Method and device for predicting parameters of lithium battery impedance model, and readable storage medium

A technology of impedance model and storage medium, which is applied in the direction of measuring devices, measuring electricity, nuclear methods, etc., can solve problems such as unsatisfactory prediction results, local optimality, and a large number of sample data, so as to improve generalization ability and reduce the upper bound , the effect of accurately predicting

Inactive Publication Date: 2020-02-14
河南工学院
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

Although the artificial neural network has achieved certain success, because the artificial neural network follows the principle of empirical risk mini...

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  • Method and device for predicting parameters of lithium battery impedance model, and readable storage medium
  • Method and device for predicting parameters of lithium battery impedance model, and readable storage medium
  • Method and device for predicting parameters of lithium battery impedance model, and readable storage medium

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Embodiment 1

[0041] see figure 1 , in an embodiment of the present invention, a method for predicting parameters of a lithium battery impedance model, the steps are as follows:

[0042] S10: Establishing a least squares support vector machine model for predicting target parameters;

[0043] The target parameter can be the final parameter or parameter value, and the number and type of target parameters can be pre-selected according to the needs;

[0044] First establish the least squares support vector machine model for predicting the target parameters. The least squares support vector machine model can be a specific calculation formula obtained, or it can not be directly expressed in the form of a calculation formula, but through a computer program implement the model;

[0045] Specifically, certain sample data can be collected in advance, and a least squares support vector machine model can be established according to the sample data.

[0046] S11: Obtain the voltage response value of ...

Embodiment 2

[0053] see figure 2, The difference between this embodiment and Embodiment 1 is that the method of establishing the least squares support vector machine model for predicting the target parameter in step S10 is as follows:

[0054] S20: Obtain sample voltage response values ​​of multiple sample lithium batteries;

[0055] Multiple lithium batteries can be selected as samples to obtain sample data for training the least squares support vector machine model. Theoretically, the larger the number of sample lithium batteries, the more sample data can be obtained. The least squares obtained in the final training The support vector machine model is more accurate, but obtaining too many samples may sometimes consume a lot of time, so it also needs to be considered in combination with the actual engineering situation. For example, the number of sample lithium batteries may be 10, and of course, in order to obtain more sample data, the number of sample lithium batteries may also be 50....

Embodiment 3

[0079] see image 3 , in the embodiment of the present invention, a lithium battery impedance model diagram, optionally, the impedance model of the battery can be as follows image 3 As shown, it includes an inductor L, a first resistor RL, a second resistor R1, a third resistor RS, a fourth resistor R2, a first electric double layer capacitor Q1, and a second electric double layer capacitor Q2. The first end and the second end of the inductance L are respectively connected to the first end and the second end of the first resistor RL, and the second end of the first resistor RL is simultaneously connected to the first electric double layer capacitor Q1 and the second end of the second resistor R1. connected at one end, the second end of the second resistor R1 is connected with the second end of the first electric double layer capacitor Q1 and the first end of the third resistor RS, and the second end of the third resistor RS is simultaneously connected with the second electric...

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Abstract

The invention discloses a method for predicting the parameters of a lithium battery impedance model. The method comprises the steps of: establishing a least squares support vector machine model for predicting target parameters; obtaining a voltage response value of a lithium battery to be tested; obtaining the target parameters of the lithium battery to be tested according to the least squares support vector machine model and the voltage response value. Therefore, the target parameters can be obtained according to the pre-established least squares support vector machine model and the obtainedvoltage response value; and the use of an artificial neural network to predict the parameters of the lithium battery impedance model is avoided, and the least squares support vector machine uses the principle of structural risk minimization, when applied to the prediction problem, the upper limit of a generalization error of the model is reduced while minimizing the sample point error, and the generalization ability of the model is improved, so that the parameters of the lithium battery impedance model can be predicted more accuracy.

Description

technical field [0001] The invention relates to the technical field of lithium batteries, in particular to a method, a device and a readable storage medium for predicting parameters of a lithium battery impedance model. Background technique [0002] With the continuous development of society, my country has achieved rapid development in new energy and energy saving and emission reduction. Lithium batteries have begun to replace traditional lead-acid, nickel-metal hydride and nickel-cadmium batteries due to their higher energy and environmental protection. When lithium batteries are used in electric vehicles, the working voltage of the lithium batteries that need to be loaded on the electric vehicle is 12V or 24V, but the working voltage of a single lithium battery is 3.7V, so multiple batteries need to be connected in series to increase the voltage, but the battery is difficult to carry out. Completely balanced charging and discharging, it is difficult to ensure the consiste...

Claims

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Application Information

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IPC IPC(8): G01R31/389G01R31/396G01R31/367G06N20/10G06N3/12
CPCG06N3/126G01R31/367G01R31/389G01R31/396G06N20/10
Inventor 杜志勇王鲜芳卢亚娟
Owner 河南工学院
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