电动汽车动力电池关键状态参数估计及管理  

Estimation and Management of Key State Parameters of Electric Vehicle Power Battery

作　　者：罗钿 赵玛龙 张文强 张建伟 安国伟 LUO Tian;ZHAO Malong;ZHANG Wenqiang;ZHANG Jianwei;AN Guowei(School of Automotive Engineering,Lanzhou Institute of Technology,Lanzhou 730050,China)

机构地区：[1]兰州工业学院汽车工程学院,甘肃兰州730050

出　　处：《兰州工业学院学报》2024年第4期83-88,共6页Journal of Lanzhou Institute of Technology

基　　金：甘肃省科技计划资助项目(23JRRA920);2023年国家级大学生创新创业训练计划项目(DC202301-53)。

摘　　要：通过分析电动汽车电池管理系统相关技术,提出了二阶等效电路模型并考虑了均衡电路的影响,建立了基于扩展卡尔曼滤波器(Extended Kalman Filter, EKF)的荷电状态估计算法规则,并将实验数据与真实数据进行了对比,验证了该模型SOC算法满足设计要求,同时对该设计现存的不足提出改进建议,并对今后的发展进行了预期。By analyzing the relevant technologies of electric vehicle battery management system,a second-order equivalent circuit model is proposed,and the influence of balanced circuits is taken into account to establish a state of charge estimation algorithm rule based on Extended Kalman Filter(EKF).Experimental data is compared with real data to verify that the SOC algorithm of the model meets the design requirements,and improvement suggestions are proposed for the existing shortcomings of the design,and expectations are made for future development.

关 键 词：电动汽车 电池管理系统 电池荷电状态(SOC)估算 卡尔曼滤波法 

分 类 号：U461.3[机械工程—车辆工程] TM911.4[交通运输工程—载运工具运用工程]