Date of Award

2025

Document Type

Open Access Dissertation

Degree Name

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

Administrative Home Department

Department of Mechanical and Aerospace Engineering

Advisor 1

Darrell Robinette

Advisor 2

Jeffrey D. Naber

Committee Member 1

Bo Chen

Committee Member 2

Nathir Rawashdeh

Abstract

This dissertation focuses on the development and validation of on-board, real-time estimation algorithms and the investigation of energy-saving strategies for modern light-duty vehicles, including connected and automated vehicles (CAVs). The accurate estimation of key vehicle parameters is critical for enhancing energy efficiency, enabling advanced driver-assistance systems (ADAS), and optimizing vehicle performance and energy efficiency. First, this research introduces a novel real-time algorithm to dynamically learn vehicle mass using readily available sensor data. Based on longitudinal vehicle dynamics, a sensitivity analysis was conducted to identify the conditions under which mass estimation is most robust. The algorithm was extensively validated through on-road testing with multiple electric and plug-in hybrid vehicles, demonstrating high accuracy and repeatability against weighed vehicle mass across various loading conditions. Second, an algorithm for real-time road load estimation was developed to determine the forces resisting a vehicle's motion, such as aerodynamic drag and rolling resistance. By deriving analytical solutions from the equations of motion, the algorithm effectively learns road load coefficients from normal driving data. The methodology was validated using simulations, controlled coast-down experiments on a dynamometer and on-road, and real-world driving tests, showing a significant improvement in the accuracy of energy consumption predictions. Building on these estimation capabilities, the dissertation investigates the energy-saving potential of automated vehicle-following. Through extensive on-road experiments with two distinct vehicles, the study quantifies the reduction in energy consumption for a following vehicle due to aerodynamic drafting. The research systematically analyzes the effects of speed, longitudinal gap, and lateral offset on the energy savings for both the lead and following vehicles. Finally, a theoretical framework for optimizing a vehicle's speed trajectory at signalized intersections is presented. Using a variational calculus approach, the study formulates and solves the problem of minimizing axle energy for Eco-Approach and Departure maneuvers, laying the groundwork for advanced, energy-efficient control systems in CAVs. Collectively, this work provides a suite of validated intelligent systems and methodologies that can significantly improve the energy efficiency and performance of next-generation automobiles.

Download

Available for download on Monday, August 10, 2026

Share

COinS