锂离子电池荷电状态估计方法综述  

Review of state of charge estimation method for Li-ion battery

作　　者：孙冬 许爽 李超 纪阿芳 SUN Dong;XU Shuang;LI Chao;JI A-fang(Engineering Training Center,Zhengzhou University of Light Industry,Zhengzhou,Henan 450002,China;College of Information Engineering,Zhengzhou Institute of Technology,Zhengzhou,Henan 450044,China;Shandong Snton Optical Material Technology Co.,Ltd.,Dongying,Shandong 257500,China;Qingdao Hiconda Mechanical Electronics Co.,Ltd.,Qingdao,Shandong 266000,China)

机构地区：[1]郑州轻工业学院工程训练中心,河南郑州450002 [2]郑州工程技术学院信息工程学院,河南郑州450044 [3]山东胜通光学材料科技有限公司,山东东营257500 [4]青岛海康达机械电子有限公司,山东青岛266000

出　　处：《电池》2018年第4期284-287,共4页Battery Bimonthly

基　　金：郑州轻工业学院博士科研基金资助项目(2017BSJJ070);河南省科技攻关项目(172102210069);河南省高校重点科研项目(18A470018);河南省高校青年骨干教师培养计划(2017GGJS182)

摘　　要：对常用锂离子电池荷电状态(SOC)估计方法分类,指出基于电池模型的闭环估计法是研究的热点。着重对比安时积分法、电压法、卡尔曼滤波法、状态观测器法和智能算法,其中卡尔曼滤波法和状态观测器法使用较多,分别从估计精度和算法设计复杂度两方面分析了这些算法的优缺点。给出SOC在线估计的要求:根据应用场合选择适合的电池模型和估计算法,通常要求估计算法具有一定通用性和较好鲁棒性,SOC估计误差小于5%。The commonly used state of charge （ SOC ） estimation methods were classified and summarized. The closed loop estimation method based on Li-ion battery model was the research hotspot. According to the classification of SOC estimation algorithm, ampere-hour integral method,voltage method, Kalman filtering method, state observer method, intelligent algorithms were compared. The Kalman filter method and state observer method were more used. The advantages and disadvantages of these algorithms were analyzed from two aspects of estimation precision and algorithm complexity. The requirements of online SOC estimation were analyzed, the suitable Li-ion battery model and the estimation algorithm must be chosen due to the application fields. It was usually required that the estimation algorithm had certain universality and good robustness, SOC estimation error should be within 5%.

关 键 词：锂离子电池 荷电状态(SOC) 估计方法 卡尔曼滤波法 状态观测器法 

分 类 号：TM912.9[电气工程—电力电子与电力传动]