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This paper proposes a deep reinforcement learning (DRL) controller based on fusion optimization for a proton exchange membrane fuel cell (PEMFC) system, which improves the dynamic and steady-state performance of the system. The DRL controller effectively coordinates the flow control of air and hydrogen, leading to enhanced output power and economic benefits. Experimental results show significant improvements over traditional control methods.

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Abstract

In a proton exchange membrane fuel cell (PEMFC) system, the flow of the air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure the real-time gas supply in the fuel cell and improve the output power and economic benefits of the system, a deep reinforcement learning (DRL) controller based on fusion optimization with deterministic policy gradient and a control optimization strategy based on net power optimization are proposed in this paper. The experimental results show that the control algorithm proposed in this paper can effectively improve the dynamic performance and steady-state performance of the system, which is embodied in the average 12.5% increase in the dynamic performance compared with the fuzzy PID control and average 99.54% increase in the steady-state performance compared with the traditional DRL control.

Key findings

  • 12.5% average increase in dynamic performance compared to fuzzy PID control
  • 99.54% average increase in steady-state performance compared to traditional DRL control
  • Improved coordination of air and hydrogen flow control

Keywords

Proton exchange membrane fuel cellReinforcement learningController (irrigation)Control theory (sociology)PID controllerControl engineering

Identifiers

DOI
10.22541/au.166623123.32285037/v1
Journal
Unknown journal
Year
2022