IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i7p1918-d527076.html
   My bibliography  Save this article

Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification

Author

Listed:
  • Ying Tian

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Qiang Zou

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Jin Han

    (Beijing Key Laboratory of Powertrain for New Energy Vehicle, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1918-:d:527076
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/7/1918/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/7/1918/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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.
    3. Zhidi Lin & Dongliang Duan & Qi Yang & Xuemin Hong & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2020. "Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources," Energies, MDPI, vol. 13(1), pages 1-16, January.
    4. 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.
    5. Xueshuang Ren & Xin Zhang & Teng Teng & Congxin Li, 2020. "Research on Estimation Method of Fuel Cell Health State Based on Lumped Parameter Model," Energies, MDPI, vol. 13(23), pages 1-13, December.
    6. Li, Zhongliang & Outbib, Rachid & Giurgea, Stefan & Hissel, Daniel & Giraud, Alain & Couderc, Pascal, 2019. "Fault diagnosis for fuel cell systems: A data-driven approach using high-precise voltage sensors," Renewable Energy, Elsevier, vol. 135(C), pages 1435-1444.
    7. 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.
    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. 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).
    2. Jiaping Xie & Chao Wang & Wei Zhu & Hao Yuan, 2021. "A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree," Energies, MDPI, vol. 14(20), pages 1-22, October.
    3. Ewa Janicka & Michal Mielniczek & Lukasz Gawel & Kazimierz Darowicki, 2021. "Optimization of the Relative Humidity of Reactant Gases in Hydrogen Fuel Cells Using Dynamic Impedance Measurements," Energies, MDPI, vol. 14(11), pages 1-11, May.
    4. Bai, Fan & Quan, Hong-Bing & Yin, Ren-Jie & Zhang, Zhuo & Jin, Shu-Qi & He, Pu & Mu, Yu-Tong & Gong, Xiao-Ming & Tao, Wen-Quan, 2022. "Three-dimensional multi-field digital twin technology for proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 324(C).
    5. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2021. "Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System," Mathematics, MDPI, vol. 9(17), pages 1-18, August.

    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. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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).
    7. 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.
    8. Akimoto, Yutaro & Okajima, Keiichi, 2021. "Simple on-board fault-detection method for proton exchange membrane fuel cell stacks using by semi-empirical curve fitting," Applied Energy, Elsevier, vol. 303(C).
    9. 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.
    10. Guk, Erdogan & Venkatesan, Vijay & Babar, Shumaila & Jackson, Lisa & Kim, Jung-Sik, 2019. "Parameters and their impacts on the temperature distribution and thermal gradient of solid oxide fuel cell," Applied Energy, Elsevier, vol. 241(C), pages 164-173.
    11. Reza Ghasemi & Mehdi Sedighi & Mostafa Ghasemi & Bita Sadat Ghazanfarpoor, 2023. "Design of a Fuzzy Adaptive Voltage Controller for a Nonlinear Polymer Electrolyte Membrane Fuel Cell with an Unknown Dynamical System," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
    12. M. Ganesan & R. Lavanya, 2023. "Simultaneous fault detection in satellite power systems using deep autoencoders and classifier chain," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 83(1), pages 1-15, May.
    13. Zou, Wei & Froning, Dieter & Shi, Yan & Lehnert, Werner, 2021. "Working zone for a least-squares support vector machine for modeling polymer electrolyte fuel cell voltage," Applied Energy, Elsevier, vol. 283(C).
    14. 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).
    15. 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.
    16. 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.
    17. 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).
    18. 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.
    19. 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.
    20. 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.

    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:gam:jeners:v:14:y:2021:i:7:p:1918-:d:527076. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.