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SOC Estimation of Li-Ion Battery Based on Improved EKF Algorithm
International Journal of Automotive Technology ( IF 1.6 ) Pub Date : 2021-03-04 , DOI: 10.1007/s12239-021-0032-4
Zhengjun Huang , Yongshou Fang , Jianjun Xu

The state of charge (SOC) is one of the important performance indicators of battery, which provides an important basis for the management and control of Battery Management System (BMS). In view of the characteristics of lithium iron phosphate battery, considering the model accuracy and calculation amount, the equivalent circuit model of improved PNGV was selected. Based on that, an improved Extended Kalman Filter (EKF) algorithm was adopted to estimate the state of charge (SOC) of Li-ion battery, which covariance matrix was modified by the Levenberg-Marquardt method. At the end of this paper, the SOC estimation algorithm was verified by MATLAB simulations. The results show that compared with the standard EKF, the improved EKF has higher estimation accuracy and anti-interference ability, and has better convergence in the estimation process.



中文翻译:

基于改进EKF算法的锂离子电池SOC估计

充电状态(SOC)是电池的重要性能指标之一,为电池管理系统(BMS)的管理和控制提供重要依据。针对磷酸铁锂电池的特性,考虑模型精度和计算量,选择了改进型PNGV的等效电路模型。在此基础上,采用改进的扩展卡尔曼滤波算法(EKF)估计锂离子电池的充电状态(SOC),并通过Levenberg-Marquardt方法对协方差矩阵进行了修正。最后,通过MATLAB仿真对SOC估计算法进行了验证。结果表明,与标准EKF相比,改进后的EKF具有更高的估计精度和抗干扰能力,并且在估计过程中具有更好的收敛性。

更新日期:2021-03-04
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