Energies 2017, 10(10), 1511; https://doi.org/10.3390/en10101511
Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK
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Received: 24 July 2017 / Revised: 8 September 2017 / Accepted: 14 September 2017 / Published: 29 September 2017
(This article belongs to the Special Issue Polymer Electrolyte Membrane Fuel Cells 2017)
Abstract
In this paper, the sensor selection approach is investigated with the aim of using fewer sensors to provide reliable fuel cell diagnostic and prognostic results. The sensitivity of sensors is firstly calculated with a developed fuel cell model. With sensor sensitivities to different fuel cell failure modes, the available sensors can be ranked. A sensor selection algorithm is used in the analysis, which considers both sensor sensitivity to fuel cell performance and resistance to noise. The performance of the selected sensors in polymer electrolyte membrane (PEM) fuel cell prognostics is also evaluated with an adaptive neuro-fuzzy inference system (ANFIS), and results show that the fuel cell voltage can be predicted with good quality using the selected sensors. Furthermore, a fuel cell test is performed to investigate the effectiveness of selected sensors in fuel cell fault diagnosis. From the results, different fuel cell states can be distinguished with good quality using the selected sensors. View Full-TextKeywords:
PEM fuel cell; PHM; sensor selection; fault diagnosis; prognosis
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Mao, L.; Davies, B.; Jackson, L. Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management. Energies 2017, 10, 1511.
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