In the context of automated highway systems (AHS), this work proposes an approach that enables a vehicle to autonomously join a platoon with optimized trajectory in the presence of dynamical traffic obstacles. A notable aspect is the use of Model Predictive Control (MPC) optimization of the planned path, in conjunction with a variant of the Rapidly-exploring Random Trees (RRT*) algorithm for the purpose of platoon formation. This combination efficiently explores the space of possible trajectories, returning trajectories that are smoothened out with respect to the dynamic constraints of the vehicle, while at the same time allowing for real-time implementation. The implementation we propose takes into consideration both localization and mapping through relevant sensors and V2V communication. The complete algorithm is tested over various nominal and worst-case scenarios, qualifying the merits of the proposed methodology.

As electric mobility gains popularity, Electric Vehicles (EVs) and their batteries are becoming more attractive due to their size and energy density advantages. However, the electric grid has not undergone similar improvements, potentially impacting power stability and affecting EV energy usage and availability. The key challenge lies in managing increasing power demands from a fully EV fleet. To address this, efforts are needed to analyze the integration of EVs into the grid and optimize power distribution. In this paper, an innovative Energy Management Strategy (EMS) is proposed to effectively control energy loads, energy sources, and EVs, incorporating Vehicle-to-Grid (V2G) capability. The EMS optimizes energy flow and storage based on time of day, potential energy production, and the cost of grid electricity. The integration of this EMS results in significant benefits, with approximately 12% savings in electricity bills compared to a reference strategy.

Connected Autonomous Vehicle (CAV) is considered as a proposal toward sustainable mobility. In order to succeed in a sustainable mobility solution, “CAV” or more precisely “CAV Transport System” should prove to be low energy, safe, and allow better performances than human-driven vehicles. This paper will propose a system architecture for a sustainable CAV Transport System on a standard scenario: crossing a roundabout. Nowadays, roundabouts are very common and practical crossing alternatives to improve the traffic flow and increase safety. This study aims to simulate and analyze the behavior of connected autonomous vehicles crossing a roundabout using a V2I (vehicle-to-infrastructure) architecture. The vehicles are exchanging information with a so-called central signaling unit. All vehicles are exchanging their position, speed, and target destination. The central signaling unit has a global view of the system compared to each ego vehicle (has more local than global information); thus, can safely and efficiently manage the traffic of the vehicles in the roundabout using a standard signaling block strategy. This strategy of decision of the central signaling unit (CSU) is performed by dividing the roundabout into several zones/blocks which can be booked by only one vehicle at a time. A solver, reproducing a vehicle’s behavior and dynamics, computes the trajectory and velocity of each vehicle depending on its surroundings. Finally, a graphical representation is used and implemented to facilitate the analysis and visualization of the roundabout crossing. The vehicle flow performance of the developed traffic control model is compared with SUMO.

In the context of CO2 emissions reduction, investigations are currently made in various directions among the automotive sector: internal combustion engine efficiency improvement, downsizing, electrification, vehicle weight reduction. Meanwhile, a new type of vehicle is emerging in the automotive sector. This study analyses the perspectives and limitations of a low voltage electric vehicle (<60V), with only two people capacity. One of the main challenges of this new type of vehicle is to identify the best suited powertrain architecture, given the different constraints and needs (power, mass, acceleration, range, etc.). With the advantage of reducing the safety constraints linked to high voltage applications, but at the expense of higher currents when power level increases, a proper powertrain components sizing is mandatory, therefore limiting the advantages of such architecture. To better evaluate the evolution of the powertrain parameters, a simulation tool has been built to analyze the implications on the performances, efficiency and cost.

In this paper, we explore the innovative academic initiatives at IFP School, France, designed to equip master’s degree students with essential skills for advancing clean mobility. We highlight the dynamic evolution of our educational programs in response to the global energy transition, providing detailed insights into course structures that focus on sustainable mobility, electrification, and smart grids. Our curriculum is uniquely integrated with practical training methods, including virtual reality, laboratory experiments, and extensive industry collaborations. This comprehensive approach ensures that our graduates are well-prepared to meet the current and future demands of the rapidly evolving automotive and energy sectors.