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A Li-ion battery life migration prediction method based on deep learning

A lithium-ion battery and deep learning technology, applied in the field of lithium battery health management, can solve problems such as fatal disasters and system failures, and achieve the effects of reducing costs, shortening the research and development cycle, and reducing test time and test volume

Active Publication Date: 2019-09-27
BEIHANG UNIV +1
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Problems solved by technology

Therefore, the performance of lithium-ion batteries is a key factor in the reliability of their overall electronic system, and its failure may cause system failure or even fatal disaster

Method used

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  • A Li-ion battery life migration prediction method based on deep learning
  • A Li-ion battery life migration prediction method based on deep learning
  • A Li-ion battery life migration prediction method based on deep learning

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Embodiment Construction

[0042] The flow of the lithium-ion battery life migration prediction method based on deep learning in the present invention is as follows: figure 1 As shown, it specifically includes the following steps:

[0043] The flow of the prediction method includes three parts: data preprocessing, similarity calculation and life prediction. Specific steps are as follows:

[0044]The first step of data preprocessing: In order to ensure the data scale consistency of cross-recipe prediction, the original data is first standardized and preprocessed. Determine the failure threshold of the battery in this study, and normalize its capacity data and remaining cycle life to obtain model input data and corresponding remaining life labels;

[0045] The second step of similarity calculation: determine the length of the test data, and calculate the average Euclidean distance between the target battery and other batteries with the same temperature, the same rate and different formulas based on the ...

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Abstract

The invention proposes a novel prediction method for service life migration of a lithium ion battery. The method comprises: a capacity degradation database of batteries with the same temperature, thesame multiplying power, and different formulas is established; after determination of a target battery, a battery similar to a capacity degradation rule of the target battery is selected based on a similarity measure; and then on the basis of the deep learning method, cross-formula battery service life migration prediction is carried out to realize prediction of the residual cycle life of the target battery. Meanwhile, with consideration of the economic target and irreversibility of battery life degradation, the design of the battery cycle life test is optimized and the battery test design issaved. With the method, the accurate residual life prediction of the lithium ion battery is realized; the test time and the test load of the service life testing at the research and development stageare reduced substantially; the research and development cycle of new products is shortened; the research and development costs are lowered; and the reliability and safety of the system are improved.

Description

technical field [0001] The present invention relates to the technical field of lithium battery health management, in particular to a lithium ion battery life migration prediction method based on deep learning. Background technique [0002] Lithium-ion batteries are currently the main energy storage devices widely used in military electronic products, avionics devices, electric vehicles and various portable electronic devices (such as laptops, digital cameras, tablet computers, mobile phones, etc.), due to their light weight, discharge With the characteristics of low efficiency and long life, lithium-ion batteries have basically replaced nickel-cadmium batteries and nickel-metal hydride batteries. At the same time, due to the current concern about climate change and the urgency of new energy development, lithium-ion electric vehicles have developed rapidly. Many automobile manufacturers and research institutions are committed to developing new energy vehicles that can replace...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367G01R31/378G01R31/392G06N3/04G06N3/08
Inventor 马剑赵万琳吕琛王振亚苏育专种晋金海族林永寿
Owner BEIHANG UNIV
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