Data-driven diagnosis of PEM fuel cell: A comparative study |
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| Affiliation: | 1. Laboratoire des Sciences de l׳Information et des Systemes (LSIS), University of Aix-Marseille, France;2. FEMTO-ST (UMR CNRS 6174), ENERGY Department, University of Franche-Comte, France;3. FCLAB (Fuel Cell Lab) Research Federation, FR CNRS 3539, rue Thierry Mieg, 90010 Belfort Cedex, France;1. FEMTO-ST UMR CNRS 6174, FCLAB Research Federation FR CNRS 3539, University Bourgogne Franche-Comte, rue Ernest Thierry Mieg, 90010 Belfort Cedex, France;2. LABEX ACTION CNRS, FEMTO-ST UMR CNRS 6174, FCLAB Research Federation FR CNRS 3539, University Bourgogne Franche-Comte, rue Ernest Thierry Mieg, 90010 Belfort Cedex, France;3. EIFER, European Institute for Energy Research, Emmy-Nother Strasse 11, Karlsruhe, Germany;1. LE2P, EA 4079, University of La Reunion, 15 Av. René Cassin, BP 7151, 97715 Saint-Denis, France;2. FCLAB Research Federation, FR CNRS 3539, FEMTO-ST/Energy Department, UMR CNRS 6174, University of Franche-Comté, Rue Thierry Mieg, 90010 Belfort Cedex, France;1. FCLAB (Fuel Cell Lab) Research Federation, FR CNRS 3539, rue Thierry Mieg, 90010 Belfort Cedex, France;2. FEMTO-ST (UMR CNRS 6174), ENERGY Department, UFC/UTBM/ENSMM, France;3. Laboratoire des Sciences de l’Information et des Systemes (LSIS), University of Aix-Marseille, France;4. CEA/LIST, 91191 Gif-sur-Yvette Cedex, France;5. CEA/LITEN, 38054 Grenoble, France;1. FCLAB (Fuel Cell Lab) Research Federation, FR CNRS 3539, rue Thierry Mieg, 90010 Belfort Cedex, France;2. Laboratoire des Sciences de l’Information et des Systemes (LSIS), University of Aix-Marseille, France;3. FEMTO-ST (UMR CNRS 6174), ENERGY Department, University of Franche-Comte, France;4. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;1. FCLAB Research Federation, FR CNRS 3539, FEMTO-ST/Energy Department, UMR CNRS 6174, France;2. University of Franche-Comté, 90010 Belfort Cedex, France;3. University of Technology of Belfort-Montbéliard, 90010 Belfort Cedex, France;4. Department of Electrical Engineering, Tsinghua University, 100084 Beijing, China;1. State Key Lab of Automotive Safety and Energy, Tsinghua University, Beijing 100084, PR China;2. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing 100081, PR China;3. Institute of Energy and Climate Research, IEK-3: Electrochemical Process Engineering, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany |
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| Abstract: | This paper is dedicated to data-driven diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC). More precisely, it deals with water related faults (flooding and membrane drying) by using pattern classification methodologies. Firstly, a method based on physical considerations is defined to label the training data. Secondly, a feature extraction procedure is carried out to pick up the significant features from vectors constructed by individual cell voltages. Finally, a classification is adopted in the feature space to realize the fault diagnosis. Various feature extraction and classification methodologies are employed on a 20-cell PEMFC stack. The performances of these methodologies are compared. |
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| Keywords: | Fault diagnosis PEMFC Water management Classification Feature extraction |
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