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Lithium ion battery residual life detection method based on relevance vector regression

A lithium-ion battery and related vector technology, applied in the field of battery life evaluation, can solve the problems of limited value, model inability to take into account both calculation efficiency and prediction accuracy, and low prediction accuracy

Inactive Publication Date: 2021-08-27
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Based on the model-driven method, the battery life prediction model can be obtained only by partial fitting. The disadvantage is that the prediction accuracy is relatively low, and most models cannot take into account both computational efficiency and prediction accuracy. The value of use in actual situations is limited.

Method used

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  • Lithium ion battery residual life detection method based on relevance vector regression
  • Lithium ion battery residual life detection method based on relevance vector regression
  • Lithium ion battery residual life detection method based on relevance vector regression

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Embodiment

[0032] A method for detecting the remaining life of a lithium-ion battery based on correlation vector regression, comprising the steps of:

[0033] (1) Data feature extraction:

[0034] According to the lithium battery charge and discharge data, the data of the discharge voltage of the lithium battery changing with time is extracted, and according to the voltage change gradient, the voltage change time in each cycle is extracted as the data feature;

[0035] Step (1) is specifically: taking NASA laboratory B5 and B6 lithium batteries as examples, set the battery health index to 1 at the initial moment, and set the battery health index to 0 when the lithium battery reaches 1.4 (Ah). According to the data of lithium battery discharge voltage changing with time, extract the lithium ion battery voltage difference between adjacent time measurement points in each cycle, and select twice the voltage difference from the plateau period in the middle of battery discharge as the threshol...

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Abstract

The invention provides a lithium ion battery residual life detection method based on relevance vector regression, which comprises the following steps of (1) data feature extraction: extracting the data of the discharge voltage of a lithium battery along with time change according to the charge and discharge data of the lithium battery, and extracting the voltage change time as the data feature according to the voltage change gradient, (2) data feature normalization: carrying out normalization processing on the data, (3) building a model: selecting a Gaussian kernel function according to a relevance vector machine (RVM) algorithm model, building mapping from a data set sample to high-dimensional data, and performing data training to obtain the RVM algorithm model, and (4) prediction: predicting the residual capacity of the lithium battery by using a relevance vector machine (RVM) algorithm model so as to effectively detect the residual life of the battery.

Description

technical field [0001] The invention belongs to the technical field of battery life evaluation, and in particular relates to a method for detecting the remaining life of a lithium-ion battery based on correlation vector regression. Background technique [0002] Lithium-ion batteries are an important part of the development of new energy vehicles. Compared with traditional lead-acid batteries, lithium-ion batteries have the characteristics of long service life, high voltage, small self-discharge, small overall battery volume, and relatively high energy density. In addition, the lithium-ion battery has high charge and discharge energy performance, which can meet the requirements of the car when it starts and accelerates quickly. At present, the use of lithium-ion batteries has achieved large-scale commercialization, and has been widely used in wind power, hydropower, and solar energy storage power stations. In the aerospace and military fields, the demand for lithium-ion batt...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/367G01R31/378G01R31/392
CPCG01R31/367G01R31/378G01R31/392
Inventor 徐自强赵开吴孟强朱洪涛郝晓明
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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