基于机器学习和器件模拟对Cu(In,Ga)Se_(2)电池中Ga含量梯度的优化分析

Optimization of Ga content gradient in Cu(In,Ga)Se_(2) solar cells through machine learning and device simulation

Cu(In,Ga)Se_(2)(CIGS)太阳能电池是一种高效薄膜太阳能电池,Ga含量(Ga/(Ga+In),GGI)梯度调控是在不损失短路电流情况下,获得高开路电压的一种有效方法.本文基于对薄膜电池效率极限的对比分析,首先评估了CIGS电池性能提升的优化空间和策略.进而,通过机器学习与电池模拟分析相结合,研究了不同类别的“V”型GGI梯度对电池性能的影响,优化了“V”型双梯度的分布,获得了高于26%的模拟效率,并探究了其内部载流子作用机理.本文的研究提供了获得高效率CIGS电池“V”型GGI梯度的优化方案,为实验优化提供了指导.

Cu(In,Ga)Se_(2)(CIGS)solar cell is a kind of highly efficient thin film solar cell,for which Ga ratio(Ga/(Ga+In),GGI)gradient engineering is an efficient approach to achieving high open circuit voltage under no short circuit current loss.In this work,we firstly evaluate the room and the strategies for improving the device performance of the CIGS solar cells based on the comparison among their theoretical efficiency limits.Then we investigate the different schemes of“V”type GGI gradient and their effects on device performance through machine learning and device simulation.Based on these studies,we optimize the scheme of“V”type GGI gradient and obtain a high efficiency of 26%from device simulation.The carrier kinetics for the effect of modifying GGI gradient on device performance are analyzed.This work provides an approach to optimizing the GGI gradient to obtain highly efficient CIGS solar cells,which is referable for experimental optimization.