Optimal Torque-Vectoring Control Strategy for Energy Efficiency and Vehicle Dynamic Improvement of Battery Electric Vehicles with Multiple Motors 2023-01-0563

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.