ANN Modelling of Nonlinear Subsystem of a PEMFC Stack for Dynamic and Steady State Operation

Author

Kong, Xin ; Yeau, Wenjie ; Khambadkone, Ashwin M.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore

fYear

2006

fDate

6-10 Nov. 2006

Firstpage

4322

Lastpage

4327

Abstract

A simple and accurate fuel cell model is required for fuel cell based power electronic applications. An artificial neural network (ANN) model is developed in this paper to model some nonlinear structures within the hybrid model of a PEM fuel cell. It improves accuracy and allows the model to work even under varying operating conditions. What is more, temperature effect on the fuel cell stack are finally represented as current effect by using ANN to represent the relationship between current and temperature. Real-time implementation of the proposed ANN model is realized via a dSPACE processor. Experimental results are provided to verify the validity of the proposed model

Keywords

neural nets; power engineering computing; proton exchange membrane fuel cells; ANN modelling; PEMFC stack; artificial neural network; dSPACE processor; nonlinear structures; nonlinear subsystem; power electronic applications; temperature effect; Artificial intelligence; Artificial neural networks; Circuit simulation; Fuel cells; Power electronics; Power system modeling; Steady-state; Temperature; Vehicle dynamics; Voltage; PEM; artificial neural network; fuel cell model; real-time;

fLanguage

English

Publisher

ieee

Conference_Titel

IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on

Conference_Location

Paris

ISSN

1553-572X

Print_ISBN

1-4244-0390-1

Type

conf

DOI

10.1109/IECON.2006.347518

Filename

4153119