基于EKF算法的纯电动汽车锂电池SOC与SOH联合估算  

Joint prediction of SOC and SOH of the Li-ion battery for a battery electric vehicles based on EKF algorithm

作　　者：李煜 蔡玉梅[1,2] 曾凯 马仪[1,2] 李茂盛 LI Yu;CAI Yumei;ZENG Kai;MA Yi;LI Maosheng(School of Mechanical and Energy Engineering,Shaoyang University,Shaoyang 422000,China;Key Laboratory of Hunan Province for Efficient Power System and Intelligent Manufacturing,Shaoyang University,Shaoyang 422000,China;Project Consultancy Studio,Hunan Pengxing Project Consultancy&Management Co.,Ltd.,Jishou 416007,China)

机构地区：[1]邵阳学院机械与能源工程学院,湖南邵阳422000 [2]邵阳学院高效动力系统智能制造湖南省重点实验室,湖南邵阳422000 [3]湖南鹏星工程咨询管理服务有限公司工程咨询服务工作室,湖南吉首416007

出　　处：《邵阳学院学报（自然科学版）》2024年第2期45-55,共11页Journal of Shaoyang University：Natural Science Edition

基　　金：湖南省教育厅一般科研项目(20C1642);邵阳学院研究生科研创新项目(CX2021SY017)。

摘　　要：为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。In order to improve the estimation accuracy of state of charge(SOC)and consider the influence of state of health(SOH)on the performance of lithium(Li)batteries,an extended kalman filter(EKF)joint estimation algorithm was proposed.According to the existing experimental data,the algorithm analyzes the characteristics of Li batteries,constructs a second-order RC equivalent circuit model,identifies parameters,and builds a MATLAB simulation platform and EKF algorithm for SOC estimation.After comparing the simulation results with the real data,the results show that the accuracy of the joint prediction SOC is about 1.2%higher than that of the EKF estimation of SOC,and the anti-interference ability is stronger.

关 键 词：EKF算法 锂电池 荷电状态 健康状态 估算 

分 类 号：TK018[动力工程及工程热物理]