Please use this identifier to cite or link to this item: https://doi.org/10.1109/IECON.2006.347518
Title: ANN modelling of nonlinear subsystem of a PEMFC stack for dynamic and steady state operation
Authors: Kong, X.
Yeau, W.
Khambadkone, A.M. 
Keywords: Artificial neural network
Fuel cell model
PEM
Real-time
Issue Date: 2006
Source: Kong, X.,Yeau, W.,Khambadkone, A.M. (2006). ANN modelling of nonlinear subsystem of a PEMFC stack for dynamic and steady state operation. IECON Proceedings (Industrial Electronics Conference) : 1-6. ScholarBank@NUS Repository. https://doi.org/10.1109/IECON.2006.347518
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. © 2006 IEEE.
Source Title: IECON Proceedings (Industrial Electronics Conference)
URI: http://scholarbank.nus.edu.sg/handle/10635/69417
ISBN: 1424401364
DOI: 10.1109/IECON.2006.347518
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