基于EKF与UKF的锂离子电池SOC估计
SOC Estimation of Lithium-Ion Battery Based on EKF and UKF
DOI: 10.12677/AEPE.2020.85010, PDF,    科研立项经费支持
作者: 韦超毅, 徐光忠, 覃小婷：广西大学机械工程学院，广西 南宁；龙佳庆*：柳州职业技术学院，广西 柳州
关键词: 锂离子电池；参数辨识；扩展卡尔曼；无迹卡尔曼；SOC；Lithium-Ion Battery； Parameter Identification； Extended Kalman； Unscented Kalman； SOC
摘要: 为了提高锂离子电池的SOC估计精度，分别使用扩展卡尔曼滤波和无迹卡尔曼滤波对电池SOC进行估计，并通过虚拟仿真实验进行验证分析。建立了二阶RC等效电路模型并利用带有遗忘因子的最小二乘法对模型参数进行辨识，然后在MATLAB中搭建EKF与UKF的算法模型对SOC进行估计，并和实际SOC进行比较分析。仿真实验结果表明：建立的二阶RC模型精度高，电压误差在40 mV内；EKF和UKF算法的精度良好，误差在3%以内，并且UKF的估计精度要比EKF的高。
Abstract: In order to improve the accuracy of lithium-ion battery SOC estimation, extended Kalman filter and unscented Kalman filter are used to estimate battery SOC respectively, and the virtual simulation experiment is used for validation and analysis. A second-order RC equivalent circuit model is estab-lished and the model parameters are identified by the least square method with forgetting factor. Then, the EKF and UKF algorithm models are built in MATLAB to estimate the SOC and compare and analyze the actual SOC. The simulation experiment results show that the established second-order RC model has high accuracy and the voltage error is within 40 mV; the accuracy of the EKF and UKF algorithms is good, with the error within 3%, and the estimation accuracy of UKF is higher than that of EKF.
文章引用：韦超毅, 徐光忠, 龙佳庆, 覃小婷. 基于EKF与UKF的锂离子电池SOC估计[J]. 电力与能源进展, 2020, 8(5): 85-94. https://doi.org/10.12677/AEPE.2020.85010

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