Study On The Control Algorithm Of Brushless DC Motor In Electric Car

With the rapid development of society,the increasing car ownership brings great pollution to the environment.With the improvement of people's environmental protection consciousness,the electric car is widely concerned for its zero-emission,low-noise and flexible operation.The key technology of electric car is the motor and its drive system.Therefore,the study of the drive control algorithm is important for improving the running efficiency and the overall performance of the electric car.This paper first established a mathematical model of brushless DC motor based on the analysis of principle,mechanical properties,and driving method of brushless DC motor.Then the motor control system scheme and hardware structure were studied using double closed-loop control strategy for speed and current loop.The traditional double closed-loop control system using double PI controllers has the disadvantages of poor anti-disturbance ability,long response time,low adjustment precision and poor robustness.This paper employed PI controller for current loop,and focused on the speed loop control algorithm.Simulation analysis was implemented using MATELAB/Simulink.The two systems respectively using PI controller and fuzzy neural network for speed loop were simulated under operating conditions of given low speed and high speed step,and the system parameters of the two systems were the same.Then the two systems were further simulated under operating conditions of sudden perturbation,load sudden change and speed sudden change.The results show that the fuzzy RBF neural network control algorithm with abilities of logical reasoning and self-learning has the advantages of fast relative speed,strong anti-disturbance ability and high adjustment precision.