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

Abstract

The present invention measures a defect in a battery material or an in-process product to rank the battery material or a manufacturing process that is the cause of the defect or detection of a change in the state of the defect in manufacturing of a secondary battery. A defect inspection device for inspecting a defect in a secondary battery member includes: an abnormality detection unit for detecting an abnormality from defect detection data pertaining to a battery material or an in-process product in a battery manufacturing step and outputting an abnormality detection time; and a cause estimation unit for ranking and estimating the cause of the abnormality on the basis of a manufacturing history for a secondary battery, the manufacturing history being stored in a manufacturing information database, and the abnormality detection time.

Description

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

[0001] The present invention relates to a defect inspection device, a defect inspection system, a defect inspection program, and a defect inspection method for inspecting defects in secondary battery components.

[0002] Secondary batteries, such as lithium-ion batteries, are capable of repeatedly charging and discharging electrical energy, and are used, for example, in automotive applications, etc. Patent Document 1 discloses a method for manufacturing a secondary battery, which involves measuring defect information in electrode materials and determining which parts to use in assembly during the manufacturing of the secondary battery.

[0003] For example, the abstract of Patent Document 1 states in the Problems section that "A high-quality secondary battery is manufactured by taking into consideration variations in quality depending on the location of use of the electrode material.", and in the Solution section that "A method for manufacturing a secondary battery, comprising: a position information acquisition step for acquiring identification information of the electrode material to be used in assembly and the location of a portion of the electrode material to be used in assembly that is to be used; a quality information acquisition step for acquiring quality information of the portion of the electrode material that is to be used based on the acquired identification information and location; and a usage portion determination step for determining a portion of the electrode material that will actually be used in assembly based on the acquired quality information."

[0004] JP 2009-266739 A

[0005] If defects occur in work-in-progress products such as electrodes or battery materials such as separators during the battery manufacturing process, this can lead to a decrease in battery capacity, an increase in self-discharge, and other problems, which can result in a decrease in battery performance, such as the ability to store electrical energy. Therefore, in a performance inspection process after battery manufacturing, battery performance, such as battery capacity and self-discharge, is inspected and non-standard products are sorted out. However, because battery sorting reduces yield, it is preferable to detect defective battery materials and work-in-progress products during the battery manufacturing process and take measures to prevent them from being sent to subsequent processes. On the other hand, to minimize yield reduction, defects that have little impact on battery performance, such as those with small defects, may be sent to subsequent processes without taking any measures.

[0006] To maintain such high-level quality control, it is important to inspect battery materials and work-in-progress products, and if defects that could affect battery performance are found, to quickly respond by changing or adjusting battery materials or manufacturing conditions.

[0007] However, simply inspecting battery materials and work-in-progress for defects does not lead to an identification of the battery materials or manufacturing process that caused the defect. In order to identify the cause of the defect and change the battery materials or manufacturing conditions that caused it, manual analysis is required. The analysis work relies heavily on the know-how of the analyst, and it can take time to identify the battery materials, manufacturing processes, and conditions that caused the defect. Therefore, there is room to shorten the time between detecting a defect and taking action.

[0008] The above-mentioned Patent Document 1 discloses a method for measuring defect information in electrode materials and determining the parts to be used in assembly, but does not describe a method for estimating the battery materials, manufacturing process, or conditions that caused the defects.

[0009] The present invention has been made in consideration of the above circumstances, and aims to provide a defect inspection device, a defect inspection system, a defect inspection program, and a defect inspection method for measuring defects in battery materials and work-in-progress, detecting changes in the defect state, and quickly estimating the battery materials, manufacturing process, and conditions that caused the defects.

[0010] One form of the present invention that solves the above problem is a defect inspection device that inspects defects in components for secondary batteries, and includes an abnormality detection unit that detects abnormalities from defect detection data of battery materials or work-in-progress in a battery manufacturing process and outputs the time the abnormality was detected, and a cause estimation unit that ranks and estimates the causes of the abnormality based on the manufacturing history of the secondary battery stored in a manufacturing information database and the time the abnormality was detected.

[0011] According to the present invention, defects can be measured in upstream manufacturing processes such as battery materials and work-in-progress, changes in the defect status of battery materials and work-in-progress can be detected, and the battery materials, manufacturing processes, and conditions that caused the defects can be estimated at an early stage. This enables early countermeasures to be taken, which has the effect of maintaining high-level quality control and improving yield.

[0012] 1 is a schematic diagram showing an example of a secondary battery manufacturing process; FIG. 2 is a configuration diagram of a defect inspection system of Example 1; FIG. 3 is a functional block diagram of the defect inspection system of Example 1; FIG. 4 is a processing flowchart of the defect inspection device of Example 1; FIG. 5 is a configuration diagram of a defect inspection system of Example 5; FIG. 6 is a functional block diagram of the defect inspection system of Example 5; FIG. 7 is a graph showing an example of calculation of similarity of defect feature amounts in Example 5; and FIG. 8 is a processing flowchart of the defect inspection device of Example 5.

[0013] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The embodiments are illustrative for explaining the present invention, and some omissions and simplifications have been made as appropriate for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0014] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0015] Although various types of information may be described using expressions such as "table," "list," and "queue" as examples, the various types of information may also be expressed using data structures other than these. For example, various types of information such as "XX table," "XX list," and "XX queue" may also be expressed as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, but these are interchangeable.

[0016] When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. When there is no need to distinguish between these multiple components, the subscripts may be omitted.

[0017] In the embodiments, processing performed by executing a program may be described. Here, a computer executes the program using a processor (e.g., a CPU or a GPU) and performs processing defined by the program using storage resources (e.g., memory) and interface devices (e.g., communication ports). Therefore, the entity performing the processing by executing the program may be the processor. Similarly, the entity performing the processing by executing the program may be a controller, device, system, computer, or node having a processor. The entity performing the processing by executing the program may be any computing unit, and may include a dedicated circuit that performs specific processing. Here, the dedicated circuit may be, for example, an FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a CPLD (Complex Programmable Logic Device).

[0018] A program may be installed on a computer from a program source. The program source may 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 may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. In addition, in the embodiments, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0019] <Explanation of Secondary Battery Manufacturing Process> First, an example of a secondary battery manufacturing process will be described with reference to the schematic diagram of FIG.

[0020] In the electrode manufacturing process P1, positive and negative electrodes were manufactured. The materials for the positive electrode were prepared by blending and kneading one or more of an active material, a conductive additive, a binder, etc. to form a positive electrode slurry. The slurry was applied to a metal foil, which serves as a current collector foil, and then dried and pressed to obtain a positive electrode sheet. The positive electrode sheet was then processed into a predetermined shape. In this manner, a positive electrode can be manufactured. A negative electrode can also be manufactured in the same manner as the positive electrode. Note that the method of applying the slurry to a current collector foil, followed by drying and pressing, was used as an example. However, depending on the materials constituting the battery, such as a solid-state battery (variously called a solid, semi-solid, quasi-solid, or pseudo-solid), the drying and pressing processes may be omitted.

[0021] In the cell manufacturing process P2, a separator (described later) was placed between the positive electrode and negative electrode manufactured in the electrode manufacturing process P1, followed by winding the resulting structure to produce a wound body. In another embodiment of the cell manufacturing process P2, a separator was placed between the positive electrode and negative electrode manufactured in the electrode manufacturing process P1, followed by stacking the resulting structure to produce a laminate. The separator is a component that prevents physical contact between the positive electrode and negative electrode while allowing Li ions to migrate. When an electrolyte (described later) is used, the separator contains a porous material to retain the electrolyte. In the case of a solid-state battery, the separator can contain a solid-state electrolyte (variously called a solid, semi-solid, quasi-solid, or pseudo-solid) that prevents physical contact between the positive electrode and negative electrode while allowing Li ions to migrate.

[0022] The wound body or laminate (hereinafter, sometimes collectively referred to as an electrode group) has tabs formed on the electrodes welded to current collecting members for extracting electricity. The electrode group is then placed in a battery outer casing (e.g., a battery can or a laminate sheet), and the outer casing is sealed by welding, thermocompression, or the like. An electrolyte solution is then injected, and the injection hole is sealed. In this manner, a battery cell can be manufactured. While the winding or stacking method has been described as an example, this method is not intended to be limiting, and any method for manufacturing a secondary battery can be widely applied. Furthermore, depending on the materials constituting the battery, such as a solid-state battery (variously called a solid, semi-solid, quasi-solid, or quasi-solid), the injection step may be omitted.

[0023] In the battery inspection process P3, the electrical characteristics of the battery cells manufactured by the above method were inspected. This process includes, for example, a step of inspecting the battery capacity by charging and discharging the battery one or more times, and a step of inspecting the presence and extent of battery self-discharge. Methods for inspecting the presence and extent of battery self-discharge include charge / discharge inspection, voltage drop inspection, internal resistance inspection, and self-discharge current inspection. The presence and extent of battery self-discharge can be inspected by performing one or a combination of these inspections.

[0024] During this battery manufacturing process, defects can occur in work-in-progress items such as electrodes and battery materials such as separators. There are many different types of defects. Anything outside the design specifications can be considered a defect, such as foreign matter, wrinkles, holes, cracks, uneven or uncoated electrodes, and electrode material slippage. Because there are many different types of defects and their causes, simply detecting the defect does not lead to improvement; it is important to also estimate the battery materials, manufacturing processes, and conditions that caused the defect. An overview of each defect is provided below.

[0025] <<Foreign Matter>> Foreign matter can occur in work-in-progress products such as electrodes and battery materials such as separators. Factors that lead to foreign matter contamination include the presence of foreign matter in battery materials, foreign matter attached to workers or present in the atmosphere that is mixed into materials or work-in-progress, foreign matter generated by scraping of manufacturing equipment or piping that is mixed into work-in-progress, and shavings and the like produced during processing of battery components that are mixed into work-in-progress. Examples of foreign matter that can be mixed into battery materials or work-in-progress as impurities include metal particles such as pure metals and alloys, ceramic particles such as metal oxides and glass, carbon particles, organic matter, and composites of these. For convenience, the term "particle" is used here to refer not only to spherical shapes but also to irregular shapes such as shavings, burrs, foils, and fibers. Furthermore, foreign matter elements are not limited to particles composed of elemental compositions that are not used in battery positive or negative electrode materials, but also include particles with irregular shapes or sizes, even if they have elemental compositions that are used in battery positive or negative electrode materials.

[0026] If foreign matter is mixed in, during battery operation, the foreign matter may cause damage or fracture of the separator between the positive and negative electrodes, causing contact between the positive and negative electrodes and resulting in an internal short circuit. Alternatively, the foreign matter may dissolve at the positive electrode, deposit at the negative electrode, and penetrate the separator, causing contact between the positive and negative electrodes through the deposit, resulting in an internal short circuit. Internal short circuits lead to excessive self-discharge of the battery, thereby reducing battery performance. In particular, foreign matter containing metal particles, even if they are as small as several tens of micrometers, may dissolve at the positive electrode, deposit at the negative electrode, and penetrate the separator, causing an internal short circuit.

[0027] However, because foreign matter is so minute, it is difficult to control, and more advanced quality control is required. As mentioned above, there are many different types of foreign matter and ways in which they can get mixed in, so in order to improve the quality, it is important not only to detect foreign matter but also to estimate the cause of the contamination.

[0028] <<Wrinkles and holes>> Wrinkles and holes can occur in work-in-progress products such as electrodes, and in battery materials such as separators. Wrinkles and holes can be present in current collector foils or separators from the beginning, or they can occur during the battery manufacturing process when excessive tension or compression is applied due to deviations from optimal manufacturing conditions during processes such as coating and drying, transportation, pressing, processing, winding, or stacking. In order to improve the situation, it is important not only to detect wrinkles and holes, but also to estimate the cause, whether it is due to the battery materials or the manufacturing conditions.

[0029] <<Unevenness and uncoated electrodes>> Unevenness and uncoated electrodes can occur in work-in-progress such as electrodes. Unevenness and uncoated electrodes occur when excessive density differences occur in the electrodes when slurry is applied to the current collector foil and then dried. Density differences cause unevenness, and when the density differences become too large, uncoated areas occur where no electrode is formed. The main causes of unevenness and uncoated electrodes include air bubbles in the slurry, coating conditions that fall outside the normal range, and uneven heating during drying. In order to improve the situation, it is important to identify the manufacturing process that caused the problem and identify the cause.

[0030] <<Cracks and electrode material slippage>> Cracks and electrode material slippage can occur in work-in-progress products such as electrodes. During the battery manufacturing process, deviations from optimal manufacturing conditions during conveying (coating, drying, etc.), pressing, processing, winding, or stacking can result in excessive tension, stress, or compression, causing cracks in the electrodes. If the impact becomes severe, it can even lead to electrode material slippage. In order to improve this, it is important to identify the manufacturing process that caused the problem and identify the cause.

[0031] The following describes a defect inspection device, system, and method for detecting defects and estimating the battery materials, manufacturing process, and conditions that caused the defects.

[0032] 2 is a configuration diagram of a defect inspection system 100 according to a first embodiment, which inspects secondary battery components for defects during secondary battery manufacturing. The defect inspection system 100 shown here includes a defect inspection device 1 that inspects battery materials and work-in-progress for defects, a measurement device 2 that measures the battery materials and work-in-progress and generates defect detection data, an external storage device 3 that is a non-volatile storage medium, and an input / output device 4 such as a keyboard, mouse, touch panel, display, etc. Note that the measurement device 2 and the external storage device 3 are not essential to this system, and the system configuration may omit one of them.

[0033] <Defect inspection device 1> The defect inspection device 1 is a computer equipped with a processor 11 such as a CPU or GPU, a memory 12 which is a volatile storage medium, an internal storage device 13 which is a non-volatile storage medium, and an interface 14 such as a communication port.

[0034] The interface 14 is connected to the measuring device 2, the external storage device 3, and the input / output device 4. In a system in which the measuring device 2 is omitted, the external storage device 3 storing defect detection data is connected instead of the measuring device 2. In a system in which the external storage device 3 is omitted, information required to execute the present invention is stored in advance in the internal storage device 13.

[0035] The internal storage device 13 stores data such as a manufacturing information database DB1, which will be described later.

[0036] Programs for the anomaly detection unit 12a and the cause estimation unit 12b are loaded into the memory 12, and the processor 11 executes each program to realize the anomaly detection unit 12a and the cause estimation unit 12b. Details of both units will be described later, but an overview is as follows. That is, the anomaly detection unit 12a calculates defect feature amounts for defect detection data acquired from the measuring device 2 or the external storage device 3, and determines deviations from a pre-specified normal range as an anomaly. The cause estimation unit 12b calculates and ranks candidate battery materials and manufacturing conditions that are estimated to be the cause of the deviation of the defect feature amounts from the normal range, based on the time when the anomaly detection unit 12a detected the anomaly.

[0037] <Functional Block Diagram of Defect Inspection System 100> Figure 3 is a functional block diagram extracting the main parts of defect inspection system 100 of Figure 2, and is also a configuration diagram outlining the processing performed by the system. This diagram shows, as the main parts of defect inspection system 100, an abnormality detection unit 12a and a cause estimation unit 12b of defect inspection device 1, a defect measurement unit 21 of measurement device 2, a display device 41 (display) which is a type of input / output device 4, and a manufacturing information database DB1 stored in internal storage device 13 or external storage device 3. Each component will be described in turn below.

[0038] <<Manufacturing Information Database DB1>> The manufacturing information database DB1 is a database that accumulates information related to the manufacturing history of battery materials and work-in-progress, such as material lots, material IDs, manufacturing lots, manufacturing IDs, manufacturing performance data, equipment maintenance data, and data related to the electrical characteristics of batteries, which are acquired in each of the processes of the electrode manufacturing process P1, the cell manufacturing process P2, and the battery inspection process P3.

[0039] <<Measuring Device 2>> The measuring device 2 is a device that measures battery materials and work-in-progress in at least one of the electrode manufacturing process P1 and the cell manufacturing process P2, and is, for example, a transmission X-ray measuring device, a fluorescent X-ray measuring device, an electron microscope, a spectroscopic analyzer, or a magnetic sensor.

[0040] The data measured here is measurement data using X-rays if the measurement device 2 is a transmission X-ray measurement device or a fluorescent X-ray measurement device, measurement data using electron beams if the measurement device 2 is an electron microscope, measurement data using visible light if the measurement device 2 is an optical microscope, measurement data using infrared, visible, or ultraviolet light if the measurement device 2 is a spectroscopic analysis device, or measurement data using magnetism if the measurement device 2 is a magnetic sensor. Note that the measurement data may be reconstructed as an image.

[0041] The defect measurement unit 21 of the measuring device 2 is a functional unit that generates defect detection data for battery materials and work-in-progress products in each process based on the above-mentioned measurement data. Note that, although the defect measurement unit 21 is disposed within the measuring device 2 in Fig. 3, the system may be configured such that the measurement data output by the measuring device 2 is processed by the defect measurement unit 21 in the memory 12 of the defect inspection device 1 to generate defect detection data.

[0042] Foreign matter, which is a type of defect to be inspected and is mixed in as an impurity, may be found not only on the surface of components such as electrodes and separators but also embedded inside them. Therefore, from the perspective of being able to penetrate and measure the interior of components, it is desirable for the measurement device 2 of this system to be a transmission X-ray measurement device or a fluorescent X-ray measurement device that uses X-rays as its measurement principle.

[0043] <<Anomaly Detection Unit 12a>> The anomaly detection unit 12a is a functional unit that generates defect features based on acquired defect detection data. Multiple types of defect features may be generated, for example, one-dimensional to multidimensional (n-dimensional) features. The defect features are obtained by converting defect detection data, and may include defect volume, defect area, defect thickness, defect minor axis, defect major axis, defect aspect ratio, defect shape, defect image data, defect position (coordinates), defect occurrence count / probability, defect elemental composition, defect detection data correlated with these, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations thereof. In this one-dimensional to n-dimensional defect feature space, a range that has little impact on battery performance is predefined as a normal range. Deviations from the normal range are detected as change points in the defect features. The normal range can be set arbitrarily and may be updated appropriately based on, for example, the evaluation results of battery performance.

[0044] The left graph in the anomaly detection unit 12a in FIG. 3 is an example of analyzing feature data in one-dimensional space. In this example, the defect feature X １ If it falls within the normal range, it is judged to be normal, and if it is outside the normal range, it is judged to be abnormal (a change point is detected). Also, the right graph in the abnormality detection unit 12a in FIG. 3 is an example in which feature data is analyzed in two-dimensional space. In this example, too, the defect feature X １ , X ２ If the plot falls within the normal range, it is judged to be normal, and if it is outside the normal range, it is judged to be abnormal (a change point is detected).

[0045] When a change point (anomaly) is detected, the anomaly detection unit 12a transmits manufacturing information, such as the time when the change point was detected (anomaly detection time), along with the defect feature to the cause estimation unit 12b. Note that the normal range with little impact on battery performance refers to, for example, if the defect is a foreign object, a range in which self-discharge due to an internal short circuit caused by the foreign object is below a standard, and can be considered to be the normal range if the foreign object size is minute. For example, if the defect is a crack in an electrode or electrode material slippage, a range in which battery capacity reduction is below a standard, and can be considered to be the normal range if the crack or electrode material slippage is minute or insignificant.

[0046] <<Cause Estimation Unit 12b>> The cause estimation unit 12b calculates and ranks candidate battery materials and manufacturing conditions that are estimated to be the cause of the deviation of the defect feature amount from the normal range, based on the manufacturing information acquired from the manufacturing information database DB1 and the anomaly detection time acquired from the anomaly detection unit 12a. Table 1 is an example of manufacturing information acquired from the manufacturing information database DB1. Here, the manufacturing unit is the manufacturing lot L, but the manufacturing unit is not limited to this and can be widely used.

[0047]

[0048] When the manufacturing information exemplified in Table 1 is used, the cause estimation unit 12b identifies a manufacturing lot in which the defect feature amount deviates from the normal range, based on the detection time of the change point transmitted from the anomaly detection unit 12a. The following describes an example in which manufacturing starts with manufacturing lot L1, and a deviation of the defect feature amount from the normal range is detected in manufacturing lot L4.

[0049] The changes synchronized with the deviation detection by the anomaly detection unit 12a, i.e., the changes synchronized with the change from production lot L3 to production lot L4, are material B and process D, as shown by the shaded areas in Table 1, and therefore the cause estimation unit 12b estimates these two changes as candidate causes of the change in defect feature quantity. Furthermore, the cause estimation unit 12b traces back the production lots and estimates material A, which changed from production lot L2 to production lot L3, as the next candidate cause. Furthermore, the cause estimation unit 12b traces back the production lots and estimates material B, which changed from production lot L1 to production lot L2, and part replacement for equipment maintenance as the next candidate causes.

[0050] Using this procedure, the cause estimation unit 12b extracts material B and process D as first candidates for the cause of the deviation of the defect feature amount from the normal range, material A as second candidates, and material B and part replacement for equipment maintenance as third candidates, and ranks these candidates.

[0051] <<Display Device 41>> The display device 41 displays, together with a ranking, candidate battery materials and manufacturing conditions that are estimated to be the cause of the deviation of the defect feature amount calculated by the cause estimation unit 12 b from the normal range. Examples of a display method on the display device 41 include a display example such as the table 41 a in FIG. 3 , but the estimated cause of the defect may also be displayed in other formats.

[0052] <Flowchart> FIG. 4 is a processing flowchart in the defect inspection apparatus 1 of the first embodiment.

[0053] First, in step S11, the abnormality detection unit 12a acquires defect detection data measured in the electrode manufacturing process P1 or the cell manufacturing process P2 from the defect measurement unit 21 of the measurement device 2 (or from the external storage device 3 that stores the defect detection data).

[0054] In step S12, the abnormality detection unit 12a converts the defect detection data acquired in step S11 into defect feature quantities.

[0055] In step S13, the anomaly detection unit 12a determines whether the defect feature amount deviates from a predetermined normal range (see FIG. 3). If the requirement is met, the process proceeds to step S14, and if the requirement is not met, the process returns to step S11.

[0056] In step S14, the abnormality detection unit 12a transmits the deviation detection time and the like to the cause estimation unit 12b.

[0057] In step S15, the cause estimation unit 12b identifies the production unit (production lot, etc.) in which the deviation was detected based on the deviation detection time, etc., and traces back the production unit to extract changes in production information (battery materials and production conditions) (see Table 1).

[0058] In step S16, the cause estimation unit 12b ranks the changes in battery materials and manufacturing conditions as possible causes in descending order of the manufacturing unit (see FIG. 3 ) and outputs them to the display device 41. As a result, the display device 41 displays the estimated causes of the abnormality in descending order of the probability of occurrence.

[0059] <Effects> According to the first embodiment described above, it is possible to detect deviations of defect features from normal ranges from defect detection data measured by a measurement device in upstream manufacturing processes such as battery materials and work-in-progress, and rank and display candidate causes. These are calculated without human intervention, and the man-hours required for estimating the causes can be reduced.

[0060] Next, a description will be given of Example 2. Note that a duplicated description of points common to Example 1 will be omitted.

[0061] Defect detection data for an electrode formed in a sheet form, which is one of the work-in-progress, and defect detection data for a separator, which is one of the battery materials, were measured using an apparatus based on X-ray measurement as the measuring apparatus 2 in Fig. 2. As the battery material and work-in-progress measured by the measuring apparatus 2, one or more of the materials and work-in-progress before, during, or after each process in Fig. 1 may be measured, but from the perspective of measuring an electrode or separator formed in a sheet form, it is preferable to measure one or more of the battery material and work-in-progress before, during, or after the coating, drying, pressing, processing, winding, or lamination process.

[0062] Electrodes such as positive and negative electrodes manufactured in the electrode manufacturing process P1 in FIG. 1 may be formed into sheet-like electrode components with thicknesses of several tens to several hundreds of micrometers by applying electrode slurry to electrode foil in a coating process. Separators may also be sheet-like components with thicknesses of several tens of micrometers. In such thick electrodes and separators, defects, particularly foreign particles, may not necessarily be exposed on the surface of the electrode or separator but may be buried internally. To measure defects buried in electrodes or separators, it is preferable to use a measurement device 2 that uses a measurement principle of penetration through components. For example, transmission X-rays or fluorescent X-rays can be measured by penetrating the electrode or separator.

[0063] X-ray transmission measurement devices irradiate electrodes or separators with X-rays and measure the intensity (sometimes referred to as brightness) of the transmitted X-rays. When X-rays penetrate an electrode or separator, they are absorbed, resulting in a decrease in brightness of the transmitted X-rays. If a defect is present, the brightness of the transmitted X-rays will decrease or increase compared to areas without defects because the amount of X-ray absorption differs between the defect and the electrode or separator. For example, if the defect is a foreign object, brightness will decrease if it contains a metal with a larger atomic weight than the base electrode or separator material, and brightness will increase if it contains a metal with a smaller atomic weight. Furthermore, brightness will increase if the defect is a hole, crack, uneven or uncoated electrode, or electrode material slippage. Therefore, by mapping the change in brightness of the transmitted X-rays, information related to the presence or absence of defects and their size can be measured as defect detection data.

[0064] Defect detection data that can be measured by a transmission X-ray measurement device include defect measurement information such as the brightness or brightness change of transmitted X-rays, defect image data obtained by mapping the defect measurement information such as the brightness or brightness change of transmitted X-rays, the number of detected defects, and the positions (coordinates) of the detected defects. The defect detection data can be used as defect feature quantities. Furthermore, the defect detection data can be converted to obtain defect volume, defect area, defect thickness, defect minor axis, defect major axis, defect aspect ratio, defect shape, defect image data, defect coordinates, defect occurrence number / probability, defect elemental composition, defect detection data correlated with these, their statistics (mean, median, maximum, minimum, standard deviation, variance, etc.), and combinations of these.

[0065] An X-ray fluorescence measurement device irradiates an electrode or separator with X-rays and measures the energy of the fluorescent X-rays. Because the energy of the fluorescent X-rays is element-specific, the elemental composition can be identified from the energy of the fluorescent X-rays. If a defect is present, the elemental composition differs between the defect and the electrode or separator, and fluorescent X-rays with different energies are measured. By mapping the energy of the fluorescent X-rays, information related to the presence or absence of defects, their size, and the elemental composition of the defects can be measured as defect detection data. Examples of defect detection data that can be measured with an X-ray fluorescence measurement device include defect measurement information such as the energy of the fluorescent X-rays, defect image data on which the defect measurement information such as the energy of the fluorescent X-rays is mapped, the number of detected defects, and the positions (coordinates) of the detected defects. The defect detection data can be used as defect feature data. Furthermore, the defect detection data can be converted and used as defect feature data. Furthermore, defect detection data can be converted to provide defect features, such as defect volume, defect area, defect thickness, defect minor axis, defect major axis, defect aspect ratio, defect shape, defect image data, defect coordinates, defect occurrence count / probability, defect elemental composition, defect detection data correlated with these, their statistics (average value, median value, maximum value, minimum value, standard deviation, variance, etc.), and combinations of these.

[0066] Other than that, a defect inspection device for secondary battery components was constructed in the same manner as in Example 1.

[0067] <Effects> In this way, by using the method described in Example 2, even if defects occur inside battery materials such as electrodes and separators or work-in-progress, deviations of defect features from the normal range can be detected from defect detection data measured by a measuring device in the upstream manufacturing process of the battery materials or work-in-progress, and candidate causes can be ranked and displayed. These are calculated without human intervention, which can reduce the man-hours required to estimate the cause.

[0068] Next, a description will be given of Example 3. Note that a duplicated description of points common to the above-mentioned examples will be omitted.

[0069] In this embodiment, the measurement process using the measurement device 2 was incorporated into the electrode manufacturing process P1 or the cell manufacturing process P2, and defect detection data was measured during these processes.

[0070] The measuring device 2, which uses transmitted X-rays as its measurement principle, is configured to include a part that irradiates the battery material or work-in-progress, which is the object to be measured, with X-rays, and a part that detects the transmitted X-rays. Therefore, in order to incorporate the measuring device 2 into manufacturing equipment, the part that irradiates X-rays and the part that detects the transmitted X-rays must be installed in the manufacturing equipment.

[0071] For example, in the coating process of the electrode manufacturing process P1, the electrode is formed into a sheet, and then transported through the manufacturing equipment in the form of a series of sheets or cut into pieces of any desired area. Furthermore, in subsequent processes (coating, drying, pressing, processing, winding, or lamination), the electrode is transported through the manufacturing equipment by roll-to-roll, belt conveyor, air chuck, or the like. Therefore, by incorporating the measuring device 2 into at least one or more manufacturing equipment, defect detection data for battery materials or work-in-progress products during transportation can be measured in real time.

[0072] <Effects> In this way, defect detection data can be measured in real time in upstream manufacturing processes such as battery materials and work-in-progress by the method described in Example 3. According to the present invention, deviations of defect features from the normal range in upstream manufacturing processes such as battery materials and work-in-progress can be detected, and the ranking of candidate causes can be displayed in real time.

[0073] Next, a fourth embodiment will be described. Note that a duplicated description of points common to the above-mentioned embodiments will be omitted.

[0074] In this embodiment, the measurement process using the measurement device 2 is provided separately from the electrode manufacturing process P1 and the cell manufacturing process P2.

[0075] For example, between the drying step, pressing step, and processing step of the electrode manufacturing process P1 and the winding or stacking step of the cell manufacturing process P2 in Fig. 1 , work-in-progress may be transferred from the manufacturing equipment of the previous step to the manufacturing equipment of the subsequent step. Therefore, by providing a measurement step using the measuring device 2 between each step, defect detection data for the battery material or work-in-progress can be measured. In Example 3, a method for incorporating the measuring device 2 into secondary battery manufacturing equipment was described, but there are cases where existing manufacturing equipment does not have space to incorporate the measuring device 2. According to the method described in Example 4, defect detection data can be measured even when there is no space to incorporate the measuring device 2 into existing manufacturing equipment.

[0076] <Effects> In this way, by using the method described in Example 4, even if there is no space to incorporate the measuring device 2 into existing manufacturing equipment, it is possible to detect deviations of defect features from the normal range in upstream manufacturing processes such as battery materials and work-in-progress from defect detection data measured by the measuring device 2, and rank candidate causes of the deviations and display them.

[0077] Next, a fifth embodiment will be described. Note that a duplicated description of points common to the above-mentioned embodiments will be omitted.

[0078] 5 is a configuration diagram of a defect inspection system 100 according to Example 5. The defect inspection device for secondary battery components according to Example 5 has the same configuration as Example 1, and further has a defect feature database DB2 stored in the internal storage device 13 or the external storage device 3.

[0079] The defect feature database DB2 stores in advance defect feature quantities generated from defect detection data that may occur in each battery material and in work-in-progress during the manufacturing process. The defect detection data to be stored in the defect feature database DB2 may be actual data obtained by measuring battery materials and work-in-progress during actual product manufacturing, or inspection data obtained by creating samples that simulate defects. Furthermore, the data stored in the defect feature database DB2 may be updated based on actual evaluation data of battery defects.

[0080] The defect feature values ​​were generated by measuring the defects using the same measurement method as that used by the measurement device 2. Multiple defect feature values ​​may be generated, and for example, one-dimensional to multidimensional (n-dimensional) feature values ​​can be generated. The defect feature values ​​are obtained by converting defect detection data, and may include defect volume, defect area, defect thickness, defect minor axis, defect major axis, defect aspect ratio, defect shape, defect image data, defect position (coordinates), defect occurrence count / probability, defect element composition, defect detection data correlated with these, their statistics (average value, median value, maximum value, minimum value, standard deviation, variance, etc.), and combinations of these.

[0081] As shown in FIG. 6, the cause estimation unit 12b of this embodiment calculates candidates for battery materials and manufacturing conditions that are estimated to be the cause of the defect feature quantity deviating from the normal range using two methods.

[0082] The first calculation method is the method described in Example 1, in which manufacturing information is acquired from the manufacturing information database DB1, and candidate causes of deviation of the defect feature amount from the normal range are ranked and calculated based on the change-point detection time transmitted from the anomaly detection unit 12 a.

[0083] The second calculation method uses defect features stored in the defect feature database DB2. First, when the anomaly detection unit 12a detects a change point, the detected defect feature is transmitted to the cause estimation unit 12b. In this case, the cause estimation unit 12b acquires defect feature values ​​related to each battery material and defects that may occur in work-in-progress during the manufacturing process, which are stored in advance in the defect feature database DB2. At this time, it is possible to acquire only defect feature values ​​from the defect feature values ​​stored in advance in the defect feature database DB2 that are related to the process before the defect inspection. For example, when a defect inspection is performed during the pressing process in FIG. 1 , it is sufficient to refer to defect feature values ​​related to the process before the pressing process. This is because it is obvious that a defect detected during the pressing process would occur before the pressing process.

[0084] Next, the cause estimation unit 12b calculates the similarity between the defect feature acquired from the defect feature database DB2 and the defect feature transmitted from the anomaly detection unit 12a. The similarity can be calculated, for example, by comparing one-dimensional to multidimensional (n-dimensional) defect feature. In the one-dimensional to multidimensional (n-dimensional) defect feature space, the similarity can be calculated for each group of defect feature of each battery material or work-in-progress in each manufacturing process based on the distance from the center of gravity of the group or the distance from the boundary surface of the group.

[0085] Using foreign matter, a type of defect, as an example, we will explain why the battery material and manufacturing conditions that caused the defect can be inferred from the similarity of defect features. Foreign matter mixed into electrode materials tends to become rounded due to the heating process involved in material manufacturing and the friction between powder materials. Foreign matter generated in the manufacturing process tends to be sharp or have a large aspect ratio, as it is formed from shavings and burrs generated during metal processing such as electrode foil or from equipment drive units. In this way, it is possible to infer the cause from defect features. It goes without saying that defects other than foreign matter also have defect features specific to battery materials and manufacturing conditions.

[0086] In addition, the defect feature amount database DB2 in FIG. 6 contains the defect feature amount X １ , X ２1 illustrates defect feature value groups for materials A, B, and process C in a two-dimensional defect feature value space consisting of: Therefore, the center of gravity of each defect feature value group can be calculated based on the distribution of each defect feature value group.

[0087] 7 is a graph showing an example of a similarity calculation method in this embodiment, which shows an example of calculating the similarity between a defect feature transmitted from the anomaly detection unit 12a and a defect feature acquired from the defect feature database DB2 in the defect feature space. For example, in the defect feature space, the closer the defect feature transmitted from the anomaly detection unit 12a is to the center of gravity or the boundary surface of the defect feature group of material A acquired from the defect feature database DB2, the higher the similarity between the defect feature transmitted from the anomaly detection unit 12a and the defect feature of material A. Note that the similarity can be calculated, for example, by using the reciprocal of the distance.

[0088] Statistical calculations or machine learning calculations can be used to calculate the center of gravity or boundary surface of the defect feature group in the defect feature space. Examples include the MT method, k-means method, and support vector machine. By calculating the similarity between the defect features of each battery material and work-in-progress products in each manufacturing process, we were able to calculate a candidate ranking for the reasons why the defect features deviated from the normal range.

[0089] As described above, the cause estimation unit 12b in Example 5 ranked and calculated candidate causes of deviation of the defect feature from the normal range based on the change-point detection time and the defect feature transmitted from the anomaly detection unit 12a, from two perspectives: the change-point detection time and the similarity of the defect feature.

[0090] The display device 41 of this embodiment then displays at least one candidate cause of the deviation of the defect feature quantity calculated by the cause estimation unit 12b from the normal range, along with the candidate rank. The candidate rank can display both or either of the candidate cause estimated from the time when the change point was detected and the candidate cause estimated from the similarity of the defect feature quantity. Furthermore, based on the candidate rank of the deviation cause of the defect feature quantity calculated by the cause estimation unit 12b, a countermeasure to suppress the deviation may be transmitted to the manufacturing equipment as a control signal. For example, if the cause is battery material, a change in material lot may be transmitted to the manufacturing equipment, or if the cause is manufacturing conditions, a change in manufacturing conditions may be transmitted. This method allows countermeasures to be implemented in real time without the intervention of an operator.

[0091] 8 is a process flowchart in the defect inspection system of Example 5. The differences from the process flowchart (FIG. 4) of Example 1 are mainly in the processes of steps S24, S26, and S27.

[0092] First, the anomaly detection unit 12a acquires defect detection data from the measurement device 2 (S21). Next, the anomaly detection unit 12a converts the defect detection data into defect feature quantities (S22). Furthermore, the anomaly detection unit 12a determines whether the defect feature quantities deviate from a predetermined normal range (S23). If a deviation is detected, the anomaly detection unit 12a transmits the time when the deviation was detected, the defect feature quantities, and the like to the cause estimation unit 12b (S24).

[0093] The cause estimation unit 12b identifies the production unit (production lot, etc.) in which the deviation was detected based on the time, etc., and traces back the production unit to extract changes in the production information (S25). The cause estimation unit 12b also calculates the similarity between the defect feature amounts and the previously acquired defect feature amounts of each battery material and work-in-progress products in each production process based on the defect feature amounts (S26). Furthermore, the cause estimation unit 12b ranks the production units in descending order of proximity, displays the similarity, and outputs the changes in the production information as cause candidates to the display device 41 (S27).

[0094] 8 shows an example in which the cause candidates are first ranked based on the time when the deviation was detected in S27, but the cause candidates may also be ranked based on the similarity of the defect feature amounts first.Furthermore, it is also possible to display only one of the ranking based on the time when the deviation was detected and the ranking based on the similarity of the defect feature amounts.

[0095] <Effects> In this way, the method described in Example 5 can detect deviations of defect features from the normal range from defect detection data measured by the measurement device 2 in upstream manufacturing processes such as battery materials and work-in-progress, and rank and display candidate causes. Calculations based on the deviation detection time and the similarity of the defect features can rank candidate causes more accurately. These calculations are performed without human intervention, and the man-hours required to estimate the cause can be reduced.

[0096] Although the technical concept of the present invention has been explained using a lithium-ion battery as an example, the technical concept of the present invention is not limited to lithium-ion batteries and can be widely applied to electricity storage devices (e.g., other batteries).

[0097] Furthermore, the present invention is not limited to the above-described embodiments and includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and are not necessarily limited to including all of the described configurations. Furthermore, it is possible to replace a portion of the configuration of one embodiment with a configuration of another embodiment, or to add a configuration of another embodiment to a configuration of one embodiment. Furthermore, the addition, deletion, or substitution of a portion of the configuration of each embodiment with another configuration can be applied alone or in combination. Furthermore, it goes without saying that the components (including element steps, etc.) are not necessarily essential unless otherwise specified or considered to be clearly essential in principle. Furthermore, the number of elements (including the number, numerical value, amount, range, etc.) is not limited to a specific number, and may be greater than or less than a specific number, unless otherwise specified or clearly limited to a specific number in principle.

[0098] Furthermore, the above-described configurations, functions, processing units, and processing means may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations and functions may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0099] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected.

[0100] REFERENCE SIGNS LIST 1 Defect inspection device 11 Processor 12 Memory 12a Abnormality detection unit 12b Cause estimation unit 13 Internal storage device 14 Interface 2 Measuring device 21 Defect measurement unit 3 External storage device 4 Input / output device 41 Display device DB1 Manufacturing information database DB2 Defect feature database P1 Electrode manufacturing process P2 Cell manufacturing process P3 Battery inspection process

Claims

1. A defect inspection device for inspecting defects in components for secondary batteries, comprising: an anomaly detection unit that detects abnormalities from defect detection data of battery materials or work-in-progress in a battery manufacturing process and outputs the time the abnormality was detected; and a cause estimation unit that ranks and estimates the causes of the abnormalities based on the manufacturing history of the secondary battery stored in a manufacturing information database and the time the abnormality was detected.

2. A defect inspection device according to claim 1, wherein the anomaly detection unit calculates a defect feature from the defect detection data, and outputs the time at which the defect feature falls outside a normal range as the anomaly detection time.

3. A defect inspection device as claimed in claim 2, characterized in that the defect features include at least one of the following: defect volume, defect area, defect thickness, defect minor axis, defect major axis, defect aspect ratio, defect shape, defect image data, defect coordinates, defect occurrence count / probability, defect elemental composition, defect detection data correlated with these, statistics thereof, and combinations thereof.

4. A defect inspection apparatus according to claim 3, characterized in that the defect is a foreign object.

5. A defect inspection device according to claim 1, wherein the defect detection data is data measured by a measuring device that uses X-rays as its measurement principle.

6. A defect inspection device according to claim 5, wherein the defect detection data is defect image data obtained by mapping measurement data obtained by X-ray measurement.

7. A defect inspection device as claimed in claim 2, characterized in that the cause estimation unit ranks and estimates the causes of anomalies based on the defect features calculated by the anomaly detection unit and the defect features stored in a defect feature database.

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

9. A defect inspection device as claimed in claim 7, characterized in that the cause estimation unit calculates the center of gravity of a group of defect features for each battery material or each process stored in the defect feature database, and calculates a similarity based on the distance between the center of gravity and the defect feature calculated by the anomaly detection unit.

10. A defect inspection system for inspecting defects in components for secondary batteries, comprising: a manufacturing information database storing the manufacturing history of secondary batteries; a measuring device that measures battery materials or work-in-progress in a battery manufacturing process to generate defect detection data; a defect inspection device that inspects the battery materials or work-in-progress for defects based on the defect detection data; and a display device that displays the output of the defect inspection device, wherein the defect inspection device has an anomaly detection unit that detects abnormalities from the defect detection data and outputs the time the abnormality was detected, and a cause estimation unit that ranks and estimates the causes of the abnormalities based on the manufacturing history and the time the abnormality was detected, and the display device ranks and displays the causes of the abnormalities estimated by the cause estimation unit.

11. A defect inspection system according to claim 10, wherein the measuring device is incorporated in a manufacturing facility used in the battery manufacturing process.

12. A defect inspection system according to claim 10, characterized in that the measuring device measures battery materials or work-in-progress in a measuring process that is provided separately from each process in the battery manufacturing process.

13. A defect inspection program for causing a computer processor to execute the anomaly detection unit and cause estimation unit of claim 1.

14. A defect inspection method for inspecting defects in components for secondary batteries, comprising: a data generating step for generating defect detection data for battery materials or work-in-progress in a battery manufacturing process; an anomaly detecting step for detecting anomalies from the defect detection data; a time output step for outputting the anomaly detection time at which the anomaly was detected; an anomaly cause estimating step for ranking and estimating causes of anomalies based on the manufacturing history of the secondary battery stored in a manufacturing information database and the anomaly detection time; and a display step for displaying the ranked anomaly causes.

15. A defect inspection method as claimed in claim 14, characterized in that in the anomaly cause estimation step, a similarity between defect features stored in a defect feature database and defect features calculated from the defect detection data is calculated, and causes of anomalies are ranked based on the similarity.

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