Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework

•An A3C-based EMS is proposed to reduce the total operation cost of the FCHEB.•Multi-process parallel computation is employed to achieve the A3C-based EMS.•EMSs based on single-agent A2C and multi-thread A3C are adopted as baselines.•The proposed EMS is evaluated by training and testing using differ...

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Vydané v:Applied energy Ročník 346; s. 121358
Hlavní autori: Huang, Ruchen, He, Hongwen, Gao, Miaojue
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.09.2023
Predmet:
algorithms
artificial intelligence
Asynchronous advantage actor-critic (A3C)
batteries
Distributed deep reinforcement learning
energy
Energy management strategy
Fuel cell hybrid electric bus
fuel cells
hydrogen
Multi-process parallel computation
operating costs
transportation industry
vehicles (equipment)
Asynchronous advantage actor-critic (A3C)
Energy management strategy
Distributed deep reinforcement learning
Fuel cell hybrid electric bus
Multi-process parallel computation
ISSN:0306-2619, 1872-9118
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Shrnutí:•An A3C-based EMS is proposed to reduce the total operation cost of the FCHEB.•Multi-process parallel computation is employed to achieve the A3C-based EMS.•EMSs based on single-agent A2C and multi-thread A3C are adopted as baselines.•The proposed EMS is evaluated by training and testing using different cycles.•Both adaptability and computational efficiency are evaluated by online testing. Deep reinforcement learning (DRL) has become the mainstream method to design intelligent energy management strategies (EMSs) for fuel cell hybrid electric vehicles with the prosperity of artificial intelligence in recent years. Conventional DRL algorithms are suffering from low sampling efficiency and unsatisfactory utilization of computing resources. Combined with distributed architecture and parallel computation, DRL algorithms can be more efficient. Given that, this paper proposes a novel distributed DRL-based energy management framework for a fuel cell hybrid electric bus (FCHEB) to shorten the development cycle of DRL-based EMSs while reducing the total operation cost of the FCHEB. To begin, to make full use of the limited computing resources, a novel asynchronous advantage actor-critic (A3C)-based energy management framework is designed by innovatively integrating with the multi-process parallel computation technique. Then, a promising EMS considering the extra operation cost caused by fuel cell degradation and battery aging is designed based on this novel framework. Furthermore, EMSs based on a conventional DRL algorithm, advantage actor-critic (A2C), and another conventional distributed DRL framework, multi-thread A3C, are employed as baselines, and the performance of the proposed EMS is evaluated by training and testing using different driving cycles. Simulation results indicate that compared to EMSs based on A2C and multi-thread A3C, the proposed EMS can efficiently accelerate the convergence speed respectively by 87.46% and 88.92%, and reduce the total operation cost respectively by 44.83% and 41.19%. The main contribution of this article is to explore the integration of multi-process parallel computation in a distributed DRL-based EMS for a fuel cell vehicle for more efficient utilization of hydrogen energy in the transportation sector.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2023.121358