Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A method for optimal speed and torque output of asynchronous motors for electric buses based on random forest regression algorithm

An asynchronous motor and random forest technology, which is applied in the direction of motor generator control, control of electromechanical brakes, control of electromechanical transmission devices, etc. The measured data is discontinuous, the current is adjusted accurately, and the effect of real-time response is realized.

Active Publication Date: 2020-10-30
ANHUI UNIVERSITY
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantages of the existing technology are: 1. Formula derivation is required, which is highly dependent on motor parameters and has many influencing factors
2. Multiple PI regulators are required to control the field weakening area (the area above the rated speed), the system is complex and parameter setting is difficult
3. The actual input Inaccurate, resulting in the problem of response lag in the output voltage vector after passing through the PI controller

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method for optimal speed and torque output of asynchronous motors for electric buses based on random forest regression algorithm
  • A method for optimal speed and torque output of asynchronous motors for electric buses based on random forest regression algorithm
  • A method for optimal speed and torque output of asynchronous motors for electric buses based on random forest regression algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0034] To solve the above problems, such as figure 1 As shown, from the perspective of machine learning, the present invention models the VC control system based on the current ratio output model of the random forest regression algorithm, so that the excitation current and torque current for controlling the output speed and torque of the asynchronous motor are online predicted , to output the optimal current vector in real time to realize the optimal speed and torque output of the asynchronous motor. The main advantages are as follows: (1) The current ratio model based on the random forest regression algorithm can basically realize the synchronization of each system parameter input into the model with the output of the excitation current and torque current, and the current vector output response basically has no lag. (2) Avoid the formula oper...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an optimal speed-torque output method of an asynchronous motor for an electric bus based on a random forest regression algorithm. First, a vector control system for asynchronous motors based on the current ratio output model is established; secondly, according to the maximum voltage (U smax ) and maximum current (I smax ) constraints, establish an analytical model for maximum torque output; thirdly, analyze the law of excitation current torque current variation corresponding to maximum torque output under different working conditions, build a voltage closed-loop vector analytical model, and embed it into a vector control system; Then, set up the AVL (AVL list GmbH) experimental platform, collect the measured sample data, and establish the random forest regression (RFR) model with working condition parameters as input and output; finally, embed this regression model into the vector control system to realize the actual motor Running regression under different working conditions not only realizes optimal speed and torque control. The method has accurate proportioning current; eliminates the division of the operating area of ​​the asynchronous motor; and improves the stability of the speed and torque output.

Description

technical field [0001] The invention relates to the technical field of asynchronous motors, in particular to a method for outputting optimal speed and torque of asynchronous motors for electric buses based on a random forest regression algorithm. Background technique [0002] Common asynchronous motor control methods include direct torque control (Direct torque control, DTC) and vector control (vector control, VC). DTC performs Bang-Bang control on torque and stator flux linkage, which avoids the transformation of rotating coordinates and makes the structure of the control system simple, but it is easy to generate torque ripple, and the speed regulation width is not high; VC decouples torque and rotor flux linkage Control, easy to achieve continuous and stable torque control, wide speed range. Therefore, according to the operation of electric buses in all working conditions (electric buses operate in different environments, there are three typical working conditions: conges...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): H02P21/22H02P21/20G06F30/27G06Q10/04G06Q50/06G06K9/62
CPCH02P21/22H02P21/20G06Q10/04G06Q50/06G06F30/367G06F18/24323
Inventor 谢芳邱臣铭吴文明
Owner ANHUI UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products