Expert-Diagnosis-1-s2.0-S0360319917306328-main.pdf (997.1 kB)
Expert diagnosis of polymer electrolyte fuel cells
journal contribution
posted on 2017-04-11, 13:26 authored by Benjamin Davies, Lisa JacksonLisa Jackson, Sarah DunnettSarah DunnettDiagnosing 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 EnergyCitation
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.11724–11734Publisher
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
2017Notes
This paper was published by Elsevier as Open Access under the Creative Commons CC BY licence.ISSN
0360-3199Publisher version
Language
- en