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A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems

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  • Polverino, Pierpaolo
  • Sorrentino, Marco
  • Pianese, Cesare

Abstract

This work illustrates an innovative diagnostic technique able to improve fault isolability in Solid Oxide Fuel Cell (SOFC) energy conversion systems. On-board sensor reduction may induce fault clustering and, thus, hinder univocal fault isolation. According to the proposed technique, isolated system component sub-models, fed with faulty inputs, can be used to solve this issue. These models provide a set of redundant residuals, which react only if the related component is under faulty state. The technique is characterized and tested in simulated environment on an SOFC Anode Off-Gas Recycling (AOGR) system. Hydrogen external leakage, fuel and air heat exchangers efficiency reduction and recirculation unit malfunction are addressed and implemented in the complete system model. This latter is used to simulate system variables in both nominal and faulty conditions and compute residuals for fault detection and isolation. The sub-models are then used to introduce further residuals, and their behaviour is investigated at different fault magnitudes. The analysis is firstly performed in an ideal case scenario, considering the fault isolability that can be theoretically achieved. Then, practical application of the diagnostic algorithm is discussed, considering quantitative residuals deviation and properly analysing the effects of feeding the sub-models with inputs provided by both faulty and nominal models. The achieved results confirm the capability of the proposed approach to univocally isolate the considered faults in all the investigated conditions. Moreover, the analysis of the real case scenario proved the proposed algorithm suitable also for real applications.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:204:y:2017:i:c:p:1198-1214
    DOI: 10.1016/j.apenergy.2017.05.069
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Ying Tian & Qiang Zou & Jin Han, 2021. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification," Energies, MDPI, vol. 14(7), pages 1-17, March.
    3. Kang, Yongzhe & Duan, Bin & Zhou, Zhongkai & Shang, Yunlong & Zhang, Chenghui, 2020. "Online multi-fault detection and diagnosis for battery packs in electric vehicles," Applied Energy, Elsevier, vol. 259(C).
    4. Sorrentino, Marco & Bruno, Marco & Trifirò, Alena & Rizzo, Gianfranco, 2019. "An innovative energy efficiency metric for data analytics and diagnostics in telecommunication applications," Applied Energy, Elsevier, vol. 242(C), pages 1539-1548.
    5. 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).
    6. Li, Da & Zhang, Zhaosheng & Zhou, Litao & Liu, Peng & Wang, Zhenpo & Deng, Junjun, 2022. "Multi-time-step and multi-parameter prediction for real-world proton exchange membrane fuel cell vehicles (PEMFCVs) toward fault prognosis and energy consumption prediction," Applied Energy, Elsevier, vol. 325(C).
    7. van Biert, L. & Visser, K. & Aravind, P.V., 2020. "A comparison of steam reforming concepts in solid oxide fuel cell systems," Applied Energy, Elsevier, vol. 264(C).
    8. Zhang, Zehan & Li, Shuanghong & Xiao, Yawen & Yang, Yupu, 2019. "Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning," Applied Energy, Elsevier, vol. 233, pages 930-942.
    9. 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).
    10. Xiao-Long Wu & Hong Zhang & Hongli Liu & Yuan-Wu Xu & Jingxuan Peng & Zhiping Xia & Yongan Wang, 2022. "Modeling Analysis of SOFC System Oriented to Working Condition Identification," Energies, MDPI, vol. 15(5), pages 1-19, February.
    11. Max Emil S. Trothe & Hamid Reza Shaker & Muhyiddine Jradi & Krzysztof Arendt, 2019. "Fault Isolability Analysis and Optimal Sensor Placement for Fault Diagnosis in Smart Buildings," Energies, MDPI, vol. 12(9), pages 1-12, April.
    12. Zhong, Xiaobo & Xu, Yuanwu & Liu, Yanlin & Wu, Xiaolong & Zhao, Dongqi & Zheng, Yi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Root cause analysis and diagnosis of solid oxide fuel cell system oscillations based on data and topology-based model," Applied Energy, Elsevier, vol. 267(C).
    13. 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.
    14. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.

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