A Coupled Electro-Thermal Battery Model Identification and State Estimation Based on Digital Twin

Lithium-ion batteries are extensively utilized in energy storage systems due to their relatively high output voltage, long cycle life, and high energy density. The integration of digital twin technology within battery management systems (BMS) enables the prediction of battery performance under varying scenarios. Accurate estimation of the State of Charge (SOC) and State of Temperature (SOT) remains a significant technical challenge in battery state estimation. To address the challenges of model uncertainty and estimation inaccuracies in lithium-ion batteries during operation, this paper proposes a novel method for battery parameter identification and state estimation based on a digital twin framework. Firstly, a digital twin architecture for lithium-ion batteries is designed. A coupled electro-thermal model is established. Secondly, the FFRLS method in proposed online to identify thermal parameters in the scenario that internal battery temperature are unavailable. Then, a Q-AUKF algorithm is proposed to address divergence in Kalman filtering and improve SOC and SOT accuracy. Finally, the developed digital twin battery model and the proposed algorithm are validated through experiments.