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Lithium battery echelon utilization residual life prediction method based on convolutional neural network

A convolutional neural network and life prediction technology, applied in the direction of measuring electricity, measuring devices, measuring electrical variables, etc., can solve problems such as inaccurate prediction of remaining service life, reduce energy consumption, improve economy, and save labor costs Effect

Active Publication Date: 2021-10-01
上海伟翔众翼新能源科技有限公司
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  • Abstract
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  • Application Information

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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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  • Lithium battery echelon utilization residual life prediction method based on convolutional neural network
  • Lithium battery echelon utilization residual life prediction method based on convolutional neural network
  • Lithium battery echelon utilization residual life prediction method based on convolutional neural network

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no. 1 example

[0043] Please refer to figure 1 , figure 2 , image 3 , Figure 4 with Figure 5 ,in, figure 1 A structural diagram of a first embodiment of a lithium battery based on a convolutional neural network provided by the present invention is a first embodiment of a residual life prediction method; figure 2 for figure 1 The first operation flow shown; image 3 for figure 1 The first operation flow shown; Figure 4 for figure 1 Schematic diagram showing the structure of the electrode positioning convolutional neural network; Figure 5 for figure 1 The residual use life predicts the structural diagram of convolutional neural network; the residual life prediction method based on the convolutional neural network, including the following steps:

[0044] S1: Using constant current voltage test methods to obtain battery capacity values ​​of training samples and battery internal resistance values;

[0045] S2: Make the internal resistance, capacity, and charge and discharge circulation curve of the tr...

no. 2 example

[0114] Please refer to Image 6 , Figure 7 , Figure 8 with Figure 9 However, a lithium battery ladder based on convolutional neural network based on the first embodiment of the present application utilizes a residual life prediction method, and a second embodiment of the present application proposes a lithium battery ladder based on convolutional neural network. The residual life prediction method. The second embodiment is merely the preferred embodiment of the first embodiment, and the implementation of the second embodiment will not affect the separate implementation of the first embodiment.

[0115] Specifically, the second embodiment of the present application is provided by a lithium battery ladder based on the convolutional neural network differs from the remaining life prediction method in that the residual life prediction method based on the lithium battery ladder based on convolutional neural network, the scan The module also includes a scanning table 1, and the surface of...

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Abstract

The invention provides a lithium battery echelon utilization residual life prediction method based on a convolutional neural network. The method comprises the following steps: determining the number of required samples according to related battery models; utilizing a constant current and voltage test method to obtain a battery capacity value and a battery internal resistance value of the training sample; taking the internal resistance, the capacity and the charge-discharge cycle curve of the training sample battery as input, calculating the remaining service life of the lithium battery, and generating a sufficient number of lithium battery service life labels; performing X-ray scanning on the lithium battery, and pairing the generated image and the service life label to form a training data set; and establishing an echelon battery remaining service life model based on the convolutional neural network. According to the lithium battery echelon utilization residual life prediction method based on the convolutional neural network provided by the invention, the convolutional neural network model is established by using the nonlinear relationship between the image scanned by the scanning module of the echelon battery and the residual service life, and the residual service life of the echelon utilization lithium battery can be quickly estimated.

Description

Technical field [0001] The present invention relates to a ladder secondary lithium battery life prediction, and more particularly to a lithium battery ladder based on a convolutional neural network, a residual life prediction method. Background technique [0002] Lithium batteries are a class of batteries that are positive and negative electrode materials from lithium metal or lithium alloys, using a nonaqueous electrolyte solution. [0003] At present, the remaining life is required after the end of the lithium battery, and the method of predicting the remaining life of the lithium battery is predicted, mainly based on mathematical models, by multi-cycle charge and discharge of lithium battery, collecting related lithium batteries The voltage and current charge and discharge curve, the aging mathematical model of lithium battery is established. [0004] Due to complex internal electrochemical properties, lithium batteries are susceptible to external factors such as temperature, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/36
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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