| Title |
Reinforcement Learning-Based Energy Management for Hybrid Power Systems: State-of-the-Art Survey, Review, and Perspectives |
| ID_Doc |
31881 |
| Authors |
Tang, XL; Chen, JX; Qin, YC; Liu, T; Yang, K; Khajepour, A; Li, S |
| Title |
Reinforcement Learning-Based Energy Management for Hybrid Power Systems: State-of-the-Art Survey, Review, and Perspectives |
| Year |
2024 |
| Published |
Chinese Journal Of Mechanical Engineering, 37, 1 |
| DOI |
10.1186/s10033-024-01026-4 |
| 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 foundational 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 comprehensive 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 envisioned, integrating Autopilot and energy-saving control. |
| Author Keywords |
New energy vehicle; Hybrid power system; Reinforcement learning; Energy management strategy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001227047800001 |
| WoS Category |
Engineering, Mechanical |
| Research Area |
Engineering |
| PDF |
https://cjme.springeropen.com/counter/pdf/10.1186/s10033-024-01026-4
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