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
Volume
362
Pages
39 - 49
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.
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
2017-07-04
Publication date
2017-07-11
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).