Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIE.2009.2026768
Title: Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based submodels
Authors: Kong, X.
Khambadkone, A.M. 
Keywords: Artificial neural network (ANN)
Fuel-cell model
Proton exchange membrane (PEM)
Real time.
Issue Date: Dec-2009
Source: Kong, X., Khambadkone, A.M. (2009-12). Modeling of a PEM fuel-cell stack for dynamic and steady-state operation using ANN-based submodels. IEEE Transactions on Industrial Electronics 56 (12) : 4903-4914. ScholarBank@NUS Repository. https://doi.org/10.1109/TIE.2009.2026768
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 protonexchange- membrane fuel-cell stack. It improves accuracy and allows the model to adapt itself to operating conditions. Moreover, the temperature effect on the fuel-cell stack is represented as the current effect by using ANN to help estimate the relationship between current and temperature. The real-time implementation of the proposed ANN model is realized via a dSPACE system. Experimental results are provided to verify the validity of the proposed model. © 2009 IEEE.
Source Title: IEEE Transactions on Industrial Electronics
URI: http://scholarbank.nus.edu.sg/handle/10635/56670
ISSN: 02780046
DOI: 10.1109/TIE.2009.2026768
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

24
checked on Dec 6, 2017

WEB OF SCIENCETM
Citations

19
checked on Nov 22, 2017

Page view(s)

30
checked on Dec 17, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.