IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2531-d359011.html
   My bibliography  Save this article

Research on the Fault Diagnosis of a Polymer Electrolyte Membrane Fuel Cell System

Author

Listed:
  • Feng Han

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

  • 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)

  • Xin Zhang

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

Abstract

In this work, the possibilistic fuzzy C-means clustering artificial bee colony support vector machine (PFCM-ABC-SVM) method is proposed and applied for the fault diagnosis of a polymer electrolyte membrane (PEM) fuel cell system. The innovation of this method is that it can filter data with Gaussian noise and diagnose faults under dynamic conditions, and the amplitude of characteristic parameters is reduced to ±10%. Under dynamic conditions with Gaussian noise, the faults of the PEM fuel cell system are simulated and the original dataset is established. The possibilistic fuzzy C-means (PFCM) algorithm is used to filter samples with membership and typicality less than 90% and to optimize the original dataset. The artificial bee colony (ABC) algorithm is used to optimize the penalty factor C and kernel function parameter g . Finally, the optimized support vector machine (SVM) model is used to diagnose the faults of the PEM fuel cell system. To illustrate the results of the fault diagnosis, a nonlinear PEM fuel cell simulator model which has been presented in the literature is used. In addition, the PFCM-ABC-SVM method is compared with other methods. The result shows that the method can diagnose faults in a PEM fuel cell system effectively and the accuracy of the testing set sample is up to 98.51%. When solving small-sized, nonlinear, high-dimensional problems, the PFCM-ABC-SVM method can improve the accuracy of fault diagnosis.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2531-:d:359011
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2531/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2531/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yancai Xiao & Yujia Wang & Zhengtao Ding, 2018. "The Application of Heterogeneous Information Fusion in Misalignment Fault Diagnosis of Wind Turbines," Energies, MDPI, vol. 11(7), pages 1-15, June.
    2. Pahon, E. & Yousfi Steiner, N. & Jemei, S. & Hissel, D. & Moçoteguy, P., 2016. "A signal-based method for fast PEMFC diagnosis," Applied Energy, Elsevier, vol. 165(C), pages 748-758.
    3. Wilberforce, Tabbi & El Hassan, Zaki & Ogungbemi, Emmanuel & Ijaodola, O. & Khatib, F.N. & Durrant, A. & Thompson, J. & Baroutaji, A. & Olabi, A.G., 2019. "A comprehensive study of the effect of bipolar plate (BP) geometry design on the performance of proton exchange membrane (PEM) fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 236-260.
    4. Pei, Pucheng & Li, Yuehua & Xu, Huachi & Wu, Ziyao, 2016. "A review on water fault diagnosis of PEMFC associated with the pressure drop," Applied Energy, Elsevier, vol. 173(C), pages 366-385.
    5. 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.
    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. 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.
    2. Weipeng Zhang & Bo Zhao & Liming Zhou & Jizhong Wang & Kang Niu & Fengzhu Wang & Ruixue Wang, 2022. "Research on Comprehensive Operation and Maintenance Based on the Fault Diagnosis System of Combine Harvester," Agriculture, MDPI, vol. 12(6), pages 1-17, June.
    3. Perčić, Maja & Vladimir, Nikola & Jovanović, Ivana & Koričan, Marija, 2022. "Application of fuel cells with zero-carbon fuels in short-sea shipping," Applied Energy, Elsevier, vol. 309(C).
    4. 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.

    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. Yu Zang & Wei Shangguan & Baigen Cai & Huashen Wang & Michael G Pecht, 2019. "Methods for fault diagnosis of high-speed railways: A review," Journal of Risk and Reliability, , vol. 233(5), pages 908-922, October.
    2. Baricci, Andrea & Mereu, Riccardo & Messaggi, Mirko & Zago, Matteo & Inzoli, Fabio & Casalegno, Andrea, 2017. "Application of computational fluid dynamics to the analysis of geometrical features in PEM fuel cells flow fields with the aid of impedance spectroscopy," Applied Energy, Elsevier, vol. 205(C), pages 670-682.
    3. 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).
    4. 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.
    5. Logan Battrell & Aubree Trunkle & Erica Eggleton & Lifeng Zhang & Ryan Anderson, 2017. "Quantifying Cathode Water Transport via Anode Relative Humidity Measurements in a Polymer Electrolyte Membrane Fuel Cell," Energies, MDPI, vol. 10(8), pages 1-16, August.
    6. 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).
    7. Li, Yuehua & Pei, Pucheng & Ma, Ze & Ren, Peng & Wu, Ziyao & Chen, Dongfang & Huang, Hao, 2019. "Characteristic analysis in lowering current density based on pressure drop for avoiding flooding in proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 248(C), pages 321-329.
    8. Ren, Peng & Pei, Pucheng & Li, Yuehua & Wu, Ziyao & Chen, Dongfang & Huang, Shangwei & Jia, Xiaoning, 2019. "Diagnosis of water failures in proton exchange membrane fuel cell with zero-phase ohmic resistance and fixed-low-frequency impedance," Applied Energy, Elsevier, vol. 239(C), pages 785-792.
    9. 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).
    10. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    11. Chu, Tiankuo & Tang, Qianwen & Wang, Qinpu & Wang, Yanbo & Du, Hong & Guo, YuQing & Li, Bing & Yang, Daijun & Ming, Pingwen & Zhang, Cunman, 2023. "Experimental study on the effect of flow channel parameters on the durability of PEMFC stack and analysis of hydrogen crossover mechanism," Energy, Elsevier, vol. 264(C).
    12. Haibo Huo & Jiajie Chen & Ke Wang & Fang Wang & Guangzhe Jin & Fengxiang Chen, 2023. "State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network," Sustainability, MDPI, vol. 15(11), pages 1-16, June.
    13. 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.
    14. Ji-Seong Kim & Keon-Soo Kim & Do-Young Kim & Min Heo & Kap-Seung Choi, 2022. "Effect of Rotational Control for Accelerating Water Discharge on the Performance of a Circular Polymer Electrolyte Membrane Fuel Cell," Energies, MDPI, vol. 15(8), pages 1-14, April.
    15. 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.
    16. 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.
    17. Yan Shi & Holger Janßen & Werner Lehnert, 2019. "A Transient Behavior Study of Polymer Electrolyte Fuel Cells with Cyclic Current Profiles," Energies, MDPI, vol. 12(12), pages 1-13, June.
    18. 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).
    19. Li, Yuehua & Pei, Pucheng & Wu, Ziyao & Ren, Peng & Jia, Xiaoning & Chen, Dongfang & Huang, Shangwei, 2018. "Approaches to avoid flooding in association with pressure drop in proton exchange membrane fuel cells," Applied Energy, Elsevier, vol. 224(C), pages 42-51.
    20. 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.

    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:13:y:2020:i:10:p:2531-:d:359011. 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.