This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short sighted traffic and road geometry information can optimize their speed profile to increase energy efficiency, depending on the powertrain types. In particular, the developed speed planner reducing the battery output power through the energy-efficiency improvement systems in electrified vehicles. Consequently, the CAV that is aware of the existence of the upcoming road gradient increases the speed on the uphill, and decreases the speed on the downhill to minimize the battery output power, which is different from the natural behaviors of human-driven vehicles on sloped roads. To consider the constraints, the model predictive control-based speed planner is developed, and its effectiveness is verified under various driving conditions. Simulation results show that our approach significantly outperforms the alternative speed profiles in terms of battery energy-saving, achieving about 27.21% of the energy efficiency improvement.INDEX TERMS Connected and automated vehicle, model predictive control, electric vehicle, energyefficiency improvement, energy-efficient driving.

Due to the battery capacity limitation of battery electric vehicles (BEVs), the importance of minimizing energy consumption has been increasing in recent years. In the mean time, for improving vehicle energy efficiency, platooning has attracted attention of several automakers. Using the connected and automated vehicles (CAVs) technology, platooning can achieve a longer driving range while preserving a closer distance from the preceding vehicle, resulting in the minimization of the aerodynamic force. However, undesired behaviors of human-driven vehicles (HVs) in the platooning group can prohibit the maximization of the energy efficiency. In this paper, we developed a speed planner based on the model predictive control (MPC) to minimize the total platooning energy consumption, and HVs were programmed to maintain a long enough distance from the preceding vehicle to avoid collision. The simulations were performed to determine how HV influences the efficiencies of the platooning group, which is composed of CAVs and HVs together, in several scenarios including the different positions and numbers of the HVs. Test results show that the CAVs planned by our approach reduces energy consumption by about 4% or more than 4% compared to that of the HVs.