卡尔曼滤波法估算电池系统荷电状态  

Estimating the State of Charge of a Battery System by Kalman Filter Method

作　　者：黄英 Huang Ying(Jiangsu United Vocational and Technical College,Changzhou Liu Guojun Branch,Jiangsu Changzhou 213025)

机构地区：[1]江苏联合职业技术学院常州刘国钧分院,江苏常州213025

出　　处：《汽车实用技术》2021年第11期6-9,共4页Automobile Applied Technology

摘　　要：电池作为电动汽车的动力源,其性能影响着电动汽车能否正常运行,而电池管理系统可以实现对电池组数据的实时检测。精确的剩余电荷容量SOC估算结果可以不间断地向电动车驾驶人提供实时的电池数据,方便其随时作出调整,这也为驾驶员能更好地使用电动汽车提供了帮助。文章重点研究了用卡尔曼滤波法来估算电池管理系统中的SOC值,并对比了不同的模型,逐一研究优点和缺点,最终确立了基础模型,最后用仿真实验验证。As the power source of electric vehicle,the performance of battery affects the normal operation of electric vehicle,and the battery management system can realize the real-time detection of battery data.ACCURATE SOC estimation results can provide real-time battery data to EV drivers without interruption,which can be easily adjusted at any time.It also helps drivers to use evs better.This paper focuses on the estimation of SOC in battery management system using the Kalman filter method,and compares the different models,studies the advantages and disadvantages one by one,finally establishes the basic model,and finally uses the simulation experiment to verify.

关 键 词：电动汽车 剩余电荷容量 卡尔曼滤波法 仿真验证 

分 类 号：U469.72[机械工程—车辆工程] TM911[交通运输工程—载运工具运用工程]