I am trying to study the effect of SOC on aging. I did a simulation with two SOC limits,
10-50% and 50-90%
model = pybamm.lithium_ion.SPM(
{
“cell geometry”: “arbitrary”,
“thermal”: “lumped”,
“SEI”: “solvent-diffusion limited”,
“SEI porosity change”: “true”,
“lithium plating”: “partially reversible”,
“lithium plating porosity change”: “true”, # alias for “SEI porosity change”
},
)
from the results, I observed SEI aging is not affected by SOC level but lithium plating do. I plotted SEI overpotential it was the same at high and low SOC windows but Lithium plating overpotential changes wrt SOC interval(high at 50-90%). The SEI capacity loss is the same at both SOCs, whereas li plating loss is high at high SOC. but literature suggests calendar aging due to SEI is affected by SOC also.
@DrSOKane , will be thankful for your valuable insights.
regards.
Hi @Thiyaga, the “solvent-diffusion limited” mechanism has no dependence on SoC. If you want to introduce SoC dependence, you will need to select a different option. I recommend “interstitial-diffusion limited” based on this paper.
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Hi @DrSOKane ,
thanks a ton for the clarification. this helps. will go through the paper.
regards
Dear Thiyaga,
How did you setup the SOC in PyBaMM? I can’t find SOC in the parameters or variables.
hi @Haoxuan ,
thanks for the note. SOC is to be calculated from discharge capacity at RPT tests. SOC = discharge capacity vector/full capacity. depending upon charging or discharging make sure the SOC values change from 0 to 1 or 1 to 0. But for setting experiments you have the option to specify initial SOC at solution object. But to terminate at certain SOC, please generate SOC vs Voltage map at RPT(similar current condition) and use voltage as your proxy for SOC. Hope this helps. Please me know in case you need any working examples on this.