增程电动汽车再生制动控制策略研究

Yongliang Li, Changlu Zhao, Ying Huang, Xu Wang, Fen Guo, Long Yang
{"title":"增程电动汽车再生制动控制策略研究","authors":"Yongliang Li, Changlu Zhao, Ying Huang, Xu Wang, Fen Guo, Long Yang","doi":"10.1109/VPPC49601.2020.9330885","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of regenerative braking energy recovery control for extended range electric vehicles, a front-rear braking force distribution strategy that maximizes braking energy recovery is proposed on the premise of ensuring vehicle braking stability and safety in this paper; then a regenerative braking energy recovery strategy based on fuzzy control is designed. In addition, the membership function of the fuzzy controller is optimized by particle swarm optimization with taking the braking energy recovery rate as the target. Finally, a quasi-static model of the whole vehicle simulation is established on the Simulink-Cruise joint simulation platform, and the simulation is performed under the NEDC, FTP72 and Ja1015 operating conditions. The simulation results show that the designed regenerative braking energy recovery control strategy has an energy recovery rate of 53.5%, 43.9% and 56.1% in the above three operating conditions, and the battery charging power does not exceed the maximum charging power in the extended range mode, proving a good control performance.","PeriodicalId":6851,"journal":{"name":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","volume":"8 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on Regenerative Braking Control Strategy for Extended Range Electric Vehicles\",\"authors\":\"Yongliang Li, Changlu Zhao, Ying Huang, Xu Wang, Fen Guo, Long Yang\",\"doi\":\"10.1109/VPPC49601.2020.9330885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of regenerative braking energy recovery control for extended range electric vehicles, a front-rear braking force distribution strategy that maximizes braking energy recovery is proposed on the premise of ensuring vehicle braking stability and safety in this paper; then a regenerative braking energy recovery strategy based on fuzzy control is designed. In addition, the membership function of the fuzzy controller is optimized by particle swarm optimization with taking the braking energy recovery rate as the target. Finally, a quasi-static model of the whole vehicle simulation is established on the Simulink-Cruise joint simulation platform, and the simulation is performed under the NEDC, FTP72 and Ja1015 operating conditions. The simulation results show that the designed regenerative braking energy recovery control strategy has an energy recovery rate of 53.5%, 43.9% and 56.1% in the above three operating conditions, and the battery charging power does not exceed the maximum charging power in the extended range mode, proving a good control performance.\",\"PeriodicalId\":6851,\"journal\":{\"name\":\"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)\",\"volume\":\"8 1\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/VPPC49601.2020.9330885\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Vehicle Power and Propulsion Conference (VPPC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VPPC49601.2020.9330885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

针对增程电动汽车再生制动能量回收控制问题,在保证车辆制动稳定性和安全性的前提下,提出了一种制动能量回收最大化的前后制动力分配策略;然后设计了一种基于模糊控制的再生制动能量回收策略。此外,以制动能量回收率为目标,采用粒子群算法对模糊控制器的隶属度函数进行优化。最后,在Simulink-Cruise联合仿真平台上建立整车仿真的准静态模型,并在NEDC、FTP72和Ja1015工况下进行仿真。仿真结果表明,所设计的再生制动能量回收控制策略在上述三种工况下的能量回收率分别为53.5%、43.9%和56.1%,且增程模式下电池充电功率不超过最大充电功率,具有良好的控制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Regenerative Braking Control Strategy for Extended Range Electric Vehicles
Aiming at the problem of regenerative braking energy recovery control for extended range electric vehicles, a front-rear braking force distribution strategy that maximizes braking energy recovery is proposed on the premise of ensuring vehicle braking stability and safety in this paper; then a regenerative braking energy recovery strategy based on fuzzy control is designed. In addition, the membership function of the fuzzy controller is optimized by particle swarm optimization with taking the braking energy recovery rate as the target. Finally, a quasi-static model of the whole vehicle simulation is established on the Simulink-Cruise joint simulation platform, and the simulation is performed under the NEDC, FTP72 and Ja1015 operating conditions. The simulation results show that the designed regenerative braking energy recovery control strategy has an energy recovery rate of 53.5%, 43.9% and 56.1% in the above three operating conditions, and the battery charging power does not exceed the maximum charging power in the extended range mode, proving a good control performance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信