Apparatus and method for managing battery

Abstract

This disclosure discloses an apparatus comprising one or more processors, wherein the one or more processors are configured to: receive from a scanner first information relating to one or more images generated by scanning a target battery; generate second information relating to a three-dimensional image of the target battery based on the first information; generate third information based on the first information by identifying one or more features of the target battery; generate fourth information based on the second and third information, wherein information relating to one or more features of the target battery is marked on the three-dimensional image of the target battery; and transmit the fourth information to an external device configured to perform a disassembly operation on the target battery.

Description

Technical Field

[0001] This disclosure relates to battery management technology.

[0002] This research was conducted by the Ministry of Environment of South Korea. This research result was conducted by the Environment Ministry of Energy (MOE) and the Korea Environment Industry & Technology Institute (KEITI) under the "Technology Development Project to Improve the Cyclic Usability of Lithium-ion Batteries". [Project Title: Demonstration of lithium pre-extraction battery recycling technology: dismantling automation and resource recovery, Project number: 00339705] Prior Technology

[0003] Currently, electric vehicles use lithium-ion batteries. These batteries consist of four components: a positive electrode material, a negative electrode material, an electrolyte, and a separator. Depending on the material used for the positive electrode, lithium-ion batteries can be categorized into various types. These include nickel-based ternary systems (e.g., nickel-cobalt-manganese (NCM) and nickel-cobalt-aluminum (NCA) batteries), cobalt-based batteries (e.g., lithium cobalt oxide (LCO)) and manganese-based batteries (e.g., lithium manganese oxide (LMO)), as well as lithium iron phosphate (LFP) batteries, which utilize iron instead of cobalt. Future development is expected to utilize various battery types, including solid-state batteries, to reduce the fire risks associated with liquid electrolytes.

[0004] Secondary batteries account for 30% of the total lifecycle carbon footprint of electric vehicle production, with the carbon footprint of raw materials, including metals such as nickel, cobalt, and manganese, contributing significantly to this proportion. Greenhouse gases such as carbon dioxide are mainly emitted during the mining and refining of secondary battery raw materials, while the reuse and recycling of secondary batteries can significantly reduce greenhouse gas emissions, playing a crucial role in achieving a carbon-neutral society transition by 2050.

[0005] As the electric vehicle market grows, the scale of discarded batteries after several years of use is constantly expanding, stimulating the booming battery recycling industry for the reuse and recycling of these batteries. Battery recycling not only recovers and reuses valuable metals (nickel, cobalt, manganese, copper, and lithium) necessary for battery production, thus bringing resource recovery opportunities and economic benefits, but also helps reduce the environmental pollution associated with the extraction of these metals. Summary of the Invention

[0006] At least one embodiment of this disclosure can provide a technique for effectively disassembling batteries (e.g., battery packs, battery modules, or battery cells) for battery recycling.

[0007] At least one embodiment of this disclosure can provide a technique for automating the battery removal process based on three-dimensional imaging of the battery.

[0008] At least one embodiment of this disclosure can provide a technique for determining whether a battery is defective so that the battery can be removed without disassembling it.

[0009] The technical problems to be solved by this disclosure are not limited to those described above. Those skilled in the art will clearly understand other technical problems not mentioned herein based on the description in the specification.

[0010] According to one embodiment of this disclosure, an apparatus is provided, comprising: one or more processors; and one or more memories storing instructions executed by the one or more processors, wherein, when the one or more processors execute the instructions, the one or more processors are configured to: receive from a scanner first information relating to one or more images generated by scanning a target battery; generate second information relating to a three-dimensional image of the target battery based on the first information; generate third information based on the first information by identifying one or more features of the target battery; generate fourth information based on the second and third information, wherein information relating to one or more features of the target battery is marked on the three-dimensional image of the target battery; and transmit the fourth information to an external device configured to perform a disassembly operation on the target battery.

[0011] In one embodiment, one or more processors are configured to, when generating the second information, generate point cloud information corresponding to the surface of the target battery for each of the one or more images, and generate a three-dimensional image of the target battery based on the point cloud information.

[0012] In one embodiment, one or more processors are configured to, when generating third information: obtain predictive information about the target battery as the output of the artificial neural network by inputting first information into an artificial neural network trained to identify one or more features of the battery from images of the battery, and generate third information based on the predictive information.

[0013] In one embodiment, one or more processors are configured to: generate training information by labeling one or more features of the target battery based on first information, and further train the artificial neural network based on the training information.

[0014] In one embodiment, the third information is information indicating one or more characteristics related to the manufacturer of the target battery.

[0015] In one embodiment, the third information is information indicating one or more characteristics of any of the electric vehicle, hybrid vehicle, fuel cell electric vehicle, or energy storage system in relation to the field in which the target battery is used.

[0016] In one embodiment, the third information is information indicating one or more characteristics related to one or more components constituting the target battery.

[0017] In one embodiment, the third information is information indicating one or more features of cylindrical, prismatic, or pouch shape as related to the packaging shape of the target battery.

[0018] In one embodiment, the third information is information indicating one or more features related to a defect in the target battery.

[0019] In one embodiment, one or more processors are configured to associate second information and third information with each other and store the second information and third information in one or more memories.

[0020] In one embodiment, one or more processors are configured to, when generating fourth information,: determine one or more locations on a three-dimensional image of the second information that correspond to one or more features, and generate fourth information by marking information associated with one or more features at one or more locations.

[0021] In one embodiment, the external device includes one or more automation devices, and wherein one or more processors are configured to, when transmitting fourth information,: determine, based on third information, a target automation device from the one or more automation devices that is configured to perform a disassembly operation associated with one or more features, and transmit the fourth information to the target automation device.

[0022] In one embodiment, the third information is information indicating a first type of one or more product types of the battery, and wherein one or more processors are configured to, when determining a target automation device, identify from one or more automation devices an automation device configured to perform a disassembly operation on the first type of battery as the target automation device.

[0023] In one embodiment, the third information is information indicating a first element among one or more elements constituting the battery, and wherein one or more processors are configured to, when determining a target automation device, identify from one or more automation devices an automation device configured to perform a disassembly operation on the first element as the target automation device.

[0024] In one embodiment, the third information is information indicating a characteristic related to the defect state of the battery, and wherein one or more processors are configured to: determine, based on the third information, whether the defect state of the target battery corresponds to a predetermined first reference state, and in response to the determination that the defect state corresponds to the first reference state, generate fifth information indicating that the target battery should not be moved to an automation device and transmit the fifth information to an external device.

[0025] A method according to an embodiment of the present disclosure, performed by an apparatus including one or more processors and one or more memories storing instructions to be executed by the one or more processors, includes the one or more processors: receiving first information from a scanner relating to one or more images generated by scanning a target battery; generating second information relating to a three-dimensional image of the target battery based on the first information; generating third information based on the first information by identifying one or more features of the target battery; generating fourth information based on the second and third information, wherein information relating to one or more features 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 on the target battery.

[0026] In one embodiment, the generation of the second information includes one or more processors generating point cloud information corresponding to the surface of the target battery for each of the one or more images, and generating a three-dimensional image of the target battery based on the point cloud information.

[0027] In one embodiment, the generation of third information includes, through one or more processors: obtaining predictive information about the target battery as the output of the artificial neural network by inputting first information into an artificial neural network trained to identify one or more features of the battery from images of the battery, and generating third information based on the predictive information.

[0028] In one embodiment, the third information is information indicating one or more characteristics related to the manufacturer of the target battery.

[0029] In one embodiment, the third information is information indicating one or more characteristics related to one or more components constituting the target battery.

[0030] One embodiment of this disclosure provides a technique for effectively disassembling batteries (e.g., battery packs, battery modules, or battery cells) for battery recycling.

[0031] One embodiment of this disclosure provides a technique for automating the battery removal process based on three-dimensional imagery of the battery.

[0032] One embodiment of this disclosure provides a technique for determining whether a battery is defective so that the battery can be removed without disassembling it.

[0033] The effects resulting from the technical ideas disclosed herein are not limited to those described above, and other effects not mentioned herein can be clearly understood by those skilled in the art from the description in the specification. Simple Explanation of the Diagram

[0034] [Figure 1] illustrates the environment in which an apparatus according to one embodiment of the present disclosure is applicable.

[0035] [Figure 2] illustrates an example of the implementation of an apparatus according to one embodiment of the present disclosure.

[0036] [Figure 3] illustrates a flowchart representing a method according to one embodiment of the present disclosure.

[0037] [Figure 4] illustrates an example of an operating environment for performing battery removal operations, which may be used as a reference in various embodiments of this disclosure.

[0038] [Figure 5] illustrates another example of the operating environment for performing battery removal operations, which may be referenced in various embodiments of this disclosure.

[0039] [Figure 6] illustrates a flowchart of an example of a method for transmitting fourth information from a device to an external device, according to various embodiments of the present disclosure.

[0040] [Figure 7] is a flowchart illustrating an example of a method for transmitting fourth information from a device to a first automated device, according to various embodiments of the present disclosure.

[0041] [Figure 8] illustrates a flowchart of an example of a method for transmitting fourth information from a device to a second automated device, according to various embodiments of the present disclosure.

[0042] [Figure 9] illustrates a flowchart of an example of a method for transmitting fourth information from a device to a third automated device, according to various embodiments of the present disclosure.

[0043] [Figure 10] illustrates a flowchart representing a method according to one embodiment of the present disclosure. Implementation

[0044] The various embodiments described in this disclosure are illustrative of the technical ideas presented herein and are not intended to limit the disclosure to any particular embodiment. The technical ideas of this disclosure include various modifications, equivalents, and alternatives to the various embodiments of this disclosure, and include embodiments that are optionally combined from all or part of the various embodiments. Furthermore, the scope of the technical ideas of this disclosure is not limited to the various embodiments set forth below or their specific descriptions.

[0045] The terminology used in this disclosure, including all technical and scientific terms, is intended to have the meaning commonly understood by a person of ordinary skill in the field to which this disclosure pertains, unless otherwise defined.

[0046] Expressions such as "comprising," "including," "may include," "provided," "available," "having," and "may have" imply the presence of features of the subject (e.g., functionality, operation, or elements) and do not exclude the presence of other additional features. In other words, these expressions should be understood as open-ended terms, implying the possibility of including other embodiments.

[0047] Unless otherwise stated, the singular forms used in this disclosure may include the meaning of the plural, and this also applies to the singular expressions stated in the claims.

[0048] The terms "first," "second," etc., used in this disclosure are used to distinguish one object from another when referring to multiple objects of the same type, unless the context otherwise indicates, and are not intended to limit the order or importance of the objects.

[0049] In this disclosure, expressions such as “A, B and C”, “A, B or C”, “at least one of A, B and C” or “at least one of A, B or C” can refer to any one of the listed items or any possible combination of the listed items. For example, “at least one of A or B” can refer to (1) at least one A, (2) at least one B, or (3) at least one A and at least one B.

[0050] The expressions used in this disclosure are "based on" one or more factors that influence the determination, judgment, or operation. These expressions are described in phrases or sentences that include the relevant expressions, and the expressions do not exclude additional factors that influence the determination, judgment, or operation.

[0051] In this disclosure, the terms "connected" or "coupled" to one element (e.g., the first element) and another element (e.g., the second element) can refer to the direct connection or coupling of one element to the other element, or it can refer to the connection or coupling through another intermediate element (e.g., the third element).

[0052] In this disclosure, the expression "configured as" has meanings such as "set as," "capable of," "changed to," "cause," "do," and "able to," depending on the context. This expression is not limited to the meaning of "specifically designed for hardware." For example, "a processor configured to perform a specific operation" can refer to a general-purpose processor that performs a specific operation by executing software, or a special-purpose computer that performs a specific operation through procedural configuration.

[0053] The embodiments described herein will be explained below with reference to the accompanying drawings. In the accompanying drawings and descriptions, identical or substantially equivalent elements may be given the same element symbols. Furthermore, in the following descriptions of the embodiments, repeated descriptions of identical or corresponding elements may be omitted, but this does not mean that these elements are not included in the embodiments.

[0054] Figure 1 illustrates an environment 100 to which the apparatus 110 according to an embodiment of this disclosure is applicable. Environment 100 may include the apparatus 110, the scanner 120, and / or an external device 130. Figure 1 only illustrates an embodiment for achieving the purposes of this disclosure, and additional elements may be added as needed. For example, environment 100 may also additionally include a user terminal used by a user who is the administrator of the apparatus 110, a transfer device configured to perform a battery transfer operation, and an operator terminal used by an operator to perform a battery scanning operation.

[0055] In this specification, the term "battery" may refer to any of the following: battery pack, battery module, or battery cell; it may also refer to an assembly of one or more battery cells, or an assembly of one or more battery modules interconnected by various components, but is not limited thereto, encompassing batteries of various forms and types as understood by those skilled in the art. Therefore, the term "battery" as used in this specification may not be limited to a specific product, form, or cell, but can be interpreted as encompassing the concept of batteries of various structures and configurations. For example, batteries may include batteries with various structures and configurations used in various application areas, including not only electric vehicles, but also hybrid electric vehicles, fuel cell electric vehicles, and energy storage systems. Furthermore, "battery" in this specification may also refer to used batteries that require disposal or recycling.

[0056] Device 110 may correspond to a server device configured to manage operations such as battery storage, battery transportation, and / or battery removal. Device 110 may execute methods according to this disclosure to generate and store various battery information, transmit the generated information to external device 130, and induce battery removal operations in external device 130.

[0057] Device 110 can be implemented as one or more computing devices. For example, all the functions of device 110 can be implemented in a single computing device. As another example, a first function of device 110 can be implemented in a first computing device, while its second function can be implemented in a second computing device. Specifically, if device 110 corresponds to a server device for managing a battery, a first function for generating battery-related information can be implemented in the first computing device, and a second function distinct from the first function can be implemented in the second computing device. Although these first and second computing devices are physically separate and real, the first and second computing devices can be referred to as device 110 as an abstract concept incorporating the first and second computing devices.

[0058] The computing devices described above may include desktop computers, laptop computers, application servers, proxy servers, cloud servers, etc., but are not limited to these, and may include any device with computing capabilities.

[0059] Scanner 120 can acquire initial information by scanning a battery. For example, scanner 120 may include an optical system, a signal processing module, and / or a data output interface, which can work together to scan an object to be photographed. The optical system may include a light source configured to emit light toward the target object and a light receiver configured to receive light reflected from the target object. For example, the light source may correspond to a laser or light-emitting diode (LED) light source, and the light receiver may correspond to a photodiode or complementary metal-oxide-semiconductor (CMOS) image sensor. The signal processing module may be configured to convert the analog signal received by the light receiver into a digital signal to generate an image of the target object. The output interface may be configured to transmit information read by the signal processing module to an external device 130. Transmission may be performed using Universal Serial Bus (USB), Bluetooth, or a wireless communication protocol. However, it should be understood that the structure of scanner 120 is not limited to this, and components may be added, removed, or modified to scan the target object.

[0060] The first information may include one or more images of the battery as the target of the image capture. Each of the one or more images may correspond to an image obtained by capturing the target battery from different directions. For example, the scanner 120 may be moved to scan the target battery in different directions, and the scanner 120 may scan the target battery at fixed time intervals to generate one or more images. As another example, the target battery may be rotated over the scanning area scanned by the scanner 120, and the scanner 120 may scan the rotating target battery at regular time intervals to generate one or more images.

[0061] Device 110 can receive first information from scanner 120 via a network. Device 110 can generate second information representing the target battery in a three-dimensional image based on the first information. In one embodiment, the second information may correspond to reverse engineering information generated by reverse engineering the geometry of the target battery, wherein the geometry of the target battery is reverse engineered via one or more images generated by scanning the target battery. For example, device 110 can generate second information to represent the shape of the target battery in a three-dimensional image associated with a three-dimensional spatial coordinate plane implemented in a computing device using computer-aided design (CAD) software.

[0062] External device 130 may correspond to a device configured to perform a disassembly operation to remove the battery. For example, external device 130 may include an automated device configured to perform a battery disassembly operation. For instance, the automated device may use a robotic arm and various tools mounted on the robotic arm (e.g., bolt driver, laser cutter, pliers, saw, etc.) to remove the battery. Laser cutting tools, bolt handling tools, cable cutting tools, cover handling tools, waste disposal tools, cable handling tools, etc., may be located around the automated device, and the automated device may change the tools connected to the robotic arm as needed. The automated device may be configured to transfer components removed from the battery pack to another location. The automated device may transfer components removed from the battery pack (e.g., top cover, bottom cover, wires, connectors, temperature sensor module, battery management system (BMS) module, battery module, battery cell, bolts, nuts, etc.) to a storage box (e.g., top cover storage box, bottom cover storage box, battery box, disassembly box, etc.).

[0063] In one embodiment, the automated device can acquire battery-related information from device 110 and perform a disassembly operation on the target battery based on this information. For example, the automated device may include a control device configured to analyze a three-dimensional image of the target battery obtained from device 110, thereby controlling the automated device. The robotic arm of the automated device may be designed to be driven by the control device, which configures the disassembly path of the target battery components based on the three-dimensional image of the target battery and performs the disassembly operation accordingly. For example, the control device may generate a virtual three-dimensional model of the target battery based on the three-dimensional image, identify the position and connection status of each component of the battery associated with the three-dimensional model, and calculate the optimal disassembly path for disassembling the connections of each component according to a pre-configured algorithm. During the disassembly operation, the operational status of each stage of the disassembly operation can be transmitted from the automated device to device 110 via a network, allowing the user of device 110 to monitor the disassembly operation of the target battery in real time.

[0064] In one embodiment, external device 130 may include a plurality of automated devices. Each of the plurality of automated devices may perform different operations. In one embodiment, each of the plurality of automated devices may correspond to a device configured to perform a disassembly operation for a battery from a particular manufacturer. For example, a battery pack from a first manufacturer may include battery cells arranged in a specific arrangement used by the first manufacturer, and the first automated device may include a specially designed automated tool for removing the battery cells arranged in that specific arrangement from the battery pack. For example, a battery pack from a second manufacturer may include components used by the second manufacturer, such as specific covers, housings, coolants, seals, cables, connectors, etc., and the second automated device may include a specially designed automated tool for removing components from the battery pack. For example, a battery pack from a third manufacturer may include a specific battery cell arrangement or specific components used by the third manufacturer, and the third automated device may include automated tools designed to be optimized for disassembling the specific battery cell arrangement or specific components. In various embodiments of this disclosure, device 110 may transmit a three-dimensional image generated for a target battery to the automated device, and the automated device may perform a disassembly operation for the target battery using a battery disassembly algorithm optimized for disassembly of a battery from a particular manufacturer.

[0065] In one embodiment, each automated device may correspond to a device configured to perform a disassembly operation on a battery having a specific product name. For example, a battery pack having a first product name may include a first element, and the first automated device may include a specially designed automated tool for disassembling the first element from the battery pack. For example, a battery pack having a second product name may include a second element, and the second automated device may include a specially designed automated tool for disassembling the second element from the battery pack. For example, a battery pack having a third product name may include a third element, and the third automated device may include a specially designed automated tool for disassembling the third element from the battery pack. In various embodiments of this disclosure, device 110 may transmit a three-dimensional image generated for the target battery to the automated device, and the automated device may perform the disassembly operation on the target battery using a battery disassembly algorithm optimized for disassembly of batteries having a specific product name.

[0066] In one embodiment, each automated device may correspond to a device configured to perform a disassembly operation on a specific component contained in the battery. For example, a first automated device may be designed to disassemble a first component contained in the battery, a second automated device may be designed to disassemble a second component contained in the battery, and a third automated device may be designed to disassemble a third component contained in the battery. In one embodiment, the first automated device may disassemble some components of the battery, and the second or third automated device may further disassemble a portion of the disassembled battery. In various embodiments of this disclosure, device 110 may transmit a three-dimensional image generated for the target battery to the automated device, and the automated device may perform the disassembly operation of the target battery using a battery disassembly algorithm optimized for the disassembly of specific components of the battery.

[0067] Therefore, in the various embodiments of this disclosure, device 110 can transmit information related to the target battery to an automation device, and the automation device can use a battery disassembly algorithm optimized for disassembling the target battery to perform the disassembly operation. Thus, automating the target battery disassembly operation can improve the speed and safety of the battery disassembly operation and increase the productivity of the battery recycling process.

[0068] According to various embodiments of this disclosure, multiple automated devices can be designed to perform distinct functions. For example, a first automated device can be optimized for cell separation of lithium-ion batteries, while a second automated device can be optimized for the recycling and sorting of electrode materials. In this way, automated devices with different functions can cooperate with each other, helping to improve the overall productivity and efficiency of the battery disassembly process.

[0069] According to various embodiments of this disclosure, each of the plurality of automated devices can independently perform a disassembly operation. Therefore, disassembly operations on multiple target batteries can be performed simultaneously and in parallel on each of the plurality of automated devices. For example, a first automated device can perform a disassembly operation on a first target battery, and a second automated device can simultaneously perform a disassembly operation on a second target battery. In other words, according to various embodiments of the present invention, by simultaneously performing disassembly operations on multiple batteries, the efficiency of disassembling multiple target batteries can be maximized, and the operation time can be shortened, thereby improving the productivity of battery disassembly operations.

[0070] In one embodiment, the external device 130 may further include various sensors for fire detection and a non-combustible firewall for effectively preventing the spread of fire. Sensors may include, for example, a carbon dioxide detection sensor, which can quickly detect a fire by real-time monitoring of changes in carbon dioxide concentration, which may occur if the battery is removed or damaged. For example, sensors may include exhaust gas detection sensors for detecting combustible gas exhaust from the lithium-ion battery. The external device 130 may even detect exhaust gas using exhaust gas detection sensors before a fire occurs, allowing for additional actions to prevent the fire from starting, such as stopping battery removal and activating a sprinkler system placed around the battery. Other sensors may include smoke detection sensors, temperature detection sensors, and flame detection sensors, each capable of providing early fire identification based on changes in particle concentration, sudden temperature increases, and the presence of a flame. Meanwhile, the non-combustible firewall may be constructed of amide fiber, glass fiber, and / or other flame-retardant materials, physically blocking the spread of fire in the event of a fire, thus preventing the fire from spreading to adjacent battery cells or surrounding structures. The external device 130 can use various sensors to detect whether a fire has occurred during the battery removal operation, and if a fire is detected, it can use a non-flammable firewall to prevent the fire from spreading to the surrounding environment or surrounding components.

[0071] Device 110, scanner 120, and / or external device 130 can communicate with each other via a network. The network can be implemented as various wired or wireless networks, such as local area network (LAN), wide area network (WAN), mobile radio communication network (MRCN), wireless broadband Internet (WiBro), etc.

[0072] Figure 2 illustrates an exemplary implementation of an apparatus 110 according to an embodiment of the present disclosure. Apparatus 110 may include one or more processors 210 and one or more memories 220. Apparatus 110 may also include communication circuitry 230. In embodiments, some elements may be removed from apparatus 110 or other elements (e.g., a display, input device, etc.) may be added to apparatus 110. Furthermore, additionally or alternatively, some elements may be integrated and implemented, or implemented as a single or multiple entities. In this disclosure, one or more processors 210 may be referred to as a single processor 210. The term "processor" 210 may refer to a group of one or more processors unless the context clearly indicates otherwise. Similarly, in this disclosure, one or more memories 220 may be referred to as a single memory 220. The term "memory" 220 may refer to a group of one or more memories 220 unless the context clearly indicates otherwise.

[0073] Processor 210 can perform calculations or data processing related to the control or communication of various elements of device 110. Specifically, processor 210 can drive software (or computer programs) received from another element to control at least one element of device 110 connected to processor 210. For example, processor 210 can load commands (e.g., instructions, codes, or code segments) or information into memory 220, process commands or information stored in memory 220, and store the result information in memory 220 according to the processing. Furthermore, processor 210 can be operatively connected to elements of device 110 to perform operations, such as various calculations, processing, generation, or manipulation related to the present disclosure.

[0074] Memory 220 can store various types of information. The information stored in memory 220 can be retrieved, processed, or used by at least one element of device 110, and may include software. The software may include one or more commands that, when loaded into memory 220, cause processor 210 to perform operations according to various embodiments of this disclosure. That is, processor 210 can execute one or more commands described above to perform operations according to various embodiments of this disclosure. Memory 220 may include, for example, transient or non-transient memory. In one embodiment, a program may correspond to the software stored in memory 220 and may include an operating system for controlling the resources of device 110, an application, middleware for providing various functions to the application so that the application can use various resources of device 110, etc.

[0075] The communication circuit 230 can establish a wired or wireless communication channel with another device and send or receive various information to or from that other device. In one embodiment, the communication circuit 230 may include at least one port for connecting to the other device via a wired cable to enable wired communication. In this case, the communication circuit 230 can communicate with the wired-connected other device through the at least one port. In one embodiment, the communication circuit 230 may include a cellular communication module and may be configured to connect to a cellular network (e.g., 3G, LTE, 5G, WiBro, or WiMAX). In one embodiment, the communication circuit 230 may include a near-field communication module to send or receive information to or from another device using near-field communication (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy (BLE), and UWB). In one embodiment, the communication circuit 230 may include a contactless communication module for contactless communication. Contactless communication may include, for example, at least one contactless proximity communication technology, such as near field communication (NFC), radio frequency identification (RFID), or magnetic secure transmission (MST). In addition to the various examples described above, device 110 may also employ a variety of other methods known in the art for communicating with another device, and the scope of this disclosure is not limited to the foregoing examples.

[0076] The processor 210, memory 220, and communication circuit 230 can be interconnected via bus, general-purpose input / output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI) to transmit and receive data or signals.

[0077] In one embodiment, device 110 may further include a display. The display may show various screens based on the control of processor 210. To display various interfaces applied thereon on the display, a web browser or dedicated application may be installed in device 110. The display may correspond to a configuration that enables interaction with the user and receive input from the user. The display may be implemented in the form of a touch sensor panel (TSP) capable of recognizing the touch or proximity of various external objects (such as fingers or styluses).

[0078] In one embodiment, device 110 may also include an input device (e.g., a mouse or keyboard). The input device can receive information that will be used in the components of device 110 from outside device 110.

[0079] The methods according to various embodiments of this disclosure will now be described in detail. Although the operations are shown in a specific order in the following figures, it should be noted that the operations do not necessarily have to be performed in the specific or sequential order shown, or that all the illustrated operations must be performed to obtain the desired result.

[0080] Furthermore, the operation of the method described below with reference to the accompanying drawings can be performed by a computing device. That is, the operation of the method can be implemented as one or more instructions executed by the processor 210 of the computing device. All operations included in this method can be performed by a single physical computing device, or the first operation of the method can be performed by a first computing device, and the second operation of the method can be performed by a second computing device.

[0081] In the following description, it will be assumed that the operation of the above method is performed by device 110. Furthermore, for ease of explanation, the entity performing the operations included in the method may be omitted. However, unless the context clearly indicates otherwise, it should be interpreted as the operation being performed by device 110.

[0082] Figure 3 illustrates a flowchart illustrating a method according to an embodiment of the present disclosure. This method may include a series of operations performed by the device 110 in conjunction with the scanner 120 and / or an external device 130.

[0083] In operation S310, processor 210 can receive first information from scanner 120 related to one or more images generated by scanning the target battery. Scanner 120 can scan the target battery to generate images about the target battery, and processor 210 can receive the images from scanner 120 via a network.

[0084] In one embodiment, scanner 120 can scan the target battery at fixed time intervals to generate one or more images. Each of the one or more images may correspond to an image obtained by photographing the target battery from a different direction. For example, with the target battery fixed, scanner 120 can move around the target battery and scan it at fixed time intervals to generate one or more images. Alternatively, with scanner 120 fixed, scanner 120 can scan the target battery on a scanning area at regular time intervals and rotate the battery on the scanning area to generate one or more images.

[0085] In one embodiment, the fixed time interval can be a predetermined interval. For example, the scanner 120 can be pre-configured to scan the target battery at intervals of 0.01 seconds. For example, when the scanner 120 scans the target battery for 1 second, it can scan at intervals of 0.01 seconds to generate 100 images within 1 second.

[0086] In one embodiment, the scanner 120 can be controlled to configure a fixed time interval to be less than a predetermined interval. For example, the processor 210 controls the scanner 120 to configure a fixed time interval of 0.005 seconds, which is less than a predetermined interval of 0.01 seconds. In this case, more images can be acquired within the same time period, and therefore, when generating a three-dimensional image of the target battery, as described below, the battery shape associated with the three-dimensional image may become more similar to the shape of the actual target battery.

[0087] In one embodiment, the scanner 120 can be controlled to configure fixed time intervals to be less than a predetermined interval. For example, the processor 210 controls the scanner 120 to configure fixed time intervals to a predetermined interval of 0.02 seconds, which is greater than a predetermined interval of 0.01 seconds. In this case, fewer images will be obtained within the same time period, and therefore, the number of images processed by the processor 210 is reduced when generating a three-dimensional image of the target battery, as described below, thereby reducing the time required to generate the three-dimensional image.

[0088] In one embodiment, a user can input user input into device 110 through the input device of device 110, and processor 210 can adjust a fixed time interval based on the user input.

[0089] In operation S320, processor 210 may generate second information related to the three-dimensional image of the target battery based on the first information. For example, the second information may include coordinate information of one or more points corresponding to the surface of the target battery associated with a three-dimensional spatial coordinate plane. For example, the second information may correspond to information representing the surface of the target battery in the form of mesh data, wherein the mesh data form is associated with a three-dimensional spatial coordinate plane.

[0090] In one embodiment, processor 210 may use each of one or more images to generate point cloud information corresponding to the surface of the target battery. Processor 210 may generate a three-dimensional image of the target battery based on the point cloud information. Processor 210 may generate the shape of the target battery relative to a three-dimensional spatial coordinate plane by connecting points on each image corresponding to the same location on the surface of the target battery, based on the point cloud information corresponding to each of the one or more images. For example, processor 210 may also process information to represent the shape of the target battery in the form of mesh data on the spatial coordinate plane. The mesh data may consist of polygonal faces and is generally composed of triangular units. When the shape of the battery is represented in the form of mesh data, processor 210 can reproduce the surface shape and contour of the target battery. However, it is not limited to this; the second information may correspond to information of various structures that represent the target battery as a three-dimensional image.

[0091] In operation S330, processor 210 may generate third information based on the first information by recognizing one or more features of the target battery. In one embodiment, processor 210 may input the first information into an artificial neural network trained to recognize one or more features of the battery from an image of the battery and obtain predictive information about the target battery as the output of the artificial neural network. Processor 210 may generate third information based on the predictive information.

[0092] For example, artificial neural networks can include classification models based on supervised learning. Artificial neural networks can be generated by learning from images of one or more batteries, and can be generated to identify one or more features of the battery from the images.

[0093] In training artificial neural networks, labels indicating categories and input images can be used as a pair of training data. Furthermore, evaluation data, which assesses the training of the artificial neural network, can be provided separately from the training data.

[0094] In one embodiment, an artificial neural network can be generated to identify the battery manufacturer by learning from a manufacturer's label and an image of the battery. For example, an image of a battery manufactured by manufacturer A ("image A") can be paired with the label "manufacturer A" as a first training data pair; an image of a battery manufactured by manufacturer B can be paired with the label "manufacturer B" as a second training data pair; and an image of a battery manufactured by manufacturer C can be paired with the label "manufacturer C" as a third training data pair. Therefore, when an image of a target battery is input, the artificial neural network can output information indicating the target battery manufacturer as predictive information. The third information may include information related to the manufacturer of the battery indicated by the predictive information.

[0095] In one embodiment, an artificial neural network can be generated to identify the product name of a battery by learning a label indicating the product name and an image of the battery. For example, an image of a battery with product name A can be paired with the label "product name A" as a first pair of training data; an image of a battery with product name B can be paired with the label "product name B" as a second pair of training data; and an image of a battery with product name C can be paired with the label "product name C" as a third pair of training data. Therefore, when an image of a target battery is input, the artificial neural network can output information indicating the product name of the target battery as predictive information. The third information may include information related to the product name of the battery indicated by the predictive information.

[0096] In one embodiment, an artificial neural network can be generated to identify the area where a battery is used by learning from labels indicating the area the battery is in and from images of the battery. For example, an image A of a battery used in the "electric vehicle" area can be paired with the label "electric vehicle" as a first training data pair; an image B of a battery used in the "hybrid vehicle" area can be paired with the label "hybrid" as a second training data pair; an image C of a battery used in the "fuel cell electric vehicle" area can be paired with the label "fuel cell electric vehicle" as a third training data pair; and an image D of a battery used in the "energy storage system" area can be paired with the label "energy storage system" as a fourth training data pair. Therefore, when an image of a target battery is input, the artificial neural network can output information indicating the area where the battery is used as predictive information. The third information may include information related to the area where the battery is used, as indicated by the predictive information.

[0097] In one embodiment, an artificial neural network can be generated by learning from labels indicating components and images of the battery to identify components contained within the battery. For example, components contained in the battery may include at least one of the following: a top cover, a bottom cover, wires, connectors, a temperature sensor module, a battery management system (BMS) module, a battery module, battery cells, bolts, nuts, etc. For example, an "image A" of a battery whose component is a wire can be paired with the label "wire" as a first pair of training data; an "image B" of a battery whose component is a battery module can be paired with the label "battery module" as a second pair of training data; and an "image C" of a battery whose component is a BMS module can be paired with the label "BMS module" as a third pair of training data. Therefore, when an image of a target battery is input, the artificial neural network can output information indicating the components of the target battery as predictive information. In one embodiment, the predictive information may further include information indicating the location of the components identified by the artificial neural network. The third information may include information related to the battery components indicated by the predictive information and / or the location of the battery components.

[0098] In one embodiment, an artificial neural network can be generated to identify the battery's packaging form by learning from markings indicating the packaging shape and images of the battery. For example, the packaging shape can include any of cylindrical, prismatic, or pouch-shaped. For instance, "Image A" of a battery with a cylindrical packaging shape and paired with the label "cylindrical" can be used as a first pair of training data; "Image B" of a battery with an angled packaging shape and paired with the label "angled" can be used as a second pair of training data; and "Image C" of a battery with a pouch-shaped packaging shape and paired with the label "pouch-shaped" can be used as a third pair of training data. Therefore, when an image of a target battery is input, the artificial neural network can output information indicating the packaging shape of the target battery as predictive information. The third information may include information related to the packaging shape of the battery indicated by the predictive information.

[0099] In one embodiment, an artificial neural network can be generated by learning from tags indicating failure-related information and images of the battery to identify failure-related information. For example, defect-related information can refer to the type of defect, such as a defect caused by impact, a defect caused by poor component connection, a defect caused by fire, or a defect caused by missing components. For instance, an image of a battery with defect A and a tag "defect A" can be used as a first pair of training data; an image of a battery with defect B and a tag "defect B" can be used as a second pair of training data; and an image of a battery with defect C and a tag "defect C" can be used as a third pair of training data. Therefore, when an image of a target battery is input, the artificial neural network can output information representing the target battery's defect as predictive information. The third information may include information related to the battery defect indicated by the predictive information.

[0100] In one embodiment, an artificial neural network can be generated to identify whether a battery is defective by learning markers indicating the presence or absence of defects and images of the battery. For example, an "image A" of a defective battery labeled "defective" can be used as a first pair of training data, and an "image B" of a non-defective battery labeled "no defect" can be used as a second pair of training data. Therefore, when an image of a target battery is input, the artificial neural network can output information indicating whether the target battery is defective as predictive information. Third information may include information related to the presence or absence of a defect in the battery indicated by the predictive information.

[0101] During training, the artificial neural network can infer patterns from fragments of multiple training data and classify corresponding images using appropriate labels when new images are input. To achieve such an artificial neural network, this disclosure references various known techniques.

[0102] In one embodiment, after training, the artificial neural network can be stored in memory 220, which can be implemented using various types of storage devices, including semiconductor memory devices, flash memory, or magnetic storage devices. In one embodiment, the artificial neural network can be implemented using a computing device such as a neuromorphic processor. A neuromorphic processor is a processor specifically designed to accelerate neural network computation at the hardware level and can be engineered to mimic the function of neurons and synapses in the human brain, thereby efficiently performing parallel computation. In one embodiment, the artificial neural network can be implemented in an optimized form using hardware accelerators (such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), and these hardware accelerators can significantly improve the training and inference speed of the neural network. According to embodiments of this disclosure, artificial neural networks can be stored and implemented using various types of storage media and computing devices, thereby improving the performance and efficiency of the neural network.

[0103] In one embodiment, training information can be generated by tagging features of the target battery to first information. The processor 210 can then additionally train the artificial neural network based on the predicted information.

[0104] In various embodiments of this disclosure, operations S320 and S330 may be executed in parallel by one or more processors 210, and according to another embodiment, the operations may be executed sequentially. The execution order of each operation may not be limited to a specific order. That is, operations S320 and S330 may be executed in parallel, or independently, taking into account the performance or capacity of processor 210.

[0105] In operation S340, processor 210 may generate fourth information based on the second and third information, wherein the fourth information marks information related to one or more features of the target battery on the three-dimensional image of the target battery. Here, marking specific information related to the three-dimensional image can indicate that the specific information is matched with a specific region or location on the three-dimensional image. For example, processor 210 may also use specific information to mark certain regions or specific locations of the three-dimensional image, use annotations to mark specific information, or generate arbitrary metadata that matches specific information with specific locations.

[0106] In one embodiment, if the third information indicates a feature related to the battery manufacturer, the processor 210 can generate the fourth information by tagging the 3D image with the battery manufacturer information. In one embodiment, if the third information indicates a feature related to the product name of the battery, the processor 210 can generate the fourth information by tagging the product name information of the battery on the 3D image.

[0107] In one embodiment, if the third information indicates features related to a battery element, the processor 210 can generate fourth information by marking the three-dimensional image with the battery element information. For example, the processor 210 can determine the position of the battery element on the three-dimensional image and mark the determined position with the corresponding element information. Therefore, the fourth information may include information indicating the battery element and information indicating the position of the battery element.

[0108] In one embodiment, if the third information indicates a feature related to a battery defect, the processor 210 can generate fourth information by marking the battery defect information on the three-dimensional image. For example, the processor 210 can determine the type and location of the battery defect on the three-dimensional image and mark the determined location with corresponding information indicating the defect type. Therefore, the fourth information may include information indicating the type (or form) of the battery defect and information indicating the location of the battery defect.

[0109] In operation S350, processor 210 may transmit fourth information to external device 130 that is performing a disassembly operation on the target battery. In one embodiment, external device 130 may include one or more automation devices. Processor 210 may transmit the fourth information to one of the one or more automation devices.

[0110] In one embodiment, processor 210 can associate second and third information with each other and store them in one or more memories 220. If the second and third information are generated for one or more target batteries, processor 210 can associate the second and third information with each of the one or more target batteries. For example, the second and third information of a first target battery can be associated with each other, and the second and third information of a second target battery can be associated with each other. Because the second and third information are associated with each other and stored in memory 220 for each of the one or more target batteries, various battery characteristics can be built into a database, thereby allowing for the systematic storage and management of various information related to various batteries, so that necessary information can be easily referenced during battery removal or recycling.

[0111] Figure 4 illustrates an example of an operating environment for performing a battery removal operation, which may be referenced in various embodiments of this disclosure. The operating environment of Figure 4 may include a scanner 120a, a storage device 410, a conveying device 420, first to third worktables 441, 442 and 443, and first to third automation devices 131, 132 and 133.

[0112] Storage device 410 may include a storage rack configured to store one or more batteries. For example, the storage rack includes one or more shelves, each structured to allow for stable placement of batteries. Each shelf may be designed to be adjustable to accommodate the size and shape of the batteries and may also include supports or clips configured to hold the batteries in place. One or more batteries may be arranged on each shelf. The distance between the storage racks and shelves is adjustable, thus accommodating batteries of various sizes. Furthermore, to enhance user convenience, the storage rack may be designed with movable wheels or a foldable structure, and may be equipped with a cover or door as needed to protect the batteries from environmental factors.

[0113] The conveying device 420 may correspond to a device configured to move batteries stored in the storage device 410 to a workbench. In various embodiments of this disclosure, the conveying device 420 configured to transfer batteries can be implemented in various forms. For example, the conveying device 420 may include a forklift, an automated guided vehicle (AGV), etc. For example, a forklift may be equipped with specially designed forks or trays for loading batteries and configured to safely and efficiently transport batteries of various sizes and weights. For example, an AGV may automatically transport loaded batteries along a pre-programmed path and may include the ability to detect and avoid obstacles via sensors.

[0114] In one embodiment, the automated guided vehicle (AGV) can deliver batteries from a starting point to a destination under the control of device 110. For example, device 110 can transmit information indicating the location of a storage rack as the starting point and information indicating the location of a target automated device among one or more automated devices as the destination to the AGV, so that the AGV can deliver batteries from the storage rack to the automated device.

[0115] In one embodiment, the automated guided vehicle (AGV) can deliver batteries from a starting point to a destination under the control of an external server. For example, device 110 can transmit location information of the starting point and destination to an external server, which can then control the AGV to move from the starting point to the destination based on the received location information.

[0116] Each of one or more automated devices can perform disassembly operations on batteries on different workbenches. In one embodiment, a first automated device 131 can perform disassembly operations on batteries manufactured by a specific manufacturer on a first workbench 441. In one embodiment, a second automated device 132 can perform disassembly operations on batteries with a specific product name on a second workbench 442. In one embodiment, a third automated device 133 can perform disassembly operations on a third workbench 443 to separate components contained in the battery from the battery.

[0117] The conveying device 420 can deliver the battery to one of the first worktable 441 to the third worktable 443. In one embodiment, the device 110 can generate fourth information for the target battery and determine a suitable target automation device from the first automation device 131 to the third automation device 133 to perform the target battery removal operation, so as to transmit the fourth information to the target automation device. In one embodiment, the device 110 can be further configured to control the conveying device 420. The device 110 can control the conveying device 420 to deliver the battery to the worktable corresponding to the target automation device to which the fourth information is transmitted.

[0118] In one embodiment, scanner 120a may be provided in a portable form. For example, scanner 120a may include a handle for easy carrying by an operator. The operator can scan batteries at various angles by moving scanner 120a. Specifically, the operator can hold scanner 120a and move it along a storage rack, continuously scanning multiple batteries stored in the rack, and device 110 can receive information about the image of each of the multiple batteries scanned by scanner 120a. Scanning operations can be particularly useful for warehouses or distribution centers managing large numbers of batteries. In one embodiment, scanner 120a can transmit scanned data to device 110 in real time via a network, so that device 110 can monitor and manage the inventory and status of scanned batteries in real time.

[0119] In one embodiment, scanner 120a can be installed in a conveying device. For example, scanner 120a can be designed to be positioned at various locations on the automated guided vehicle (AGV) to perform automated scanning operations. For instance, the scanner can be mounted on the upper part of the AGV's forks to scan batteries during storage rack insertion or removal. Furthermore, the scanner can include structures attached to the side surface or lower part of the AGV's racks to scan batteries loaded on the racks. Additionally, the scanner can be located at the top of the AGV to allow simultaneous scanning of multiple batteries loaded on the AGV while the AGV is in motion, enabling rapid scanning of multiple batteries during battery delivery. Furthermore, the scanner can transmit information generated from the scanning results to device 110 via a network. In various embodiments of this disclosure, the arrangement and structure of scanner 120a can automate the scanning operation, thereby reducing the workload of the operator and improving operational efficiency.

[0120] Figure 5 illustrates another example of the operating environment for performing battery removal operations, which can be referenced in various embodiments of this disclosure. In the following, descriptions of elements substantially the same as those described in Figure 4 will be omitted, and differences will be discussed in detail. The operating environment diagram of Figure 5 may include a scanner 120b, a storage device 410, a first transfer device 521, a scanning stage 530, a second transfer device 522, first to third stages 441, 442, 443, and first to third automation devices 131, 132, 133. The first transfer device 521 may deliver one or more batteries stored in the storage device 410 to the scanning stage 530. In one embodiment, the second transfer device 522 may deliver a battery 535 scanned on the scanning stage 530 to one of the first to third stages 441, 442, 443.

[0121] In one embodiment, the scanner 120b can be provided in a fixed form. A rotatable turntable can be mounted on the scanning stage 530 on which the scanner 120b is mounted. A specific target object, such as a battery, is loaded onto the upper turntable, and as the turntable rotates, the object moves circumferentially within the field of view of the scanner 120b. The scanner 120b can utilize this mechanical rotational motion to capture the overall outline of the target object from various angles, thereby accurately scanning the object's three-dimensional shape information. The rotation speed and direction of the turntable can be adjusted via a control device, thereby achieving efficient and flexible scanning operations to adapt to various scanning needs. In the various embodiments of this disclosure, the structural feature of fixing the scanner 120b to the scanning stage 530 can reduce the impact of vibrations occurring during the scanning operation, thereby improving the accuracy of the images acquired by the scanner 120b.

[0122] However, the structure and / or arrangement of the scanner 120b are not limited to this, and the scanner 120b can be rotated or moved to scan the battery on the scanning stage 530 while the scanning stage 530 is stationary. For example, the scanner 120b can be designed to rotate 360 ​​degrees around the battery, or move hemispherically around the top of the battery to scan the front surface of the battery. In the various embodiments of this disclosure, due to the structural features of the scanner 120b moving on the scanning stage 530, the battery can be accurately scanned from various angles. In this way, the rotation and movement of the scanner can be appropriately adjusted according to the shape and size of the battery on the scanning stage, and an optimal scanning path can be provided according to the structural features of the target battery.

[0123] Figure 6 illustrates a flowchart illustrating a method for transmitting fourth information from device 110 to external device 130 according to various embodiments of the present disclosure. External device 130 may include one or more automation devices. The flowchart of Figure 6 may be an example of a method for processing third and / or fourth information generated by performing the method 300 of Figure 3 on a target battery in various embodiments of the present disclosure.

[0124] In operation S610, processor 210 can determine, based on third information, a target automation device configured to perform a disassembly operation related to one or more characteristics of the target battery. In operation S620, processor 210 can transmit fourth information to the target automation device.

[0125] In one embodiment, memory 220 may store information corresponding to various automated devices performing the battery removal operation. This information may include information about the type of automated device corresponding to each automated device, information about the type of battery suitable for removal by the automated device, and information about the type of component suitable for removal by the automated device. Processor 210 may determine a target automated device from one or more automated devices based on the third information and the information stored in memory 220.

[0126] Figure 7 illustrates a flowchart illustrating a method for transmitting fourth information from device 110 to a first automation device according to various embodiments of the present disclosure. In operation S710, the processor 210 of device 110 may obtain third information indicating the manufacturer of the target battery. In one implementation, the third information may correspond to information indicating the first manufacturer of the battery.

[0127] In operation S720, the processor 210 of device 110 can identify a target automation device from one or more automation devices optimized for disassembling batteries manufactured by the first manufacturer. For example, the memory 220 of device 110 can store information indicating that the first automation device is suitable for performing the battery disassembly operation of the first manufacturer, and the processor 210 can use this information to identify the first automation device as the target automation device.

[0128] In operation S730, when the first automation device is identified as the target automation device, device 110 can transmit fourth information to the first automation device. In one embodiment, when the fourth information is transmitted to the first automation device, device 110 can control the automated guided vehicle to move the target battery to the first workbench, whereby the first automation device performs a disassembly operation on the target battery.

[0129] In operation S740, the first automated device can perform a disassembly operation on the target battery based on fourth information. The fourth information may include a three-dimensional image of the target battery. The first automated device can use the three-dimensional image of the target battery to perform the disassembly operation on the target battery.

[0130] Figure 8 illustrates a flowchart illustrating a method for transmitting fourth information from device 110 to a second automation device according to various embodiments of the present disclosure. In operation S810, device 110 may obtain third information indicating the product name of a target battery. In one embodiment, the third information may correspond to information indicating a specific product name of the battery.

[0131] In operation S820, the processor 210 of device 110 can identify a target automation device from one or more automation devices optimized for removing batteries with a specific product name. For example, the memory 220 of device 110 can store information indicating that a second automation device is suitable for performing the removal operation on batteries with a specific product name, and the processor 210 can use this information to identify the second automation device as the target automation device.

[0132] In operation S830, when the second automation device is identified as the target automation device, device 110 can transmit fourth information to the second automation device. In one embodiment, when the fourth information is transmitted to the second automation device, device 110 can control the automated guided vehicle to move the target battery to the second workbench, where the second automation device performs a disassembly operation on the target battery.

[0133] In operation S840, the second automation device can perform a disassembly operation on the target battery based on fourth information. The fourth information may include a three-dimensional image of the target battery. The second automation device can use the three-dimensional image of the target battery to perform the disassembly operation.

[0134] Figure 9 illustrates a flowchart illustrating a method for transmitting fourth information from device 110 to a third automation device according to various embodiments of the present disclosure. In operation S910, device 110 may obtain third information indicating one or more elements of a target battery. For example, the third information may correspond to third information indicating a first element.

[0135] In operation S920, the processor 210 of device 110 can identify an automation device optimized for the removal of the first component of the battery from one or more automation devices as a target automation device. For example, the memory 220 of device 110 can store information indicating a third automation device as an automation device suitable for the removal operation of the first component, and the processor 210 can use this information to identify the third automation device as the target automation device.

[0136] In operation S930, when the third automation device is identified as the target automation device, device 110 can transmit fourth information to the third automation device. In one embodiment, when the fourth information is transmitted to the third automation device, device 110 can control the automated guided vehicle to move the target battery to the third workbench, where the third automation device performs a disassembly operation on the target battery.

[0137] In operation S940, the third automation device can perform a disassembly operation on the target battery based on fourth information. The fourth information may include a three-dimensional image of the target battery. In one embodiment, the fourth information may correspond to information indicating the location of a first element that is marked at the position of the first element on the three-dimensional image. The third automation device can perform the disassembly operation on the target battery using the three-dimensional image of the target battery and the information indicating the position of the first element.

[0138] Figure 10 illustrates a flowchart illustrating a method according to one embodiment of the present disclosure. The method according to Figure 10 can correspond to a method for determining whether to perform a disassembly operation on a target battery based on the defect state of the target battery identified by third information.

[0139] In operation S1010, the processor 210 can determine whether the defect state of the target battery corresponds to a predetermined first reference state based on third information.

[0140] In one embodiment, the predetermined first reference state may correspond to a defective state of the battery. For example, when third information indicating that the target battery is defective is obtained, the processor 210 may determine that the defective state of the target battery corresponds to the first reference state.

[0141] In one embodiment, a predetermined first reference state may correspond to a state in which the battery has a first type of defect. For example, when third information indicating that the target battery has a first type of defect is obtained, the processor 210 can determine that the defect state of the target battery corresponds to the first reference state. As another example, when third information indicating that the target battery has a defect of a type other than the first type (e.g., the second type) is obtained, the processor 210 can determine that the defect state of the target battery does not correspond to the first reference state. For example, a first type of defect might indicate that the battery has discolored due to a fire, while a second type of defect might indicate that the battery has deformed due to an impact. Thus, in various embodiments of this disclosure, the processor 210 can determine that the defect state of the target battery corresponds to the first reference state based on the defect type indicated by the third information.

[0142] In operation S1020, in response to determining that the defective state corresponds to the first reference state, processor 210 may generate fifth information indicating that the target battery will not move to the automation device and transmit the fifth information to external device 130. External device 130 may include a conveying device. In one embodiment, upon receiving the fifth information, external device 130 may not perform a disassembly operation on the target battery. For example, external device 130 may control the conveying device and deliver the target battery to an area configured for storing waste batteries.

[0143] In various embodiments of this disclosure, by preventing defective or fire-risk batteries from entering the automatic disassembly device, potential fire hazards during the disassembly process can be prevented in advance. This also prevents damage to the automated device during battery disassembly.

[0144] In the flowcharts of this disclosure, the operations of the method or algorithm are described sequentially. However, in addition to being executed sequentially, these operations can also be executed in any combination of orders as per this disclosure. The description of the flowcharts in this disclosure does not preclude changes or modifications to the method or algorithm, and does not imply that any operation is necessary or desirable. In one embodiment, at least some operations can be executed in parallel, iteratively, or heuristically. In another embodiment, at least some operations may be omitted, or additional operations may be added.

[0145] Various embodiments of this disclosure can be implemented as software on a machine-readable storage medium (MRSM) readable by a computing device. This software may correspond to software used to implement the various embodiments described in this disclosure. This software can be inferred by a programmer skilled in the art to which this disclosure pertains based on the various embodiments described in this disclosure. For example, the software may be a computer program including instructions readable by a computing device. A computing device is a means of operating according to instructions retrieved from a storage medium, such as, interchangeably, an electronic device. In one embodiment, the processor 210 of the computing device can execute the retrieved instructions, causing elements of the computing device to perform functions corresponding to those instructions. Storage medium can refer to any type of recording medium that stores information and can be read by a device. Storage media may include: ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical information storage device, etc. In one embodiment, the storage medium may be implemented in a distributed manner, for example, in a computer system connected to a network. In this case, the software can be distributed and stored on and executed on the computer system, etc. In another embodiment, the storage medium may correspond to a non-transitory storage medium. Non-transitory media refers to tangible media that exist regardless of whether the information is stored in a semi-permanent or temporary manner, excluding signals that are temporarily transmitted.

[0146] Although the technical concepts according to this disclosure have been described through various embodiments, the technical ideas of this disclosure include various substitutions, modifications, and alterations that can be understood by those skilled in the art to which this disclosure pertains. Furthermore, these substitutions, modifications, and alterations should be considered to be included within the scope of the patent applications.

[0147] 100: Environment 110: Device 120, 120a, 120b: Scanner 130: External device 131, 132, 133: First to third automated devices 210: Processor 220: Memory 230: Communication circuit 300: Method S310~S350, S610~S620, S710~S740, S810~S840, S910~S940, S1010~S1020: Operation 410: Storage device 420: Conveying device 441, 442, 443: First to third workbenches 521: First Conveying Device 522: Second Conveying Device 530: Scanning workbench 535: Battery

Claims

1. A device for managing a battery, comprising: one or more processors; and one or more memories storing instructions executed by the one or more processors, wherein, When the one or more processors execute the instruction, the one or more processors are configured to: receive first information related to one or more images generated by scanning a target battery from a scanner; generate second information related to a three-dimensional image of the target battery based on the first information; generate third information based on the first information by identifying one or more features of the target battery; generate fourth information based on the second and third information, wherein information related to the one or more features of the target battery is marked on the three-dimensional image of the target battery; and transmit the fourth information to an external device configured to perform a disassembly operation on the target battery.

2. The apparatus as claimed in claim 1, wherein, The one or more processors are configured to, when generating the second information, generate point cloud information corresponding to the surface of the target battery for each of the one or more images, and generate the three-dimensional image of the target battery based on the point cloud information.

3. The apparatus as claimed in claim 1, wherein, The one or more processors are configured to, when generating the third information,: obtain predictive information about the target battery as the output of the artificial neural network by inputting the first information into an artificial neural network trained to identify one or more features of the battery from an image of the battery, and generate the third information based on the predictive information.

4. The apparatus as claimed in claim 3, wherein, The one or more processors are configured to: generate training information by labeling the one or more features of the target battery based on the first information, and further train the artificial neural network based on the training information.

5. The apparatus as claimed in claim 3, wherein, The third piece of information is information indicating one or more characteristics related to the manufacturer of the target battery.

6. The apparatus as claimed in claim 3, wherein, The third piece of information is information indicating one or more characteristics of any of the electric vehicles, hybrid vehicles, or energy storage systems in relation to the field in which the target battery is used.

7. The apparatus as claimed in claim 3, wherein, The third information is information indicating one or more characteristics related to one or more components constituting the target battery.

8. The apparatus as claimed in claim 3, wherein, The third piece of information is information indicating one or more features of cylindrical, prismatic, or pouch shape as related to the packaging shape of the target battery.

9. The apparatus as claimed in claim 3, wherein, The third piece of information is information indicating one or more features related to the defect of the target battery.

10. The apparatus as claimed in claim 1, wherein, The one or more processors are configured to associate the second information and the third information with each other and store the second information and the third information in the one or more memories.

11. The apparatus as claimed in claim 1, wherein, The one or more processors are configured to, when generating the fourth information,: determine one or more locations on the three-dimensional image of the second information that correspond to the one or more features, and generate the fourth information by marking information related to the one or more features at the one or more locations.

12. The apparatus as claimed in claim 1, wherein, The external device includes one or more automation devices, and wherein the one or more processors are configured to, when transmitting the fourth information: determine, based on the third information, a target automation device from the one or more automation devices that is configured to perform a disassembly operation related to the one or more features, and transmit the fourth information to the target automation device.

13. The apparatus as claimed in claim 12, wherein, The third information is information indicating a first type of one or more product types of batteries, and wherein the one or more processors are configured to, when determining the target automation device, identify from the one or more automation devices an automation device configured to perform a disassembly operation on the first type of battery as the target automation device.

14. The apparatus as claimed in claim 12, wherein, The third information is information indicating a first element among one or more elements constituting a battery, and wherein the one or more processors are configured to, when determining the target automation device, determine from the one or more automation devices an automation device configured to perform a disassembly operation on the first element as the target automation device.

15. The apparatus as claimed in claim 1, wherein, The third information is information indicating the characteristic related to the defect state of the battery, and wherein the one or more processors are configured to: determine, based on the third information, whether the defect state of the target battery corresponds to a predetermined first reference state, and generate fifth information in response to the determination that the defect state corresponds to the first reference state, the fifth information indicating not to move the target battery to the automation device and transmitting the fifth information to the external device.

16. A method for managing a battery, performed by a means including one or more processors and one or more memories storing instructions executed by the one or more processors, the method comprising, through the one or more processors: receiving first information from a scanner relating to one or more images generated by scanning a target battery; generating second information relating to a three-dimensional image of the target battery based on the first information; generating third information based on the first information by identifying one or more features of the target battery; generating fourth information based on the second information and the third information, wherein information relating to the one or more features of the target battery is marked on the three-dimensional image of the target battery; and transmitting the fourth information to an external means configured to perform a disassembly operation on the target battery.

17. The method as described in claim 16, wherein, The generation of the second information includes, through the one or more processors: generating point cloud information corresponding to the surface of the target battery for each of the one or more images, and generating the three-dimensional image of the target battery based on the point cloud information.

18. The method as described in claim 16, wherein, The generation of the third information includes, through the one or more processors: inputting the first information into an artificial neural network trained to identify one or more features of the battery from an image of the battery, obtaining predictive information about the target battery as the output of the artificial neural network, and generating the third information based on the predictive information.

19. The method as described in claim 18, wherein, The third piece of information is information indicating one or more characteristics related to the manufacturer of the target battery.

20. The method as described in request item 18, wherein, The third information is information indicating one or more characteristics related to one or more components constituting the target battery.

Images