AWGLFuser: A Global–Local Feature Fusion Network with Adaptive Wavelet Filter for Lithium-Ion Battery State-of-Health Estimation

Accurate estimation of the State of Health (SOH) of lithium-ion batteries is critical to ensuring the safety and stability of energy storage and power systems. However, existing SOH evaluation methods fall short in adaptive denoising, multi-scale feature extraction, and effective fusion of global and local information. To overcome these limitations, this paper proposes a global–local feature fusion network with an adaptive wavelet filter (AWGLFuser) for end-to-end SOH estimation. The proposed model consists of three modules: an adaptive wavelet filter (AWF) module to suppress high-frequency noise and highlight key information in the frequency domain; a global–local feature extraction (GLFE) module to capture both global and local temporal dependencies at multiple scales; and a bidirectional cross-attention fusion (BCAF) module to enable deep interaction between global and local features, thereby facilitating their effective fusion. Comparison experiments on the NASA and XJTU datasets demonstrate that the proposed model yields lower estimation errors than the eight competing models. The average reductions in MAE, MAPE, and RMSE are 44.158%, 44.209%, and 38.014%, respectively, and this improvement is statistically significant against every comparison model. Furthermore, ablation studies clarify what each component contributes, with the modules proving mutually reinforcing when combined. AWGLFuser also attains a compact parameter count and storage footprint with competitive inference latency, despite comparatively higher FLOPs.