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Remaining life prediction method of lithium battery echelon utilization based on convolutional neural network

A convolutional neural network and life prediction technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as inaccurate prediction of remaining service life, reduce energy consumption, improve economy, and save The effect of labor costs

Active Publication Date: 2022-07-29
上海伟翔众翼新能源科技有限公司
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Problems solved by technology

[0006] The invention provides a method for predicting the remaining service life of a lithium battery step-by-step utilization based on a convolutional neural network, which solves the problem that the existing detection equipment will affect the prediction of the remaining service life and become inaccurate

Method used

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  • Remaining life prediction method of lithium battery echelon utilization based on convolutional neural network
  • Remaining life prediction method of lithium battery echelon utilization based on convolutional neural network
  • Remaining life prediction method of lithium battery echelon utilization based on convolutional neural network

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no. 1 example

[0043] Please refer to figure 1 , figure 2 , image 3 , Figure 4 and Figure 5 ,in, figure 1 It is a schematic structural diagram of the first embodiment of the method for predicting the remaining life of a lithium battery based on a convolutional neural network provided by the present invention; figure 2 for figure 1 The schematic diagram of the first operation flow shown; image 3 for figure 1 The schematic diagram of the first operation flow shown; Figure 4 for figure 1 The schematic diagram of the structure of the cell electrode localization convolutional neural network shown; Figure 5 for figure 1 Shown is a schematic diagram of the remaining service life prediction convolutional neural network structure; the remaining life prediction method of lithium battery echelon utilization based on convolutional neural network includes the following steps:

[0044] S1: Use the constant current voltage test method to obtain the battery capacity value and battery inte...

no. 2 example

[0114] Please refer to Image 6 , Figure 7 , Figure 8 and Figure 9 , based on the convolutional neural network-based remaining life prediction method for lithium batteries provided by the first embodiment of the present application, the second embodiment of the present application proposes another convolutional neural network-based remaining life prediction method for lithium batteries. The second embodiment is only a preferred mode of the first embodiment, and the implementation of the second embodiment will not affect the independent implementation of the first embodiment.

[0115] Specifically, the difference between the convolutional neural network-based remaining life prediction method of lithium battery echelon utilization provided by the second embodiment of the present application is that the convolutional neural network-based lithium battery echelon utilization remaining life prediction method, the scanning The module also includes a scanning table 1, a surface ...

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Abstract

The present invention provides a method for predicting the remaining life of a lithium battery in a cascade utilization based on a convolutional neural network. value; use the internal resistance, capacity and charge-discharge cycle curve of the training sample battery as input, calculate the remaining service life of the lithium battery, and generate a sufficient number of lithium battery service life labels; perform X-ray scanning on the lithium battery, and compare the generated images and The service life labels are paired to form a training data set; a convolutional neural network-based remaining service life model of the battery is established. The method for predicting the remaining service life of a lithium battery based on a convolutional neural network provided by the present invention utilizes the nonlinear relationship between the image scanned by the scanning module of the cascaded battery and the remaining service life to establish a convolutional neural network model, which can quickly estimate the echelon. Take advantage of the remaining life of the lithium battery.

Description

technical field [0001] The invention relates to the field of life prediction of lithium battery in cascade utilization, in particular to a method for predicting the remaining life of lithium battery in cascade utilization based on a convolutional neural network. Background technique [0002] Lithium battery is a kind of battery that uses lithium metal or lithium alloy as positive and negative electrode materials and uses non-aqueous electrolyte solution. [0003] At present, it is necessary to detect the remaining life of the lithium battery after the use of the lithium battery, and the method of predicting the remaining life of the lithium battery at this stage is mainly based on the method of mathematical model. The voltage and current charge-discharge curves are used to establish the aging mathematical model of the lithium battery. [0004] Due to the complex internal electrochemical characteristics of existing detection devices, lithium batteries are easily affected by ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/73G06N3/04G06N3/08
CPCG01R31/36
Inventor 顾颖中张蓓刘楠伯乐本薛頔陆斌印言伟杨琴华
Owner 上海伟翔众翼新能源科技有限公司
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