Human-Centered Torque Vectoring Control for Distributed Drive Electric Vehicle Considering Driving Characteristics

Most of the existing torque vectoring control (TVC) strategies are vehicle-centered, hence, fail to consider how driving characteristics affect the vehicle's motion. To address this issue, we propose a hierarchically structured human-centered TVC (HCTVC) for distributed drive electric vehicles (EVs). To do this we design the experiment to characterize the drivers' behavior with different skills. Then a global objective function is formulated considering the driver's physical and mental workload, as well as the agility and stability of the vehicle, to optimize the overall performance of the driver-vehicle system. Next, an adaptive second-order sliding mode controller (ASOSM) based on the backstepping method is designed as the upper-level controller to improve system robustness. Finally, a multi-objective optimization algorithm based on the weight self-tuning strategy is developed as the lower-level controller to solve the torque distribution problem. We further obtain the analytical solution to this optimization problem. Experimental results on the asphalt and icy roads confirm that the proposed HCTVC significantly enhances the vehicle transient response and reduces the driver's workload.

Automated summary

A human-centered torque vectoring control strategy was proposed, characterizing driver behavior through experiments to optimize overall performance, with upper and lower level controllers designed to solve torque distribution problems.