Date of Award


Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Bo Chen

Committee Member 1

Jeffrey D. Naber

Committee Member 2

Darrell L. Robinette

Committee Member 3

Stephen A. Hackney


In this dissertation, the development of eco-driving and charging planning algorithms in the connected and automated vehicle environment (CAV) are presented. CAV technologies provide opportunities for potential energy savings and efficiency improvement of transportation networks, which are explored through multiple research tasks in this study.

The objective of the first study presented in Chapter 2 is to reduce vehicle dynamic losses and required tractive force while completing trip distance within a given travel time. Sequential Quadratic Programming method is employed for this nonlinearly constrained optimization problem. The validation result illustrates the benefits of optimal velocity trajectories. The objective of the second study presented in Chapter 3 is to generate an optimal velocity trajectory for real-time vehicle control. Optimization considers the distance-based traffic dynamics and road conditions compared to time-based optimization in the first study. The developed algorithm reduces energy consumption by avoiding wasteful driving maneuvers and utilizes opportunities for regeneration. The algorithm is implemented by the ACADO toolkit for real-time execution.

The objective of the third research presented in Chapter 4 is to develop algorithms for predictive velocity control of CAVs at automated signalized intersections using SPAT messages. To validate the algorithm with realistic scenarios, the test strategies are developed to consider the automated traffic lights dynamics due to the actuation of waiting pedestrians, car queues for left turns and human driven vehicles. This research task also develops an eco-stopping algorithm for vehicles stopping at stop signs energy efficiently. The algorithm is compared with the velocity profile segments of US06 cycles to measure the improvement.

The fourth research task presented in Chapter 5 targets the development of charging control algorithm for battery electric vehicles during long road trips. To consider the waiting time at fast -charging stations, a waiting time forecasting method is developed using real-world data from open-source survey of NHTS, daily trip distribution, daily trip mileage and alternate fuel corridors. Dynamic Programming based mixed-integer optimization method for charging control algorithm that selects charging stations along the trip route to reach the destination.

Available for download on Sunday, December 15, 2024