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| 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 |
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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.
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Received: 17 February 2022
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