Dissertation - Open Access
Doctor of Philosophy (PhD)
Department / School
Electrical Engineering and Computer Science
Perovskite solar cells have received considerable attention in recent years due to their low processing cost and high energy conversion efficiency. However, the mechanisms of perovskite solar cell performance are not fully understood. Models based on probabilistic and statistical approaches can be used to simulate, optimize, and predict perovskite solar cell photovoltaic performance, and they can also guide experimental processing and fabrication conditions to achieve higher photovoltaic efficiency. This work developed a 3D model based on the kinetic Monte Carlo (KMC) approach to simulate 3D morphology of perovskite-based solar cells and predict their photovoltaic performance. The model incorporated the physical behavior of perovskite cells with respect to their charge generation, transport, and recombination characteristics. KMC simulation results showed that perovskite films with the pin holes-free and a homogenous perovskite capping layer of 400 nm thickness produced a maximum photovoltaic efficiency of 20.85%, resulting in minimal charge transport time (τt) and maximum charge carrier recombination lifetime (τr). Photovoltaic performance from the fabricated device has been used to validate this simulation model. This model provides significant conceptual advances in identifying current performance constraints and guiding novel device designs that enhance overall perovskite photovoltaic performance.
Library of Congress Subject Headings
Solar cells -- Design.
Solar cells -- Materials.
Monte Carlo method.
South Dakota State University
Bahrami, Behzad, "A Kinetic Monte Carlo Study of Mesoscopic Perovskite Solar Cell Performance Behavior" (2019). Electronic Theses and Dissertations. 3185.