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Method for predicting remaining service life of lithium ion battery

A lithium-ion battery, life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve the problems of few parameters, weak exploration ability, strong development ability, etc., to improve efficiency, improve stability and predict results The effect of precision

Pending Publication Date: 2021-03-12
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

This algorithm is an optimized search method inspired by the prey activity of gray wolves. It has the characteristics of strong convergence performance, few parameters, and easy implementation. However, its gray wolf (individual) position update equation has development Disadvantages of strong ability but weak exploration ability

Method used

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  • Method for predicting remaining service life of lithium ion battery
  • Method for predicting remaining service life of lithium ion battery
  • Method for predicting remaining service life of lithium ion battery

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Embodiment Construction

[0049] The specific implementation manners of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments. Using the lithium battery data set provided by NASA Center for Excellence in Fault Prediction (NASA), three batteries B5, B6, and B7 are selected as the data sets in the specific embodiment.

[0050] A method for predicting the remaining service life of lithium-ion batteries based on VMD-HGWO-SVR, such as figure 1 As shown, the specific steps of this fusion algorithm are as follows:

[0051] Step 1, for the battery capacity data set 200, use the VMD method to decompose the original capacity data set 200, that is, the original signal x(t) into several intrinsic mode functions IMF, where IMF is an amplitude modulation frequency modulation signal, and calculate each IMF component According to the correlation coefficient with the original signal, an appropriate threshold is set according to the correlation coefficient...

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Abstract

The invention discloses a method for predicting the remaining service life of a lithium ion battery based on VMD-HGWO-SVR. Prediction of the remaining service life of the lithium ion battery is an important part of battery health management. The method comprises the following specific steps: firstly, carrying out multi-scale decomposition on lithium battery capacity degradation data by using a variational mode decomposition method, setting a proper threshold value according to correlation coefficient analysis, and reconstructing a mode function meeting conditions to obtain battery capacity data after capacity regeneration and noise fluctuation are eliminated; then, training an SVR model based on the preprocessed battery capacity data, and optimizing hyper-parameters of the SVR by adoptingan improved grey wolf optimization algorithm HGWO; and finally, predicting the remaining service life of the lithium battery by using the trained VMD-HGWO-SVR model. According to the method, the influence of capacity regeneration and noise fluctuation in the lithium battery capacity data on the prediction precision of the residual life of the lithium battery is solved, the grey wolf optimization algorithm is improved in three aspects to prevent the prediction model from falling into a local optimal solution during training, and the proposed method is stable in prediction performance and more accurate in prediction result.

Description

technical field [0001] The invention relates to the technical field of testing the electrical condition of a battery, in particular to a method for predicting the remaining service life of a lithium battery. [0002] technical background [0003] Lithium-ion batteries have the advantages of long cycle life, wide operating temperature range, high energy density, and no pollution. Therefore, they are widely used in electronic equipment, electric vehicles, energy storage systems, aerospace and other fields, and play an important role in modern society. Role. However, in the process of repeated use, the performance of lithium batteries will gradually degrade and become invalid, which will cause the batteries to easily leak and short-circuit to affect the normal operation of equipment systems, and even cause economic losses and explosive disasters. Therefore, it is very necessary to accurately predict the remaining useful life (RUL) of lithium batteries. RUL refers to the time wh...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G01R31/367G01R31/392G06F111/06G06F119/04
CPCG06F30/27G01R31/392G01R31/367G06F2111/06G06F2119/04
Inventor 李祖欣叶乙福周哲蔡志端钱懿
Owner HUZHOU TEACHERS COLLEGE
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