Optimization of straight-line driving torque vectoring for energy-efficient operation of electric vehicles with multiple motors and disconnect clutches

Abstract

Battery electric vehicles with multiple motors are characterized by actuator redundancy, which calls for application of instantaneously optimized distribution of motor/wheel torques, thus minimizing the energy consumption, i.e., maximizing the vehicle range. If the e-motors are equipped with disconnect clutches, the energy saving potential becomes even higher due to the avoidance of drag of inactive electric motors. However, in this case optimization through time and predictive control techniques should be used to provide globally minimal energy consumption. To this end, the paper proposes the following modeling, optimization, and model predictive control method for straight-line driving mode: (i) a dynamic backward-looking model of electric vehicle propelled by disconnect clutch-equipped four wheel motors, which takes into account the clutch synchronization-related drivetrain transient loss; (ii) globally optimal, dynamic programming (DP)-based off-line optimization of e-motor torque and clutch state control trajectories, and (iii) a model predictive torque vectoring control (MPC) strategy. The MPC strategy is verified by simulation for various certification driving cycles, and the results are compared with the DP-optimal benchmark for different values of a user-defined weighting coefficient, which penalizes frequent clutch disconnects for improved durability. The DP optimization results reveal that the energy consumption reduction achieved through the disconnect clutch functionality is up to 7%, on top of up to 5% reduction achieved by torque distribution itself. The MPC control strategy relying on the prediction horizon of 10 steps approach the DP energy consumption benchmark within the margin of 1%.