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Lithium ion battery SOC estimation method based on 3DCNN

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: 2022-02-18
NANTONG UNIVERSITY
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  • 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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  • Lithium ion battery SOC estimation method based on 3DCNN
  • Lithium ion battery SOC estimation method based on 3DCNN
  • Lithium ion battery SOC estimation method based on 3DCNN

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Embodiment 1

[0038] see Figure 1 to Figure 3, The technical solution provided by the present invention is a 3DCNN-based method for estimating the SOC of lithium-ion batteries. In this embodiment, the Panasonic lithium-ion battery NCR-18650B is used as the object of research. The nominal voltage is 3.7V and the battery capacity is 3400mAh. The battery is fully charged by constant current-constant voltage charging method. After standing for 1 hour, the battery is fully charged. The battery was discharged under constant current discharge, DST working condition, FUDS working condition and US06 working condition, and the experiment was repeated until the voltage dropped to the discharge cut-off voltage.

[0039] In order to better realize the purpose of the present invention, the present embodiment is based on the 3DCNN lithium-ion battery SOC estimation method, comprising the following steps:

[0040] Step 1) At different temperatures, fully charge the new lithium battery, and then repeatedl...

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Abstract

The invention provides a lithium ion battery SOC estimation method based on 3DCNN, and belongs to the technical field of lithium ion batteries. The problem that in an SOC estimation method, a 3DCNN convolutional neural network is difficult to be used for SOC estimation is solved. According to the technical scheme, the method comprises the following steps of: 1) repeatedly measuring data such as current through a discharge experiment; 2) preprocessing data and constructing a data set; and 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 invention are that the convolutional neural network structure can explore the relation of input data at the same time point between adjacent discharge periods, the convolution kernel in the time dimension not only can consider the number of cycles, but also can extract the feature relation between the cycles, and by means of the high adaptability of the convolution kernel. various parameters of the battery, such as battery residual capacity and battery residual life, can be predicted.

Description

technical field [0001] The invention relates to the technical field of lithium-ion batteries, in particular to a method for estimating the SOC of lithium-ion batteries based on 3DCNN. Background technique [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 have become the most important energy storage components due to their lifespan, specific energy and many other characteristics. Lithium batteries are also divided into lithium acid batteries, lithium manganate batteries, lithium manganese dioxide batteries and iron phosphate according to the different materials of positive and negative electrodes. lithium battery. The measurement accuracy of battery life and remaining power of electric vehicles has also become the focus of attention. In reality, it is still a challenging task to complete the estimation of el...

Claims

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Application Information

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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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