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Online Short-Term Remaining Useful Life Prediction of Fuel Cell Vehicles Based on Cloud System

Author

Listed:
  • Tiancai Ma

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Jianmiao Xu

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Ruitao Li

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Naiyuan Yao

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Yanbo Yang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

The durability of automotive fuel cells is one of the main factors restricting their commercial application. Therefore, establishing a remaining useful life (RUL) prediction model and developing an online operational method to apply it to the RUL optimization of fuel cell vehicles is an urgent academic problem. In this work, a short-term RUL prediction model and an online operation scheme for fuel cell vehicles are proposed. Firstly, based on historical data of a fuel cell bus under multiple conditions, the daily mode of stack voltage under a 75 A operation condition was selected as a health indicator that could better reflect the health status of a fuel cell stack. Then, an adaptive locally weighted scatterplot smoothing (LOWESS) algorithm was developed to adjust the most appropriate step size to smooth the original data automatically. Furthermore, for better prediction accuracy and stronger adaptability, a short-term RUL prediction model consisting of the adaptive LOWESS and bi-directional long short-term memory was established. Finally, an online operation scheme of the RUL prediction model based on a cloud system gave the model a strong powerful practicability. After validation, this work demonstrated good application prospects in the prognostic and health management of automotive fuel cells.

Suggested Citation

  • Tiancai Ma & Jianmiao Xu & Ruitao Li & Naiyuan Yao & Yanbo Yang, 2021. "Online Short-Term Remaining Useful Life Prediction of Fuel Cell Vehicles Based on Cloud System," Energies, MDPI, vol. 14(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2806-:d:554220
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    References listed on IDEAS

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    2. Ma, Rui & Yang, Tao & Breaz, Elena & Li, Zhongliang & Briois, Pascal & Gao, Fei, 2018. "Data-driven proton exchange membrane fuel cell degradation predication through deep learning method," Applied Energy, Elsevier, vol. 231(C), pages 102-115.
    3. Liu, Hao & Chen, Jian & Hissel, Daniel & Lu, Jianguo & Hou, Ming & Shao, Zhigang, 2020. "Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    4. Jouin, Marine & Gouriveau, Rafael & Hissel, Daniel & Péra, Marie-Cécile & Zerhouni, Noureddine, 2016. "Degradations analysis and aging modeling for health assessment and prognostics of PEMFC," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 78-95.
    5. Hua, Zhiguang & Zheng, Zhixue & Péra, Marie-Cécile & Gao, Fei, 2020. "Remaining useful life prediction of PEMFC systems based on the multi-input echo state network," Applied Energy, Elsevier, vol. 265(C).
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    Cited by:

    1. Tiancai Ma & Jiajun Kang & Weikang Lin & Xinru Xu & Yanbo Yang, 2022. "Highly Integrated Online Multi-Channel Electrochemical Impedance Spectroscopy Measurement Device for Fuel Cell Stack," Energies, MDPI, vol. 15(9), pages 1-23, May.

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