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Development and application of a comprehensive model-based methodology for fault mitigation of fuel cell powered systems

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  • Gallo, Marco
  • Costabile, Carmine
  • Sorrentino, Marco
  • Polverino, Pierpaolo
  • Pianese, Cesare

Abstract

The present paper describes an innovative and generalizable approach to apply fault mitigation strategies to fuel cell powered systems, upon information on system State of Health and Remaining Useful Life. Model-based approach is proposed to derive useful performance-related indicators for each system component. The model comprises two main parts: a nominal part, providing the key variables behavior in nominal conditions, and a faulty part that can be used for fault identification purposes. The framework of the algorithm firstly addresses a monitoring phase, through which residuals are computed, and if one or more residuals overcome defined thresholds, a fault detection is triggered. Afterwards, fault isolation is performed by means of a Fault Signature Matrix and the fault identification (i.e., its magnitude and time-behavior definition) is performed thanks to the faulty sub-models. Once characterized the fault, several strategies (according to different fault magnitudes) are considered, and the most suitable one can be chosen and applied. A case study is then presented to validate the methodology on a fuel starvation fault caused in a 6-cells solid oxide fuel cell stack by a fuel leakage in the anode pipeline. Once applied the mitigation strategy, it has been verified that the power output of the system safely bounds within 20% of its nominal value, whereas stack efficiency variation is negligible. The methodology herein proposed could substantially help the commercial success of solid oxide fuel cell technology, allowing increasing lifetime, with a much focused control of the main variable for diagnostic and maintenance-oriented applications. Indeed, if used in real applications, the proposed approach will speed up maintenance actions even setting the system in a soft condition to be properly prepared for replacement as well.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:279:y:2020:i:c:s0306261920311934
    DOI: 10.1016/j.apenergy.2020.115698
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    References listed on IDEAS

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    Cited by:

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    2. Young Park, Jin & Seop Lim, In & Ho Lee, Yeong & Lee, Won-Yong & Oh, Hwanyeong & Soo Kim, Min, 2023. "Severity-based fault diagnostic method for polymer electrolyte membrane fuel cell systems," Applied Energy, Elsevier, vol. 332(C).
    3. Roberto Spotorno & Fiammetta Rita Bianchi & Daniele Paravidino & Barbara Bosio & Paolo Piccardo, 2022. "Test and Modelling of Solid Oxide Fuel Cell Durability: A Focus on Interconnect Role on Global Degradation," Energies, MDPI, vol. 15(8), pages 1-19, April.
    4. Yuanwu Xu & Hao Shu & Hongchuan Qin & Xiaolong Wu & Jingxuan Peng & Chang Jiang & Zhiping Xia & Yongan Wang & Xi Li, 2022. "Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter," Energies, MDPI, vol. 15(7), pages 1-17, March.
    5. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Zeng, Jiawei & Fernandez, Carlos, 2024. "Remaining useful life prediction and state of health diagnosis of lithium-ion batteries with multiscale health features based on optimized CatBoost algorithm," Energy, Elsevier, vol. 300(C).
    6. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    7. Chen, Kui & Badji, Abderrezak & Laghrouche, Salah & Djerdir, Abdesslem, 2022. "Polymer electrolyte membrane fuel cells degradation prediction using multi-kernel relevance vector regression and whale optimization algorithm," Applied Energy, Elsevier, vol. 318(C).
    8. Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Kai Xiong & Chuangting Chen & Mumin Rao & Zhiping Peng & Xi Li & Jingxuan Peng, 2022. "A Data-Driven Fault Diagnosis Method for Solid Oxide Fuel Cell Systems," Energies, MDPI, vol. 15(7), pages 1-16, March.

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