A battery pack remaining life prediction method based on migration deep learning

A technology of deep learning and life prediction, which is applied in the field of batteries, can solve problems such as an efficient and accurate prediction method for the life of battery packs and battery cells that has not been proposed, and achieve the effect of optimizing design and improving prediction accuracy

Active Publication Date: 2022-06-24
CHONGQING UNIV
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Problems solved by technology

[0003] In view of the above problems, there is no effective and accurate prediction method for the remaining life of the battery pack and the distribution of the life of the battery pack monomer based on migration deep learning.

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  • A battery pack remaining life prediction method based on migration deep learning
  • A battery pack remaining life prediction method based on migration deep learning
  • A battery pack remaining life prediction method based on migration deep learning

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Embodiment Construction

[0048] The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of ​​the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0049] Among them, the accompanying drawings are only used for exemplary description, and represent only schematic diagrams, not physical drawings, and should not be ...

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Abstract

The invention relates to a method for predicting the remaining life of a battery pack based on migration deep learning, which belongs to the technical field of batteries. The method includes the following steps: Step S1: collecting power battery aging data sets, and establishing a battery aging database; Step S2: extracting multiple health factors according to the aging data of battery cells, and screening the health factors according to correlation analysis and capacity estimation errors; step S3: Train the recursive model of the health factor based on the aging data set of the battery cell life cycle, and the capacity estimation model based on the health factor; Step S4: Establish a machine learning model based on the battery cell health factor set and the capacity decay of the battery pack ; Step S5: Predict the capacity of each cell in the future based on the cell capacity estimation model, and obtain the cell capacity distribution of the battery pack in the future cycle. The combination of transfer learning and deep learning can effectively use the existing complete information and improve the prediction accuracy of the remaining life of the battery pack.

Description

technical field [0001] The invention belongs to the technical field of batteries, and relates to a method for predicting the remaining life of a battery pack based on migration deep learning. Background technique [0002] The remaining life prediction methods of batteries can generally be divided into two categories: model-based and data-driven. A model-based method is mainly based on empirical or semi-empirical models established by historical data and cycle times for prediction. Usually an exponential model, a double exponential model, or a polynomial model. Use advanced filters such as Kalman filter, particle filter, etc. to perform curve fitting to obtain a fitted curve, so as to use the fitted curve for capacity estimation or remaining life prediction. Physical model-based is another model-based method, which establishes the aging mechanism model of the battery, so as to realize the charging and discharging simulation of future cycles through simulation, and then obta...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367G01R31/392G06F30/27G06N3/04G06N3/08G06N20/00G06F111/08
CPCG01R31/367G01R31/392G06F30/27G06N3/08G06N20/00G06F2111/08G06N3/044G06N3/045
Inventor 胡晓松车云弘李佳承邓忠伟唐小林
Owner CHONGQING UNIV
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