Mathematical modeling, parameter identification, and electrical performance of a DSSC based on nature-inspired optimization techniques

Abstract

The aim of this research is to achieve the highest efficiency for a dye-sensitized solar cell (DSSC) before the fabrication process. For DSSC efficiency improvement, six different optimization algorithms are used for the DSSC parameter extraction. The algorithms used are the genetic algorithm, grey wolf algorithm, dragonfly algorithm, moth flame algorithm, ant-lion algorithm, and whale algorithm, developed based on MATLAB coding. The physical parameters for the DSSC are the electron lifetime, electrode thickness, ideality factor, absorption coefficient, and diffusion coefficient. A comparative study is carried out among the six algorithms based on the highest efficiency and computational speed. Finally, a sensitivity analysis of environmental conditions (solar irradiance and temperature) and physical parameters is implemented and analyzed to simulate the DSSC performance for different values of these parameters. The DSSC parameters studied are short-circuit current density, open-circuit voltage, fill factor, and efficiency. The optimal electron lifetime is 100 ms, and the optimal thickness of the photoanode layer is 1 μm, reaching maximum efficiency equal to 11.79%.