By Topic

Modeling of a PEM Fuel-Cell Stack for Dynamic and Steady-State Operation Using ANN-Based Submodels

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xin Kong ; Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore ; Ashwin M. Khambadkone

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 proton-exchange-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.

Published in:

IEEE Transactions on Industrial Electronics  (Volume:56 ,  Issue: 12 )