A Novel Approach to the Optimization of a Solid Oxide Fuel Cell Anode Using Evolutionary Algorithms

Abstract

Solid oxide fuel cell (SOFC) has a high energy conversion efficiency and emits a low level of pollutants in the environment. One of the crucial elements is an anode that, typically, is a composite of nickel and yttria-stabilized zirconia (Ni-YSZ). The microstructure morphology of an anode plays an important role in determining the electrochemical performances of a single cell and, consequently, a stack of cells. Therefore, the microstructure optimization design should be included in the development of a system at a very early stage. The anode material microstructure can be tailored to fulfill the role it has at the particular location in the stack. This paper presents a novel approach of using an evolutionary algorithm to optimize the microstructure of an SOFC's anode. The optimization problem consists of 16 microstructural parameters connected by the mesh of the dependencies. One group of algorithms that can face this challenge is an evolutionary algorithm family. In this paper, a genetic algorithm and a particle swarm optimization are employed to optimize the cell microstructure and to help in improving the performance of an SOFC. The developed mathematical model can correctly predict the performance of the SOFC anode and is employed in the evolutionary algorithms to select the optimal microstructure. The results show that the optimal microstructure leads to better cell performance than the conventional one.

Keywords

• Anodes
• evolutionary computation
• fuel cells
• genetic algorithms
• microstructure
• particle swarm optimization