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Multi-parameter optimization of stepwise distribution of parameters of gas diffusion layer and catalyst layer for PEMFC peak power density

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  • Yu, Rui Jiao
  • Guo, Hang
  • Ye, Fang
  • Chen, Hao

Abstract

Gas diffusion layer and catalyst layer are important parts in a proton exchange membrane fuel cell, and the structure parameters can affect cell performance. Because the electrochemical reaction inside the cell is not uniform, it is particularity important to explore the uneven distribution of each structure parameter. Thus, a fuel cell model including agglomerate and optimization algorithm is built to study the optimal step-wise parameter distribution. These parameters include cathode gas diffusion layer porosity, platinum loading, ionomer content and carbon loading inside cathode catalyst layer. The four parameters are calculated comprehensively to obtain the optimal three segments distribution along three directions of the coordinate axis. The results show that the parameters obtained are various at different voltages and directions. The results along Z-Axis has the best positive effect on cell performance. In the results no matter which direction is, the maximum peak power density all occurs when the optimization voltage is 0.4 V. The cell can operate in wide voltage range by selecting the parameters obtained at 0.4 V along Z-Axis, and the current density can be improved at least 1.859 %. Bidirectional mass transfer at the interface between two domains can be enhanced for the model with optimal parameters.

Suggested Citation

  • Yu, Rui Jiao & Guo, Hang & Ye, Fang & Chen, Hao, 2022. "Multi-parameter optimization of stepwise distribution of parameters of gas diffusion layer and catalyst layer for PEMFC peak power density," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922010467
    DOI: 10.1016/j.apenergy.2022.119764
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    References listed on IDEAS

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    Cited by:

    1. Siwen Gu & Jiaan Wang & Xinmin You & Yu Zhuang, 2023. "Investigating the Parameter-Driven Cathode Gas Diffusion of PEMFCs with a Piecewise Linearization Model," Energies, MDPI, vol. 16(9), pages 1-12, April.
    2. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    3. Yu, Ruijiao & Guo, Hang & Chen, Hao & Ye, Fang, 2023. "Effect of ionomer volume fraction within cathode catalyst layer on performance of a PEMFC," Energy, Elsevier, vol. 277(C).

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