首页 | 本学科首页   官方微博 | 高级检索  
     检索      

电动汽车远程监控平台的电池SOC估算
引用本文:柳炽伟,郭美华,景玉军,钟子文.电动汽车远程监控平台的电池SOC估算[J].农业装备与车辆工程,2022,60(2):39-43.
作者姓名:柳炽伟  郭美华  景玉军  钟子文
作者单位:528403 广东省 中山市 中山职业技术学院 机电工程学院;528455 广东省 中山市 中山市公共交通集团有限公司
基金项目:2020年广东省教育厅“特色创新”科研项目(2020KTSCX334);2019年中山市社会公益科技研究项目(2019B2045)。
摘    要:为充分发挥远程监控平台的监控和故障预警作用,针对电动汽车动力电池荷电状态(State OfCharge,SOC)精准估算对汽车控制和安全运行的重要性,利用车辆上传监控平台的运行数据进行SOC估算研究.通过减聚类算法计算隐层中心数,用量子粒子群(Quantum Particle Swarm Optimization,QP...

关 键 词:动力电池  远程监控  量子粒子群算法  神经网络  荷电状态SOC

Estimation of Battery SOC on Electric Vehicle Remote Monitoring Platform
Liu Chiwei,Guo Meihua,Jing Yujun,Zhong Ziwen.Estimation of Battery SOC on Electric Vehicle Remote Monitoring Platform[J].Agricultural Equipment & Vehicle Engineering,2022,60(2):39-43.
Authors:Liu Chiwei  Guo Meihua  Jing Yujun  Zhong Ziwen
Institution:(Institute of Mechanical and Electrical Engineering,Zhongshan Polytechnic,Zhongshan City,Guangdong Province 528403,China;Zhongshan Public Transport Holdings Limited,Zhongshan City,Guangdong Province 528455,China)
Abstract:In order to give full play to the monitoring and fault early warning function of remote monitoring platform,aiming at the importance of accurate estimation of state of charge(SOC)of electric vehicle power battery for vehicle control and safe operation,SOC estimation research was carried out by using the operation data uploaded from vehicle monitoring platform.The parameters and structure of radial basis function neural network(RBFNN)were optimized by quantum-behaved particle swarm optimization(QPSO),and the width,center and connection weight of kernel function in RBFNN were determined.Experiments show that the data collected by the remote monitoring platform and the optimized neural network model can achieve more accurate and rapid SOC estimation.The test results show that this method can avoid the dependence on the original SOC output value and its deviation.It can effectively monitor the battery condition of electric vehicles during operation.
Keywords:power battery  remote monitoring  quantum-behaved particle swarm optimization  neural network  SOC
本文献已被 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号