锂离子电池建模及其荷电状态鲁棒估计  

Modelingand State of Charge Robust Estimation for Lithium-ion Batteries

作　　者：陈息坤[1] 孙冬[1,2] 陈小虎[1] 

机构地区：[1]上海大学机电工程与自动化学院,上海200072 [2]郑州轻工业学院,郑州450002

出　　处：《电工技术学报》2015年第15期141-147,共7页Transactions of China Electrotechnical Society

基　　金：国家高技术研究发展(863)计划(2011AA11A247);上海市经信委重大技术装备项目(ZB-ZBYZ-02-14-0825)资助

摘　　要：锂离子电池动态建模和荷电状态估计是锂电池管理系统的关键技术。针对锂电池工作状态受外部环境因素和负载变化的影响,以二阶RC等效电路模型为基础,采用变遗忘因子最小二乘法辨识模型参数。针对锂电池系统存在不确定性噪声问题,提出基于离散H∞滤波的SOC鲁棒估计方法,并与常用的扩展卡尔曼滤波法进行对比实验研究。实验结果表明,变遗忘因子最小二乘法可提高二阶RC模型的性能,鲁棒估计法可将锂电池SOC的估计误差控制在3%左右,具有较好的鲁棒性。In lithium-ion battery power management system, the dynamic modeling and the estimation of state of charge （SOC） are the key techniques. The battery working states are affected by external environment factors and load changes. So the variable forgetting factorleast squares method is used to identify the model parameters based on the second-order RC equivalent circuit model. For the uncertainty noise problem in actual applications, the SOC robust estimation method is proposed based on the discrete-time H-infinity filter. The experiments are carried to compare the suggested method with the commonly used extended Kalman filter. The results show that the performance of the second-order RC model can be improved by the variable forgetting factor least squares method; the robust estimation method can be used to compute battery SOC accurately with 3% error, and the proposed algorithm has better robustness than the extended Kalman filter.

关 键 词：锂离子电池 荷电状态 离散H∞ 滤波器 扩展卡尔曼滤波器 

分 类 号：U463[机械工程—车辆工程]