基于改进扩展卡尔曼滤波算法的SOC估算

The SOC Estimation Based on Improved Extended Kalman Filter Algorithm

在电动车、储能系统和移动设备等领域中,电池管理系统是保障电池组性能和安全性的关键技术之一,而电池荷电状态(SOC)估算是其重要的组成部分。文章重点针对18650型号的磷酸铁锂电池(单体电池)SOC估算展开研究和设计,首先选择双阶远程控制(RC)模型作为电池模型,通过电池容量标定实验、开路电压(OCV)-SOC标定实验、混合功率脉冲特性(HPPC)实验确定了双阶RC模型的各个动态参数,在MATLAB/Simulink中搭建动力电池仿真模型,验证了所选模型的可靠性。然后,为了解决单体电池SOC估算精度和成本等问题,以扩展卡尔曼滤波(EKF)算法为基础提出了一种改进方法,即在预测第k个时间步的误差协方差矩阵时,引入了时变渐消因子,在更新方差Q和R时引入自适应分子。最后,通过不同循环工况对提出的算法进行仿真分析,结果显示,提出的算法提升了SOC估算的精度,实用性强。

In the fields of electric vehicles,energy storage systems and mobile devices,the battery management system is one of the key technologies to ensure the performance and safety of the battery pack.The state of charge(SOC)is estimated to be an important part of it.The research of the article focuses on the lithium-iron phosphate battery of 18650 models,which is based on the estimation of the SOC of the single battery.First of all,select the dual-order remote control(RC)model as the battery model and determine the dynamic parameters of the dual-order RC model through the battery capacity calibration experiment,the open circuit voltage(OCV)-SOC calibration experiment and the hybrid pulse power characteristic(HPPC)experiment.The power battery simula-tion model is built in MATLAB/Simulink,and the reliability of the selected model is verified.Then,in order to solve the problem of accuracy and cost of the single battery SOC estimation,based on the extended Kalman filter(EKF)algorithm,a improvement method is proposed,that is,the time-varying fade factor is introduced when predicting the error covariance matrix of the time step k,and the adaptive molecule is introduced when the variance Q and R are updated.Finally,the simulation analysis of the proposed algorithm is carried out under different cyclic conditions.Through comparison,the proposed algorithm improves the accuracy of SOC estimation and has strong practicality.