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Mining lithium battery life prediction method based on grey vector machine and management system

A life prediction, lithium battery technology, applied in the direction of measuring electricity, measuring electrical variables, measuring devices, etc., can solve problems such as low-level status, achieve the effect of accurate prediction, prolong battery life, and optimize management system

Inactive Publication Date: 2019-04-16
CHINA UNIV OF MINING & TECH
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Problems solved by technology

However, due to the existence of noise, measurement errors, etc., the prediction of lithium battery life by the existing lithium battery management system is still at a low level.

Method used

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  • Mining lithium battery life prediction method based on grey vector machine and management system
  • Mining lithium battery life prediction method based on grey vector machine and management system
  • Mining lithium battery life prediction method based on grey vector machine and management system

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Embodiment Construction

[0035] The present invention will be further described below in conjunction with the drawings:

[0036] A method for predicting the life of lithium batteries for mines based on gray vector machine, including the following steps:

[0037] The first step is to select the prediction model DGM(1,1), which is defined as follows:

[0038] x (1) (k+1)=β 1 x (1) (k)+β 2 ;

[0039] Through the simulation analysis of the mining lithium battery cycle life test data, DGM(1,1) is to further refine the GM(1,1) model, which improves the stability of prediction to a certain extent.

[0040] The second step is to select mining lithium-ion battery cycle life capacity sample data as the initial training data, normalize the samples, convert all data into numbers between [-1, 1], and eliminate the number of cycles and capacity The magnitude difference between

[0041] The third step is to initialize the parameters of the RVM model: the kernel function selects the Gaussian kernel function, K(x,x i )=exp(-||x...

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Abstract

The invention discloses a mining lithium battery life prediction method based on a grey vector machine and a management system. The life prediction method adopts a mining lithium battery capacity as atraining sample to establish a grey RVM regression prediction model; a DGM is built according to the training sample data, a predicted value of the DGM is used as input, the original training sampleis used as output, and an RVM regression prediction model is obtained by training; a DGM(1,1) is adopted to performing capacity short-term prediction, a predicted value is taken as the input of the RVM regression prediction model to obtain a short-term regression prediction result and a prediction probability value of the obtained capacity, a metabolic process is introduced, so that the training sample data are updated; and relevance is analyzed and judged through grey correlation, the RVM model is dynamically updated according to the result so as to obtain a new relevance vector, thereby obtaining a long-term trend prediction result of the method. According to the method, lithium battery monitoring data is acquired in real time, so that the service life prediction precision of the mininglithium battery is more accurate.

Description

Technical field [0001] The invention relates to a method and a management system for predicting the life of lithium batteries for mines based on a gray vector machine, and belongs to the technical field of lithium battery management. Background technique [0002] Lithium batteries have the advantages of high single working voltage, small size, light weight, high energy density, long cycle life, small self-discharge current, no memory effect, no pollution and high cost performance, so they are widely used in communications, transportation, mining, etc. Various industries. Lithium battery includes two parts: battery cell and protection circuit. The protection circuit of large lithium battery is powerful, also known as management system. Its main function is to ensure the uniformity of battery capacity, diagnose battery problems in time, and prevent battery damage. Overcharge and overdischarge, accurately obtain the state of charge of the battery, etc. [0003] Due to the high price...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/392
Inventor 张晓光赵志科孙佳胜徐桂云孙正蔺康张然
Owner CHINA UNIV OF MINING & TECH
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