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Application of the sensor selection approach in polymer electrolyte membrane fuel cell prognostics and health management

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journal contribution
posted on 15.12.2017, 14:01 authored by Lei Mao, Benjamin Davies, Lisa JacksonLisa Jackson
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.

Funding

This work is supported by grant EP/K02101X/1 for Loughborough University, Department of Aeronautical and Automotive Engineering from the UK Engineering and Physical Sciences Research Council (EPSRC). Authors also acknowledge Intelligent Energy for its close collaboration in providing necessary information for the paper.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Energies

Volume

10

Issue

10

Pages

1511 - 1511

Citation

MAO, L., DAVIES, B. and JACKSON, L., 2017. Application of the sensor selection approach in polymer electrolyte membrane fuel cell prognostics and health management. Energies, 10, 1511.

Publisher

© 2017 by the authors. Licensee MDPI, Basel, Switzerland

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/

Acceptance date

14/09/2017

Publication date

2017

Notes

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/

eISSN

1996-1073

Language

en