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Effects of operating parameters on hydrogen crossover rate through Nafion® membranes in polymer electrolyte membrane fuel cells

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  • Baik, Kyung Don
  • Hong, Bo Ki
  • Kim, Min Soo

Abstract

This study examines the effects of operating parameters—comprising temperature, relative humidity, hydrogen pressure, and membrane thickness—on hydrogen crossover rate in a polymer electrolyte membrane fuel cell (PEMFC). It is found that the hydrogen crossover rate increases proportional to both temperature and relative humidity for all membrane samples. Increased hydrogen crossover rate is also observed with increasing hydrogen pressure. The hydrogen crossover rate increases gradually with the decrease of membrane thickness from 258 to 135 μm. When the membrane thickness decreases from 63 to 21 μm, there is a dramatic increase of hydrogen crossover. Multiple linear regression analysis was used to analyze the effects of all the operating parameters on hydrogen crossover rate. The results indicate that increased hydrogen crossover rate is mainly determined by the inverse of the logarithmic membrane thickness, followed by hydrogen pressure, relative humidity, and temperature, respectively.

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  • Baik, Kyung Don & Hong, Bo Ki & Kim, Min Soo, 2013. "Effects of operating parameters on hydrogen crossover rate through Nafion® membranes in polymer electrolyte membrane fuel cells," Renewable Energy, Elsevier, vol. 57(C), pages 234-239.
    • Handle: RePEc:eee:renene:v:57:y:2013:i:c:p:234-239
    DOI: 10.1016/j.renene.2013.01.046
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    References listed on IDEAS

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    1. Nam, Jinmoo & Chippar, Purushothama & Kim, Whangi & Ju, Hyunchul, 2010. "Numerical analysis of gas crossover effects in polymer electrolyte fuel cells (PEFCs)," Applied Energy, Elsevier, vol. 87(12), pages 3699-3709, December.
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    Cited by:

    1. Colmenar-Santos, Antonio & Alberdi-Jiménez, Lucía & Nasarre-Cortés, Lorenzo & Mora-Larramona, Joaquín, 2014. "Residual heat use generated by a 12 kW fuel cell in an electric vehicle heating system," Energy, Elsevier, vol. 68(C), pages 182-190.
    2. Pengcheng Liu & Sichuan Xu, 2022. "Experimental Research on the Dynamic Characteristics and Voltage Uniformity of a PEMFC Stack under Subzero Temperatures," Energies, MDPI, vol. 15(9), pages 1-14, April.
    3. Baik, Kyung Don & Hong, Bo Ki & Han, Kookil & Kim, Min Soo, 2014. "Effects of anisotropic bending stiffness of gas diffusion layers on the performance of polymer electrolyte membrane fuel cells with bipolar plates employing different channel depths," Renewable Energy, Elsevier, vol. 69(C), pages 356-364.
    4. Sayadi, Parvin & Rowshanzamir, Soosan & Parnian, Mohammad Javad, 2016. "Study of hydrogen crossover and proton conductivity of self-humidifying nanocomposite proton exchange membrane based on sulfonated poly (ether ether ketone)," Energy, Elsevier, vol. 94(C), pages 292-303.
    5. Kannan, Vishvak & Xue, Hansong & Raman, K. Ashoke & Chen, Jiasheng & Fisher, Adrian & Birgersson, Erik, 2020. "Quantifying operating uncertainties of a PEMFC – Monte Carlo-machine learning based approach," Renewable Energy, Elsevier, vol. 158(C), pages 343-359.
    6. Chang, Huawei & Cai, Fengyang & Yu, Xianxian & Duan, Chen & Chan, Siew Hwa & Tu, Zhengkai, 2023. "Experimental study on the thermal management of an open-cathode air-cooled proton exchange membrane fuel cell stack with ultra-thin metal bipolar plates," Energy, Elsevier, vol. 263(PA).
    7. Jia, Fei & Tian, Xiaodi & Liu, Fengfeng & Ye, Junjie & Yang, Chengpeng, 2023. "Oxidant starvation under various operating conditions on local and transient performance of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 331(C).
    8. Pineda-Delgado, J.L. & Chávez-Ramirez, A.U. & Gutierrez B, Cynthia K. & Rivas, S. & Marisela, Cruz-Ramírez & de Jesús Hernández-Cortes, Ramiro & Menchaca-Rivera, J.A. & Pérez-Robles, J.F., 2022. "Effect of relative humidity and temperature on the performance of an electrochemical hydrogen compressor," Applied Energy, Elsevier, vol. 311(C).
    9. Kong, Im Mo & Jung, Aeri & Kim, Beom Jun & Baik, Kyung Don & Kim, Min Soo, 2015. "Experimental study on the start-up with dry gases from normal cell temperatures in self-humidified proton exchange membrane fuel cells," Energy, Elsevier, vol. 93(P1), pages 57-66.
    10. Ren, Peng & Pei, Pucheng & Chen, Dongfang & Li, Yuehua & Wu, Ziyao & Zhang, Lu & Li, Zizhao & Wang, Mingkai & Wang, He & Wang, Bozheng & Wang, Xizhong, 2022. "Novel analytic method of membrane electrode assembly parameters for fuel cell consistency evaluation by micro-current excitation," Applied Energy, Elsevier, vol. 306(PB).
    11. Jouin, Marine & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine, 2016. "Degradations analysis and aging modeling for health assessment and prognostics of PEMFC," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 78-95.
    12. Pei, Pucheng & Wu, Ziyao & Li, Yuehua & Jia, Xiaoning & Chen, Dongfang & Huang, Shangwei, 2018. "Improved methods to measure hydrogen crossover current in proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 215(C), pages 338-347.
    13. Pan, Mingzhang & Pan, Chengjie & Li, Chao & Zhao, Jian, 2021. "A review of membranes in proton exchange membrane fuel cells: Transport phenomena, performance and durability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    14. Li, Sida & Wei, Xuezhe & Jiang, Shangfeng & Yuan, Hao & Ming, Pingwen & Wang, Xueyuan & Dai, Haifeng, 2022. "Hydrogen crossover diagnosis for fuel cell stack: An electrochemical impedance spectroscopy based method," Applied Energy, Elsevier, vol. 325(C).
    15. Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    16. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.

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