Defect inspection device, defect inspection system, defect inspection program, and defect inspection method for secondary battery component

Abstract

In the manufacturing of a secondary battery, defects of a battery material and a semi-finished product are measured, changes in the state of the defects are detected, and the battery material and the manufacturing process which cause the defects are sorted. A defect inspection device for inspecting a defect of a secondary battery member comprises: an abnormality detection unit for detecting an abnormality on the basis of defect detection data of a battery material or a semi-finished product in a battery manufacturing process, and outputting an abnormality detection timing; and a cause estimation unit which, on the basis of the manufacturing history of the secondary battery stored in the manufacturing information database and the abnormality detection time, estimates the causes of the abnormality by sorting the causes.

Description

Technical Field

[0001] This invention relates to a defect inspection apparatus, a defect inspection system, a defect inspection procedure, and a defect inspection method for inspecting defects in secondary battery components. Background Technology

[0002] Secondary batteries, such as lithium-ion batteries, are batteries that can be repeatedly charged and discharged, for example, in automotive applications. In the manufacture of secondary batteries, a method for measuring defects in electrode materials and determining the parts to be assembled is known, as described in Patent Document 1.

[0003] For example, in the abstract of Patent Document 1, the subject line states "manufacturing a high-quality secondary battery considering quality deviations caused by the application location of the electrode material." The solution line states "a method for manufacturing a secondary battery, comprising: a position information acquisition step, acquiring identification information of the electrode material used for assembly and the position of a predetermined portion of the electrode material used for assembly; a quality information acquisition step, acquiring quality information of the predetermined portion of the electrode material based on the acquired identification information and position; and a usage portion determination step, determining the portion actually used for assembly in the electrode material based on the acquired quality information."

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2009-266739 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] During battery manufacturing, defects in semi-finished products such as electrodes and battery materials such as separators can sometimes lead to reduced battery capacity, increased self-discharge, and decreased battery performance, including energy storage capacity. Therefore, in the post-manufacturing performance inspection process, battery capacity, self-discharge, and other battery performance parameters are checked to screen for non-standard products. However, battery screening can reduce the yield rate. Therefore, it is preferable to detect defective battery materials and semi-finished products during battery manufacturing and take countermeasures to prevent them from flowing to subsequent processes. On the other hand, to minimize the reduction in yield, defects with minimal impact on battery performance, such as small defects, are sometimes left unaddressed and allowed to flow to subsequent processes.

[0009] To maintain such a high level of quality management, it is important to inspect battery materials and semi-finished products, and to quickly change or adjust battery materials and manufacturing conditions in the event of defects that affect battery performance.

[0010] However, simply inspecting for defects in battery materials and semi-finished products is insufficient to determine the underlying battery materials and manufacturing processes that might be causing the defects. To determine the cause of a defect, changes to the causative battery materials and manufacturing conditions are necessary, requiring manual analysis. This analysis relies heavily on the technical expertise of the person in charge, and determining the root cause of the defect can be time-consuming. Therefore, there is room to shorten the time from defect detection to appropriate action.

[0011] Patent Document 1 disclosed a method for measuring defect information in electrode materials and determining the parts to be used in assembly, but it did not describe the battery materials, manufacturing processes, or methods for estimating the causes of defects.

[0012] The present invention was made in view of the above circumstances, and its object is to provide a defect inspection device, defect inspection system, defect inspection procedure and defect inspection method for measuring defects in battery materials or semi-finished products, and for detecting changes in defect states and causes of defects in battery materials or manufacturing processes and conditions at an early stage.

[0013] Methods for solving problems

[0014] One aspect of the present invention that solves the above-mentioned problems is a defect inspection device for inspecting defects in components for secondary batteries, wherein the defect inspection device comprises: an anomaly detection unit that detects anomalies based on defect detection data of battery materials or semi-finished products in the battery manufacturing process and outputs the anomaly detection time; and a cause estimation unit that sorts and estimates the causes of the anomalies based on the manufacturing history of secondary batteries stored in a manufacturing information database and the anomaly detection time.

[0015] Invention Effects

[0016] According to the present invention, defects can be measured in upstream manufacturing processes such as battery materials and semi-finished products, and changes in the defect state of battery materials and semi-finished products can be detected at an early stage, as well as the battery materials, manufacturing processes, and conditions that may be the causes of defects. This enables early intervention, maintains high-level quality management, and improves yield. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating an example of the manufacturing process of a secondary battery.

[0018] Figure 2 This is a structural diagram of the defect inspection system in Example 1.

[0019] Figure 3 This is a functional block diagram of the defect inspection system in Example 1.

[0020] Figure 4 This is a flowchart of the processing in the defect inspection device of Example 1.

[0021] Figure 5 This is a structural diagram of the defect inspection system in Example 5.

[0022] Figure 6 This is a functional block diagram of the defect inspection system in Example 5.

[0023] Figure 7 This is a graph representing a calculation example of the similarity of the defect feature quantities in Example 5.

[0024] Figure 8 This is a flowchart of the processing in the defect inspection device of Example 5. Detailed Implementation

[0025] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. These embodiments are illustrative of the invention, and omissions and simplifications have been appropriately made for clarity. The invention can also be implemented in various other ways. Unless otherwise specified, each structural element may be a single element or a plurality of elements.

[0026] To facilitate understanding of the invention, the positions, sizes, shapes, and extents of the structural elements shown in the accompanying drawings may not represent their actual positions, sizes, shapes, or extents. Therefore, the invention is not necessarily limited to the positions, sizes, shapes, and extents disclosed in the accompanying drawings.

[0027] As examples of various types of information, they are sometimes represented by data structures such as "table," "list," and "queue," but various types of information can also be represented by data structures other than these. For example, information such as "XX table," "XX list," and "XX queue" can also be set as "XX information." When describing identification information, terms such as "identification information," "identifier," "name," "ID," and "number" are used, but these terms can be interchanged.

[0028] When multiple structural elements exist that are identical or have the same function, different subscripts are sometimes used to describe the same reference numerals in the accompanying drawings. Conversely, when it is not necessary to distinguish between these multiple structural elements, the subscripts are sometimes omitted in the description.

[0029] In the embodiments, the processing performed by executing the program is sometimes described. Here, the computer executes the program via a processor (e.g., CPU, GPU), using storage resources (e.g., memory), interface devices (e.g., communication ports), etc., while performing processing determined by the program. Therefore, the processor can also be considered as the subject of the processing performed by executing the program. Similarly, the subject of the processing performed by executing the program can also be a controller, device, system, computer, or node having a processor. The subject of the processing performed by executing the program can be any arithmetic unit, or it can include dedicated circuitry for performing specific processing. Here, dedicated circuitry includes, for example, FPGA (Field Programmable Gate Array), ASIC (Application Specific Integrated Circuit), CPLD (Complex Programmable Logic Device), etc.

[0030] The program can also be installed onto the computer from a program source. A program source can be, for example, a program distribution server or a computer-readable storage medium. When the program source is a program distribution server, the program distribution server includes a processor and storage resources for storing the program to be distributed. The processor of the program distribution server can also distribute the program to other computers. Furthermore, in embodiments, two or more programs can be implemented as a single program, or a single program can be implemented as two or more programs.

[0031] <Explanation of the secondary battery manufacturing process>

[0032] First, use Figure 1 The diagram illustrates an example of the secondary battery manufacturing process.

[0033] In electrode manufacturing process P1, a positive electrode and a negative electrode are manufactured. For the positive electrode, one or more of an active material, conductive additive, and binder are blended and mixed to form a positive electrode slurry. The slurry is coated onto a metal foil serving as a current collector, dried, and pressed to obtain a positive electrode sheet. The positive electrode sheet is then processed into a predetermined shape. This allows the positive electrode to be manufactured. The negative electrode can also be manufactured using the same method as the positive electrode. Furthermore, the method of coating the slurry onto the current collector foil and then drying and pressing it has been described as an example; however, depending on the materials used to construct batteries such as solid-state batteries (which may have various names such as solid, semi-solid, quasi-solid, and similar solid), the drying and pressing processes may sometimes be omitted.

[0034] In cell manufacturing process P2, a wound body is manufactured by placing the separator (described later) between the positive and negative electrodes manufactured in electrode manufacturing process P1 and then winding it. Alternatively, a laminated body is manufactured by placing the separator between the positive and negative electrodes produced in electrode manufacturing process P1 and then stacking them. The separator is a component that allows Li ions to move while preventing physical contact between the positive and negative electrodes. When using the electrolyte (described later), the separator is configured to contain a porous material to maintain the electrolyte. Furthermore, in the case of solid-state batteries, the separator can be configured to contain a solid electrolyte (with various names such as solid, semi-solid, quasi-solid, and similar solids) that allows Li ions to move while preventing physical contact between the positive and negative electrodes.

[0035] The wound or laminated body (hereinafter sometimes collectively referred to as the electrode assembly) welds the tabs formed on the electrodes to the current collector for extracting electricity. Then, the electrode assembly is placed into the battery casing (such as a battery can, laminate, etc.), and the casing is sealed by welding, heat pressing, etc.

[0036] Then, electrolyte is injected, and the injection hole is sealed. This allows the manufacture of a battery cell. Furthermore, while examples of winding or stacking methods have been provided, this method is not intended to limit the application to this specific method; any method for manufacturing secondary batteries can be widely used. Additionally, depending on the materials used to construct batteries such as solid-state batteries (which may have various names such as solid, semi-solid, quasi-solid, and similar solids), the electrolyte injection step may sometimes be omitted.

[0037] In battery inspection step P3, the electrical characteristics of the battery cells manufactured by the above method are inspected. This includes, for example, a step of performing one or more charge-discharge cycles on the battery and checking its capacity, and a step of checking for and to what extent self-discharge exists in the battery. Methods for checking for and to what extent self-discharge exists in the battery include charge-discharge checks, voltage drop checks, internal resistance checks, and self-discharge current checks. By performing one or a combination of these checks, the presence and extent of self-discharge in the battery can be determined.

[0038] During the battery manufacturing process, defects sometimes occur in semi-finished products such as electrodes, and battery materials such as separators. These defects are of various types. Any phenomenon outside the design specifications can be considered a defect, such as foreign objects, wrinkles, holes, cracks, uneven electrode coating, and electrode material slippage. Because the types and causes of defects are diverse, simply detecting defects is insufficient for improvement; therefore, it is crucial to estimate the battery materials, manufacturing processes, and conditions that may be causing the defects. A summary of each type of defect is described below.

[0039] <<Foreign Objects>>

[0040] Foreign matter may be generated in semi-finished products such as electrodes, and battery materials such as separators. The main causes of foreign matter contamination include: foreign matter being mixed into battery materials; foreign matter adhering to the operator or existing in the atmosphere; foreign matter generated during the cutting of manufacturing equipment or piping; and foreign matter such as chips from the processing of battery components. Foreign matter that can be mixed into battery materials and semi-finished products as impurities includes pure metals, alloys, metal oxides, ceramic particles such as glass, carbon particles, organic matter, and their complexes. Furthermore, for convenience, although it appears as particles, its shape is not limited to spheres; it also includes deformed shapes such as chips, burrs, foil, and fibers. In addition, the elements constituting foreign matter are not only particles composed of elements not used in the positive and negative electrode materials of the battery, but also particles of shapes and sizes that are outside the specifications of the elements used in the positive and negative electrode materials of the battery; these will be treated as foreign matter.

[0041] If foreign matter is introduced, during battery operation, there are several possibilities: the foreign matter can become the starting point for damage or breakage of the separator between the positive and negative electrodes, leading to contact between the positive and negative electrodes and causing an internal short circuit; or the foreign matter can dissolve at the positive electrode and precipitate at the negative electrode, with the precipitate penetrating the separator and causing contact between the positive and negative electrodes through the precipitate, also resulting in an internal short circuit. Internal short circuits lead to excessive self-discharge of the battery, thus reducing its performance. In particular, foreign matter containing metal particles, even those as small as tens of micrometers, can dissolve at the positive electrode, precipitate at the negative electrode, and penetrate the separator, thereby causing an internal short circuit.

[0042] However, due to the small size of the foreign matter, it is difficult to manage and requires a higher level of quality control. As mentioned above, since there are multiple types of foreign matter and different entry paths, it is important not only to detect the foreign matter but also to determine the cause of its entry in order to improve the situation.

[0043] <<Folds, Holes>>

[0044] Wrinkles and pores can occur in semi-finished products such as electrodes and battery materials such as separators. Sometimes wrinkles and pores are present in the current collector foil and separator from the beginning, and sometimes they are caused by excessive tension or compression applied during the coating, drying, transportation, pressing, processing, winding, or stacking processes, which deviate from optimal manufacturing conditions. In addition to detecting wrinkles and pores, it is important to determine whether the cause is due to the battery material or the manufacturing conditions in order to improve the situation.

[0045] <<Uneven electrode coating>>

[0046] Electrode unevenness and lack of coating can occur in semi-finished products such as electrodes. Electrode unevenness and lack of coating refer to excessive density differences in the electrodes during the coating and drying of the slurry on the current collector foil. If a density difference occurs, it becomes uneven; if the density difference is large, uncoated areas where no electrode is formed are created. The main causes of unevenness and lack of coating include air bubbles mixed into the slurry, deviations from the normal range of coating conditions, and uneven heating during drying. To improve this, it is important to determine which manufacturing process caused the problem.

[0047] <<Cracks, Electrode Material Slippage>>

[0048] Cracks and electrode material slippage can occur in semi-finished products such as electrodes. During battery manufacturing, excessive tension, stress, or compression can be applied, sometimes deviating from optimal manufacturing conditions, such as during coating, drying, conveying, pressing, processing, winding, or laminating, which can sometimes lead to cracks in the electrodes. If the impact is significant, it can sometimes cause electrode material slippage. To improve this, it is important to determine which manufacturing process caused the crack.

[0049] The following describes the defect inspection apparatus, system, and method for detecting defects and presuming the cause of defects in battery materials, manufacturing processes, and conditions.

[0050] Example 1

[0051] Figure 2 This is a structural diagram of a defect inspection system 100 according to Embodiment 1, which is used to inspect for defects in components used in secondary battery manufacturing. The defect inspection system 100 shown here includes: a defect inspection device 1 that inspects for defects in battery materials or semi-finished products; a measuring device 2 that measures the battery materials or semi-finished products to generate defect detection data; an external storage device 3, which is a non-volatile storage medium; and input / output devices 4 such as a keyboard, mouse, touch panel, and display. Furthermore, the measuring device 2 and the external storage device 3 are not essential in this system, and either one can be omitted from the system structure.

[0052] <Defect Inspection Device 1>

[0053] The defect inspection device 1 is a computer equipped with a CPU, GPU and other processors 11, a memory 12 as a volatile storage medium, an internal storage device 13 as a non-volatile storage medium, and an interface 14 such as a communication port.

[0054] The interface 14 is connected to the measuring device 2, the external storage device 3, and the input / output device 4. Furthermore, in systems where the measuring device 2 is omitted, the external storage device 3, which stores defect detection data, is connected instead of the measuring device 2. Additionally, in systems where the external storage device 3 is omitted, the information required to execute this invention is pre-stored in the internal storage device 13.

[0055] The internal storage device 13 stores data such as the manufacturing information database DB1, which will be described later.

[0056] The processor 11 loads the programs for the anomaly detection unit 12a and the cause estimation unit 12b into the memory 12, and executes each program, thereby implementing the anomaly detection unit 12a and the cause estimation unit 12b. Details of both parts will be described later, and are summarized below. Specifically, the anomaly detection unit 12a calculates defect characteristic quantities based on defect detection data obtained from the measuring device 2 or the external storage device 3, and determines anomalies as deviations from a pre-specified normal range. The cause estimation unit 12b calculates and ranks candidate battery materials and manufacturing conditions that are presumed to be causes of the defect characteristic quantity deviating from the normal range, based on the time when the anomaly is detected by the anomaly detection unit 12a.

[0057] <Functional Block Diagram of Defect Inspection System 100>

[0058] Figure 3 It was extracted Figure 2 This is a functional block diagram of the main components of the defect inspection system 100, and also a structural diagram that outlines the processing of the system. In this diagram, the main components of the defect inspection system 100 include the anomaly detection unit 12a and cause estimation unit 12b of the defect inspection device 1, the defect measurement unit 21 of the measuring device 2, the display device 41 (display) which is an input / output device 4, and the manufacturing information database DB1 stored in the internal storage device 13 or the external storage device 3. Each structure will be described in turn below.

[0059] <<Manufacturing Information Database DB1>>

[0060] The manufacturing information database DB1 is a database that stores information related to the manufacturing history of battery materials and semi-finished products, such as material batches, material IDs, manufacturing batches, manufacturing IDs, manufacturing performance data, equipment maintenance data, and data related to the electrical characteristics of batteries, obtained in each process of electrode manufacturing process P1, unit manufacturing process P2, and battery inspection process P3.

[0061] <<Measuring Device 2>>

[0062] The measuring device 2 is a device for measuring battery materials or semi-finished products in at least one of the electrode manufacturing process P1 or the unit manufacturing process P2. For example, it is a transmission X-ray measuring device, a fluorescence X-ray measuring device, an electron microscope, a spectral analysis device, or a magnetic sensor.

[0063] The data measured here are X-ray based measurements if the measuring device 2 is a transmission X-ray measuring device or a fluorescence X-ray measuring device; electron beam based measurements if it is an electron microscope; visible light based measurements if it is an optical microscope; infrared, visible light, or ultraviolet light based measurements if it is a spectral analysis device; and magnetic based measurements if it is a magnetic sensor. Furthermore, the measuring device 2 can also reconstruct images from the measurement data.

[0064] The defect measurement unit 21 of the measuring device 2 is a functional unit that generates defect detection data for battery materials and semi-finished products in each process based on the aforementioned measurement data. Furthermore, in Figure 3 In this system, a defect measurement unit 21 is provided in the measuring device 2, but it can also be configured as a system structure in which the defect measurement unit 21 in the memory 12 of the defect inspection device 1 processes the measurement data output by the measuring device 2 to generate defect detection data.

[0065] Foreign matter, which is a defect that is inspected and is mixed in as an impurity, is sometimes not only buried on the surface of components such as electrodes and diaphragms, but also buried inside. Therefore, from the point of view that it can penetrate into the inside of the component for measurement, the measuring device 2 of this system preferably uses a transmission X-ray measuring device or a fluorescence X-ray measuring device that uses X-rays as the measurement principle.

[0066] <<Anomaly Detection Department 12a>>

[0067] The anomaly detection unit 12a is a functional unit that generates defect feature quantities based on the acquired defect detection data. Various defect feature quantities can be generated, such as 1-dimensional to multi-dimensional (n-dimensional) feature quantities. These defect feature quantities are obtained by transforming the defect detection data and can include defect volume, defect area, defect thickness, defect minor diameter, defect major diameter, defect aspect ratio, defect shape, defect image data, defect location (coordinates), defect occurrence number and probability, defect element composition, related defect detection data, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof. For this 1-dimensional to n-dimensional defect feature quantity space, the range with minimal impact on battery performance is predefined as the normal range. When the range deviates from the normal range, it is detected as a point of change in the defect feature quantity. Furthermore, the normal range can be arbitrarily set, and for example, it can be appropriately updated based on actual battery performance evaluation results.

[0068] Figure 3 The left figure in the anomaly detection unit 12a is an example of parsing feature quantity data in one-dimensional space. In this example, if the defect feature quantity X1 converges within the normal range, it is judged as normal; if the defect feature quantity X1 falls outside the normal range, it is judged as abnormal (a change point is detected). Furthermore, Figure 3 The right figure in the anomaly detection unit 12a is an example of parsing feature quantity data in two-dimensional space. In this example, if the plotted points of defect feature quantities X1 and X2 converge within the normal range, it is judged as normal; if the plotted points are outside the normal range, it is judged as abnormal (a change point is detected).

[0069] When a change point (abnormality) is detected, the abnormality detection unit 12a sends manufacturing information, such as the time when the change point was detected (abnormality detection time), along with defect characteristic quantities to the cause estimation unit 12b. Furthermore, the normal range with minimal impact on battery performance refers to, for example, in the case of a foreign object, a range below the self-discharge caused by an internal short circuit due to the foreign object; if the foreign object is small, this can be considered normal. Additionally, for example, in the case of an electrode crack or electrode material slippage, this refers to a range below the battery capacity reduction threshold; if the crack or electrode material slippage is minor or negligible, this can be considered normal.

[0070] <<Cause Prediction Section 12b>>

[0071] In the cause estimation unit 12b, based on the manufacturing information obtained from the manufacturing information database DB1 and the anomaly detection time obtained from the anomaly detection unit 12a, candidate battery materials and manufacturing conditions that are presumed to be the cause of defect feature quantities deviating from the normal range are calculated and ranked. Table 1 is an example of the manufacturing information obtained from the manufacturing information database DB1. Here, the manufacturing batch L is used as the manufacturing unit, but the manufacturing unit is not limited to this and can be widely used.

[0072] [Table 1]

[0073] Table 1

[0074]

[0075] When using the manufacturing information illustrated in Table 1, the cause estimation unit 12b determines the manufacturing batch in which the defect feature quantity deviates from the normal range based on the detection time of the change point sent from the anomaly detection unit 12a. The following explanation will take the case where manufacturing begins in manufacturing batch L1, and a defect feature quantity deviating from the normal range is detected in manufacturing batch L4 as an example.

[0076] As shown in the shaded area in Table 1, the changes synchronized with the departure detection of the anomaly detection unit 12a, i.e., the changes synchronized with the change from manufacturing batch L3 to manufacturing batch L4, are material B and process D. Therefore, in the cause estimation unit 12b, these two changes are estimated as candidates for the cause of the change in defect characteristic quantity. In addition, the cause estimation unit 12b traces back to the manufacturing batch and estimates material A, which changed from manufacturing batch L2 to manufacturing batch L3, as the next candidate. Furthermore, the cause estimation unit 12b traces back to the manufacturing batch and estimates material B, which changed from manufacturing batch L1 to manufacturing batch L2, and the replacement of equipment maintenance components as the next candidates.

[0077] Through these steps, in the cause estimation section 12b, material B and process D are extracted as the first candidate for the cause of the defect feature quantity deviating from the normal range, material A is extracted as the second candidate, and material B and the replacement of equipment maintenance components are extracted as the third candidate, and they are ranked.

[0078] <<Display Device 41>>

[0079] The display device 41 displays, along with the candidate and order of battery materials and manufacturing conditions that are estimated by the cause estimation unit 12b to be the cause of the defect feature quantity deviating from the normal range. Figure 3 The example shown in Table 41a is one, but the presumed cause of the defect can also be shown in other ways.

[0080] <Flowchart>

[0081] Figure 4 This is a flowchart of the processing in the defect inspection device 1 of Embodiment 1.

[0082] First, in step S11, the anomaly detection unit 12a obtains the defect detection data measured in electrode manufacturing process P1 and unit manufacturing process P2 from the defect measurement unit 21 of the measuring device 2 (or the external storage device 3 that stores defect detection data).

[0083] In step S12, the anomaly detection unit 12a converts the defect detection data obtained in step S11 into defect feature quantities.

[0084] In step S13, the anomaly detection unit 12a determines whether the defect feature quantity has deviated from the predetermined normal range (refer to...). Figure 3 Then, if the requirements are met, proceed to step S14; otherwise, return to step S11.

[0085] In step S14, the anomaly detection unit 12a sends information such as the time of departure from detection to the cause estimation unit 12b.

[0086] In step S15, the cause estimation unit 12b determines the manufacturing unit (manufacturing batch, etc.) where the separation was detected based on the separation detection time, and traces the manufacturing unit to extract changes in manufacturing information (battery material, manufacturing conditions) (see Table 1).

[0087] In step S16, the cause estimation unit 12b sorts the manufacturing units from nearest to farthest, and outputs changes in battery materials and manufacturing conditions as candidate causes to the display device 41 (see reference). Figure 3 As a result, in the display device 41, the presumed causes of the anomaly are displayed in order of probability from highest to lowest.

[0088] <Effect>

[0089] According to Embodiment 1 described above, it is possible to detect deviations of defect feature quantities from the normal range based on defect detection data measured by measuring devices in upstream manufacturing processes such as battery materials and semi-finished products, and to sort and display candidate causes. These are not calculated manually, thus reducing the time required for presuming causes.

[0090] Example 2

[0091] Next, Example 2 will be described. Furthermore, the commonalities with Example 1 will be omitted from repeated descriptions.

[0092] As Figure 2 The measuring device 2, using an X-ray-based measurement principle, measured defect detection data of a sheet-shaped electrode (one of the semi-finished products) and a separator (one of the battery materials). The battery materials and semi-finished products measured by the measuring device 2 can be measured... Figure 1 One or more of the materials or semi-finished products before, during, or after each process, but from the viewpoint of measuring the electrode or separator formed into a sheet, it is preferable to measure one or more of the battery materials or semi-finished products before, during, or after the coating, drying, pressing, processing, winding, or stacking processes.

[0093] exist Figure 1 In the electrode manufacturing process P1, electrodes such as positive and negative electrodes are sometimes coated with electrode slurry in the coating process, resulting in sheet-like electrode components with a thickness of tens to hundreds of μm. Similarly, diaphragms are sometimes sheet-like components with a thickness of tens of μm. In electrodes and diaphragms with such thickness, defects, especially foreign matter, may not be exposed on the surface of the electrodes or diaphragms; sometimes they are embedded inside. To measure defects embedded in the electrodes and diaphragms, it is preferable to use a measuring device 2 based on the principle of measuring through components.

[0094] For example, transmitted X-rays and fluorescent X-rays can be measured through electrodes and diaphragms.

[0095] In a transmission X-ray measuring apparatus, X-rays are irradiated onto electrodes and diaphragms, and the intensity (sometimes called brightness) of the transmitted X-rays is measured. If X-rays pass through the electrodes and diaphragms, they are absorbed, and the brightness of the transmitted X-rays decreases. In the presence of defects, the amount of X-ray absorption differs between the defect and the electrodes / diaphragms, thus the brightness of the transmitted X-rays may decrease or increase compared to the defect-free portion. For example, if the defect is a foreign object, the brightness decreases when it contains a metal with a larger atomic weight than the electrode / diaphragm material used as the base material, and increases when it contains a metal with a smaller atomic weight.

[0096] Furthermore, brightness increases when defects include holes, cracks, uneven electrodes, lack of coating, or electrode material slippage. Therefore, by mapping the brightness change of transmitted X-rays, information related to the presence and size of defects can be measured as defect detection data.

[0097] As defect detection data that can be measured by a transmission X-ray measuring device, it includes defect measurement information such as the brightness or brightness variation of transmission X-rays, defect image data that maps the defect measurement information such as the brightness or brightness variation of transmission X-rays, the number of detected defects, and the location (coordinates) of the detected defects. Defect detection data can be used as defect characteristic quantities. Furthermore, as defect characteristic quantities, defect detection data can be transformed to use defect volume, defect area, defect thickness, defect minor diameter, defect major diameter, defect aspect ratio, defect shape, defect image data, defect coordinates, defect occurrence number and probability, defect elemental composition, related defect detection data, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof.

[0098] In a fluorescence X-ray measuring apparatus, X-rays are irradiated onto electrodes and a diaphragm, and the energy of the fluorescence X-rays is measured. The energy of the fluorescence X-rays is taken from the inherent value of the elements, thus the elemental composition can be determined based on the energy of the fluorescence X-rays. In the presence of defects, since the elemental composition of the defects differs from that of the electrodes and diaphragm, fluorescence X-rays with different energies are measured. By mapping the energy of the fluorescence X-rays, information related to the presence or absence of defects, the size of defects, and the elemental composition of defects can be measured as defect detection data. As defect detection data that can be measured by the fluorescence X-ray measuring apparatus, it includes defect measurement information such as the energy of the fluorescence X-rays, defect image data mapped with the defect measurement information such as the energy of the fluorescence X-rays, the number of detected defects, and the location (coordinates) of the detected defects. This defect detection data can be used as defect characteristic quantities. Furthermore, as defect characteristic quantities, the defect detection data can be converted and used. In addition, as defect feature quantities, defect detection data can be transformed using defect volume, defect area, defect thickness, defect minor diameter, defect major diameter, defect aspect ratio, defect shape, defect image data, defect coordinates, defect occurrence number and probability, defect element composition, defect detection data related to them, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof.

[0099] In addition, a defect inspection device for secondary battery components was constructed using the same method as in Example 1.

[0100] <Effect>

[0101] Thus, using the method described in Example 2, even defects generated inside battery materials such as electrodes and separators, as well as semi-finished products, can be detected based on defect detection data measured by a measuring device in upstream manufacturing processes such as battery materials and semi-finished products. This allows for the detection of defect characteristic quantities deviating from the normal range, and the potential causes can be ranked and displayed. These are not calculated manually, thus reducing the time required for presuming causes.

[0102] Example 3

[0103] Next, Example 3 will be described. Furthermore, the commonalities with the above examples will be omitted from repeated descriptions.

[0104] In this embodiment, the measurement process of the measuring device 2 is incorporated into the electrode manufacturing process P1 or the unit manufacturing process P2, and defect detection data is measured in these processes.

[0105] The measuring device 2, which uses X-ray transmission as its measurement principle, is configured to include a part that irradiates the battery material or semi-finished product, which is the object of measurement, with X-rays, and a part that detects the transmitted X-rays. Therefore, in order to assemble the measuring device 2 into the manufacturing equipment, it is necessary to install the part that irradiates the X-rays and the part that detects the transmitted X-rays into the manufacturing equipment.

[0106] For example, in the coating process of electrode manufacturing step P1, after the electrode is formed into a sheet, it is conveyed in the manufacturing equipment as a series of sheets or cut into arbitrary sizes. Furthermore, in subsequent processes (coating, drying, pressing, processing, winding, or lamination), it is conveyed within the manufacturing equipment using roller-to-roll conveyors, belt conveyors, pneumatic chucks, etc. Therefore, by assembling the measuring device 2 into at least one piece of manufacturing equipment, it is possible to measure defect detection data of the battery material or semi-finished product in real time during transport.

[0107] <Effect>

[0108] Thus, the method described in Example 3 enables real-time measurement of defect detection data in upstream manufacturing processes such as battery materials and semi-finished products. According to the present invention, it is possible to detect deviations of defect characteristic quantities from the normal range in upstream manufacturing processes such as battery materials and semi-finished products, and to display the order of potential causes in real time.

[0109] Example 4

[0110] Next, Example 4 will be described. Furthermore, the commonalities with the above examples will be omitted from the description.

[0111] In this embodiment, the measurement process of the measuring device 2 is set up separately from the various processes in the electrode manufacturing process P1 and the unit manufacturing process P2.

[0112] For example, in Figure 1 Between the drying, pressing, and processing steps of electrode manufacturing process P1, and the winding or laminating step of unit manufacturing process P2, the semi-finished product is sometimes changed from the manufacturing equipment of the previous step to the manufacturing equipment of the subsequent step. Therefore, by setting up a measurement step based on the measuring device 2 between each step, it is possible to measure defect detection data of battery materials or semi-finished products. In Example 3, a method for assembling the measuring device 2 in a secondary battery manufacturing equipment was described, but sometimes there is no space to assemble the measuring device 2 in existing manufacturing equipment. According to the method described in Example 4, even when there is no space to assemble the measuring device 2 in existing manufacturing equipment, defect detection data can be measured.

[0113] <Effect>

[0114] Thus, by means of the method described in Example 4, even in the absence of space to assemble the measuring device 2 in existing manufacturing equipment, it is possible to detect defects that deviate from the normal range based on the defect detection data measured by the measuring device 2 in upstream manufacturing processes such as battery materials and semi-finished products, and to sort and display the candidate causes.

[0115] Example 5

[0116] Next, Example 5 will be described. Furthermore, the commonalities with the above examples will be omitted from the description.

[0117] Figure 5 This is a structural diagram of the defect inspection system 100 of Embodiment 5. In addition to the structure of Embodiment 1, the defect inspection device for secondary battery components in Embodiment 5 also stores the defect feature quantity database DB2 in the internal storage device 13 or the external storage device 3.

[0118] The Defect Feature Database DB2 pre-stores defect feature quantities generated based on defect detection data that may occur in semi-finished products during various battery materials and manufacturing processes. The defect detection data stored in the Defect Feature Database DB2 can be actual data obtained by measuring battery materials and semi-finished products during actual product manufacturing, or it can be data from specimens that simulate defects. Furthermore, the data stored in the Defect Feature Database DB2 can be updated based on actual battery defect evaluation data.

[0119] As defect characteristic quantities, defects are measured using the same measurement method as measuring device 2, and defect characteristic quantities are generated. Multiple defect characteristic quantities can be generated, for example, characteristic quantities ranging from 1-dimensional to multi-dimensional (n-dimensional). Defect characteristic quantities are obtained by transforming defect detection data and can include defect volume, defect area, defect thickness, defect minor diameter, defect major diameter, defect aspect ratio, defect shape, defect image data, defect location (coordinates), defect occurrence number and probability, defect elemental composition, related defect detection data, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof.

[0120] like Figure 6 As shown, in the cause estimation section 12b of this embodiment, candidates for battery materials and manufacturing conditions that are estimated to be the cause of defect feature quantities deviating from the normal range are calculated using two methods.

[0121] The first calculation method is the method described in Example 1, which obtains manufacturing information from the manufacturing information database DB1, sorts and calculates the candidates for reasons why the defect feature quantity deviates from the normal range based on the change point detection time sent from the anomaly detection unit 12a.

[0122] The second calculation method uses the defect feature quantity calculation method stored in the defect feature quantity database DB2. First, when a change point is detected by the anomaly detection unit 12a, the detected defect feature quantity is sent to the cause estimation unit 12b. In this case, the cause estimation unit 12b obtains the defect feature quantities pre-stored in the defect feature quantity database DB2 related to each battery material and defects that may occur in the semi-finished product during the manufacturing process. At this time, only the defect feature quantities from the defect feature quantity database DB2 prior to the process in which defect inspection was performed can be obtained. For example, in... Figure 1 If defect inspection is performed during the pressing process, the defect characteristic quantities can be referenced for the period prior to pressing. This is because defects detected during the pressing process are obvious to have occurred before the pressing process.

[0123] Next, the cause estimation unit 12b calculates the similarity between the defect feature quantities obtained from the defect feature quantity database DB2 and the defect feature quantities sent from the anomaly detection unit 12a. The similarity calculation can, for example, compare and calculate defect feature quantities in one-dimensional to multi-dimensional (n-dimensional) space. In the one-dimensional to multi-dimensional (n-dimensional) defect feature quantity space, for each group of defect feature quantities of each battery material and each semi-finished product in each manufacturing process, the similarity can be calculated based on the distance from the center of gravity of the group and the distance from the boundary surface of the group.

[0124] Taking foreign matter as an example of a defect, the reasons why the cause of a defect can be inferred from the similarity of defect characteristic quantities will be explained. Foreign matter mixed into the electrode material tends to have rounded corners due to the heating process accompanying the material manufacturing and the friction between powder materials. Foreign matter generated during the manufacturing process is such as chips or burrs from metal processing of electrode foils or equipment drive units, so it tends to become sharp or have a large longitudinal and transverse ratio. In this way, the cause can be inferred from the defect characteristic quantities. For defects other than foreign matter, there are also defect characteristic quantities inherent to the battery material and manufacturing conditions.

[0125] In addition, Figure 6 The defect feature quantity database DB2 illustrates defect feature quantity clusters related to materials A, B, and process C in a two-dimensional defect feature quantity space composed of defect feature quantities X1 and X2. Therefore, the centroid of each defect feature quantity cluster can be calculated based on its distribution.

[0126] <An example of similarity calculation methods>

[0127] Figure 7This is a graph illustrating an example of the similarity calculation method in this embodiment. It shows an example of calculating the similarity between a defect feature quantity sent from the anomaly detection unit 12a and a defect feature quantity obtained from the defect feature quantity database DB2 in the defect feature quantity space. For example, in the defect feature quantity space, the closer the defect feature quantity sent from the anomaly detection unit 12a is to the centroid or boundary surface relative to the defect feature quantity group of material A obtained from the defect feature quantity database DB2, the higher the similarity to the defect feature quantity of material A. Furthermore, as a similarity measure, for example, the reciprocal of the distance can be taken.

[0128] The centroid or boundary surface of a defect feature cluster in the defect feature space can be calculated using statistical or machine learning methods. For example, the MT method, k-means method, and support vector machine can be used. By calculating the similarity of defect features for each battery material and each manufacturing process's semi-finished product, a candidate ranking can be calculated for reasons why defect features deviate from the normal range.

[0129] As described above, in the cause estimation unit 12b of Embodiment 5, based on the change point detection time and defect feature quantity sent from the anomaly detection unit 12a, the candidate causes of the defect feature quantity deviating from the normal range are sorted and calculated from the perspectives of the change point detection time and the similarity of the defect feature quantity.

[0130] Furthermore, the display device 41 of this embodiment displays at least one candidate cause for the defect feature quantity deviating from the normal range, calculated by the cause estimation unit 12b, along with a candidate order. The candidate order can display only one or both of the candidate causes estimated based on the time of detection of the change point and the candidate causes estimated based on the similarity of the defect feature quantity. Additionally, based on the candidate order of causes for the deviation of the defect feature quantity calculated by the cause estimation unit 12b, a countermeasure method to suppress the deviation can be sent as a control signal to the manufacturing equipment. For example, if the cause is battery material, a material batch change can be sent to the manufacturing equipment; if the cause is manufacturing conditions, a change in manufacturing conditions can be sent to the manufacturing equipment. In such a method, countermeasures can be executed in real time without the operator's intervention.

[0131] Figure 8 This is a flowchart of the defect inspection system in Example 5. Furthermore, it is similar to the flowchart of Example 1. Figure 4 The main difference lies in the processing of steps S24, S26, and S27.

[0132] First, the anomaly detection unit 12a acquires defect detection data from the measuring device 2 (S21). Next, the anomaly detection unit 12a converts the defect detection data into defect feature quantities (S22). Then, the anomaly detection unit 12a determines whether the defect feature quantity has deviated from a predetermined normal range (S23). If it has deviated, the anomaly detection unit 12a sends the time of detection of the deviation and the defect feature quantity, etc., to the cause estimation unit 12b (S24).

[0133] The cause estimation unit 12b determines the manufacturing unit (manufacturing batch, etc.) that was detected as having detached based on the time, etc., and traces the manufacturing unit to extract changes in manufacturing information (S25). Furthermore, the cause estimation unit 12b calculates the similarity to the defect feature quantities of each battery material and semi-finished product from each manufacturing process, based on the defect feature quantities obtained in advance (S26). Then, the cause estimation unit 12b sorts the manufacturing units from nearest to farthest, displays the similarity, and outputs the changes in manufacturing information as candidate causes to the display device 41 (S27).

[0134] In addition, Figure 8 The example described is of sorting the candidate causes based on the time when the detachment is detected in S27, but it is also possible to sort the candidate causes based on the similarity of the defect feature quantities first. Alternatively, only one of the sorting based on the time of detachment detection or the sorting based on the similarity of the defect feature quantities may be displayed.

[0135] <Effect>

[0136] Thus, using the method described in Example 5, it is possible to detect deviations of defect feature quantities from the normal range based on defect detection data measured by the measuring device 2 in upstream manufacturing processes such as battery materials and semi-finished products, and to rank and display candidate causes. By calculating based on the similarity between the deviation detection time and the defect feature quantity, the candidate causes can be ranked with higher accuracy. These calculations are performed manually, reducing the time required to presuppose causes.

[0137] Furthermore, this invention uses a lithium-ion battery as an example to illustrate the technical concept of the invention, but the technical concept of the invention is not limited to lithium-ion batteries and can be widely applied to energy storage devices (such as other batteries).

[0138] Furthermore, the present invention is not limited to the above-described embodiments, but also includes various modifications.

[0139] For example, the embodiments described above are detailed examples for the purpose of easily understanding and illustrating the present invention, and are not necessarily limited to including all the structures described. Furthermore, a portion of the structure of one embodiment can be replaced with the structure of another embodiment, and the structure of another embodiment can be added to the structure of one embodiment. Additionally, the addition, deletion, or replacement of other structures with respect to a portion of the structure of each embodiment can be applied individually or in combination. Moreover, structural elements (including element steps, etc.) are not necessarily required except where specifically stated or where they are clearly considered essential in principle. Furthermore, the quantity of elements (including number, value, quantity, range, etc.) is not limited to a specific quantity except where specifically stated or where it is clearly limited to a specific quantity in principle; it can be more than or less than a specific quantity.

[0140] Furthermore, the aforementioned structures, functions, processing units, and processing modules can be partially or entirely implemented in hardware, for example, through integrated circuit design. Alternatively, the aforementioned structures and functions can be implemented in software by a processor interpreting and executing programs that implement each function. The programs, tables, files, and other information implementing these functions can be stored in recording devices such as memory, hard disks, SSDs (Solid State Drives), or recording media such as IC cards, SD cards, and DVDs.

[0141] Furthermore, control lines and information lines are considered necessary for the instructions, but may not all control lines and information lines be represented on the product itself. In fact, almost all structures can be considered interconnected.

[0142] Explanation of reference numerals in the attached figures

[0143] 1. Defect inspection device

[0144] 11 processors

[0145] 12 memory,

[0146] 12a Anomaly Detection Department

[0147] 12b Cause Prediction Section

[0148] 13 Internal storage devices

[0149] 14 interfaces

[0150] 2. Measuring device

[0151] 21 Defect Measurement Department

[0152] 3. External storage devices

[0153] 4. Input / output devices

[0154] 41 Display devices

[0155] DB1 Manufacturing Information Database

[0156] DB2 Defect Feature Database

[0157] P1 electrode manufacturing process,

[0158] P2 unit manufacturing process

[0159] P3 battery inspection procedure.

Claims

1. A defect inspection device for inspecting defects in components used in secondary batteries, characterized in that, The defect inspection device has the following features: The anomaly detection department detects anomalies based on defect detection data of battery materials or semi-finished products in the battery manufacturing process and outputs the anomaly detection time. The cause estimation unit sorts and estimates the causes of the anomaly based on the manufacturing history of the secondary battery stored in the manufacturing information database and the time of the anomaly detection.

2. The defect inspection device according to claim 1, characterized in that, The anomaly detection unit calculates the defect feature quantity based on the defect detection data and outputs the moment when the defect feature quantity is outside the normal range as the anomaly detection moment.

3. The defect inspection device according to claim 2, characterized in that, The defect features include at least one of the following: defect volume, defect area, defect thickness, defect minor diameter, defect major diameter, defect aspect ratio, defect shape, defect image data, defect coordinates, defect occurrence number and probability, defect elemental composition, defect detection data associated with them, their statistics, and combinations thereof.

4. The defect inspection device according to claim 3, characterized in that, The defect is a foreign object.

5. The defect inspection device according to claim 1, characterized in that, The defect detection data is measured by a measuring device based on X-rays.

6. The defect inspection device according to claim 5, characterized in that, The defect detection data is defect image data mapped from X-ray-based measurement data.

7. The defect inspection device according to claim 2, characterized in that, The cause estimation unit sorts and estimates the causes of the anomalies based on the defect feature quantity calculated by the anomaly detection unit and the defect feature quantity stored in the defect feature quantity database.

8. The defect inspection device according to claim 7, characterized in that, The cause estimation unit calculates the similarity between the defect feature quantity calculated by the anomaly detection unit and the defect feature quantity stored in the defect feature quantity database.

9. The defect inspection device according to claim 7, characterized in that, The cause estimation unit calculates the centroid of the group of defect features stored in the defect feature quantity database for each battery material or each process, and calculates the similarity based on the distance between the centroid and the defect feature quantity calculated by the anomaly detection unit.

10. A defect inspection system for inspecting defects in components used in secondary batteries, characterized in that, The defect inspection system has the following features: A manufacturing information database that stores the manufacturing history of secondary batteries; A measuring device that measures battery materials or semi-finished products in the battery manufacturing process and generates defect detection data; A defect inspection device that inspects battery materials or semi-finished products for defects based on the defect detection data; and A display device that displays the output of the defect inspection device. The defect inspection device includes: an anomaly detection unit that detects anomalies based on the defect detection data and outputs the anomaly detection time; and a cause estimation unit that sorts and estimates the causes of the anomalies based on the manufacturing history and the anomaly detection time. The display device sorts and displays the causes of the anomalies estimated by the cause estimation unit.

11. The defect inspection system according to claim 10, characterized in that, The measuring device is assembled into the manufacturing equipment used in the battery manufacturing process.

12. The defect inspection system according to claim 10, characterized in that, The measuring device measures battery materials or semi-finished products in a measuring process that is separate from the various processes of the battery manufacturing process.

13. A defect inspection procedure, characterized in that, The computer's processor executes the anomaly detection unit and cause estimation unit as described in claim 1.

14. A defect inspection method for inspecting defects in components used in secondary batteries, characterized in that, The defect inspection method has the following characteristics: The data generation step generates defect detection data for battery materials or semi-finished products during the battery manufacturing process. Anomaly detection step: Detect anomalies based on the defect detection data; Output the steps at which an anomaly is detected, including the anomaly detection time. The abnormality cause estimation step involves sorting and estimating the causes of the abnormality based on the manufacturing history of the secondary battery stored in the manufacturing information database and the time of the abnormality detection. as well as The steps are displayed, along with the reasons for any errors after sorting.

15. The defect inspection method according to claim 14, characterized in that, In the step of estimating the cause of the anomaly, the similarity between the defect feature quantity stored in the defect feature quantity database and the defect feature quantity calculated based on the defect detection data is calculated, and the causes of the anomaly are sorted based on the similarity.

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