Unlocking the full potential of solar cell materials: parameter sensitivity analysis and optimization using response surface modelling

Abstract

This study introduces a novel approach for predicting solar cell efficiency and conducting sensitivity analysis of key parameters and their interactions, leveraging response surface modeling to optimize interacting solar cell structure parameters for the best performance. Integrating response surface modeling with solar cell simulation software enhances the efficiency prediction process that enables the solar cell capacitance simulator (SCAPS)-1D software to unlock the true potential of a material. Through the utilization of central composite design (CCD) and response surface modeling (RSM), this research minimizes material waste during synthesis, saves time, and conserves energy. The methodology involves selecting five input parameters within specific ranges, modeling them using the least square method to create a polynomial regression model, and validating the model through efficiency predictions compared to SCAPS-1D simulations. The parameter sensitivity analysis is validated using analysis of variance (ANOVA) test results, demonstrating the precision of the RSM in predicting solar cell efficiency with a maximum error of 1.93%.