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A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues

Author

Listed:
  • Pedro Andrade

    (CISE-Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal)

  • Khaled Laadjal

    (CISE-Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal)

  • Adérito Neto Alcaso

    (CISE-Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal
    Polytechnic of Guarda, School of Technology and Management, P-6300-559 Guarda, Portugal)

  • Antonio J. Marques Cardoso

    (CISE-Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal)

Abstract

The complexity of Fuel Cell (FC) systems demands a profound and sustained understanding of the various phenomena occurring inside of it. Thus far, FCs, especially Proton Exchange Membrane Fuel Cells (PEMFCs), have been recognized as being among the most promising technologies for reducing Green House Gas (GHG) emissions because they can convert the chemical energy bonded to hydrogen and oxygen into electricity and heat. However, their efficiency remains limited. To enhance their efficiency, two distinct factors are suggested. First, the quality of materials plays a significant role in the development of more robust and efficient FCs. Second, the ability to identify, mitigate, and reduce the occurrence of faults through the use of robust control algorithms is crucial. Therefore, more focused on the second point, this paper compiles, distinguishes, and analyzes several publications from the past 25 years related to faults and their diagnostic techniques in FCs. Furthermore, the paper presents various schemes outlining different symptoms, their causes, and corresponding fault algorithms.

Suggested Citation

  • Pedro Andrade & Khaled Laadjal & Adérito Neto Alcaso & Antonio J. Marques Cardoso, 2024. "A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues," Energies, MDPI, vol. 17(3), pages 1-45, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:657-:d:1329549
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    References listed on IDEAS

    as
    1. Pahon, E. & Yousfi Steiner, N. & Jemei, S. & Hissel, D. & Moçoteguy, P., 2016. "A signal-based method for fast PEMFC diagnosis," Applied Energy, Elsevier, vol. 165(C), pages 748-758.
    2. 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).
    3. Sutharssan, Thamo & Montalvao, Diogo & Chen, Yong Kang & Wang, Wen-Chung & Pisac, Claudia & Elemara, Hakim, 2017. "A review on prognostics and health monitoring of proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 440-450.
    4. Pu, Zonghua & Zhang, Gaixia & Hassanpour, Amir & Zheng, Dewen & Wang, Shanyu & Liao, Shijun & Chen, Zhangxin & Sun, Shuhui, 2021. "Regenerative fuel cells: Recent progress, challenges, perspectives and their applications for space energy system," Applied Energy, Elsevier, vol. 283(C).
    5. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Jemei, Samir & Giraud, Alain & Rosini, Sebastien, 2016. "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied Energy, Elsevier, vol. 164(C), pages 284-293.
    6. Song Yan & Mingyang Yang & Chuanyu Sun & Sichuan Xu, 2023. "Liquid Water Characteristics in the Compressed Gradient Porosity Gas Diffusion Layer of Proton Exchange Membrane Fuel Cells Using the Lattice Boltzmann Method," Energies, MDPI, vol. 16(16), pages 1-18, August.
    7. Kim, Kyunghyun & Kim, Jaeyeon & Choi, Heesoo & Kwon, Obeen & Jang, Yujae & Ryu, Sangbong & Lee, Heeyun & Shim, Kyuhwan & Park, Taehyun & Cha, Suk Won, 2023. "Pre-diagnosis of flooding and drying in proton exchange membrane fuel cells by bagging ensemble deep learning models using long short-term memory and convolutional neural networks," Energy, Elsevier, vol. 266(C).
    8. Kurnia, Jundika C. & Chaedir, Benitta A. & Sasmito, Agus P. & Shamim, Tariq, 2021. "Progress on open cathode proton exchange membrane fuel cell: Performance, designs, challenges and future directions," Applied Energy, Elsevier, vol. 283(C).
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