Exploiting Capacity Regeneration Based on a Hybrid Model to Accurately Predict the Remaining Useful Life of Lithium-Ion Batteries

Abstract

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for the reliability and safety of modern energy systems. However, the capacity regeneration phenomenon, a temporary recovery of capacity during cycling or rest, introduces non-monotonic fluctuations in degradation trajectories, posing significant challenges to existing prediction models.Many current approaches treat CR as noise or overlook its physical significance, limiting interpretability and accuracy. To address this, we propose a hybrid framework that explicitly models both regenerative and degenerative battery behaviors. The method uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose capacity sequences into high-and low-frequency components. A Graph Convolutional Network-Long Short-Term Memory (GCN-LSTM) branch captures dynamic regeneration features, while a Deep Neural Network (DNN) branch learns long-term degradation trends. These are fused to reconstruct the full degradation path and predict RUL. The CEEMDAN-GCN-LSTM-DNN hybrid model achieves a mean absolute percentage error below 0.15%, outperforming several state-of-the-art baselines. It also demonstrates strong robustness under data-limited conditions and effectively captures complex capacity regeneration patterns often missed by conventional methods. This study offers a new perspective for handling non-monotonic battery degradation and provides a useful tool for battery health management and predictive maintenance.