Li, Yiming;Lo, I Hsiu;Yu, Chia-Hui;Cheng, Hui-Wen, Electrical Characteristic Optimization of Silicon Solar Cells using Genetic Algorithm

Abstract:

Solar cell has been a very important clean energy technology in the world. Thin-film solar cell with tandem structures is one of the most popular types for its improved conversion efficiency and cost-effectively practical technology. However, optimal design of thin-film-based solar cell for pursuing the highest efficiency is difficult to achieve in a trial-and-error way. In this paper, a computational intelligence technique is for the first time applied to extract and simulate the dark and illuminated properties of thin-film solar cells. A set of solar cell equations formulated with the photo-generation and drift-diffusion models is solved numerically. The results obtained are used for the optimization of the electrical characteristics with a genetic algorithm (GA) method; therefore, we can deduce optimal parameters of the cell structure, including the energy band gap of thin-film materials, the doping concentration of each layer, and the thickness of emitter, base and intrinsic layers. The iteration of evolutionary can be terminated when the final convergent solution is obtained. The results of our optimization have allowed us to calculate associated electrical characteristics, including short-circuited current, open-circuited voltage, quantum efficiency, filling-factor, and maximum efficiency to analyze the properties of the solar cell. This approach has practical applications in solar cell characterization and structure optimal design. PS: This submission is for the "Mini-symposium on Genetic algorithms in materials design and processing"


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@article(li11elec,
author = "Li, Yiming;Lo, I Hsiu;Yu, Chia-Hui;Cheng, Hui-Wen",
title = "Electrical Characteristic Optimization of Silicon Solar Cells using Genetic Algorithm",
journal = "Computer Methods in Materials Science",
volume = "11",
address = "National Chiao Tung University , Hsinchu, TWN",
pages = "23-27",
year = "2011",
url = "www.cmms.agh.edu.pl/abstract.php?p_id=307"}