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.

This work proposes a systematic methodology for designing an active leveling system (ALS) actuator for lunar landing application. The ALS actuator is integrated into an inverted tripod leg layout, exploiting a honeycomb crushable damper as a shock absorber. The proposed ALS actuator is fitted within the leg’s primary strut and features a custom permanent-magnet synchronous machine rigidly coupled with a lead screw. The actuator aims to both provide proper leg deployment functioning and compensate for the different shock absorber deformations during landing. The leg dynamic behavior is simulated through a parameterized multi-body model to investigate different landing scenarios. First, a parametric sensitivity approach is used to optimize the transmission system and the electric machine characteristics. Then, the electric motor model is numerically validated and optimized through electromagnetic finite element analysis. To validate the proposed ALS design methodology, a virtual test bench is used to assess the ALS performances under different load scenarios. It is found that the proposed methodology is able to yield a compact, well-sized actuator which is numerically validated with the EL3 platform as a case study.

<div class="section abstract"><div class="htmlview paragraph">Electric vehicles comprising multiple motors allow the individual wheel torque allocation, i.e. torque-vectoring. Powertrain configurations with multiple motors provide additional degree of freedom to improve system level efficiencies while ensuring handling performances and active safety. However, most of the works available on this topic do not simultaneously optimize both vehicle dynamic performance and energy efficiency while considering the real-time implementability of the controller. In this work, a new and systematic approach in designing, modeling, and simulating the main layers of a torque-vectoring control framework is introduced. The high level control combines the actions of an adaptive Linear Quadratic Regulator (A-LQR) and of a feedforward controller, to shape the steady-state and transient vehicle response by generating the reference yaw moment. A novel energy efficient torque allocation method is proposed as a low level controller. The torque is allocated on each wheel by solving a quadratic programming problem. The latter is solved in real-time to guarantee the desired yaw moment and the requested driver power demand while minimizing the system losses. The objective function of the quadratic problem accounts for the efficiency map of the electric machine as well as the dissipations due to tire slip phenomena. The torque-vectoring is evaluated in a co-simulation environment. Matlab/Simulink is used for the control strategy and VI-CarRealTime for the vehicle model and driver. The vehicle model represents a high performance pure electric SUV with four e-motors. The performance of the proposed controller is assessed using open loop maneuvers and in closed loop track lap scenarios. The results demonstrate that the proposed controller enhances the vehicle’s performance in terms of handling. Additionally, a significant improvement in energy saving in a wide range of lateral acceleration conditions is: presented. Moreover, the control strategy is validated using rapid control prototyping, thus guaranteeing a deterministic real-time implementation.</div></div>