中国机械工程学报2024，Vol.37Issue(3) ：1-25.DOI:10.1186/s10033-024-01026-4

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

Xiaolin Tang Jiaxin Chen Yechen Qin Teng Liu Kai Yang Amir Khajepour Shen Li
中国机械工程学报2024，Vol.37Issue(3) ：1-25.DOI:10.1186/s10033-024-01026-4
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Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

Xiaolin Tang 1Jiaxin Chen 1Yechen Qin 2Teng Liu 1Kai Yang 1Amir Khajepour 3Shen Li4
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作者信息

  • 1. College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China
  • 2. School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China
  • 3. Department of Mechanical and Mechatronics Engineering,University of Waterloo,Waterloo,ON N2L 3G1,Canada
  • 4. School of Civil Engineering,Tsinghua University,Beijing 100084,China
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Abstract

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foun-dational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehen-sive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as"Alpha HEV"are envi-sioned,integrating Autopilot and energy-saving control.

Key words

New energy vehicle/Hybrid power system/Reinforcement learning/Energy management strategy

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基金项目

National Natural Science Foundation of China(52222215)

National Natural Science Foundation of China(52072051)

Fundamental Research Funds for the Central Universities in China(2023CDJXY-025)

Chongqing Municipal Natural Science Foundation of China(CSTB2023NSCQ-JQX0003)

出版年

2024
中国机械工程学报
中国机械工程学会

中国机械工程学报

CSTPCD
影响因子：0.765
ISSN：1000-9345
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