Abstract
The electrical power generation of a thermophotovoltaic (TPV) device can be enhanced if the vacuum gap between the thermal emitter and TPV cell is at the nanoscale owing to the photon tunneling of evanescent waves. Multi-junction TPV cells with multiple bandgaps have gained interest as a method of improving their conversion efficiency by selectively absorbing the spectral radiation in each subcell. In this paper, we comprehensively analyze an optimized near-field tandem TPV converter consisting of a tungsten emitter covered by an ITO thin film (at 1500 K) and a GaInAsSb/InAs monolithic interconnected tandem TPV cell (at 300 K). We developed a simulation model by coupling the near-field radiation solved by fluctuational electrodynamics and diffusion-recombination-based charge transport equations. The optimal configuration of the near-field tandem TPV converter obtained using the genetic algorithm achieved an electrical power output of 8.42 W/cm2 and a conversion efficiency of 35.6% at a vacuum gap of 100 nm. We demonstrated that two resonance modes (i.e., surface plasmon polaritons supported by the ITO-vacuum interface and the confined waveguide mode in the tandem TPV cell) significantly contribute to the enhanced performance of the optimized system. Through loss analysis, we also demonstrate that the near-field tandem TPV converter is superior to the single-cell-based near-field TPV converter in terms of both power output and conversion efficiency. The optimization performed with the objective function of the conversion efficiency resulted in the current matching condition for the tandem TPV converter, regardless of the vacuum gap distance. In addition, the impacts of the additional series resistance and shadowing losses are explored by introducing the front contact grid.
Original language | English |
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Article number | 111522 |
Journal | Solar Energy Materials and Solar Cells |
Volume | 236 |
DOIs | |
Publication status | Published - Mar 2022 |
Bibliographical note
Publisher Copyright:© 2021 Elsevier B.V.
Keywords
- Genetic algorithm
- Near-field radiation
- Tandem cell
- Thermophotovoltaic