Evaluating and Interpreting the Predictive Power of Features in Battery Lifetime Prediction

Our study addresses the critical need for interpretable models in early Li-ion battery cycle life prediction. We evaluate the predictive power of features derived from high-rate, low-rate, and resistance-based cycling measurements using a unique formation dataset with identical postformation aging cycles.