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

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.