I. Introduction
Thanks to their ability in enhancing road safety while decreasing road congestion, autonomous vehicles can bring huge benefits to the automotive industry [1], [2], as well as improving energy saving when considering electrical vehicle (EV) [3]. According to the CCAM paradigm, energy-saving performance could be further improved thanks to the vehicle-to-everything (V2X) communication technology, which allows information sharing with smart infrastructure and other vehicles, thereby enabling access to previously unavailable surrounding road traffic environment information [4]. This brings traffic management to an entirely new level and contributes to sustainable mobility, i.e. the shared information allows the optimization of vehicles control motion in a completely sustainable manner [5]. Within this framework, research interests in designing eco-driving control strategies for Connected Autonomous Vehicles (CAVs), as well as Connected Autonomous Electric Vehicles (CAEVs), have been increased [6]. A first attempt is presented in [7] where, without taking into account the power train dynamics, a Model Predictive Controller (MPC) is adopted for the designing of an Adaptive Cruise Control (ACC) able to ensure fuel economy and robustness to external disturbances in urban scenarios.