Connected and Automated Vehicles (CAV) provide new prospects for energy-efficient driving due to their improved information accessibility, enhanced processing capacity, and precise control. The idea of the Eco-Driving (ED) control problem is to perform energy-efficient speed planning for a connected and automated vehicle using data obtained from high-resolution maps and Vehicle-to-Everything (V2X) communication. With the recent goal of commercialization of autonomous vehicle technology, more research has been done to the investigation of autonomous eco-driving control. Previous research for autonomous eco-driving control has shown that energy efficiency improvements can be achieved by using optimization techniques. Most of these studies are conducted through simulations, but many more physical vehicle integrated test application studies are needed. This paper addresses this research gap by highlighting the Vehicle Hardware-In-the-Loop (VHIL) energy saving potential of autonomous eco-driving control for connected and automated vehicles. A comprehensive system description of autonomous eco-driving control is presented by describing subsystems and their functionalities. Validated autonomous eco-driving optimization methods, including Dynamic Programming (DP), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO) were tested with a control-enabled electric Kia Soul using a 2-wheel-drive chassis dynamometer. VHIL test performance of these methods is evaluated relative to each other as well as a baseline scenario. The conclusions were derived from examinations that were carried out on a chassis dynamometer. The results show that energy efficiency may be enhanced by anywhere from 5 to 15 %, depending on the method that is used. When compared to our earlier simulation results, it is demonstrated that the VHIL outcomes achieve the predicted gain in energy efficiency. The overall results show that the use of the dynamic programming method is the most effective strategy for enhancing energy efficiency. It is shown that the application of methods that are derived from genetic algorithms has the potential to increase energy efficiency when integrated in the test vehicle.