Lithium battery failure judgment method, model generation method, equipment and storage medium

A failure judgment model, lithium battery technology, applied in character and pattern recognition, design optimization/simulation, instruments, etc., can solve problems such as difficulty in predicting battery failure

Pending Publication Date: 2022-04-08
SUNWODA ELECTRONICS
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The embodiment of the present invention provides a lithium battery failure judgment method, model generation method, equipment and storage medium, so as to at least solve the problem in the prior art that it is very difficult to predict the failure of the battery by quantifying the uncontrollable chemical side reactions inside the battery , at the same time, the classical capacity fading model cannot well fit the technical problem of the battery capacity mutation problem

Method used

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  • Lithium battery failure judgment method, model generation method, equipment and storage medium
  • Lithium battery failure judgment method, model generation method, equipment and storage medium
  • Lithium battery failure judgment method, model generation method, equipment and storage medium

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Embodiment 1

[0029] According to an embodiment of the present invention, an embodiment of a method for generating a lithium battery failure judgment model is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions , and, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.

[0030] figure 1 is a flowchart of a method for generating a lithium battery failure judgment model according to an embodiment of the present invention, such as figure 1 As shown, the method includes the following steps:

[0031] Step S102, acquiring historical data of a plurality of lithium battery samples, the historical data including a plurality of node parameter data;

[0032] The above multiple node parameter data include, but are not limited to, numerical standardized ...

Embodiment 2

[0051] figure 2 is a flow chart of a lithium battery failure determination method according to an embodiment of the present invention, such as figure 2 As shown, the method includes the following steps:

[0052] Step S202, obtaining sample parameter data of the lithium battery to be tested;

[0053] Step S204, judge the lithium battery to be tested according to the pre-trained failure judgment model and sample parameter data, and obtain the judgment result of the failure of the lithium battery to be tested, and the failure judgment model is generated according to any one of the above lithium battery failure judgment model generation methods .

[0054] It should be noted that the index type F or S, risk level, and parameter column contribution included in the above-mentioned extended judgment result.

[0055] Through the above steps, the historical data of the lithium battery sample can be used to establish a failure judgment model for the failure prediction of the lithium...

Embodiment 3

[0082] According to another aspect of the embodiments of the present invention, a lithium battery failure judgment model generation device is also provided, Figure 6 is a schematic diagram of a lithium battery failure judgment model generation device according to an embodiment of the present invention, such as Figure 6 As shown, the lithium battery failure judgment model generation device includes: a first acquisition module 62 , a second acquisition module 64 , a standardization processing module 66 and a generation module 68 . The device for generating the lithium battery failure judgment model will be described in detail below.

[0083] The first acquisition module 62 is used to acquire the historical data of a plurality of lithium battery samples, and the historical data includes a plurality of node parameter data; the second acquisition module 64 is connected to the above-mentioned first acquisition module 62 and is used for based on the preset correlation coefficient ...

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Abstract

The invention discloses a lithium battery failure judgment method, a model generation method, equipment and a storage medium. The method comprises the following steps: acquiring historical data of a plurality of lithium battery samples, the historical data comprising a plurality of node parameter data; screening the plurality of node parameter data based on a preset correlation coefficient to obtain a plurality of node correlation data; carrying out standardization processing on the related data of the plurality of nodes to obtain a plurality of standard node data; and training according to the plurality of standard node data and the failure result of the corresponding lithium battery sample, and generating a failure judgment model for performing failure pre-judgment on the lithium battery. The technical problems that in the prior art, it is very difficult to prejudge the failure of the battery by quantifying uncontrollable chemical side reactions in the battery, and meanwhile, a classic capacity recession model cannot well fit the battery capacity sudden change problem are solved.

Description

technical field [0001] The invention relates to the field of lithium batteries, in particular to a lithium battery failure judgment method, a model generation method, equipment and a storage medium. Background technique [0002] Compared with traditional batteries, lithium batteries have the advantages of light weight, small size, long life, high specific energy, low spontaneous discharge rate, no memory effect, and high safety. They are commonly used power supplies for 3C products and are widely used in mobile phones, notebook computers, Products such as tablet computers and smart homes have become ideal energy storage batteries at present. [0003] Due to complex electrochemical reactions in the charging and discharging process of lithium batteries, it is still impossible to effectively predict the failure of lithium batteries. In addition, with the continuous accumulation of charge and discharge times, the crystal structure of the electrode active material will be gradua...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06K9/62G06F30/27
Inventor 万猛崔彦洲冯倩
Owner SUNWODA ELECTRONICS
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