Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

Lei Mao, Ben Davies and Lisa Jackson
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Lei Mao: Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK
Ben Davies: Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK
Lisa Jackson: Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3RX, UK

Energies, 2017, vol. 10, issue 10, 1-13

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.

Keywords: PEM fuel cell; PHM; sensor selection; fault diagnosis; prognosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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