Comparative study on prediction of fuel cell performance using machine learning approaches
journal contributionposted on 19.09.2016 by Lei Mao, Lisa Jackson
Any type of content formally published in an academic journal, usually following a peer-review process.
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 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).
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering