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Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

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journal contribution
posted on 31.08.2016 by Lei Mao, Lisa Jackson
In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

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).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Journal of Power Sources

Citation

MAO, L. and JACKSON, L.M., 2016. Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell. Journal of Power Sources, 328 (October), pp. 151-160.

Publisher

© 2016 The Author(s). Published by Elsevier B.V.

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

03/08/2016

Publication date

2016

Notes

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

ISSN

1873-2755

Language

en

Licence

Exports