IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v214y2021ics0360544220321691.html
   My bibliography  Save this article

Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell

Author

Listed:
  • Park, Jin Young
  • Lim, In Seop
  • Choi, Eun Jung
  • Kim, Min Soo

Abstract

One of the barriers for commercialization of polymer electrolyte membrane fuel cell system is its reliability. One good solution for reliability improvement is to develop fault diagnosis model. This study suggests a diagnostic method for polymer electrolyte membrane fuel cell thermal management system. With the increase of fuel cell stack power these days, waste heat from the stack also increased and thereby thermal management system is also receiving attention. In this study, thermal management system fault experiments are repeatedly performed under various loads and stack degradation conditions. With the degradation of stack, waste heat from the stack increases and fault response as well as its impact on the system varies. The diagnosis model developed in this study detects faults on component-level and diagnoses severity of the faulty components. Furthermore, the diagnosis model is applicable to fuel cell system with degraded stack. In the process of model development, single-task learning technique is applied to neural network diagnosis model for higher diagnostic accuracy and compared with conventional multi-task learning technique.

Suggested Citation

  • Park, Jin Young & Lim, In Seop & Choi, Eun Jung & Kim, Min Soo, 2021. "Fault diagnosis of thermal management system in a polymer electrolyte membrane fuel cell," Energy, Elsevier, vol. 214(C).
    • Handle: RePEc:eee:energy:v:214:y:2021:i:c:s0360544220321691
    DOI: 10.1016/j.energy.2020.119062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220321691
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.119062?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. Shao, Meng & Zhu, Xin-Jian & Cao, Hong-Fei & Shen, Hai-Feng, 2014. "An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system," Energy, Elsevier, vol. 67(C), pages 268-275.
    2. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Jemei, Samir & Giraud, Alain & Rosini, Sebastien, 2016. "Online implementation of SVM based fault diagnosis strategy for PEMFC systems," Applied Energy, Elsevier, vol. 164(C), pages 284-293.
    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. Taehyung Koo & Rockkil Ko & Dongwoo Ha & Jaeyoung Han, 2023. "Development of Model-Based PEM Water Electrolysis HILS (Hardware-in-the-Loop Simulation) System for State Evaluation and Fault Detection," Energies, MDPI, vol. 16(8), pages 1-18, April.
    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. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    4. Laifa Tao & Haifei Liu & Jiqing Zhang & Xuanyuan Su & Shangyu Li & Jie Hao & Chen Lu & Mingliang Suo & Chao Wang, 2022. "Associated Fault Diagnosis of Power Supply Systems Based on Graph Matching: A Knowledge and Data Fusion Approach," Mathematics, MDPI, vol. 10(22), pages 1-28, November.
    5. Lin, Rui & Wang, Hong & Zhu, Yu, 2021. "Optimizing the structural design of cathode catalyst layer for PEM fuel cells for improving mass-specific power density," Energy, Elsevier, vol. 221(C).
    6. Hui, Jiuwu & Yuan, Jingqi, 2022. "Neural network-based adaptive fault-tolerant control for load following of a MHTGR with prescribed performance and CRDM faults," Energy, Elsevier, vol. 257(C).
    7. Pang, Ran & Zhang, Caizhi & Dai, Haifeng & Bai, Yunfeng & Hao, Dong & Chen, Jinrui & Zhang, Bin, 2022. "Intelligent health states recognition of fuel cell by cell voltage consistency under typical operating parameters," Applied Energy, Elsevier, vol. 305(C).
    8. Cai, Yonghua & Wu, Di & Sun, Jingming & Chen, Ben, 2021. "The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC," Energy, Elsevier, vol. 222(C).

    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. 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).
    2. Oh, Hwanyeong & Lee, Won-Yong & Won, Jinyeon & Kim, Minjin & Choi, Yoon-Young & Han, Soo-Bin, 2020. "Residual-based fault diagnosis for thermal management systems of proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 277(C).
    3. Andújar, J.M. & Segura, F. & Isorna, F. & Calderón, A.J., 2018. "Comprehensive diagnosis methodology for faults detection and identification, and performance improvement of Air-Cooled Polymer Electrolyte Fuel Cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 193-207.
    4. Wu, Kangcheng & Du, Qing & Zu, Bingfeng & Wang, Yupeng & Cai, Jun & Gu, Xin & Xuan, Jin & Jiao, Kui, 2021. "Enabling real-time optimization of dynamic processes of proton exchange membrane fuel cell: Data-driven approach with semi-recurrent sliding window method," Applied Energy, Elsevier, vol. 303(C).
    5. Gao, Q.W. & Liu, W.Y. & Tang, B.P. & Li, G.J., 2018. "A novel wind turbine fault diagnosis method based on intergral extension load mean decomposition multiscale entropy and least squares support vector machine," Renewable Energy, Elsevier, vol. 116(PA), pages 169-175.
    6. Xu, Shuhui & Wang, Yong & Wang, Zhi, 2019. "Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method," Energy, Elsevier, vol. 173(C), pages 457-467.
    7. 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).
    8. Won, Jinyeon & Oh, Hwanyeong & Hong, Jongsup & Kim, Minjin & Lee, Won-Yong & Choi, Yoon-Young & Han, Soo-Bin, 2021. "Hybrid diagnosis method for initial faults of air supply systems in proton exchange membrane fuel cells," Renewable Energy, Elsevier, vol. 180(C), pages 343-352.
    9. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
    10. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    11. Özçelep, Yasin & Sevgen, Selcuk & Samli, Ruya, 2020. "A study on the hydrogen consumption calculation of proton exchange membrane fuel cells for linearly increasing loads: Artificial Neural Networks vs Multiple Linear Regression," Renewable Energy, Elsevier, vol. 156(C), pages 570-578.
    12. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    13. 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.
    14. Feng Han & Ying Tian & Qiang Zou & Xin Zhang, 2020. "Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System," Energies, MDPI, vol. 13(10), pages 1-18, May.
    15. 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.
    16. Zhang, Caizhi & Zhang, Yuqi & Wang, Lei & Deng, Xiaozhi & Liu, Yang & Zhang, Jiujun, 2023. "A health management review of proton exchange membrane fuel cell for electric vehicles: Failure mechanisms, diagnosis techniques and mitigation measures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    17. Pedro Andrade & Khaled Laadjal & Adérito Neto Alcaso & Antonio J. Marques Cardoso, 2024. "A Comprehensive Review on Condition Monitoring and Fault Diagnosis in Fuel Cell Systems: Challenges and Issues," Energies, MDPI, vol. 17(3), pages 1-45, January.
    18. Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    19. Chen, Huicui & Zhao, Xin & Qu, Bingwang & Zhang, Tong & Pei, Pucheng & Li, Congxin, 2018. "An evaluation method of gas distribution quality in dynamic process of proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 232(C), pages 26-35.
    20. Heng Zhang & Zhongyong Liu & Weilai Liu & Lei Mao, 2022. "Diagnosing Improper Membrane Water Content in Proton Exchange Membrane Fuel Cell Using Two-Dimensional Convolutional Neural Network," Energies, MDPI, vol. 15(12), pages 1-15, June.

    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:energy:v:214:y:2021:i:c:s0360544220321691. 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.journals.elsevier.com/energy .

    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.