State of Charge Estimation Using Adaptive Kalman Filter with Improved Sliding Mode Observer
Qian Wei1,2, Wang Haoyu1, Guo Xiangwei1,2, Li Wan1
1. School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454003 China; 2. Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic University Jiaozuo 454003 China
Abstract：Accurate estimation of a lithium battery’s state of charge (SOC) is of great significance for improving energy utilization efficiency and ensuring a safe operation. This paper addresses the study of SOC estimation based on the Kalman filter (KF). The traditional KF algorithms rely on all past measurements to estimate the state variables at the next moment, ignoring the weight of current measurements, resulting in weak tracking ability and low estimation accuracy under steady-state conditions of KF. This paper proposes an adaptive extended Kalman filter (AEKF) algorithm with a jointly improved sliding mode observer (ISMO) to improve SOC estimation accuracy and robustness. First, an improved sliding mode observer incorporating the saturation function is built based on a dual-polarization (DP) equivalent circuit model that can balance estimation accuracy and computational complexity. The saturation function can switch the control outside the boundary layer in real-time and implement the linearized feedback control inside the boundary layer, significantly reducing the chattering of the traditional sliding mode observer. Second, a novel adaptive fading factor is introduced based on the extended Kalman filter (EKF), which strengthens the role of the present observation data while weakening the unfavorable influence of the stale measurements. Thus, the EKF algorithm’s tracking performance and estimation accuracy are enhanced. Finally, the robustness of the improved sliding mode observer system is utilized to predict the state vectors of the system and alleviate the problem of filter dispersion caused by modeling inaccuracies. Accordingly, an adaptive extended Kalman filter with the improved sliding mode observer (ISMO_AEKF) algorithm is established, which combines the advantages of AEKF and ISMO. Based on the self-built experimental platform, the Sanyo NCR18650GA 3.5 A·h lithium ternary battery is taken as the experimental object to obtain the measured simulated working condition data. The estimation accuracy and robustness of the joint algorithm are verified by the simulation models. The results show that the mean absolute error (MAE) and the root mean square error (RMSE) of the joint algorithm ISMO_AEKF are both less than 0.35% for SOC under the dynamic stress test (DST) condition and less than 0.4% under the world light vehicle test cycle (WLTC) condition. The estimation accuracy is improved compared to the traditional KF and other joint algorithms. A random perturbation signal with zero mean and 0.1 variance and obeying Gaussian distribution is chosen to be added to the rated capacity. Under this perturbation signal, the MAE and RMSE of the joint algorithm are both less than 0.4% for SOC in the DST condition and less than 0.6% for the WLTC condition. The proposed ISMO_AEKF algorithm has better estimation accuracy under DST and WLTC conditions, verifying that the ISMO_AEKF algorithm has good robustness. Future research mainly focuses on applying novel algorithms to the battery management system of new energy vehicles to further improve its practicality.
钱伟, 王浩宇, 郭向伟, 李万. 联合改进滑模观测器的自适应卡尔曼滤电状态估计[J]. 电工技术学报, 2025, 40(6): 1984-1994.
Qian Wei, Wang Haoyu, Guo Xiangwei, Li Wan. State of Charge Estimation Using Adaptive Kalman Filter with Improved Sliding Mode Observer. Transactions of China Electrotechnical Society, 2025, 40(6): 1984-1994.
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