The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS
Single-junction solar cells are the most available in the market and the most simple in terms of the realization and fabrication comparing to the other solar devices. However, these single-junction solar cells need more development and optimization for higher conversion efficiency. In addition to th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2017/8269358 |
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| author | Kamal Attari Lahcen Amhaimar Ali El yaakoubi Adel Asselman Mounir Bassou |
| author_facet | Kamal Attari Lahcen Amhaimar Ali El yaakoubi Adel Asselman Mounir Bassou |
| author_sort | Kamal Attari |
| collection | DOAJ |
| description | Single-junction solar cells are the most available in the market and the most simple in terms of the realization and fabrication comparing to the other solar devices. However, these single-junction solar cells need more development and optimization for higher conversion efficiency. In addition to the doping densities and compromises between different layers and their best thickness value, the choice of the materials is also an important factor on improving the efficiency. In this paper, an efficient single-junction solar cell model of GaAs is presented and optimized. In the first step, an initial model was simulated and then the results were processed by an algorithm code. In this work, the proposed optimization method is a genetic search algorithm implemented in Matlab receiving ATLAS data to generate an optimum output power solar cell. Other performance parameters such as photogeneration rates, external quantum efficiency (EQE), and internal quantum efficiency (EQI) are also obtained. The simulation shows that the proposed method provides significant conversion efficiency improvement of 29.7% under AM1.5G illumination. The other results were Jsc = 34.79 mA/cm2, Voc = 1 V, and fill factor (FF) = 85%. |
| format | Article |
| id | doaj-art-d0295d5303ab43e5a453ee5e30d7628e |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-d0295d5303ab43e5a453ee5e30d7628e2025-02-03T01:23:39ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2017-01-01201710.1155/2017/82693588269358The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLASKamal Attari0Lahcen Amhaimar1Ali El yaakoubi2Adel Asselman3Mounir Bassou4Optics & Photonics Team, Faculty of Science, Abdelmalek Essaadi University, Tetuan, MoroccoOptics & Photonics Team, Faculty of Science, Abdelmalek Essaadi University, Tetuan, MoroccoOptics & Photonics Team, Faculty of Science, Abdelmalek Essaadi University, Tetuan, MoroccoOptics & Photonics Team, Faculty of Science, Abdelmalek Essaadi University, Tetuan, MoroccoOptics & Photonics Team, Faculty of Science, Abdelmalek Essaadi University, Tetuan, MoroccoSingle-junction solar cells are the most available in the market and the most simple in terms of the realization and fabrication comparing to the other solar devices. However, these single-junction solar cells need more development and optimization for higher conversion efficiency. In addition to the doping densities and compromises between different layers and their best thickness value, the choice of the materials is also an important factor on improving the efficiency. In this paper, an efficient single-junction solar cell model of GaAs is presented and optimized. In the first step, an initial model was simulated and then the results were processed by an algorithm code. In this work, the proposed optimization method is a genetic search algorithm implemented in Matlab receiving ATLAS data to generate an optimum output power solar cell. Other performance parameters such as photogeneration rates, external quantum efficiency (EQE), and internal quantum efficiency (EQI) are also obtained. The simulation shows that the proposed method provides significant conversion efficiency improvement of 29.7% under AM1.5G illumination. The other results were Jsc = 34.79 mA/cm2, Voc = 1 V, and fill factor (FF) = 85%.http://dx.doi.org/10.1155/2017/8269358 |
| spellingShingle | Kamal Attari Lahcen Amhaimar Ali El yaakoubi Adel Asselman Mounir Bassou The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS International Journal of Photoenergy |
| title | The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS |
| title_full | The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS |
| title_fullStr | The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS |
| title_full_unstemmed | The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS |
| title_short | The Design and Optimization of GaAs Single Solar Cells Using the Genetic Algorithm and Silvaco ATLAS |
| title_sort | design and optimization of gaas single solar cells using the genetic algorithm and silvaco atlas |
| url | http://dx.doi.org/10.1155/2017/8269358 |
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