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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25755

Title: Investigation of polymer electrolyte membrane fuel cell internal behaviour during long term operation and its use in prognostics
Authors: Mao, Lei
Jackson, Lisa M.
Jackson, Thomas
Keywords: PEM fuel cell
Internal behaviour
Polarization curve
Particle filtering approach
ANFIS
Issue Date: 2017
Publisher: © The Author(s). Published by Elsevier B.V
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.
Abstract: 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.
Description: 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).
Sponsor: 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.
Version: Published
DOI: 10.1016/j.jpowsour.2017.07.018
URI: https://dspace.lboro.ac.uk/2134/25755
Publisher Link: https://doi.org/10.1016/j.jpowsour.2017.07.018
Related Resource: https://doi.org/10.17028/rd.lboro.5208826
ISSN: 0378-7753
Appears in Collections:Published Articles (Business School)
Published Articles (Aeronautical and Automotive Engineering)

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