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http://dx.doi.org/10.7316/KHNES.2019.30.6.556

Model-based Fault Detection Method for the Air Supply System of a Residential PEM Fuel Cell  

WON, JINYEON (Department of Mechanical engineering, Yonsei University)
KIM, MINJIN (Fuel Cell Research Center, Korea Institute of Energy Research)
LEE, WON-YONG (Fuel Cell Research Center, Korea Institute of Energy Research)
CHOI, YOON-YOUNG (Fuel Cell Research Center, Korea Institute of Energy Research)
HONG, JONG SUP (Department of Mechanical engineering, Yonsei University)
OH, HWANYEONG (Fuel Cell Research Center, Korea Institute of Energy Research)
Publication Information
Transactions of the Korean hydrogen and new energy society / v.30, no.6, 2019 , pp. 556-566 More about this Journal
Abstract
Recently, as the supply of residential polymer electrolyte membrane fuel cells (PEMFCs) increases, the durability and lifetime of the PEMFC system are becoming important. The related studies have been mainly focused on the durability and lifetime of materials while the research on the durability and maintenance of the system level is insufficient. In this paper, a model-based fault detection method is developed considering an air supply system that is dominant to the system performance and efficiency. A commercial 1 kW residential fuel cell system is built, and experiments are conducted under various operation loads and states (normal, 6 faults). From the experimental data, nominal models and residuals are generated. With the residual pattern obtained from real-time data, the detection and classification of various faults can be possible. The technical importance of this paper is to minimize extra sensor installation by using the empirical model rather than a complex mathematical model, and to decrease the number of models by using the applicable model at three loads. Finally, the model-based fault detection method for the air supply system of a PEMFC is established and is expected to be applicable to other subsystems.
Keywords
Polymer electrolyte membrane fuel cell; Air supply system; Fault detection; Model based; Residual pattern;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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