Quality control systems for energy devices, methods for controlling the quality of energy devices

Abstract

In the manufacturing process of energy devices composed of multiple electrode layers and electrolyte layers, this invention provides a quality control system for energy devices that can efficiently acquire characteristic quantities of intermediate products that strongly affect the performance of the energy device without significantly reducing the operating rate of the manufacturing line, and can manage the performance quality of the energy device during shipment inspection. [Solution] This is a quality control system for energy devices that determines the quality of intermediate products in the energy device manufacturing process, comprising: a device for acquiring first inspection data relating to the material of the intermediate product; a device for acquiring second inspection data which are characteristic quantities relating to the intermediate product; and a data storage and calculation device that takes the first and second inspection data as input and calculates a predicted performance value of the energy device based on previously accumulated correlation data.

Description

[Technical Field]

[0001] This invention relates to the configuration and method of a quality control system for managing the quality of energy devices, and more particularly to a technology that is effective when applied to energy devices using coated sheets as electrodes or carrier conduction layers. [Background technology]

[0002] To reduce CO2 emissions, there is a growing need to use fossil fuel-free secondary batteries and capacitors as the driving force for mobile vehicles and other equipment. Furthermore, in power grids, there is a need to increase the proportion of renewable energy sources such as solar and wind power, replacing fossil fuels as the energy source for electricity. Therefore, there is a growing need for electrical energy storage systems using secondary batteries or capacitors to provide adjustment capabilities to mitigate output fluctuations.

[0003] Furthermore, there is a growing need for dye-sensitized solar cells using perovskite crystals as one form of solar power generation. Energy devices such as secondary batteries, capacitors, and perovskite solar cells consist of a laminate of an electrode layer and an electrolyte layer for conducting carrier charges to the electrode layer. These energy devices are essential for realizing a decarbonized society, and the need for their manufacture is increasing.

[0004] The challenges in manufacturing these energy devices include improving energy efficiency and resource efficiency during production. Therefore, it is necessary to increase the yield of manufactured devices. However, improving the yield is difficult due to variations in the performance of materials that make up components (intermediate products) such as electrodes and electrolyte layers for carrier ions, and poor quality of components due to conditions that are difficult to control.

[0005] In the following, we will describe the challenges in energy device manufacturing in detail, using lithium-ion batteries, a type of secondary battery, as a specific example. Among the internal materials of lithium-ion batteries, electrode composite sheets are particularly influential on yield. These sheets contain an active material that stores electric charge, a conductive additive that conducts electrons to the active material, and a binder that holds them together. It is believed that the presence or absence of defects during manufacturing, the size of the aggregates, and the dispersion state of the constituent materials all affect battery performance.

[0006] As background technology for this field, for example, there is technology such as that described in Patent Document 1. Patent Document 1 discloses "a method for inspecting all-solid-state batteries that improves the efficiency of inspection of all-solid-state batteries."

[0007] Furthermore, Patent Document 2 discloses "a method for manufacturing a non-aqueous electrolyte secondary battery having a heat-resistant layer with reduced binder content."

[0008] Furthermore, Patent Document 3 discloses "a method for manufacturing secondary batteries that enables the production of a large number of products with limited resources, or improves the manufacturing yield." [Prior art documents] [Patent Documents]

[0009] [Patent Document 1] Japanese Patent Publication No. 2024-46206 [Patent Document 2] Japanese Patent Publication No. 2016-152066 [Patent Document 3] Japanese Patent Publication No. 2021-057272 [Overview of the project] [Problems that the invention aims to solve]

[0010] Incidentally, while conventional inspection methods for electrode composite sheets can identify obvious defects such as electrode defects, it is difficult to grasp mixing defects or non-uniform distribution of constituent materials in electrode composite sheets that do not have obvious defects. This has the drawback of making it difficult to detect performance defects in advance during manufacturing, shipping, or battery operation.

[0011] The above-mentioned Patent Document 1 describes a method for determining an appropriate range of electrode slurry viscosity based on a correlation between the viscosity of the electrode slurry and battery performance obtained in advance, and excluding those that deviate from this range from the manufacturing process. However, even with slurries within the range, it is difficult to detect cracks or abnormalities in compositional distribution that occur during the coating and drying processes.

[0012] Furthermore, while Patent Documents 2 and 3 show that structural information, including the compositional distribution of electrode composite sheets, can be detected and used as an indicator to determine performance quality, a challenge is that performing inspections to acquire this structural information, such as the scanning electron microscope (SEM)-energy-dispersive X-ray spectroscopy (EDX) shown in Patent Document 2, on all manufactured electrode composite sheets reduces the manufacturing utilization rate. In addition, the inability to detect performance defects during the shipping inspection leads to a decrease in yield during the manufacturing process, and the use of defective electrodes in the production of batteries results in a loss of time and energy. Moreover, the inability to detect performance defects during battery operation leads to a decrease in the utilization rate of equipment and systems using the affected batteries.

[0013] Generally, during the material and process design phase of a battery, if the battery performance after fabrication is not as desired, it is possible to investigate the reason by observing the electrodes using an electron microscope or similar equipment to check for the presence of poor mixing or non-uniform distribution, and to reflect the results in the material and process design. However, since these observations are time-consuming, they are often performed as offline evaluations by taking the relevant samples after manufacturing and evaluation, and have not been used for electrode inspection during the manufacturing process.

[0014] In addition, even if electrode structure information can be obtained through sampling inspection of the manufactured electrode composite sheets, a challenge remains in reducing the operating rate of the manufacturing line by unnecessarily sampling the material. Furthermore, methods for extracting manufacturing and structural information that affect battery performance have not yet been established, and the correlation between manufacturing and structural information and battery performance at the time of shipment inspection and degradation characteristics during operation is unclear, making it difficult to provide feedback to the manufacturing process based on the results of shipment inspection.

[0015] As mentioned above, conventional electrode inspection methods fail to detect non-uniform distribution, structural defects, and insufficient voids in electrode materials that may occur during the manufacturing of electrode composite sheets used as positive or negative electrodes. As a result, these issues lead to poor performance during final inspection and reduced manufacturing efficiency. Furthermore, even when information is obtained through electrode inspection, maintaining the operating rate of the manufacturing line is challenging. Additionally, the lack of clear correlation between the information and manufacturing conditions and battery performance makes it difficult to provide feedback to the electrode manufacturing process, which also contributes to reduced manufacturing efficiency.

[0016] The above concerns the manufacturing process of secondary batteries, such as lithium-ion batteries, but it is also a common challenge in the manufacturing process of energy devices such as perovskite solar cells and capacitors, which are produced by laminating multiple functional materials into a sheet through a slurry process.

[0017] Therefore, the object of the present invention is to provide an energy device quality control system and an energy device quality control method that enable efficient acquisition of intermediate product characteristics that strongly affect the performance of an energy device without significantly reducing the operating rate of the manufacturing line in the manufacturing process of an energy device composed of multiple electrode layers and an electrolyte layer, and that enable management of the performance quality of the energy device at the time of shipment inspection. [Means for solving the problem]

[0018] To solve the above problems, the present invention provides a quality control system for an energy device that determines the quality of an intermediate product in the manufacturing process of an energy device, comprising: a first inspection data acquisition device that acquires first inspection data relating to the material of the intermediate product; a second inspection data acquisition device that acquires second inspection data which are characteristic quantities relating to the intermediate product; a data storage and calculation device that takes the first inspection data and the second inspection data as input and calculates a performance prediction value of the energy device based on previously accumulated correlation data; and a data output device that outputs the first inspection data, the second inspection data, and the performance prediction value, wherein the intermediate product is a functional sheet that functions as an electrode layer or carrier conduction layer of the energy device, and a slurry obtained by mixing a functional material constituting the functional sheet, a solvent, and a binder, and the data storage and calculation device has a correlation database that stores manufacturing conditions, structural information, and performance information of the energy device of the functional sheet and the slurry, and the structural information of the intermediate product acquired from at least one of the first inspection data and the second inspection data is input into the correlation database to calculate a performance prediction value of the energy device and determine the quality of the intermediate product.

[0019] The present invention also relates to a quality control method for an energy device that determines the quality of an intermediate product in an energy device manufacturing process. The intermediate product is a functional sheet that functions as an electrode layer or a carrier conduction layer of the energy device, and a slurry in which a functional material, a solvent, and a binder that constitute the functional sheet are mixed. The energy device manufacturing process includes a slurry process of forming a mixture of the functional material, the solvent, and the binder, and a sheet process of obtaining a functional sheet by coating and drying the slurry on a substrate. The method also includes a first inspection data acquisition process of acquiring information regarding the properties of the mixture and the functional sheet, a process of determining an extraction region from the functional sheet based on the first inspection data acquired in the first inspection data acquisition process, a correlation database storing the manufacturing conditions, structural information, and performance information of the energy device, and a second inspection data acquisition process of acquiring structural data of the functional sheet corresponding to the extraction region. By inputting the structural information of the intermediate product obtained from at least one of the first inspection data and the second inspection data into the correlation database, an arithmetic device calculates a predicted performance value of the energy device and determines the quality of the intermediate product.

Advantages of the Invention

[0020] According to the present invention, in the manufacturing process of an energy device composed of a plurality of electrode layers and electrolyte layers, it is possible to efficiently acquire characteristic quantities of intermediate products that strongly affect the performance of the energy device without significantly reducing the operating rate of the production line, and to manage the energy device performance quality during the outgoing inspection. A quality control system for an energy device and a quality control method for an energy device can be realized.

[0021] Thereby, by feeding back the characteristic quantities of the obtained intermediate products as management items to the manufacturing conditions, it is possible to contribute to improving the manufacturing yield of the energy device and reducing the abnormal risk during the operation of the energy device.

[0022] Problems, configurations, and effects other than those described above will be clarified by the description of the following embodiments.

Brief Description of the Drawings

[0023] [Figure 1] It is a diagram schematically showing the structure in the planar (upper surface) direction of a secondary battery which is an example of an energy device targeted by the present invention. [Figure 2] It is a diagram showing the cross-section A - A' of FIG. 1. [Figure 3] It is a diagram schematically showing the cross-sectional structure of an electrode mixture sheet of a secondary battery. [Figure 4] It is a diagram schematically showing the manufacturing process of a secondary battery and the quality management system of an energy device according to the present invention. [Figure 5] It is a diagram schematically showing the manufacturing process and inspection process of an electrode mixture sheet according to the present invention. [Figure 6] It is a flowchart showing the manufacturing process and inspection process of an electrode mixture sheet according to the present invention. [Figure 7] It is a flowchart showing the process of creating a correlation database of manufacturing information, structural information, and performance information according to the present invention. [Figure 8] It is a diagram schematically showing the analysis procedure by a scanning electron microscope image of the electrode surface which is the second inspection data according to the present invention. [Figure 9] It is a diagram showing an example of the correlation coefficient of each item in a correlation database storing manufacturing conditions, structural information, and performance information of an energy device according to the present invention.

Embodiments for Carrying Out the Invention

[0024] Hereinafter, embodiments for carrying out the present invention (hereinafter referred to as "embodiments") will be described as appropriate while referring to the drawings.

[0025] The secondary batteries, capacitors, and solar cells that are the target of the quality control system for energy devices of the present invention are common in that the components that contribute to power generation and energy storage are composed of functional sheets containing functional inorganic or organic materials, and are obtained by coating and drying a slurry containing the functional material, and the effects of the quality control system described below are the same. Here, the functional sheet is an electrode composite sheet containing at least an active material capable of intercalating and releasing ions, and a binder.

[0026] In describing the quality control system for energy devices of the present invention, an embodiment of a quality control system for a secondary battery, which is one example, will be described, and then embodiments of other energy devices, such as solar cells and capacitors, will be described.

[0027] In explaining the quality control system for the secondary battery manufacturing process, we will first describe the structure and constituent materials of lithium-ion batteries, a typical secondary battery, and the manufacturing process of lithium-ion batteries. Then, we will describe the quality control system, which is an embodiment of the present invention. It should be noted that the secondary batteries to which the present invention applies are not limited to lithium-ion batteries; they are merely examples. The invention is also applicable to sodium-ion secondary batteries, potassium-ion secondary batteries, and secondary batteries using polyvalent cations as carriers, among others.

[0028] <Structure and constituent materials of lithium-ion batteries> The structure of one form of lithium-ion battery will be explained using Figures 1 and 2. Figure 1 is a schematic diagram showing the planar (top) structure of one form of lithium-ion battery, which is a secondary battery.

[0029] In Figure 1, the cell (corresponding to a battery cell, secondary battery cell, secondary battery, or lithium-ion battery) 100 comprises an electrode occupancy portion 1, a positive electrode terminal (tab) 2, a negative electrode terminal (tab) 3, an electrolyte 4, a separator 5, and an outer casing material 6. The outer casing material 6 is made of a laminate film or a similar material. Although Figure 1 shows a laminate with multiple separators and electrodes stacked and sealed with a laminate film, the present invention also applies to laminates that are wound and enclosed in a cylindrical outer casing material, or enclosed in a rectangular outer casing material.

[0030] Figure 2 shows a cross-section of line A-A' in Figure 1, schematically illustrating an example of a cross-section of the energy storage element (component of the energy storage mechanism) of cell 100. In Figure 2, the energy storage element of cell 100 comprises a positive electrode 11, a negative electrode 12, and a separator 5. The electrolyte 4, which functions as a battery, is impregnated into the micropores of the positive electrode 11, negative electrode 12, separator 5, etc. Therefore, the electrolyte 4 is not shown in Figure 2.

[0031] Furthermore, in Figure 2, the energy storage element has positive electrodes 11 and negative electrodes 12 arranged alternately with a separator 5 in between. For example, polypropylene can be used for the separator 5. However, in addition to polypropylene, microporous films or nonwoven fabrics made of polyolefins such as polyethylene can also be used as the separator 5. A solid electrolyte sheet using a lithium-ion conductive solid electrolyte can also be applied as the separator 5. Note that Figures 1 and 2 are schematic diagrams showing an example of a cross-section of a lithium-ion battery and an example of a cross-section of the energy storage element of a cell, and the positional relationships and sizes of each component are not limited to those shown.

[0032] The positive electrode 11 and the negative electrode 12 are each formed in sheet form on a suitable metal current collector foil, containing a mixture of suitable electrode active material, conductive agent, binder, etc. In this invention, these are referred to as "electrode composite sheets." In lithium-ion battery cells, which are one of the subjects of testing in this invention, any current collector can be used without being limited by material, shape, manufacturing method, etc.

[0033] <Positive electrode 11> The current collector foil 112 of the positive electrode 11 can be made of one of the following: aluminum foil with a thickness of 10 μm to 100 μm, perforated aluminum foil with a thickness of 10 μm to 100 μm and a pore size of 0.1 mm to 10 mm, expanded metal, foamed metal sheet, etc. In addition to aluminum, stainless steel, titanium, etc. can also be used as materials.

[0034] The electrode active material used in the electrode composite layer 111 of the positive electrode 11 is preferably one that contains a reactant. The reactant in a lithium-ion battery is lithium ions. In this case, the electrode active material contains a lithium-containing compound that can reversibly insert and remove lithium ions. Examples of the electrode active material of the positive electrode 11 include lithium cobaltate, manganese-substituted lithium cobaltate, lithium manganeseate, lithium nickelate, lithium iron phosphate (olivine type), and other lithium transition metals. w Ni x Co y Mn z One example is O2 (where w, x, y, and z are 0 or positive values).

[0035] <Negative electrode 12> The current collector foil 122 of the negative electrode 12 can be made of copper foil with a thickness of 10 μm to 100 μm, perforated copper foil with a thickness of 10 μm to 100 μm and a pore size of 0.1 mm to 10 mm, expanded metal, foamed metal sheet, etc. In addition to copper, stainless steel, titanium, etc. can also be used as materials.

[0036] The electrode active material used in the electrode composite layer 121 of the negative electrode 12 contains a material that allows for the reversible insertion and removal of lithium ions. The types of electrode active materials for the negative electrode 12 include, for example, natural graphite, composite carbonaceous materials formed by coating natural graphite using a dry CVD method or a wet spray method, artificial graphite produced by firing using resin materials such as epoxy or phenol or pitch-based materials obtained from petroleum or coal as raw materials, silicon (Si), silicon-mixed graphite, non-graphitizable carbon materials, and lithium titanate Li4Ti5O 12 The following can be used.

[0037] Furthermore, the active materials of the positive electrode 11 and negative electrode 12 described above can be selected and applied from multiple materials as needed. Figure 3 is a schematic diagram showing the cross-sectional structure of an electrode (electrode composite sheet) in a lithium-ion battery, which is a secondary battery. (a) is an electrode consisting of a single active material A(312), and (b) is a composite electrode consisting of two active materials A(312) and active material B(315).

[0038] <Electrolyte> In addition to the positive electrode 11, negative electrode 12, and separator 5, the energy storage elements (components of the energy storage mechanism) include an electrolyte 4. In the case of a lithium-ion battery, the electrolyte 4 can be an aprotic organic solvent such as ethylene carbonate (EC), propylene carbonate (PC), butylene carbonate (BC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC), methyl propyl carbonate (MPC), or ethyl propyl carbonate (EPC).

[0039] Alternatively, an electrolyte solution may be obtained by dissolving lithium salts such as lithium hexafluoride phosphate, lithium tetrafluoroborate, lithium perchlorate, lithium iodide, lithium chloride, lithium bromide, LiB(OCOCF3)4, LiB(OCOCF2CF3)4, LiPF4(CF3)2, LiN(SO2CF3)2, or LiN(SO2CF2CF3)2 in a solvent of two or more of the above-mentioned mixed organic compounds. Another example is an electrolyte solution obtained by dissolving two or more of the above-mentioned mixed lithium salts.

[0040] The constituent solvents of the electrolyte 4 described above are generally highly volatile, and the volatilization temperature of the electrolyte 4 is often below 25°C. In addition, in one embodiment of the present invention, a lithium-ion conductive liquid with a volatilization temperature raised to, for example, 100°C or higher can be used. Specifically, ionic liquids and solvated ionic liquids can be mentioned. Furthermore, a lithium-ion conductive solid electrolyte can be used instead of the electrolyte 4.

[0041] <Lithium-ion battery manufacturing process> The upper part of Figure 4 schematically shows an example of the manufacturing process for lithium-ion batteries. After mixing the constituent materials, electrode active material, conductive additive, and binder, an electrode composite sheet is formed. In this process, it is common to disperse each material in a solvent to form a slurry, which is then coated onto a current collector foil using a blade coater or die coater and dried to form the sheet. However, it is also possible to mix the solid active material, conductive additive, and binder under pressure using a roll or the like without dispersing them in a solvent, and then disperse them onto the current collector foil and pressurize to form the electrode composite sheet.

[0042] Furthermore, for solid-state batteries where the electrolyte 4 is replaced with a solid electrolyte, an electrode composite sheet can be manufactured by adding and mixing a solid electrolyte to the above material. Generally, the electrode composite sheet formed on the current collector foil is cut to a predetermined size, laminated with a separator 5 and a solid electrolyte, housed in an outer casing 6, injected with electrolyte, and sealed to form a battery cell. In addition, by charging and discharging the manufactured battery under appropriate conditions to form a stable film between the electrode composite sheet and the electrolyte, the performance during subsequent battery operation can be maintained at a high level. This is called conditioning or aging treatment.

[0043] Subsequently, performance tests are conducted before shipment, and only batteries that meet the specified performance standards are shipped as normal products. Examples of performance test items include the capacity and charge / discharge efficiency (the ratio of capacity to discharge capacity during the first charge / discharge), the battery capacity (rate characteristics) when the charge or discharge current is changed, and the AC resistance and DC resistance at a specified charge rate (SOC: State of Charge).

[0044] If any cells fail to meet the guaranteed performance standards during inspection, they are deemed defective and either discarded without being shipped or dismantled and materials recovered as needed. This process also consumes energy, so in manufacturing processes with high defect rates, the amount of energy consumed per battery cell and the associated costs become problematic, leading to a decline in manufacturing efficiency. Even if defects occur in intermediate products during the manufacturing process, if these defects cannot be identified until the final stage of the manufacturing line, the energy required to manufacture defective batteries, including those with poor performance intermediate products, is wasted, further worsening efficiency.

[0045] <Inspection methods in the manufacturing process> As one way to solve the aforementioned problem of reduced manufacturing efficiency, the present invention proposes a method in which a sample is extracted from a portion of the electrode composite sheet after its manufacture, and in the structural inspection process, the structural information is quantified as a feature and used as a control item to determine the quality of the electrode composite sheet. The lower part of Figure 4 shows an overview of a quality control system, which is one embodiment of the present invention.

[0046] The energy device quality control system 200 of the present invention comprises, as its main components, a first inspection data acquisition device 410, a second inspection data acquisition device 420, a data storage and calculation device 430, and a data output device 440. In the battery manufacturing process, the first inspection data (411) obtained for the slurry formed in the mixing process and the electrodes after coating is transmitted to the data storage and calculation device 430, which has pre-stored correlation data of manufacturing information, structural information, and performance information, where it is determined whether or not to acquire second inspection data. If it is necessary to acquire second inspection data (421), the region (second inspection data acquisition region (412)) is determined and transmitted to the second inspection data acquisition device 420.

[0047] The second inspection data acquisition device 420 acquires structural information of the electrode composite sheet and transmits it to the data storage and calculation device 430. When the first inspection data (411) and second inspection data (421) for the target intermediate product are input to the data storage and calculation device 430, a predicted value (431) of its performance as a final product is calculated based on previously accumulated correlation data, and this is visualized in the data output device 440 along with the first inspection data (411), the second inspection data (421), and the second inspection data acquisition area (412). This data output device 440 allows users of this quality management system to visualize the quality of the relevant intermediate product. Furthermore, when the data storage and calculation device 430 calculates the predicted product performance value (431), it also calculates the prediction accuracy (432) for the predicted product performance value (431), and can display the prediction accuracy (432) for the predicted product performance value (431) in the data output device 440 as well. Furthermore, the second inspection data acquisition device 420 may acquire structural information of the electrode composite sheet as well as manufacturing information of the electrode composite sheet, and the data storage and calculation device 430 may calculate a predicted value (431) of the performance of the final product based on the correlated data.

[0048] Figure 5 schematically shows the manufacturing and inspection processes for electrode composite sheets. In the quality control system of the present invention, three types of information are acquired: manufacturing information, structural information, and performance information, and stored in the data storage and calculation device 430 within the energy device quality control system 200 shown in Figure 4. Here, the first inspection data (411) and the second inspection data (421) are stored as part of the structural information.

[0049] Manufacturing information includes material specifications for forming the electrode composite sheet, material manufacturing lot, slurry mixing conditions (mixing method, speed, solvent addition amount, timing, atmosphere), coating conditions (atmosphere, coating speed, drying temperature), and pressing conditions (pressure, roll temperature, pressurizing time).

[0050] Among the inspection data for the intermediate product, the first inspection data (411) can be data on the properties of the slurry obtained as a preliminary process before forming the electrode composite sheet (functional sheet). Examples of such data include viscosity, electrical conductivity, dispersibility, and blackness. The method for measuring viscosity is not particularly limited, but examples include a method in which a cone-shaped plate is brought into contact with the target slurry and measured from the rotational resistance, or a method in which the resistance value is measured when the slurry is extruded.

[0051] Furthermore, the electrical conductivity and dispersibility of a slurry can be obtained from the impedance spectrum when an AC signal is applied between two electrodes separated by the slurry. Electrical conductivity can be calculated using the real impedance component in the high-frequency or low-frequency region of the impedance spectrum and the distance between the electrodes. Dispersibility can also be evaluated from the capacitive semicircular shape in the Nyquist plot, which plots the real and imaginary components of the impedance spectrum.

[0052] Furthermore, the blackness of a slurry can be evaluated by measuring the color of the slurry liquid surface or the surface of a glass container containing the slurry using a spectrophotometer, and using the lightness L* and the chromaticity a* and b* which indicate hue and saturation. The L*a*b* color space is a color system used to represent the color of objects, standardized by the International Commission on Illumination (CIE), and adopted in Japan by JIS (JIS Z 8781-4). a* and b* indicate the direction of color, with a* indicating the red direction, -a* indicating the green direction, b* indicating the yellow direction, and -b* indicating the blue direction. When the coordinate formed by a* and b* is close to the origin, and L* is low, the blackness is high. When a* and b* are large, the color becomes vivid, and when the lightness L* is large, the color becomes glossy.

[0053] Furthermore, the amount of coating applied to the electrode composite sheet obtained through the coating and drying processes, the thickness after pressing, and the degree of blackness of the electrode surface after pressing can also be used as first inspection data.

[0054] The second inspection data (421) is obtained by extracting specific areas from the formed electrode composite sheet (functional sheet), acquiring image data from those areas, and storing the extracted features. The image data can also be stored. The second inspection data (421) can include the presence or absence of defects or cracks within the electrode, the ratio of constituent materials (active material, conductive additive, binder) within the electrode composite sheet surface and thickness direction, the size of aggregates made of the constituent materials, cracks occurring within the electrode active material particles, and the void ratio where no constituent material exists.

[0055] Furthermore, the three-dimensional information of these items, that is, the distribution of each of the above-mentioned items in the sheet thickness direction, can also be used as structural information.

[0056] Methods for obtaining the aforementioned structural information include scanning electron microscopes (SEMs) and optical microscopes. Furthermore, elemental distribution information in a sample obtained using energy-dispersive X-ray spectroscopy (EDX), which is often attached to scanning electron microscopes, can also be applied as structural information.

[0057] The second inspection data (421) is obtained by extracting a specific area of ​​the electrode during the manufacturing process and observing it with a scanning electron microscope. Figure 5 shows a simplified flow for determining the acquisition area of ​​the second inspection data (421) using the first inspection data (411). In Figure 5, after passing from the slurry coating section 510 to the sheet drying section (drying oven) 520, the sheet inspection section (visual inspection section) 530, the pressing section 540, and the sheet inspection section (visual inspection section) 550 after pressing, structural information of the electrode requiring structural inspection is acquired in the second inspection data area extraction section 560 as needed.

[0058] As a method for extracting the second inspection data area, as shown in the lower part of Figure 5, a portion of the wound body made of the electrode composite sheet after manufacturing is extracted by punching and fixed to a sample holder for the inspection device. Other methods for extracting the sample include, but are not limited to, methods using laser processing. In addition, in Figure 5, the second inspection area is extracted after coating and pressing but before winding onto the roll, but the relevant part may also be extracted after winding onto the roll but before proceeding to the electrode cutting process.

[0059] By acquiring a secondary electron image (SEM) of the electrode composite sheet surface fixed to a sample holder and observing the surface morphology, defects within the electrode can be detected, and their degree can be quantified based on the number of defects per unit area and their area ratio. Similarly, cracks within active material particles can also be detected by SEM secondary electron images, and their degree can be quantified using the total number of cracks or crack lengths per unit area within the active material.

[0060] Furthermore, by using backscattered electron images from a scanning electron microscope (SEM), it is possible to obtain observation images with contrast that reflects the atomic weights of the constituent elements. By quantifying the size and area ratio of regions with different contrasts, it is possible to quantify the aggregate size, the ratio of constituent materials (active material, conductive additive, binder), and the void ratio.

[0061] Furthermore, to obtain information in the thickness direction of the electrode composite sheet, cross-sectional observation after punching can be used, or the electrode composite sheet extracted from the wound body can be peeled off from the current collector foil, and surface information of the peeled surface near the current collector foil can be obtained using the method described above.

[0062] Figure 6 shows the acquisition of first inspection data and second inspection data in the quality control system of the present invention, and the quality control flow utilizing each data. In steps S1 to S6, the viscosity, electrical conductivity, dispersibility, blackness, and drying temperature of the slurry, the amount of coating on the sheet immediately after coating, the thickness after pressing, and the blackness of the electrode surface after pressing are acquired as first inspection data (411), and then this information is input to the data storage and calculation device 430 shown in Figures 4 and 5.

[0063] The data storage and calculation device 430 stores a datasheet showing battery performance, or the correlation coefficient between the second inspection data (421) and the first inspection data (411). If the value of the first inspection data (411), which shows a high correlation coefficient, differs from the normal value, it may affect the second inspection data (421) and battery performance in subsequent steps, potentially leading to variations in battery performance and the occurrence of defective products. Therefore, in step S7, it is determined that there is a high need to acquire the second inspection data (Yes), and in step S8, the extraction of electrode pieces (sample pieces) shown in the lower part of Figure 5 is performed.

[0064] If the first inspection data (411) is obtained from the slurry, a sample can be taken from any location in the coated and dried electrode area using the slurry. The sample can be taken from one or more locations, but fewer locations are preferable from the perspective of production line utilization. Furthermore, if the first inspection data (411) is extracted from a specific area within the coated electrode composite sheet, a sample can be extracted from that area, and the process can proceed to obtaining the second inspection data.

[0065] In step S7, the determination of whether the first inspection data (411) differs from normal conditions can be arbitrarily determined by the user of this quality control system. For example, if the average value of the first inspection data (411) when good device performance is obtained is set to 100%, then if the measured value differs from the average by 1% to 5%, it can be considered different from normal conditions, and the user can proceed to acquire the second inspection data.

[0066] The device performance information stored in the data storage and processing unit 430 is, for example, acquired during the aging and shipping inspection of the manufactured cells in the case of a lithium-ion secondary battery, and includes the capacity and charge / discharge efficiency (which is the ratio of the capacity during the initial charge / discharge), the battery capacity (rate characteristics) when the charge or discharge current is changed, and the AC resistance and DC resistance at a predetermined state of charge (SOC).

[0067] In the quality management system of the present invention, the correlation between acquired manufacturing information, structural information, and performance information is formulated and used as a performance prediction model. In step S9, either the acquired first inspection data (411) or second inspection data (421) is used as input to the performance prediction model, along with the structural information of a newly manufactured electrode composite sheet, to predict the battery performance when the electrode composite sheet is applied. If the predicted performance does not meet the guaranteed performance, the electrode composite sheet can be removed from the manufacturing process, thereby preventing battery failure in subsequent stages.

[0068] Specifically, in step S10, it is determined whether there are any cracks or delaminations within the electrode as structural information. Then, in step S11, it is determined whether the performance output from the performance prediction model meets the guaranteed performance. Only the electrode composite sheets that pass both determinations are cut, laminated, and the battery is assembled (steps S12 to S17).

[0069] On the other hand, in step S10 or step S11, electrodes that do not meet the criteria are removed from the manufacturing process, and it is determined that the manufacturing conditions used to form the electrode composite sheet were inappropriate, and these manufacturing conditions can be updated (steps S18, S19). By repeatedly performing the above inspections and updating the manufacturing conditions, the risk of defects in the manufacturing process can be suppressed and manufacturing efficiency can be improved.

[0070] The performance information in Figure 6 (step S17) only shows data from aging (step S15) and the factory inspection (step S16), but it is also possible to add battery operation information after shipment. Here, operation information refers to the usage history of the battery cells incorporated into the battery system after shipment, and mainly time-series data such as battery voltage, current, and temperature detected by the battery management system (BMS) of the battery system can be used. Furthermore, degradation information refers to the change in battery performance due to the operation described above. This degradation information can also be estimated by analyzing the BMS data.

[0071] When a battery storage system is used under operating conditions appropriate to the battery specifications, its performance will degrade, but since the performance progresses according to a predetermined trend, the system can be used systematically. However, if a sudden degradation occurs that deviates from the predicted trend, the system may be judged to have malfunctioned, and its operation may be stopped. In this case, it is necessary to identify the reason for the abnormality and take corrective measures according to the reason. By comparing the degradation information obtained by inputting structural information with the abnormality information, it is possible to determine whether the cause of the abnormality occurred during the manufacturing of the battery and electrode composite sheet, or whether the operating conditions were unsuitable.

[0072] <Formalization method for the correlation between structural information and performance information> Figure 6 illustrates how formulating the correlation between structural information and performance or degradation information can reduce the manufacturing defect rate of batteries and clarify the causes of system failures. Two examples of this formulation method are described below. To implement this formulation, it is necessary to create multiple electrode composite sheets when considering materials and processes to determine the specifications of the batteries to be manufactured, or when designing and commissioning the manufacturing line, and to obtain structural information and performance / degradation information for each. The following shows, but is not limited to, a method for formulating the correlation using the acquired information.

[0073] (i) During the design phase of the materials and processes for the target battery, or during the commissioning of the manufacturing line, manufacturing conditions are intentionally controlled to produce electrode slurries with different properties and electrode composite sheets with different structures. Structural information is obtained from electrode inspections for electrode groups in which only specific manufacturing conditions have been changed, and performance information is obtained from pre-shipment inspections of the manufactured batteries.

[0074] Here, feature quantities indicating structural information and performance information (hereinafter referred to as structural parameters, performance parameters, and degradation parameters) are extracted, and the relationship between the performance parameter or degradation parameter and the structural parameter is formulated by regression analysis and stored in the data storage and calculation device 430. At this time, the first inspection data (411) and second inspection data (421) in this quality control system are input and analyzed as structural parameters.

[0075] In Figure 6, performance is predicted by inputting structural information of the electrode composite sheet extracted from the manufacturing process into the corresponding formula. Figure 7 shows an example of the performance / degradation prediction model construction flow using the above method. In step S1, the manufacturing conditions for the electrode composite sheet for information acquisition are determined, and electrodes and batteries are manufactured based on these conditions (steps S2 to S9). The process of obtaining corresponding structural parameters and performance / degradation parameters is repeated multiple times (steps S10 and S11, steps S12 to S15), and a prediction model is constructed by regression analysis (step S16).

[0076] In step S17, this process is repeated (from step S1 to step S16) until the correlation coefficient falls below a predetermined value, thereby obtaining a performance / degradation prediction model (step S18). The correlation equation obtained by regression analysis is not specified and can be a polynomial function, exponential function, logarithmic function, power function, etc., and any function that clearly shows the correlation can be appropriately selected.

[0077] (ii) In the same manner as in (i), manufacturing conditions are intentionally controlled during the design phase of the materials and processes of the target battery, or during the commissioning of the manufacturing line, to produce electrodes with different structures. In this process, multiple sets of electrode composite sheets with different structural parameters are prepared by changing multiple manufacturing conditions, and the performance parameters of batteries to which each electrode is applied are evaluated.

[0078] By performing multiple regression analysis on the obtained set of parameters, the performance parameters are expressed as multidimensional functions of the structural parameters and stored in the data storage and calculation unit 430. This process is the same as in Figure 7. However, as the number of structural parameters to be handled increases, the amount of information required for formulation also increases. To perform efficient formulation with a large amount of information, machine learning can also be used.

[0079] The performance prediction formula in method (ii) above can be obtained by quantifying the correlation between the manufacturing information and structural information (first inspection data, second inspection data) used as explanatory variables and the performance information used as the dependent variable. The standardized regression coefficient can be cited as a parameter that quantitatively indicates the correlation. The higher this coefficient, the more closely each parameter influences the others. In the quality control system of the present invention, this standardized regression coefficient is stored in a database in advance, and the second inspection data (421) that affects battery performance is acquired preferentially. Furthermore, by prioritizing the acquisition of performance information and the first inspection data (411) which has a high standardized regression coefficient with the second inspection data (421), efficient quality control becomes possible.

[0080] By inputting the first or second inspection data into the performance prediction formula obtained as described above, a performance prediction value can be obtained. If this value deviates from the performance guarantee range, the corresponding intermediate product can be excluded from the manufacturing process, thereby reducing the quality defect rate during the final inspection.

[0081] In this process, the coefficient of determination (R²) in the regression equation used to obtain the predicted value, or the error between the predicted and actual values ​​(root mean square error (RMSE)) in the test data and battery performance data used to obtain the regression equation, can be used as indicators of accuracy.

[0082] In the following sections, we will introduce an example to more specifically illustrate a quality control system for the manufacturing process of lithium-ion secondary batteries, as one example of the quality control system for energy devices according to the present invention. The performance prediction model and degradation prediction model used in the quality control system of the present invention are constructed by controlling numerous manufacturing conditions and using the structural and performance information obtained at that time. However, in order to clearly explain the concept of the quality control system, a simpler example will be used here. [Examples]

[0083] <Quality control system for cathode composite sheets> This example describes a case where a positive electrode composite sheet with NCM (Li(Ni,Co,Mn)O2) as the active material was used as the subject of inspection, and the manufacturing conditions, including the active material composition, mixing ratio in the electrode, amount of solvent added to the slurry, drying temperature, pressing temperature, and pressing pressure, were intentionally changed to form slurries and electrode sheets with different properties, and the correlation between their structural information and performance information was examined. In actual manufacturing lines, quality control is carried out using many parameters, but in this example, as a simple case, the target intermediate product is only the positive electrode composite sheet, and the first inspection data (411) is the slurry viscosity, and the second inspection data (421) is the data extracted from electron microscope images of the extracted sample surface.

[0084] A slurry was prepared by blending NCM powder, the active material, acetylene black as a conductive additive, and PVdF (Polyvinylidene Fluoride) as a binder in a predetermined weight ratio, and adding N-methyl-2-pyrrolidone as a solvent. The prepared slurry was coated onto 15 μm thick aluminum foil using a comma coater and dried in a drying oven for approximately 10 minutes. The coating amount was 24 mg / cm² by weight after drying. 2(Single-sided) was used. In this case, NCM powder was used with the elemental ratios of Ni, Co, and Mn changed to 1:1:1 and 6:2:2. The drying temperature was varied between 100°C and 160°C. In addition, a total of 35 cathode specifications were created as shown in Table 1. Table 1 summarizes the sample preparation conditions and structural information that form the basis of the correlation database in this example.

[0085] [Table 1]

[0086] The slurry viscosity was measured as the first inspection data (411). Additionally, features from scanning electron microscope (SEM) images of the cathode sample surface were used as the second inspection data (421). The observation conditions and extracted features are described below. The cathode sample was cut into several-millimeter squares, attached to a dedicated holder using carbon tape, and introduced into the SEM chamber. A Hitachi field-effect electron microscope (FE-SEM, S-4800) was used for observation.

[0087] During observation, an acceleration voltage of 5kV was used, and measurements were performed in two modes: secondary electron imaging (SE imaging) and backscattered electron imaging (BSE imaging). The magnification was 5000x for SE imaging and 500x for BSE imaging. SE imaging yields images that reflect the surface shape, while BSE imaging exhibits different contrasts depending on the crystal structure and atomic weight of the observed object. Therefore, active material cracks were quantified by image analysis from the SE imaging. In addition, binder distribution, material aggregation structure, and void density were quantified using BSE imaging, which provides information from the material. ImageJ from National Institutes of Health was used for quantitative analysis.

[0088] <Crack density> Cracks were marked on the acquired SE images, and then the average value per particle was calculated.

[0089] <Intermediate color ratio and void ratio> Figure 8 schematically shows the analysis procedure using scanning electron microscope images (BSE images) of the electrode surface, which is the second inspection data (421).

[0090] In BSE images, the contrast varies depending on the crystal structure and atomic weight of the object being observed. For example, in the BSE image of a positive electrode active material shown in Figure 8(a), white represents the positive electrode active material, intermediate colors represent the mixture of conductive additive and binder, and black represents voids. Figure 8(b) shows the image analysis flow. When extracting the area ratio of the intermediate color region, which is the ratio of binder to conductive additive, the intermediate color region is extracted after acquiring the BSE image. The ratio of the intermediate color to the total number of pixels is calculated to obtain the intermediate color ratio. On the other hand, when extracting the area ratio of the black region, which is the void ratio, the black region is extracted after acquiring the BSE image, and then the ratio of black to the total number of pixels is calculated.

[0091] <Percentage of aggregated bodies> After acquiring the BSE image, it was hypothesized that fine voids within the active material would affect the area ratio of the white region during white extraction. Therefore, this effect on the image was removed using a median filter. A median filter was used for noise reduction. Here, to analyze the degree of aggregation of the active material, the white region, which represents the active material, was extracted from the BSE image. Particle analysis was performed using analysis software to extract aggregates.

[0092] <Positive electrode performance and battery performance evaluation> The following describes the method for obtaining performance information for the positive electrode fabricated as described above and the battery cell using it.

[0093] To obtain the capacity and discharge rate characteristics of the fabricated positive electrode, a positive electrode composite sheet was punched out to a diameter of φ15 mm and mounted in a model cell. A three-electrode cell was fabricated using lithium foil as the reference electrode and counter electrode after impregnating the positive electrode and a separator made of polyolefin with 1MLiPF6 EC:DMC:EMC=2:2:4 as the electrolyte, and then under room temperature conditions, the charge and discharge capacities were evaluated within a potential range (upper limit 4.3V, lower limit 3.0V). The current rates during charge and discharge were set to 0.2C and 5.0C, and the ratio of the discharge capacity at 5C to the reference value of 0.2C was calculated.

[0094] To evaluate the ionic conduction resistance within the fabricated positive electrode, a symmetric cell composed of a positive electrode composite layer sheet was fabricated, and electrochemical impedance (EIS) measurement was carried out. The impedance observed in the symmetric cell includes, in addition to the electrolyte resistance of the separator portion between the electrodes, the contact resistance between the electrolyte and the positive electrode and the resistance components inside the electrode. In the Nyquist plot where the imaginary component and the real component of the impedance are plotted on the x-axis and y-axis respectively, the impedance derived from the lithium ion conduction resistance appears as a straight line with a slope of 45°, and the ionic conduction resistance Ri within the electrode was evaluated from the magnitudes of its real and imaginary components (R 45° ) (Ri = R 45° / 3). As the symmetric cell, a small laminate cell with a positive electrode area of 12 cm 2 [ was used.

[0095] Subsequently, the evaluation of the initial performance of the laminate cell was carried out in the following procedure. The positive electrode and the negative electrode were punched out to the desired electrode size (coating part: 47×74 mm), and burrs and fallen composite powder were removed by roll coating and brushing work. As the separator, a separator composed of a polyolefin material was used, and it was processed into a bag shape by heat welding three sides in advance with a width of 1 mm. The obtained bag-shaped separator was inserted with the positive electrode and laminated together with the negative electrode to obtain a laminated electrode group. After alternately laminating two positive electrodes and three negative electrodes inserted into the bag-shaped separator so that the outermost layer is the negative electrode, the electrode group was fixed with a polyimide tape, and the uncoated parts formed at the ends of the positive and negative electrodes were bundled and integrally welded to the positive electrode terminal and the negative electrode terminal by ultrasonic waves respectively. The terminals are made of Al for the positive electrode and Ni for the negative electrode. To ensure sealing performance, an insulating film was pasted on the part contacting the laminate film. Also, the welded part was protected with a polyimide tape to prevent short circuit.

[0096] These electrode groups were sandwiched between laminate films, and three sides (including the tab portion) were heat-sealed at 180°C using a lamination sealing device, leaving one side open for electrolyte injection, to create pre-injection laminated cells. For pre-injection drying, the cells were vacuum-dried at 60°C, injected with electrolyte, and vacuum-sealed. The fabricated cells were left to stand for a certain period of time before being initialized. They were charged with a constant current at a design capacity of 0.2C up to an upper voltage limit of 4.2V, and then charged at a constant voltage of 4.2V until the current dropped to 0.02C. Subsequently, a constant current discharge of 0.2C was performed at a lower voltage limit of 2.7V. The discharge capacity evaluated under these conditions was defined as the "rated capacity". After that, the discharge capacity was measured when the discharge rate was 0.2 and 3.0, and the ratio of each discharge capacity was obtained.

[0097] <Correlation Data Analysis> For the positive electrode obtained in Table 1, electrode and battery performance were acquired, and the results in Table 2 were obtained. Table 2 summarizes the electrode and battery performance that form the basis of the correlation database in this embodiment.

[0098] [Table 2]

[0099] Based on Tables 1 and 2, a correlation equation was created using machine learning, with manufacturing information and structural information as explanatory variables and performance information as the dependent variable. The model's accuracy was evaluated using the leave-one-out method. This method involves training with n-1 data points and evaluating with the remaining 1 data point. The training and evaluation process is repeated n times, and an evaluation index is calculated for all predicted values.

[0100] The data used as explanatory variables included manufacturing information (active material, binder, composition ratio of the mixture, amount of solvent added, drying temperature, drying time, target density, and press temperature) and structural information obtained from SEM (amount of cracks in the active material, aggregate size, binder ratio (intermediate color ratio), and void ratio), while battery information (capacity (unipolar performance), rate performance (unipolar), ion resistance, capacity (laminate cell performance), and rate characteristics (laminate cell performance)) was used as the dependent variable.

[0101] Figure 9 shows the standardized regression coefficients for each parameter obtained during this process. A positive coefficient indicates a positive correlation between the parameters, while a negative coefficient indicates a negative correlation. Furthermore, a larger absolute value in the cells where the parameter axes intersect indicates a stronger correlation between the two parameters. In Figure 9, the values ​​overlap in the upper right and lower left, so the upper right is left blank. Investigating the correlation between the performance information, specifically rate characteristics_unipolar performance, ion resistance, and rate characteristics_lamicell performance, and the second inspection data (421), it can be seen that the regression coefficients with void ratio are large, at 0.50, -0.58, and 0.66, respectively. From this, it can be seen that the surface void ratio has a high influence on the second inspection data (421). Furthermore, this void ratio is thought to be strongly correlated with the amount of solvent. Among the first inspection data (411), the absolute value of the regression coefficient between solvent slurry viscosity, which has a strong correlation with the amount of solvent, is a relatively large 0.47. From the above, it can be seen that by monitoring the solvent slurry viscosity, which is the first inspection data (411), a change from the normal state will cause the second inspection data (421) and performance data to change.

[0102] Based on this knowledge, the slurry viscosity is continuously monitored as the first inspection data (411). Only when a change in viscosity is observed, an electrode created using the relevant slurry is sampled and the void ratio is monitored as the second inspection data (421) by surface observation, thereby efficiently determining the quality of the slurry and electrode composite sheet. Furthermore, when the blackness of the surface of the obtained positive electrode composite sheet was evaluated with a spectrophotometer, it was confirmed that positive electrode composite sheets with a higher void ratio tended to have a lower brightness L*. From this, it is also possible to manage the quality of the positive electrode composite sheet by continuously monitoring the blackness of the electrode surface after coating and pressing as the first inspection data, and only when a change in this value is observed, monitoring the void ratio of the relevant area as the second inspection data.

[0103] The above is an example focused on the positive electrode of a secondary battery, but it can be applied to quality control of other components of secondary batteries, as well as various other energy devices such as solid-state batteries, solar cells, capacitors, and electric double-layer capacitors obtained by laminating functional sheets made of oxides.

[0104] It should be noted that the present invention is not limited to the embodiments described above, and various modifications are included. For example, the embodiments described above are described in detail to make the present invention easier to understand, and are not necessarily limited to those having all the configurations described. Furthermore, it is possible to replace parts of the configuration of one embodiment with the configuration of another embodiment, and it is also possible to add configurations from other embodiments to the configuration of one embodiment. In addition, it is possible to add, delete, or replace parts of the configuration of each embodiment with other configurations.

[0105] Furthermore, each of the above configurations, functions, processing units, and processing means may be implemented in hardware, either partially or entirely, by designing them as integrated circuits, for example. Alternatively, each of the above configurations and functions may be implemented in software by having the processor interpret and execute programs that implement each function. Information such as programs, tables, and files that implement each function can be stored in memory, or in recording devices and recording media such as hard disks and SSDs (Solid State Drives). [Explanation of Symbols]

[0106] 1...Electrode occupied part 2…Positive terminal (tab) 3…Negative terminal (tab) 4...Electrolyte 5... Separator 6… Exterior materials 11...Positive electrode 12...Negative electrode 100...cell 111... Positive electrode composite layer 112... Positive electrode current collector foil 121... Negative electrode composite layer 122... Negative electrode current collector foil 200…Energy device quality control system 311...Current collector foil 312...Active material A 313... Conductive additive 314…Binding agent (binder) 315...Active material B 410...First inspection data acquisition device 411…First test data 412...Second inspection data acquisition area 420...Second inspection data acquisition device 421…Second test data 430...Data storage and processing unit 431…Predicted product performance 432…Prediction accuracy 440...Data output device 510...Slurry Coating Department 520...Sheet drying section (drying oven) 530, 550... Sheet Inspection Department (Visual Inspection Department) 540…Press Department 560... Extraction unit for the second inspection data area.

Claims

1. A quality control system for energy devices that determines the quality of intermediate products in the energy device manufacturing process, A first inspection data acquisition device for acquiring first inspection data relating to the material of the intermediate product, A second inspection data acquisition device for acquiring second inspection data, which is a characteristic quantity relating to the aforementioned intermediate product, A data storage and calculation device that takes the first inspection data and the second inspection data as input and calculates a predicted performance value of the energy device based on previously accumulated correlation data, A data output device that outputs the first inspection data, the second inspection data, and the performance prediction value, is provided, The intermediate product is a functional sheet that functions as an electrode layer or carrier conduction layer of the energy device, and a slurry obtained by mixing the functional material constituting the functional sheet with a solvent and a binder. The data storage and calculation device has a correlation database that stores manufacturing conditions, structural information, and performance information of the functional sheet and slurry, A quality control system for an energy device, characterized by inputting structural information of the intermediate product obtained from at least one of the first inspection data and the second inspection data into the correlation database, thereby calculating a performance prediction value for the energy device and determining the quality of the intermediate product.

2. A quality control system for an energy device according to claim 1, A quality control system for an energy device, characterized by inputting structural information of the intermediate product and manufacturing information of the intermediate product obtained from at least one of the first inspection data and the second inspection data into the correlation database, thereby calculating a performance prediction value for the energy device and determining the quality of the intermediate product.

3. A quality control system for an energy device according to claim 1, The data storage and arithmetic device calculates the prediction accuracy of the performance prediction value, The data output device is a quality control system for energy devices, characterized in that it outputs the prediction accuracy.

4. A quality control system for an energy device according to claim 1, The first inspection data includes at least one of the composition ratio of the functional material, the solvent, and the binder, the viscosity of the slurry, the electrical conductivity in the slurry, the degree of dispersion of the functional material in the slurry, and the blackness of the slurry, for an energy device quality control system.

5. A quality control system for an energy device according to claim 1, The first inspection data includes at least one of the temperature during coating and drying of the slurry, the amount of coating, and the blackness of the functional sheet after coating, in a quality control system for an energy device.

6. A quality control system for an energy device according to claim 1, The quality control system for energy devices is characterized in that the second inspection data is structural data or elemental data of the intermediate product obtained using a scanning electron microscope, an optical microscope, a spectrophotometer, or an X-ray spectrometer attached to the scanning electron microscope.

7. A quality control system for an energy device according to claim 1, The quality control system for energy devices is characterized in that the second inspection data includes at least one of the following: the ratio of defects or cracks within the surface of the functional sheet; the ratio of the functional material and the binder in the plane or thickness direction of the functional sheet; the size of aggregates made of the functional material and the binder; the ratio of cracks occurring inside the functional material particles; and the void ratio where the functional material and the binder are not present.

8. A quality control system for an energy device according to claim 1, The second inspection data is structural information of the functional sheet, The aforementioned structural information is obtained by removing a portion of the functional sheet after manufacturing through punching or laser processing, fixing it in an inspection holder, and then acquiring the information using a scanning electron microscope, an optical microscope, a spectrophotometer, or an X-ray spectrometer attached to the scanning electron microscope, as a quality control system for energy devices.

9. A quality control system for an energy device according to claim 8, A quality control system for energy devices, characterized in that the structural information includes at least one of the following: information on the number of defects per unit area between active materials and area ratios based on secondary electron images of a scanning electron microscope for a portion of the functional sheet fixed to the inspection holder; information on the total number of cracks or crack lengths per unit area within the active material based on secondary electron images of a scanning electron microscope; aggregate size obtained by quantifying the size and area ratio of regions with different contrasts reflecting the atomic weights of constituent elements based on backscattered electron images of a scanning electron microscope; and information on the ratio of constituent materials and void ratio in the in-plane or thickness direction of the functional sheet.

10. A quality control system for an energy device according to claim 1, The data storage and processing device has a function of relating the manufacturing information of the intermediate product, the structural information of the intermediate product, and the performance information of the energy device. The structural information includes at least one of the first inspection data and the second inspection data. The relationship between the manufacturing information, the structural information, and the performance information is characterized in that specific manufacturing conditions are controlled to produce slurries with different properties and functional sheets having different structures, and the relationship between the first inspection data obtained from the slurry, the second inspection data obtained from the functional sheet, and the performance information of the energy device produced from the functional sheet is formulated by regression analysis.

11. A quality control system for an energy device according to claim 10, A quality management system for energy devices, characterized by repeatedly performing regression analysis on the relationship between the structural information obtained from the functional sheet with only the specific manufacturing conditions changed and the performance information of the energy device made from the functional sheet, until the correlation coefficient of the correlation function obtained by the regression analysis becomes less than or equal to a predetermined value, thereby constructing a performance prediction model in the data storage and computing device.

12. A quality control system for an energy device according to claim 11, The items of the second inspection data that have a high correlation with the performance of the energy device obtained from the regression analysis are extracted, Extract the items from the first test data that have a high correlation with the items from the second test data. A quality control system for energy devices, characterized by extracting a portion of a functional sheet coated using a slurry in which the first inspection data falls outside the specified range, and obtaining the second inspection data.

13. A quality control system for an energy device according to claim 1, The energy device is characterized by being one of the following: a lithium-ion secondary battery, a sodium-ion secondary battery, a potassium-ion secondary battery, or a secondary battery using a polyvalent cation as a carrier.

14. A quality control system for an energy device according to claim 1, The quality control system for an energy device is characterized in that the functional sheet is an electrode composite sheet comprising at least an active material capable of intercalating and releasing ions, and a binder.

15. A quality control system for an energy device according to claim 14, The aforementioned functional sheet is characterized by containing a solid electrolyte capable of conducting carrier ions of a secondary battery, and is a quality control system for an energy device.

16. A quality control system for an energy device according to claim 1, The energy device is characterized in that it is either a solar cell device containing a perovskite crystal or an electric double-layer capacitor obtained by stacking the functional sheets made of oxide.

17. A method for controlling the quality of energy devices, which determines the quality of intermediate products in the energy device manufacturing process, The intermediate product is a functional sheet that functions as an electrode layer or carrier conduction layer of the energy device, and a slurry obtained by mixing the functional material constituting the functional sheet with a solvent and a binder. The energy device manufacturing process includes a slurry step of forming a mixture of the functional material, the solvent, and the binder, and a sheet step of obtaining a functional sheet by coating and drying the slurry on a substrate, and further includes a first inspection data acquisition step of acquiring information on the properties of the mixture and the functional sheet, a step of determining a region to be extracted from the functional sheet based on the first inspection data acquired in the first inspection data acquisition step and a correlation database that stores manufacturing conditions, structural information, and performance information of the functional sheet and the slurry, and a second inspection data acquisition step of acquiring structural data of the functional sheet corresponding to the extracted region. A method for quality control of an energy device, characterized in that a computing device calculates a predicted performance value of the energy device and determines the quality of the intermediate product by inputting structural information of the intermediate product obtained from at least one of the first inspection data and the second inspection data into the correlation database.

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