IMECS2016_pp52-57.pdf (1.23 MB)

Comparative study on prediction of fuel cell performance using machine learning approaches

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journal contribution
posted on 19.09.2016, 12:22 by Lei Mao, Lisa Jackson
This paper provides a comparative study to evaluate the effectiveness of machine learning techniques in predicting fuel cell performance. Several methods applied in fuel cell prognostics are selected, including a neural network, an adaptive neuro-fuzzy inference system, and a particle filtering approach. Test data from a fuel cell system is used for the evaluation. From the results, the advantages and disadvantages of these approaches are compared, which can provide a general framework for the selection of the necessary algorithms for fuel cell prognostics under different conditions.

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 (EPSRC).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Lecture Notes in Engineering and Computer Science

Volume

2221

Issue

1

Pages

52 - 52 (57)

Citation

2016. Comparative study on prediction of fuel cell performance using machine learning approaches. IN: Ao, S.I. ...et al. (eds.) Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2016, 16-18 March, 2016, Hong Kong, pp. 52-57.

Publisher

Published by Newswood Limited for IAENG

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2016-03-18

Notes

This is a conference paper.

ISBN

9789881925381

ISSN

2078-0966;2078-0958

Language

en

Exports