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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
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    References listed on IDEAS

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    Citations

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    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. Song, Ke & Huang, Xing & Huang, Pengyu & Sun, Hui & Chen, Yuhui & Huang, Dongya, 2024. "Data-driven health state estimation and remaining useful life prediction of fuel cells," Renewable Energy, Elsevier, vol. 227(C).
    4. 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.
    5. 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).
    6. 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.

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