The transition to clean energy and electrification of transportation requires accurate, real-time monitoring of the state of health (SoH) of lithium-ion batteries, which serve as critical components for energy storage. Conventional SoH estimation methods typically rely on fixed statistical feature extraction, have poor generalization ability, and are unsuitable for multiple battery chemistry and temperature conditions. In this work, we propose a deep learning framework based on a transformer encoder and XGBoost to extract ageing-related electrochemical impedance spectroscopy (EIS) features, capturing low-, mid-, and high-frequency ageing characteristics, directly from daily operation profiles for capacity estimation. The approach requires only current, voltage, and temperature time-series data, making it suitable for edge deployment without the need for explicit EIS measurements. Validation on a dataset with two battery chemistries and three temperature conditions yields a root-mean-square error of 0.16% to 0.20% in capacity estimation. These results establish the feasibility of accurate SoH estimation during multiple operation of battery energy storage systems and electric vehicles.
Xiang et al. (2026) studied this question.