Piecewise Arrhenius–Long Short-Term Memory Residual Correction Modeling for Whole-Life State of Health Estimation and Remaining Useful Life Prediction of Lithium-Ion Batteries

Lithium-ion batteries exhibit nonlinear capacity degradation under variable-temperature and complex operating conditions, which makes remaining useful life (RUL) prediction challenging. To address this issue, we propose a hybrid Arrhenius-long short-term memory (LSTM) method that combines piecewise Arrhenius thermal-damage modeling with LSTM-based residual correction. The method constructs a piecewise Arrhenius capacity-degradation model around the knee point and introduces an equivalent thermal-damage factor to accumulate temperature-induced aging effects. An LSTM residual branch then learns the discrepancy between measured capacity and the physical baseline from historical capacity, temperature, voltage, current, and prior physical predictions. A three-stage optimization strategy, comprising physical-parameter pre-fitting, residual-branch training, and physics-constrained joint fine-tuning, stabilizes residual learning while preserving mechanistic interpretability. Experiments on four CALCE CX2 validation cells yield mean SOH RMSE and MAE values of 0.020 and 0.016, respectively. The corresponding mean RUL RMSE and MAE are 41.52 and 31.58 cycles. Compared with Ade-lwT, the proposed method reduces mean SOH RMSE and MAE by 22.33% and 35.05%, respectively. For RUL prediction, the corresponding reductions are 58.06% and 62.29%. These results support the effectiveness of coupling a piecewise Arrhenius baseline with capacity-residual learning for cross-cell SOH estimation and RUL prediction.