基于价值函数学习的混合动力汽车最优能量管理方法

Akito Saito, T. Shen
{"title":"基于价值函数学习的混合动力汽车最优能量管理方法","authors":"Akito Saito, T. Shen","doi":"10.1109/CVCI51460.2020.9338540","DOIUrl":null,"url":null,"abstract":"This paper presents two learning-based approaches to solve the optimal energy management problem for hybrid electric vehicles. It will be shown that by applying a learning algorithm to the interpolation of value-function, which is an optimal approximate value-function in continuous state space, the discretization error can be rejected when performing dynamic programming. Extreme Learning Machine and Gaussian Process Regression are exploited as learning tools. Finally, numerical simulation results with a parallel HEV will be demonstrated to show the effort of value-function learning.","PeriodicalId":119721,"journal":{"name":"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Value-Function Learning-based Solutions to Optimal Energy Management Problem of HEVs\",\"authors\":\"Akito Saito, T. Shen\",\"doi\":\"10.1109/CVCI51460.2020.9338540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents two learning-based approaches to solve the optimal energy management problem for hybrid electric vehicles. It will be shown that by applying a learning algorithm to the interpolation of value-function, which is an optimal approximate value-function in continuous state space, the discretization error can be rejected when performing dynamic programming. Extreme Learning Machine and Gaussian Process Regression are exploited as learning tools. Finally, numerical simulation results with a parallel HEV will be demonstrated to show the effort of value-function learning.\",\"PeriodicalId\":119721,\"journal\":{\"name\":\"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVCI51460.2020.9338540\",\"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 4th CAA International Conference on Vehicular Control and Intelligence (CVCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVCI51460.2020.9338540","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

提出了两种基于学习的混合动力汽车最优能量管理方法。结果表明,将学习算法应用于连续状态空间中最优近似值函数的插值,可以有效地抑制动态规划时的离散化误差。利用极限学习机和高斯过程回归作为学习工具。最后,将用并行混合动力汽车的数值仿真结果来展示值函数学习的成果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Value-Function Learning-based Solutions to Optimal Energy Management Problem of HEVs
This paper presents two learning-based approaches to solve the optimal energy management problem for hybrid electric vehicles. It will be shown that by applying a learning algorithm to the interpolation of value-function, which is an optimal approximate value-function in continuous state space, the discretization error can be rejected when performing dynamic programming. Extreme Learning Machine and Gaussian Process Regression are exploited as learning tools. Finally, numerical simulation results with a parallel HEV will be demonstrated to show the effort of value-function learning.
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