Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

Abstract The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variatio...

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Autores principales:	Mansur Mohammed Ali Gamel, Pin Jern Ker, Hui Jing Lee, Wan Emilin Suliza Wan Abdul Rashid, M. A. Hannan, J. P. R. David, M. Z. Jamaludin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/df8ebe9b4fcb4eb89a71ba637b39c0c2
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spelling	oai:doaj.org-article:df8ebe9b4fcb4eb89a71ba637b39c0c22021-12-02T14:37:07ZMulti-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm10.1038/s41598-021-86175-52045-2322https://doaj.org/article/df8ebe9b4fcb4eb89a71ba637b39c0c22021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86175-5https://doaj.org/toc/2045-2322Abstract The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.Mansur Mohammed Ali GamelPin Jern KerHui Jing LeeWan Emilin Suliza Wan Abdul RashidM. A. HannanJ. P. R. DavidM. Z. JamaludinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mansur Mohammed Ali Gamel Pin Jern Ker Hui Jing Lee Wan Emilin Suliza Wan Abdul Rashid M. A. Hannan J. P. R. David M. Z. Jamaludin Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm
description	Abstract The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain < 15%. Therefore, this work develops a multi-variable or multi-dimensional optimization of In0.53Ga0.47As TPV cell using the real coded genetic algorithm (RCGA) at various radiation temperatures. RCGA was developed using Visual Basic and it was hybridized with Silvaco TCAD for the electrical characteristics simulation. Under radiation temperatures from 800 to 2000 K, the optimized In0.53Ga0.47As TPV cell efficiency increases by an average percentage of 11.86% (from 8.5 to 20.35%) as compared to the non-optimized structure. It was found that the incorporation of a thicker base layer with the back-barrier layers enhances the separation of charge carriers and increases the collection of photo-generated carriers near the band-edge, producing an optimum output power of 0.55 W/cm2 (cell efficiency of 22.06%, without antireflection coating) at 1400 K radiation spectrum. The results of this work demonstrate the great potential to generate electricity sustainably from industrial waste heat and the multi-dimensional optimization methodology can be adopted to optimize semiconductor devices, such as solar cell, TPV cell and photodetectors.
format	article
author	Mansur Mohammed Ali Gamel Pin Jern Ker Hui Jing Lee Wan Emilin Suliza Wan Abdul Rashid M. A. Hannan J. P. R. David M. Z. Jamaludin
author_facet	Mansur Mohammed Ali Gamel Pin Jern Ker Hui Jing Lee Wan Emilin Suliza Wan Abdul Rashid M. A. Hannan J. P. R. David M. Z. Jamaludin
author_sort	Mansur Mohammed Ali Gamel
title	Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm
title_short	Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm
title_full	Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm
title_fullStr	Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm
title_full_unstemmed	Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm
title_sort	multi-dimensional optimization of in0.53ga0.47as thermophotovoltaic cell using real coded genetic algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/df8ebe9b4fcb4eb89a71ba637b39c0c2
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Multi-dimensional optimization of In0.53Ga0.47As thermophotovoltaic cell using real coded genetic algorithm

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