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Energies 2017, 10(12), 1981; https://doi.org/10.3390/en10121981

Extremely Efficient Design of Organic Thin Film Solar Cells via Learning-Based Optimization

Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843, USA
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Received: 3 October 2017 / Revised: 21 November 2017 / Accepted: 22 November 2017 / Published: 30 November 2017
(This article belongs to the Section Energy Fundamentals and Conversion)
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Abstract

Design of efficient thin film photovoltaic (PV) cells require optical power absorption to be computed inside a nano-scale structure of photovoltaics, dielectric and plasmonic materials. Calculating power absorption requires Maxwell’s electromagnetic equations which are solved using numerical methods, such as finite difference time domain (FDTD). The computational cost of thin film PV cell design and optimization is therefore cumbersome, due to successive FDTD simulations. This cost can be reduced using a surrogate-based optimization procedure. In this study, we deploy neural networks (NNs) to model optical absorption in organic PV structures. We use the corresponding surrogate-based optimization procedure to maximize light trapping inside thin film organic cells infused with metallic particles. Metallic particles are known to induce plasmonic effects at the metal–semiconductor interface, thus increasing absorption. However, a rigorous design procedure is required to achieve the best performance within known design guidelines. As a result of using NNs to model thin film solar absorption, the required time to complete optimization is decreased by more than five times. The obtained NN model is found to be very reliable. The optimization procedure results in absorption enhancement greater than 200%. Furthermore, we demonstrate that once a reliable surrogate model such as the developed NN is available, it can be used for alternative analyses on the proposed design, such as uncertainty analysis (e.g., fabrication error). View Full-Text
Keywords: organic photovoltaics; plasmonics; neural networks; surrogate-based analysis and optimization; uncertainty analysis organic photovoltaics; plasmonics; neural networks; surrogate-based analysis and optimization; uncertainty analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kaya, M.; Hajimirza, S. Extremely Efficient Design of Organic Thin Film Solar Cells via Learning-Based Optimization. Energies 2017, 10, 1981.

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