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.
Highlights
The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power
Voc is influenced by dark current densities (J01 and J02), where J01 is contributed to the dark current due to surface and bulk recombination losses, and J02 is related to recombination due to traps in the space charge region (SCR)[69]
The single variable optimization is used to investigate the effect of thickness and doping concentration of layers, which demonstrated the significant impact of the base layer to achieve high performance In0.53Ga0.47As TPV cell
Summary
The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. 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. 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 nonoptimized structure. Device performance depends collectively on all the design v ariables[45,46], and a more heuristic optimization that considers the effect of all important variables for the In0.53Ga0.47As TPV cell is necessary to achieve the optimum cell efficiency. This study investigates the effect of each variable through single variable optimization and performs multi-dimensional (simultaneous multi-variables) optimization using real coded genetic algorithm (RCGA) to obtain the optimum configuration of In0.53Ga0.47As TPV cell
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