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Consistently Tuned Battery Lifetime Predictive Model of Capacity Loss, Resistance Increase, and Irreversible Thickness Growth

Sravan Pannala,H. Movahedi,Taylor R. Garrick,Anna G. Stefanopoulou,Jason B. Siegel

Published 2023 in Journal of the Electrochemical Society

ABSTRACT

Predicting changes in cell resistance and thickness as the battery ages can be as important as capacity fade in informing vehicle resale value, pack replacement schedules, and viability for repurposing before eventual recycling. Three well-known degradation mechanisms, solid electrolyte interphase (SEI) growth, lithium plating, and electrode particle fracture due to reversible expansion and contraction, are revisited and tuned using capacity fade data from cells experiencing plating and having significant loss of anode active material as the dominant degradation mechanism. Therefore, the lifetime prediction achieved cannot rely on SEI degradation that can be tuned mostly with calendar aging data. More importantly, our model uses capacity-predictive irreversible SEI growth, net growth in plating, and accumulation of particle fracture as the cells cycle to predict the irreversible increases in cell thickness through a single set of tuned parameters. Last but not least, the capacity-predictive degradation can also predict the increased resistance through another single set of parameters. These two sets of scaling parameters achieve for the first time a consistently tuned model of degradation mechanisms for lifetime prediction of changes in battery capacity, resistance, and thickness as the battery ages. The model prediction has been validated in conditions not used for parameter tuning.

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