All-Wheel Drive Electric Vehicle Performance Optimization: From Modelling to Subjective Evaluation on a Static Simulator

Abstract

Powertrain electrification is undoubtedly recognized as a major trend in the automotive industry. The elimination of the internal combustion engine opens to different vehicles architecture designs, to improve habitability and reduce cost. The paper focus on an All-Wheel-Drive Full Electric high-performance vehicle equipped with wheel-hub motors, a layout that offers a significant potential in controlling each wheel individually. The objective is to develop a control algorithm capable of handling wheels torques independently to enhance vehicle's dynamic, keeping into consideration the model's energy performance. The control algorithm is entirely developed in Matlab-Simulink and implemented in the vehicle dynamic model, in a co-simulation environment with VI-CarRealTime software. Offline simulations are performed to tune the controllers and evaluate their impact on vehicle dynamics and energy efficiency. Finally, the model is tested in a real static simulator to be validated and to have a subjective interpretation of the dynamic behavior of the vehicle. Handling improvements are evaluated through a racetrack lap time performed by the VI-Grade virtual driver. Energy efficiency protocols instead will be assessed by monitoring the battery State of Charge variation and their impact on vehicle's behavior will be analyzed on the static simulator. The results point out to an improvement in the lap time thanks to the more agile and less understeering vehicle. Energy optimization algorithms and regenerative braking displays a promising energy reduction without compromising vehicle dynamics. The same racetrack from the offline simulations is used to test the model on the static simulator. Torque vectoring impact on driver's feeling is found to be noticeable and helpful in improving vehicle's response during cornering while energy optimization protocols are not affecting the dynamic performance.