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Lithium battery life prediction method based on feature screening

A technology for life prediction and feature screening, which is applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., and can solve problems such as large deviation of prediction results.

Pending Publication Date: 2020-10-27
HUST WUXI RES INST
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  • Application Information

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Problems solved by technology

However, the elastic network is very sensitive to the selection of hyperparameters, and nonlinear characteristics will lead to large deviations in prediction results

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  • Lithium battery life prediction method based on feature screening
  • Lithium battery life prediction method based on feature screening
  • Lithium battery life prediction method based on feature screening

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with specific drawings and embodiments.

[0052] In the embodiment that the present invention proposes, a kind of lithium battery life prediction method based on characteristic screening comprises the following steps:

[0053] Step S1, dividing the 124 public lithium battery data into a training set and a test set; both the training set and the test set include multiple samples;

[0054] In a practical example, the training set includes 41 lithium battery data samples, the test set 1 includes 43 lithium battery data samples, and the test set 2 includes 40 lithium battery data samples;

[0055] Step S2, performing pairwise non-repetitive subtraction of the discharge SOC in the first 100 charge-discharge cycles of the lithium battery samples in the training set and the test set;

[0056] The dimension of SOC in this step is 1000; that is, 1000 SOC data points are taken in each discharge cycle;

[0057]...

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Abstract

The invention provides a lithium battery life prediction method based on feature screening. The lithium battery life prediction method comprises the following steps: S1, dividing acquired lithium battery data into a training set and a test set, each of the training set and the test set comprising a plurality of samples; S2, performing pairwise non-repeated subtraction on the discharge SOCs in thefirst m charge-discharge cycles of the lithium battery samples in the training set and the test set; setting m to be larger than 20; S3, obtaining a variance of the SOC subtraction result of each lithium battery, and obtaining the features of each lithium battery; S4, inputting the features and the service life of the training samples in the training set into a Gaussian process regression model with ARD for model training; S5, visualizing the sparse feature weight of the trained model; and S6, inputting the features of the samples in the test set into the training model for life prediction. According to the invention, high correlation characteristics can be automatically extracted, so that the lithium battery life prediction is more accurate.

Description

technical field [0001] The invention belongs to the technical field of lithium battery degradation state monitoring, in particular to a lithium battery life prediction method based on feature screening. Background technique [0002] Lithium-ion batteries have become an indispensable energy source in life due to their advantages of high energy and high power density. Accurate prediction of lithium battery life can assist users to use battery energy reasonably, which is of great significance in practical applications. [0003] The data-driven lithium battery life prediction method is currently a commonly used method, which can be divided into small-sample single-battery prediction methods and large-sample feature-life prediction methods from the perspective of data. Limited by the sample size, the small-sample single-battery prediction method predicts the capacity degradation curve of the lithium battery and calculates the battery life through the threshold. However, this pr...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06K9/62G06F17/18G01R31/36G01R31/367G01R31/387G01R31/392G06F119/04
CPCG06F30/27G06F17/18G01R31/387G01R31/392G01R31/367G01R31/3648G06F2119/04G06F18/214
Inventor 袁烨马贵君华丰丁汉
Owner HUST WUXI RES INST
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