【Technical Field】 5
The present invention relates to a system and method for predicting a vent occurrence time of a battery cell.
This application claims the benefit of priority based on Korean Patent Application No. 10-2021-0086372, filed on July 1, 2021, and the entire contents of the Korean patent application are incorporated herein by reference. 10
【Background Art】
Recently, rechargeable and dischargeable secondary batteries have been widely used as energy sources of wireless mobile devices. In addition, secondary batteries have attracted attention as energy sources of electric vehicles, hybrid electric vehicles, etc. that have been introduced as a solution to air pollution due to existing gasoline vehicles and diesel vehicles 15 using fossil fuels. Therefore, the types of applications using a secondary battery are diversifying due to the advantages of the secondary battery, and it is expected that secondary batteries will be applied to more fields and products in the future than now.
Secondary batteries may be classified into a lithium ion battery, a lithium ion polymer battery, a lithium polymer battery, etc. according to electrodes and a composition of an 20
3
electrolyte, and among these batteries, the use of the lithium ion polymer battery, which is less prone to leakage of the electrolyte and easy to manufacture, is increasing. In general, secondary batteries are classified into a cylindrical or prismatic battery in which an electrode assembly is included in a cylindrical or rectangular metal can according to a shape of a battery case, and a pouch type battery in which an electrode assembly is included in a pouch-type case 5 of an aluminum laminate sheet. An electrode assembly included into a battery case is a chargeable and dischargeable power generating device that includes a positive electrode, a negative electrode, and a separator between the positive electrode and the negative electrode. Electrode assemblies are classified into a jelly-roll type formed by interposing a separator between a positive electrode and a negative electrode, which are long sheet types coated with 10 an active material, and winding a resultant structure, and a stack type in which positive electrodes and negative electrodes each having a certain size are sequentially stacked while a separator is interposed therebetween.
FIG. 1 is a schematic diagram illustrating a form of a general pouch type battery cell.
Referring to FIG. 1, the pouch type battery cell has a structure in which an electrode 15 assembly 20 is accommodated in a pouch type battery case 10, electrode leads 30 are protruding from opposite ends of the battery case 10, and a sealing portion 11a is formed around an outer periphery of the battery case 10. In this case, the sealing portion 11a and a gas pocket portion 11b, which is an empty space between the sealing portion 11a and an accommodation space, are formed in a terrace portion 11, which is a space between the space in which the electrode 20 assembly is accommodated and an end of the battery case. The gas pocket portion 11b is a space in which gas generated in a battery due to various causes gathers.
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Various high-temperature storage experiments are performed on a battery cell as described above according to the needs of customers. The experiments are performed to determine the safety and durability of the battery cell under severe conditions. In this case, the durability and performance of the battery cell can be estimated by predicting a vent occurrence time of the battery cell through a high-temperature storage experiment. 5
Specifically, when the amount of gas generated in the battery cell increases for various causes such as the high-temperature environment experiment, a width of the sealing portion 11a gradually decreases and eventually bursts, thus causing discharging of the gas, i.e., a venting phenomenon.
In the related art, the vent occurrence time of the sealing portion 11a is predicted by 10 directly measuring the width of the sealing portion 11a with the naked eye and checking for the presence of a vent. Thus, it takes a lot of time to perform the process, and the experiment should be performed again when a vent occurred at a point in time when measurement was not performed.
[Related Art Literature] 15
[Patent Document]
Korean Registered Patent Publication No. 10-2125238
【Disclosure】
【Technical Problem】
To address the above problem, the present invention is directed to providing a system 20 for predicting a vent occurrence time of a battery cell, the system being capable of automatically
5
predicting a vent occurrence time of a vent will occur and improving the accuracy of prediction.
【Technical Solution】
A system according to the present invention for predicting a vent occurrence time of a battery cell, which includes a terrace portion having a sealing portion and formed on at least one side of a pouch type battery case and an electrode lead protruding from an end of the terrace 5 portion, includes a storage unit configured to collect data about vent occurrence times according to widths of a remaining sealing portion, a measurement unit configured to periodically measure a width of a remaining sealing portion of a battery cell to be measured, and a determination unit configured to predict a vent occurrence time of the battery cell to be measured by comparing a measured width of the remaining sealing portion with the collected data. 10
The measurement unit may include a camera configured to capture an image or video of the terrace portion, and a calculator configured to calculate a width of the remaining sealing portion in the captured image or video.
The measurement unit measures a width of the remaining sealing portion by reducing a measurement time interval as a vent occurrence time is imminent. 15
As a specific example, the collecting of the data and the measuring of the width of the remaining sealing portion may be performed at a high temperature of 60 °C or higher.
As a specific example, the determination unit may predict a vent occurrence time of the battery cell to be measured through machine learning or deep learning.
Specifically, the determination unit may derive a correlation between a width of the 20 remaining sealing portion and a corresponding vent occurrence time from the data.
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Also, the determination unit may predict a vent occurrence time of the battery cell according to a measured width of the remaining sealing portion for each measurement time interval of a width of the remaining sealing portion on the basis of the correlation.
As another example, the system according to the present invention for predicting a vent occurrence time of a battery cell may further include a learning unit configured to learn a result 5 of predicting a vent occurrence time.
As a specific example, the learning unit may configure training data for predicting a vent occurrence time of the battery cell, and the determination unit may newly derive a correlation between a width of the remaining sealing portion and a corresponding vent occurrence time from the training data, and predict a vent occurrence time of the battery cell 10 according to a measured width of the remaining sealing portion from the correlation.
Specifically, the learning unit may configure the training data by verifying the validity of the data by comparing the predicted vent occurrence time and an actual vent occurrence time and updating the data collected in the storage unit with a result of verifying the validity of the data. 15
In addition, the present invention provides a method of predicting a vent occurrence time of a battery cell using a system for predicting a vent occurrence time of a battery cell.
A method according to the present invention of predicting a vent occurrence time of a battery cell includes collecting data about vent occurrence times according to widths of a remaining sealing portion, periodically measuring a width of a remaining sealing portion of a 20 battery cell to be measured, and predicting a vent occurrence time of the battery cell to be measured by comparing a measured width of the remaining sealing portion with the collected
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data.
As a specific example, the periodically measuring of the width of the remaining sealing portion may include capturing an image or video of a terrace portion by the camera and calculating the width of the remaining sealing portion in the captured image or video.
As a specific example, the predicting of the vent occurrence time of the battery cell to 5 be measured may include deriving a correlation between a width of a remaining sealing portion and a corresponding vent occurrence time, and periodically predicting a vent occurrence time of the battery cell to be measured according to a measured width of the remaining sealing portion on the basis of the correlation.
As a specific example, the method according the present invention may further include 10 learning a result of predicting a vent occurrence time.
As a specific example, the learning of the result of predicting the vent occurrence time may include verifying the validity of the data by comparing the predicted vent occurrence time with an actual vent occurrence time, and configuring training data by updating the data with a result of verifying the validity of the data. 15
The predicting of the vent occurrence time of the battery cell to be measured may include newly deriving a correlation between a width of a remaining sealing portion and a corresponding vent occurrence time from the training data, and predicting a vent occurrence time of the battery cell according to a measured width of the remaining sealing portion from the correlation. 20
【Advantageous Effects】
According to the present invention, a vent occurrence time is predicted on the basis of
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machine learning, thereby automatically predicting the vent occurrence time of the battery cell and improving the accuracy of prediction.
【Brief Description of the Drawings】
FIG. 1 is a schematic diagram illustrating a form of a general pouch type battery cell.
FIG. 2 is a block diagram illustrating a configuration of a system for predicting a vent 5 occurrence time of a battery cell according to an embodiment of the present invention.
FIG. 3 is a schematic diagram illustrating a process of measuring a width of a remaining sealing portion.
FIG. 4 is a photograph showing an image captured by a camera.
FIG. 5 is a block diagram illustrating a configuration of a system for predicting a vent 10 occurrence time of a battery cell according to another embodiment of the present invention.
FIG. 6 is a flowchart of a process of learning a result of prediction.
FIG. 7 is a schematic diagram illustrating a learning method based on deep learning.

[Claim 1]
A system for predicting a vent occurrence time of a battery cell, which includes a terrace portion having a sealing portion and provided on at least one side of a pouch type battery case and an electrode lead protruding from an end of the terrace portion, the system comprising: 5
a storage unit configured to collect data about vent occurrence times according to widths of a remaining sealing portion;
a measurement unit configured to periodically measure a width of the remaining sealing portion of the battery cell to be measured; and
a determination unit configured to predict a vent occurrence time of the battery cell to 10 be measured by comparing the measured width of the remaining sealing portion with the collected data.
[Claim 2]
The system of claim 1, wherein the measurement unit comprises:
a camera configured to capture an image or video of the terrace portion; and 15
a calculator configured to calculate a width of the remaining sealing portion in the captured image or video.
[Claim 3]
The system of claim 1, wherein the measurement unit is configured to measure a width of the remaining sealing portion by reducing a measurement time interval as the vent occurrence 20 time is imminent.
[Claim 4]
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The method of claim 1, wherein the collecting of the data and the measuring of the width of the remaining sealing portion are to be performed at a high temperature of 60 °C or higher.
[Claim 5]
The system of claim 1, wherein the determination unit is configured to predict the vent 5 occurrence time of the battery cell to be measured through machine learning or deep learning.
[Claim 6]
The system of claim 5, wherein the determination unit is configured to derive a correlation between a width of the remaining sealing portion and a corresponding vent occurrence time from the data. 10
[Claim 7]
The system of claim 6, wherein the determination unit is configured to predict the vent occurrence time of the battery cell according to a measured width of the remaining sealing portion for each measurement time interval of a width of the remaining sealing portion on a basis of the correlation. 15
[Claim 8]
The system of claim 1, further comprising a learning unit configured to learn a result of predicting the vent occurrence time.
[Claim 9]
The system of claim 8, wherein the learning unit is to configure training data for 20 predicting the vent occurrence time, and
the determination unit is configured to newly derive a correlation between a width of
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the remaining sealing portion and a corresponding vent occurrence time from the training data, and is configured to predict the vent occurrence time of the battery cell according to a measured width of the remaining sealing portion from the correlation.
[Claim 10]
The system of claim 8, wherein the learning unit is to configure the training data by 5 verifying validity of the data by comparing the predicted vent occurrence time and an actual vent occurrence time and updating the data collected in the storage unit with a result of verifying the validity of the data.
[Claim 11]
A method of predicting the vent occurrence time of the battery cell using the system of 10 claim 1, the method comprising:
collecting the data about vent occurrence times according to the widths of the remaining sealing portion;
periodically measuring the width of the remaining sealing portion of the battery cell to be measured; and 15
predicting the vent occurrence time of the battery cell to be measured by comparing the measured width of the remaining sealing portion with the collected data.
[Claim 12]
The method of claim 11, wherein the periodically measuring of the width of the remaining sealing portion comprises capturing an image or video of the terrace portion by a 20 camera, and calculating a width of the remaining sealing portion in the captured image or video.
[Claim 13]
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The method of claim 11, wherein the predicting of the vent occurrence time of the battery cell to be measured comprises deriving a correlation between a width of the remaining sealing portion and a corresponding vent occurrence time, and periodically predicting the vent occurrence time of the battery cell to be measured according to the measured width of the remaining sealing portion on a basis of the correlation. 5
[Claim 14]
The method of claim 11, further comprising learning a result of predicting the vent occurrence time.
[Claim 15]
The method of claim 14, wherein the learning of the result of predicting the vent 10 occurrence time comprises verifying validity of the data by comparing the predicted vent occurrence time with an actual vent occurrence time, and configuring training data by updating the data with a result of verifying the validity of the data.
[Claim 16]
The method of claim 15, wherein the predicting of the vent occurrence time of the 15 battery cell to be measured comprises newly deriving a correlation between a width of a remaining sealing portion and a corresponding vent occurrence time from the training data, and predicting the vent occurrence time of the battery cell to be measured according to a measured width of the remaining sealing portion from the correlation.