Data analysis device and method for analyzing battery manufacturing process

Abstract

A data analysis device according to one embodiment of the present invention is linked to: a battery inspection device for determining whether batteries to be inspected are defective; and an identifier collection device for collecting the identifiers of batteries to be packaged, and may comprise: at least one processor; and a memory for storing at least one instruction executed through the at least one processor. The at least one instruction can include the instructions of: collecting, from the battery inspection device, inspection information about the batteries to be inspected; collecting, from the identifier collection device, packaging information including the identifiers of the batteries to be packaged; and generating performance information about the battery inspection device by using the inspection information and the packaging information.

Description

Data analysis device and method for analyzing battery manufacturing processes

[0001] This application claims the benefit of Korean Patent Application No. 10-2024-0092833 filed with the Korean Intellectual Property Office on July 15, 2024, the entire contents of which are incorporated herein by reference.

[0002] The present invention relates to a data analysis device and method, and more particularly, to a data analysis device and method for analyzing a battery manufacturing process.

[0003] Secondary batteries are batteries that can be reused by 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 are also used as an energy source for medium and large devices such as automobiles and ESS (Energy Storage Systems) for smart grids.

[0004] Secondary batteries are batteries that can be reused by 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 are also used as medium- to large-scale energy sources such as personal mobility, automobiles, and ESS (Energy Storage Systems) for smart grids.

[0005] Battery cells are manufactured through assembly and activation processes. Because battery cells are assembled in a discharged state, an activation process is required after the assembly process. This activates the positive electrode active material and forms a surface film (SEI, Solid Electrolyte Interface) on the negative electrode, enabling the battery to function. This activation process is called the formation process.

[0006] During the activation process, gas generated within the cell may cause the cell to expand, resulting in appearance defects. Furthermore, external impacts, etc., may also cause appearance defects in the cell during transport after the activation process.

[0007] Typically, to detect these defective batteries, a visual inspection using a battery tester or a visual inspection by a worker may be performed after the activation process. Defective batteries are discarded, while those deemed normal are packaged using a packaging device and then shipped.

[0008] As a related prior literature, there is KR 10-2019-0035199.

[0009] The purpose of the present invention to solve the above problems is to provide a data analysis device for analyzing a battery manufacturing process.

[0010] Another object of the present invention to solve the above problems is to provide a data analysis method performed in such a data analysis device.

[0011] Another object of the present invention to solve the above problems is to provide a battery manufacturing system including such a data analysis device.

[0012] According to one embodiment of the present invention for achieving the above purpose, a data analysis device may include a battery inspection device for determining whether batteries to be inspected are defective; and a data analysis device linked to an identifier collection device for collecting identifiers of batteries to be packaged, the data analysis device including at least one processor; and a memory for storing at least one command executed through the at least one processor.

[0013] The at least one command may include a command for collecting inspection information on the batteries to be inspected from the battery inspection device; a command for collecting packaging information, including identifiers of the batteries to be packaged, from the identifier collection device; and a command for generating performance information on the battery inspection device using the inspection information and packaging information.

[0014] The command for collecting the above inspection information may include a command for collecting identifiers of the batteries to be inspected and inspection results indicating whether each of the batteries to be inspected is defective.

[0015] The command for collecting the above packaging information may include a command for collecting identifiers of the batteries to be packaged, which are determined to be the final normal and are packaged by the packaging device.

[0016] The above-mentioned battery to be packaged may include a battery that is judged to be normal by the battery inspection device, and a battery that is judged to be defective by the battery inspection device but is judged to be normal upon inspection by a worker.

[0017] The command for collecting the above packaging information may include a command for collecting an identifier of each of the batteries to be packaged from an identifier collection device that recognizes an identification code displayed on the outer surface of the battery.

[0018] The command to generate performance information of the battery inspection device may include a command to calculate an over-inspection rate of the battery inspection device using the inspection information and packaging information.

[0019] The command for calculating the over-inspection rate of the battery inspection device may include a command for selecting batteries included in the batteries to be packaged among the batteries determined to be defective by the battery inspection device; and a command for calculating the over-inspection rate of the battery inspection device based on the number of selected batteries.

[0020] The command for calculating the over-inspection rate of the battery inspection device may include a command for calculating the ratio of the number of selected batteries and the number of batteries determined to be defective by the battery inspection device.

[0021] The command to generate performance information of the battery inspection device may include a command to calculate the actual failure rate for the batteries to be inspected using the inspection information and packaging information.

[0022] The command for calculating the actual failure rate for the above-mentioned batteries to be inspected may include a command for calculating the actual failure rate based on the difference between the number of the above-mentioned batteries to be inspected and the number of the above-mentioned batteries to be packaged.

[0023]

[0024] According to an embodiment of the present invention for achieving the above-described other object, a data analysis method is provided, which comprises: a battery inspection device for determining whether batteries to be inspected are defective; and a data analysis device linked to an identifier collection device for collecting identifiers of batteries to be packaged, the data analysis method comprising: a step of collecting inspection information on the batteries to be inspected from the battery inspection device; a step of collecting packaging information, including identifiers of the batteries to be packaged, from the identifier collection device; and a step of generating performance information on the battery inspection device using the inspection information and the packaging information.

[0025] The step of collecting the above inspection information may include a step of collecting identifiers of the batteries to be inspected and inspection results indicating whether each of the batteries to be inspected is defective.

[0026] The step of collecting the above packaging information may include a step of collecting identifiers of the batteries to be packaged, which are determined to be the final normal and are packaged by the packaging device.

[0027] The above-mentioned battery to be packaged may include a battery that is judged to be normal by the battery inspection device, and a battery that is judged to be defective by the battery inspection device but is judged to be normal upon inspection by a worker.

[0028] The step of collecting the above packaging information may include a step of collecting an identifier of each of the batteries to be packaged from an identifier collection device that recognizes an identification code displayed on the outer surface of the battery.

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

[0030] The step of calculating the over-inspection rate of the battery inspection device may include the step of selecting batteries included in the batteries to be packaged among the batteries determined to be defective by the battery inspection device; and the step of calculating the over-inspection rate of the battery inspection device based on the number of selected batteries.

[0031] The step of calculating the over-inspection rate of the battery inspection device may include a step of calculating a ratio between the number of selected batteries and the number of batteries determined to be defective by the battery inspection device.

[0032] The step of generating performance information of the battery inspection device may include a step of calculating an actual failure rate for the batteries to be inspected using the inspection information and packaging information.

[0033] The step of calculating the actual failure rate for the batteries to be inspected may include a step of calculating the actual failure rate based on the difference between the number of the batteries to be inspected and the number of the batteries to be packaged.

[0034]

[0035] According to one embodiment of the present invention for achieving the above-described further object, a battery manufacturing system may include a battery inspection device for determining whether batteries to be inspected are defective; a packaging device for packaging batteries finally determined to be normal; an identifier collection device for collecting identifiers of each of the batteries to be packaged; and a data analysis device for generating performance information about the battery inspection device.

[0036] Here, the data analysis device can collect inspection information on the batteries to be inspected from the battery inspection device, collect packaging information including identifiers of the batteries to be packaged from the identifier collection device, and generate performance information on the battery inspection device using the inspection information and packaging information.

[0037] According to the above-described embodiment of the present invention, accurate performance indicators for a battery inspection device can be quickly calculated using battery inspection information and packaging information.

[0038] Figure 1 shows a typical battery manufacturing process.

[0039] Figure 2 is a flowchart of the operation of a battery inspection method performed after the activation process.

[0040] Figure 3 is a block diagram of a battery manufacturing system according to an embodiment of the present invention.

[0041] Figure 4 is a flowchart of the operation of a data analysis method according to an embodiment of the present invention.

[0042] Figure 5 is a flowchart of the operation of a data analysis method according to another embodiment of the present invention.

[0043] Figure 6 is a flowchart of the operation of a data analysis method according to another embodiment of the present invention.

[0044] Figure 7 is a block diagram of a data analysis device according to an embodiment of the present invention.

[0045] 100: Battery Tester

[0046] 200: Packaging device

[0047] 300: Identifier collection device

[0048] 400: Data Analysis Device

[0049] The present invention is susceptible to various modifications and embodiments. Specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the present invention. Throughout the description of each drawing, similar reference numerals have been used to designate similar components.

[0050] Terms such as "first," "second," "A," and "B" may be used to describe various components, but these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, the first component could be referred to as the "second component," and similarly, the second component could also be referred to as the "first component." The term "and / or" includes any combination of multiple related items listed or any one of multiple related items listed.

[0051] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0052] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0053] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0054]

[0055] Figure 1 shows a typical battery manufacturing process.

[0056] A battery can be manufactured by sequentially performing multiple unit processes. More specifically, the battery manufacturing process can be categorized into N unit processes, and the battery can be manufactured by sequentially performing the first through Nth processes.

[0057] For example, a battery cell can be manufactured by sequentially performing 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).

[0058] During the course of individual unit processes, or after battery manufacturing is complete, performance tests may be conducted to determine whether the battery exhibits the intended performance, and failure tests may be conducted to determine whether the battery is defective.

[0059] Specifically, since battery cells are assembled in a discharged state, they must undergo an activation process (fourth process) after the battery cell assembly process (third process) to activate the positive electrode active material and form a surface film (SEI, Solid Electrolyte Interface) on the negative electrode, enabling it to function as a battery. This activation process is also called the formation process.

[0060] During the activation process, gas generated within the cell may cause the cell to expand, resulting in appearance defects. Furthermore, external impacts, etc., may also cause appearance defects in the cell during transport after the activation process.

[0061] Typically, to detect these visually defective batteries, after the activation process, defective batteries can be screened through visual inspection by a battery inspector or visual inspection by a worker. Defective batteries are discarded, while those deemed normal are packaged using a packaging device and then shipped.

[0062]

[0063] Figure 2 is a flowchart of the operation of a battery inspection method performed after the activation process.

[0064] Once the activation process (S210) is completed, a defect inspection by a battery inspection device can be performed (S220). Here, the battery inspection device can determine whether each battery cell sequentially transported along the transport lane is defective. For example, the battery inspection device can use a non-destructive inspection method using an optical sensor to determine whether the appearance of each battery cell is outside the specification range, thereby determining whether each battery cell is defective (normal or defective).

[0065] A battery that is determined to be normal (N of S230) as a result of inspection by a battery inspection device can be shipped after being determined to be normal (S270).

[0066] Batteries that are determined to be defective (Y in S230) as a result of inspection by the battery inspection device may undergo a visual inspection by a worker (S240). For example, a visual inspection may be conducted by a worker directly inspecting the appearance of cells determined to be defective by the battery inspection device and selecting defective batteries.

[0067] Batteries that are determined to be defective (Y in S250) as a result of visual inspection may be judged as finally defective (S260) and discarded.

[0068] A battery that is determined to be normal (N of S250) through visual inspection can be finally determined to be normal (S270) and shipped. Here, a battery determined to be normal through visual inspection can be considered a battery that was over-inspected by the battery inspection device (a battery that was actually normal but determined to be defective) as it was determined to be a defective battery by the battery inspection device.

[0069] To analyze the cause of a battery inspection device's performance degradation and implement measures to address it, a performance analysis of the battery inspection device must be performed first. At this time, a method of calculating the over-inspection rate and actual defect rate of the battery inspection device using an additional precision inspection device can be considered. For example, the precision inspection device can determine whether a sample of batteries (e.g., 100 out of 10,000) have been inspected by the battery inspection device, determine whether they are defective, and compare the inspection results of the battery inspection device with those of the precision inspection device to calculate the over-inspection rate and actual defect rate of the battery inspection device.

[0070] However, the performance indicators calculated using this method are unreliable as they are calculated using a portion of sampled batteries, and performance analysis may take a lot of time.

[0071] The present invention relates to a technology capable of quickly calculating accurate performance indicators for a battery testing device. Hereinafter, various embodiments of the present invention will be described in detail with reference to the attached drawings.

[0072]

[0073] Figure 3 is a block diagram of a battery manufacturing system according to an embodiment of the present invention.

[0074] Referring to FIG. 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).

[0075] The battery inspection device (100) is a device that determines whether batteries to be inspected are defective. Here, the battery inspection device (100) can determine whether each battery cell sequentially transported on a transport lane is defective.

[0076] For example, the battery inspection device (100) can determine whether each battery cell is defective (normal or defective) by checking whether the appearance of the battery cell is outside the standard range through a non-destructive inspection method using an optical sensor.

[0077] The packaging device (200) is a device that packages batteries before they are shipped. Here, the battery to be packaged by the packaging device (200) may be a battery that has been finally determined to be normal.

[0078] The batteries to be packaged may include batteries that are judged as normal by the battery inspection device (100) and batteries that are judged as defective by the battery inspection device (100) but are judged as normal by visual inspection.

[0079] Batteries that have been inspected by the battery inspection device (100) may be transported by branching into a first transport lane and a second transport lane according to the inspection results. Specifically, batteries that are judged as normal (OK) by the battery inspection device (100) may be transported through the first transport lane (OK lane). Additionally, batteries that are judged as defective (NG) by the battery inspection device (100) may be transported through the second transport lane (NG lane).

[0080] A visual inspection by a worker may be performed on at least some of the batteries that have been inspected by the battery inspection device (100). For example, as illustrated in FIG. 2, batteries inspected by the battery inspection device (100) may be transported by branching into a first transport lane (OK lane) and a second transport lane (NG lane) according to the inspection results, and a visual inspection by a worker may be performed in Zone A.

[0081] A visual inspection may be performed on batteries determined to be defective (NG) by the battery inspection device (100). Specifically, the visual inspection may be conducted on batteries transported via the second transport lane (NG lane). For example, the visual inspection may be conducted by having a worker inspect the appearance of each battery transported via the second transport lane (NG lane) and select defective batteries.

[0082] Batteries that are visually determined to be defective may be deemed defective and disposed of. For example, batteries that are visually determined to be defective may be transported to a waste battery collection area located at the end of the second transport lane (NG lane) and subsequently disposed of by a disposal device.

[0083] As a result of the visual inspection, batteries that are determined to be normal as a result of the visual inspection can be finally judged as normal and shipped. For example, batteries that are determined to be normal as a result of the visual inspection can be moved by a worker 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 the visual inspection are batteries that were determined to be defective by the battery inspection device (100), but are ultimately judged to be normal, and are thus over-inspected batteries by the battery inspection device (batteries that are actually normal, but judged to be defective).

[0084] Batteries that are determined to be normal as a result of inspection by the battery inspection device (100) may be finally judged to be normal and shipped. For example, batteries that are determined to be normal as a result of inspection by the battery inspection device (100) may be transported to the packaging device (200) via the first transport lane (OK lane) without performing a visual inspection, and may be packaged by the packaging device (200) and then shipped.

[0085] The identifier collection device (300) is a device that collects the identifier of a battery.

[0086] The battery to be inspected according to the present invention may be pre-assigned an identifier, and an identification code corresponding to the identifier (ID) may be displayed on the exterior of the battery. For example, a barcode or a Quick Response code (QR code) corresponding to the identifier of the battery cell may be printed or affixed to the exterior of the battery cell.

[0087] Here, the identifier collection device (300) can recognize an identification code displayed or attached to the exterior surface of the battery to be inspected and confirm an identifier corresponding to the identification code. For example, the identifier collection device (300) may include a barcode scanner or a QR scanner, and scan the identification code of the battery cell to confirm the identifier of the corresponding battery cell.

[0088] The identifier collection device (300) can collect identifiers for batteries to be packaged. For example, as illustrated in FIG. 3, the identifier collection device (300) can be positioned at the end of the first transport lane (OK lane) and collect identifiers of batteries to be packaged immediately before being packaged by the packaging device (200). That is, the identifiers collected by the identifier collection device (300) are identifiers of batteries that have been finally determined to be normal.

[0089] The data analysis device (400) can be linked with the battery inspection device (100) and the identifier collection device (300) and generate performance information of the battery inspection device (100). Here, the performance information can include at least one of the over-inspection rate of the battery inspection device and the actual defect rate of the battery to be inspected.

[0090] That is, the data analysis device (400) according to an embodiment of the present invention can calculate the performance index of the battery inspection device by using the identifiers of the batteries to be packaged (batteries that have been finally determined to be normal) collected immediately before packaging and the inspection information of the battery inspection device. Accordingly, the performance index of the battery inspection device calculated according to the present invention exhibits high accuracy compared to the performance index calculated using some of the sampled batteries, and real-time monitoring is possible.

[0091]

[0092] Figure 4 is a flowchart of the operation of a data analysis method according to an embodiment of the present invention.

[0093] The data analysis device can collect inspection information from the battery inspection device (S410). Here, the inspection information can include an identifier of each battery to be inspected and an inspection result (normal or defective). For example, when inspection of 100 batteries to be inspected is completed, the data analysis device can receive inspection information including {[#1; OK], [#2; OK], [#3; NG], [#4; OK], ... [#99; NG], [#100; OK]} from the battery inspection device.

[0094] The data analysis device can collect packaging information from the identifier collection device (S420). Here, the packaging information may include identifiers for each battery to be packaged. For example, the data analysis device may receive packaging information containing {#1; #2; #4; #5; ... #100} from a barcode scanner.

[0095] The data analysis device can generate performance information about the battery inspection device using the inspection information collected in S410 and the packaging information collected in S420. Here, the performance information can include at least one of the over-inspection rate of the battery inspection device and the actual defect rate of the battery being inspected.

[0096] For example, the data analysis device can derive the number of over-inspected batteries using inspection information and packaging information, and calculate the over-inspection rate of the battery inspection device based on the number of over-inspected batteries. Here, the over-inspection rate can be defined as the ratio (N_od / N_d_ng) of the number of over-inspected batteries (N_od) and the number of batteries (N_d_ng) determined as defective by the battery inspection device.

[0097] As another example, the data analysis device can use inspection information and packaging information to derive the number of defective batteries (final quantity judgment batteries) and calculate the actual failure rate of the batteries to be inspected based on the number of defective batteries. Here, the actual failure rate can be defined as the ratio (N_r_ng / N_all) of the number of defective batteries (N_r_ng) and the number of batteries to be inspected (N_all).

[0098]

[0099] Figure 5 is a flowchart illustrating the operation of a data analysis method according to an embodiment of the present invention. Below, with reference to Figure 5, a method for calculating the over-inspection rate of a battery inspection device according to an embodiment of the present invention will be described in more detail.

[0100] The data analysis device can collect inspection information including the identifiers of each battery to be inspected and the inspection results (normal or defective) from the battery inspection device (S510). For example, when inspection of 100 batteries to be inspected is completed, the data analysis device can receive inspection information including {[#1; OK], [#2; OK], [#3; NG], [#4; OK], ... [#99; NG], [#100; OK]} from the battery inspection device.

[0101] The data analysis device can collect packaging information including identifiers for each of the batteries to be packaged from the identifier collection device (S520). For example, the data analysis device can receive packaging information including {#1; #2; #3; #4; #5; ... #100} from a barcode scanner.

[0102] The data analysis device can use the inspection information collected in S410 and the packaging information collected in S420 to select over-inspected batteries (S530). Here, over-inspected batteries may refer to batteries that were judged as defective by the battery inspection device but are actually normal.

[0103] Specifically, the data analysis device can select batteries included in the packaging target batteries among the batteries determined to be defective by the battery inspection device as over-inspected batteries. Here, the data analysis device can select over-inspected batteries by comparing the identifiers of the batteries determined to be defective among the identifiers included in the inspection information with the identifiers included in the packaging information.

[0104] 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 select the battery corresponding to #3 as an over-inspection battery. That is, the batteries corresponding to #7, #15, #31, and #99 are determined to be defective by the battery inspection device, and are also determined to be defective in a subsequent visual inspection, and can be classified as batteries that are disposed of (final defective determination). On the other hand, the battery corresponding to #3 is determined to be defective by the battery inspection device, but is determined to be normal in a subsequent visual inspection, and can be classified as an over-inspection battery that was recognized just before packaging.

[0105] Thereafter, the data analysis device can calculate the over-inspection rate of the battery inspection device based on the number of over-inspected batteries (S540). Here, the over-inspection rate can be defined as the ratio (N_od / N_d_ng) of the number of over-inspected batteries (N_od) and the number of batteries (N_d_ng) determined as defective by the battery inspection device.

[0106] In the above example, the number of over-inspected batteries (#3) (N_od) is 1, and the number of batteries (#3, #7, #15, #31, #99) judged as defective by the battery inspection device (N_d_ng) is 5, so the over-inspection rate can be calculated as 20% (1 / 5).

[0107]

[0108] Figure 6 is a flowchart illustrating the operation of a data analysis method according to another embodiment of the present invention. Below, with reference to Figure 6, a method for calculating the actual failure rate of a battery under inspection according to an embodiment of the present invention will be described in more detail.

[0109] The data analysis device can collect inspection information including the identifier of each battery to be inspected and the inspection result (normal or defective) from the battery inspection device (S610). For example, when inspection of 100 batteries to be inspected is completed, the data analysis device can receive inspection information including {[#1; OK], [#2; OK], [#3; NG], [#4; OK], ... [#99; NG], [#100; OK]} from the battery inspection device.

[0110] The data analysis device can collect packaging information including identifiers for each of the batteries to be packaged from the identifier collection device (S620). For example, the data analysis device can receive packaging information including {#1; #2; #3; #4; #5; ... #100} from a barcode scanner.

[0111] The data analysis device can calculate the number of defective batteries using the inspection information collected in S410 and the packaging information collected in S420 (S630). Here, a defective battery refers to a battery that was determined to be defective by the battery inspection device and subsequently determined to be defective in a subsequent visual inspection, resulting in a final defective judgment.

[0112] Specifically, the data analysis device can calculate the number of defective batteries based on the difference between the number of batteries subject to inspection and the number of batteries subject to packaging. Here, the data analysis device can calculate the number of defective batteries by calculating the difference between the number of identifiers included in the inspection information and the number of identifiers included in the packaging information.

[0113] 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 number of defective batteries can be calculated as 4.

[0114] Thereafter, the data analysis device can calculate the actual failure rate of the batteries to be inspected based on the number of batteries to be inspected and the number of defective batteries (S640). Here, the actual failure rate can be defined as the ratio (N_r_ng / N_all) of the number of defective batteries (N_r_ng) and the number of batteries to be inspected (N_all).

[0115] In the above example, the number of defective batteries (N_r_ng) is 4, and the number of batteries to be inspected (N_all) is 100, so the defective rate can be calculated as 4% (4 / 100).

[0116]

[0117] Figure 7 is a block diagram of a data analysis device according to an embodiment of the present invention.

[0118] The data analysis device (700) according to an embodiment of the present invention may be linked to a battery inspection device included in a battery manufacturing system that determines whether batteries to be inspected are defective; and an identifier collection device that collects identifiers of batteries to be packaged. Here, the data analysis device (700) may correspond to an integrated monitoring device of the battery manufacturing system or may be included in the integrated monitoring device.

[0119] A data analysis device (700) according to an embodiment of the present invention may include at least one processor (710), a memory (720) that stores at least one command executed through the processor, and a transmission / reception device (730) that is connected to a network and performs communication.

[0120] The at least one command may include a command for collecting inspection information on the batteries to be inspected from the battery inspection device; a command for collecting packaging information, including identifiers of the batteries to be packaged, from the identifier collection device; and a command for generating performance information on the battery inspection device using the inspection information and packaging information.

[0121] The command for collecting the above inspection information may include a command for collecting identifiers of the batteries to be inspected and inspection results indicating whether each of the batteries to be inspected is defective.

[0122] The command for collecting the above packaging information may include a command for collecting identifiers of the batteries to be packaged, which are determined to be the final normal and are packaged by the packaging device.

[0123] The above-mentioned battery to be packaged may include a battery that is judged to be normal by the battery inspection device, and a battery that is judged to be defective by the battery inspection device but is judged to be normal upon inspection by a worker.

[0124] The command for collecting the above packaging information may include a command for collecting an identifier of each of the batteries to be packaged from an identifier collection device that recognizes an identification code displayed on the outer surface of the battery.

[0125] The command to generate performance information of the battery inspection device may include a command to calculate an over-inspection rate of the battery inspection device using the inspection information and packaging information.

[0126] The command for calculating the over-inspection rate of the battery inspection device may include a command for selecting batteries included in the batteries to be packaged among the batteries determined to be defective by the battery inspection device; and a command for calculating the over-inspection rate of the battery inspection device based on the number of selected batteries.

[0127] The command for calculating the over-inspection rate of the battery inspection device may include a command for calculating the ratio of the number of selected batteries and the number of batteries determined to be defective by the battery inspection device.

[0128] The command to generate performance information of the battery inspection device may include a command to calculate the actual failure rate for the batteries to be inspected using the inspection information and packaging information.

[0129] The command for calculating the actual failure rate for the above-mentioned batteries to be inspected may include a command for calculating the actual failure rate based on the difference between the number of the above-mentioned batteries to be inspected and the number of the above-mentioned batteries to be packaged.

[0130] The data analysis device (700) may also include an input interface device (740), an output interface device (750), a storage device (760), etc. Each component included in the data analysis device (700) may be connected by a bus (770) to communicate with each other.

[0131] Here, the processor (710) may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed. The memory (or storage device) may be comprised of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory may be comprised of at least one of a read-only memory (ROM) and a random access memory (RAM).

[0132]

[0133] The operations of the method according to an embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. A computer-readable recording medium includes any type of recording device that stores data readable by a computer system. Furthermore, a computer-readable recording medium can be distributed across network-connected computer systems, allowing the computer-readable program or code to be stored and executed in a distributed manner.

[0134]

[0135] While some aspects of the present invention have been described in the context of a device, they may also represent a description of a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described as a corresponding block or item or a feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most significant method steps may be performed by such a device.

[0136] Although the present invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. A battery inspection device that determines whether batteries to be inspected are defective; and a data analysis device that is linked to an identifier collection device that collects identifiers of batteries to be packaged. at least one processor; and A memory that stores at least one instruction to be executed through at least one processor, At least one of the above commands, A command to collect inspection information on the batteries to be inspected from the battery inspection device; A command to collect packaging information, including identifiers of the batteries to be packaged, from the identifier collection device; and A data analysis device comprising a command to generate performance information for the battery inspection device using the above inspection information and packaging information.

2. In claim 1, The command to collect the above inspection information is: A data analysis device comprising an identifier of the batteries to be inspected and a command for collecting inspection results indicating whether each of the batteries to be inspected is defective.

3. In claim 1, The command to collect the above packaging information is: A data analysis device comprising a command for collecting identifiers of the batteries to be packaged, which are determined to be the final normal and are packaged by the packaging device.

4. In claim 3, The above packaging target battery is, A data analysis device including a battery that is judged to be normal by the battery inspection device, and a battery that is judged to be defective by the battery inspection device but is judged to be normal as a result of inspection by an operator.

5. In claim 3, The command to collect the above packaging information is: A data analysis device comprising a command to collect an identifier of each of the batteries to be packaged from an identifier collection device that recognizes an identification code displayed on the outer surface of the battery.

6. In claim 1, A command to generate performance information of the above battery test device is: A data analysis device including a command for calculating an over-inspection rate of the battery inspection device using the above inspection information and packaging information.

7. In claim 6, The command for calculating the over-inspection rate of the above battery inspection device is: A command to select batteries included in the packaging target batteries from among the batteries determined to be defective by the battery inspection device; and A data analysis device comprising a command for calculating an over-inspection rate of the battery inspection device based on the number of selected batteries.

8. In claim 7, The command for calculating the over-inspection rate of the above battery inspection device is: A data analysis device comprising a command for calculating a ratio between the number of the selected batteries and the number of batteries determined to be defective by the battery inspection device.

9. In claim 1, A command to generate performance information of the above battery test device is: A data analysis device including a command for calculating the actual defect rate for the batteries to be inspected using the above inspection information and packaging information.

10. In claim 9, The command to calculate the actual failure rate for the above-mentioned batteries is: A data analysis device including a command for 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.

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

12. In claim 11, The steps of collecting the above inspection information are: A data analysis method comprising a step of collecting identifiers of the batteries to be inspected and inspection results indicating whether each of the batteries to be inspected is defective.

13. In claim 1, The steps of collecting the above packaging information are: A data analysis method comprising a step of collecting identifiers of the batteries to be packaged, which are determined to be the final normal and are packaged by a packaging device.

14. In claim 13, The above packaging target battery is, A data analysis method including a battery determined to be normal by the battery inspection device, and a battery determined to be defective by the battery inspection device but determined to be normal as a result of inspection by an operator.

15. In claim 13, The steps of collecting the above packaging information are: A data analysis method comprising a step of collecting an identifier of each of the batteries to be packaged from an identifier collection device that recognizes an identification code displayed on the outer surface of the battery.

16. In claim 11, The step of generating performance information of the above battery test device is: A data analysis method comprising a step of calculating an over-inspection rate of the battery inspection device using the above inspection information and packaging information.

17. In claim 16, The step of calculating the over-inspection rate of the above battery inspection device is: A step of selecting batteries included in the packaging target batteries from among the batteries determined to be defective by the battery inspection device; and A data analysis method comprising a step of calculating an over-inspection rate of the battery inspection device based on the number of selected batteries.

18. In claim 17, The step of calculating the over-inspection rate of the above battery inspection device is: A data analysis method comprising a step of calculating a ratio between the number of the selected batteries and the number of batteries determined to be defective by the battery inspection device.

19. In claim 11, The step of generating performance information of the above battery test device is: A data analysis method comprising a step of calculating the actual defect rate for the batteries to be inspected using the above inspection information and packaging information.

20. In claim 19, The step of calculating the actual defect rate for the above-mentioned batteries is as follows: A data analysis method, comprising a 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 the batteries to be inspected are defective; A packaging device that packages batteries that have been determined to be final and normal; An identifier collection device that collects the identifier of each battery to be packaged; and A data analysis device comprising: a data analysis device that generates performance information for the battery inspection device; The above data analysis device, A battery manufacturing system that collects inspection information on the batteries to be inspected from the battery inspection device, collects packaging information including identifiers of the batteries to be packaged from the identifier collection device, and generates performance information on the battery inspection device using the inspection information and packaging information.

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