On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring
Author/s
Parra, Alberto; Tavernini, Davide; Gruber, Patrick; Sorniotti, Aldo; Zubizarreta, Asier; [et al.]Date
2021-01Keywords
Torque-vectoring
Nonlinear model predictive control
Powertrain power loss
Tire slip power loss
Reference yaw rate
Control allocation
Weight adaptation
Abstract
A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies – not using MPC – show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear ...
Type
article