SOC estimation of power battery based on Kalman filter
XU Liyou， MA Ke， YANG Qingxia， SONG Lintao, MA Xiaobin
1. College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang, Henan 471000, China; 2. Henan Caeri Vehicle Testing and Certification Center Co., Ltd., Jiaozuo, Henan 454000, China； 3. Jiaozuo Coal Group Supplies Department，Jiaozuo, Henan 454000， China
摘要 由于传统无迹卡尔曼滤波估算方法具有局限性，为了能准确估算动力电池荷电状态（state of charge，SOC），提出了一种基于无迹卡尔曼粒子滤波的动力电池SOC估算方法.以三元锂电池为研究对象，建立了电池二阶RC等效电路模型，通过对电池进行充放电试验辨识出模型参数，并验证模型准确性.采集了实际工况下的电池数据，分别用无迹卡尔曼滤波算法、粒子滤波算法和无迹卡尔曼粒子滤波算法估算电池SOC，在MATLAB中进行了仿真试验，并对估算的电池SOC进行比较.结果表明：无迹卡尔曼粒子滤波算法可以快速准确地估算出电池SOC，误差小于2.5%，优于另外2种算法.
Abstract： Due to the limitations of traditional unscented Kalman filter estimation methods, to accurately estimate the state of charge（SOC） of power battery, a method for SOC estimation of power battery based on unscented Kalman particle filter was proposed. Taking the ternary lithium battery as research object, the secondorder RC equivalent circuit model of the battery was established to identify the model parameters through the battery charging and discharging test, and the accuracy of the model was verified. The battery data under actual working conditions were collected, and the SOC was estimated by untracked Kalman filter algorithm, particle filter algorithm and untracked Kalman particle filter algorithm， respectively. The simulation experiments were carried out in MATLAB, and the estimated SOC values were compared. The results show that the untracked Kalman particle filter algorithm can estimate the SOC quickly and accurately, and the error is less than 2.5%, which is better than those of other two algorithms.
[J]. 江苏大学学报（自然科学版）, 2024, 45(1): 24-29.
XU Liyou， MA Ke， YANG Qingxia， SONG Lintao, MA Xiaobin. SOC estimation of power battery based on Kalman filter[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2024, 45(1): 24-29.
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