Data analysis apparatus and method for analyzing battery manufacturing processes

Abstract

A data analysis device according to one embodiment of the present disclosure is a data analysis device that works in conjunction with a battery inspection device that determines whether a battery to be inspected is defective or not, and an identifier collection device that collects identifiers of batteries to be packaged, and includes at least one processor; and a memory that stores at least one instruction executed through the at least one processor. The at least one instruction may include an instruction to collect inspection information relating to the battery to be inspected from the battery inspection device; an instruction to collect packaging information including the identifier of the battery to be packaged from the identifier collection device; and an instruction to generate performance information relating to the battery inspection device using the inspection information and the packaging information.

Description

Technical Field

[0001] This application claims the benefit of the filing date of Korean Patent Application No. 10-2024-0092833 filed with the Korean Intellectual Property Office on July 15, 2024, and the entire contents disclosed in the document of the said Korean patent application are incorporated herein by reference.

[0002] The present disclosure relates to a data analysis apparatus and method, and more specifically, to a data analysis apparatus and method for analysis of a battery manufacturing process. Background Art

[0003] A secondary battery is a battery that can be reused through charging even after discharge, and can be used as an energy source for small devices such as mobile phones, tablet PCs, and vacuum cleaners, and is also used as an energy source for medium and large devices such as automobiles and ESS (Energy Storage System) for smart grids.

[0004] A secondary battery is a battery that can be reused through charging even after discharge, and can be used as an energy source for small devices such as mobile phones, tablet PCs, and vacuum cleaners, and is also used as a medium and large energy source for Personal Mobility, automobiles, and ESS (Energy Storage System) for smart grids.

[0005] Battery cells are manufactured through an assembly process and an activation process. Since battery cells are assembled in a discharged state, after the assembly process of battery cells, an activation process is performed to activate the positive electrode active material, and a surface film (SEI, Solid Electrolyte Interface) is formed on the negative electrode, allowing the battery to function as a battery. Such an activation process is referred to as a formation process.

[0006] During the activation process, the cells may expand due to gases generated inside them, which can cause cosmetic defects. Furthermore, after the activation process is complete, cosmetic defects may occur during transport due to external impacts or other factors.

[0007] Generally, to detect such defective batteries, after the activation process, a visual inspection using a battery inspector or a visual inspection by an operator can be performed. Batteries deemed defective are discarded, while batteries deemed normal can be packaged by a packaging device and then shipped.

[0008] A related prior document is KR 10-2019-0035199. [Prior art documents] [Patent Documents]

[0009] [Patent Document 1] KR 10-2019-0035199 [Overview of the Initiative] [Problems that the invention aims to solve]

[0010] The purpose of this disclosure, in order to solve the problems described above, is to provide a data analysis device for analyzing the battery manufacturing process.

[0011] Another purpose of this disclosure, in order to solve the problems described above, is to provide a method of data analysis performed by such a data analysis device.

[0012] Another objective of this disclosure, in order to solve the problems described above, is to provide a battery manufacturing system that includes such a data analysis device. [Means for solving the problem]

[0013] A data analysis device according to one embodiment of the present disclosure for achieving the above objective is a data analysis device that works in conjunction with a battery inspection device for determining whether a battery to be inspected is defective or not, and an identifier collection device for collecting identifiers of batteries to be packaged, and includes at least one processor and a memory for storing at least one instruction executed through the at least one processor.

[0014] The above-mentioned at least one instruction may include an instruction to collect inspection information relating to the battery to be inspected from the battery inspection device; an instruction to collect packaging information including the identifier of the battery to be packaged from the identifier collection device; and an instruction to generate performance information relating to the battery inspection device using the inspection information and the packaging information.

[0015] The command for collecting the above-mentioned inspection information may include a command for collecting the identifier of the battery to be inspected and an inspection result indicating whether or not each of the batteries to be inspected is defective.

[0016] The instruction for collecting the above-mentioned packaging information may include an instruction for collecting the identifier of the battery to be packaged, which is determined to be in the final normal state and packaged by the packaging device.

[0017] The batteries subject to packaging as described above may include batteries that are determined to be normal by the battery inspection device described above, and batteries that were determined to be defective by the battery inspection device described above but were determined to be normal as a result of inspection by a worker.

[0018] The command for collecting the above-mentioned packaging information may include a command for collecting the identifier of each of the batteries to be packaged from an identifier collection device that recognizes the identification code displayed on the outside of the battery.

[0019] The command for generating performance information related to the above-mentioned battery testing device may include a command for calculating the over-detection rate of the battery testing device using the above-mentioned testing information and packaging information.

[0020] The instructions for calculating the over-detection rate of the battery inspection device may include: instructions for selecting batteries included in the packaging target batteries from among batteries determined to be defective by the battery inspection device; and instructions for calculating the over-detection rate of the battery inspection device based on the number of selected batteries.

[0021] The instructions for calculating the over-detection rate of the battery inspection device may include instructions for calculating a ratio of the number of the selected batteries to the number of batteries determined to be defective by the battery inspection device.

[0022] The instructions for generating performance information related to the battery inspection device may include instructions for calculating an actual defect rate for the inspection target batteries using the inspection information and the packaging information.

[0023] The instructions for calculating the actual defect rate for the inspection target batteries may include instructions for calculating the actual defect rate based on a difference between the number of the inspection target batteries and the number of the packaging target batteries.

[0024] A data analysis method performed by a data analysis device that interworks with a battery inspection device configured to determine whether a battery to be inspected is defective and an identifier collection device configured to collect identifiers of batteries to be packaged, according to an embodiment of the present disclosure for achieving another object, may comprise: collecting inspection information related to the batteries to be inspected from the battery inspection device; collecting packaging information including the identifiers of the batteries to be packaged from the identifier collection device; and generating performance information related to the battery inspection device using the inspection information and the packaging information.

[0025] The step of collecting the inspection information may include a step of collecting identifiers of the inspection target batteries and inspection results indicating the presence / absence of a defect for each of the inspection target batteries.

[0026] The step of collecting the packaging information may include a step of collecting identifiers of the packaging target batteries that are determined to be finally normal and are packaged by a packaging device.

[0027] The batteries to be packaged described above may include batteries judged to be normal by the battery inspection device described above, and batteries that have been judged to be defective by the battery inspection device described above but are judged to be normal as a result of inspection by an operator.

[0028] The step of collecting the packaging information described above may include a step of collecting respective identifiers of the batteries to be packaged described above from an identifier collection device that recognizes identification codes displayed on the outer surfaces of the batteries.

[0029] The step of generating performance information related to the battery inspection device described above may include a step of calculating an over-detection rate of the battery inspection device described above using the inspection information and the packaging information described above.

[0030] The step of calculating the over-detection rate of the battery inspection device described above may include: a step of sorting out batteries included in the batteries to be packaged described above from among batteries judged to be defective by the battery inspection device described above; and a step of calculating the over-detection rate of the battery inspection device described above based on the number of sorted batteries.

[0031] The step of calculating the over-detection rate of the battery inspection device described above may include a step of calculating a ratio of the number of the sorted batteries described above to the number of batteries judged to be defective by the battery inspection device described above.

[0032] The step of generating performance information related to the battery inspection device described above may include a step of calculating an actual defect rate for the inspection target batteries described above using the inspection information and the packaging information described above.

[0033] The step of calculating the actual defect rate for the inspection target batteries described above may include a step of calculating the actual defect rate described above based on a difference between the number of the inspection target batteries described above and the number of the batteries to be packaged described above.

[0034] A battery manufacturing system according to one embodiment of the present disclosure for achieving the above or other objectives may include a battery inspection device for determining whether a battery to be inspected is defective; a packaging device for packaging batteries that have been determined to be in good condition; an identifier collection device for collecting identifiers for each battery to be packaged; and a data analysis device for generating performance information related to the battery inspection device.

[0035] Here, the data analysis device can collect inspection information regarding the battery to be inspected from the battery inspection device, collect packaging information including the identifier of the battery to be packaged from the identifier collection device, and generate performance information regarding the battery inspection device using the inspection information and packaging information. [Effects of the Invention]

[0036] According to the embodiments of this disclosure described above, accurate performance indicators for a battery inspection device can be quickly calculated using battery inspection information and packaging information. [Brief explanation of the drawing]

[0037] [Figure 1] This shows a typical battery manufacturing process. [Figure 2] This is a flowchart illustrating the operation of the battery inspection method performed after the activation process. [Figure 3] This is a block diagram of a battery manufacturing system according to an embodiment of the present disclosure. [Figure 4] This is a flowchart illustrating the operation of the data analysis method according to an embodiment of the present disclosure. [Figure 5] This is a flowchart illustrating the operation of a data analysis method according to another embodiment of the present disclosure. [Figure 6] This is a flowchart illustrating the operation of a data analysis method according to another embodiment of this disclosure. [Figure 7] This is a block diagram of a data analysis device according to an embodiment of the present disclosure. [Modes for carrying out the invention]

[0038] This disclosure can be modified in various ways and may have many different embodiments. Therefore, specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this should not be understood as limiting the disclosure to specific embodiments, but rather as including all modifications, equivalents, or substitutes that fall within the spirit and technical scope of this disclosure. Similar reference numerals are used for similar components in the description of each drawing.

[0039] Terms such as 1, 2, A, B, etc., may be used to describe various components, but the components should not be limited by such terms. The terms are used solely for the purpose of distinguishing one component from another. For example, without exceeding the scope of the rights of this disclosure, 1 component may be named 2 component, and similarly, 2 component may be named 1 component. The term "and / or" includes a combination of multiple related items or one of multiple related items.

[0040] When it is stated that one component is "linked" or "connected" to another component, it should be understood that this may mean that it is directly linked or connected to that other component, but that there may also be another component in between. Conversely, when it is stated that one component is "directly linked" or "directly connected" to another component, it should be understood that there is no other component in between.

[0041] The terms used in this application are used solely to describe specific embodiments and are not intended to limit the disclosure. Singular expressions include plural expressions unless they are clearly different in context. In this application, terms such as “includes” or “having” are intended to specify the presence of features, figures, steps, actions, components, parts, or combinations thereof as described in the specification, and should not be understood to preemptively exclude the presence or possibility of adding one or more other features, figures, steps, actions, components, parts, or combinations thereof.

[0042] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as those generally understood by a person of ordinary skill in the art to which this disclosure pertains. Terms as defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art, and not as ideal or overly formal unless expressly defined herein.

[0043] Figure 1 shows a typical battery manufacturing process.

[0044] Batteries can be manufactured by sequentially carrying out multiple unit processes. More specifically, the battery manufacturing process can be classified into N unit processes, and a battery can be manufactured by sequentially carrying out processes 1 through N.

[0045] For example, a battery cell can be manufactured by sequentially carrying out unit processes classified as an electrode coating process (first process), an electrode rolling process (second process), an assembly process (third process), an activation process (fourth process), and an EOL (End Of Line) process (fifth process).

[0046] During the process of individual unit processes, or after the completion of battery manufacturing, performance tests can be conducted to confirm whether the battery exhibits the intended performance, and defect tests can be conducted to determine whether the battery is defective.

[0047] Specifically, since battery cells are assembled in a discharged state, the positive electrode active material is activated through an activation process (the fourth process) after the battery cell assembly process (the third process), and a surface film (SEI, Solid Electrolyte Interface) is formed on the negative electrode, allowing the battery to function. This activation process is also called the formation process.

[0048] During the activation process, the cells may expand due to gases generated inside them, which can cause cosmetic defects. Furthermore, after the activation process is complete, cosmetic defects may occur during transport due to external impacts or other factors.

[0049] Generally, to detect such defective batteries, after the activation process, defective batteries can be sorted out through visual inspection using a battery inspector or visual inspection by an operator. Batteries determined to be defective are discarded, while batteries determined to be normal can be packaged by a packaging device and then shipped.

[0050] Figure 2 is a flowchart illustrating the operation of the battery inspection method performed after the activation process.

[0051] Once the activation process (S210) is complete, the battery inspection device can proceed with the defect inspection (S220). Here, the battery inspection device can determine whether each battery cell being transported sequentially on the transport lane is defective or not. For example, the battery inspection device can determine whether each battery cell is defective (normal or defective) by checking whether the appearance of the battery cell is outside the specified range using a non-destructive inspection method with an optical sensor.

[0052] Batteries that are determined to be normal (N in S230) as a result of inspection by the battery inspection device can be determined to be final normal (S270) and shipped.

[0053] Batteries identified as defective (Y in S230) by the battery inspection device can be subjected to visual inspection by an operator (S240). For example, the visual inspection can be carried out by an operator directly examining the appearance of the cells that have been identified as defective by the battery inspection device and selecting the defective batteries.

[0054] Batteries that are determined to be defective (Y in S250) based on visual inspection can be deemed final defective (S260) and discarded.

[0055] Batteries that are determined to be normal (N in S250) as a result of visual inspection can be determined to be final normal (S270) and shipped. Here, batteries that are determined to be normal as a result of visual inspection are batteries that were determined to be defective by the battery inspection device but are ultimately determined to be normal, and can be considered as batteries that were over-detected by the battery inspection device (batteries that are actually normal but were determined to be defective).

[0056] In order to analyze the cause of the deterioration in the inspection performance of the battery inspection device and to take measures to resolve it, a performance analysis of the battery inspection device must be performed first. At this time, a method can be considered in which an additional precision inspection device is used to calculate the over-detection rate and actual defect rate of the battery inspection device. For example, the precision inspection device can determine whether or not a battery is defective by sampling a portion of the batteries that have been inspected by the battery inspection device (for example, 100 out of 10,000 batteries), and then compare the inspection results of the battery inspection device with the inspection results of the precision inspection device to calculate the over-detection rate and actual defect rate of the battery inspection device.

[0057] However, performance metrics calculated using this method are based on a sample of batteries, making them unreliable and potentially time-consuming to analyze.

[0058] This disclosure relates to a technology that can quickly calculate accurate performance indicators for a battery testing device. Various embodiments of this disclosure will be described in detail below with reference to the attached drawings.

[0059] Figure 3 is a block diagram of a battery manufacturing system according to an embodiment of the present disclosure.

[0060] Referring to Figure 3, the battery manufacturing system may include a battery inspection device 100, a packaging device 200, an identifier collection device 300, and a data analysis device 400.

[0061] The battery inspection device 100 is a device that determines whether or not a battery under inspection is defective. Here, the battery inspection device 100 can determine whether or not each battery cell is defective as it is sequentially transported on the transport lane.

[0062] For example, the battery inspection device 100 can determine whether the appearance of a battery cell is outside the specified range by using a non-destructive inspection method with an optical sensor, and can determine whether each battery cell is defective (normal or defective).

[0063] The packaging device 200 is a device that packages batteries before they are shipped. Here, the batteries to be packaged by the packaging device 200 may be batteries that have been determined to be in perfect working order.

[0064] The batteries to be packaged may include batteries that have been determined to be normal by the battery inspection device 100, and batteries that have been determined to be defective by the battery inspection device 100 but have been determined to be normal as a result of visual inspection.

[0065] Batteries that have been inspected by the battery inspection device 100 can be transported via a first transport lane and a second transport lane depending on the inspection result. Specifically, batteries that are judged to be normal (OK) by the battery inspection device 100 can be transported via the first transport lane (OK lane). Batteries that are judged to be defective (NG) by the battery inspection device 100 can be transported via the second transport lane (NG lane).

[0066] At least some of the batteries that have been inspected by the battery inspection device 100 can be visually inspected by an operator. For example, as shown in Figure 2, batteries that have been inspected by the battery inspection device 100 can be transported to a first transport lane (OK lane) and a second transport lane (NG lane) depending on the inspection results, and a visual inspection by an operator can be carried out in area A.

[0067] Visual inspection can be performed on batteries that have been determined to be defective (NG) by the battery inspection device 100. That is, visual inspection can be carried out on batteries being transported via the second transport lane (NG lane). For example, visual inspection can be carried out in a manner in which an operator checks the appearance of each battery being transported via the second transport lane (NG lane) and sorts out the defective batteries.

[0068] Batteries identified as defective through visual inspection can be deemed final defects and discarded. For example, batteries identified as defective through visual inspection can be transferred to a waste battery collection space located at the end of the second transfer lane (NG lane), and then discarded by a waste disposal device.

[0069] Batteries that are determined to be normal as a result of visual inspection can be finalized as normal and shipped. For example, batteries that are determined to be normal as a result of visual inspection can be moved by an operator from the second transfer lane (NG lane) to the first transfer lane (OK lane), and then transferred to the packaging device 200 located at the end of the first transfer lane (OK lane). Here, batteries that are determined to be normal as a result of visual inspection are batteries that were determined to be defective by the battery inspection device 100 but are ultimately determined to be normal, and are batteries that were over-detected by the battery inspection device (batteries that are actually normal but were determined to be defective).

[0070] Batteries that are determined to be normal as a result of inspection by the battery inspection device 100 can be deemed final and shipped. For example, batteries that are determined to be normal as a result of inspection by the battery inspection device 100 can be transferred to the packaging device 200 via the first transfer lane (OK lane) without visual inspection, and can be shipped after being packaged by the packaging device 200.

[0071] The identifier collection device 300 is a device for collecting the identifier of a battery.

[0072] The batteries subject to inspection according to this disclosure are pre-assigned identifiers, and an identification code corresponding to the identifier (ID) may be displayed on the outer surface of the battery. For example, a barcode or QR code (Quick Response code) corresponding to the battery cell identifier may be printed or affixed to the outer surface of the battery cell.

[0073] Here, the identifier collection device 300 can recognize an identification code displayed or affixed to the outer surface of the battery to be inspected and confirm the identifier corresponding to the identification code. For example, the identifier collection device 300 may include a barcode scanner or a QR scanner and can scan the identification code of a battery cell to confirm the identifier of the battery cell.

[0074] The identifier collection device 300 can collect identifiers for batteries to be packaged. For example, as shown in Figure 3, the identifier collection device 300 is positioned at the end of the first transfer lane (OK lane) and can collect identifiers for batteries to be packaged immediately before they are packaged by the packaging device 200. In other words, the identifiers collected by the identifier collection device 300 are the identifiers of batteries that have been determined to be in good working order.

[0075] The data analysis device 400 can generate performance information for the battery inspection device 100 in conjunction with the battery inspection device 100 and the identifier collection device 300. Here, the performance information may include one or more of the following: the over-detection rate of the battery inspection device and the actual defect rate of the batteries being inspected.

[0076] In other words, the data analysis device 400 according to the embodiment of this disclosure can calculate a performance index for a battery inspection device using the identifier of the battery to be packaged (the battery that has been determined to be in the final normal state) collected immediately before packaging, and the inspection information of the battery inspection device. Therefore, the performance index of the battery inspection device calculated by this disclosure shows higher accuracy and enables real-time monitoring compared to a performance index calculated using a sample of batteries.

[0077] Figure 4 is a flowchart illustrating the operation of the data analysis method according to an embodiment of this disclosure.

[0078] The data analysis device can collect inspection information from the battery inspection device (S410). Here, the inspection information may include the identifier and inspection result (normal or defective) of each battery being inspected. For example, when inspection of 100 batteries has been completed, the data analysis device can receive inspection information from the battery inspection device that includes {[#1;OK], [#2;OK], [#3;NG], [#4;OK], ...[#99;NG], [#100;OK]}.

[0079] The data analysis device can collect packaging information from the identifier collection device (S420). Here, the packaging information may include identifiers for each of the batteries being packaged. For example, the data analysis device can receive packaging information containing {#1;#2;#4;#5;…#100} from a barcode scanner.

[0080] The data analysis device can generate performance information regarding the battery inspection device using the inspection information collected in S410 and the packaging information collected in S420. Here, the performance information may include one or more of the following: the over-detection rate of the battery inspection device and the actual defect rate of the batteries being inspected.

[0081] For example, a data analysis device can use inspection information and packaging information to derive the number of over-detected batteries, and calculate the over-detection rate of the battery inspection device based on the number of over-detected batteries. Here, the over-detection rate can be defined as the ratio (N_od / N_d_ng) of the number of over-detected batteries (N_od) to the number of batteries determined to be defective by the battery inspection device (N_d_ng).

[0082] As another example, a data analysis device can use inspection information and packaging information to derive the number of actual defective batteries (batteries that are ultimately deemed defective), and calculate the actual defect rate of the batteries under inspection based on the number of actual defective batteries. Here, the actual defect rate can be defined as the ratio (N_r_ng / N_all) of the number of actual defective batteries (N_r_ng) to the number of batteries under inspection (N_all).

[0083] Figure 5 is a flowchart of the operation of the data analysis method according to an embodiment of the present disclosure. Below, with reference to Figure 5, the method for calculating the over-detection rate of the battery inspection device according to an embodiment of the present disclosure will be described in more detail.

[0084] The data analysis device can collect inspection information from the battery testing device, including the identifier and inspection result (normal or defective) of each battery under inspection (S510). For example, when the inspection of 100 batteries under inspection is completed, the data analysis device can receive inspection information from the battery testing device that includes {[#1;OK], [#2;OK], [#3;NG], [#4;OK], ...[#99;NG], [#100;OK]}.

[0085] The data analysis device can collect packaging information from the identifier collection device, including identifiers for each of the batteries to be packaged (S520). For example, the data analysis device can receive packaging information containing {#1;#2;#3;#4;#5;…#100} from the barcode scanner.

[0086] The data analysis device can sort out over-detected batteries using the inspection information collected in S410 and the packaging information collected in S420 (S530). Here, an over-detected battery can refer to a battery that was judged as defective by the battery inspection device but is actually normal.

[0087] Specifically, the data analysis device can select batteries that were found to be defective by the battery inspection device and that were included in the packaged batteries as over-detected batteries. Here, the data analysis device can select over-detected batteries by comparing the identifier of the battery found to be defective among the identifiers included in the inspection information with the identifier included in the packaging information.

[0088] For example, if the identifiers of batteries determined to be defective among the identifiers included in the inspection information are #3, #7, #15, #31, and #99, and the packaging information includes all identifiers from #1 to #100 except for #7, #15, #31, and #99, the data analysis device can sort the battery corresponding to #3 as an over-detected battery. That is, batteries corresponding to #7, #15, #31, and #99, respectively, can be classified as batteries that were determined to be defective by the battery inspection device, and subsequently determined to be defective in the visual inspection that follows, and are therefore disposed of (final defective determination). On the other hand, the battery corresponding to #3 was determined to be defective by the battery inspection device, but subsequently determined to be normal in the visual inspection that follows, and is therefore classified as an over-detected battery recognized immediately before packaging.

[0089] Subsequently, the data analysis device can calculate the over-detection rate of the battery inspection device based on the number of over-detected batteries (S540). Here, the over-detection rate can be defined as the ratio (N_od / N_d_ng) of the number of over-detected batteries (N_od) to the number of batteries determined to be defective by the battery inspection device (N_d_ng).

[0090] In the example above, the number of over-detected batteries (#3) (N_od) is 1, and the number of batteries (#3, #7, #15, #31, #99) determined to be defective by the battery testing device (N_d_ng) is 5. Therefore, the over-detection rate can be calculated as 20% (1 / 5).

[0091] Figure 6 is a flowchart of the operation of a data analysis method according to another embodiment of the present disclosure. Below, with reference to Figure 6, the method for calculating the actual defect rate of a battery under inspection according to an embodiment of the present disclosure will be described in more detail.

[0092] The data analysis device can collect inspection information from the battery testing device, including the identifier and inspection result (normal or defective) of each battery under inspection (S610). For example, when the inspection of 100 batteries under inspection is completed, the data analysis device can receive inspection information from the battery testing device that includes {[#1;OK], [#2;OK], [#3;NG], [#4;OK], ...[#99;NG], [#100;OK]}.

[0093] The data analysis device can collect packaging information from the identifier collection device, including identifiers for each of the batteries to be packaged (S620). For example, the data analysis device can receive packaging information containing {#1;#2;#3;#4;#5;…#100} from the barcode scanner.

[0094] The data analysis device can calculate the number of actual defective batteries using the inspection information collected in S410 and the packaging information collected in S420 (S630). Here, an actual defective battery can mean a battery that has been identified as defective by the battery inspection device, and subsequently identified as defective by a visual inspection, and thus has been finally determined to be defective.

[0095] Specifically, the data analysis device can calculate the actual number of defective batteries based on the difference between the number of batteries to be tested and the number of batteries to be packaged. Here, the data analysis device can calculate the actual number of defective batteries by calculating the difference between the number of identifiers included in the test information and the number of identifiers included in the packaging information.

[0096] For example, if the number of identifiers included in the inspection information is 100 and the number of identifiers included in the packaging information is 96, the actual number of defective batteries can be calculated to be 4.

[0097] Subsequently, the data analysis device can calculate the actual failure rate of the batteries under test based on the number of batteries under test and the number of actual defective batteries (S640). Here, the actual failure rate can be defined as the ratio (N_r_ng / N_all) of the number of actual defective batteries (N_r_ng) to the number of batteries under test (N_all).

[0098] In the example above, the actual number of defective batteries (N_r_ng) is 4, and the number of batteries to be tested (N_all) is 100, so the actual defect rate can be calculated as 4% (4 / 100).

[0099] Figure 7 is a block diagram of a data analysis device according to an embodiment of the present disclosure.

[0100] The data analysis device 700 according to the embodiment of this disclosure can be linked with a battery inspection device included in a battery manufacturing system, which determines whether or not a battery under inspection is defective, and an identifier collection device that collects identifiers of batteries under packaging. Here, the data analysis device 700 corresponds to or can be included in an integrated monitoring device of the battery manufacturing system.

[0101] The data analysis apparatus 700 according to the embodiment of the present disclosure may include at least one processor 710, a memory 720 for storing at least one instruction executed through the processor, and a transceiver 730 connected to a network for communication.

[0102] The above-mentioned at least one instruction may include an instruction to collect inspection information relating to the battery to be inspected from the battery inspection device; an instruction to collect packaging information including the identifier of the battery to be packaged from the identifier collection device; and an instruction to generate performance information relating to the battery inspection device using the inspection information and the packaging information.

[0103] The command for collecting the above-mentioned inspection information may include a command for collecting the identifier of the battery to be inspected and an inspection result indicating whether or not each of the batteries to be inspected is defective.

[0104] The instruction for collecting the above-mentioned packaging information may include an instruction for collecting the identifier of the battery to be packaged, which is determined to be in the final normal state and packaged by the packaging device.

[0105] The batteries subject to packaging as described above may include batteries that are determined to be normal by the battery inspection device described above, and batteries that were determined to be defective by the battery inspection device described above but were determined to be normal as a result of inspection by a worker.

[0106] The command for collecting the above-mentioned packaging information may include a command for collecting the identifier of each of the batteries to be packaged from an identifier collection device that recognizes the identification code displayed on the outside of the battery.

[0107] The command for generating performance information of the battery testing device may include a command for calculating the over-detection rate of the battery testing device using the testing information and packaging information.

[0108] The command for calculating the over-detection rate of the battery inspection device may include a command to select batteries that are included in the packaged batteries from among the batteries determined to be defective by the battery inspection device, and a command to calculate the over-detection rate of the battery inspection device based on the number of batteries selected.

[0109] The command for calculating the over-detection rate of the battery inspection device may include a command for calculating the ratio of the number of batteries that have been sorted to the number of batteries that have been determined to be defective by the battery inspection device.

[0110] The command for generating performance information for the battery testing device may include a command to calculate the actual defect rate for the battery being tested using the test information and packaging information.

[0111] The command for calculating the actual defect rate for the batteries under inspection may include a command for calculating the actual defect rate based on the difference between the number of batteries under inspection and the number of batteries to be packaged.

[0112] The data analysis device 700 may further include an input interface device 740, an output interface device 750, a storage device 760, and the like. Each component included in the data analysis device 700 can communicate with one another via a bus 770.

[0113] Here, processor 710 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which the method according to the embodiments of this disclosure is performed. Memory (or storage device) may consist of at least one of volatile storage media and non-volatile storage media. For example, memory may consist of at least one of read-only memory (ROM) and random access memory (RAM).

[0114] The operation of the methods according to the embodiments of this disclosure can be embodied as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes all types of recording devices on which data that can be read by a computer system is stored. Furthermore, computer-readable recording media can be distributed across networked computer systems, allowing computer-readable programs or code to be stored and executed in a distributed manner.

[0115] Some aspects of this disclosure have been described in the context of apparatus, but they can also be described by corresponding methods, where a block or apparatus corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method can be described by corresponding blocks or items or features of corresponding apparatus. Some or all of the method steps can be carried out by (or using) hardware devices such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most important method steps can be carried out by such devices.

[0116] While preferred embodiments of the present disclosure have been described above with reference to those skilled in the art, a person skilled in the art will understand that the present disclosure can be modified and altered in various ways without departing from the spirit and scope of the present disclosure as set forth in the following claims. [Explanation of Symbols]

[0117] 100: Battery inspection device 200: Packaging equipment 300: Identifier Collection Device 400: Data analysis equipment

Claims

1. A battery inspection device that determines whether a battery under inspection is defective; and a data analysis device that works in conjunction with an identifier collection device that collects identifiers of batteries under packaging, At least one processor; and Includes memory for storing at least one instruction executed through the at least one processor, The at least one instruction is, An order from the battery inspection device to collect inspection information relating to the battery to be inspected; An instruction to collect packaging information, including the identifier of the battery to be packaged, from the identifier collection device; and A data analysis device that includes a command to generate performance information relating to the battery inspection device using the inspection information and the packaging information.

2. The order to collect the aforementioned inspection information is, The data analysis apparatus according to claim 1, comprising an instruction to collect an identifier for the battery to be inspected and an inspection result indicating whether each of the batteries to be inspected is defective or not.

3. The instruction to collect the aforementioned packaging information is, The data analysis apparatus according to claim 1, comprising an instruction to collect identifiers of the batteries to be packaged, which are determined to be in perfect condition and packaged by a packaging device.

4. The aforementioned batteries to be packaged are The data analysis apparatus according to claim 3, which includes batteries determined to be normal by the battery inspection device, and batteries that were determined to be defective by the battery inspection device but were determined to be normal as a result of inspection by an operator.

5. The instruction to collect the aforementioned packaging information is, The data analysis apparatus according to claim 3, comprising an instruction to collect identifiers for each of the batteries to be packaged from the identifier collection device which recognizes an identification code displayed on the outer surface of the batteries to be packaged.

6. The command for generating performance information relating to the aforementioned battery testing device is: The data analysis device according to claim 1, which includes an instruction to calculate the over-detection rate of the battery inspection device using the inspection information and the packaging information.

7. The command for calculating the over-detection rate of the battery inspection device is: A command to select batteries that are included in the packaged batteries from among the batteries that have been determined to be defective by the battery inspection device; and The data analysis device according to claim 6, which includes an instruction to calculate the over-detection rate of the battery inspection device based on the number of selected batteries.

8. The command for calculating the over-detection rate of the battery inspection device is: The data analysis device according to claim 7, which includes a command to calculate the ratio of the number of selected batteries to the number of batteries determined to be defective by the battery inspection device.

9. The command for generating performance information relating to the aforementioned battery testing device is: The data analysis apparatus according to claim 1, which includes an instruction to calculate the actual defect rate for the battery to be inspected using the inspection information and the packaging information.

10. The command to calculate the actual defect rate for the battery under inspection is: The data analysis apparatus according to claim 9, which includes an instruction to calculate the actual defect rate based on the difference between the number of batteries to be inspected and the number of batteries to be packaged.

11. A battery inspection device for determining whether a battery under inspection is defective; and a data analysis method using a data analysis device linked to an identifier collection device for collecting identifiers of batteries under packaging, A step of collecting inspection information relating to the battery to be inspected from the battery inspection device; A step of collecting packaging information, including the identifier of the battery to be packaged, from the identifier collection device; and A data analysis method comprising the step of generating performance information relating to the battery inspection device using the inspection information and the packaging information.

12. The step of collecting the aforementioned inspection information is: The data analysis method according to claim 11, further comprising the step of collecting an identifier for the battery to be inspected and inspection results indicating whether each of the batteries to be inspected is defective or not.

13. The step of collecting the aforementioned packaging information is: The data analysis method according to claim 11, further comprising the step of collecting identifiers for the batteries to be packaged, which are determined to be in perfect condition and packaged by a packaging device.

14. The aforementioned batteries to be packaged are The data analysis method according to claim 13, comprising batteries determined to be normal by the battery inspection device, and batteries that were determined to be defective by the battery inspection device but were determined to be normal as a result of inspection by an operator.

15. The step of collecting the aforementioned packaging information is: The data analysis method according to claim 13, further comprising the step of collecting identifiers for each of the batteries to be packaged from the identifier collection device which recognizes an identification code displayed on the outer surface of the batteries to be packaged.

16. The step of generating performance information relating to the battery testing device is: The data analysis method according to claim 11, comprising the step of calculating the over-detection rate of the battery inspection device using the inspection information and the packaging information.

17. The step of calculating the over-detection rate of the battery inspection device is: A step of selecting batteries that are included in the packaged batteries from among the batteries that have been determined to be defective by the battery inspection device; and The data analysis method according to claim 16, further comprising the step of calculating the over-detection rate of the battery inspection device based on the number of selected batteries.

18. The step of calculating the over-detection rate of the battery inspection device is: The data analysis method according to claim 17, further comprising the step of calculating the ratio of the number of selected batteries to the number of batteries determined to be defective by the battery inspection device.

19. The step of generating performance information relating to the battery testing device is: The data analysis method according to claim 11, further comprising the step of calculating the actual defect rate for the battery to be inspected using the inspection information and the packaging information.

20. The step of calculating the actual defect rate for the battery under inspection is: The data analysis method according to claim 19, further comprising the step of calculating the actual defect rate based on the difference between the number of batteries to be inspected and the number of batteries to be packaged.

21. A battery inspection device that determines whether or not a battery under inspection is defective; A packaging device for packaging batteries that have been determined to be in perfect working order; An identifier collection device for collecting the identifiers of each battery to be packaged; and Includes a data analysis device that generates performance information related to the aforementioned battery testing device, The aforementioned data analysis device is A battery manufacturing system that collects inspection information relating to the battery to be inspected from the battery inspection device, collects packaging information including the identifier of the battery to be packaged from the identifier collection device, and generates performance information relating to the battery inspection device using the inspection information and the packaging information.

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