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Expert diagnosis of polymer electrolyte fuel cells

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journal contribution
posted on 11.04.2017, 13:26 by Benjamin Davies, Lisa Jackson, Sarah Dunnett
Diagnosing faulty conditions of engineering systems is a highly desirable process within control structures, such that control systems may operate effectively and degrading operational states may be mitigated. The goal herein is to enhance lifetime performance and extend system availability. Difficulty arises in developing a mathematical model which can describe all working and failure modes of complex systems. However the expert's knowledge of correct and faulty operation is powerful for detecting degradation, and such knowledge can be represented through fuzzy logic. This paper presents a diagnostic system based on fuzzy logic and expert knowledge, attained from experts and experimental findings. The diagnosis is applied specifically to degradation modes in a polymer electrolyte fuel cell. The defined rules produced for the fuzzy logic model connect observed operational modes and symptoms to component degradation. The diagnosis is then tested against common automotive stress conditions to assess functionality.

Funding

This report is supported by the Engineering and Physical Science Research Council (grant number EP/L015749/1).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

International Journal of Hydrogen Energy

Citation

DAVIES, B., JACKSON, L.M. and DUNNETT, S.J., 2017. Expert diagnosis of polymer electrolyte fuel cells. International Journal of Hydrogen Energy, 42 (16), pp.11724–11734

Publisher

Elsevier Ltd on behalf of Hydrogen Energy Publications LLC (© 2017 The Authors)

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: https://creativecommons.org/licenses/by/4.0/

Publication date

2017

Notes

This paper was published by Elsevier as Open Access under the Creative Commons CC BY licence.

ISSN

0360-3199

Language

en

Licence

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