Inspection method, inspection device, and inspection pogram

Abstract

To improve the estimation accuracy of a defect rate of a secondary battery in comparison to a case of estimating the defect rate of the secondary battery only from a manufacturing history until assembling the secondary batter.SOLUTION: An inspection method makes a computer execute processing of: by using an estimation expression for estimating a battery characteristic of a secondary battery generated by use of manufacturing history data representing a manufacturing history of the secondary battery and an amount of characteristic representing a charge / discharge history of the second battery when performing charge / discharge to the secondary battery, generating a distribution of the battery characteristic from respective estimated battery characteristics by changing the amount of characteristic after addition of an estimation error of the battery characteristic in the estimation expression to the amount of characteristic, relative to the same manufacturing history; calculating a process capability in a manufacturing process of the secondary battery from the generated distribution of the battery characteristic; and outputting a defect rate of the secondary battery from the calculated process capability.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] The present disclosure relates to an inspection method, an inspection device, and an inspection program. [Background technology]

[0002] For example, in a manufacturing process of secondary batteries such as lithium ion batteries, a total inspection or a random inspection may be performed at the time of shipping inspection of the manufactured secondary batteries depending on the manufacturing status of the secondary batteries.

[0003] Regarding such shipping inspection, a method of inspecting secondary batteries has been disclosed in which the characteristic values ​​of the secondary batteries are predicted, and if the predicted characteristic values ​​fall within a standard range, random inspection is performed on some secondary batteries selected from a plurality of secondary batteries, and if the predicted characteristic values ​​do not fall within the standard range, a full inspection is performed (see Patent Document 1).

[0004] Also disclosed is an inspection device that switches the inspection mode to perform sampling inspection instead of 100% inspection when it is determined that the defect rate is below a predetermined value based on the inspection results of a predetermined number or more of inspection objects stored in an inspection result storage means (see Patent Document 2). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2020-129466 A [Patent Document 2] JP 2002-286778 A Summary of the Invention [Problem to be solved by the invention]

[0006] In Patent Documents 1 and 2, characteristic values ​​of a product are estimated from the characteristics of the material that constitutes the product, the production of parts using the material, and the manufacturing history related to the assembly of the produced parts, and the estimated characteristic values ​​are used to determine whether to conduct a full inspection or a sampling inspection.

[0007] In order to determine whether to perform 100% inspection or sampling inspection, a defect rate calculated from an estimated characteristic value, for example, is often used.

[0008] On the other hand, when manufacturing a secondary battery, a process called "activation" is required for the secondary battery to start charging and discharging. Therefore, if the defect rate is estimated only from the manufacturing history obtained when the internal structure and external shape of the secondary battery are manufactured, the error from the actual defect rate may be large. In addition, the characteristic value estimated from the manufacturing history contains an error, and if the characteristic value is not estimated after taking the error into consideration, the error from this point of view may also be large.

[0009] The present disclosure has been made in consideration of the above circumstances, and aims to provide an inspection method, inspection device, and inspection program that can improve the accuracy of estimating the defect rate of a secondary battery compared to estimating the defect rate of a secondary battery only from the manufacturing history up to the assembly of the secondary battery. [Means for solving the problem]

[0010] In the inspection method according to the first aspect, a computer executes a process of using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of a secondary battery and second data representing the charge / discharge history of the secondary battery when the secondary battery is charged and discharged, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after taking into account an estimation error of the battery characteristics in the estimation equation for the same first data, calculating a process capacity in a manufacturing process of the secondary battery from the generated distribution of the battery characteristics, and outputting a defect rate of the secondary battery from the calculated process capacity.

[0011] The inspection device of the second aspect includes a distribution generation unit that uses an estimation equation for estimating predetermined battery characteristics of the secondary battery, generated using first data representing a manufacturing history of the secondary battery and second data representing the charge / discharge history of the secondary battery when the secondary battery is charged and discharged, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after adding an estimation error of the battery characteristics in the estimation equation to the second data for the same first data, a calculation unit that calculates a process capability in a manufacturing process of the secondary battery from the distribution of the battery characteristics generated by the distribution generation unit, and an output unit that outputs the defect rate of the secondary battery from the process capability calculated by the calculation unit.

[0012] An inspection program according to a third aspect causes a computer to execute a process of using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of a secondary battery and second data representing the charge / discharge history of the secondary battery when the secondary battery is charged and discharged, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after taking into account an estimation error of the battery characteristics in the estimation equation for the same first data, calculate a process capacity in a manufacturing process of the secondary battery from the generated distribution of the battery characteristics, and output a defect rate of the secondary battery from the calculated process capacity. Effect of the Invention

[0013] According to the present disclosure, it is possible to improve the accuracy of estimating the defect rate of a secondary battery compared to estimating the defect rate of a secondary battery only from the manufacturing history up to the assembly of the secondary battery. [Brief description of the drawings]

[0014] [Figure 1] FIG. 2 is a diagram showing an example of a flow of each process from manufacturing to shipping of a secondary battery. [Diagram 2] FIG. 13 is a diagram showing an example of measurement of the section capacity IV resistance of a secondary battery. [Diagram 3] FIG. 2 is a diagram illustrating an example of a functional configuration of an inspection apparatus. [Figure 4] FIG. 2 is a diagram showing an example of the configuration of a main part of an electrical system of the inspection device; [Diagram 5] 13 is a flowchart showing an example of the flow of an estimation expression generating process. [Figure 6] FIG. 13 is a diagram showing an example of an estimation equation for reaction resistance generated by the estimation equation generation process. [Figure 7] FIG. 13 is a diagram showing an example of an estimation equation for reaction resistance generated using only manufacturing data. [Figure 8] 13 is a flowchart showing an example of the flow of an inspection level output process. [Figure 9] FIG. 13 is a diagram showing an example of a reaction resistance distribution. [Figure 10] FIG. 1 is a diagram showing the relationship between process capability and defect rate. [Figure 11] FIG. 13 is a diagram illustrating an example of an estimation equation for section capacity generated by the estimation equation generation process. [Figure 12] FIG. 13 is a diagram showing an example of an estimation formula for section capacity generated using only manufacturing data. [Figure 13] FIG. 13 is a diagram illustrating an example of a distribution of section capacities. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Note that the same components and processes are denoted by the same reference numerals throughout the drawings, and duplicated descriptions will be omitted.

[0016] Moreover, the secondary battery in the embodiment is a battery that can be used repeatedly by charging, and examples of the secondary battery include a nickel-metal hydride battery, a nickel-cadmium battery, a lithium-ion battery, a lithium polymer battery, a lead-acid battery, etc. In the following, the secondary battery will be described using a lithium-ion battery as an example.

[0017] Fig. 1 is a diagram of a workflow showing an example of the flow of each process from manufacturing to shipping of a secondary battery. Although the actual workflow includes other processes than those shown in Fig. 1, in order to easily explain the technology of the present disclosure, Fig. 1 shows an outline of the workflow.

[0018] In FIG. 1, "Battery Materials" refers to the process of measuring whether the characteristics of the materials constituting the secondary battery components, such as the positive electrode, negative electrode, electrolytic solution, electrolyte, separator, and exterior (e.g., active material, conductive additive, binder, etc.), meet the specified requirements.

[0019] "Source" is a process in which secondary battery components are fabricated using procured materials and the characteristics of the fabricated components, such as the film thickness of the positive and negative electrodes, are measured.

[0020] "Assembly" is a process in which the manufactured parts are assembled into the shape of a secondary battery, and assembly characteristics such as the distance between parts are measured.

[0021] "Activation" is the process of charging and discharging an assembled secondary battery to give it the functionality of a secondary battery. During activation, the secondary battery is repeatedly charged and discharged using charging and discharging equipment, and the characteristics obtained during the charging and discharging process are measured. The characteristics obtained during the charging and discharging process of a secondary battery are called "charge and discharge data."

[0022] Various data related to the manufacture of the secondary battery are obtained in each process from the battery material to activation. Hereinafter, the characteristics obtained in each process from the battery material to activation are referred to as "manufacturing history data" in the sense of data obtained in the process of manufacturing the secondary battery. Among the manufacturing history data, the characteristics obtained in each process of the battery material, source, and assembly are particularly referred to as "manufacturing data". The manufacturing data representing the characteristics obtained up to the assembly of the secondary battery is an example of first data, and among the manufacturing history data, the characteristics representing the charge and discharge history of the secondary battery obtained in the activation process, that is, the charge and discharge data, is an example of second data. Note that the manufacturing history data can be used to trace the manufacturing process of the secondary battery back later, so the manufacturing history data is also called "traceability data". The manufacturing history data is stored in the manufacturing history DB 16 of FIG. 3 described later. Note that "DB" is an abbreviation for database.

[0023] The charge / discharge data obtained by activation includes various types of data. For example, Fig. 2 shows an example of the charge / discharge data, which is a measurement example of the section capacity IV resistance of a secondary battery. In the measurement of the section capacity IV resistance, a charged secondary battery is discharged at a specified current for a specified time, and the resistance value in section 2 is measured from the relationship between the voltage and current of the secondary battery in section 2 set within the specified time.

[0024] The charge / discharge data represents the charge / discharge characteristics of the assembled secondary battery, and is therefore an example of a charge / discharge history.

[0025] 1 estimates the defect rate of secondary batteries manufactured in a predetermined period, for example. There is no restriction on the length of the period for estimating the defect rate, and the length of the period can be changed by the user.

[0026] In order to manage secondary batteries manufactured from the same materials during the same period as a single product group, each secondary battery may be assigned production lot information indicating when it was manufactured using the materials procured from where and when. Therefore, the defect rate of the manufactured secondary batteries is estimated for each production lot, for example.

[0027] The defect rate of the secondary battery is estimated using a distribution of a predetermined type of characteristics estimated from the manufacturing data and the charge / discharge data obtained by activating the secondary battery. Therefore, the user sets in advance optimal characteristics to be used for estimating the defect rate of the secondary battery. Hereinafter, the characteristics of the secondary battery estimated from the manufacturing data and the charge / discharge data to be used for estimating the defect rate are referred to as "battery characteristics."

[0028] Not all charge / discharge data is used to estimate the battery characteristics, but predetermined types of charge / discharge data that are correlated with the battery characteristics are used depending on the type of battery characteristics to be estimated. Hereinafter, the charge / discharge data obtained by activation that is used to estimate the predetermined battery characteristics is referred to as a "feature amount." Since the feature amount is also the charge / discharge data, the feature amount is also an example of the second data.

[0029] Once the distribution of battery characteristics is obtained, a process capability Cpk in the manufacturing process of a secondary battery can be calculated from the distribution using a known method. The process capability Cpk represents the capability of producing products within a set specification range, and therefore the defect rate of the secondary battery can be obtained from the process capability Cpk.

[0030] Therefore, if the defective rate of the manufactured secondary batteries is equal to or greater than a predetermined threshold (hereinafter referred to as the "first threshold"), a "shipment inspection" process is carried out before shipping the secondary batteries for quality control purposes, so that secondary batteries that do not meet the required specifications are not shipped. In this case, the larger the value of the defective rate, the more preferable it is to increase the number of batteries to be subjected to the shipment inspection. Therefore, for example, a second threshold value larger than the first threshold value may be set in advance, and the inspection level for the secondary batteries may be determined according to the magnitude of the defective rate, such that a full inspection is carried out when the defective rate is equal to or greater than the second threshold, and a random inspection is carried out when the defective rate is equal to or greater than the first threshold and less than the second threshold.

[0031] Once the shipping inspection is complete, secondary batteries that meet the required standards are shipped in the "shipping" process.

[0032] On the other hand, if the defect rate of the manufactured secondary batteries is less than the first threshold, it means that there are no major problems in the manufacturing process of the secondary batteries that require improvement. In particular, in the case where the first threshold is set to a value that is considered to prevent the manufacture of defective products in reality, for example, in consideration of the number of manufactured secondary batteries, if the defect rate is less than the first threshold, there is no problem in terms of quality control even if all the manufactured secondary batteries are shipped without a shipping inspection. Therefore, if the defect rate of the manufactured secondary batteries is less than the first threshold, the secondary batteries are shipped without a shipping inspection.

[0033] In this way, the inspection content of the manufactured secondary battery changes according to the inspection level of the secondary battery set according to the defective rate.

[0034] Next, an inspection device 10 that estimates the defect rate of manufactured secondary batteries and outputs an inspection level for the secondary batteries will be described.

[0035] 3 is a diagram showing an example of a functional configuration of the inspection device 10. The inspection device 10 includes the following functional units: an estimation equation generating unit 11, an estimation unit 12, a distribution generating unit 13, a calculation unit 14, and an output unit 15.

[0036] The estimation equation generation unit 11 uses the manufacturing data for each process of battery materials, sourcing, and assembly stored in the manufacturing history DB 16, and the feature values ​​included in the charge / discharge data for the activation process, to generate an estimation equation in which the manufacturing data and the feature values ​​are explanatory variables and the battery characteristics are objective variables.

[0037] The estimation unit 12 estimates battery characteristics corresponding to each feature amount from the estimation equation generated by the estimation equation generation unit 11 by varying the feature amount.

[0038] The distribution generating unit 13 generates a distribution of the battery characteristics using the battery characteristics estimated by the estimating unit 12 .

[0039] The calculation unit 14 calculates the process capability Cpk in the manufacturing process of the secondary battery from the distribution of the battery characteristics generated by the distribution generation unit 13. The process capability Cpk is an index representing the capability to produce a product within a standard width of a predetermined required specification.

[0040] The output unit 15 obtains the defect rate of the manufactured secondary battery from the process capability Cpk calculated by the calculation unit 14, and outputs the result to the user. In this disclosure, "output" refers to an operation of making the user recognize information. Therefore, for example, each operation such as displaying information on a display, notifying information by voice, printing information on paper, transmitting information through a communication line, storing information in a storage device, and notifying information by light or vibration represents a specific operation form of output. In this disclosure, an example will be described in which information is displayed on a display.

[0041] It should be noted that the generation of the estimation equation does not necessarily have to be performed by the inspection device 10, and may be performed by another device. In this case, the estimation unit 12 of the inspection device 10 estimates the battery characteristics using the estimation equation generated by the other device. Therefore, the inspection device 10 does not need to include the estimation equation generation unit 11, but in the present disclosure, as an example, the inspection device 10 is described as including the estimation equation generation unit 11.

[0042] Although FIG. 3 shows a situation in which the manufacturing history DB 16 is provided outside the inspection device 10, the inspection device 10 may include the manufacturing history DB 16.

[0043] The inspection device 10 shown in Fig. 3 is configured, for example, using a computer 20. Fig. 4 is a diagram showing an example of the configuration of a main part of an electrical system of the inspection device 10 configured using the computer 20.

[0044] The computer 20 includes a CPU (Central Processing Unit) 21, which is an example of a processor that executes the processing of each functional unit shown in Fig. 3. The computer 20 also includes a RAM (Random Access Memory) 22, which is used as a temporary working area for the CPU 21, a non-volatile memory 23, and an input / output interface (I / O) 24. The CPU 21, the RAM 22, the non-volatile memory 23, and the I / O 24 are each connected via a bus 25.

[0045] The nonvolatile memory 23 is an example of a storage device that maintains stored information even if the power supplied to the nonvolatile memory 23 is cut off, and may be, for example, a semiconductor memory or a hard disk. The nonvolatile memory 23 stores information that must not be erased every time the computer 20 is restarted or a power outage occurs, such as an inspection program that causes the computer 20 to function as the inspection device 10 and various parameters such as the first and second thresholds.

[0046] To the I / O 24, for example, a communication unit 26, an input unit 27, and an output unit 28 are connected.

[0047] The communication unit 26 is connected to a communication line and has a communication protocol for transmitting and receiving data to and from an external device connected to the communication line. For example, when the external device generates an estimated equation, the inspection device 10 receives the estimated equation from the external device via the communication unit 26.

[0048] The input unit 27 is a device that receives instructions from a user and notifies the CPU 21 of the instructions, and includes, for example, a button, a touch panel, a mouse, a keyboard, and a pointing device.

[0049] The output unit 28 outputs to a user information obtained by the inspection device 10, such as the defect rate of the secondary battery and the inspection level of the secondary battery. Specifically, the output unit 28 is a display device that displays information processed by the CPU 21 as an image, and is configured with a liquid crystal display, an organic EL (Electro Luminescence) display, or the like.

[0050] Note that the units connected to the I / O 24 are not limited to the communication unit 26, the input unit 27, and the output unit 28, and various units are connected to the I / O 24 according to the functions of the inspection device 10. For example, when the inspection device 10 prints the inspection results on paper, a print unit (not shown) is connected to the I / O 24. Also, when the inspection device 10 operates offline, it is not necessarily necessary to connect the communication unit 26 to the I / O 24.

[0051] Next, a description will be given of an estimation equation generation process executed by the inspection device 10. Fig. 5 is a flowchart showing an example of the flow of the estimation equation generation process executed by the CPU 21 of the inspection device 10 when an instruction to generate an estimation equation for estimating predetermined battery characteristics is received.

[0052] A testing program that defines the estimation equation generation process is stored in advance, for example, in non-volatile memory 23 of testing device 10. CPU 21 of testing device 10 reads the testing program stored in non-volatile memory 23 and executes the estimation equation generation process.

[0053] Here, the estimation equation generation process will be explained using an example of generating an estimation equation that estimates reaction resistance as an example of an estimation equation, but when generating an estimation equation for other battery characteristics, the same process as the estimation equation generation process shown below is performed to generate an estimation equation.

[0054] First, in step S10, the CPU 21 acquires manufacturing data of items correlated with the reaction resistance, which is the objective variable of the estimation formula, from among the manufacturing history data stored in the manufacturing history DB 16. The items of manufacturing data correlated with the reaction resistance are designated in advance by the user, and the CPU 21 acquires the designated manufacturing data from the manufacturing history DB 16 according to the user's instructions. For example, manufacturing data of the negative electrode plate BET, the negative electrode basis weight, the water content in the electrode body, and the positive electrode active material Ni is used to estimate the reaction resistance. Note that the manufacturing data may differ for each secondary battery or each manufacturing lot of the secondary battery, but for example, a representative value for each item (for example, a standard median value) determined in advance by the user is used as the manufacturing data for each item.

[0055] In step S20, the CPU 21 acquires charge / discharge data, i.e., feature quantities, designated in advance by the user as charge / discharge data to be used for estimating the reaction resistance, from among the charge / discharge data stored in the manufacturing history DB 16. For example, the section capacity IV resistance is used as the feature quantity for estimating the reaction resistance. Note that, in order to generate an estimation formula for the reaction resistance, the non-volatile memory 23 is assumed to have stored in advance the actual measured values ​​of the reaction resistance of each secondary battery for which the section capacity IV resistance was measured.

[0056] In step S30, the CPU 21 generates an estimation equation using, for example, multiple regression analysis, with the reaction resistance as the objective variable and the negative electrode plate BET, the negative electrode coating weight, the water in the electrode body, the positive electrode active material Ni, and the section capacity IV resistance as explanatory variables. The method of least squares is used for the multiple regression analysis.

[0057] The CPU 21 stores the generated estimation equation for the reaction resistance in, for example, the non-volatile memory 23, and ends the estimation equation generation process shown in FIG.

[0058] Fig. 6 is a diagram showing an example of a prediction result of the reaction resistance generated by the estimation equation generation process shown in Fig. 5. Fig. 7 is a diagram showing an example of a prediction result of the reaction resistance generated using only the manufacturing data without using the section capacity IV resistance, which is an example of the charge / discharge data, for comparison with the prediction result shown in Fig. 6. The straight line 1 shown in Figs. 6 and 7 represents an ideal line when the estimation error of the reaction resistance is "0".

[0059] To evaluate the prediction accuracy of the reaction resistance, we calculated the MAE (Mean Absolute Error) of the prediction results in Figure 6 and the MAE of the prediction results in Figure 7. The MAE of the prediction results in Figure 6 was 0.96, and the MAE of the prediction results in Figure 7 was 1.56. In addition, the R 2 (The square of the correlation coefficient (R)) and the R of the prediction results in Figure 7 2 When we calculate each of these, the R of the predicted results in Figure 6 is 2 is 0.79, and the R 2 was 0.68.

[0060] The closer the actual value and the estimated value are, the smaller the MAE becomes. 2 Since is an index that approaches 1 as the actual measurement value and the estimated value approach each other, it can be seen that the predicted reaction resistance generated by the estimation equation generation process shown in Fig. 5 is closer to the actual reaction resistance than the reaction resistance predicted without using the section capacity IV resistance. In other words, the estimation equation for the battery characteristics generated by adding the feature quantity obtained in the activation process to the manufacturing data was able to obtain an estimation equation with higher estimation accuracy than the estimation equation for the battery characteristics generated only from the manufacturing data.

[0061] The CPU 21 calculates the error between the estimated value of the battery characteristics estimated using the estimation formula and the actual measured value of the estimated battery characteristics, that is, the estimation error of the battery characteristics in the estimation formula. The estimation error of the battery characteristics is represented by a standard deviation e that indicates the degree of variation of the estimation error in the estimated value estimated using the estimation formula. The CPU 21 stores the calculated standard deviation e in the non-volatile memory 23. That is, the CPU 21 calculates the estimation error of the reaction resistance in the prediction result shown in FIG. 6.

[0062] Next, a process of the inspection device 10 that outputs an inspection level of a secondary battery using the estimation equation for reaction resistance generated by the estimation equation generation process shown in FIG. 5 will be described.

[0063] FIG. 8 is a flowchart showing an example of the flow of an inspection level output process executed by the CPU 21 of the inspection device 10 when an instruction to determine the inspection level for a secondary battery is received.

[0064] An inspection program that defines the inspection level output process is stored in advance, for example, in non-volatile memory 23 of inspection device 10. CPU 21 of inspection device 10 reads the inspection program stored in non-volatile memory 23 and executes the inspection level output process.

[0065] First, in step S100, the CPU 21 calculates the reaction resistance using the estimation equation generated by the estimation equation generation process shown in Fig. 5, and generates a distribution of each reaction resistance (hereinafter referred to as "reaction resistance distribution"). Specifically, the CPU 21 generates the reaction resistance distribution using each reaction resistance calculated using a requested value, i.e., a random number generated within a range of ±4σ centered on a representative value, as the values ​​of the manufacturing data and the feature amount (in this case, the section capacitance IV resistance). Here, "σ" is the standard deviation of each of the manufacturing data and the feature amount. Although the standard deviation σ takes a different value for each manufacturing data and feature amount, for convenience of explanation, it will be represented as "σ".

[0066] Since the manufacturing data and feature values ​​of the manufactured secondary batteries are within a range of ±4σ with a probability of 0.9999366 around the representative value, by adjusting the random number value within the range of ±4σ, a distribution that takes into account the variability of the manufacturing data and feature values ​​of each secondary battery can be obtained. Note that the adjustment range of the random number is not limited to ±4σ, and may be set to other ranges such as ±3σ or ±5σ.

[0067] In the above, random numbers generated within a range of ±4σ centered on each representative value are used for the manufacturing data as well. However, the manufacturing data may be fixed to a representative value, and the reaction resistance distribution may be generated using random numbers generated only for the feature quantities within a range of ±4σ centered on the representative value.

[0068] FIG. 9 is a diagram showing an example of the generated reaction resistance distribution. In FIG. 9, distribution 3A represents the reaction resistance distribution when manufacturing data and feature values ​​(i.e., section capacity IV resistance) generated by random numbers are used, that is, the reaction resistance distribution without taking into account the estimation error of the reaction resistance in the estimation formula. Distribution 3B represents the reaction resistance distribution when the estimation error of the reaction resistance in the estimation formula is further taken into account for each manufacturing data and feature value generated by random numbers. The estimation error of the reaction resistance in the estimation formula is represented by the standard deviation e. That is, distribution 3B is a reaction resistance distribution generated using an explanatory variable in which a random number generated within the range of ±e is added as an estimation error to each manufacturing data and feature value generated by random numbers. The lower limit 7A represents the lower limit value L1 of the reaction resistance specified as the required specifications, and the upper limit 7B represents the upper limit value L2 of the reaction resistance specified as the required specifications.

[0069] In addition, when the manufacturing data is fixed to a representative value and the reaction resistance is calculated using random numbers generated only for the feature values ​​within a range of ±4σ centered on the representative value, it goes without saying that a random number generated within a range of ±e may be further added to the feature values ​​as an estimated error to calculate the reaction resistance and generate a reaction resistance distribution.

[0070] In step S110 of FIG. 8, the CPU 21 calculates the process capability Cpk of the secondary battery manufacturing process from the reaction resistance distribution generated by the process of step S100.

[0071] For this purpose, the CPU 21 acquires the lower limit value L1 and the upper limit value L2 of the reaction resistance defined as the required specifications. The lower limit value L1 and the upper limit value L2 of the reaction resistance may be stored in advance in the non-volatile memory 23, for example.

[0072] Furthermore, the CPU 21 calculates the average value μ0 and the standard deviation σ0 of the reaction resistance distribution from the reaction resistance distribution generated by the process of step S100.

[0073] The process capability Cpk is expressed by the smaller of Cpk1 and Cpk2 in formula (1) using the lower limit L1 and upper limit L2 of the reaction resistance, and the average value μ0 and standard deviation σ0 of the reaction resistance distribution.

[0074]

number

[0075] Since the process capability Cpk and the defect rate are associated in advance, in step S120, the CPU 21 calculates the defect rate of the manufactured secondary battery from the process capability Cpk calculated in the processing of step S110. A correspondence table associating the process capability Cpk with the defect rate may be stored in advance in, for example, the non-volatile memory 23.

[0076] 10 is a diagram showing the relationship between process capability Cpk and the defect rate. As process capability Cpk increases, the defect rate indicated by point 8 decreases. Therefore, process capability Cpk is divided into, for example, section 4 below Cpk1, section 5 equal to or greater than Cpk1 and less than Cpk2 (Cpk2>Cpk1), and section 6 equal to or greater than Cpk2. The defect rate in section 4 is higher than the defect rates in sections 5 and 6, the defect rate in section 6 is lower than the defect rate in sections 4 and 5, and the defect rate in section 5 is lower than the defect rate in section 4 and higher than the defect rate in section 6.

[0077] Therefore, CPU21 outputs 100% inspection for production lots having a defect rate included in interval 4, sampling inspection for production lots having a defect rate included in interval 5, and no inspection required for production lots having a defect rate included in interval 6. In this way, CPU21 outputs the inspection level for the secondary batteries using the defect rate.

[0078] When the inspection level is determined to be sampling inspection, CPU 21 may also output the number of secondary batteries to be sampled to output unit 28. The number of secondary batteries to be sampled is calculated from the defect rate using a known statistical method.

[0079] Furthermore, the inspection levels output by the inspection device 10 are not limited to the three levels of full inspection, sampling inspection, and no inspection required. For example, section 5 may be further divided into a plurality of sections, and a sampling inspection category may be added in which the number of secondary batteries to be sampled is set to be smaller for a section that has a smaller range of defect rates among the divided sections.

[0080] Furthermore, CPU 21 may output the defect rate of the secondary batteries together with the inspection level, and may further output information obtained from the defect rate. Information obtained from the defect rate includes, for example, the number of defective secondary batteries. The number of defective secondary batteries is calculated by multiplying the defect rate of the secondary batteries by the number of secondary batteries manufactured. With the above, the inspection level output process shown in FIG. 8 is completed.

[0081] Up to this point, the battery characteristic that determines the inspection level has been described using reaction resistance, but it goes without saying that the battery characteristic that determines the inspection level is not limited to reaction resistance.

[0082] For example, the inspection level of the secondary battery may be determined using the section capacity of the secondary battery. In this case, in step S10 of Fig. 5, the CPU 21 acquires manufacturing data of items correlated with the section capacity that is the objective variable of the estimation formula from among the manufacturing history data stored in the manufacturing history DB 16. For example, the manufacturing data of the positive electrode plate thickness, the winding_original roll diameter, and the electrode body height are used to estimate the section capacity.

[0083] 5, the CPU 21 acquires a feature quantity designated in advance by the user as the charge / discharge data to be used for estimating the section capacity from among the charge / discharge data included in the manufacturing history data stored in the manufacturing history DB 16. For example, the initial charge capacity of the secondary battery is used as the feature quantity for estimating the section capacity. The initial charge capacity is the capacity stored in the secondary battery at the time of the first charge.

[0084] In step S30 of FIG. 5, the CPU 21 generates an estimation equation using, for example, multiple regression analysis, with the section capacity as the objective variable and the positive plate thickness, the wound_original roll diameter measurement, the electrode body height, and the initial charge capacity as explanatory variables.

[0085] Fig. 11 is a diagram showing an example of a prediction result of a section capacity generated by the estimation equation generation process shown in Fig. 5. Fig. 12 is a diagram showing an example of a prediction result of a section capacity generated using only manufacturing data without using the initial charge capacity, which is an example of charge / discharge data, for comparison with the prediction result shown in Fig. 11. The straight line 1 shown in Figs. 11 and 12 represents an ideal line when the estimation error of the section capacity is "0".

[0086] R of the forecast results in Fig. 11 2 and the R of the prediction results in Fig. 12 2 When we calculate each of these, the R of the prediction results in Figure 11 is 2 is 0.743, and the R 2 was 0.628.

[0087] In other words, even if the objective variable and explanatory variables of the estimation formula are changed, generating an estimation formula for the battery characteristics by adding the features obtained in the activation process to the manufacturing data can obtain an estimation formula that is closer to the actual battery characteristics than generating an estimation formula for the battery characteristics from the manufacturing data alone.

[0088] On the other hand, Fig. 13 is a diagram showing an example of the distribution of interval capacity generated when the inspection level output process shown in Fig. 8 is executed using the estimation formula generated by the estimation formula generation process shown in Fig. 5. In Fig. 13, distribution 3C represents the distribution of interval capacity taking into account the estimation error of the interval capacity in the estimation formula. The lower limit 7C represents the lower limit value L1 of the interval capacity defined as the required specifications, and the upper limit 7D represents the upper limit value L2 of the interval capacity defined as the required specifications. Moreover, line segment 9A represents the value of the average value μ1 of the interval capacity minus four times the standard deviation σ1 of the interval capacity (i.e., 4σ1), and line segment 9B represents the value of μ1 + 4σ1.

[0089] If the process capability Cpk in the manufacturing process of the secondary battery calculated from the distribution of the section capacities shown in FIG. 13 is a value included in section 6 shown in FIG. 10, the CPU 21 outputs "no inspection required" as the inspection level for the manufactured secondary battery.

[0090] Although one embodiment of the inspection device 10 has been described above, the disclosed form of the inspection device 10 is merely an example, and the form of the inspection device 10 is not limited to the scope of the disclosure. Various modifications or improvements can be made without departing from the gist of the present disclosure, and forms with such modifications or improvements are also included in the technical scope of the disclosure. For example, the internal processing order of each process shown in Figures 5 and 8 may be changed without departing from the gist of the present disclosure.

[0091] In the above embodiment, as an example, the processes shown in Fig. 5 and Fig. 8 are implemented by software. However, the processes equivalent to the flowcharts of the processes shown in Fig. 5 and Fig. 8 may be implemented by hardware. In this case, the process speed can be increased compared to when the processes are implemented by software.

[0092] In the above embodiment, the term "processor" refers to a processor in a broad sense, including a general-purpose processor (e.g., CPU 21) and a dedicated processor (e.g., GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

[0093] In addition, the operations of the processor in the above embodiment may be performed not only by one processor but also by multiple processors located at physically separate locations working together. The order of the operations of the processor is not limited to the order described in the above embodiment and may be changed as appropriate.

[0094] In the above embodiment, an example in which the inspection program is stored in the non-volatile memory 23 has been described, but the storage destination of the inspection program is not limited to the non-volatile memory 23. The inspection program of the present disclosure can also be provided in a form recorded in a storage medium readable by the computer 20. For example, the inspection program may be provided in a form recorded in an optical disk such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a Blu-ray disk. The inspection program may also be provided in a form recorded in a portable semiconductor memory such as a Universal Serial Bus (USB) memory or a memory card. The non-volatile memory 23, the CD-ROM, the DVD-ROM, the Blu-ray disk, the USB, and the memory card are examples of non-transitory storage media.

[0095] Furthermore, the inspection device 10 may download an inspection program from an external device connected to a communication line via the communication unit 26, and store the downloaded inspection program in a storage device.

[0096] In this case, the CPU 21 of the inspection device 10 reads from the storage device the inspection program downloaded from the external device, and executes the processes shown in FIGS.

[0097] The following are notes related to this disclosure.

[0098] (Appendix 1) using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of the secondary battery and second data representing a charge / discharge history of the secondary battery when charging / discharging the secondary battery, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after taking into account an estimation error of the battery characteristics in the estimation equation for the same first data; Calculating a process capability in a manufacturing process of the secondary battery from the distribution of the battery characteristics thus generated; A computer executes a process of outputting a defect rate of the secondary battery from the calculated process capability. Testing method.

[0099] (Appendix 2) As the second data, data obtained in the activation step of the secondary battery is used. The test method described in Appendix 1.

[0100] (Appendix 3) The battery characteristics are estimated using a predetermined estimation formula in which the first data and the second data are explanatory variables and the battery characteristics are objective variables. 2. The method of claim 1 ,

[0101] (Appendix 4) The estimation error of the battery characteristics is obtained from an error between the battery characteristics of the secondary battery estimated using the estimation formula and the battery characteristics of the secondary battery actually measured. The inspection method according to any one of appendix 1 to appendix 3.

[0102] (Appendix 5) determining an inspection level for the secondary battery using the defect rate; Outputting an inspection level for the secondary battery The inspection method according to any one of appendices 1 to 4.

[0103] (Appendix 6) As an inspection level for the secondary battery, one of total inspection, random inspection, and no inspection is required is determined. The test method described in Appendix 5.

[0104] (Appendix 7) a distribution generating unit that generates a distribution of the battery characteristics from each of the battery characteristics estimated by using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of the secondary battery and second data representing a charge / discharge history of the secondary battery when charging / discharging the secondary battery, for the same first data, by varying the second data while taking into account an estimation error of the battery characteristics in the estimation equation; a calculation unit that calculates a process capability of a manufacturing process of the secondary battery from the distribution of the battery characteristics generated by the distribution generation unit; an output unit that outputs a defect rate of the secondary battery based on the process capability calculated by the calculation unit; An inspection device equipped with

[0105] (Appendix 8) using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of the secondary battery and second data representing a charge / discharge history of the secondary battery when charging / discharging the secondary battery, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after taking into account an estimation error of the battery characteristics in the estimation equation for the same first data; Calculating a process capability in a manufacturing process of the secondary battery from the distribution of the battery characteristics thus generated; and causing a computer to execute a process of outputting a defect rate of the secondary battery from the calculated process capability. Inspection program. [Explanation of symbols]

[0106] 1 ideal line 2, 4~6 Sections Distribution of 3A, 3B, 3C battery characteristics 7A, 7C Lower limit of battery specification requirements 7B, 7D Upper limit of required specifications for battery characteristics 8 points 9A, 9B line segments 10 Inspection equipment 11 Estimation formula generator 12 Estimation part 13 Distribution generator 14 Calculation section 15 Output section 16 Manufacturing history DB 20. Computers 21 CPU 22 RAM 23 Non-volatile memory 24 I / O 25 Bus 26 Communication Unit 27 Input Unit 28 Output Unit Cpk process capability

Claims

1. using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of the secondary battery and second data representing a charge / discharge history of the secondary battery when charging / discharging the secondary battery, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after taking into account an estimation error of the battery characteristics in the estimation equation for the same first data; Calculating a process capability in a manufacturing process of the secondary battery from the distribution of the battery characteristics thus generated; A computer executes a process of outputting a defect rate of the secondary battery from the calculated process capability. Testing method.

2. As the second data, data obtained in the activation step of the secondary battery is used. The inspection method according to claim 1 .

3. The battery characteristics are estimated using a predetermined estimation formula in which the first data and the second data are explanatory variables and the battery characteristics are objective variables. The inspection method according to claim 2.

4. The estimation error of the battery characteristics is obtained from an error between the battery characteristics of the secondary battery estimated using the estimation formula and the battery characteristics of the secondary battery actually measured. The inspection method according to claim 3.

5. determining an inspection level for the secondary battery using the defect rate; Outputting an inspection level for the secondary battery The inspection method according to any one of claims 1 to 4.

6. As an inspection level for the secondary battery, one of total inspection, random inspection, and no inspection is determined. The inspection method according to claim 5.

7. a distribution generating unit that generates a distribution of the battery characteristics from each of the battery characteristics estimated by using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of the secondary battery and second data representing a charge / discharge history of the secondary battery when charging / discharging the secondary battery, for the same first data, by varying the second data while taking into account an estimation error of the battery characteristics in the estimation equation; a calculation unit that calculates a process capability of a manufacturing process of the secondary battery from the distribution of the battery characteristics generated by the distribution generation unit; an output unit that outputs a defect rate of the secondary battery based on the process capability calculated by the calculation unit; An inspection device equipped with

8. using an estimation equation for estimating predetermined battery characteristics of the secondary battery, the estimation equation being generated using first data representing a manufacturing history of the secondary battery and second data representing a charge / discharge history of the secondary battery when charging / discharging the secondary battery, to generate a distribution of the battery characteristics from each of the battery characteristics estimated by varying the second data after taking into account an estimation error of the battery characteristics in the estimation equation for the same first data; Calculating a process capability in a manufacturing process of the secondary battery from the distribution of the battery characteristics thus generated; and causing a computer to execute a process of outputting a defect rate of the secondary battery from the calculated process capability. Inspection program.

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