温度自适应SMO算法估计锂离子电池的SOC  

SOC estimation for Li-ion battery used by temperature adaptive SMO algorithm

作　　者：吕高[1] 樊郭宇 张嘉蕾[1] 杜君莉 史书怀 LYU Gao;FAN Guoyu;ZHANG Jialei;DU Junli;SHI Shuhuai(School of Electric Power and Architecture,Shanxi University,Taiyuan,Shanxi 030000,China;Electric Power Science Research Institute,State Grid Henan Electric Power Company,Zhengzhou,Henan 450000,China)

机构地区：[1]山西大学电力与建筑学院,山西太原030000 [2]国网河南省电力公司电力科学研究院,河南郑州450000

出　　处：《电池》2024年第3期334-339,共6页Battery Bimonthly

基　　金：中国博士后科学基金第三批特别资助(站前)(2021TQ0097)。

摘　　要：现有对锂离子电池荷电状态(SOC)的估计,没有考虑温度变化导致的SOC估计准确度降低。提出一种考虑温度的滑模观测(SMO)法进行SOC估计。基于混合脉冲功率测试(HPPC)实验的数据,得到18650型LiFePO4锂离子电池的SOC与温度、参数之间的拟合式,通过台风(Typhoon)系统进行半实物实验分析。温度自适应SMO算法在低温或常温工况下的平均误差较传统SMO算法降低0.3~0.5个百分点,直接通过拟合式所快速估计的SOC较温度自适应SMO算法平均误差在2%左右,常温25℃工况下误差低于1%,能够实现较高的估计精准度,为快速估计SOC提供了较好的算法参考。Existing estimation of the state of charge(SOC)of Li-ion battery does not take into account the reduced accuracy of SOC estimation due to temperature variations.A sliding mode observation(SMO)method considering temperature is proposed for SOC estimation.Based on the data of the hybrid pulse power testing(HPPC)experiments,the fitting equations between the SOC of 18650 type LiFePO4 Li-ion batteries and the temperature and parameters are obtained,and analyzed by semi-physical experiments with the Typhoon system.The average error of the temperature-adaptive SMO algorithm is reduced by 0.3-0.5 percentage points compared with the traditional SMO algorithm under low or normal temperature conditions,and the average error of the SOC quickly estimated directly by the fitting equation is about 2%compared with that of the temperature-adaptive SMO algorithm,and the error is less than 1%under the normal temperature of 25℃,which is able to realize a higher estimation accuracy and provide a good algorithm reference for the quick estimation of SOC.

关 键 词：荷电状态(SOC)估计 滑模观测(SMO) 温度影响 锂离子电池 半实物实验分析 

分 类 号：TM912.9[电气工程—电力电子与电力传动]