电动汽车动力锂离子电池SOC估算方法比较分析  

State of Charge Estimation Comparative Analysis for Traction Li-ion Batteries in Electric Vehicle

作　　者：吴广顺[1] 李真铁 王昊[1] WU Guangshun;LI Zhentie;WANG Hao(Tianjin Internal Combustion Engine Research Institute,Tianjin 300072,China)

机构地区：[1]天津内燃机研究所,天津300072

出　　处：《汽车实用技术》2023年第15期47-53,共7页Automobile Applied Technology

摘　　要：由于电动汽车动力锂离子电池在使用过程中是时变、非线性的复杂电化学系统,精确评估电池荷电状态(SOC)难度很大。文章对电动汽车锂离子电池的SOC估算方法进行了详细综述,根据各类算法的不同特征将其分为传统方法、基于学习算法的方法和基于模型的方法,阐明了各种估算方法的特征及应用条件,对各种方法的优缺点进行了分析探讨,对将来在线估算电动汽车锂离子电池SOC的方法进行了展望。结果表明,基于学习算法的SOC估算方法将是未来的发展方向。Because the lithium-ion battery of electric vehicles is a time-varying and nonlinear complex electrochemical system during use,it is difficult to accurately evaluate state of charge(SOC).In this paper,SOC estimation methods for lithium-ion batteries of electric vehicles are reviewed in detail,according to the different characteristics of various algorithms,they are divided into traditional methods,learning algorithm-based methods and model-based methods,the characteristics and application conditions of various estimation methods are expounded,and the advantages and disadvantages of each method are analyzed and discussed,and the future online SOC estimation methods for lithium-ion batteries of electric vehicles are prospected.The results show that the SOC estimation method based on learning algorithm will be the future development direction.

关 键 词：电动汽车 锂离子电池 荷电状态 卡尔曼滤波 

分 类 号：U469.72[机械工程—车辆工程]