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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22318

Title: Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell
Authors: Mao, Lei
Jackson, Lisa M.
Keywords: Sensor selection approaches
PEM fuel cell
Sensitivity analysis
Performance prediction
Adaptive neuro-fuzzy inference system
Issue Date: 2016
Publisher: © 2016 The Author(s). Published by Elsevier B.V.
Citation: MAO, L. and JACKSON, L.M., 2016. Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell. Journal of Power Sources, 328 (October), pp. 151-160.
Abstract: In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.
Description: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor: 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).
Version: Published
DOI: 10.1016/j.jpowsour.2016.08.021
URI: https://dspace.lboro.ac.uk/2134/22318
Publisher Link: http://dx.doi.org/10.1016/j.jpowsour.2016.08.021
ISSN: 1873-2755
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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