A Fuzzy Logic Direct Yaw-Moment Control System for All-Wheel-Drive Electric Vehicles

Abstract

Summary In-wheel-motors are revolutionary new electric drive systems that can be housed in vehicle wheel assemblies. Such E-wheels permit packaging flexibility by eliminating the central drive motor and the associated transmission and driveline components, including the transmission, the differential, the universal joints and the drive shaft. Apart from many advantages of such a system, unequalled independent wheel control allows vehicle dynamic improvement to assist the driver in enhancing cornering and straight-line stability on slippery roads and in adverse ground conditions. In this paper a Fuzzy logic driver-assist stability system for all-wheel-drive electric vehicles based on a yaw reference DYC is introduced. The system assists the driver with path correction, thus enhancing cornering and straight-line stability and providing enhanced safety. A feed-forward neural network is employed to generate the required yaw rate reference. The neural net maps the vehicle speed and the steering angle to give the yaw rate reference. The vehicle true speed is estimated using a multi-sensor data fusion method. Data from wheel sensors and an embedded accelerometer are fed into an estimator, where a Fuzzy logic system decides which input is more reliable. The efficiency of the proposed system is approved by conducting a computer simulation. The proposed control system is an effective and easy to implement method to enhance the stability of all-wheel-drive electric vehicles.