DFFNet: A Dual-Branch Feature Fusion Network with Improved Dynamic Elastic Weight Consolidation for Accurate Battery State of Health Prediction

Accurately estimating the state of health (SOH) of lithium-ion batteries is vital for guaranteeing battery safety and prolonging their operational lifespan. However, current data-driven approaches often suffer from limited utilization of health indicators, weak noise suppression, and inefficient feature fusion across multiple branches. To overcome these limitations, this paper proposes DFFNet, a Dual-Branch Feature Fusion Network with Improved Dynamic Elastic Weight Consolidation (IDEWC) for battery SOH estimation. The proposed DFFNet captures both local degradation behaviors and global aging trends through two parallel branches, while a gated attention mechanism adaptively integrates multi-scale features. Moreover, IDEWC dynamically updates the Fisher Information Matrix to retain previously learned knowledge and adapt to new data, thereby mitigating catastrophic forgetting. Experimental validation on the NASA and CALCE datasets shows that DFFNet outperforms baseline methods in accuracy, demonstrating its robustness and generalizability for precise battery SOH estimation.