Fitting Battery Degradation: Across Different C-rates and Temperatures

General
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I’ve been working with LG21700 battery cycling data (current, voltage, charge and discharge capacity, time steps) collected experimentally at different C-rates (0.5C, 1C, 2C) and temperatures (10°C, 20°C, 40°C). Now I’m trying to develop a physical degradation model that captures behavior across these conditions.

For those who have experience with battery degradation modeling, which mechanisms would you prioritize? I’m thinking about screening a grid of different PyBamm parameters for a DFN model and choose the model which has the loss between simulated and experimental capacity curves.

My specific questions:

  • Which degradation mechanisms are most important to include given the C-rate and temperature ranges and what parameters would you recommend screening first and how would you choose the grid?
  • Is PyBOP a good tool for this kind of fitting?
  • Any performance tips for parameter optimization across multiple conditions?

Thanks in advance for the help

1 Like
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Hi @t-schilt, I have extensive experience in degradation modelling of the LG M50 21700 cell. I found that SEI (interstitial-diffusion limted) and stress-drive loss of active material are the most important mechanisms for that cell under the conditions you describe. Please see my former PhD student’s recent paper for more details.

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Hi 'SEI reaction exchange current density [A.m-2]' and 'SEI lithium interstitial diffusivity [m2.s-1]' the parameters vary with respect to C-rates as I manually combined interstitial diffusion limited and reaction limited models . 'SEI growth activation energy [J.mol-1]' parameter can be used to fit temperature values. I suggest u can manually try to change these parameters and fit the experimental degradation curve and later you can fine tune the parameters using scipy.optimize.minimize using L-BFGS method with tight bounds. This will be time saving to find the parameters to fit the model.