BATWO: Bayesian Adaptive Time Window Optimization for Feature Extraction in SOH Estimation of Li-Ion Batteries Under Dynamic Operating Conditions

Accurate state of health (SOH) estimation of lithium-ion batteries is critical to the reliability of electric vehicles. However, under dynamic operating conditions, conventional feature extraction based on fixed time window often exhibits poor generalization, as it fails to account for the multi-timescale parameter couplings inherent in the non-stationary voltage responses. To address this issue, this paper proposes a Bayesian Adaptive Time Window Optimization (BATWO) framework for feature extraction in battery SOH estimation. Within this framework, the time window length is treated as a learnable structural parameter and is adaptively optimized via Bayesian optimization to identify the most informative observation timescale for extracting degradation-sensitive statistical features under given operating conditions. Evaluations on a cycle-aging dataset containing 69 lithium-ion battery samples subjected to distinct dynamic operating profiles show that the optimal time window lengths vary significantly, ranging from 500 s to 27,630 s. The BATWO framework achieves an average root-mean-square error (RMSE) of 2.07% and a mean absolute error (MAE) of 1.45%, outperforming the best fixed time window strategy by reducing the RMSE and MAE by 2.35% and 2.68%, respectively. Moreover, compared with LSTM- and Transformer-based models without feature extraction, the BATWO framework reduces training time by over 97%. These results highlight the superior generalization capability and computational efficiency of the BATWO framework, demonstrating its great potential for practical deployment in battery management system.