Static and dynamic modeling of solid oxide fuel cell using genetic programming

Uday Kumar Chakraborty

Energy, 2009, vol. 34, issue 6, 740-751

Abstract: Modeling of solid oxide fuel cell (SOFC) 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 in the past been reported for the modeling of solid oxide fuel cell stacks. However, all of these models have their limitations. This paper presents an efficient genetic programming approach to SOFC modeling and simulation. This method, belonging to the computational intelligence paradigm, is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling. Both static (fixed load) and dynamic (load transient) analyses are provided. Statistical tests of significance are used to validate the improvement in solution quality.

Keywords: Solid oxide fuel cell; SOFC stack; Dynamic model; Transient response; Genetic programming; Neural network (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544209000449
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:34:y:2009:i:6:p:740-751

DOI: 10.1016/j.energy.2009.02.012

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().