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Lithium ion battery remaining service life prediction method and system based on IGS-SVM

A lithium-ion battery, life prediction technology, applied in nuclear methods, neural learning methods, measuring electricity, etc.

Pending Publication Date: 2021-08-20
CHANGAN UNIV
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Problems solved by technology

The above studies have achieved good results, but how to select the optimal parameters based on the SVR method to ensure high accuracy of RUL prediction is still an existing problem

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  • Lithium ion battery remaining service life prediction method and system based on IGS-SVM
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  • Lithium ion battery remaining service life prediction method and system based on IGS-SVM

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Embodiment Construction

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0061] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or Presence or addition of multiple other features, integers, steps, operations, elements, components and / or collections thereof.

[0062] It should also be understood that the terminology used ...

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Abstract

The invention discloses a lithium ion battery remaining service life prediction method and system based on IGS-SVM. The method comprises the following steps: using a discharge end voltage to construct a health factor HI having the same degradation capability as the NASA lithium battery capacity; establishing a lithium battery RUL prediction data set according to the health factor HI and the extracted historical data, and dividing the data set into a training set and a test set; optimizing the parameters of the support vector machine on the training set by using an improved grid search method to obtain optimal parameters, and updating the parameters of the support vector machine model by using the optimal parameters to obtain an IGS-SVM model; and putting the test set into the IGS-SVM model to obtain a mean absolute error value and a root-mean-square error value of the test set in model training and a value of a fitting degree decision coefficient, measuring a capacity prediction error between a predicted value and a true value, and predicting the remaining service life of the lithium battery in real time. The method and the system are suitable for online RUL prediction of the lithium battery and has good practicability.

Description

technical field [0001] The invention belongs to the technical field of battery detection, and in particular relates to a method and system for predicting the remaining service life of lithium-ion batteries based on IGS-SVM. Background technique [0002] With the development of science and technology, lithium-ion batteries have gradually become an important energy storage and energy supply carrier in many industries due to their comprehensive advantages such as small size, high energy density, high working voltage, and long life cycle. Lithium-ion battery Remaining Useful Life (RUL) prediction, as a cutting-edge technology for lithium-ion battery fault diagnosis and health management (Prognostics and Health Management, PHM), has been valued by more and more researchers, and has gradually become an important part of electronic technology. Research hotspots in system health management and fault diagnosis. Existing lithium battery RUL prediction methods can be divided into fail...

Claims

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Application Information

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IPC IPC(8): G01R31/392G01R31/367G06N3/08G06N20/10
CPCG01R31/392G01R31/367G06N3/08G06N20/10
Inventor 李杰张志新李润然贾渊杰孟凡熙张子辰闫柯朴赵世明牛惠萌
Owner CHANGAN UNIV
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