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Title: Expert diagnosis of polymer electrolyte fuel cells
Authors: Davies, Ben
Jackson, Lisa M.
Dunnett, Sarah J.
Keywords: Fuel cell
Polymer electrolyte fuel cell
PEFC
Diagnosis
Fuzzy logic
Knowledge based system
Issue Date: 2017
Publisher: Elsevier Ltd on behalf of Hydrogen Energy Publications LLC (© 2017 The Authors)
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
Abstract: 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.
Description: This paper was published by Elsevier as Open Access under the Creative Commons CC BY licence.
Sponsor: This report is supported by the Engineering and Physical Science Research Council (grant number EP/L015749/1).
Version: Published
DOI: 10.1016/j.ijhydene.2017.02.121
URI: https://dspace.lboro.ac.uk/2134/24700
Publisher Link: http://dx.doi.org/10.1016/j.ijhydene.2017.02.121
ISSN: 0360-3199
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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