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.
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.
This is a conference paper.
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).