两种联合算法的三元锂电池SOC估计比较  

Comparison of two combined algorithms for SOC estimation of ternary lithium battery

作　　者：葛才安 郑燕萍[1] 虞杨 GE Cai’an;ZHENG Yanping;YU Yang(College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China;College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China)

机构地区：[1]南京林业大学汽车与交通工程学院,南京210037 [2]南京林业大学机械与电子工程学院,南京210037

出　　处：《重庆理工大学学报（自然科学）》2022年第8期29-35,共7页Journal of Chongqing University of Technology：Natural Science

基　　金：江苏省重点研发计划项目(BE2017008)。

摘　　要：电池荷电状态(SOC)估计的准确性受到电池模型精度的影响。为了提高复杂工况下电池SOC估计精度,比较基于遗忘因子递推最小二乘法-扩展卡尔曼滤波(FFRLS-EKF)和双扩展卡尔曼滤波(DEKF)联合算法的三元锂电池SOC估计方法。分别利用FFRLS和EKF算法在线辨识电池模型参数,然后与EKF算法联合进行三元锂电池SOC估计。在动态应力测试(DST)工况下,两种联合算法的SOC估计结果表明:FFRLS-EKF联合算法的估计误差在2.49%之内,DEKF联合算法的估计误差在2.62%之内;FFRLS建立的电池模型精度更高,端电压平均误差为0.37 mV。In order to improve estimation accuracy of lithium battery state of charge(SOC)under complex working conditions,based on combined algorithm of forgetting factor recursive least squares-extended Kalman filter(FFRLS-EKF)and dual extended Kalman filter(DEKF),SOC estimation methods are compared in this paper.FFRLS and EKF algorithms are used to identify parameters of equivalent circuit model online,and then combined with EKF algorithm to estimate battery SOC.Under dynamic stress testing(DST)condition,the SOC estimation results of the two combined algorithms show that:SOC estimation error of FFRLS-EKF combined algorithm is within 2.49%,and that of DEKF combined algorithm is within 2.62%.The equivalent circuit model established by FFRLS has higher accuracy,and the average terminal voltage error is 0.37 mV.

关 键 词：三元锂电池 SOC估计 在线辨识 遗忘因子递推最小二乘法 扩展卡尔曼滤波 

分 类 号：U469.72[机械工程—车辆工程] TM912.8[交通运输工程—载运工具运用工程]