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Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

Author

Listed:
  • Lei Mao

    (Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK)

  • Ben Davies

    (Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK)

  • Lisa Jackson

    (Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK)

Abstract

In this paper, the sensor selection approach is investigated with the aim of using fewer sensors to provide reliable fuel cell diagnostic and prognostic results. The sensitivity of sensors is firstly calculated with a developed fuel cell model. With sensor sensitivities to different fuel cell failure modes, the available sensors can be ranked. A sensor selection algorithm is used in the analysis, which considers both sensor sensitivity to fuel cell performance and resistance to noise. The performance of the selected sensors in polymer electrolyte membrane (PEM) fuel cell prognostics is also evaluated with an adaptive neuro-fuzzy inference system (ANFIS), and results show that the fuel cell voltage can be predicted with good quality using the selected sensors. Furthermore, a fuel cell test is performed to investigate the effectiveness of selected sensors in fuel cell fault diagnosis. From the results, different fuel cell states can be distinguished with good quality using the selected sensors.

Suggested Citation

  • Lei Mao & Ben Davies & Lisa Jackson, 2017. "Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management," Energies, MDPI, vol. 10(10), pages 1-13, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1511-:d:113661
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    Cited by:

    1. Devin Fowler & Vladimir Gurau & Daniel Cox, 2019. "Bridging the Gap between Automated Manufacturing of Fuel Cell Components and Robotic Assembly of Fuel Cell Stacks," Energies, MDPI, vol. 12(19), pages 1-14, September.

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