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

Modeling of solid oxide fuel cell (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. This paper presents an efficient genetic programming approach for modeling and simulation of SOFC output voltage versus fuel utilization behavior. This method is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling.