Quality management system for secondary battery and quality management method using quality management system for secondary battery

Abstract

In order to provide a quality management system for a secondary battery and a quality management method for a secondary battery, which are capable of predicting the performance of a completed secondary battery on the basis of configuration information of an electrode composite sheet manufactured in a manufacturing step, the following configuration is adopted. A secondary battery quality management system and a secondary battery quality management method, the secondary battery quality management system comprising: a storage unit for storing a performance prediction model for formulating the correlation between the configuration information of an electrode composite sheet and the performance information of a secondary battery manufactured by using the electrode composite sheet; and a performance prediction unit that inputs the structure information of a newly manufactured electrode composite sheet into the performance prediction model, and predicts the battery performance of a secondary battery manufactured by applying the electrode composite sheet.

Description

Technical Field

[0001] This invention relates to a quality management system for secondary batteries and a quality management method using the quality management system for secondary batteries. Background Technology

[0002] To suppress CO2 emissions, the demand for fossil fuel-free secondary batteries as propulsion for vehicles and other applications is increasing. Furthermore, as energy sources that also provide electricity in power systems, replacing fossil fuels, there is a need to increase the proportion of renewable energy sources such as solar and wind power. As a means to mitigate fluctuations in their output, the demand for energy storage systems using secondary batteries is also increasing. Against this backdrop, the demand for secondary battery manufacturing, represented by lithium-ion batteries, is rising.

[0003] As a research topic in rechargeable batteries, there are issues related to improving energy efficiency and resource efficiency. Therefore, it is necessary to increase the yield rate of manufactured batteries. However, due to material deviations within the battery and poor quality of internal components caused by uncontrollable conditions, it is difficult to improve the yield rate. Among the internal components of the battery, electrode composite sheets are a prime example of materials that have a significant impact on the yield rate.

[0004] A secondary battery is considered to consist of an active material that stores charge in a secondary battery, a conductive agent that conducts electrons to the active material, and a binder that binds them together. The battery performance is affected by the presence or absence of defects during manufacturing, the size of the aggregates, and the dispersion state of the constituent materials. As a known technique for improving yield, methods for inspecting manufactured electrode composite sheets using various methods are disclosed.

[0005] Patent Document 1 discloses the following method: an infrared camera is used to photograph an electrode composite material sheet after it has been pressed, the obtained infrared image is processed to a high resolution, and the defect is detected by comparing the image after the high resolution processing with a previously obtained defect image, and it is determined whether there are defects and the type of defects in the roller-shaped electrode composite material sheet on at least one side of the current collector.

[0006] Patent Document 2 discloses a method that detects the inclusion of bends, wrinkles, etc. by having a process of marking a predetermined position on an electrode composite sheet before winding and a process of detecting the positional offset of the markings applied to adjacent electrode sheets among the markings applied to the multiple wound electrode composite sheets.

[0007] Patent document 3 discloses the following: an inspection light is irradiated onto the surface of a thin electrode composite sheet wound into a roller shape, and the transmitted light, reflected light and scattered light are photographed. The photographed data are analyzed by an image processing unit, thereby recording the types of defects existing in the sheet (black defects, white defects, gloss defects, fiber bundle defects, resin deficiency defects, pinhole defects), the location and size of the defects on a recording medium.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent Document 1: Japanese Patent Application Publication No. 2016-143641

[0011] Patent Document 2: Japanese Patent Application Publication No. 2015-133259

[0012] Patent Document 3: Japanese Patent Application Publication No. 2010-272250 Summary of the Invention

[0013] The problem that the invention aims to solve

[0014] However, while patent documents 1 to 3 can identify obvious defects such as electrode flaws, it is difficult to detect poor mixing and uneven distribution of the constituent materials in electrode composite sheets without such obvious defects. This results in the inability to detect performance defects during manufacturing and battery use beforehand. Because performance defects at the manufacturing stage cannot be detected beforehand, the yield rate in the manufacturing process decreases, and the use of defective electrodes in battery manufacturing leads to time and energy losses. Furthermore, the inability to detect performance defects during battery use beforehand reduces the operating rate of equipment and systems using such batteries.

[0015] Typically, during the battery material and process design phase, if the performance of the manufactured battery does not meet expectations, electron microscopy or similar instruments can be used to observe the electrodes to identify any issues such as poor mixing or uneven distribution, and the results are then incorporated into the material and process design. However, these observations are time-consuming, and therefore, as offline evaluations, they are mostly performed on samples taken after manufacturing and evaluation, without being used for inspection during the manufacturing process. Furthermore, even when electrode structure information can be obtained through sampling inspections of manufactured electrode composite sheets, methods for extracting structural factors that affect battery performance have not been established. Additionally, the correlation between these factors and battery performance at the time of inspection and degradation characteristics during use is unclear, making it difficult to provide feedback to the manufacturing process based on inspection results.

[0016] Based on the above, in the manufacturing process of secondary batteries based on currently available information, issues such as uneven distribution, structural defects, and insufficient porosity of the electrode material, which may arise during the manufacturing of the electrode composite sheet used as the positive or negative electrode, are not detected during the manufacturing process. As a result, there are problems with poor performance during factory inspection and decreased manufacturing efficiency. Furthermore, even when the above information is obtained during electrode inspection, the correlation between battery performance and degradation characteristics is unclear, making feedback to the electrode manufacturing process difficult. Consequently, decreased manufacturing efficiency is also a problem.

[0017] The purpose of this invention is to provide a quality management system and a quality management method for secondary batteries that can predict the performance of the completed secondary battery based on the structural information of the electrode composite material sheet produced in the manufacturing process.

[0018] Methods for solving problems

[0019] The present invention, which addresses the above-mentioned problems, is configured as follows. A quality management system and a method for secondary batteries are disclosed. The quality management system comprises: a storage unit storing a performance prediction model that formulates the correlation between structural information of an electrode composite sheet and performance information of a secondary battery manufactured using the electrode composite sheet; and a performance prediction unit that inputs structural information of a newly manufactured electrode composite sheet into the performance prediction model to predict the battery performance of the secondary battery manufactured using the electrode composite sheet. The structural information is information related to the structure of the electrode composite sheet, measured after manufacturing of at least one of the positive and negative electrodes, including at least one of defects in the positive and negative electrodes, presence or absence of cracks, ratio of constituent materials in the in-plane or thickness direction of the electrode composite sheet, size of aggregates formed by the constituent materials, cracks generated inside the particles of the electrode active material, and a void ratio where no constituent material is present. The performance information is information related to the performance of the secondary battery manufactured using the electrode composite sheet, including at least one of the following: capacity at initial charge and discharge, ratio of capacity during charge and discharge (i.e., charge and discharge efficiency), battery capacity when the charging or discharging current changes, AC resistance at a predetermined charging rate, and DC resistance.

[0020] Invention Effects

[0021] According to the present invention, a quality management system and a quality management method for secondary batteries are provided that can predict the performance of the completed secondary battery based on the structural information of the electrode composite material sheet produced in the manufacturing process.

[0022] Other issues, structures, and effects not mentioned above will be clarified through the following description of the implementation methods. Attached Figure Description

[0023] Figure 1 This is a schematic diagram illustrating an example of the planar orientation configuration of a battery, which is the object of inspection in this invention.

[0024] Figure 2 This is a schematic diagram illustrating an example of the cross-sectional orientation of a battery, which is the object of inspection in this invention.

[0025] Figure 3 This is a schematic diagram illustrating an example of the cross-sectional orientation of the electrode composite sheet constituting the battery subject to inspection in this invention.

[0026] Figure 4 This diagram schematically illustrates an example of the manufacturing process of a battery that is the subject of inspection in this invention.

[0027] Figure 5 This is a diagram illustrating an example of the processing flow of the inspection system of the present invention.

[0028] Figure 6 This diagram schematically illustrates the process of extracting a sample of the object to be inspected from an electrode composite sheet under manufacturing in the inspection process of the present invention.

[0029] Figure 7 This is a diagram illustrating an example of the processing flow of the inspection system of the present invention.

[0030] Figure 8 This is a diagram illustrating an example of the processing flow for constructing a performance and degradation prediction model for the inspection system of the present invention.

[0031] Figure 9 This is a schematic diagram illustrating the performance of the inspection system of the present invention and the correlation between the battery's internal state parameters, the battery's voltage-capacity curve, and the resistance-capacity curve used to construct the degradation prediction model.

[0032] Figure 10 This is a diagram illustrating an example of the processing flow for constructing a performance and degradation prediction model for the inspection system of the present invention.

[0033] Figure 11 This is a graph showing a scanning electron microscope image of a surface, which is an example of the structural information of an embodiment of the present invention, and the results of compositional analysis based on energy-dispersive X-ray spectroscopy.

[0034] Figure 12 This is a diagram showing an example of structural information from an electrode cross section, along with the compositional distribution results based on energy-dispersive X-ray spectroscopy, representing an embodiment of the present invention.

[0035] Figure 13 This is a diagram illustrating an example of structural information in an embodiment of the present invention, showing an image analysis process for evaluating the distribution of constituent materials based on scanning electron microscope images of a surface.

[0036] Figure 14 This is a diagram illustrating an example of the results of evaluating the distribution of constituent materials based on a scanning electron microscope image of the surface, which is an example of the structural information of an embodiment of the present invention.

[0037] Figure 15 This is a graph showing the discharge rate characteristics as an example of battery performance information in an embodiment of the present invention.

[0038] Figure 16 This is a graph showing the AC impedance measurement results for evaluating the ion conduction resistance within the electrode, as an example of battery performance information in an embodiment of the present invention.

[0039] Figure 17 This is a graph showing the discharge capacity retention rate during a charge-discharge cycle, which is an example of battery performance information as an embodiment of the present invention.

[0040] Figure 18 This refers to the area ratio of the intermediate color region in the reflected electron image, which is one of the structural information of an embodiment of the present invention, and its relationship with battery performance ((a) ion conduction resistance within the electrode, (b) the ratio of 5C discharge capacity to 0.2C discharge capacity in the discharge rate characteristics, (c) discharge capacity retention rate in charge-discharge cycle tests, and (d) positive electrode active material utilization rate in charge-discharge cycles (m). p A graph showing the correlation between the rate of change of ( ). Detailed Implementation

[0041] Hereinafter, the embodiments for carrying out the present invention (hereinafter referred to as "implementation methods") will be described with appropriate reference to the accompanying drawings.

[0042] When describing the inspection system for the battery manufacturing process of the present invention, the structure and constituent materials of lithium-ion batteries and the manufacturing process of lithium-ion batteries will be described first, followed by a description of the inspection system in the secondary battery manufacturing process as an embodiment of the present invention. Furthermore, the secondary battery to which the present invention is aimed is not limited to lithium-ion batteries, but is shown only as an example.

[0043] <Structure and constituent materials of lithium-ion batteries>

[0044] use Figure 1 and Figure 2 This paper describes one method of constructing a lithium-ion battery. Figure 1 This is a diagram that conceptually represents an example of a cross-section of a lithium-ion battery.

[0045] Figure 1 In this battery unit (battery cell, secondary battery) 100, there are electrode portions 1, a positive terminal (tab) 2, a negative terminal (tab) 3, an electrolyte 4, a separator 5, and outer packaging material 6. The outer packaging material 6 is made of laminated film or similar raw materials. Figure 1 In this process, multiple diaphragms and electrodes are stacked and sealed using a laminated film. However, structures that roll up the stacked body and seal it into cylindrical outer packaging materials, as well as structures that seal it into square outer packaging materials, are also subject to inspection.

[0046] Figure 2 It is a conceptual representation Figure 1 A diagram illustrating an example of a cross-section of a unit's energy storage element (a component of the energy storage mechanism). In Figure 2 In this battery, the energy storage elements include a positive electrode 11, a negative electrode 12, and a separator 5. Furthermore, the electrolyte 4, serving as the battery, is contained within the micropores of the positive electrode 11, negative electrode 12, and separator 5. Therefore, the electrolyte 4... Figure 2 Not shown in the image.

[0047] In addition, Figure 2 In this configuration, the positive electrode 11 and negative electrode 12 of the energy storage element are alternately arranged with a separator 5 between them. The separator 5 is, for example, made of polypropylene. However, besides polypropylene, microporous membranes made of polyolefins such as polyethylene or nonwoven fabrics can also be used as the separator 5. Furthermore, Figure 1 and Figure 2 This is a conceptual diagram illustrating an example of the cross-section of a lithium-ion battery and an example of the cross-section of the energy storage element of a cell. The positional relationship and size of the constituent components are not limited to the situation shown in the diagram.

[0048] The positive electrode 11 and the negative electrode 12 are respectively formed into sheets by forming a mixture of appropriate electrode active material, conductive agent, binder, etc. on a current-collecting foil of appropriate metal. In this invention, they are referred to as "electrode composite material sheets".

[0049] In the lithium-ion battery cell, which is one of the objects of inspection in this invention, any current collector can be used without being limited by material, shape, manufacturing method, etc.

[0050] <Positive Electrode 11>

[0051] The current collector foil of the positive electrode 11 can be any one of the following: aluminum foil with a thickness of 10~100μm, perforated aluminum foil with a thickness of 10~100μm and a pore size of 0.1~10mm, expanded metal, or foamed metal plate. In addition to aluminum, stainless steel, titanium, etc. can also be used as the above materials.

[0052] The electrode active material of the positive electrode 11 preferably contains reactants internally. The reactant in a lithium-ion battery is lithium ions. In this case, the electrode active material contains a lithium-containing compound capable of reversibly inserting and releasing lithium ions. Examples of electrode active materials for the positive electrode 11 include, for example, lithium cobalt oxide, manganese-substituted lithium cobalt oxide, lithium manganese oxide, lithium nickel oxide, olivine-type lithium iron phosphate, and other transition metal phosphates such as lithium phosphate and Li. w Ni x Co y Mn z O2 (where w, x, y, and z are 0 or positive values).

[0053] <Negative Electrode 12>

[0054] The current collector foil of the negative electrode 12 can be made of copper foil with a thickness of 10~100μm, copper perforated foil with a thickness of 10~100μm and a pore size of 0.1~10mm, expanded metal, foamed metal plate, etc. In addition to copper, stainless steel, titanium, etc., can also be used. The electrode active material of the negative electrode 12 contains substances capable of reversibly inserting and releasing lithium ions. Types of electrode active materials for the negative electrode 12 include, for example, natural graphite, composite carbonaceous materials coated on natural graphite by dry CVD or wet spraying, artificial graphite manufactured from epoxy, phenol, or asphalt-based materials derived from petroleum or coal through sintering, silicon (Si), graphite mixed with silicon, non-graphitizable carbon materials, and lithium titanate (Li4Ti5O). 12 wait.

[0055] In addition, the active materials for the positive and negative electrodes mentioned above can be selected from a variety of materials as needed. Figure 3 The diagrams are conceptual representations of the cross-sections of electrodes within a lithium-ion battery. (a) is an electrode formed from a single active material, A312, and (b) is a composite electrode composed of two active materials, A312 and B315.

[0056] Electrolyte

[0057] In the energy storage elements (the constituent elements of the energy storage mechanism), in addition to the positive electrode 11, negative electrode 12, and separator 5, there is also an electrolyte 4. In the case of lithium-ion batteries, the electrolyte can be, for example, aprotic organic solvents such as ethylene carbonate (EC), propylene carbonate (PC), butyl carbonate (BC), dimethyl carbonate (DMC), ethyl methyl carbonate (EMC), diethyl carbonate (DEC), methyl propyl carbonate (MPC), and ethyl propyl carbonate (EPC). Alternatively, electrolytes containing lithium salts such as lithium hexafluorophosphate, lithium tetrafluoroborate, lithium perchlorate, lithium iodide, lithium chloride, lithium bromide, LiB(OCOCF3)4, LiB(OCOCF2CF3)4, LiPF4(CF3)2, LiN(SO2CF3)2, and LiN(SO2CF2CF3)2 dissolved in solvents containing two or more of the above-mentioned mixed organic compounds can be cited. Alternatively, electrolytes containing two or more of the above-mentioned mixed lithium salts can be cited.

[0058] The solvents constituting the aforementioned electrolytes are typically highly volatile, with evaporation temperatures mostly below 25°C. Alternatively, in one embodiment of the present invention, a lithium-ion conductive liquid with an evaporation temperature raised to, for example, 100°C or higher can be used. Specifically, ionic liquids and solvated ionic liquids can be cited as examples. Furthermore, a solid electrolyte can be used instead of the electrolyte.

[0059] <Manufacturing Process of Lithium-ion Batteries>

[0060] Figure 4 This diagram illustrates an example of a lithium-ion battery manufacturing process. Electrode active materials, conductive additives, and binders, which serve as constituent materials, are mixed to form an electrode composite sheet. Typically, this sheet is formed by coating a slurry containing materials dispersed in a solvent onto a current collector foil using a doctor blade coater or die coater, followed by drying. However, it is also possible to mix the solid active materials, conductive additives, and binders under pressure using rollers or similar methods without dispersing them in a solvent, then disperse them on the current collector foil in this state and apply pressure to form the sheet.

[0061] Alternatively, as an electrode composite sheet for solid-state batteries, which replaces the electrolyte with a solid electrolyte, an electrode composite sheet formed by adding a solid electrolyte to the aforementioned material can also be manufactured. Typically, the electrode composite sheet formed on the current collector foil is cut to a predetermined size, laminated with a separator and solid electrolyte, housed in a battery outer packaging, and then filled and sealed to form a battery cell. Furthermore, by charging and discharging the manufactured battery under appropriate conditions, a stable film is formed between the electrode composite sheet and the electrolyte, which can better maintain the battery's performance during subsequent operation. This is called conditioning or aging treatment.

[0062] Afterwards, a performance check is performed before the product leaves the factory, and batteries that indicate the expected performance are shipped as normal products.

[0063] As performance testing items, examples include the initial charge and discharge capacity, the charge and discharge efficiency as a ratio, the battery capacity (rate characteristics) when the charging or discharging current changes, the AC resistance and DC resistance at a predetermined charge rate (SOC).

[0064] Here, if any unit fails to meet the guaranteed performance requirements, it is deemed a defective product and discarded without leaving the factory, or disassembled and its materials recycled as needed. Since energy is also consumed in this process, the energy consumption and cost of battery cell manufacturing become issues in high-defect manufacturing processes. Furthermore, if defect identification cannot reach the final stage of the production line, energy related to manufacturing defective products is wasted, further deteriorating efficiency.

[0065] <Inspection Methods in Manufacturing Processes>

[0066] As a method to address the aforementioned problem of deteriorating manufacturing efficiency, the present invention proposes the following method: after the electrode composite sheet is manufactured, a sample sheet is extracted from a portion of it, and in the process of inspecting its structure, the structural information is quantified as a characteristic quantity and flexibly used as a management item to determine the quality of the electrode composite sheet.

[0067] Figure 5 A flowchart illustrating an inspection method as one embodiment of the present invention is shown. In this flowchart, manufacturing information, structural information, and performance information are acquired as three types of information and stored in the storage unit (also called a "storage device") of the inspection system (also called a "quality management system"). The quality management system includes a control unit that performs various calculations, such as regression analysis, on the manufacturing information, structural information, and performance information stored in the storage unit, and a display unit that displays the calculation results from the control unit. Furthermore, although not shown, it has the same configuration as a general computer system, including an input unit such as a keyboard for inputting various data into the control unit, a printer for printing calculation results, and various interfaces for receiving control signals from various devices involved in the manufacturing of the electrode composite sheet.

[0068] Manufacturing information includes the material specifications used to form the electrode composite sheet, the manufacturing batch of the material, mixing conditions, coating conditions (atmosphere, slurry viscosity, coating speed, drying temperature), pressing conditions (pressure, pressing time), etc.

[0069] As structural information, besides the presence or absence of defects or cracks within the electrode, examples include the ratio of constituent materials (active material, conductive additives, binders) within the sheet and along the thickness direction of the electrode composite material, the size of aggregates formed by the constituent materials, cracks generated within the active material particles of the electrode, and the ratio of voids lacking constituent materials. Furthermore, the three-dimensional information of these items, i.e., the distribution of the aforementioned items along the sheet thickness direction, can also be used as structural information. Methods for obtaining this structural information include scanning electron microscopy (SEM), optical microscopy, and blackness measurement. Additionally, elemental distribution information in the sample obtained using energy dispersive X-ray spectroscopy (EDX), which is attached to a scanning electron microscope, can also be used as structural information.

[0070] Furthermore, in order to perform inspections during the manufacturing process, it is necessary to simply sample the test specimens. As one embodiment of the present invention, examples such as... Figure 6 A portion of the wound body formed from the manufactured electrode composite sheet is extracted through a punching process and fixed to the holder used in the aforementioned inspection device. Other methods for sample extraction include extraction using laser processing, but are not limited to these.

[0071] For the surface of the electrode composite sheet fixed to the inspection bracket, a secondary electron image of SEM is obtained to observe the surface morphology, thereby enabling the detection of defects within the electrode. The severity of these defects can be quantified by the number of defects per unit area or the area ratio. Similarly, cracks within the active material particles can also be detected using secondary electron imaging. The severity can be quantified by using the number of cracks or the total crack length per unit area of ​​the active material.

[0072] Furthermore, by using a back-scattered electron image (SEM), an observation image with contrast reflecting the atomic weights of the constituent elements can be obtained. By quantifying the size and area ratio of regions with different contrasts, the size of the aggregate, the ratio of constituent materials (active material, conductive additive, binder), and the porosity ratio can be quantified. Additionally, in obtaining information about the thickness direction of the electrode composite sheet, besides observing the cross-section after punching, the electrode composite sheet extracted from the winding can be peeled from the current collector foil, and surface information close to the peeled surface of the current collector foil can be obtained using the methods described above.

[0073] Performance information is obtained during the aging of manufactured cells and factory inspection, including the capacity at the first charge and discharge, the charge and discharge efficiency as a ratio, the battery capacity (rate characteristics) when the charging or discharging current changes, the AC resistance and DC resistance at the predetermined charge rate (SOC).

[0074] In the inspection system of this invention, the correlation between the acquired structural information and performance information is formulated and used as a performance prediction model. The structural information of the newly manufactured electrode composite sheet is used as input to the performance prediction model to predict the battery performance when this electrode composite sheet is applied. In this case, if the predicted performance does not meet the guaranteed performance, the electrode composite sheet can be omitted from the manufacturing process, preventing subsequent battery defects. Specifically, as structural information, it is determined whether cracks, peeling, etc., exist within the electrode. Then, it is determined whether the performance output from the performance prediction model meets the guaranteed performance. Only electrode composite sheets that pass the determination are cut and stacked to proceed with battery manufacturing. The formulation of the correlation between structural information and performance information, the prediction of battery performance using the formulated performance prediction model, and the determination of whether the predicted performance does not meet the guaranteed performance are all handled by… Figure 5 The "Control Department" within the described quality management system performs this function. Sometimes, the part (function) within the "Control Department" that predicts battery performance is called the "Performance Prediction Department." Additionally, if the Control Department determines that the predicted performance does not meet the guaranteed performance, the determination result can be displayed in the "Display Department" of the quality management system.

[0075] On the other hand, electrodes that do not meet the criteria in the judgment can be omitted from the process, and it can be determined that the manufacturing conditions when forming the electrode composite sheet are inappropriate, and the manufacturing conditions are updated. By repeatedly performing the above checks and updating the manufacturing conditions, the risk of defects in the manufacturing process can be suppressed and the manufacturing efficiency can be improved.

[0076] Figure 7 An inspection system (also called a "quality management system"), which is one embodiment of the present invention, is represented as a flowchart. Here, as information used for inspection, in addition to... Figure 5 In addition to the manufacturing and structural information shown, operational, degradation, and anomaly information are also stored in the storage unit (also known as the "storage device"). The structure and... Figure 5The configuration shown is the same, so descriptions are omitted. Here, operational information refers to the usage history of the battery cells assembled as part of the battery system after leaving the factory. This is primarily based on time-series data such as battery voltage, current, and temperature monitored by the battery management system (BMS). Additionally, degradation information refers to changes in battery performance caused by the aforementioned operational changes. This degradation information can also be inferred by analyzing BMS data.

[0077] When a battery system is used under operating conditions appropriate to its specifications, performance deteriorates, but within a predictable trend, allowing for planned and flexible system utilization. However, if a sharp deterioration occurs that deviates from the predicted trend, the system is considered to be in a malfunctioning state, sometimes resulting in operational stoppage. In such cases, it is necessary to determine the cause of the anomaly and implement corresponding corrective measures. Figure 7 In this process, by comparing the degradation information obtained from the input structural information with the anomaly information, it is possible to determine whether the cause of the anomaly occurred during the manufacturing of the battery and electrode composite sheet or due to inappropriate operating conditions.

[0078] <Formulaic methods for the correlation between construction information, performance information, and degradation information>

[0079] exist Figure 5 and Figure 7 In this process, by formulating the correlation between structural information and performance or degradation information, it is possible to reduce the manufacturing defect rate of batteries and clarify the causes of system defects. Three examples of this formulating method are described below. For this formulating, multiple electrode composite sheets need to be fabricated during material and process research to determine the specifications of the batteries to be manufactured, or during the design and trial operation of the production line, and structural information and performance / degradation information need to be obtained for each sheet. The following shows a formulating method using the correlation of the obtained information, but it is not limited to this method. Furthermore, the formulating of the correlation between structural information and degradation information, the execution of the formulated degradation prediction model, and the formulating and execution of the performance prediction model are all performed by the control department of the quality management system. Sometimes, the part (function) in the "control department" that performs degradation prediction is called the "degradation prediction department."

[0080] (i) During the design phase of the battery materials and processes, or during the trial operation of the production line, manufacturing conditions are intentionally controlled to produce electrode composite sheets with different structures. For electrode assemblies where only specific manufacturing conditions are changed, structural information is obtained from electrode inspection, and performance information is obtained from pre-shipment inspection of the manufactured battery. Here, feature quantities representing structural and performance information (hereinafter referred to as structural parameters and performance parameters) are extracted. The performance parameters or degradation parameters are used as target variables, and the structural parameters are used as target variables. Their correlation is formulated through regression analysis and stored in a storage device.

[0081] exist Figures 5 to 7 In this method, the performance is predicted by inputting the structural information of the electrode composite sheet extracted from the manufacturing process into the formula. Figure 8 This illustrates an example of the performance / deterioration prediction model construction process described above. The manufacturing conditions for the electrode composite material sheet used to obtain information are determined repeatedly. Based on this, electrodes and batteries are fabricated, and the corresponding structural parameters and performance / deterioration parameters are obtained. A prediction model is then constructed using regression analysis. This process is repeated until the correlation coefficient falls below a predetermined value, resulting in the performance prediction model. The correlation expression obtained through regression analysis can be a polynomial function, exponential function, logarithmic function, power function, etc. There are no specific restrictions; any function that explicitly represents the correlation can be appropriately chosen.

[0082] (ii) Similarly to (i), manufacturing conditions are intentionally controlled during the design phase of the battery materials and processes, or during the trial run of the production line, to produce electrodes with different structures. At this time, multiple manufacturing conditions are changed to prepare multiple sets of electrode composite materials with different structural parameters, and the performance parameters of the batteries using each electrode are evaluated. By performing multiple regression analysis on the obtained parameter sets, the performance parameters are expressed using a multidimensional function of the structural parameters and stored in a storage device (also called a "storage unit"). This process is similar to... Figure 8 The same applies. However, by increasing the variety of construction parameters to be processed, the amount of information required for formulation increases.

[0083] Machine learning can also be used to formulate information with limited data.

[0084] (iii) Furthermore, in formulating the formula, it is also possible to use a battery model that effectively utilizes phenomena occurring inside the battery. Specifically, it is possible to use battery internal state parameters that are related to structural information to reproduce the charge-discharge curve of the battery and predict performance information and degradation information. Regarding battery internal state parameters, known techniques, such as those described in Kohei Honkura, Ko Takahashi, and Tatsuo Horiba, Journal of PowerSources Vol.196 Issue 23 (2011), Pages 10141-10147, can be used to estimate "battery internal state parameters" as characteristic quantities of materials originating from inside the battery, based on the shape changes of voltage-capacity curves and resistance-capacity curves obtained during the charging or discharging of the manufactured battery.

[0085] Figure 9 This represents an example of the reactions that occur inside a battery and their corresponding internal state parameters.

[0086] The capacity dependence of the potential and resistance of the manufactured electrodes (positive and negative electrodes) is inherent to the type of active material used in the electrodes, provided there are no fatal structural anomalies. The voltage-capacity curve of the battery can be expressed as the difference between the potential-capacity curves of the positive and negative electrodes. In addition, the resistance-capacity curve of the battery can be expressed as the sum of the resistance-capacity curves of each electrode and the electronic resistance and ionic resistance (ohmic resistance) of the components.

[0087] If structural abnormalities occur within the electrodes, or if material degradation intensifies due to operation, the lengths of the capacity or resistance axes of the positive and negative electrodes change, or their relative positions change, resulting in alterations in battery performance. The aforementioned literature describes "internal battery state parameters," indicators of the battery's internal material state, which can be quantitatively evaluated based on changes in the shape of the voltage-capacity and resistance-capacity curves of batteries immediately after manufacturing or during operation.

[0088] Figure 9 In (a), the utilization rate (m) of the active material in the positive electrode is shown as an internal state parameter of the battery. p ), utilization rate of active material in negative electrode (m) n ), ion loss (δ) caused by electrolyte decomposition on the surfaces of the positive and negative electrodes. p δ n ), the ohmic resistance within the battery (R0, electronic resistance of the constituent components + ionic resistance), and the rate of increase in ion diffusion resistance within the positive and negative electrodes (a p a n ).like Figure 9As shown in (b) and (c), m p and m n It affects the magnification / reduction of the positive and negative curves in the voltage-capacitance and resistance-capacitance curves along the horizontal axis. Furthermore, δ p and δ n This represents the parallel shift (deviation) of each curve along the capacity axis. Additionally, Ro corresponds to the baseline of the battery resistance value in the resistance-capacity curve, and a... p a n It is the ratio of the vertical axis (resistance axis) of the resistance-capacity curves of the positive and negative terminals.

[0089] Structural information obtained through SEM, optical microscopy, etc., is closely related to the internal state parameters of these batteries. For example, in the aforementioned structural information, the ratio of constituent materials (active material, conductive additive, binder) within the electrode composite material sheet is related to m. p m n and R o This has an impact. For example, in electrodes where the binder material is heavily concentrated on the surface, the surface electronic and ionic resistance tends to increase, resulting in R... o Increase. Furthermore, it is believed that the condensate size has an effect on R0 and a. p a n The cracks generated inside the electrode active material particles have an impact on a. p a n The porosity affects R0.

[0090] Furthermore, as material degradation intensifies with battery operation, various material parameters change in the direction of degradation (m p m n : Reduce, δ p δ n a p a n R o The rate of change is affected not only by operating conditions (time-series data of temperature, voltage, and current) but also by initial structural defects. For example, in positive electrodes where the binder material is segregated on the surface, due to the relative insufficiency of the binder material on the current collector foil side, peeling occurs due to electrode volume changes during charging and discharging, resulting in a lower utilization rate (m). p Resistance increase rate a p Ohmic resistors R0 are prone to change, and their operating parameters, such as operating time, vary, i.e., dm. p / dt、da p / dt、dR o The absolute value of / dt tends to increase. The same applies to the negative electrode.

[0091] Figure 10This diagram schematically illustrates the process of preliminary experiments and regression analysis used to construct a performance / degradation prediction model by using internal battery information parameters. To obtain the aforementioned internal battery state parameters... Figure 5 The correlation between the required "structural information" and "performance information" is determined first by preparing electrode composite material sheets built under various manufacturing conditions during pre-production testing. Next, "structural parameters" are extracted as numerical values ​​from image information representing the structure of the electrode composite sheet, and "cell internal state parameters" are extracted from the battery using the electrode. Furthermore, the correlation between these two parameters is obtained through regression analysis, thereby enabling the prediction of the internal state parameters of a battery using an electrode with arbitrary structural parameters. Then, by using the predicted internal state parameters, such as... Figure 9 As in (b) and (c), the voltage-capacity curve and resistance-capacity curve of the battery can be reproduced, and the initial performance information (capacity, resistance, input and output characteristics) can be predicted.

[0092] In addition, Figure 7 In deriving the correlation between the required "structural information" and "degradation information," preliminary experiments are conducted to obtain the rate of change of each battery's internal parameters relative to battery operating quantities (operating time, total charge / discharge capacity, and number of charge / discharge cycles). Regression analysis is then used to formulate the correlation between this rate of change and the "structural parameters." Next, the structural parameters extracted from the electrode composite material sheet during manufacturing are input, and the operating quantities are integrated. This allows for the prediction of the battery's internal parameters after a certain period. The discharge curve is then reconstructed using the battery's internal state parameters, thereby predicting the battery's performance after a certain period.

[0093] The following describes an embodiment of an inspection system (quality management system) used in the manufacturing process of the secondary battery of the present invention to illustrate more specifically. The performance prediction model and degradation prediction model used in the inspection system of the present invention are constructed by controlling multiple manufacturing conditions and using the construction information and performance information at that time; however, for the purpose of clearly illustrating the concept of the inspection system, a simpler example is used here.

[0094] Example 1

[0095] <Inspection system focusing on the binder distribution in positive electrode composite sheets>

[0096] This paper presents an example of studying the correlation between structural and performance information by setting the test object as a cathode composite sheet with NCM (Li(Ni,Co,Mn)O2) as the active material, and changing the drying conditions as the manufacturing conditions. Table 1 shows an example of cathode composite sheets (cathode A to cathode D) applied to the construction of performance prediction models and degradation prediction models.

[0097] [Table 1]

[0098]

[0099] NCM powder (as the active material), acetylene black (as the conductive agent), and PVdF (as the binder) were mixed in a weight ratio of 90:6.5:3.5 to prepare a slurry with N-methyl-2-pyrrolidone as the solvent. The slurry was then coated onto a 15 μm thick Al foil using a corner-shaped coating machine and dried in a drying oven for approximately 10 minutes. The coating weight, based on the dried weight, was 24 mg / cm² (single-sided). The drying temperature varied between 100°C and 160°C, as shown in Table 1.

[0100] Figure 11 The figures show the morphology of two cathode composite sheets, cathode B (dried at 120°C) and cathode D (dried at 160°C), observed using a scanning electron microscope (SEM), and the elemental composition of the surface was evaluated using EDX. In cathode D, a large amount of fluoropolymer from the binder was observed. This means that, as shown in the figure, when the solvent rapidly dries from the current collector foil side to the electrode surface under high-temperature conditions, the fluoropolymer dissolved in the solvent segregates and precipitates on the electrode surface. This phenomenon is called binder migration.

[0101] As a method to obtain the material inhomogeneity within the electrode composite sheet caused by the migration of the binder, in addition to Figure 7 In addition to the SEM-EDX analysis from the surface of the slide, we can also cite cross-sectional observations and contrast analysis from surface SEM.

[0102] Figure 12 This figure shows cross-sectional SEM observations as an embodiment of the inspection system of the present invention. The slices in the figure are... Figure 11 The same electrodes B and D were used. In this measurement, in order to determine the quality of the sample during the manufacturing process in a short time, the sample was extracted by punching through a machining fixture containing a metal or ceramic blade, and the cross-section was observed without grinding.

[0103] Figure 12 (a) is a secondary electron image, and (b) is a graph mapping the presence ratio of fluorine (F) from the binder material as measured by EDX. It can be seen that during high-temperature drying, at the positive electrode D where binder migration is intensified, fluorine is unevenly distributed and concentrated on the surface layer. This information can also be accumulated as structural information.

[0104] Figure 13This schematically illustrates a method for quantifying and extracting structural information from surface SEM images as one embodiment of the inspection system of the present invention. (i) is an observation image (reflected electron image, BSE image) obtained in SEM by detecting back scattered electrons (BSE) from the object in response to irradiation. In the secondary electron image, the greater the atomic weight of the object being observed, the higher the brightness. Figure 13 The contrast was observed to be high in the NCM (Natural Chemical Material) regions and light in the surrounding binder and conductive agent regions. Additionally, the void regions, where nothing is present, were observed to be black. These characteristics can be effectively utilized in… Figure 13 In (ii), after removing noise from the image, in (iii), the active material region, the binder and conductive additive region, and the void region are identified based on the brightness of the contrast, and the composition of the constituent materials on the sheet surface is quantified based on their area ratio.

[0105] The results of quantifying the area ratio of the binder material and conductive additive regions (intermediate color regions) in positive electrodes A to D using the above method are shown below. Figure 14 In this measurement, to correct for measurement bias caused by the measurement site, evaluation results are shown in multiple observation areas of a sheet surface. Figure 14 The height of the bars represents the average value, and deviations are evaluated using error bars. Regarding the area ratio of the intermediate colored regions, the higher the drying temperature and the more easily the binder material tends to accumulate on the surface of the electrode, the higher the average value. Figure 14 The results can be regarded as a quantification of the partial sets of the bonding material as structural information.

[0106] In addition, as another measurement method, highly adhesive conductive strips were attached to the surfaces of positive electrodes A to D. These strips were then used to peel positive electrodes A to D from the current collector foil. Electrode peeling was confirmed in the region close to the current collector foil. Therefore, SEM observation was also performed on the peeled surface on the current collector foil side, using... Figure 13 The same method was used to calculate the proportion of intermediate color regions in the peeled surface. The results confirmed that the intermediate color regions were the least in the positive electrode D with high drying temperature. This method also confirmed that the binder segregation to the electrode surface caused by drying at high temperature can be detected.

[0107] Figure 15 This refers to the discharge rate characteristic, which is one of the battery performance parameters measured before the battery leaves the factory.

[0108] The horizontal axis represents the discharge rate, and xC refers to the current that can charge or discharge the battery's storeable capacity at a rate of 1 / x per hour. Figure 15It is generally believed that the higher the discharge rate (higher current value), the lower the discharge capacity due to the influence of internal battery resistance. However, this trend is significant in cathode A, which has a low drying temperature, and cathode D, which has a high drying temperature. Here, in order to quantitatively evaluate the effect of changes in electrode structure dependent on manufacturing conditions on the ion conduction resistance within the electrode, symmetrical units made by sandwiching a separator between two identical cathode sheets were prepared for cathodes A to D, and electrochemical impedance (EIS) measurements were performed at 25°C.

[0109] The results are shown in Figure 16 The length of the region with a 45° inclination, close to the curve in the figure, corresponds to the magnitude of the ion conduction resistance, thus evaluating the ion conduction resistance of each positive electrode. The results are shown in Table 1. This suggests that if the binder is concentrated on the surface, as in positive electrode D, the surface binding material of the electrode hinders the movement of electrons and ions towards the active material, increasing the resistance.

[0110] Furthermore, in cathode A, which has a low drying temperature and a low surface binder dosage, it is expected that the binder segregation near the current collector foil will be relatively aggravated, resulting in an increase in ion conduction resistance. Table 1 shows, as an example of performance information, the ratio of discharge capacity at 0.2C (coulombs) to discharge capacity at 5C, but in the order of cathode B ~ cathode C > cathode A > cathode D, which matches the sequence of ion conduction resistance (from low to high cathode B ~ cathode C < cathode A < cathode D).

[0111] Figure 17 This indicates the results of a charge-discharge cycle test simulating battery performance degradation during operation. Laminated battery cells were fabricated by combining positive electrodes A through D with negative electrodes of the same specifications formed from graphite. These cells were then subjected to a charge-discharge cycle test at a rate of 1C (coulomb) under a constant temperature environment of 25°C.

[0112] Compared to cells using cathodes B and C, battery cells using cathodes A and D exhibit a greater capacity degradation rate, particularly noticeable with cathode D. Furthermore, by comparing the voltage-capacity curves and resistance-capacity curves before and after degradation, [further details are needed]. Figure 9 The battery internal state parameters shown indicate the utilization rate m of the positive electrode active material. p The variation varied across the cathodes, with values ​​for cathode A (93.1%), cathode B (99.5%), cathode C (99.0%), and cathode D (76.5%). This sequence also showed a good match with the sequence for capacity reduction rate.

[0113] Furthermore, according to the battery disassembly test after the experiment, in the positive electrode D where the utilization rate of active material decreased significantly, a defect occurred between the electrode composite sheet and the Al current collector foil. Figures 11 to 14The confirmed bias of the adhesive material towards the surface leads to poor bonding between the electrode composite and the current collector foil, impairing the utilization of the positive electrode portion in the non-bonded areas. Consequently, the utilization rate of the active material in the positive electrode becomes significantly reduced. Table 1 shows, as an example of degradation information, the discharge capacity retention rate after 150 cycles in a cyclic test, compared with... Figure 15 , Figure 16 Similarly, it can be seen that the sequence becomes positive electrode B ~ positive electrode C > positive electrode A > positive electrode D.

[0114] Figure 18 The table shows the performance information (ion conduction resistance within the electrode, 5C capacity / 0.2C capacity in the discharge rate test) and the degradation information (discharge capacity retention after 150 cycles, degradation rate of active material utilization of the positive electrode: normalized active material utilization of the positive electrode in each cycle m). p The graph is plotted as the ratio of the reduction rate to the area of ​​the intermediate colored region, which is one of the structural information. Additionally, the graph also shows the results of a regression analysis (fitting a quadratic function in this case) with performance or degradation information as the objective variable and structural information as the explanatory variable. This study demonstrates that the area ratio of the intermediate colored region is highly sensitive to both performance and degradation information.

[0115] This means that the initial performance and degradation characteristics of the subsequently manufactured battery can be determined based on the binder ratio present on the surface, obtained from observations of the positive electrode surface. This demonstrates that by examining and managing this structural information during manufacturing, it is possible to identify electrode composite material layers that ensure battery performance. Specifically, through... Figure 6 The method shown extracts a portion of the continuously manufactured electrode composite sheet and automatically performs the following processes under pre-set conditions: fixing to the inspection frame, conveying to the SEM device, atmosphere adjustment, acquisition of reflected electron images, and evaluation of the area ratio of the intermediate color region. The obtained area ratio is input into a pre-built performance prediction model and a degradation prediction model, which can predict the initial performance and operational degradation performance when using the winding body formed from the corresponding electrode composite sheet to manufacture battery cells.

[0116] If the predicted result is lower than the expected benchmark (predetermined performance), the electrode can be identified as having a risk of failure. In cases where an electrode is identified as having a risk of failure, this situation can be notified. The notification is preferably displayed on the monitor screen of the quality management system's computer. The system operator can observe this display on the monitor and remove batteries with a risk of failure from the production line in advance. Alternatively, a mechanism can be used to automatically remove batteries with electrodes identified as having a risk of failure from the production line.

[0117] In addition, through such Figure 18Formulating the relevant relationships as illustrated in the charts allows for the prediction of performance or degradation parameters based on the numericalized structural parameters of the structural information. This also helps in efficiently determining manufacturing conditions at the start of the battery manufacturing process. Furthermore, in this study, for simplicity, only the area ratio of the intermediate color region was considered as structural information. However, porosity and the size of aggregates formed by multiple active materials vary depending on the drying temperature. By performing multiple regression analysis with these factors, the prediction accuracy of performance and degradation characteristics for quality management is increased.

[0118] Furthermore, the present invention is not limited to the above-described embodiments, but includes various modifications.

[0119] For example, the above embodiments, in order to facilitate understanding of the present invention, specifically illustrate examples of positive electrodes where the drying temperature in the manufacturing information varies widely and the drying speed is quite different. However, in actual production lines, the drying temperature set by the equipment is mostly constant, but the drying conditions also vary depending on the batch of active material, solvent mixing ratio, slurry viscosity, production line environment, etc., resulting in changes in the material distribution in the electrode. In such cases, defects in the electrode composite sheet that cause poor performance can also be detected by following the inspection method and steps of Example 1.

[0120] in addition, Figure 18 As a simple example, the correlation between the structural, performance, and degradation information of four positive electrode composite sheets was plotted. However, in practice, by discovering and formulating correlations from information sets for multiple electrodes, the system can automatically determine the quality of the electrode. At this point, it is also possible to determine the coefficients of a pre-defined model formula, or to derive the correlation formula by analyzing information sets for multiple electrodes using machine learning.

[0121] Regarding structural information, in addition to the distribution of the constituent materials in Example 1, the aggregate size of the active material particles, the shape of the voids formed between the materials, and the area ratio of the electrode surface can also be used as structural information. Furthermore, not only the electrode surface, but also the distribution in the thickness direction and the tortuosity of the voids can be examined and quantitatively evaluated using scanning electron microscopy and optical microscopy, and can be applied to structural information. As a method for confirming the distribution in the thickness direction, besides… Figure 12 Besides such observations and analyses from the cross-sectional direction, in addition to Figure 13 In addition to observing and analyzing the electrode composite sheet from the surface direction, the electrode composite sheet is also peeled off from the current collector foil, and the peeled surface is observed, analyzed and compared with the two, thereby evaluating the uniformity in the thickness direction.

[0122] Symbol Explanation

[0123] 100 units (battery unit, secondary battery unit, secondary battery, lithium-ion battery)

[0124] 1 Electrode Occupying Part

[0125] 2 Positive end (lever)

[0126] 3 negative extremes (pole ears)

[0127] 4 Electrolyte

[0128] 5. Diaphragm

[0129] 6. Outer Packaging Materials

[0130] 11 positive electrode

[0131] 12 negative electrodes

[0132] 111 positive electrode composite material layer

[0133] 112 positive electrode current collector foil

[0134] 121 negative electrode composite material layer

[0135] 122 negative electrode current collector foil

[0136] 311 collector foil

[0137] 312 Active Substance A

[0138] 313 conductive additive

[0139] 314 adhesive (binder)

[0140] 315 Active substance B.

Claims

1. A quality management system for secondary batteries, characterized in that, have: The storage unit stores a performance prediction model that formulates the correlation between the structural information of the electrode composite sheet and the performance information of the secondary battery manufactured using the electrode composite sheet; and The performance prediction unit inputs the structural information of the newly manufactured electrode composite sheet into the performance prediction model to predict the battery performance of the secondary battery manufactured using the electrode composite sheet. The construction information is information related to the construction of the electrode composite sheet, determined after the manufacture of at least one of the positive and negative electrodes used in a secondary battery. It includes at least one of the following: the presence or absence of defects or cracks in at least one of the positive and negative electrodes; the ratio of constituent materials in the in-plane or thickness direction of the electrode composite sheet; the size of aggregates formed by the constituent materials; cracks generated within the electrode active material particles; and the ratio of voids in the constituent materials. The performance information is information related to the performance of a secondary battery manufactured using the electrode composite material sheet, including at least one of the following: capacity during initial charge and discharge, the ratio of capacity during charge and discharge (i.e., charge and discharge efficiency), battery capacity when the charging or discharging current is varied, AC resistance at a predetermined charge rate, and DC resistance.

2. The quality management system for secondary batteries according to claim 1, characterized in that, The quality management system includes a performance prediction model generation unit, which generates the performance prediction model by formulating the correlation between the construction information and the performance information. The storage unit stores the construction information and the performance information.

3. The quality management system for secondary batteries according to claim 1, characterized in that, The structural information is obtained by analyzing at least one of the following: elemental analysis of a sample using a scanning electron microscope, an optical microscope, a blackness measurement method, or an X-ray spectrophotometer associated with the scanning electron microscope.

4. The quality management system for secondary batteries according to claim 3, characterized in that, The structural information is obtained by removing a portion of the manufactured electrode composite sheet through punching or laser processing, fixing it to an inspection bracket, and analyzing it using at least one of the scanning electron microscope, the optical microscope, the density measurement method, or the X-ray spectrophotometry method associated with the scanning electron microscope.

5. The quality management system for secondary batteries according to claim 4, characterized in that, The construction information includes at least one of the following: The information is based on the number of defects per unit area of ​​active material, the area ratio, the number of cracks or the length of cracks per unit area of ​​active material, and the information about a portion of the electrode composite sheet fixed to the inspection bracket. as well as Information on the size of the aggregate, the ratio of constituent materials in the in-plane or thickness direction, and the porosity ratio obtained by quantifying the size and area ratio of regions with different contrasts reflecting the atomic weights of the constituent elements from the reflected electron image of the scanning electron microscope.

6. The quality management system for secondary batteries according to claim 1, characterized in that, The performance prediction model is obtained by controlling manufacturing conditions to produce electrode composite sheets with different structures, and by formulating the correlation between the structure information obtained from the electrode composite sheets and the performance information of the battery made from the electrode composite sheets through regression analysis.

7. The quality management system for secondary batteries according to claim 6, characterized in that, The performance prediction model is constructed by repeatedly performing regression analysis on the correlation between the construction information obtained from the electrode composite sheet with only a change in specific manufacturing conditions and the performance information of the battery made from the electrode composite sheet, until the correlation coefficient of the correlation function obtained by the regression analysis becomes below a predetermined value.

8. The quality management system for secondary batteries according to claim 1, characterized in that, The quality management system includes a control unit. The control unit controls the storage unit in the following manner: it stores manufacturing information in the storage unit, which is information used in the manufacture of an electrode composite sheet suitable for at least one of the positive and negative electrodes of a secondary battery. The manufacturing information includes at least one of the material specifications for forming the electrode composite sheet, the manufacturing batch of the material, mixing conditions, coating conditions, and pressing conditions. If the battery performance predicted by the performance prediction unit does not meet a predetermined benchmark, it determines that any of the manufacturing information is inappropriate and updates the manufacturing information stored in the storage unit.

9. The quality management system for secondary batteries according to claim 1, characterized in that, The information stored in the storage unit also includes degradation information, which indicates changes in battery performance caused by use after the battery is assembled as a battery system after leaving the factory. This degradation information includes at least one of capacity retention rate, resistance rise rate, the rate of decrease in the utilization rate of the active materials of the positive and negative electrodes, and the rate of increase in the ion conduction resistance within the battery.

10. The quality management system for secondary batteries according to claim 9, characterized in that, The quality management system includes: a degradation prediction model generation unit, which generates a degradation prediction model, the degradation prediction model being a model obtained by formulating the correlation between the structural information obtained from the electrode composite material sheets with different structures manufactured under controlled manufacturing conditions and the degradation information of the batteries made from the electrode composite material sheets through regression analysis; and The degradation prediction unit inputs the structural information of the newly manufactured electrode composite sheet into the degradation prediction model to predict the degradation performance of the battery manufactured using the electrode composite sheet during operation. The degradation information refers to information on changes in battery performance due to use of a battery assembled as a battery system after leaving the factory, including at least one of capacity retention rate, rate of increase in resistance, rate of decrease in the utilization rate of active materials in the positive and negative electrodes, and rate of increase in ion conduction resistance within the battery.

11. The quality management system for secondary batteries according to claim 10, characterized in that, The degradation prediction model generation unit repeatedly performs regression analysis on the correlation between the structural information obtained from the electrode composite material sheet with only a change in specific manufacturing conditions and the degradation information of the battery made from the electrode composite material sheet, until the correlation coefficient of the correlation function obtained by the regression analysis becomes below a predetermined value, thereby constructing the degradation prediction model.

12. The quality management system for secondary batteries according to claim 1, characterized in that, The secondary battery is any one of lithium-ion secondary batteries, sodium-ion secondary batteries, potassium-ion secondary batteries, and secondary batteries using multivalent cations as charge carriers.

13. The quality management system for secondary batteries according to claim 1, characterized in that, The electrode composite sheet used in the secondary battery contains a solid electrolyte.

14. A quality management method for secondary batteries, characterized in that, Includes the following steps: The performance prediction model generation step involves formulating the correlation between the structural information of the electrode composite sheet and the performance information of the secondary battery manufactured using the electrode composite sheet to generate a performance prediction model. as well as The performance prediction step involves inputting the structural information of the newly manufactured electrode composite sheet into the performance prediction model to predict the battery performance of a battery manufactured using the electrode composite sheet. The construction information is information related to the construction of the electrode composite sheet, determined after the manufacture of at least one of the positive and negative electrodes used in a secondary battery. It includes at least one of the following: the presence or absence of defects or cracks in at least one of the positive and negative electrodes; the ratio of constituent materials in the in-plane or thickness direction of the electrode composite sheet; the size of aggregates formed by the constituent materials; cracks generated within the electrode active material particles; and the ratio of voids in the constituent materials. The performance information is information related to the performance of a secondary battery manufactured using the electrode composite material sheet, including at least one of the following: capacity during initial charge and discharge, the ratio of capacity during charge and discharge (i.e., charge and discharge efficiency), battery capacity when the charging or discharging current is varied, AC resistance at a predetermined charge rate, and DC resistance.

15. The quality management method for secondary batteries according to claim 14, characterized in that, The performance prediction model is obtained by controlling manufacturing conditions to produce electrode composite sheets with different structures, and by formulating the correlation between the structural information obtained from the electrode composite sheets and the performance information of the battery made from the electrode composite sheets through regression analysis.

16. The quality management method for secondary batteries according to claim 15, characterized in that, The performance prediction model is constructed by repeatedly performing regression analysis on the correlation between the construction information obtained from the electrode composite sheet with only a change in specific manufacturing conditions and the performance information of the battery made from the electrode composite sheet, until the correlation coefficient of the correlation function obtained by the regression analysis becomes below a predetermined value.

17. The quality management method for secondary batteries according to claim 14, characterized in that, Includes the following steps: The manufacturing information storage step stores manufacturing information, which is information used in the manufacture of an electrode composite sheet suitable for at least one of the positive and negative electrodes of a secondary battery, including at least one of the material specifications for forming the electrode composite sheet, the manufacturing batch of the material, mixing conditions, coating conditions, and pressing conditions. as well as In the manufacturing information update step, if the battery performance predicted in the performance prediction step does not meet the predetermined benchmark, it is determined that any one of the manufacturing information is inappropriate, and the manufacturing information is updated.

18. The quality management method for secondary batteries according to claim 14, characterized in that, Includes the following steps: The degradation prediction model generation step generates a degradation prediction model, which is obtained by formulating the correlation between the structural information obtained from the electrode composite material sheet with different structures manufactured under controlled manufacturing conditions and the degradation information of the battery made from the electrode composite material sheet through regression analysis. as well as The degradation prediction step involves inputting the structural information of the newly manufactured electrode composite sheet into the degradation prediction model to predict the degradation performance of the battery manufactured using the electrode composite sheet during operation. The degradation information indicates changes in battery performance due to use of the battery assembled as a battery system after leaving the factory, including at least one of capacity retention rate, rate of increase in resistance, rate of decrease in the utilization rate of the active materials of the positive and negative electrodes, and rate of increase in the ion conduction resistance within the battery.

19. The quality management method for secondary batteries according to claim 18, characterized in that, The degradation prediction model is constructed by repeatedly performing regression analysis on the correlation between the structural information obtained from the electrode composite sheet with only a change in specific manufacturing conditions and the degradation information of the battery made from the electrode composite sheet, until the correlation coefficient of the correlation function obtained by the regression analysis becomes below a predetermined value.

20. The quality management method for secondary batteries according to claim 14, characterized in that, The electrode composite sheet is formed by slurrying a mixture of constituent materials with a liquid, coating it, drying it, and then pressing it.

21. The quality management method for secondary batteries according to claim 14, characterized in that, The electrode composite sheet is formed by dispersing a mixture of constituent materials into a solid state into a sheet and then applying pressure.

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