Research on Control Method of DC Speed Regulation Electric Energy Vehicle Based on Neural Network

Abstract

Nowadays, the New energy vehicles(NEVs) have been developed in a key way as an industry adjusted by China’s national strategy. Aiming at the problems of poor parameter adjustment and low response speed existing in traditional PID control of energy vehicles in the complex driving environment, a speed control method of DC motor based on BP neural network and traditional PID was proposed, which greatly improved the speed control efficiency and accuracy of new energy vehicles. According to the nonlinear and multivariable characteristics of brushless DC motor control system, the motor and control principle are modeled and analyzed by Simulink. Then based on Proteus, STC89C51 single-chip microcomputer combined with the drive control module is used to simulate motor speed control. Meanwhile, in order to enhance the adaptability and rapidity of the parameter adjustment in speed regulation system, BP neural network and traditional PID control are applied in the designation of controller, which can implement the self-adaptation control of system and self-tuning of PID parameters. At last, the simulation is verified by MATLAB. The experimental results show that the BP-PID neural network controller has the advantages of good robustness, strong stability and fast response. At the same time, the BP-PID neural network controller has stronger anti-disturbance performance under the disturbed conditions, which can prove the correctness and feasibility of the designed model above. This approach provides a new solution to improve the performance of NEVs.