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A Lithium-ion Battery Life Prediction Method

A lithium-ion battery and life prediction technology, which is applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of difficulty in lithium battery life prediction, and achieve the effect of improving accuracy

Active Publication Date: 2020-01-21
TAIYUAN UNIV OF TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] For this reason, what the present invention is to solve is the difficult problem of lithium battery life prediction under specific operating conditions. Based on big data storage technology, various data are provided for analysis, and then a battery life prediction method is provided.

Method used

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  • A Lithium-ion Battery Life Prediction Method
  • A Lithium-ion Battery Life Prediction Method
  • A Lithium-ion Battery Life Prediction Method

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Embodiment

[0038] A lithium-ion battery life prediction method, said method comprising the following processes:

[0039] Collect the operating data of the same type of in-service or decommissioned batteries, and establish a database including battery operating temperature, battery discharge rate, battery internal resistance, real-time discharge capacity, service time i and total service life parameters;

[0040] Establish a linear regression function model for battery life prediction:

[0041] h(x)=h θ (x) = θ 0 +θ 1 x 1 +θ 2 x 2 +θ 3 x 3

[0042] Among them, x={x 1 , x 2 , x 3} is a parameter affecting battery life, x 1 is the battery operating temperature, x 2 is the battery discharge rate, x 3 is the internal resistance of the battery, h(x) is the total service life of the battery, θ={θ 1 , θ 2 , θ 3} is the influence coefficient of each parameter on life decay, θ 0 is noise, which obeys a normal distribution with a mean of 0 and a variance of σ;

[0043] Introduce ...

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Abstract

The invention relates to a lithium ion battery, and further relates to a lithium ion battery service life prediction method. The method comprises the following steps of collecting the running data ofthe in-service or retired battery of the same model, and establishing a database which comprises the battery operating temperature, the battery discharge rate, the battery internal resistance and thetotal service life parameter; establishing a battery service life prediction linear regression function model: h (x) = h theta (x) = theta 0+theta 1*1+theta 2*2 + theta 3*3, and the operating temperature, the discharge rate, the internal resistance of the battery in a specified model are substituted into the regression model, and the total service life of the battery is obtained. The operating temperature, the discharge rate and the internal resistance of the battery are key factors affecting the service life of the battery, and the operating temperature, and by introducing the discharge rateand the internal resistance of the battery to be used as the influence parameters of the battery service life, the modeling prediction is effective.

Description

Technical field: [0001] The invention relates to a lithium ion battery, and further relates to a lithium ion battery life prediction method. Background technique: [0002] As the application fields of lithium batteries become more and more extensive, its design capacity gradually increases, and the inconsistency of battery cells and different operating conditions make the service life of batteries vary greatly, and there are many factors for battery performance attenuation. The internal chemical reaction mechanism is relatively complex, making battery life prediction difficult to achieve. [0003] Existing battery life prediction models are usually based on two modeling methods, one is the empirical model. Empirical models usually require a large number of tests to obtain test data, and obtain empirical data of capacity fading by obtaining parameter values. It takes a long time, and a large amount of resources must be invested in testing to obtain data. The second is a phy...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/392
CPCG01R31/382
Inventor 陈泽华柴晶赵哲峰刘晓峰刘帆李伟
Owner TAIYUAN UNIV OF TECH
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