In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin’s Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV that is controlled by the automated driving system follows this Eco-Cruise speed profile as long as there is no preceding vehicle impeding its motion or upcoming traffic light or STOP sign ahead. When the ego CAV approaches a traffic light, then a V2I algorithm called Pass-at-Green (PaG) calculates a fuel-economic and Signal Phase and Timing (SPaT) dependent speed profile. When the ego CAV approaches a STOP sign, the eHorizon electronic horizon unit is used to get STOP sign location while the Eco-Stop algorithm calculates a fuel optimal Eco-Approach speed trajectory for the ego CAV, so that the ego vehicle smoothly comes to a complete stop at the STOP sign. When the ego CAV departs from the traffic light or STOP sign, then the Eco-Departure algorithm calculates a fuel optimal speed trajectory to smoothly accelerate to a higher speed for the ego CAV. Other than the interaction of the CAV with road infrastructure, there could also be other vehicles around the ego vehicle. When there is a preceding vehicle in front of the ego CAV, typically, an Adaptive Cruise Control (ACC) is used to follow the lead vehicle keeping a constant time gap. Lead vehicle acceleration that was received by the ego CAV through V2V can be utilized in Cooperative Adaptive Cruise Control (CACC) to follow the preceding vehicle better than the ACC. If the ego CAV is found to be erratic, then the Ecological Cooperative Adaptive Cruise Control (Eco-CACC) takes over and calculates a fuel efficient speed trajectory for car following. If the preceding vehicle acts too erratically or slows down too much, and the ego CAV has a chance to change its lane, then the Lane Change mode takes control and changes the lane. The default driving mode in all these scenarios is the Eco-Cruise mode, which is the optimal fuel economic and route-dependent solution acquired using DP. Unmanned Aerial Vehicles (UAVs) are part of Intelligent Transportation Systems (ITS) and can communicate with CAVs and other transportation agents. Whenever there are UAVs with communication capabilities around the ego CAV, information can be transferred between the UAV and CAV. As part of this communication capability, when the ego CAV approaches a bottleneck or a queue, information regarding the queue can be broadcast either from a Roadside Unit (RSU) or a Connected UAV (C-UAV) acting like an RSU with Dedicated Short Range Communication (DSRC). The queue information can be received by the On-Board-Unit (OBU), which is the vehicle communication unit using DSRC protocol in the ego CAV. Using the queue information, the Dynamic Speed Harmonization (DSH) model can be activated to take the main driver role for generating a smooth deceleration profile while the ego CAV approaches the queue. Once the queue is passed, the ego CAV goes back to the default Eco-Cruise mode. The elements of the proposed Eco-Driving method outlined above are first treated individually and then integrated in a holistic manner in this dissertation. The organization of this dissertation is as follows. Firstly, a summary is given on the topic of CAVs and various ways that connectivity is utilized in CAV research in Chapter 1 Introduction and Literature Review. Then, in Chapter 2 Modelling, Simulation and Testing Environment, details about the state-of-the-art simulation environment used for this dissertation are presented. Chapter 3 Scenario Development and Selection focuses on test route development procedure and the types of roadways tested in this work. Chapter 4 Fuel Economic Driving for a Single CAV with V2I in No Traffic explains the different models developed for fuel optimal speed trajectory calculation using roadway infrastructure. Chapter 5 Fuel Economic Driving for a CAV with V2V in Traffic gives details about the models developed for an ego CAV travelling among other connected vehicles. The Model-in-the-Loop (MIL) simulation results for the Eco-Driving algorithms developed for Chapter 4 and Chapter 5 are presented in Chapter 6. The Hardware-in-the-Loop (HIL) simulation results for the Eco-Driving algorithms in Chapter 4 and Chapter 5 are presented in Chapter 7. Chapter 8 shows results about testing the complete Eco-Driving strategy in a traffic simulator with realistic traffic flow. Chapter 9 touches on CAV and UAV communication and presents Dynamic Speed Harmonization (DSH) as a use case scenario. Chapter 10 Conclusion presents the results of this dissertation and draws conclusions about this work.