IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i6p2294-d776101.html
   My bibliography  Save this article

Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine

Author

Listed:
  • Weiwei Huo

    (School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100092, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Information Science & Technology University, Beijing 100092, China
    Key Laboratory of Modern Measurement and Control Technology, Ministry of Education, Beijing Information Science & Technology University, Beijing 100192, China)

  • Weier Li

    (School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100092, China)

  • Chao Sun

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Qiang Ren

    (Guangzhou Automobile Group Co., Ltd., Automotive Engineering Research Institute, Guangzhou 510006, China)

  • Guoqing Gong

    (School of Mechanical and Electrical Engineering, Beijing Information Science & Technology University, Beijing 100092, China)

Abstract

The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization.

Suggested Citation

  • Weiwei Huo & Weier Li & Chao Sun & Qiang Ren & Guoqing Gong, 2022. "Research on Fuel Cell Fault Diagnosis Based on Genetic Algorithm Optimization of Support Vector Machine," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2294-:d:776101
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/6/2294/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/6/2294/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Su Zhou & Jie Jin & Yuehua Wei, 2021. "Research on Online Diagnosis Method of Fuel Cell Centrifugal Air Compressor Surge Fault," Energies, MDPI, vol. 14(11), pages 1-15, May.
    3. Chen, Hui & Zhang, Zehui & Guan, Cong & Gao, Haibo, 2020. "Optimization of sizing and frequency control in battery/supercapacitor hybrid energy storage system for fuel cell ship," Energy, Elsevier, vol. 197(C).
    4. Samuel Simon Araya & Fan Zhou & Simon Lennart Sahlin & Sobi Thomas & Christian Jeppesen & Søren Knudsen Kær, 2019. "Fault Characterization of a Proton Exchange Membrane Fuel Cell Stack," Energies, MDPI, vol. 12(1), pages 1-17, January.
    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. Jiang, Deyin & Chen, Tianyu & Xie, Juanzhang & Cui, Weimin & Song, Bifeng, 2023. "A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    2. Rendao Ye & Mengyao Yang & Peng Sun, 2023. "Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    3. Yue Ren & Chunhua Jin & Shu Fang & Li Yang & Zixuan Wu & Ziyang Wang & Rui Peng & Kaiye Gao, 2023. "A Comprehensive Review of Key Technologies for Enhancing the Reliability of Lithium-Ion Power Batteries," Energies, MDPI, vol. 16(17), pages 1-38, August.
    4. Danqi Su & Jiayang Zheng & Junjie Ma & Zizhe Dong & Zhangjie Chen & Yanzhou Qin, 2023. "Application of Machine Learning in Fuel Cell Research," Energies, MDPI, vol. 16(11), pages 1-32, May.

    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. 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).
    3. 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).
    4. Yuan, Yi & Chen, Li & Lyu, Xingbao & Ning, Wenjing & Liu, Wenqi & Tao, Wen-Quan, 2024. "Modeling and optimization of a residential PEMFC-based CHP system under different operating modes," Applied Energy, Elsevier, vol. 353(PA).
    5. 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).
    6. Reveles-Miranda, María & Ramirez-Rivera, Victor & Pacheco-Catalán, Daniella, 2024. "Hybrid energy storage: Features, applications, and ancillary benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    7. Huang, Jiangfan & An, Qing & Zhou, Mingyu & Tang, Ruoli & Dong, Zhengcheng & Lai, Jingang & Li, Xin & Yang, Xiangguo, 2024. "A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics," Applied Energy, Elsevier, vol. 361(C).
    8. 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.
    9. 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.
    10. Benmouna, A. & Becherif, M. & Boulon, L. & Dépature, C. & Ramadan, Haitham S., 2021. "Efficient experimental energy management operating for FC/battery/SC vehicles via hybrid Artificial Neural Networks-Passivity Based Control," Renewable Energy, Elsevier, vol. 178(C), pages 1291-1302.
    11. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(C).
    12. Xie, Peilin & Tan, Sen & Bazmohammadi, Najmeh & Guerrero, Josep. M. & Vasquez, Juan. C. & Alcala, Jose Matas & Carreño, Jorge El Mariachet, 2022. "A distributed real-time power management scheme for shipboard zonal multi-microgrid system," Applied Energy, Elsevier, vol. 317(C).
    13. Pinthurat, Watcharakorn & Hredzak, Branislav, 2021. "Fully decentralized control strategy for heterogeneous energy storage systems distributed in islanded DC datacentre microgrid," Energy, Elsevier, vol. 231(C).
    14. 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.
    15. 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.
    16. Ouyang, Tiancheng & Zhao, Zhongkai & Zhang, Mingliang & Xie, Shutao & Wang, Zhiping, 2022. "A micro off-grid power solution for solid oxide fuel cell waste heat reusing enabled peak load shifting by integrating compressed-air energy storage," Applied Energy, Elsevier, vol. 323(C).
    17. 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.
    18. 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).
    19. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    20. 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.

    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:15:y:2022:i:6:p:2294-:d:776101. 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.