Diagnosis of \CO\ Pollution in \HTPEM\ Fuel Cell using Statistical Change Detection
By Christian Jeppesen and Mogens Blanke and Fan Zhou and S
Published in IFAC-PapersOnLine
NULL
2015
Abstract
The fuel cell technologies are advancing and maturing for commercial markets. However proper diagnostic tools needs to be developed in order to insure reliability and durability of fuel cell systems. This paper presents a design of a data driven method to detect \CO\ content in the anode gas of a high temperature fuel cell. In this work the fuel cell characterization is based on an experimental equivalent electrical circuit, where model parameters are mapped as a function of the load current. The designed general likelihood ratio test detection scheme detects whether a equivalent electrical circuit parameter differ from the non-faulty operation. It is proven that the general likelihood ratio test detection scheme, with a very low probability of false alarm, can detect \CO\ content in the anode gas of the fuel cell.
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