Abstract
One of the problems of industrial development of fuel cells is the reliability of their performances with time. The solution of this problem is through by the development of improved diagnostic methods such as the identification of parameters. This work focuses on the modeling and the identification of the impedance model parameters of a Proton Exchange Membrane (PEM) fuel cell. It is based on the Randles model represented by specific complex impedance at each cell. We have used the “Least square” method to determine the parameters model using measured reference values. The proposed authentication method is valid for Randles model, but it can be generalized to be applied to others.
Highlights
The fuel cell is not a source of energy, but a converter that directly transforms the chemical energy of a fuel into electrochemically powered and its working principle was discovered in 1839 by W
The modeling of a fuel cell is an important factor in describing the operation of the fuel cell system through a well-defined model, and more precisely the fuel cell impedance model that describes the frequency behavior
The objective of this article is to judge the methodology for identifying the impedance model parameters of the fuel cell used and to validate it by experimental results
Summary
The fuel cell is not a source of energy, but a converter that directly transforms the chemical energy of a fuel into electrochemically powered and its working principle was discovered in 1839 by W. Grove [1], [2]. It is an efficient means of electrical production in terms of efficiency. Behind the displayed simplicity of its operating principle, it may appear as a relatively complex to be technically implemented. The modeling of a fuel cell is an important factor in describing the operation of the fuel cell system through a well-defined model, and more precisely the fuel cell impedance model that describes the frequency behavior. The objective is not to describe in detail and exhaustively all the models present in the fuel, but rather to highlight the dynamic model in which one can identify its parameters
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