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.

Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies.

The transport sector is under an intensive renovation process. Innovative concepts such as shared and intermodal mobility, mobility as a service, and connected and autonomous vehicles (CAVs) will contribute to the transition toward carbon neutrality and are foreseen as crucial parts of future mobility systems, as demonstrated by worldwide efforts in research and industry communities. The main driver of CAVs development is road safety, but other benefits, such as comfort and energy saving, are not to be neglected. CAVs analysis and development usually focus on Information and Communication Technology (ICT) research themes and less on the entire vehicle system. Many studies on specific aspects of CAVs are available in the literature, including advanced powertrain control strategies and their effects on vehicle efficiency. However, most studies neglect the additional power consumption due to the autonomous driving system. This work aims to assess uncertain CAVs’ efficiency improvements and offers an overview of their architecture. In particular, a combination of the literature survey and proper statistical methods are proposed to provide a comprehensive overview of CAVs. The CAV layout, data processing, and management to be used in energy management strategies are discussed. The data gathered are used to define statistical distribution relative to the efficiency improvement, number of sensors, computing units and their power requirements. Those distributions have been employed within a Monte Carlo method simulation to evaluate the effect on vehicle energy consumption and energy saving, using optimal driving behaviour, and considering the power consumption from additional CAV hardware. The results show that the assumption that CAV technologies will reduce energy consumption compared to the reference vehicle, should not be taken for granted. In 75% of scenarios, simulated light-duty CAVs worsen energy efficiency, while the results are more promising for heavy-duty vehicles.