Research On Swarm Intelligence Fusion Control Algorithm For PMSM Of Low Speed Electric Vehicle

Low speed electric vehicles with low energy consumption and high environmental protection characteristics have broken through the development bottleneck of traditional fuel vehicles caused by ecological energy problems,and gradually become a new development direction of the automobile industry.The battery and electronic control system based on driving motor replace the traditional mechanical transmission system in electric vehicle.Because of the complexity and randomness of driving conditions,the motor drive system becomes a nonlinear,multi parameter,strong coupling and time-varying control system.PMSM,with its excellent performance in output efficiency,power density and system reliability,has become the preferred driving motor of electric vehicles.Based on the vector control theory of PMSM,this paper introduces the intelligent control theory of particle swarm,artificial fish swarm and artificial immune system into the research of key technologies,such as motor parameter identification,speed estimation and parameter setting,so as to solve the problems of torque ripple suppression,motor speed estimation accuracy improvement and system stability improvement under complex working conditions.The main contents and innovations of this paper are summarized as follows:;(1)Aiming at the problem of determining the noise matrix of PMSM EKF speed estimation,an improved particle swarm optimization algorithm is introduced to optimize the R and Q parameters of EKF noise matrix.The noise matrix parameters are used as the optimization particles,and the time integral of the absolute difference between the actual speed and the estimated speed is used as the optimization particle.The fitness function makes the particles move to the target extreme point iteratively in the optimization space,and finally determines the noise matrix of the EKF estimation model with the optimal particle position,so as to achieve high-precision speed estimation,enhance the immunity of the motor speed waveform,and realize the smooth speed regulation.(2)Aiming at the problem of PI controller parameter tuning in PMSM current and speed double closed-loop control,an improved particle swarm optimization algorithm based on artificial immune system is proposed to optimize the parameters of PI controller.In the algorithm,KP and Ki parameters in the double loop control are used as the optimization particles,and the artificial immune system is used to enhance the diversity and migration of particle swarm optimization,effectively improve the premature convergence of the optimization system,and improve the speed and accuracy of particle swarm optimization.Therefore,the optimized control system can suppress the speed ripple,reduce the response time when the torque changes,and significantly improve the robustness and stability of PMSM control system.(3)Aiming at the precise identification of PMSM parameters,such as stator winding,dq axis inductance and rotor flux,a hybrid control algorithm is proposed to optimize the initial weight and threshold of BPNN identification model of PMSM parameters by using chaos artificial intelligence fish swarm CAFS.The optimized network identification model of motor parameters has the characteristics of weak initial value sensitivity,good parameter setting robustness and good system stability under complex conditions.Compared with other intelligent algorithms,cafs-bpnna has higher identification accuracy and faster convergence speed for PMSM motor parameters.(4)The PMSM swarm intelligence fusion control algorithm proposed in this paper is studied in practice,and the hardware and software system of the motor drive controller is constructed according to the principle of hierarchical separation component design.Through the comparative analysis of indoor platform test and outdoor real vehicle test results,the swarm intelligence fusion control algorithm and motor controller system proposed in this paper can meet the performance requirements of the practical application of low-speed electric vehicles.Under the same cost of hardware and software,the PMSM controller system designed by the research group has good performance in speed control accuracy,torque ripple suppression,vehicle driving comfort and other performance.In this paper,the application of swarm intelligent control fusion algorithm in PMSM motor vector control is studied in theory and practice,which has a certain practical significance for improving the performance of low-speed electric vehicle system and realizing the rapid and good development of low-speed electric vehicle.