RT Journal Article
T1 On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring
A1 Parra, Alberto
A1 Tavernini, Davide
A1 Gruber, Patrick
A1 Sorniotti, Aldo
A1 Zubizarreta, Asier
A1 Perez, Joshue
AB 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 model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer.
SN 0018-9545
YR 2021
FD 2021-01
LA eng
NO Parra , A , Tavernini , D , Gruber , P , Sorniotti , A , Zubizarreta , A & Perez , J 2021 , ' On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring ' , IEEE Transactions on Vehicular Technology , vol. 70 , no. 1 , 9186728 , pp. 173-188 . https://doi.org/10.1109/tvt.2020.3022022
NO Publisher Copyright: © 1967-2012 IEEE.
DS TECNALIA Publications
RD 23 jul 2024