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MC-WPT system load and mutual inductance recognition model, method and system based on tensorflow

A technology of MC-WPT and recognition model, which is applied in the field of MC-WPT, can solve problems such as the inability to realize dual-parameter recognition at the same time, and achieve the effects of real-time control, easy implementation, and high precision

Active Publication Date: 2021-10-15
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a TensorFlow-based MC-WPT system load and mutual inductance identification model, method and system. The technical problem to be solved is that the current load and mutual inductance identification method of the MC-WPT system cannot simultaneously realize dual-parameter identification, fast identification speed, Low cost, high recognition accuracy

Method used

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  • MC-WPT system load and mutual inductance recognition model, method and system based on tensorflow
  • MC-WPT system load and mutual inductance recognition model, method and system based on tensorflow
  • MC-WPT system load and mutual inductance recognition model, method and system based on tensorflow

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Embodiment 1

[0076] This embodiment provides a TensorFlow-based MC-WPT system load and mutual inductance identification model based on the above-mentioned dual LCC type MC-WPT system. The generation steps include:

[0077] S1. Construct a fully connected neural network model based on the TensorFlow framework;

[0078] S2. set up the COSMOL and Simulink simulation model of MC-WPT system, obtain the input current value of multiple groups of described MC-WPT systems, transmission distance simulation data between coils, and described simulation data is divided into training set and test set;

[0079] S3. Input the training set into the fully connected neural network model for model training, and continuously optimize the parameters in the fully connected neural network model according to the training error value;

[0080] S4. When the training error rate of the fully connected neural network model is as low as the preset error rate, the training is ended, and the trained MC-WPT system load and...

Embodiment 2

[0106] Based on the MC-WPT system load and mutual inductance identification model described in Embodiment 1, an embodiment of the present invention provides an MC-WPT system load and mutual inductance identification method, including steps:

[0107] X1. Detect the current input current value of the MC-WPT system and the transmission distance between the coils;

[0108] X2. Input the current input current value and the transmission distance between the coils into the MC-WPT system load and mutual inductance identification model, and calculate the corresponding load value and mutual inductance value.

[0109] Wherein, in the step X2, the formula for calculating the MC-WPT system load and mutual inductance identification model is:

[0110]

[0111] Among them, l 1 Represents the intermediate variable of the first hidden layer, [h I m ] represents the matrix composed of the transmission distance between the coils and the input current value of the MC-WPT system, and respe...

Embodiment 3

[0119] The embodiment of the present invention provides a TensorFlow-based MC-WPT system load and mutual inductance identification system, including a controller and a current detection module and a distance measurement module connected to the controller; the current detection module is used to detect the transmitter LCC in the MC-WPT system The input current value of the circuit topology I m and sent to the controller; the ranging module is used to detect the transmission distance h between the transmitting coil and the receiving coil in the MC-WPT system and sent to the controller; the controller is used to install the load and mutual inductance of the MC-WPT system described in Embodiment 1 For the identification model, according to the MC-WPT system load and mutual inductance identification method described in Embodiment 2, the load value and mutual inductance value corresponding to the input current value and the transmission distance are calculated.

[0120] Preferably, ...

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Abstract

The present invention relates to the technical field of MC-WPT, and specifically discloses a TensorFlow-based MC-WPT system load and mutual inductance identification model, method and system. The problem of load and mutual inductance recognition is equivalent to the problem of solving nonlinear equations, and then transformed into a deep learning nonlinear fitting problem, and the training set is used to train the model tens of thousands of times, and finally the MC‑ WPT system load and mutual inductance identification model. On the whole, the present invention trains the model offline and imports the trained model into the micro-controller, which can realize the simultaneous identification of load and mutual inductance online, with fast identification speed and high precision, which is beneficial to the real-time control of the system, and the cost is low and easy Realization is conducive to engineering popularization and application.

Description

technical field [0001] The present invention relates to the technical field of MC-WPT (magnetic field coupled wireless power transmission), in particular to a TensorFlow-based MC-WPT system load and mutual inductance identification model, method and system. Background technique [0002] With the development of economy and society, the use of traditional wire power supply for mobile electrical equipment, such as rail trains, mobile hoisting equipment, household appliances, rotating machinery and other equipment will affect its flexibility, and in some special environments will increase The potential safety hazards of electricity use have brought challenges to the practical application of engineering. The emergence of Wireless Power Transfer (WPT, Wireless PowerTransfer) technology provides a safe, environmentally friendly, convenient, and easy-to-maintain power supply method, which has attracted the attention and research of many scholars at home and abroad, and jointly promo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J7/02H02J50/12H02J50/90G06N3/04G06N3/08
CPCH02J7/02H02J50/12H02J50/90G06N3/08G06N3/048G06N3/045
Inventor 苏玉刚阳剑王智慧孙跃戴欣唐春森
Owner CHONGQING UNIV