INTRODUCTION The AUTO21EV vehicle model with four direct-drive in- wheel motors and an active steering system is an exciting platform on which to apply advanced motion control techniques, such as advanced slip control and torque vectoring systems, since the motor speeds and torques can be generated and controlled quickly, precisely, and independently at each wheel. An advanced fuzzy slip control system is developed and evaluated in [ 9] using predefined test maneuvers and a novel path-following driver model [ 10]. In addition, a genetic fuzzy yaw moment controller is developed in [ 11], the objective of which is to determine the corrective yaw moment required to minimize the vehicle yaw rate and sideslip errors. This genetically-tuned fuzzy yaw moment controller acts as a high-level supervisory module that assigns tasks to the lower-level controllers and actuators. In this paper, an advanced torque vectoring controller is developed for the AUTO21EV that distributes the task of generating the calculated corrective yaw moment to the in- wheel motors. The developed advanced torque vectoring controller consists of left-to-right and front-to-rear torquevectoring components, which work together to distribute the calculated corrective yaw moment in an integrated approach. Figure 1. AUTO21EV concept vehicle Figure 1 illustrates the AUTO21EV, which is a two- passenger, all-wheel-drive urban electric vehicle developed and modeled using the ADAMS/View software. Table 1 lists some of the relevant parameters used for the AUTO21EV model. The use of small but powerful direct-drive in-wheel motors allows for the implementation of the most advanced 2013-01-0698 Published 04/08/2013 Copyright © 2013 SAE International doi:10.4271/2013-01-0698 saealtpow.saejournals.org Development of an Advanced Torque Vectoring Control System for an Electric Vehicle with In-Wheel Motors using Soft Computing Techniques Kiumars Jalali, Thomas Uchida, Steve Lambert and John McPhee Univ. of Waterloo ABSTRACT A two-passenger, all-wheel-drive urban electric vehicle (AUTO21EV) with four direct-drive in-wheel motors has been designed and developed at the University of Waterloo. A 14-degree-of-freedom model of this vehicle has been used to develop a genetic fuzzy yaw moment controller. The genetic fuzzy yaw moment controller determines the corrective yaw moment that is required to stabilize the vehicle, and applies a virtual yaw moment around the vertical axis of the vehicle. In this work, an advanced torque vectoring controller is developed, the objective of which is to generate the required corrective yaw moment through the torque intervention of the individual in-wheel motors, stabilizing the vehicle during both normal and emergency driving maneuvers. Novel algorithms are developed for the left-to-right torque vectoring control on each axle and for the front-to-rear torque vectoring distribution action. Several maneuvers are simulated to demonstrate the performance and effectiveness of the proposed advanced torque vectoring controller, and the results are compared to those obtained using the ideal genetic fuzzy yaw moment controller. The advanced torque vectoring controller is also implemented in a hardware- and operator-in-the-loop driving simulator to further evaluate its performance. CITATION: Jalali, K., Uchida, T., Lambert, S. and McPhee, J., "Development of an Advanced Torque Vectoring Control System for an Electric Vehicle with In-Wheel Motors using Soft Computing Techniques," SAE Int. J. Alt. Power. 2(2):2013, doi:10.4271/2013-01-0698. ____________________________________ 261Downloaded from SAE International by Univ of California, Thursday, August 02, 2018all-wheel-drive system in which the optimal traction force can be generated and controlled on each wheel. Table 1. AUTO21EV model parameters TORQUE VECTORING CONTROL SYSTEMS In conventional four-wheel-drive (4WD) vehicles, either all the wheels of the vehicle are permanently driven, which is referred to a