Date of Award


Degree Name

Doctor of Philosophy


Mechanical and Aerospace Engineering

First Advisor

Zachary D. Asher, Ph.D.

Second Advisor

Richard T. Meyer, Ph.D.

Third Advisor

Alvis C Fong, Ph.D.

Fourth Advisor

Damon Miller, Ph.D.


Connected and automated vehicles (CAVs), eco-driving, intelligent transportation system (ITS), long-short memory (LSTM), mobility energy productivity, vehicle-to-vehicle (V2V) - vehicle-to-infrastructure (V2I)


The transportation sector is a significant contributor to global energy consumption and emissions, necessitating the development of sustainable transportation systems. In this regard, connected and automated vehicles (CAVs) have emerged as a potential solution to transform the transportation industry. By harnessing advanced mapping and location technologies, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, CAVs offer the promise of improving efficiency, reducing traffic congestion, and enhancing safety and comfort. However, the adoption of CAVs also brings about various challenges, including energy efficiency concerns that need to be addressed to fully realize their potential benefits. This dissertation investigates energy-efficient control techniques for transportation vehicles using connected and automated vehicles. The primary research question driving this study is: How can energy-efficient controls be implemented with consideration of current and near future CAV technologies? By exploring this question, the study aims to examine the practical implementation of energy-efficient control techniques specifically within the context of connected and automated vehicles. The research hypothesis posits that the implementation of energy-efficient control techniques in transportation vehicles, leveraging the capabilities of connected and automated technologies, will lead to significant reductions in energy consumption and emissions while maintaining or improving overall vehicle performance. This hypothesis will be empirically tested through comprehensive data analysis and evaluation. By addressing this research question and hypothesis, this dissertation seeks to contribute to the development of effective energy-efficient control strategies for transportation vehicles. The investigation will provide insights and recommendations for integrating energyefficient controls in connected and automated vehicles, with the ultimate goal of promoting sustainability and reducing energy consumption within the transportation sector. Artificial neural networks (ANNs), predictive optimal energy management strategies (POEMS), and autonomous eco-driving control are studied to improve fuel economy, energy efficiency, drivability, and safety. The study compares optimal energy management strategies and evaluates predictive optimal energy management strategies in hybrid electric vehicles and connected vehicles. It also integrates POEMS with optimal traffic management to enhance system efficiency and develops accurate models for predicting emissions and fuel economy in light-duty vehicles. The research identifies research gaps in energy-efficient control of electrified autonomous vehicle eco-driving and evaluates the energy-saving potential of autonomous eco-driving control. The findings demonstrate that connected and automated vehicles offer new prospects for energy-efficient driving, and the application of these methods can significantly improve energy efficiency and reduce emissions in transportation vehicles. The results have implications for the development of energy-efficient control strategies for future transportation systems. By addressing the challenges of energy efficiency and sustainability in transportation, this dissertation contributes to the development of more sustainable transportation systems.

Access Setting

Dissertation-Open Access