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
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Zusammenfassung: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 computing provides a reliable algorithm basis for promoting adaptability. Dedicated to trying more efficient deep reinforcement learning (DRL) algorithms, this paper proposed a deep q-network (DQN)-based energy and emission management strategy (E&EMS) at first. Then, two distributed DRL algorithms, namely asynchronous advantage actor-critic (A3C) and distributed proximal policy optimization (DPPO), were adopted to propose EMSs, respectively. Finally, emission optimization was taken into account and then distributed DRL-based E&EMSs were proposed. Regarding dynamic programming (DP) as the optimal benchmark, simulation results show that three DRL-based control strategies can achieve near-optimal fuel economy and outstanding computational efficiency, and compared with DQN, two distributed DRL algorithms have improved the learning efficiency by four times.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3107734