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Real-Time Control of Gas Supply System for a PEMFC Cold-Start Based on the MADDPG Algorithm

Author

Listed:
  • Lei Pan

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Tong Zhang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Yuan Gao

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

During the cold-start process of a PEMFC, the supply of air and hydrogen in the gas supply system has a great influence on the cold-start performance. The cold-start of a PEMFC is a complex nonlinear coupling process, and the traditional control strategy is not sensitive to the real-time characteristics of the system. Inspired by the strong perception and decision-making abilities of deep reinforcement learning, this paper proposes a cold-start control strategy for a gas supply system based on the MADDPG algorithm, and designs an air supply controller and a hydrogen supply controller based on this algorithm. The proposed strategy can optimize the control parameters of the gas supply system in real time according to the temperature rise rate of the stack during the cold-start process, the fluctuation of the OER, and the voltage output characteristics. After the strategy is trained offline according to the designed reward function, the detailed in-loop simulation experiment results are given and compared with the traditional control strategy for the gas supply system. From the results, it can be seen that the proposed MADDPG control strategy has a more effective coordination control effect.

Suggested Citation

  • Lei Pan & Tong Zhang & Yuan Gao, 2023. "Real-Time Control of Gas Supply System for a PEMFC Cold-Start Based on the MADDPG Algorithm," Energies, MDPI, vol. 16(12), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4655-:d:1168966
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    References listed on IDEAS

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    1. Zuo, Jian & Lv, Hong & Zhou, Daming & Xue, Qiong & Jin, Liming & Zhou, Wei & Yang, Daijun & Zhang, Cunman, 2021. "Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application," Applied Energy, Elsevier, vol. 281(C).
    2. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    3. Li, Linjun & Wang, Shixue & Yue, Like & Wang, Guozhuo, 2019. "Cold-start method for proton-exchange membrane fuel cells based on locally heating the cathode," Applied Energy, Elsevier, vol. 254(C).
    4. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
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