An optimal coordinated proton exchange membrane fuel cell heat management method based on large-scale multi-agent deep reinforcement learning

To improve the operating efficiency of proton exchange membrane fuel cells (PEMFCs), an optimal coordinated control strategy for addressing the poor coordination problem between the water pump and radiator in a PEMFC stack heat management system is proposed in this paper. To this end, a cooperative...

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Veröffentlicht in:	Energy reports Jg. 7; S. 6054 - 6068
Hauptverfasser:	Li, Jiawen, Li, Yaping, Yu, Tao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2021 Elsevier
Schlagworte:	Cooperative exploration strategy large-scale multi-agent twin-delay deep policy gradient (CESL-MATD3), proton exchange membrane fuel cell (PEMFC), coordinated control of stack temperature Distributed deep reinforcement learning Stack heat management system Distributed deep reinforcement learning Cooperative exploration strategy large-scale multi-agent twin-delay deep policy gradient (CESL-MATD3), proton exchange membrane fuel cell (PEMFC), coordinated control of stack temperature Stack heat management system
ISSN:	2352-4847, 2352-4847
Online-Zugang:	Volltext
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