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An evaluation method of gas distribution quality in dynamic process of proton exchange membrane fuel cell

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  • Chen, Huicui
  • Zhao, Xin
  • Qu, Bingwang
  • Zhang, Tong
  • Pei, Pucheng
  • Li, Congxin

Abstract

The proton exchange membrane fuel cell is considered to be the best alternative to next generation power system due to its high efficiency without emission. It has been found that the lack of gas supply due to frequent load change and start-stop is a very crucial factor in fuel cell life decay and restricts its commercialization. Gas starvation is the state in which the reaction gas of proton exchange membrane fuel cell operates at a sub-stoichiometric level. The diagnosis of local gas starvation without additional expensive and intrusive sensors is still a tough problem not to be solved. In this paper, the area of gas starvation is calculated to evaluate the degree of gas starvation. A novel statistical analysis based method is applied as the index to measure the uniformity of the reaction gas concentration directly on the surface of electrode. The distribution quality of reaction gas under both steady-state and dynamic conditions was evaluated by simulating different operating conditions through the simulation model established. A suitable working area of the fuel cell under steady state is proposed to achieve more stable cell performance under load change conditions. The distribution quality of reaction gas concentration on the whole electrode surface under dynamic condition is evaluated to identify the specific areas where gas starvation is likely to occur. The results of this study will provide the basis for the optimal control and structural design of the fuel cell against dynamic gas starvation, and contribute to the long-life study of proton exchange membrane fuel cells.

Suggested Citation

  • Chen, Huicui & Zhao, Xin & Qu, Bingwang & Zhang, Tong & Pei, Pucheng & Li, Congxin, 2018. "An evaluation method of gas distribution quality in dynamic process of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 232(C), pages 26-35.
    • Handle: RePEc:eee:appene:v:232:y:2018:i:c:p:26-35
    DOI: 10.1016/j.apenergy.2018.09.031
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    References listed on IDEAS

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    4. Milos Milanovic & Verica Radisavljevic-Gajic, 2019. "Multi-Timescale-Based Partial Optimal Control of a Proton-Exchange Membrane Fuel Cell," Energies, MDPI, vol. 13(1), pages 1-24, December.
    5. Chen, Kui & Laghrouche, Salah & Djerdir, Abdesslem, 2019. "Degradation model of proton exchange membrane fuel cell based on a novel hybrid method," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Deng, Shutong & Zhang, Jun & Zhang, Caizhi & Luo, Mengzhu & Ni, Meng & Li, Yu & Zeng, Tao, 2022. "Prediction and optimization of gas distribution quality for high-temperature PEMFC based on data-driven surrogate model," Applied Energy, Elsevier, vol. 327(C).
    7. Chen, Dongfang & Pei, Pucheng & Ren, Peng & Song, Xin & Wang, He & Zhang, Lu & Wang, Mingkai, 2022. "Analytical methods for the effect of anode nitrogen concentration on performance and voltage consistency of proton exchange membrane fuel cell stack," Energy, Elsevier, vol. 258(C).
    8. Wang, Xuechao & Chen, Jinzhou & Quan, Shengwei & Wang, Ya-Xiong & He, Hongwen, 2020. "Hierarchical model predictive control via deep learning vehicle speed predictions for oxygen stoichiometry regulation of fuel cells," Applied Energy, Elsevier, vol. 276(C).
    9. Zhang, Xiaojie & Zhang, Tong & Chen, Huicui & Cao, Yinliang, 2021. "A review of online electrochemical diagnostic methods of on-board proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 286(C).
    10. Quan, Shengwei & He, Hongwen & Chen, Jinzhou & Zhang, Zhendong & Han, Ruoyan & Wang, Ya-Xiong, 2023. "Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method," Energy, Elsevier, vol. 278(PA).
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