Metaheuristic optimization of perovskite solar cell using hybrid L₃₂ Taguchi DoE-based genetic algorithm - Salehuddin, Fauziyah; Ahmad Jalaludin, Nabilah; Kaharudin, Khairil Ezwan; Arith, Faiz; Mohd Zain, Anis Suhaila; Md Junos@Yunus, Siti Aisah; R Apte, Prakash (Penerbit Akademia Baru, 2024)
Solar cells convert sunlight into electricity, and the efficiency of this conversion process largely depends on the material parameters. Optimizing these parameters, like thickness and carrier concentration, could significantly increase the efficiency of solar cells. This paper emphasizes the metaheuristic optimization approach in searching for the optimum input parameters of perovskite solar cell (PSC). The proposed approach is realized using Solar Cell Capacitance Simulator (SCAPS-1D) software incorporated with a hybrid L32 Taguchi DoE-based Genetic Algorithm. Based on Multiple Linear Regression (MLR) analysis, the thickness of mix halide perovskite (CH3NH3PbI3-XClX) was discovered to be the most crucial input parameter affecting the Power Conversion Efficiency (PCE) variations. Based on the result of the Genetic algorithm, the optimal values of the input parameters: Fluorine doped tin oxide (FTO) thickness, FTO donor density, Titanium Dioxide (TiO2) layer thickness, TiO2 donor density, CH3NH3PbI3-XClX layer thickness, CH3NH3PbI3-XClX donor density, graphene oxide (GO) layer thickness, and GO acceptor density are predicted to be 0.187 μm, 9.965x1021 cm-3, 0.033 μm, 9.629x1021 cm-3, 0.926 μm, 9.983x1021 cm-3, 0.039 μm and 9.671x1021 cm-3 respectively. Using the predicted optimum input parameters, the simulation generates the best value of open voltage (Voc), current density (Jsc), fill factor (FF), and PCE measured at 1.647 V, 25.68 mA/cm2, 92.03%, and 38.93%, respectively.