A Hybrid Framework for Lithium‐Ion Battery State‐of‐Health Estimation via Dynamic Local–Global Feature Fusion

Accurate estimation of the state of health (SOH) is essential for ensuring the safety and reliability of lithium‐ion batteries. Conventional data‐driven approaches often struggle to simultaneously capture long‐term temporal dependencies and dynamically adapt to evolving degradation behaviors. To address this limitation, a hybrid deep learning framework, termed TCN–SE–TE, is developed. The framework integrates a temporal convolutional network (TCN), a squeeze‐and‐excitation (SE) attention module, and a transformer encoder (TE). Based on experimental cycling data, four health indicators (HIs) highly correlated with SOH are extracted through data preprocessing combined with Pearson correlation analysis. The TCN captures local degradation dynamics between adjacent cycles, while the SE module adaptively adjusts feature channel weights to emphasize dominant HIs at different aging stages. The TE further models global temporal dependencies to characterize overall degradation trends. Evaluated using leave‐one‐out cross‐validation on the University of Maryland CS2 battery dataset, the proposed framework effectively combines local–global feature fusion and dynamic feature attention to enhance the adaptability and stability of SOH estimation across different degradation stages.