Research On Control Technology Of Automotive Brushless DC Motor Based On Improved Particle Swarm Algorithm Parameter Identification

Brushless DC motors have the advantages of simple structure,small size,high power density and simple control method,which are widely used in daily life and industrial applications.In the field of small and medium-sized electric vehicles,brushless DC motors are often used as the main drive motor in order to take into account both the control performance and cost of the motor system.The stator resistance of brushless DC motors in electric vehicles,as well as the direct-axis inductance and the quadrature-axis inductance,are important parameters for optimizing the internal loop control of motor dq-axis currents.If we want to achieve high-performance vector control of motors,it is necessary to study the identification techniques of motor stator resistance and direct-axis inductance and quadrature-axis inductance.Firstly,this paper introduces the current research status of brushless DC motor in the field of vector control and parameter identification,and gives a detailed introduction to its i_d=0 control,MTPA control,flux-weakening control,as well as offline identification method and online identification method,respectively,to explain the important significance of brushless DC motor vector control and parameter identification.Meanwhile,the basic physical structure and mathematical model of brushless DC motor are explained,the mathematical principles of CLARK transform,PARK transform and SVPWM are analyzed,and the block diagram of brushless DC motor vector control and space vector pulse width modulation technique are introduced.Secondly,the integrated brushless DC motor controller for vehicles is designed,the general overview diagram of the integrated controller is given,and the voltage stabilizing circuit of control power supply,MCU control circuit,MOSFET driver circuit,MOSFET-based main power circuit,voltage and current sampling circuit,sensor and control signal conditioning circuit are designed respectively.This paper solves the problem of controller wiring layout by connecting the control board and driver board up and down to form an integrated controller,as well as improving the controller structure to further flatten it,and finally building an integrated brushless DC motor experimental platform for vehicles.Thirdly,the MTPA-based brushless DC motor vector control technology is studied.In the process of implementing MTPA-based brushless DC motor vector control for vehicles using STM32 microprocessor,the principle of detecting position based on Hall sensor is studied,the main program flowchart of vector control is designed,and the inner current loop program,overcurrent protection program and outer speed loop program are designed.Finally,the experiments in constant speed control state and variable speed control state are conducted to verify the reliability of the MTPA-based brushless DC motor vector control program designed in this paper.Finally,the offline identification of motor parameters and the optimization method of current PI regulator parameters based on particle swarm algorithm are studied.In this paper,we propose an improved particle swarm algorithm based on extended dimensionality to address the shortcomings of the traditional particle swarm algorithm in solving multi-dimensional optimization problems,and use the improved particle swarm algorithm with extended dimensionality to identify the stator resistance,direct-axis inductance and quadrature-axis inductance of the brushless DC motor.Considering the current PI regulators K_p andK_i of the brushless DC motor parameters can be adjusted according to the motor stator resistance,direct-axis inductance and quadrature-axis inductance,the vector control method of brushless DC motor based on the optimization of current PI parameters is proposed,and its improved control performance of the dq-axis current PI regulator is verified by experiments.