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 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.
Highlights
E LECTRIC vehicles (EVs) are the subject of intensive research as they are considered a key solution to reduce air Manuscript received January 21, 2020; revised June 10, 2020 and July 28, 2020; accepted August 21, 2020
The previous studies show the energy saving potential of shaping the reference cornering response, to be practically useful, the implementation of this approach should: i) simultaneously account for the power losses associated with the powertrains and tire slip; ii) be based on feedback control structures, e.g., capable of compensating unexpected EV behavior caused by the variation of system parameters or transients, rather than using simplified feedforward or rule-based algorithms; iii) integrate the reference direct yaw moment generation and control allocation functions, to prevent conflicts between different control layers involved in the power loss management; and iv) provide significant operational flexibility depending on the actual driving situation, i.e., prioritize energy efficiency during normal driving, and vehicle safety and stability in extreme maneuvers
The nonlinear model predictive control (NMPC) TV system is implemented in the simulation framework defined in Fig. 1, and compared with other controller configurations, which are introduced in Sections V.A and V.B
Summary
E LECTRIC vehicles (EVs) are the subject of intensive research as they are considered a key solution to reduce air Manuscript received January 21, 2020; revised June 10, 2020 and July 28, 2020; accepted August 21, 2020. The previous studies show the energy saving potential of shaping the reference cornering response, to be practically useful, the implementation of this approach should: i) simultaneously account for the power losses associated with the powertrains and tire slip; ii) be based on feedback control structures, e.g., capable of compensating unexpected EV behavior caused by the variation of system parameters or transients, rather than using simplified feedforward or rule-based algorithms; iii) integrate the reference direct yaw moment generation and control allocation functions, to prevent conflicts between different control layers involved in the power loss management; and iv) provide significant operational flexibility depending on the actual driving situation, i.e., prioritize energy efficiency during normal driving, and vehicle safety and stability in extreme maneuvers.
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