Device and method for controlling a battery

Claims

1. A device comprising: one or more processors; and one or more memory devices that store instructions executed by one or more processors, wherein, when one or more processors execute instructions, one or more processors are configured to: receiving from the scanner first information associated with one or more images generated by scanning the target battery, generating, based on the first information, second information associated with the three-dimensional image of the target battery, generating, based on the first information, a third information by identifying one or more characteristics of the target battery, generating, based on the second information and the third information, a fourth information in which information related to one or more characteristics of the target battery is marked on the three-dimensional image of the target battery, and transmitting the fourth information to an external device configured to perform a disassembly operation for the target battery.

2. The device according to paragraph 1, characterized in that one or more processors are configured to, when generating the second information: generating point cloud information for each of one or more images corresponding to the surface of the target battery, and generating a three-dimensional image of the target battery based on the point cloud information.

3. The device according to paragraph 1, characterized in that one or more processors are configured to, when generating third information: obtaining, by inputting first information into an artificial neural network that is trained to identify one or more characteristics of a battery from an image of the battery, predictive information regarding a target battery as output data of the artificial neural network, and generating third information based on predictive information.

4. The device according to paragraph 3, characterized in that one or more processors are configured to: generating, based on the first information, training information by labeling one or more characteristics of the target battery and learning, based on information for learning, artificial neural network in addition.

5. The device of claim 3, wherein the third information is information indicating one or more characteristics associated with the manufacturer of the target battery.

6. The device of claim 3, wherein the third information is information indicating, as one or more characteristics related to the field in which the target battery is used, any of an electric vehicle, a hybrid electric vehicle, a fuel cell electric vehicle, or an energy storage system.

7. The device of claim 3, wherein the third information is information indicating one or more characteristics associated with one or more components that make up the target battery.

8. The device of claim 3, wherein the third information is information indicating, as one or more characteristics associated with the packaging shape of the target battery, any of a cylindrical shape, a prismatic shape, or a bag-shaped shape.

9. The device of claim 3, wherein the third information is information indicating one or more characteristics associated with a defect in the target battery.

10. The device according to claim 1, characterized in that one or more processors are configured to compare the second information and the third information with each other and store the second information and the third information in one or more storage devices.

11. The device according to paragraph 1, characterized in that one or more processors are configured to, when generating the fourth information: determining one or more locations corresponding to one or more characteristics on a three-dimensional image of the second information, and generating fourth information by marking information associated with one or more characteristics in one or more locations.

12. The device according to item 1, characterized in that the external device contains one or more automated devices, and wherein one or more processors are configured to, when transmitting the fourth information: determining, on the basis of the third information, a target automated device configured to perform a disassembly operation associated with one or more characteristics, among one or more automated devices, and transmission of the fourth information to the target automated device.

13. The device of claim 12, wherein the third information is information indicating a first type among one or more types of battery products, and wherein one or more processors are configured to, when determining a target automated device, determine from among the one or more automated devices an automated device configured to perform a disassembly operation for a battery of the first type, as the target automated device.

14. The device of claim 12, wherein the third information is information indicating a first component among one or more components constituting the battery, and wherein one or more processors are configured to, when determining the target automated device, determine an automated device from among one or more automated devices configured to perform the disassembly operation for the first component, as the target automated device.

15. The device according to claim 1, characterized in that the third information is information indicating a characteristic associated with the battery fault condition, and wherein one or more processors are configured to: determining, based on the third information, whether the fault state of the target battery corresponds to a predetermined first reference state, and generating, in response to determining that the fault condition corresponds to the first reference condition, fifth information indicating not to move the target battery to the automated device, and transmitting the fifth information to the external device.

16. A method performed by a device comprising one or more processors and one or more memory devices storing instructions executable by the one or more processors, the method including, by means of the one or more processors: receiving from the scanner first information associated with one or more images generated by scanning the target battery; generating, based on the first information, second information associated with a three-dimensional image of the target battery; generating, based on the first information, third information by identifying one or more characteristics of the target battery; generating, based on the second information and the third information, a fourth information in which information related to one or more characteristics of the target battery is marked on the three-dimensional image of the target battery; and transmitting the fourth information to an external device configured to perform a disassembly operation for the target battery.

17. The method according to claim 16, characterized in that generating the second information includes, by means of one or more processors: generating point cloud information for each of one or more images corresponding to the surface of the target battery, and generating a three-dimensional image of the target battery based on the point cloud information.

18. The method according to claim 16, characterized in that generating the third information includes, by means of one or more processors: obtaining, by inputting first information into an artificial neural network that is trained to identify one or more characteristics of a battery from an image of the battery, predictive information regarding a target battery as output data of the artificial neural network, and generating third information based on predictive information.

19. The method of claim 18, wherein the third information is information indicating one or more characteristics associated with the manufacturer of the target battery.

20. The method of claim 18, wherein the third information is information indicating one or more characteristics associated with one or more components making up the target battery.