An improved electrochemical-thermal coupling model for state-of-charge estimation of lithium-ion battery

Accurate estimation of the state-of-charge (SOC) of lithium-ion batteries is critical to improving the safety performance and user experience of electric vehicles. The electrochemical model-based SOC estimation methods can describe different electrochemical behaviors inside batteries, but their practical implementation in battery management systems remains constrained by computational complexity and temperature sensitivity. To address these limitations, this paper proposes an innovative electrochemical-thermal coupling model with three key advancements for SOC estimation of lithium-ion batteries. First, a three-parameter parabolic approximation is developed to simplify lithium-ion concentration dynamics in electrode particles, enabling real-time computation while preserving electrochemical fidelity. Second, a bidirectional electrochemical-thermal coupling model is established, integrating a cylindrical battery thermal model that accounts for ambient temperature variations and heat production and release during battery operation. To verify the accuracy of the coupled model, experimental tests and COMSOL simulation are carried out. Third, the coupled model-based SOC estimation method is designed by integrating the extended Kalman filter, leveraging physics-informed state-space equations derived from the coupled model for dynamic error correction. The accuracy of the SOC estimation method is verified under different ambient temperatures and dynamic conditions. Compared with the two reference methods, the errors of SOC estimation are reduced by approximately 50 %, and the maximum errors of SOC estimation are mostly less than 1 %. It is verified that the proposed methodology achieves an optimal balance between computational efficiency and model accuracy, advancing physics-based SOC estimation methods for practical automotive applications.