Reinforced Dynamic Degradation Evolution Modeling Network: A New Remaining Useful Life Prediction Method for Lithium-Ion Batteries

To address the limitations of insufficient multi-scale feature mining and rigid fusion strategies in existing remaining useful life (RUL) prediction methods, this paper proposes a novel approach based on a Reinforced Dynamic Degradation Evolution Modeling Network (RDDEMN). The proposed model integrates a Dynamic State Transition Network (DST-Net) and Sequence Pattern Attention (SPA) to jointly capture local capacity fluctuations and global degradation trends while adaptively weighting critical temporal patterns. Furthermore, a reinforcement learning-based adaptive gating mechanism is introduced to intelligently adjust feature fusion ratios according to the current degradation states. Ex-tensive experiments on the NASA dataset demonstrate that RDDEMN significantly out-performs mainstream models across MSE, RMSE, and MAE metrics. The results verify the model’s high accuracy and robustness under complex degradation scenarios, providing a reliable solution for battery health management.