The Effect of Optimal Vehicle Velocity Trajectory and Optimal Hybrid Energy Storage on Electric Vehicle Energy Consumption

Waiard Saikong, T. Kulworawanichpong
{"title":"The Effect of Optimal Vehicle Velocity Trajectory and Optimal Hybrid Energy Storage on Electric Vehicle Energy Consumption","authors":"Waiard Saikong, T. Kulworawanichpong","doi":"10.1109/RI2C48728.2019.8999922","DOIUrl":null,"url":null,"abstract":"This article proposed a method to increase effectiveness of energy consumption and presented comparison of effects of energy and power consumption from algorithms to investigate optimal velocity trajectory under the condition of late arrival, algorithms to investigate optimal hybrid energy storage system, and cases that collaborated both algorithms. The testing was on the route New York City Cycle - NYCC and route SUT - Suranaree University of Technology Route. The real field measurement was used to find load profile for SUT route. Both were urban traffic routes. The researcher created a mathematic model and tested for optimal velocity trajectory and optimal hybrid energy storage system using particle swarm optimization: PSO methodology. The test revealed that the algorithm to investigate optimal velocity trajectory under the condition of delayed arrival together with the algorithm to investigate optimal hybrid energy storage system - HESS can reduce energy consumption and maximum peak power at most which was at 46.653% and 60.543% respectively on NYCC route and can reduce energy consumption at 21.435% and reduce maximum power at 23.973% on SUT route.","PeriodicalId":404700,"journal":{"name":"2019 Research, Invention, and Innovation Congress (RI2C)","volume":"854 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Research, Invention, and Innovation Congress (RI2C)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RI2C48728.2019.8999922","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This article proposed a method to increase effectiveness of energy consumption and presented comparison of effects of energy and power consumption from algorithms to investigate optimal velocity trajectory under the condition of late arrival, algorithms to investigate optimal hybrid energy storage system, and cases that collaborated both algorithms. The testing was on the route New York City Cycle - NYCC and route SUT - Suranaree University of Technology Route. The real field measurement was used to find load profile for SUT route. Both were urban traffic routes. The researcher created a mathematic model and tested for optimal velocity trajectory and optimal hybrid energy storage system using particle swarm optimization: PSO methodology. The test revealed that the algorithm to investigate optimal velocity trajectory under the condition of delayed arrival together with the algorithm to investigate optimal hybrid energy storage system - HESS can reduce energy consumption and maximum peak power at most which was at 46.653% and 60.543% respectively on NYCC route and can reduce energy consumption at 21.435% and reduce maximum power at 23.973% on SUT route.
最优车速轨迹和最优混合储能对电动汽车能耗的影响
本文提出了一种提高能耗效率的方法,并从考察迟到点条件下最优速度轨迹的算法、考察最优混合储能系统的算法以及两种算法协同使用的情况,对能源和功耗的效果进行了比较。测试是在纽约城市自行车- NYCC路线和SUT - Suranaree科技大学路线上进行的。通过现场实测,确定了SUT线路的负荷分布。两条都是城市交通路线。研究人员建立了数学模型,并利用粒子群优化方法对最优速度轨迹和最优混合储能系统进行了测试。试验表明,研究延迟到达条件下最优速度轨迹的算法与研究最优混合储能系统HESS的算法在NYCC路线上的能耗和最大峰值功率降幅分别为46.653%和60.543%,在SUT路线上的能耗和最大功率降幅分别为21.435%和23.973%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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