Reinforcement learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management Recent Advances and Prospects

被引：169

作者：

Hu, Xiaosong ^{[1
,2
]}

Liu, Teng ^{[3
,4
]}

Qi, Xuewei ^{[5
,6
,7
]}

Barth, Matthew ^{[8
]}

机构：

[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing, Peoples R China

[2] Chongqing Univ, Dept Automot Engn, Chongqing, Peoples R China

[3] Vehicle Intelligence Pioneers, Beijing, Peoples R China

[4] Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON, Canada

[5] Natl Acad Sci Engn & Med, Alternat Transportat Fuels & Technol Standing Com, Washington, DC USA

[6] Natl Acad Sci Engn & Med, Artificial Intelligence Standing Comm, Washington, DC USA

[7] Natl Acad Sci Engn & Med, Adv Comp Standing Comm, Transportat Res Board, Washington, DC USA

[8] Natl Acad Sci Engn & Med, Transportat Res Board, Washington, DC USA

来源：

IEEE INDUSTRIAL ELECTRONICS MAGAZINE | 2019年 / 13卷 / 03期

基金：

中国国家自然科学基金;

关键词：

POWER MANAGEMENT; STRATEGY;

D O I：

10.1109/MIE.2019.2913015

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Energy management is a critical technology in plug-in hybrid-electric vehicles (PHEVs) for maximizing efficiency, fuel economy, and range, as well as reducing pollutant emissions. At the same time, deep reinforcement learning (DRL) has become an effective and important methodology to formulate model-free and realtime energy-management strategies for HEVs and PHEVs. In this article, we describe the energy-management issues of HEVs/PHEVs and summarize a variety of potential DRL applications for onboard energy management. We also discuss the prospects for various DRL approaches in the energy-management field. © 2007-2011 IEEE.

引用

页码：16 / 25

页数：10

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