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The Development of a Model-Based Methodology to Implement a Fused Health Indicator for a Solid Oxide Fuel Cell

Author

Listed:
  • Andrea Ambrosino

    (Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy)

  • Giovanni Bove

    (Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy)

  • Marco Sorrentino

    (Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy)

  • Fabio Postiglione

    (Department of Industrial Engineering (DIIn), University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano, Italy)

Abstract

Hydrogen-based technologies are growing, thanks to recent advancements in systems such as fuel cells and electrolyzers. The present work aims to develop a methodology for the definition of a fused health indicator to monitor the operating and health conditions of a solid oxide fuel cell system. A suitable degradation model was built to yield four trendable output indicators, which were subsequently merged to create the fused health indicator. Subsequently, the assessment of off-design conditions and two realistic scenarios (leakage and constant excess of air working regime) was carried out. The health indicator has proved suitable for fault detection, prognostic applications, control strategy improvement, and health management. In particular, the methodology has underlined the necessity of making the control strategy adaptive with respect to degradation. Through this approach, it is observed that reducing the solid oxide fuel cell temperature difference by 10 °C can result in a 1.2% increase in lifetime. In contrast, the leakage simulation reveals a decrease of about 10.5% in the health state after 100 h, resulting in about a 21% lower end-of-life.

Suggested Citation

  • Andrea Ambrosino & Giovanni Bove & Marco Sorrentino & Fabio Postiglione, 2025. "The Development of a Model-Based Methodology to Implement a Fused Health Indicator for a Solid Oxide Fuel Cell," Energies, MDPI, vol. 18(18), pages 1-22, September.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4822-:d:1746728
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    References listed on IDEAS

    as
    1. Polverino, Pierpaolo & Sorrentino, Marco & Pianese, Cesare, 2017. "A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems," Applied Energy, Elsevier, vol. 204(C), pages 1198-1214.
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    3. Gallo, Marco & Costabile, Carmine & Sorrentino, Marco & Polverino, Pierpaolo & Pianese, Cesare, 2020. "Development and application of a comprehensive model-based methodology for fault mitigation of fuel cell powered systems," Applied Energy, Elsevier, vol. 279(C).
    4. Shan-Jen Cheng & Wen-Ken Li & Te-Jen Chang & Chang-Hung Hsu, 2021. "Data-Driven Prognostics of the SOFC System Based on Dynamic Neural Network Models," Energies, MDPI, vol. 14(18), pages 1-17, September.
    5. Jingxuan Peng & Dongqi Zhao & Yuanwu Xu & Xiaolong Wu & Xi Li, 2023. "Comprehensive Analysis of Solid Oxide Fuel Cell Performance Degradation Mechanism, Prediction, and Optimization Studies," Energies, MDPI, vol. 16(2), pages 1-23, January.
    6. Gallo, Marco & Polverino, Pierpaolo & Mougin, Julie & Morel, Bertrand & Pianese, Cesare, 2020. "Coupling electrochemical impedance spectroscopy and model-based aging estimation for solid oxide fuel cell stacks lifetime prediction," Applied Energy, Elsevier, vol. 279(C).
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