Consistently Tuned Battery Lifetime Predictive Model of Capacity Loss, Resistance Increase, and Irreversible Thickness Growth
Year: 2023
Type: article
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, namely SEI growth, lithium plating, and electrode particle fracture due to reversible expansion and contraction, are revisited and tuned using cycling data from cells experiencing plating and having significant loss of anode active material as the dominant degradation mechanism. Therefore, lifetime prediction achieved in this work cannot rely only on SEI growth rates calibrated from calendar aging test. 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 model 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 predictions are validated with conditions not used for parameter tuning.
Institutions: University of Michigan, General Motors (United States)
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,785
Funder: National Science Foundation
OpenAlex ID: W4389328831