Battery Cross-Operation-Condition Lifetime Prediction via Interpretable Feature Engineering Assisted Adaptive Machine Learning

We develop an adaptive machine-learning framework that addresses cross-operation-condition battery lifetime prediction, particularly under extreme conditions. This framework uses correlation alignment to correct feature divergence under fast-charging and extremely fast-charging conditions. We report a linear correlation between feature adaptability and prediction accuracy. Higher adaptability generally leads to better prediction accuracy, aiding efficient feature engineering. Our analysis shows that the first 120 cycles provide sufficient information for lifetime prediction, and extending data to the first 320 cycles only marginally improves prediction accuracy. An early prediction using only one feature at the 20th cycle produces a 93.3% accuracy, saving up to 99.4% computation time and repetitive tests. Our quantitative adaptability evaluation enhances prediction accuracy while reducing information redundancy via proper feature and cycle selections. The proposed framework is validated under another unseen complex operation condition with a 90.3% accuracy without prior knowledge.