}




"""
"r_n_prim":
"r_p_prim":
"r_n_sec":
"r_p_sec":
"y":
"z":
"R_n":
"R_p":
"""




experiment1 = pybamm.Experiment(




[
(
"Charge at 2C until 4.2 V",
"Rest for 1 hours",
"Discharge at 0.1C until 3.0 V", #charge at some values
"Rest for 5 hours", #rest to obtain equilibrium
),
]* 1000,
termination = '30% capacity' #terminate the experiment when discharge capacity is 70%
)


sim1 = pybamm.Simulation(model, experiment=experiment1, parameter_values=param, var_pts = var_pts)
#solver=pybamm.CasadiSolver(mode="fast")
sim_solved1 = sim1.solve(initial_soc = 1) #say battery is initially charged to 100% SoC



sim1.plot(
[
"Current [A]",
"Total current density [A.m-2]",
"Voltage [V]",
"Discharge capacity [A.h]",
"Electrolyte potential [V]",
"X-averaged total SEI thickness [m]",
"X-averaged cell temperature [K]",
[
"Total lithium lost [mol]",
"Loss of lithium to SEI [mol]",
],
]
)


pybamm.plot_summary_variables(sim_solved1,["Capacity [A.h]",
"Loss of lithium inventory [%]",
"Loss of capacity to SEI [A.h]", ])

[brosaplanella]

The ageing rate is extremely sensitive on the parameter values, so you would have to calibrate the values to the battery you are simulating.