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Autocorrelation standard deviation and root mean square frequency analysis of polymer electrolyte membrane fuel cell to monitor for hydrogen and air undersupply

Title
Autocorrelation standard deviation and root mean square frequency analysis of polymer electrolyte membrane fuel cell to monitor for hydrogen and air undersupply
Authors
Kim, Joo GonMukherjee, SantanuBates, AlexZickel, BenjaminPark, SamSon, Byung RakChoi, Jae SungKwon, OsungLee, Dong HaChung, Hyun-Youl
DGIST Authors
Son, Byung Rak; Lee, Dong Ha
Issue Date
2015-12-30
Citation
Journal of Power Sources, 300, 164-174
Type
Article
Article Type
Article
Keywords
AutocorrelationElectrolytesEnergy ConversionEnergy Conversion DevicesFrequency VariationFuel Cell PerformanceFuel CellsHealth MonitoringHydrogen SupplyHydrogen Supply CapacityMembrane Electrode AssembliesMembranesPEMFCPolyelectrolytesProton-Exchange Membrane Fuel Cells (PEMFC)RMSFStarvation ConditionsStatistics
ISSN
0378-7753
Abstract
Proton exchange membrane fuel cells are a promising energy conversion device which can help to solve urgent environmental and economic problems. Among the various types of fuel cells, the air breathing proton exchange membrane fuel cell, which minimizes the balance of plant, has drawn a lot of attention due to its superior energy density. In this study a compact, air breathing, proton exchange membrane fuel cell based on Nafion and a Pt/C membrane electrode assembly was designed. The fuel cell was tested using a Scribner Associates 850e fuel cell test station. Specifically, the hydrogen fuel and oxygen starvation of the fuel cell were accurately and systematically tested and analyzed using a frequency analysis method which can analyze the input and output frequency. The analysis of the frequency variation under a fuel starvation condition was done using RMSF (root mean square frequency) and ACSD (autocorrelation standard deviation). The study reveals two significant results: first, the fuel starvations show entirely different phenomenon in both RMSF and ACSD and second, the results of the Autocorrelation show clearer results for fuel starvation detection than the results with RMSF. © 2015 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/20.500.11750/5152
DOI
10.1016/j.jpowsour.2015.09.062
Publisher
ELSEVIER SCIENCE BV
Related Researcher
  • Author Son, Byungrak  
  • Research Interests 센서네트워크; 연료전지; 드론
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Wellness1. Journal Articles


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