Application of Genetic Algorithm for More Efficient Multi-Layer Thickness Optimization in Solar Cells

Premkumar Vincent, Gwenaelle Cunha Sergio, Jaewon Jang, In Man Kang, Jaehoon Park, Hyeok Kim, Minho Lee and Jin-Hyuk Bae
Additional contact information
Premkumar Vincent: School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
Gwenaelle Cunha Sergio: School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
Jaewon Jang: School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
In Man Kang: School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
Jaehoon Park: College of Software, Hallym University, Chuncheon 24252, Korea
Hyeok Kim: Department of Electrical and Computer Engineering, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
Minho Lee: Department of Artificial Intelligence, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea
Jin-Hyuk Bae: School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Korea

Energies, 2020, vol. 13, issue 7, 1-13

Abstract: Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.

Keywords: genetic algorithm; solar cell optimization; finite difference time domain; optical modelling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/7/1726/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/7/1726/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:7:p:1726-:d:341572

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().