On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

A recently growing literature discusses the topics of direct yaw moment control based on model predictive (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, available TV studies focus allocation layer, which calculates individual wheel torque levels to generate total reference longitudinal force moment, specified by higher level algorithms provide desired lateral vehicle dynamics. In fact, a system redundant actuators, vehicle-level objectives can be achieved distributing actions minimize an optimality criterion, e.g., reduction different power loss contributions. However, preliminary simulation experimental - not using MPC show that further important savings are possible through appropriate design rate. This paper presents nonlinear (NMPC) implementation TV, is concurrent optimization rate allocation. The NMPC cost function weights varied fuzzy logic algorithm adaptively prioritize dynamics or efficiency, depending driving conditions. results adaptive configuration allows stable cornering performance lower consumption than benchmarking controller layer.