The transportation sector accounts for a substantial share of global energy consumption and carbon emissions, making the development of energy-efficient vehicle control strategies an essential step toward sustainable mobility. With the rapid advancement of Connected and Automated Vehicles (CAVs), predictive control technologies are increasingly applied to improve vehicle efficiency through the anticipation of future road and traffic conditions. In order to take full advantage of this feature, this study develops a predictive eco-driving control framework based on Model Predictive Control (MPC) within a traffic-in-the-loop simulation platform. The framework enables dynamic interaction among vehicles, surrounding traffic, and infrastructure, thereby optimizing speed and acceleration trajectories in real time.