Optimising standard solar cell designs for maximum efficiency using genetic algorithms

Abstract

ABSTRACT Solar energy is an alternative to conventional fossil fuels, and maximising the energy collected through solar cell design improvements is worthwhile. Genetic algorithms (GAs) have been historically useful in pursuing design improvements in solar cell architecture and manufacturing, particularly in combination with cell modelling software. This study demonstrates solar cell structural optimisation using PC3D software in combination with a genetic algorithm (GA) to maximise solar cell power conversion efficiency. PC3D is an Excel-based tool for modelling solar cells. The cell models examined here are: Passivated-Emitter Rear Contact (PERC), Interdigitated Back Contact (IBC), Aluminium-Back Surface Field (Al-BSF), and Passivated-Emitter Rear Locally Diffused (PERL). These are all silicon PV cells with varying structural features that significantly alter the performance of each cell. Absolute efficiency improvements of 2% for PERC, 1.6% for Al-BSF, 0.9% for IBC, and 0.5% for PERL cells are achieved. The main parameters impacting cell efficiency in this model are cell thickness and minority charge carrier lifetime, which feature a necessary trade-off between the higher absorption resulting from a thicker cell and the increased likelihood of charge collection arising from longer carrier lifetimes. These parameters are managed through other related values such as cell doping.