基于鲁棒H_(∞)滤波的锂离子电池SOC估计  

Lithium-ion battery state of charge estimation based on a robust H_(∞)filter

作　　者：潘凤文 弓栋梁 高莹[1,2] 寇亚林 PAN Feng-wen;GONG Dong-liang;GAO ying;KOU Ya-lin(State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130025,China;College of Automotive Engineering,Jilin University,Changchun 130025,China)

机构地区：[1]吉林大学汽车仿真与控制国家重点实验室,长春130025 [2]吉林大学汽车工程学院,长春130025

出　　处：《工程科学学报》2021年第5期693-701,共9页Chinese Journal of Engineering

基　　金：国家重点研发计划资助项目(2016YFB0100300)。

摘　　要：荷电状态(State of charge,SOC)估计是电池管理系统的核心功能之一,它在电动汽车的生命周期中起着重要作用.针对锂离子电池温度影响模型参数,进而导致SOC估计不准确的问题,本文提出了基于鲁棒H_(∞)滤波的SOC估计方法.首先,以二阶Thevenin等效电路模型做为锂离子电池基础模型,并将温度对电池模型参数的影响建模为标称电阻值和电池总容量的加性变量,视温度变化为系统的外部扰动.其次,采用滑动线性法对电池模型进行线性化,并在此基础上运用线性矩阵不等式技术设计了对SOC进行估计的鲁棒H_(∞)滤波器.最后,分别采用四种不同类型的动态电流激励进行仿真实验验证,并将SOC的估计结果与kalman滤波对SOC的估计结果进行对比.结果表明所设计的鲁棒H_(∞)滤波器能够实现对SOC更为准确的跟踪,同时对外部扰动具有较好的鲁棒性.The state of charge(SOC)estimation is one of the core functions of the battery management system;it can play a significant role in the life cycle of electric vehicles.The SOC estimation method has attracted considerable research attention in recent years,particularly about improving estimation accuracy.However,most studies are limited by only focusing on known or fixed battery model parameters and not considering their temperature dependence.This indicates a need to explore how the lithium-ion battery temperature affects the model parameters,which leads to inaccurate SOC estimation.The principal objective of this study is to investigate the robust H_(∞)filter-based method for the problem that temperature affects battery model parameters and thus leads to inaccurate SOC estimation.First,the second-order Thevenin equivalent circuit model with two parallel resistor–capacitor pairs is taken as the basic model of the lithium-ion battery.The influence of temperature on battery model parameters is modeled as an additive variable of the nominal resistance value and the total battery capacity,and the temperature change is considered an external disturbance of the system.Afterward,the sliding linear method is used to linearize this battery model;on this basis,a robust H_(∞)filter for SOC estimation is designed using linear matrix inequality technology.Finally,the effectiveness of the proposed approach is verified using four different types of dynamic current load profiles(the BJDST-Beijing Dynamic Stress Test,FUDS-Federal Urban Driving Schedule,US06-US06 Highway Driving Schedule and BJDST-FUDS-US06 joint dynamic test)compared with the Kalman filter-based SOC estimation method.The simulation analysis results indicate that the proposed SOC estimation approach can realize a higher SOC estimation accuracy even if the model parameters vary with temperature,and it has good robustness to external disturbances.

关 键 词：锂离子电池 SOC估计 模型参数摄动 模型线性化 H_(∞)滤波器 

分 类 号：TM911.3[电气工程—电力电子与电力传动]