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Performance prediction and analysis of a dead-end PEMFC stack using data-driven dynamic model

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  • Barzegari, Mohammad Mahdi
  • Rahgoshay, Seyed Majid
  • Mohammadpour, Lliya
  • Toghraie, Davood

Abstract

In this paper, we derive a data-driven dynamic model of a dead-end cascade-type proton exchange membrane (PEM) fuel cell. We employ an Artificial neural network (ANN) method to build the nonlinear black-box model of the PEM fuel cell stack. Both anode and cathode sides of the stack are composed of two stages which the second stages of them operate in a dead-end condition. Identification experiments are accomplished for a 400 W PEM fuel cell stack consisting of 4 cells with a 225 cm2 membrane. The empirical model inputs are time, stack current, inlet reactant gases pressures and purge interval time, and the model output is stack voltage. The ANN is trained with a set of experimental data, and the trained model is then tested and validated with an independent set of data. The results reveal good agreement between the proposed black-box model and experimental data with adequate certainty. The proposed methodology can be applied to guide the controller design and fault diagnosis of the PEM fuel cell in the near future.

Suggested Citation

  • Barzegari, Mohammad Mahdi & Rahgoshay, Seyed Majid & Mohammadpour, Lliya & Toghraie, Davood, 2019. "Performance prediction and analysis of a dead-end PEMFC stack using data-driven dynamic model," Energy, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:energy:v:188:y:2019:i:c:s036054421931744x
    DOI: 10.1016/j.energy.2019.116049
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    References listed on IDEAS

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    1. Hemmat Esfe, Mohammad & Hajmohammad, Hadi & Toghraie, Davood & Rostamian, Hadi & Mahian, Omid & Wongwises, Somchai, 2017. "Multi-objective optimization of nanofluid flow in double tube heat exchangers for applications in energy systems," Energy, Elsevier, vol. 137(C), pages 160-171.
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    Citations

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    Cited by:

    1. Liu, Yang & Tu, Zhengkai & Chan, Siew Hwa, 2023. "Water management and performance enhancement in a proton exchange membrane fuel cell system using optimized gas recirculation devices," Energy, Elsevier, vol. 279(C).
    2. Bai, Xingying & Luo, Lizhong & Huang, Bi & Jian, Qifei & Cheng, Zongyi, 2022. "Performance improvement of proton exchange membrane fuel cell stack by dual-path hydrogen supply," Energy, Elsevier, vol. 246(C).
    3. Meng, Kai & Zhou, Haoran & Chen, Ben & Tu, Zhengkai, 2021. "Dynamic current cycles effect on the degradation characteristic of a H2/O2 proton exchange membrane fuel cell," Energy, Elsevier, vol. 224(C).
    4. Meng, Kai & Chen, Ben & Zhou, Haoran & Shen, Jun & Shen, Zuguo & Tu, Zhengkai, 2022. "Investigation on degradation mechanism of hydrogen–oxygen proton exchange membrane fuel cell under current cyclic loading," Energy, Elsevier, vol. 242(C).
    5. 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).
    6. Chu, Tiankuo & Zhang, Ruofan & Wang, Yanbo & Ou, Mingyang & Xie, Meng & Shao, Hangyu & Yang, Daijun & Li, Bing & Ming, Pingwen & Zhang, Cunman, 2021. "Performance degradation and process engineering of the 10 kW proton exchange membrane fuel cell stack," Energy, Elsevier, vol. 219(C).
    7. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2021. "Working zone for a least-squares support vector machine for modeling polymer electrolyte fuel cell voltage," Applied Energy, Elsevier, vol. 283(C).
    8. Chen, Ben & Zhou, Haoran & He, Shaowen & Meng, Kai & Liu, Yang & Cai, Yonghua, 2021. "Numerical simulation on purge strategy of proton exchange membrane fuel cell with dead-ended anode," Energy, Elsevier, vol. 234(C).

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