Arrhenius Equation-Based Temperature-Compensated Extended Kalman Filter for Improved Battery State-of-Charge Estimation

Battery State-of-Charge (SOC) estimation in batteries presents significant challenges under varying temperature conditions, affecting the accuracy of traditional Extended Kalman Filter (EKF) methods. This paper proposed an Arrhenius equation-based temperature-compensated EKF approach to improve SOC estimation accuracy across diverse temperature conditions. The proposed method incorporates Arrhenius equation relationships directly into the EKF framework, providing dynamic temperature compensation for both battery model parameters and filter characteristics. The algorithm was evaluated using LG HG2 lithium-ion cell data across various operating conditions including different driving cycles and a temperature range of 40°C to -20°C. Results demonstrate significant improvement in estimation accuracy, with the temperature-compensated EKF achieving root mean square errors (RMSE) of 4.63% to 7.44% across all temperatures, compared to 5.73% to 8.86% with traditional EKF in systematic comparative assessment. The method exhibits particular robustness at extreme temperatures, addressing the significant parameter variations that affect estimation accuracy while maintaining computational efficiency. The proposed approach offers a physically motivated solution for temperature-dependent SOC estimation, suitable for real-time battery management systems in electric vehicles and energy storage applications.