Data‐Driven Machine Learning Model for Battery Life Prediction Across Electrode Materials

Accurately predicting the remaining lifespan of lithium‐ion batteries (LIBs) is crucial for manufacturing processes and safe, reliable usage. Battery lifespan prediction continues to face major challenges due to varying degradation processes, fluctuating operating conditions, and differences in electrode materials. Here, we combine commercial battery data charged and discharged under different electrodes and temperature conditions to build a data‐driven machine learning model for cycle life prediction. The datasets include three types of commercial cathodes: LiFePO 4 (LFP), LiNi 0.86 Co 0.11 Al 0.03 O 2 (NCA), and LiNi 0.83 Co 0.11 Mn 0.07 O 2 (NCM), which were cycled under various conditions and temperatures. The charging and discharging dataset under a single cathode material, trained using the Elastic Net model, shows that the root mean square error (RMSE) reaches over 1528 cycles under different electrodes. Furthermore, our findings reveal that temperature plays a critical role in predictive accuracy, emphasizing the importance of incorporating cycling conditions into prediction models. With both cathode diversity and temperature effects considered during model training, all RMSE values dropped below 200 cycles. Notably, the mean absolute percentage error (MAPE) for NCA decreased from 64% to 27%. These outcomes highlight a promising approach for developing robust machine learning models capable of accurate battery performance prediction across varied conditions, contributing to safer and more reliable battery technology.