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On the origin of degradation in fuel cells and its fast identification by applying unconventional online-monitoring tools

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  • Subotić, Vanja
  • Menzler, Norbert H.
  • Lawlor, Vincent
  • Fang, Qingping
  • Pofahl, Stefan
  • Harter, Philipp
  • Schroettner, Hartmuth
  • Hochenauer, Christoph

Abstract

The key advantage of solid oxide fuel cells (SOFC) – high fuel flexibility – still remains the main challenge disturbing their stability, reliability and durability. Specific operating conditions induce and accelerate various degradation mechanisms and reduce the overall fuel cell lifetime. Identifying and predicting the onset of degradation at the preliminary stage is of crucial importance, in order to provoke appropriate countermeasures and to prolong the service time of the fuel cell technology. This is not possible when using available conventional monitoring tools. When employing appropriate online-monitoring tools the principle of which differs from the most common measurement of a linear stationary system, relevant information about the occurring failure modes can be obtained. An example for it is a total harmonic distortion (THD) tool, which is based on identification of the system non-linearity and its alternation from the stable state. Taking this into account, this study moves from the traditional concepts and we show that: (i) non-conventional methodologies can be used to identify relevant failure modes at their preliminary stage, (ii) it is possible to in-operando differentiate individual degradation mechanisms, and (iii) advanced unconventional online-monitoring tools are time-efficient and required measuring time can be reduced by factor up to 20.

Suggested Citation

  • Subotić, Vanja & Menzler, Norbert H. & Lawlor, Vincent & Fang, Qingping & Pofahl, Stefan & Harter, Philipp & Schroettner, Hartmuth & Hochenauer, Christoph, 2020. "On the origin of degradation in fuel cells and its fast identification by applying unconventional online-monitoring tools," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920311107
    DOI: 10.1016/j.apenergy.2020.115603
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    References listed on IDEAS

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    1. Subotić, Vanja & Stoeckl, Bernhard & Lawlor, Vincent & Strasser, Johannes & Schroettner, Hartmuth & Hochenauer, Christoph, 2018. "Towards a practical tool for online monitoring of solid oxide fuel cell operation: An experimental study and application of advanced data analysis approaches," Applied Energy, Elsevier, vol. 222(C), pages 748-761.
    2. Ma, Rui & Liu, Chen & Breaz, Elena & Briois, Pascal & Gao, Fei, 2018. "Numerical stiffness study of multi-physical solid oxide fuel cell model for real-time simulation applications," Applied Energy, Elsevier, vol. 226(C), pages 570-581.
    3. Lyu, Zewei & Meng, Hao & Zhu, Jianzhong & Han, Minfang & Sun, Zaihong & Xue, Huaqing & Zhao, Yongming & Zhang, Fudong, 2020. "Comparison of off-gas utilization modes for solid oxide fuel cell stacks based on a semi-empirical parametric model," Applied Energy, Elsevier, vol. 270(C).
    4. Gaber, Christian & Demuth, Martin & Prieler, René & Schluckner, Christoph & Schroettner, Hartmuth & Fitzek, Harald & Hochenauer, Christoph, 2019. "Experimental investigation of thermochemical regeneration using oxy-fuel exhaust gases," Applied Energy, Elsevier, vol. 236(C), pages 1115-1124.
    5. Barelli, L. & Bidini, G. & Cinti, G. & Gallorini, F. & Pöniz, M., 2017. "SOFC stack coupled with dry reforming," Applied Energy, Elsevier, vol. 192(C), pages 498-507.
    6. Wu, Xiao-long & Xu, Yuan-Wu & Xue, Tao & Zhao, Dong-qi & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2019. "Health state prediction and analysis of SOFC system based on the data-driven entire stage experiment," Applied Energy, Elsevier, vol. 248(C), pages 126-140.
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    Cited by:

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    2. Yuan, Hao & Zhou, Shulin & Zhang, Shaozhe & Tang, Wei & Jiang, Bo & Wei, Xuezhe & Dai, Haifeng, 2024. "Unconventional frequency response analysis of PEM fuel cell based on high-order frequency response function and total harmonic distortion," Applied Energy, Elsevier, vol. 357(C).
    3. Shin, Donghoon & Yoo, Seungryeol, 2023. "Diagnostic method for PEM fuel cell states using probability Distribution-Based loss component analysis for voltage loss decomposition," Applied Energy, Elsevier, vol. 330(PB).

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