A 3dcnn-based soc estimation method for lithium-ion batteries

A lithium-ion battery, lithium battery technology, applied in the field of lithium-ion batteries, to achieve the effect of high adaptability

Active Publication Date: 2022-05-20
NANTONG UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

In reality, it is still a challenging task to complete the estimation of electric vehicle battery state of charge

Method used

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  • A 3dcnn-based soc estimation method for lithium-ion batteries
  • A 3dcnn-based soc estimation method for lithium-ion batteries
  • A 3dcnn-based soc estimation method for lithium-ion batteries

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Embodiment 1

[0038] See Figures 1 through 3, the present invention provides a technical solution for a lithium-ion battery SOC estimation method based on 3DCNN, the present embodiment of the Panasonic lithium-ion battery NCR-18650B as an object of study, the calibration voltage is 3.7V, the battery capacity is 3400mAh. The battery is charged in a constant current-constant voltage charging method, and after 1h of standing, the battery is fully charged. The battery is discharged under constant current discharge, DST condition, FUDS condition and US06 working condition until the voltage drops to the discharge cut-off voltage, and the experiment is repeated.

[0039] For a better realization of the object of the present invention, the present embodiment is based on a 3DCNN lithium-ion battery SOC estimation method, comprising the following steps:

[0040] Step 1) At different temperatures, the new lithium battery is fully charged, and then through the constant current discharge experiment, DST con...

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Abstract

The invention provides a method for estimating the SOC of a lithium-ion battery based on 3DCNN, which belongs to the technical field of lithium-ion batteries. It solves the problem that the 3DCNN convolutional neural network is difficult to use for SOC estimation in the SOC estimation method. The technical solution is as follows: the method includes the following steps: Step 1) Repeatedly measure current and other data through discharge experiments; Step 2) Data preprocessing and constructing a data set; Step 3) Training the data set through a 3DCNN convolutional neural network , to obtain a 3DCNN model for real-time estimation. The beneficial effects of the present invention are: the convolutional neural network structure used in the present invention can explore the connection of input data at the same time point between adjacent discharge cycles, and the convolution kernel in the time dimension can not only consider the number of cycles, but also extract each The characteristic relationship between cycles, and with its high adaptability, it can also predict various parameters of the battery such as the remaining capacity of the battery and the remaining life of the battery.

Description

Technical field [0001] The present invention relates to the field of lithium-ion battery technology, in particular to a 3DCNN-based lithium-ion battery SOC estimation method. Background [0002] New energy vehicles are currently developing rapidly in China, among which electric vehicles powered by on-board lithium batteries have been vigorously promoted in the "13th Five-Year Plan". Lithium batteries with life, specific energy and other aspects of the characteristics of the most important energy storage components, lithium batteries are also divided into lithium oxide batteries, lithium manganese oxide batteries, lithium manganese dioxide batteries and lithium iron phosphate batteries according to the positive and negative electrode materials. The measurement accuracy of electric vehicles such as endurance and residual power has also become the focus of attention. In reality, completing an estimate of the state of charge of an electric vehicle's battery remains a challenging task...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/387G01R31/367G01R31/392G06F30/27G06N3/04G06N3/08G06F119/08
CPCG01R31/387G01R31/367G01R31/392G06F30/27G06N3/08G06F2119/08G06N3/045Y02T10/70
Inventor 李俊红蒋泽宇顾菊平宗天成李磊褚云琨芮佳丽李政张泓睿
Owner NANTONG UNIVERSITY
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