关键词: 分布式驱动；电动车辆；滑模控制；稳定性；转矩分配；CarSim；MATLAB/Simulink

Abstract: The four-wheel torque of distributed drive electric vehicle is independent and controllable, the drive motor response is fast, and the transmission chain is short, but the vehicle is prone to sideslip risk due to the coupling relationship between the longitudinal and lateral forces of the wheel when turning. Therefore, in order to improve the steering stability of distributed drive electric vehicle, an improved sliding mode control strategy based on radial basis function (RBF) neural network is proposed in this paper. The vehicle adopts a layered control structure, and the upper layer adopts a sliding mode controller to control the yawing moment. In order to avoid the "buffeting" problem, a RBF neural network is introduced to optimize and improve the sliding mode gain in the traditional sliding mode control algorithm. The lower driving force distribution layer distributes the four-wheel torque reasonably according to the dynamic transfer characteristics of the vertical load of the tire, so that it can meet the driving force and additional yaw torque requirements of the vehicle. Finally, the joint simulation is carried out in CarSim and MATLAB/Simulink. The results show that the improved sliding mode controller can reduce the front wheel angle at high speed, the yaw speed and the side yaw angle of center of mass can be reduced by 0.1 rad/s and 0.01 rad, respectively, and the yaw speed and the side yaw angle of center of mass can be reduced by 0.12 rad/s and 0.013 rad at low speed, which can effectively improve the vehicle stability.

Key words: distributed drive; electric vehicle; sliding mode control; stability; torque distribution; CarSim; MATLAB/Simulink