Kaya Mine, Hajimirza Shima
Department of Mechanical Engineering, Texas A&M University, 3123 TAMU, College Station, TX, 77843-3123, USA.
Sci Rep. 2018 May 25;8(1):8170. doi: 10.1038/s41598-018-26469-3.
This paper uses surrogate modeling for very fast design of thin film solar cells with improved solar-to-electricity conversion efficiency. We demonstrate that the wavelength-specific optical absorptivity of a thin film multi-layered amorphous-silicon-based solar cell can be modeled accurately with Neural Networks and can be efficiently approximated as a function of cell geometry and wavelength. Consequently, the external quantum efficiency can be computed by averaging surrogate absorption and carrier recombination contributions over the entire irradiance spectrum in an efficient way. Using this framework, we optimize a multi-layer structure consisting of ITO front coating, metallic back-reflector and oxide layers for achieving maximum efficiency. Our required computation time for an entire model fitting and optimization is 5 to 20 times less than the best previous optimization results based on direct Finite Difference Time Domain (FDTD) simulations, therefore proving the value of surrogate modeling. The resulting optimization solution suggests at least 50% improvement in the external quantum efficiency compared to bare silicon, and 25% improvement compared to a random design.
本文采用代理模型对薄膜太阳能电池进行极快速设计，以提高太阳能到电能的转换效率。我们证明，基于多层非晶硅的薄膜太阳能电池的波长特定光吸收率可以用神经网络精确建模，并且可以作为电池几何形状和波长的函数有效地近似。因此，可以通过在整个辐照光谱上以有效方式平均代理吸收和载流子复合贡献来计算外部量子效率。使用该框架，我们优化了由ITO前涂层、金属背反射器和氧化层组成的多层结构，以实现最大效率。与基于直接时域有限差分（FDTD）模拟的最佳先前优化结果相比，我们对整个模型进行拟合和优化所需的计算时间减少了5到20倍，从而证明了代理模型的价值。所得的优化解决方案表明，与裸硅相比，外部量子效率至少提高了50%，与随机设计相比提高了25%。
