Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

JOURNAL OF POWER SOURCES

卷:359

Article

Jeppesen, Christian¹  Araya, Samuel Simon¹  Sahlin, Simon Lennart¹  Thomas, Sobi¹  Andreasen, Soren Juhl²  Kaer, Soren knudsen¹ 

[1] Aalborg Univ, Dept Energy Technol, Pontoppidanstraede 111, DK-9220 Aalborg O, Denmark

[2] Serenergy AS, Lyngvej 8, DK-9000 Aalborg, Denmark

关键词: Fault diagnosis;    Classification;    Pattern recognition;    Fuel cell;    PEM;    Electrochemical impedance spectroscopy (EIS);   

DOI  :  10.1016/j.jpowsour.2017.05.021

来源: Elsevier

【 摘 要 】

This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode-and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%. (C) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free