Physics-Prompt Cross-Attention Network for Generalizable and Interpretable Battery State-of-Health Estimation under Domain Shift

Accurate state-of-health (SOH) estimation is essential for safe and reliable lithium-ion battery management, yet practical deployment requires not only point accuracy but also strong generalization, explainability, interpretability, and transparency under domain shift. To address this need, we propose a Physics-Prompt Cross-Attention Network (PPCAN) that incorporates electrochemical degradation knowledge into a data-driven predictor through a cross-attention mechanism. Unlike physics-informed neural networks (PINNs), which impose physical laws as soft penalties in the loss function, PPCAN represents key electrochemical variables—including Ohmic resistance ( R 0 ), SEI resistance ( R sei ), lithium-ion diffusion coefficient ( D s ), and cyclable lithium inventory ( C li )—as structured physical prompts that guide feature extraction and prediction. Because the current-cycle lithium-inventory proxy C li ( t ) is capacity-derived and may act as a label proxy, we distinguish this setting from leakage-safer controls rather than treating it as a strictly label-independent input. A dedicated leakage-stress matrix shows that the current-cycle setting provides a diagnostic upper-bound result (OOD RMSE 0.0056), while a one-cycle-delayed cycle-level setting using C li ( t − 1) achieves a similar OOD RMSE of 0.0065 without using the target-cycle capacity. In this setting, C li ( t − 1) is a historical state available after the previous cycle has been completed, and the model estimates cycle- t SOH from the sensor sequence observed in cycle t ; it is therefore a quasi-online SOH estimation protocol rather than a pre-cycle prediction protocol. The model also transfers across battery chemistries, retaining useful performance on mixed-chemistry batteries when initialized from single-material pretraining. Overall, the proposed framework provides a transparent and physically grounded route to battery SOH estimation under changing operating conditions.