Accurate Real‐Time Internal Temperature Estimation of Lithium‐Ion Batteries With an Aquila‐Optimized Adaptive Strong Tracking Extended Kalman Filter

To address the challenge of directly and accurately measuring the internal temperature of lithium‐ion power batteries in electric vehicles, this paper proposes an online precise estimation method for battery internal temperature based on the Aquila Optimizer‐optimized Adaptive Strong Tracking Extended Kalman Filter (AO‐ASTEKF). Building on a battery equivalent thermal model with parameters identified using the Genetic Algorithm (GA), the Aquila Optimizer (AO) is employed to optimize the initial noise covariance settings of the traditional Extended Kalman Filter (EKF), thereby mitigating the impact of improper initialization. To resolve the estimation deviation caused by fixed noise covariance in EKF, the Sage‐Husa adaptive filtering technique is introduced to enable adaptive adjustment of noise covariance values. Furthermore, to counteract the estimation accuracy degradation of the filter due to sudden temperature changes in high‐temperature environments, the Strong Tracking (ST) filter is incorporated to enhance the tracking capability of the EKF. Through co‐simulation in AMESim and MATLAB/Simulink, the accuracy of the proposed AO‐ASTEKF algorithm in estimating battery internal temperature is validated under different ambient temperatures and operating conditions. Experimental results demonstrate that the AO‐ASTEKF algorithm improves estimation accuracy by at least 58.46% compared to both the traditional EKF and the Strong Tracking Extended Kalman Filter (STEKF). This method effectively overcomes the limitations of conventional algorithms in accurately estimating battery internal temperature, holding significant importance for ensuring battery safety and enhancing battery performance.