Please use this identifier to cite or link to this item:
|Title:||ANN modelling of nonlinear subsystem of a PEMFC stack for dynamic and steady state operation||Authors:||Kong, X.
|Keywords:||Artificial neural network
Fuel cell model
|Issue Date:||2006||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.