工程科学学报.第43卷，第5期：693-701.2021年5月 Chinese Journal of Engineering,Vol.43,No.5:693-701,May 2021 https://doi.org/10.13374/j.issn2095-9389.2020.09.21.002;http://cje.ustb.edu.cn 基于鲁棒H滤波的锂离子电池SOC估计 潘凤文2)，弓栋梁12，高莹12四，寇亚林2) 1)吉林大学汽车仿其与控制国家重点实验室，长春1300252)吉林大学汽车工程学院.长春130025 ☒通信作者，E-mail:gaoying@jlu.edu.cn 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Hm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 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 Lithium-ion battery state of charge estimation based on a robust H filter PAN Feng-wen2.GONG Dong-liang2),GAO ying2,KOU Ya-lin2) 1)State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130025,China 2)College of Automotive Engineering,Jilin University,Changchun 130025,China Corresponding author,E-mail:gaoying@jlu.edu.cn ABSTRACT 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 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. 收稿日期：2020-09-21 基金项目：国家重点研发计划资助项目(2016YFB0100300)基于鲁棒 H∞滤波的锂离子电池 SOC 估计 潘凤文1,2)，弓栋梁1,2)，高    莹1,2) 苣，寇亚林1,2) 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 苣通信作者，E-mail：gaoying@jlu.edu.cn 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. 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 Lithium-ion battery state of charge estimation based on a robust H∞ filter PAN Feng-wen1,2) ，GONG Dong-liang1,2) ，GAO ying1,2) 苣 ，KOU Ya-lin1,2) 1) State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China 2) College of Automotive Engineering, Jilin University, Changchun 130025, China 苣 Corresponding author, E-mail: gaoying@jlu.edu.cn ABSTRACT    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. 收稿日期: 2020−09−21 基金项目: 国家重点研发计划资助项目（2016YFB0100300） 工程科学学报，第 43 卷，第 5 期：693−701，2021 年 5 月 Chinese Journal of Engineering, Vol. 43, No. 5: 693−701, May 2021 https://doi.org/10.13374/j.issn2095-9389.2020.09.21.002; http://cje.ustb.edu.cn