Cross-domain battery SOH and RUL estimation via Domain-Adaptive Transformer

Accurate estimation of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries across heterogeneous chemistries and cycling protocols remains challenging because of significant domain shifts and complex degradation dynamics. To overcome these issues, this study proposes a Domain-Adaptive Transformer (DAT) framework for cross-domain battery health prognostics. The framework integrates a protocol-independent feature representation—constructed from voltage (V), capacity (Q), and their differential features (ΔV, ΔQ)—with a hybrid CNN–Transformer backbone enhanced by rotary positional embedding and a lightweight Extreme Learning Machine (ELM) module. This design enables efficient modeling of long-range temporal dependencies, rapid fine-tuning on new domains, and high computational efficiency. Comprehensive experiments on three transfer tasks (cross-discharge-protocol, cross-charge-protocol, and cross-chemistry) demonstrate that the proposed model consistently outperforms state-of-the-art baselines including BiLSTM, TCN, PINN, and SDE-BiLSTM. After fine-tuning, our model achieves RUL RMSE = 178 cycles (R2 = 0.853) and SOH MAE = 0.138% (R2 = 0.999), confirming its robustness and adaptability across domains. These results highlight the potential of the framework for generalizable, data-efficient, and practical battery health prognostics in real-world applications.