State of Health Estimation for Lithium-Ion Batteries Using Hybrid Signal Decomposition and Deep Feature Fusion

With the rapid growth in the use of lithium-ion batteries (LIBs), particularly in electric vehicles, accurate state of health (SOH) estimation has become essential for ensuring battery safety and reliability. This study proposes a hybrid framework for SOH prediction that integrates advanced signal decomposition with deep temporal feature fusion. The approach combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO)-based variational mode decomposition (VMD), and a Transformer-BiLSTM network enhanced by a cross-attention mechanism. The methodology begins by applying ICEEMDAN to decompose the original battery signals into intrinsic mode functions (IMFs). IMFs with the highest multi-scale permutation entropy (MPE) are selected for further analysis, reflecting their rich dynamic information. These selected components are then decomposed again using VMD, with parameters tuned via GWO to adaptively capture key signal patterns. The resulting features are fed into a Transformer-BiLSTM network to learn both global and local temporal dependencies. A cross-attention layer is employed to enhance feature integration by weighting informative temporal dimensions. Experimental evaluations on two public datasets—NASA and Oxford—demonstrate that the proposed method significantly outperforms baseline models in SOH prediction accuracy. The results confirm the effectiveness of combining signal decomposition and deep feature fusion for robust battery health estimation