Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

The dramatic global climate change has driven governments to drastically tackle pollutant emissions. In the transportation field, one of the technological responses has been powertrain electrification for passengers’ cars. Nevertheless, the large amount of possible powertrain designs does not help the development of an exhaustive sizing process. In this research, a multi-objective particle swarm optimization algorithm is proposed to find the optimal layout of a parallel P2 hybrid electric vehicle powertrain with the aim of maximizing fuel economy capability and minimizing production cost. A dynamic programming-based algorithm is used to ensure the optimal vehicle-level energy management. The results show that diverse powertrain layouts may be suggested when different weights are assigned to the sizing targets related to fuel economy and production cost, respectively. Particularly, upsizing the power sources and increasing the number of gears might be advised to enhance HEV fuel economy capability through the efficient exploitation of the internal combustion engine (ICE) operation. On the other hand, reduction of the HEV production cost could be achieved by downsizing the power sources and limiting the number of gears with respect to conventional ICE-powered vehicles thanks to the interaction between ICE and electric motor.

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred.

Autonomous driving systems are among the most interesting technologies in the transportation field nowadays, ensuring a high level of safety and comfort while also enhancing energy saving. For the following case study, a Battery Electric Vehicle (BEV) is considered able to communicate with other vehicles through vehicle-to-vehicle (V2V) technology by exchanging information on position and velocity. In this framework, an innovative real-time velocity planner has been developed aiming at maximizing the battery energy savings while improving the passenger comfort as well. This controller uses the principles of the equivalent consumption minimization strategy (ECMS) when the preceding vehicle is accelerating. Simulation results demonstrate improvements in comfort ranging from 26% to 42% ca. and in energy consumption (from 0.4% to 1.3%) when performing different drive cycles in V2V automated driving mode thanks to the proposed controller.

Techniques exploiting the communication between vehicles, infrastructure or anything capable of, are being developed in the recent years due to their effectiveness in improving energy efficiency, comfort and safety. The scenario analyzed in this paper is of four vehicles platooning, in which the leader (i.e. the first in the platoon) is set to travel through different drive cycles and is followed by three other vehicles. An optimization-based algorithm based on Dynamic Programming (DP) is implemented to find the benchmark optimal control solution for the speed trajectory of the three following Battery Electric Vehicles (BEVs). Optimal control targets for planning the three automated vehicle velocity profiles involve both reducing aggressive changes in velocity, thus enhancing passenger comfort, and decreasing energy consumption. Results show a potential range of 1.8 - 7.6 % energy reduction when comparing the energy consumptions of the lead and first follower vehicle, whereas the implemented optimization-based velocity planner predicts enhanced energy economy for the second and third follower BEVs. In general, the highest advantages both in energy consumption and comfort are predicted in the urban scenarios due to the high number of acceleration/deceleration phases.