Accurate state-of-charge(SOC)estimation is crucial for enhancing vehicle performance,range,and overall efficiency,as well as ensuring battery health and longevity.While the traditional extended Kalman filter(EKF)algorithm is widely employed,its accuracy is susceptible to the noise covariance matrix.To address this issue,this paper proposes an improved EKF algorithm based on the Gray Wolf Optimization(GWO)algorithm,aiming to enhance the accuracy of SOC estimation for lithium batteries.The algorithm is validated on a lithium battery test platform using Hybrid Pulse Power Characterization(HPPC)dynamic operating current data.The results demonstrate that compared to the traditional EKF algorithm,the SOC estimation error of the improved algorithm is significantly reduced,substantially improving the estimation accuracy.
关键词
荷电状态估计/EKF算法/灰狼算法/噪声协方差矩阵
Key words
State of Charge Estimation/EKF Algorithm/GWO/Noise Covariance Matrix
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