Document Type
Article
Publication Date
2023
Abstract
Tandem structures have been introduced to the photovoltaics (PV) market to boost power conversion efficiency (PCE). Single-junction cells’ PCE, either in a homojunction or heterojunction format, are clipped to a theoretical limit associated with the absorbing material bandgap. Scaling up the single-junction cells to a multi-junction tandem structure penetrates such limits. One of the promising tandem structures is the perovskite over silicon topology. Si junction is utilized as a counter bare cell with perovskites layer above, under applying the bandgap engineering aspects. Herein, we adopt BaTiO3/CsPbCl3/MAPbBr3/CH3NH3PbI3/c-Si tandem structure to be investigated. In tandem PVs, various input parameters can be tuned to maximize PCE, leading to a massive increase in the input combinations. Such a vast dataset directly reflects the computational requirements needed to simulate the wide range of combinations and the computational time. In this study, we seed our random-forest machine learning model with the 3×106 points’ dataset with our optoelectronic numerical model in SCAPS. The machine learning could estimate the maximum PCE limit of the proposed tandem structure at around 37.8%, which is more than double the bare Si-cell reported by 18%.
Recommended Citation
Ganoub, Moustafa; Elsaban, Omar; Abdellatif, Sameh O. Dr; Kirah, Khaled; and Ghali, Hani, "Utilizing machine learning algorithm in predicting the power conversion efficiency limit of a monolithically perovskites/silicon tandem structure" (2023). Electrical Engineering. 19.
https://buescholar.bue.edu.eg/elec_eng/19
