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A method and device for estimating lithium battery capacity based on visual cognition

A lithium battery and visual technology, applied in the field of battery capacity estimation, can solve the problems of cumbersome battery capacity estimation methods and high cost, and achieve the effects of avoiding electrochemical mechanism research and degradation modeling process, good applicability, and simple test data.

Active Publication Date: 2020-07-03
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0004] The technical problem solved by the solution provided according to the embodiment of the present invention is to solve the cumbersome and costly method for estimating the battery capacity in the prior art

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  • A method and device for estimating lithium battery capacity based on visual cognition
  • A method and device for estimating lithium battery capacity based on visual cognition
  • A method and device for estimating lithium battery capacity based on visual cognition

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Embodiment Construction

[0052] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described below are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0053] figure 1 It is a flow chart of a method for estimating lithium battery capacity based on visual cognition provided by an embodiment of the present invention, such as figure 1 shown, including:

[0054] Step S101: Obtain the charging current data set during each charging process or the discharge voltage data set during each discharging process by collecting the charging current during each charging process or the discharging voltage during each discharging process of the lithium battery;

[0055] Step S102: forming a charging current two-dimensional image or a discharging voltage two-dimensional image by performing data arrangement processing on th...

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Abstract

The invention discloses a lithium batter capacity estimation method and device based on visual recognition, and relates to the technical field of battery capacity estimation. The method comprises thesteps that the capacity of a battery is obtained through estimation after processing is conducted on visual recognition images which are obtained through the conversion performed on current and voltage data in each loop obtained from battery loop service life based on non sub-sampled contourlet transformation and a Laplacian eigenmaps method. According to the method, applying the visual recognition to the field of battery capacity estimation is proposed for the first time, battery capacity estimation can be conducted by simply adopting a battery charging current value or a discharging voltagevalue, compared with an existing method, the accuracy of the method is higher, and the method is more efficient.

Description

technical field [0001] The invention relates to the technical field of battery capacity estimation, in particular to a method and device for estimating lithium battery capacity based on visual cognition. Background technique [0002] Battery capacity is considered an important indicator of battery performance, which is greatly affected by various internal and external mechanisms such as ambient temperature, aging, and usage methods. These factors cause battery performance to gradually decline over time. Therefore, the available battery capacity needs to be accurately estimated to ensure reliability and reasonable battery usage management. [0003] Research in recent years has proposed various methods for estimating battery capacity. Most of the existing methods are model-based methods, including electrochemical, equivalent circuit and analytical models. These models are mainly based on complex physical and chemical processes considering the dynamic characteristics of batte...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/367G01R31/396
Inventor 程玉杰吕琛
Owner BEIHANG UNIV
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