BO-PatchDiffFormer with Interpretable Feature Segments for State of Health Estimation of Lithium-Ion Batteries

Abstract

Reliable state of health (SOH) estimation provides important support for safety management and efficient operation of lithium-ion battery energy storage systems. To address the limitations of existing SOH estimation methods in terms of feature interpretability, joint modeling of local variations and overall morphological characteristics within each feature segment, and the rationality of model hyperparameter configuration, this study proposes a hybrid data-driven SOH estimation method integrating interpretable feature construction, an improved Transformer, and Bayesian optimization (BO). Specifically, raw charging data are first converted into incremental capacity (IC) curves based on incremental capacity analysis, and IC peaks are dynamically located in different cycles. Local voltage–capacity segments around the IC peak voltage are then extracted as interpretable input features closely related to battery aging. Subsequently, the Transformer encoder is improved by incorporating patch embedding and a multi-head differential self-attention mechanism, thereby enhancing the model’s ability to jointly capture local variations and overall morphological characteristics within each cycle-wise feature segment. BO is further employed to adaptively optimize key model hyperparameters. Experimental results on the CALCE-CS2 and CALCE-CX2 battery datasets show that the proposed BO-PatchDiffFormer model can provide accurate and stable SOH estimation in both comparative and generalization experimental scenarios. Compared with the best-performing baseline model, the maximum reductions in RMSE and MAE on the four CS2 test batteries reach 31.81% and 23.76%, respectively. In the generalization experiments, the average RMSE and MAE on the two CX2 test batteries are 1.6039% and 1.3081%, respectively. In addition, the maximum model size is only 2.07 MB, and the single-sample inference time remains within 0.84–1.18 ms, indicating good potential for practical deployment and application.