ADSTAN: Adversarial Dynamic Spatiotemporal Attention Networks for Unsupervised Cross-Domain Battery SOH Estimation

State of health (SOH) estimation is essential for battery health monitoring, particularly in cross-domain scenarios where data variability and domain shifts present significant challenges. To address these issues, this study proposes the adversarial dynamic spatiotemporal attention network (ADSTAN), which integrates a graph attention network for spatial feature extraction, a gated recurrent unit for temporal dependency modeling, and a gradient reversal layer-based domain alignment module for unsupervised domain adaptation. Representing battery health data as dynamic graphs, with each cycle serving as a node, ADSTAN effectively captures spatiotemporal dependencies and dynamically aligns feature distributions between source and target domains. Experiments on cross-domain datasets, including the CALCE and NASA battery datasets, demonstrate the model’s effectiveness. Using data from 10 cycles to predict SOH for 5, 10, and 15 horizons, ADSTAN achieved RMSE values of 2.49%, 2.61%, and 2.94%, respectively. Ablation experiments validated the model’s design, highlighting the superiority of its spatial, temporal, and alignment modules. These results underscore ADSTAN’s robust performance and its suitability for accurate and generalizable SOH estimation in diverse cross-domain settings.