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Channel geometry optimization of a polymer electrolyte membrane fuel cell using genetic algorithm


  • Yang, Woo-Joo
  • Wang, Hong-Yang
  • Lee, Dae-Hyung
  • Kim, Young-Bae


The study presents the use of genetic algorithm (GA) to optimize the bipolar plate channel geometry of a polymer electrolyte membrane fuel cell (PEMFC). Contrary to previous optimization techniques, laborious fuel cell design steps, including boundary setting, mesh generation, and numerical computation at every design parameter variation step, are avoided by developing an automated program via Matlab and Comsol Multiphysics software. GA with Matlab automatically checks the optimality of the present fuel cell design with a performance index obtained from Comsol Multiphysics. If a global optimal is not reached, the new geometry set of a fuel cell is generated according to GA rules toward better fuel cell performance. The new set is then fed back to Comsol Multiphysics to have the new performance index calculated through updated boundary conditions, element and mesh generations, and numerical analysis. This automated optimization technique not only saves numerous calculations, but also obtains the global optimal result of a given fuel cell geometry. Therefore, it provides a fast and efficient optimization process and renders optimal results. In this study, two channel and rib geometry arrangements are studied: one with a symmetric anode and cathode channel arrangement, wherein channels and ribs face each other; and another with an asymmetric arrangement, wherein a channel faces a rib and vice versa. First, the two-dimensional (2D) CFD model is used to obtain the optimal result in order to speed up the optimization calculation, slightly sacrificing the model accuracy. Afterwards, the three-dimensional CFD model is utilized and experimental verification is made with the same geometries to support the validation of the 2D optimization result.

Suggested Citation

  • Yang, Woo-Joo & Wang, Hong-Yang & Lee, Dae-Hyung & Kim, Young-Bae, 2015. "Channel geometry optimization of a polymer electrolyte membrane fuel cell using genetic algorithm," Applied Energy, Elsevier, vol. 146(C), pages 1-10.
  • Handle: RePEc:eee:appene:v:146:y:2015:i:c:p:1-10
    DOI: 10.1016/j.apenergy.2015.01.130

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    References listed on IDEAS

    1. Pei, Pucheng & Chen, Huicui, 2014. "Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: A review," Applied Energy, Elsevier, vol. 125(C), pages 60-75.
    2. Chiu, Han-Chieh & Jang, Jer-Huan & Yan, Wei-Mon & Li, Hung-Yi & Liao, Chih-Cheng, 2012. "A three-dimensional modeling of transport phenomena of proton exchange membrane fuel cells with various flow fields," Applied Energy, Elsevier, vol. 96(C), pages 359-370.
    3. Jang, Jiin-Yuh & Cheng, Chin-Hsiang & Liao, Wang-Ting & Huang, Yu-Xian & Tsai, Ying-Chi, 2012. "Experimental and numerical study of proton exchange membrane fuel cell with spiral flow channels," Applied Energy, Elsevier, vol. 99(C), pages 67-79.
    4. Besseris, George J., 2014. "Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 128(C), pages 15-26.
    5. Cheng, Shan-Jen & Miao, Jr-Ming & Wu, Sheng-Ju, 2013. "Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)," Applied Energy, Elsevier, vol. 105(C), pages 161-169.
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    Cited by:

    1. Luo, Lei & Wen, Fengbo & Wang, Lei & Sundén, Bengt & Wang, Songtao, 2016. "Thermal enhancement by using grooves and ribs combined with delta-winglet vortex generator in a solar receiver heat exchanger," Applied Energy, Elsevier, vol. 183(C), pages 1317-1332.
    2. Wu, Horng-Wen, 2016. "A review of recent development: Transport and performance modeling of PEM fuel cells," Applied Energy, Elsevier, vol. 165(C), pages 81-106.
    3. Zhang, Heng & Xiao, Liusheng & Chuang, Po-Ya Abel & Djilali, Ned & Sui, Pang-Chieh, 2019. "Coupled stress–strain and transport in proton exchange membrane fuel cell with metallic bipolar plates," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Yin, Yan & Wu, Shiyu & Qin, Yanzhou & Otoo, Obed Nenyi & Zhang, Junfeng, 2020. "Quantitative analysis of trapezoid baffle block sloping angles on oxygen transport and performance of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 271(C).
    5. Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
    6. Gao, Xueping & Tian, Ye & Sun, Bowen, 2018. "Multi-objective optimization design of bidirectional flow passage components using RSM and NSGA II: A case study of inlet/outlet diffusion segment in pumped storage power station," Renewable Energy, Elsevier, vol. 115(C), pages 999-1013.
    7. Cai, Yonghua & Fang, Zhou & Chen, Ben & Yang, Tianqi & Tu, Zhengkai, 2018. "Numerical study on a novel 3D cathode flow field and evaluation criteria for the PEM fuel cell design," Energy, Elsevier, vol. 161(C), pages 28-37.
    8. Cai, Genchun & Liang, Yunmin & Liu, Zhichun & Liu, Wei, 2020. "Design and optimization of bio-inspired wave-like channel for a PEM fuel cell applying genetic algorithm," Energy, Elsevier, vol. 192(C).


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