Comparative analysis of torque vectoring control strategies in electric vehicles

Abstract

Vehicle dynamics control systems have an important role in accident prevention by decreasing the difference between the desired and actual vehicle response. Torque Vectoring Control is one of these systems which is developed to enhance the steering and handling performances of vehicles. This thesis focuses on the comparison of the control strategies for the potential of improving the steering and handling performances of electric vehicles through torque vectoring control systems. In this thesis, a non-linear multi-body dynamics model is used to represent the real electric vehicle. This vehicle model adopts three independently controllable electric motors; one of them is on the front axle for traction and the other two are on the rear axle. With the help of these two motors on the rear axle, the yaw moment can be controlled with the torque difference between the two electric motors. Furthermore, the control system needs a target value to follow the desired behavior of the system. To achieve this aim, the lateral dynamics reference generation has been included. When working with the vehicle lateral dynamics in the study, the yaw rate response is considered as a representative of steering and handling performance. Then, the system steering and handling capabilities using different maneuvers are evaluated with closed-loop test cases performed in a simulation environment. Various controllers can be used to assess the steering and handling performance on the vehicle. In the first part of this thesis study, the effects of PID controller parameters on system performance for a single maneuver are examined, and then these parameters are adjusted to minimize the integral square error performance metric. The durability of this optimal controller under maneuver changes is also investigated. Simulation results show that under different maneuvers, particularly at high speeds and steering angles, the system performance significantly deteriorates. To overcome this problem, possible maneuvers the vehicle might encounter are determined, and then optimal PID controller parameters are found for each maneuver to minimize the integral square error performance metric. Using the optimal PID parameters found for possible maneuvers, an adaptive PID controller design is proposed with the help of the cubic spline method. The performance of the adaptive PID controller adapted to speed and steering angle is tested under various maneuver changes, yielding satisfactory results. In the second part of the thesis study, the effects of a fuzzy PID controller on system performance are examined due to its success in controlling nonlinear systems. For this purpose, fuzzy PID parameters, i.e. input-output scaling factors are found using the rule table and membership functions from the literature to minimize the integral square error, similar to the optimal PID controller. The fuzzy PID controller is compared to the optimal PID controller using a similar maneuver. Although the fuzzy PID controller does not produce superior results for a single maneuver compared to the optimal PID controller, it shows quite satisfactory results when faced with maneuver changes. The reason for the fuzzy PID controller not producing superior results for similar maneuvers is that the rule table and membership functions from the literature are not suitable for the system. For this purpose, the output membership functions are adjusted (shifted outward and inward), and then a fuzzy PID design is designed for a similar maneuver. When the output membership functions are shifted outward, the fuzzy PID controller produces superior results compared to the optimal PID controller.