An Adaptive Machine Learning Framework for Early Lithium-Ion Battery Lifetime Prediction Under Heterogeneous Operating Conditions

Abstract

This study presents PAGER-Life, an adaptive hybrid framework for the early prediction of lithium-ion battery service life under heterogeneous operating conditions. The system integrates physically interpretable degradation features and standardized charge–discharge curves using linear regression, decision tree-based models, and neural networks. An error-aware routing mechanism combines expert predictions based on out-of-fold errors, while conformal prediction enables uncertainty quantification. The framework was evaluated using the MATR and HUST datasets with observation horizons of 20, 50, and 100 cycles, following a protocol that accounts for the grouped data structure and prevents information leakage. A comparative analysis involving 114 physical cells and 14 alternative methods demonstrated the following performance metrics for PAGER-Life: Mean Absolute Percentage Error (MAPE) of 12.827%, Mean Absolute Error (MAE) of 166.766 cycles, Root Mean Square Error (RMSE) of 210.715 cycles, and a macro-averaged coefficient of determination (R2) of 0.495. Ablation studies indicated that removing the physically grounded degradation features substantially degraded predictive performance. The results indicate that the adaptive integration of complementary prognostic models offers a promising approach for the early estimation of battery service life under heterogeneous operating conditions.