Authors: Uday K. Chakraborty
Addresses: Department of Mathematics and Computer Science, One University Blvd., University of Missouri – St. Louis, St. Louis, MO 63121, USA
Abstract: Models that predict performance are important tools in understanding and designing solid oxide fuel cells (SOFCs). Modelling of SOFC stack-based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. Several algorithmic approaches have already been reported for the modelling of solid oxide fuel cell stack-based systems. This paper presents a new, genetic programming approach to SOFC modelling. Initial simulation results obtained with the proposed approach outperform the state-of-the-art radial basis function neural network method for this task.
Keywords: solid oxide fuel cells; SOFC stack; genetic programming; modelling; nonlinear dynamics; simulation.
International Journal of Information and Communication Technology, 2008 Vol.1 No.3/4, pp.453 - 461
Published online: 23 Mar 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article