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Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics

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journal contribution
posted on 12.07.2017 by Lei Mao, Lisa Jackson, Tom Jackson
This paper investigates the polymer electrolyte membrane (PEM) fuel cell internal behaviour variation at different operating condition, with characterization test data taken at predefined inspection times, and uses the determined internal behaviour evolution to predict the future PEM fuel cell performance. For this purpose, a PEM fuel cell behaviour model is used, which can be related to various fuel cell losses. By matching the model to the collected polarization curves from the PEM fuel cell system, the variation of fuel cell internal behaviour can be obtained through the determined model parameters. From the results, the source of PEM fuel cell degradation during its lifetime at different conditions can be better understood. Moreover, with determined fuel cell internal behaviour, the future fuel cell performance can be obtained by predicting the future model parameters. By comparing with prognostic results using adaptive neuro fuzzy inference system (ANFIS), the proposed prognostic analysis can provide better predictions for PEM fuel cell performance at dynamic condition, and with the understanding of variation in PEM fuel cell internal behaviour, mitigation strategies can be designed to extend the fuel cell performance.

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. Authors also acknowledge Intelligent Energy for its close collaboration in providing necessary information for the paper.

History

School

  • Business and Economics

Department

  • Business

Published in

Journal of Power Sources

Citation

MAO, L., JACKSON, L. and JACKSON, T., 2017. Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics. Journal of Power Sources, 362, (September), pp. 39–49.

Publisher

© 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

04/07/2017

Publication date

2017

Notes

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Model and experimental data discussed in this work can be found at Loughborough Data Repository (https://lboro.figshare. com).

ISSN

0378-7753

Language

en

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