In the context of automated driving, Electric Power Steering (EPS) systems represent an enabling technology. They introduce the ergonomic function of reducing the physical effort required by the driver during the steering maneuver. Furthermore, EPS gives the possibility of high precision control of the steering system, thus paving the way to autonomous driving capability. In this context, the present work presents a performance assessment of an EPS system designed for a full-electric all-wheel-drive electric prototype racing in Formula Student Driverless (FSD) competitions. Specifically, the system is based on the linear actuation of the steering rack by using a ball screw. The screw nut is rotated through a belt transmission driven by a brushless DC motor. Modeling and motion control techniques for this system are presented. Moreover, the numerical model is tuned through a grey-box identification approach. Finally, the performance of the proposed EPS system is tested experimentally on the vehicle through both sine-sweep profiles and co-simulated driverless sessions. The system performance is assessed in terms of reference tracking capability, thus showing favorable results for the proposed actuation solution.

Recent studies show that hybrid powertrains with dual clutch transmission are becoming more common. This is mainly influenced by the fact that they have higher efficiency, allow power-on shifting, and are more compact. However, the electric motor attached to the primary shaft of the transmission might represent some critical issues in statics and dynamics due to additional torque, inertia, and working irregularities. This paper aims to investigate the influence of the electric motor integration in the transmission on the reliability of the overall system. The focus is on the investigation of potential over stresses of the ICE bearings due to the bending of the gearbox primary shaft. To achieve the aim, the static and the rotor dynamic analysis of the inner and outer primary shafts is performed. The numerical analyses show the potential critical issues that can arise from the excessive manufacturing and assembling tolerances, the interaction of the e-machine torque irregularities and inertia with the bending dynamics of the primary shafts.

This paper presents a controller dedicated to the lateral and longitudinal vehicle dynamics control for autonomous driving. The proposed strategy exploits a Model Predictive Control strategy to perform lateral guidance and speed regulation. To this end, the algorithm controls the steering angle and the throttle and brake pedals, in order to minimize the vehicle’s lateral deviation

*This paper presents a technique based on the probabilistic road map algorithm for the optimal trajectory generation in autonomous driving. The objective is to provide an algorithm allowing to optimize the trajectory of the vehicle by reducing the distance traveled and minimizing the lateral deviation and relative yaw angle of the vehicle with respect to the reference trajectory,