Enhancing Solar Cell Conversion Efficiency Through Evolutionary Optimization Using Genetic Algorithms

In this study, we propose a new method based on genetic algorithms to optimize the performance of intermediate-band solar cells (IBSC). Our approach aims to maximize photovoltaic conversion efficiency by judiciously optimizing the geometric and physical parameters of the IBSC structure., which must be partially filled. This filling ensures the presence of both empty states in the intermediate band (IB) to receive electrons from the valence band (VB), and filled states to provide electrons to the conduction band (CB). Recently, studies have observed the effect of IB occupancy on cell efficiency, and calculated the optimal efficiency for IB devices. The analytical expression for optimal IB filling has been utilized for different scenarios involving IB-CB coupling strength and IB region width. In this work we have studied the influence of the intermediate band energy level, the effects of doping on efficiency, short-circuit current, open-circuit voltage, fill factor, and in order to validate our approach on parasitic effects such as series and shunt resistance.