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| Estimation of state of charge of battery based on improved multi-innovation extended Kalman filter |
Ke-bing LEI( ),Zi-qiang CHEN*() |
| State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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Abstract An improved multi-innovation extended Kalman filter was proposed based on the forgetting factor in order to improve the accuracy of SOC estimation. Dual-polarization equivalent circuit model of lithium-ion battery was established, and open-circuit voltage testing was conducted. Recursive least squares method was used to realize online battery model parameter identification. FMIEKF was proposed for SOC estimation based on the fusion of multi-innovation identification theory and Kalman filtering. A forgetting factor was introduced to weaken the historical data correction weight and solve the problem of data oversaturation. The method was verified through experiments and hardware-in-the-loop. The experimental results show that FMIEKF has higher accuracy and convergence. The maximum estimation error was 0.948%, the average error was 0.214%, and the FMIEKF converged within 20 seconds under different initial values of SOC. The method can be applied to the actual battery management system.
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Received: 26 November 2020
Published: 27 October 2021
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| Fund: 国家自然科学基金资助项目（51677119） |
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Corresponding Authors:
Zi-qiang CHEN
E-mail: Kebing_Lei@sjtu.edu.cn;chenziqiang@sjtu.edu.cn
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基于改进多新息扩展卡尔曼滤波的电池SOC估计
为了提高SOC估计精度，提出基于遗忘因子改进多新息扩展卡尔曼滤波（FMIEKF）方法. 建立锂离子电池的双极化等效电路模型，开展开路电压测试. 通过递归最小二乘法，实现电池模型参数在线辨识. 提出FMIEKF进行SOC估计，该方法在融合多新息辨识理论和卡尔曼滤波基础上，引入遗忘因子削弱历史数据修正权重，解决数据过饱和问题. 通过实验和硬件在环进行验证. 结果表明，FMIEKF具有较高的准确性和收敛性，最大估计误差为0.948%，平均误差为0.214%，在不同SOC初值下20 s内收敛，可以适用于实际的电池管理系统中.
关键词：
锂离子电池,
多新息辨识,
卡尔曼滤波,
SOC估计,
硬件在环
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