Back to Search
Start Over
Distributed Deep Reinforcement Learning-Based Energy and Emission Management Strategy for Hybrid Electric Vehicles.
- Source :
- IEEE Transactions on Vehicular Technology; Oct2021, Vol. 70 Issue 10, p9922-9934, 13p
- Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 70
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 153712168
- Full Text :
- https://doi.org/10.1109/TVT.2021.3107734