An Energy-Oriented Torque-Vector Control Framework for Distributed Drive Electric Vehicles

The over-actuated characteristics of distributed drive electric vehicles (DDEVs) provide a flexible platform to pursue higher holistic performance. This article proposes a dual-model predictive control (MPC)-based hierarchical framework to realize the energy saving while improving the handling stability for DDEVs. The upper layer allocates the torque vector through the front/rear axles, which can provide a high-efficiency zone for the in-wheel motors and reduce the energy consumption. The lower layer generates a direct-yaw-moment (DYC) control input by differential longitudinal forces of the left/right wheels to ensure the vehicle handling stability. Considering the time-varying state variables, a linear-time-varying MPC (LTV-MPC) method is adopted to guarantee the accuracy of the model. The combined magic formula tire model is used to modify the tire parameters, including tire longitudinal stiffness and cornering stiffness. The soft constraint constructed by
$\beta $
–
$\gamma $
phase plane is introduced in the LTV-MPC to ensure the vehicle stability, based on which, a relaxation factor is designed to reduce the energy consumption due to the excessive DYC inputs. The simulation and hardware-in-the-loop (HIL) test results show that the proposed control framework can effectively reduce the energy consumption for DDEVs while ensuring the vehicle handling stability.