Abstract: To ensure the safety and efficiency of the battery during use, it is crucial to accurately calculate the remaining battery charge (State of Charge, SOC) in real time. To address the shortcomings of sudden situation tracking, prior information, and dynamic environments, an SOC estimation method based on the Adaptive Strong Tracking Unscented Kalman Filter (ASTFUKF) is proposed. Considering that the accuracy of SOC estimation is highly dependent on the accuracy of the battery model, the ASTFUKF and Extended Kalman Filter (EKF) algorithms are subsequently used to jointly estimate the battery SOC and SOH (State of Health). Distinguishing and processing different time scales based on the time-varying characteristics of SOC and SOH can optimize the calculation strategy and save resources. Experimental comparisons of different algorithms in dynamic environments show that the proposed algorithm has an average error of 0.27% and a maximum error of 0.48% under the HWFET condition, and an average error of 0.44% and a maximum error of 0.91% under the UDDS condition, significantly improving the estimation accuracy of battery SOC and SOH.
张忠亮, 刘兴德, 梁家瑞, 孙成, 杭文宇 . 基于自适应强跟踪无迹卡尔曼滤波的SOC和SOH联合估计#br#[J]. 吉林化工学院学报, 2025, 42(3): 17-24.
ZHANG Zhongliang, LIU Xingde, LIANG Jiarui , SUN Cheng, HANG Wenyu, SUN Minghong. Joint SOC and SOH Estimation based on Adaptive Strong Tracking Untracked Kalman Filter. Journal of Jilin Institute of Chemical Technology, 2025, 42(3): 17-24.
