Prediction of state-of-health and remaining useful life for lithium-ion batteries using short-term relaxation voltage

Accurate estimation of state-of-health (SOH) and remaining useful life (RUL) is critical for battery management systems. However, existing methods often depend on complex feature engineering or extensive historical cycling data, posing challenges for real-time online monitoring. To address this limitation, we innovatively propose a dual-task collaborative Gaussian process regression framework based on short-term relaxation voltage (RV). By extracting RV data after a single charging cycle, online SOH estimation is achieved. Additionally, time-series voltage features are integrated to establish an RUL prediction model. Validation is conducted on 41 batteries with varying temperatures, C-rates, and chemistries. When using merely 10-minute RV data, the SOH prediction achieves MAE and RMSE below 1.3 % and 1.6 % respectively, while RUL estimation maintains MAE and RMSE within 50 cycles. Furthermore, incorporating voltage decay rate as an additional input further reduces errors. Finally, the transfer learning model enhanced with a linear transformation layer achieves an MAE below 2.8 % for SOH prediction and 65 cycles for RUL prediction. This work presents a lightweight, interpretable, and highaccuracy paradigm for battery health assessment under variable conditions.