Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Electrical and Computer Engineering

Committee Chair

Muhammad A. Alam

Committee Member 1

Mark S. Lundstrom

Committee Member 2

Peide P. Ye

Committee Member 3

Zhihong Chen


Thanks to technology advancement in recent decades, the levelized cost of electricity (LCOE) of solar photovoltaics (PV) has finally been driven down close to that of traditional fossil fuels. Still, PV only provides approximately 0.5% of the total electricity consumption in the United States. To make PV more competitive with other energy resources, we must continuously reduce the LCOE of PV through improving their performance and reliability. As PV efficiencies approach the theoretical limit, however, further improvements are difficult. Meanwhile, solar modules in the field regularly fail prematurely before the manufacturers 25-year warranty. Therefore, future PV research needs innovative approaches and inventive solutions to continuously drive LCOE down. In this work, we present a novel approach to PV system design and analysis. The approach, comprised of three components: multiscale, multiphysics, and time, aims at systemically and collaboratively improving the performance and reliability of PV. First, we establish a simulation framework for translating the cell-level characteristics to the module level (multiscale). This framework has been demonstrated to reduce the cell-to-module efficiency gap. The framework also enables the investigation of module-level reliability. Physics-based compact models -the building blocks for this multiscale framework are, however, still missing or underdeveloped for promising materials such as perovskites and CIGS. Hence, we have developed compact models for these two technologies, which analytically describe salient features of their operation as a function of illumination and temperature. The models are also suitable for integration into a large-scale circuit network to simulate a solar module. In the second aspect of the approach, we study the fundamental physics underlying the notorious self-heating effects for PV and examine their detrimental influence on the electrical performance (multiphysics). After ascertaining the sources of self-heating, we propose novel optics-based self-cooling methodologies to reduce the operating temperature. The cooling technique developed in this work has been predicted to substantially enhance the efficiency and durability of commercial Si solar modules. In the third and last aspect of the approach, we have established a simulation framework that can forward predict the future energy yield for PV systems for financial scrutiny and inversely mine the historical field data to diagnose the pathology of degraded solar modules (time). The framework, which physically accounts for environmental factors (e.g., irradiance, temperature), can generate accurate projection and insightful analysis of the geographic-and technology-specific performance and reliability of solar modules. For the forward modeling, we simulate the optimization and predict the performance of bifacial solar modules to rigorously evaluate this emerging technology in a global context. For the inverse modeling, we apply this framework to physically mine the 20-year field data for a nearly worn-out silicon PV system and successfully pin down the primary degradation pathways, something that is beyond the capability of conventional methods. This framework can be applied to solar farms installed globally (an abundant yet unexploited testbed) to establish a rich database of these geographic-and technology-dependent degradation processes, a knowledge prerequisite for the next-generation reliability-aware design of PV systems. Finally, we note that the research paradigm for PV developed in this work can also be applied to other applications, e.g., battery and electronics, which share similar technical challenges for performance and reliability.