Keywords

EV adoption, energy demand, energy forecasting, econometric model

Abstract

The adoption of Electric Vehicles (EVs) represents a transformative change in the automotive industry. As more households make the transition to EVs, the traditional dependence on fossil fuels for transportation is being replaced by electricity as the primary energy source. This transition has the potential to increase the electricity consumption within households as well as the demand on the power grids. To maximize the environmental and economic benefits of EV adoption, strategies regarding efficient energy management, integration of renewable energy source, and grid capacity are becoming essential considerations. The current dissertation research is motivated toward evaluating the adoption of EV and its impact on the US household’s energy demand. In pursuit of these goals, this research has made several contributions. First, we proposed an econometric framework to estimate the factors influencing customers’ vehicle purchase decisions. Second, a comparative analysis is conducted between two econometric frameworks –panel mixed random utility maximization MNL model and panel mixed random regret minimization MNL model to estimate the evolving landscape of EV adoption over time. Third, we employed an advanced econometric framework- Multiple Discrete Continuous Extreme Value (MDCEV) model– to evaluate the factors that influence the energy consumption profile of various household end-uses along with their alternating trends over time. Fourth, we employed a novel fusion approach to the MDCEV model to assess the impact of travel behavior along with several household socioeconomic characteristics on various energy end-uses. Finally, by predicting household EV ownership, projections of total household energy demand for the city of Atlanta for the years 2030, 2040 and 2050 are performed. A set of independent variables including vehicle attributes, socio-economic attributes, travel behavior-related attributes, dwelling attributes, appliance-use related attributes, and climate-related attributes from various data sources are employed in this study. The research concludes with an analysis of different policy implications.

Completion Date

2024

Semester

Summer

Committee Chair

Naveen Eluru

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Department of Civil, Environmental and Construction Engineering

Degree Program

Ph.D. in Civil Engineering (Transportation Engineering)

Format

application/pdf

Identifier

DP0028603

URL

https://purls.library.ucf.edu/go/DP0028603

Language

English

Rights

In copyright

Release Date

August 2027

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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Restricted to the UCF community until August 2027; it will then be open access.

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