摘要
电池荷电状态(SOC)的精确估算可以为电池管理系统控制策略提供有效判据,能够提高电池的工作效率,并延长其使用寿命。本文运用MATLAB/Simulink平台对锂电池单体进行了建模与仿真,并通过MATLAB的cftool工具箱辨识出二阶模型的各个参数,然后利用扩展卡尔曼滤波算法(EKF)对电池SOC进行了精确估算。分别在确定和不确定SOC初始值两种情况下对算法进行了仿真与验证,结果发现SOC平均绝对误差小于0.01,电压平均绝对误差小于0.005 V,这表明EKF算法在电池SOC估算方面具有精度高、收敛快和实用性较强的优点,可以用于电池SOC的精确估算。
Accurate estimation of battery state of charge(SOC)can provide effective criteria for the control strategy of battery management system,which improves the efficiency of battery and extends its service life.In this paper,the lithium battery monomer is modeled and simulated by MATLAB/Simulink platform,and the parameters of the two-order model are identified through the cftool toolbox of MATLAB.Then,the extended SOC filter algorithm(EKF)is used to accurately estimate the battery SOC.The results show that the average absolute error of SOC is less than 0.01 and the average absolute error of voltage is less than 0.005 V.This shows that EKF algorithm has the advantages of high accuracy,fast convergence and strong practicability in battery SOC estimation,and can be used to accurately estimate battery SOC.
作者
查伟民
ZHA Weimin(Fangda New Materials(Jiangxi)Company Limited,Nanchang 330096,China)
出处
《山东理工大学学报（自然科学版）》
CAS
2020年第6期41-45,共5页
Journal of Shandong University of Technology:Natural Science Edition
基金
国家自然科学基金项目(11401508)
福建省中青年教师教育科研项目(JAT170675)。
关键词
荷电状态
电池管理系统
辨识
EKF
鲁棒性
state of charge
battery management system
identified
EKF
robustness
作者简介
第一作者:查伟民,男,zhaweim@fangda.com。
