Optimization of perovskite solar cell with MoS2 -based HTM layer using hybrid L27 Taguchi-GRA based genetic algorithm
Khairil Ezwan Kaharudin, Fauziyah Salehuddin, Nabilah Ahmad Jalaludin, Anis Suhaila Mohd Zain, Faiz Arith, Siti Aisah Mat Junos, Ibrahim Ahmad
Abstract
This article proposes an optimization method to predictively model the perovskite solar cell with molybdenum disulfide (MoS2 ) based inorganic hole transport material (HTM) for improved fill factor (FF) and power conversion efficiency (PCE) by finding the most optimum thickness and donor/acceptor concentration for each layer via a hybrid L27 Taguchi grey relational analysis (GRA) based genetic algorithm (GA). Numerical simulation of the device is carried out by employing one-dimensional solar cell capacitance simulator (SCAPS-1D) while the optimization procedures are developed based on combination of multiple methods; L27 Taguchi orthogonal array, GRA, multiple linear regression (MLR), and GA. The results of post-optimization reveal that the most optimum layer parameters for improved FF and PCE are predicted as follows; SnO2 F thickness (0.855 μm), SnO2 F donor concentration (9.206×1018 cm-3 ), TiO2 thickness (0.011 μm), TiO2 donor concentration (9.306×1016 cm-3 ), CH3 NH3 PbI3 thickness (0.897 μm), CH3 NH3 PbI3 donor concentration (0.906×1013 cm-3 ), MoS2 thickness (0.154 μm), and MoS2 acceptor concentration (9.373×1017 cm-3 ). Both FF and PCE of the device are improved by ~1.1% and ~12.6% compared to the pre-optimization.
Keywords
Fill factor; Genetic algorithm; Grey relational analysis; Power conversion efficiency; Taguchi grey relational analysis
DOI:
https://doi.org/10.11591/eei.v14i1.8455
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Bulletin of Electrical Engineering and Informatics (BEEI) ISSN: 2089-3191, e-ISSN: 2302-9285 This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU) .