Development of a Path-following and a Speed Control Driver Model for an Electric Vehicle

Abstract

A two-passenger all-wheel-drive urban electric vehicle (AUTO21EV) with four in-wheel motors and an active steering system has been designed and developed at the University of Waterloo. In order to evaluate the handling and performance of such a vehicle in the design stage and analyze the effectiveness of different chassis control systems before implementing them in the real vehicle, the simulation of a large number of different open-loop and closed-loop test maneuvers is necessary. Thus, in the simulation environment, not only is a mathematical vehicle model needed for every test maneuver, but a driver model must also be designed to simulate the closed-loop test maneuvers. The role of the driver model is to calculate the control inputs required to successfully follow a predefined path. Such a driver model can be implemented as an inverse dynamics problem or by a representation of a driver that can look ahead, preview the path, and change the steering wheel angle and acceleration or brake pedal positions accordingly. In this regard, a path-following driver model is developed in this work with an advanced path previewing technique. In addition, a gain scheduling speed control driver model is developed for the AUTO21EV, which adjusts the drive torques of the wheels to minimize the deviation between the desired and actual vehicle speeds.

Development of an Advanced Fuzzy Active Steering Controller and a Novel Method to Tune the Fuzzy Controller

“…To confirm the performance of the tuned fuzzy active steering controller, the AUTO21EV is first driven through a double-lane-change maneuver using a driver model [16] with an initial speed of 70 km/h, both with and without the fuzzy active steering controller. Note that this driver model is considered to represent a professional driver, not an inexperienced average driver.…”

“…The ISO double-lane-change maneuver is typically performed as a closed-loop driving test, and is used to adjust the dynamics of a vehicle based on the subjective evaluations of professional drivers. Consequently, simulation of this maneuver demands a driver model [16] that can dynamically adjust the steering wheel according to the vehicle trajectory and yaw rate response at every time step of a simulation. Since the membership functions of the fuzzy controller must be tuned in a general sense and not based on the response of a specific driver or driver model, and also noting that the fuzzy controller is responsible only for the stability of the vehicle, the double-lane-change maneuver used in this work is performed as an open-loop driving test rather than a closedloop test.…”

Development of an Integrated Control Strategy Consisting of an Advanced Torque Vectoring Controller and a Genetic Fuzzy Active Steering Controller

“…As can be seen, the driver model is able to keep the vehicle on the predefined circular path while severely braking when the integrated controller is active, and the lateral deviation of the vehicle from the desired path remains negligible throughout the maneuver. Figure 16-a illustrates the driver's steering wheel input as a function of time, and indicates that the driver model [10] is able to control the vehicle very smoothly and with little steering effort when the integrated controller is used. This figure also shows the effort of the active steering controller when using the integrated control approach as it superimposes a corrective signal atop the driver's steering wheel input.…”

“…The performance of the integrated control system consisting of the ATVC and the GFASC is first evaluated by driving the AUTO21EV through the ISO 3888 double-lanechange maneuver [17] with an initial speed of 75 km/h and using the path-following driver model developed previously [10]. Figure 6 illustrates the vehicle trajectory and demonstrates that the driver is able to negotiate the maneuver without striking the cones when the integrated control strategy is used.…”

“…Table 1 lists some of the relevant parameters used for the AUTO21EV model. Based on this vehicle model, an advanced fuzzy slip control system [9] was developed and tested using some predefined test maneuvers and a novel path-following and speed-controlling driver model [10]. A genetic fuzzy yaw moment controller was also developed [11], the objective of which was to determine the corrective yaw moment required to minimize the vehicle yaw rate and sideslip errors.…”