Distributed Deep Reinforcement Learning-Based Energy and Emission Management Strategy for Hybrid Electric Vehicles

Advanced algorithms can promote the development of energy management strategies (EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) with distributed structure can significantly improve training efficiency in complex environments, and multi-threaded parallel com...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 70; H. 10; S. 9922 - 9934
Hauptverfasser:	Tang, Xiaolin, Chen, Jiaxin, Liu, Teng, Qin, Yechen, Cao, Dongpu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Asynchronous advantage actor-critic Deep learning deep reinforcement learning distributed proximal policy optimization Dynamic programming Efficiency Electric vehicles Emission Energy management energy management strategy Engines Fuel economy Heuristic algorithms hybrid electric vehicle Hybrid electric vehicles Machine learning Optimization Real-time systems Training
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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