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

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

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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.

New energy vehicleHybrid power systemReinforcement learningEnergy management strategy

Xiaolin Tang、Jiaxin Chen、Yechen Qin、Teng Liu、Kai Yang、Amir Khajepour、Shen Li

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College of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China

School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China

Department of Mechanical and Mechatronics Engineering,University of Waterloo,Waterloo,ON N2L 3G1,Canada

School of Civil Engineering,Tsinghua University,Beijing 100084,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central Universities in ChinaChongqing Municipal Natural Science Foundation of China

52222215520720512023CDJXY-025CSTB2023NSCQ-JQX0003

2024

10.1186/s10033-024-01026-4

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
年,卷(期)：2024.37(3)