IMECS2016_pp52-57.pdf (1.23 MB)
Comparative study on prediction of fuel cell performance using machine learning approaches
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 ScienceVolume
2221Issue
1Pages
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 IAENGVersion
- 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
2016-03-18Notes
This is a conference paper.ISBN
9789881925381ISSN
2078-0966;2078-0958Publisher version
Language
- en