Genetic programming model of solid oxide fuel cell stack: first results

Authors:
Uday K. Chakraborty
Affiliations:
Department of Mathematics and Computer Science, One University Blvd., University of Missouri – St. Louis, St. Louis, MO 63121, USA
Venue:
International Journal of Information and Communication Technology
Year:
2008

Citing 5
Cited 1

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An evolutionary computation approach to predicting output voltage from fuel utilization in SOFC stacks

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation

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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.