Design and characterization of effective solar cells

Ojha, V. ORCID: https://orcid.org/0000-0002-9256-1192, Jansen, G., Patanè, A., La Magna, A., Romano, V. and Nicosia, G. ORCID: https://orcid.org/0000-0002-0650-3157 (2022) Design and characterization of effective solar cells. Energy Systems, 13. pp. 355-382. ISSN 1868-3967 doi: 10.1007/s12667-021-00451-x

Abstract/Summary

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency (Qe) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, Qe of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/98150
Identification Number/DOI	10.1007/s12667-021-00451-x
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE) Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Springer
Download/View statistics	View download statistics for this item