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Online Prediction of Vehicular Fuel Cell Residual Lifetime Based on Adaptive Extended Kalman Filter

Author

Listed:
  • Ke Song

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

  • Yimin Wang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

  • Xiao Hu

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

  • Jing Cao

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

Abstract

The limited lifetime of proton exchange membrane fuel cell (PEMFC) inhibits the further development of the fuel cell industry. Prediction is one of the most effective means for managing the lifetime of a fuel cell because it can assist in the implementation of mitigation actions before a vehicular fuel cell fails by estimating the residual lifetime. Therefore, this study aimed to develop a PEMFC lifetime prediction method for online applications. This paper presents the online prediction method developed for the residual lifetime of a vehicular fuel cell, which utilises data processing with an adaptive extended Kalman filter and a prediction formula. The formula considers different operating conditions and the external environment, which is in accord with the actual operating conditions of fuel cell vehicles. This method realises the online prediction of the residual lifetime of a vehicular fuel cell by updating weight coefficients for the operating conditions and environmental factors. This prediction method was validated and analysed using a simulation. The influences of key parameters on the stability and prediction accuracy of the algorithm were evaluated. The prediction method proposed in this paper can provide a reference for studies on fuel cell lifetime prediction.

Suggested Citation

  • Ke Song & Yimin Wang & Xiao Hu & Jing Cao, 2020. "Online Prediction of Vehicular Fuel Cell Residual Lifetime Based on Adaptive Extended Kalman Filter," Energies, MDPI, vol. 13(23), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6244-:d:451839
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    References listed on IDEAS

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    1. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
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    Cited by:

    1. Huu Linh Nguyen & Jaesu Han & Hoang Nghia Vu & Sangseok Yu, 2022. "Investigation of Multiple Degradation Mechanisms of a Proton Exchange Membrane Fuel Cell under Dynamic Operation," Energies, MDPI, vol. 15(24), pages 1-21, December.
    2. Juhui Gim & Minsu Kim & Changsun Ahn, 2022. "Energy Management Control Strategy for Saving Trip Costs of Fuel Cell/Battery Electric Vehicles," Energies, MDPI, vol. 15(6), pages 1-15, March.
    3. Jiawei Guo & Chao He & Jiaqiang Li & Heng Wei, 2022. "Slope Estimation Method of Electric Vehicles Based on Improved Sage–Husa Adaptive Kalman Filter," Energies, MDPI, vol. 15(11), pages 1-17, June.
    4. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.

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