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

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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).
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    Cited by:

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    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.

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