Electrochemistry Degradation Representation for Causal SOH Prediction in Batteries

Battery state-of-health (SOH) forecasting is essential for reliable battery management, lifetime assessment, and risk-aware operation in electric vehicles and energy storage systems. However, accurate SOH prediction remains challenging because battery aging is governed by latent electrochemical degradation processes that are only indirectly reflected by heterogeneous cycle-level measurements, while historical SOH information may be incomplete or unreliable during deployment. To address these challenges, we propose EDR-BAT, an electrochemistry degradation representation framework for sequential causal SOH prediction in batteries. Guided by physically motivated degradation priors, EDR-BAT first transforms raw cycling measurements into multi-modal electrochemical embedding-based health features and uses series-level identity as a shared prior to query recent electrochemical history. This yields cycle-specific degradation representations that capture both shared aging regularities across similar batteries and cycle-specific variations within individual cells. To reduce drift under online prediction, EDR-BAT further introduces an SOH memory mechanism with memory-anchored training, allowing the model to operate with measured, incomplete, or autoregressively predicted SOH histories and reducing training-inference mismatch. Experiments on five battery aging datasets show that EDR-BAT consistently improves SOH prediction accuracy over representative baselines, reducing MAE/RMSE by 12.9%/11.4% on average compared with the best-performing deep learning baseline on each dataset.