State of Health Estimation of Large-Capacity Energy Storage Batteries Based on Mechanical–Electrical–Thermal Multi-Modal Features

This paper proposes an SOH estimation method that fuses mechanical–electrical–thermal multi-modal features by introducing expansion force monitoring. Aging tests on 16 prismatic 530 Ah LiFePO4 batteries from two brands are conducted at 25 and 45 °C. Each full cycle is divided into charge, post-charge rest, discharge, and post-discharge rest, with SOH defined by the capacity ratio. From cycle-level data, 37 candidate features are extracted and cleaned using local median and median absolute deviation. Using only training cells, Spearman correlation eliminates highly redundant features, and 12 key features are retained via internal validation. Under 4-fold cross-validation with complete battery grouping, Random Forest, XGBoost, LightGBM, and LSTM are compared. LightGBM achieves the best performance with an average MAE of 0.0032, RMSE of 0.0037, and R2 of 91.36%. Ablation shows multi-modal fusion outperforms single-type features; five-category fused features reduce RMSE by ~75.57% versus electrical-only features. Removing expansion force features increases RMSE to 0.0064 and drops R2 to 75.66%. These findings confirm that expansion force supplies critical mechanical degradation information, significantly improving SOH estimation for large-capacity energy storage batteries.