Research On Motor Control Of Four Wheel Independent Drive Electric Vehicle

In recent years,with the continuous development of China’s industrial technology,as well as the pressure brought about by environmental energy problems,new energy vehicles have attracted widespread attention.Permanent magnet synchronous motor is widely used in the field of electric vehicles because of its small size,high flux density and large starting torque.In this paper,through the research of Permanent magnet synchronous motor control algorithm,we choose the appropriate control strategy for the motor control of electric vehicles,in order to optimize the operation performance of electric vehicles.Aiming at the speed tracking problem of electric vehicle at high speed,a nonlinear generalized predictive control system based on reduced order load torque observer is designed.In the speed loop control law,the load torque is considered as the disturbance value,the reduced order load torque observer is combined with the generalized predictive control,and the estimated torque value is used to replace the actual value.The simulation results show that the proposed control strategy can effectively achieve speed tracking.Compared with the classical proportional integral control,it has the advantages of short stabilization time,small overshoot,strong anti-interference ability and high robustness of speed loop control.This paper studies the torque ripple of electric vehicle at low speed,and designs a double closed loop iterative learning control method to reduce the interference of motor torque ripple on the control stability of electric vehicle.The simulation results show that the proposed control strategy can effectively suppress the output torque ripple when the motor is running at low speed,which is conducive to the smooth operation of the motor and improves the driving performance of Permanent magnet synchronous motor under different conditions.Two improved control strategies are proposed for multi motor cooperative control of electric vehicles.(1)An improved deviation coupling control strategy based on torque state observer.The co simulation results show that the synchronization error and tracking error of the system can be reduced by 69.23% and64.38% respectively compared with the traditional deviation coupling control method.(2)An iterative learning control strategy with adjacent cross coupling for multiple motors is proposed.Compared with the traditional adjacent cross coupling control method,the synchronization error and tracking error can be reduced by 78.17% and62.40% respectively.These two control algorithms can not only reduce the synchronization error and tracking error of the system,but also effectively reduce the complexity of the control system.