IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v277y2020ics0306261920311107.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920311107
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115603?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    4. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Preininger, Michael & Stoeckl, Bernhard & Subotić, Vanja & Mittmann, Frank & Hochenauer, Christoph, 2019. "Performance of a ten-layer reversible Solid Oxide Cell stack (rSOC) under transient operation for autonomous application," Applied Energy, Elsevier, vol. 254(C).
    2. Wang, Chao & Liao, Mingzheng & Liang, Bo & Jiang, Zhiqiang & Zhong, Weilin & Chen, Ying & Luo, Xianglong & Shu, Riyang & Tian, Zhipeng & Lei, Libin, 2021. "Enhancement effect of catalyst support on indirect hydrogen production from propane partial oxidation towards commercial solid oxide fuel cell (SOFC) applications," Applied Energy, Elsevier, vol. 288(C).
    3. Xu, Liangfei & Fang, Chuan & Li, Jianqiu & Ouyang, Minggao & Lehnert, Werner, 2018. "Nonlinear dynamic mechanism modeling of a polymer electrolyte membrane fuel cell with dead-ended anode considering mass transport and actuator properties," Applied Energy, Elsevier, vol. 230(C), pages 106-121.
    4. 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).
    5. Petrone, Giovanni & Zamboni, Walter & Spagnuolo, Giovanni, 2019. "An interval arithmetic-based method for parametric identification of a fuel cell equivalent circuit model," Applied Energy, Elsevier, vol. 242(C), pages 1226-1236.
    6. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    7. Zarabi Golkhatmi, Sanaz & Asghar, Muhammad Imran & Lund, Peter D., 2022. "A review on solid oxide fuel cell durability: Latest progress, mechanisms, and study tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    8. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).
    9. 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.
    10. Li, Haolong & Wei, Wei & Liu, Fengxia & Xu, Xiaofei & Li, Zhiyi & Liu, Zhijun, 2023. "Identification of internal polarization dynamics for solid oxide fuel cells investigated by electrochemical impedance spectroscopy and distribution of relaxation times," Energy, Elsevier, vol. 267(C).
    11. Mumin Rao & Li Wang & Chuangting Chen & Kai Xiong & Mingfei Li & Zhengpeng Chen & Jiangbo Dong & Junli Xu & Xi Li, 2022. "Data-Driven State Prediction and Analysis of SOFC System Based on Deep Learning Method," Energies, MDPI, vol. 15(9), pages 1-15, April.
    12. Wang, Yang & Wu, Chengru & Zhao, Siyuan & Wang, Jian & Zu, Bingfeng & Han, Minfang & Du, Qing & Ni, Meng & Jiao, Kui, 2022. "Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell," Applied Energy, Elsevier, vol. 315(C).
    13. 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.
    14. Li, Hui & Eghbalian, Nasrin, 2021. "Numerical studies of effect of integrated through-plane array flow field on novel PEFC performance using BWO algorithm under uncertainties," Energy, Elsevier, vol. 231(C).
    15. Yaping Wu & Xiaolong Wu & Yuanwu Xu & Yongjun Cheng & Xi Li, 2023. "A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    16. Habibollahzade, Ali & Rosen, Marc A., 2021. "Syngas-fueled solid oxide fuel cell functionality improvement through appropriate feedstock selection and multi-criteria optimization using Air/O2-enriched-air gasification agents," Applied Energy, Elsevier, vol. 286(C).
    17. Baccioli, Andrea & Liponi, Angelica & Milewski, Jarosław & Szczęśniak, Arkadiusz & Desideri, Umberto, 2021. "Hybridization of an internal combustion engine with a molten carbonate fuel cell for marine applications," Applied Energy, Elsevier, vol. 298(C).
    18. Pongratz, G. & Subotić, V. & Schroettner, H. & Hochenauer, C. & Skrzypkiewicz, M. & Kupecki, Jakub & Anca-Couce, A. & Scharler, R., 2021. "Analysis of H2S-related short-term degradation and regeneration of anode- and electrolyte supported solid oxide fuel cells fueled with biomass steam gasifier product gas," Energy, Elsevier, vol. 218(C).
    19. Xiaowei Fu & Yanlin Liu & Xi Li, 2020. "Source Diagnosis of Solid Oxide Fuel Cell System Oscillation Based on Data Driven," Energies, MDPI, vol. 13(16), pages 1-13, August.
    20. Polverino, Pierpaolo & Bove, Giovanni & Sorrentino, Marco & Pianese, Cesare & Beretta, Davide, 2019. "Advancements on scaling-up simulation of Proton Exchange Membrane Fuel Cells impedance through Buckingham Pi theorem," Applied Energy, Elsevier, vol. 249(C), pages 245-252.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920311107. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.