The characteristics of the electric vehicle hub motor drive enable the wheels to operate independently of each other,and a variety of intelligent drive modes can be developed to lay the foundation for the development of intelligent connected vehicles.This paper focuses on the research on the driving and control strategies of permanent magnet brushless DC wheel motors for electric vehicles,and combined with the test vehicle to perform the cycle test and semi-physical simulation verification.The traditional motor drive adopts double closed-loop control of speed and torque output,but its dynamic and static characteristics cannot meet the performance requirements of the hub motor.This paper uses a fuzzy internal model control strategy to improve the output characteristics of the hub motor.In the fuzzy internal model control strategy,the control signal first enters the inner loop for fuzzy PI control,and the output results are fed back to the outer loop for internal model control to achieve intelligent optimization under different operating conditions.In order to verify the actual effect of the control strategy,a longitudinal dynamic model of the in-wheel electric vehicle was built,and the new European cycle driving conditions were selected,and the required motor speed and torque at the corresponding speed were calculated according to the relevant parameters of the test vehicle,and the relevant data The actual performance of the control strategy is verified.Because the brushless DC motor will produce large torque ripple during operation,it is suppressed by the use of traceless Kalman filtering.The traceless Kalman filter module is added to the control system to optimize the feedback signal quality,improve the feedback effect,and achieve torque ripple suppression.Using the method of semi-physical simulation,the wheel motor is connected to the circuit and dSPACE for testing.According to the overall requirements of the motor controller design,the hardware circuit design and software program were written.The experimental results show that the control strategy is real and feasible. |