CN218733972U

CN218733972U - Compound control system for vector control of permanent magnet synchronous motor for electric automobile

Info

Publication number: CN218733972U
Application number: CN202222421080.0U
Authority: CN
Inventors: 李晨鑫; 刘二林; 李杉杉
Original assignee: Lanzhou Jiaotong University
Current assignee: Lanzhou Jiaotong University
Priority date: 2022-09-14
Filing date: 2022-09-14
Publication date: 2023-03-24
Anticipated expiration: 2032-09-14

Abstract

The utility model discloses a PMSM vector control's composite control system for electric automobile, including fuzzy PID controller (1), BP neural network PID controller (2), PI controller (3), park inverse transform module (4), SVPWM module (5), three-phase inverter (6), PMSM (7), sensor detection module (8), clark transform module (9), park transform module (10). The composite control system for the vector control of the permanent magnet synchronous motor for the electric automobile adopts a fuzzy PID controller in the control of a speed loop, adopts a BP neural network PID controller in a current loop, makes up for the deficiency, combines the advantages of respective controllers, and improves the control precision compared with the traditional PID controller, thereby being capable of more flexibly coping with the variable-inertia working scene of the electric automobile.

Description

Compound control system for vector control of permanent magnet synchronous motor for electric automobile

Technical Field

The utility model relates to a PMSM voltage modulation field for electric automobile, concretely relates to PMSM vector control's combined control system for electric automobile.

Background

With the increasing living standard of people, automobiles become one of indispensable transportation means in people's lives gradually. Due to the increasing oil crisis and the serious environmental pollution problem, the electric automobile has unique advantages as a green and healthy trip option. In recent years, government awareness of environmental protection and shortage of non-renewable resources have given economic support to policies of the electric vehicle industry.

The motor and the control system in the electric automobile are used as the core of automobile driving control, and the quality of the control performance directly determines the quality of the control performance of the electric automobile. With the development of rare earth permanent magnet materials and the great progress of research and control technology of power electronic devices for a long time, a permanent magnet synchronous motor adopting a high-performance permanent magnet to provide rotor magnetic flux does not need additional exciting current, so that the power factor is greatly improved, and the permanent magnet synchronous motor is favored by various electric automobile industries by virtue of various advantages of simple rotor structure, high efficiency, high power density, rapid dynamic response performance and the like, and is gradually taken as a mainstream motor. The permanent magnet synchronous motor is a complex system with strong coupling and multivariable, and the precise control of the complex system with high order is difficult. In the early 70 s of the 20 th century, german scholars proposed a vector control principle in order to reduce the complexity of their mathematical models to some extent, and adopted a coordinate transformation theory to transform them into a synchronous rotating coordinate system and make them have the form of a mathematical model of a dc motor. At present, a permanent magnet synchronous motor vector control system generally adopts a double closed loop control strategy, wherein a speed loop and a current loop both adopt a traditional PI controller, although the control principle is simple and the robustness is strong. However, in the normal driving process of the automobile, the rotational inertia of the motor is changed due to different road conditions, the stability of the system is affected by the change of the rotational inertia in the system, and the system becomes unstable in extreme cases.

SUMMERY OF THE UTILITY MODEL

In order to overcome the not enough of prior art, the utility model provides a PMSM vector control's composite control system for electric automobile, its problem that can reply motion inertia change in the electric automobile motion process effectively guarantees the stability of PMSM controller, robustness to and the control requirement that reaches the car fast and accurately.

In order to solve the above problems, the utility model is realized according to the following technical scheme.

The utility model relates to a PMSM vector control's composite control system for electric automobile, including fuzzy PID controller, convert the difference between given rotational speed and actual rotational speed into q axle command current; the BP neural network PID controller converts the difference between the q-axis instruction current and the actual current into q-axis voltage; a PI controller for converting the difference between the d-axis command current and the actual current into a d-axis voltage; a Park inverse transformation module, which is a coordinate transformation module from two-phase rotation to two-phase static rotation; the SVPWM module generates six paths of PWM waves according to the voltage under the two-phase static coordinate system; the three-phase inverter is used for generating three-phase voltage according to the six paths of PWM waves to drive the permanent magnet synchronous motor; a permanent magnet synchronous motor; the sensor detection module is used for detecting the rotating speed and the three-phase current of the permanent magnet synchronous motor; the Clark conversion module is a coordinate conversion module from three-phase stillness to two-phase stillness; and the Park conversion module is a coordinate conversion module from two-phase static to two-phase rotation.

Further, the fuzzy PID controller includes an adjustment to the PI parameter.

Further, the input layer of the BP neural network PID controller is 3 layers, the hidden layer is 8 layers, and the output layer is 3 layers.

Compared with the prior art, the utility model has the beneficial effects that a PMSM vector control's composite control system for electric automobile, the fuzzy PID controller and the BP neural network PID controller of adoption can be according to load torque's difference, and each parameter of automatic adjustment PID makes rotational speed and electric current converge to the error field fast, the effectual quick response nature that has improved the system, further makes the system have stronger system robustness simultaneously.

Drawings

The following is a more detailed description of embodiments of the present invention, taken in conjunction with the accompanying drawings.

Fig. 1 is a block diagram of the overall structure of the present invention.

Fig. 2 is a schematic diagram of the fuzzy PID controller of the present invention.

Fig. 3 is a schematic diagram of a BP neural network PID controller in the present invention.

Detailed Description

The present invention will be further explained with reference to the accompanying drawings.

According to the composite control system for vector control of the permanent magnet synchronous motor for the electric vehicle shown in the attached drawing 1, the system adopts a vector control double closed loop control strategy and mainly comprises a fuzzy PID controller 1, a BP neural network PID controller 2, a PI controller 3, a Park inverse transformation module 4, an SVPWM module 5, a three-phase inverter 6, a permanent magnet synchronous motor 7, a sensor detection module 8, a Clark transformation module 9 and a Park transformation module 10.

Specifically, in the working process of the motor, the whole control system is supplied with direct current, the input direct current firstly enables the system to generate an initial PWM signal to drive the three-phase inverter 6 to work, and the three-phase inverter 6 is used for converting the direct current into alternating current to drive the permanent magnet synchronous motor 7 to work.

Further, in order to enable the permanent magnet synchronous motor 7 to meet the expected control requirement, when the permanent magnet synchronous motor 7 works, the sensor detection module 8 detects the three-phase current and the angular speed of the permanent magnet synchronous motor 7.

Further, the detected three-phase stator current value is subjected to Clark conversion module 9 to obtain stator current under a two-phase static coordinate system, and then is subjected to Park conversion module 10 to obtain d-axis and q-axis current under a two-phase arbitrary coordinate system.

Further, the collected angular speed of the permanent magnet synchronous motor 7 is input to the front end of the fuzzy PID controller 1, the front end of the fuzzy PID controller 1 performs difference processing on the actual rotating speed and the target rotating speed of the permanent magnet synchronous motor 7, and the difference value is used as the input of the fuzzy PID controller 1.

According to the schematic diagram of the fuzzy PID controller shown in FIG. 2, the fuzzy control table rule in the fuzzy controller corresponding to the input error and the error change rate is analyzed and compared, and is used as a parameter in the PID controller which is preferably adjusted on line.

Further, the q-axis current under the two-phase arbitrary coordinate system is output after being controlled by the fuzzy PID controller 1, the difference between the acquired q-axis current and the q-axis current output by the fuzzy PID controller 1 is compared, and the difference value of the q-axis current is used as the input of the BP neural network PID controller 2.

According to the schematic diagram of the BP neural network PID controller shown in the attached figure 3, q-axis current errors and error change rates are analyzed and calculated, and parameters in the PID controller are preferably adjusted on line through a learning algorithm and the BP neural network.

Specifically, the d-axis current generally takes a value of 0, the difference between the acquired d-axis current and 0 is made, and the difference value of the d-axis current is used as the input of the PI controller 3.

The output of the BP neural network PID controller 2 is q-axis voltage, and the output of the PI controller 3 is d-axis voltage.

Furthermore, the voltages of the d and q axes are converted into voltages under a two-phase static coordinate system through a Park inverse transformation module and input to the front end of the SVPWM module 5, the voltages pass through the SVPWM module 5 to obtain six optimal PWM waves, and the three-phase inverter 6 is driven through the six PWM waves.

Further, the three-phase inverter 6 drives the permanent magnet synchronous motor 7 to work according to three-phase voltage generated by the input six-path PWM waves, and a closed-loop control effect is formed.

Other structures of the composite control system for vector control of the permanent magnet synchronous motor for the electric automobile are disclosed in the prior art.

Although the present invention has been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without inventive work are still within the scope of the present invention.

Claims

1. A composite control system for vector control of a permanent magnet synchronous motor for an electric automobile is characterized by comprising a fuzzy PID controller, a fuzzy PID controller and a fuzzy PID controller, wherein the difference between a given rotating speed and an actual rotating speed is converted into a q-axis instruction current; the BP neural network PID controller converts the difference between the q-axis instruction current and the actual current into q-axis voltage; a PI controller for converting the difference between the d-axis command current and the actual current into a d-axis voltage; a Park inverse transformation module, which is a coordinate transformation module from two-phase rotation to two-phase static rotation; the SVPWM module generates six paths of PWM waves according to the voltage under the two-phase static coordinate system; the three-phase inverter is used for generating three-phase voltage according to the six paths of PWM waves to drive the permanent magnet synchronous motor; a permanent magnet synchronous motor; the sensor detection module is used for detecting the rotating speed and the three-phase current of the permanent magnet synchronous motor; the Clark conversion module is a coordinate conversion module from three-phase stillness to two-phase stillness; and the Park conversion module is a coordinate conversion module from two-phase static to two-phase rotation.