Application of Soft Computing Techniques for Porosity Optimization of Dye Sensitized Solar Cell

Abstract

In this paper, the evolutionary computation-based techniques have been introduced for porosity optimization of dye-sensitized solar cell (DSSC) with comparative analysis. The diffusion differential equation-based model of DSSC achieves the goal. The porosity has been considered for optimization as it influences the light absorption and electron diffusion rate. Due to that reason, the cell performance differs at different porosities. This parameter with proper tuning can help to extract the maximum efficiency irrespective of environmental factors. The search and optimization tools, such as artificial bee colony, differential evolution, genetic algorithm, particle swarm optimization, and simulated annealing (SA), is used and applied to the DSSC model for the optimization. The classic optimization algorithms have been compared, and an investigation has been carried out at different thickness values of the titanium dioxide ((Formula presented.)) layer. This study results the realization of the best approach in terms of convergence and computational time, and the consistency of the optimized porosity has been examined at distinct porosity. It is convenient for the practical model improvement of DSSCs with better efficiency.