Battery cell sorting method and battery cell handling device

Abstract

To achieve shortening of a read time, appropriate determination of reuse of a battery cell, and appropriate maintenance management of a battery cell.SOLUTION: Regularity information among respective data regarding non-defective or defective battery cells is extracted from a plurality of past data regarding non-defective or defective battery cells in respective steps, and the regularity information among the respective data regarding the non-defective or defective battery cells is input to extract common points of the non-defective or defective battery cells and singular points of the non-defective and defective battery cells. Then, a determination model based on a conditional expression that can discriminate a non-defective battery cell and a defective battery cell from each other on the basis of the singular points is used to collect data necessary for determination using the determination model from data of the battery cells obtained in the respective steps of a manufacturing process, etc., and each management process, and the determination model is used to determine whether a battery cell is non-defective or defective.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a battery cell sorting method and a battery cell handling device. [Background technology]

[0002] Regarding the characteristics of battery cells for long-term use, a cycle charge-discharge test is conducted in which the manufactured battery cells are repeatedly charged and discharged. For example, if the discharge capacity after 3,000 cycles (one cycle is when a battery cell is charged and discharged once) is 80% or more of the capacity at the initial manufacturing stage, the battery cell is deemed to be of good quality.

[0003] Also, in an aging test, a charged battery cell is left for a long period of time, and the presence or absence of an internal short circuit or the like in the battery cell is judged based on the change in voltage.

[0004] However, conducting these tests requires a significant amount of time. Therefore, rather than conducting these tests on all battery cells, the above tests are conducted on randomly sampled battery cells for each production lot, and if the results indicate that the battery cells are good, the other battery cells in the same lot are also deemed to be good.

[0005] However, with the above screening method, the evaluation of the battery cells in the entire lot depends on the judgment results of the sampled battery cells, so it cannot be said to be sufficient in terms of reliability.

[0006] In addition, a single battery cell that has been determined to be non-defective in the manufacturing process, or a battery module (also called a battery pack) made by combining multiple such battery cells into a module, is mounted in the device as the storage unit of an energy storage device, and is known to be connected to a charge / discharge control unit that controls the charging and discharging of the battery cells, or a CMU (Cell Management Unit: this monitors and manages each battery cell in the storage unit to prevent them from going into an abnormal state, and includes a "Cell Monitoring Unit" that only has the function of monitoring the voltage and temperature of the battery cell) or a BMU (Battery Management Unit: this monitors and manages the battery cells that make up the battery module in the storage unit to prevent them from going into an abnormal state), which not only controls the charging and discharging, but also detects battery abnormalities and estimates the battery life based on the data obtained by measuring the battery voltage and temperature of the battery cells.

[0007] In recent years, various predictions have been made using machine learning, in particular by organizing large amounts of information obtained through improvements in artificial intelligence (AI) technology and analyzing this information using machine learning to find patterns, which are then used in sales strategies, advertising strategies, and the like. Attempts to utilize such patterns discovered through analysis using AI are also being considered for the manufacture and sale of battery cells.

[0008] Examples of technologies using AI include a technology that uses the initial characteristics of a battery to predict its long-term characteristics, thereby quickly determining whether the battery is defective (see, for example, Patent Document 1), and a technology that enables early prediction of battery life from life trends according to various characteristic factors of the battery cell before the battery cell is manufactured and its lifespan is evaluated, thereby improving the reliability of the lifespan prediction (see, for example, Patent Document 2). [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Special Publication No. 2010-539473 [Patent Document 2] JP 2013-217897 A Summary of the Invention [Problem to be solved by the invention]

[0010] As mentioned above, advances in AI technology have led to proposals to use machine learning to find patterns and rules from various data obtained through measurements, etc., and to classify the objects being measured based on those patterns. In particular, it has become possible to classify large amounts of diverse digitized data, known as big data, and to discover patterns from the data. With this type of machine learning, deep learning makes it possible to automatically extract feature points without the need for manual extraction, and it is now possible to extract feature points that would not be noticed through manual extraction.

[0011] However, although the above-mentioned techniques are applicable to predicting the life span of a battery cell, there is room for improvement in applying them as management indicators in the production process, monitoring process, and management process of the battery cell.

[0012] Therefore, the present invention has been made in consideration of the above-mentioned problems, and has an object to provide a battery cell sorting method and battery cell handling device that derives a pattern between good and defective battery cells at each process from data obtained by measurement or the like, extracts singular points between good and defective battery cells based on this pattern, generates a judgment model, and uses the judgment model as input data for battery cell data at each process to perform calculations to properly judge whether a battery cell is good or defective at each process, thereby shortening lead times, making appropriate decisions about reusing battery cells, and achieving appropriate maintenance management of the battery cells. [Means for solving the problem]

[0013] Mode 1: One or more embodiments of the present invention include the steps of: creating a judgment model capable of distinguishing between a good battery cell and a defective battery cell, using data obtained in each step of a battery cell handling process having a plurality of steps; acquiring various data for each battery cell in each of the steps; using the judgment model to judge the good or defective battery cell using data necessary for judging the battery cell using the judgment model as input data from the data acquired in each of the steps; and handling the battery cell judged as a good or defective battery cell according to the judgment result, respectively, wherein one of the steps is a charge / discharge step, and when the battery cell is judged as a good or defective battery cell in the step of judging as a good or defective battery cell using the judgment model, The process of charging and leaving it for a specified period of time to check the amount of self-discharge The present invention proposes a battery cell sorting method characterized by comprising a step of determining whether or not a battery cell needs to be sorted.

[0014] Mode 2: In one or more embodiments of the present invention, when the battery cell is determined to be defective in the step of determining whether the battery cell is defective or not using the determination model, The battery cell Removed from production line To or not to For the purpose of judgment, The process of charging and leaving it for a specified period of time to check the amount of self-discharge The present invention proposes a battery cell sorting method characterized by comprising a step of determining whether or not a battery cell needs to be sorted.

[0016] form 3 One or more embodiments of the present invention propose a battery cell sorting method, characterized in that the judgment model is created by extracting regularity information between each piece of data relating to good and defective battery cells from multiple past data relating to good and defective battery cells, extracting singular points of the good and defective battery cells from common points for each of the good and defective battery cells based on the extracted regularity information, and using data obtained in each process to create a conditional equation that can distinguish between good and defective battery cells based on the singular points.

[0017] form 4 One or more embodiments of the present invention propose a battery cell sorting method, characterized in that the data obtained in each of the above steps is data obtained during the manufacturing process of the battery cells.

[0018] form 5 One or more embodiments of the present invention include a judgment model generation unit that creates a judgment model capable of distinguishing between a good battery cell and a defective battery cell by using data obtained in each step of battery cell handling having a plurality of steps, a data acquisition unit that acquires various data for each battery cell in each of the steps, a quality judgment unit that uses the judgment model to judge the quality of the battery cell as input data from the data acquired in each of the steps, and judges the quality of the battery cell as good or defective as a result of the judgment, and a battery cell handling unit that handles battery cells that are judged to be good and battery cells that are judged to be defective differently depending on the judgment result, one of the steps being a charging and discharging step, Judging by It was judged to be good The above For battery cells The process of charging and leaving it for a specified period of time to check the amount of self-discharge Determine whether or not Abandonment There is proposed a battery cell handling device characterized by including a determination unit.

[0019] form 6 One or more embodiments of the present invention include Abandonment The determination unit is The quality judgement unit judges A step of charging the battery cell that has been determined to be defective and leaving it for a predetermined period of time to check the amount of self-discharge is determined. As a result of this judgment, the battery cells that are determined not to require the above-mentioned leaving treatment are excluded from the production line. The present invention proposes a battery cell handling device characterized by the above.

[0021] form 7One or more embodiments of the present invention propose a battery cell handling device comprising: a regularity information extraction unit that extracts regularity information between each piece of data related to good or defective battery cells from a plurality of past data related to good or defective battery cells in each process; a memory unit that stores the regularity information between each piece of data related to good or defective battery cells extracted by the regularity information extraction unit; a singularity extraction unit that inputs the regularity information between each piece of data related to the good or defective battery cells from the memory unit and extracts common points for the good or defective battery cells and singular points for the good and defective battery cells; a conditional equation calculation unit that calculates a conditional equation capable of distinguishing between good and defective battery cells based on the singular points; and a judgment model generation unit that verifies the validity of the conditional equation calculated by the conditional equation calculation unit and generates a judgment model.

[0022] form 8 In one or more embodiments of the invention, the data is The above We propose a battery cell handling device that is characterized by data obtained during the manufacturing process of the battery cells. Effect of the Invention

[0023] According to one or more embodiments of the present invention, a regularity between good and bad battery cells at each process is derived from data obtained by measurement or the like, and specific points between good and bad battery cells are extracted based on this regularity to generate a judgment model. The data of the battery cells at each process is used as input data and calculations are performed using the judgment model, thereby appropriately judging whether the battery cells are good or bad at each process, thereby achieving the effects of shortening lead times, appropriately determining whether the battery cells can be reused, and appropriately maintaining and managing the battery cells. [Brief description of the drawings]

[0024] [Figure 1] FIG. 1 is an electrical configuration diagram of a battery cell handling device according to a first embodiment of the present invention. [Diagram 2]1 is a functional block diagram of the inside of a CPU in a battery cell handling device according to a first embodiment of the present invention. FIG. [Diagram 3] FIG. 2 is a process flow diagram of a CPU in the battery cell handling device according to the first embodiment of the present invention. [Figure 4] FIG. 2 is a process flow diagram of the battery cell handling device according to the first embodiment of the present invention. [Diagram 5] FIG. 11 is an electrical configuration diagram of a battery cell handling device according to a second embodiment of the present invention. [Figure 6] FIG. 11 is a functional block diagram of the inside of a CPU in a battery cell handling device according to a second embodiment of the present invention. [Figure 7] FIG. 11 is a process flow diagram of a battery cell handling device according to a second embodiment of the present invention. [Figure 8] FIG. 11 is a process flow diagram relating to a modified example of the process of the battery cell handling device according to the second embodiment of the present invention. [Figure 9] FIG. 11 is an electrical configuration diagram of a battery cell handling device according to a third embodiment of the present invention. [Figure 10] FIG. 11 is a functional block diagram of the inside of a CPU in a battery cell handling device according to a third embodiment of the present invention. [Figure 11] FIG. 11 is a process flow diagram of a battery cell handling device according to a third embodiment of the present invention. [Figure 12] FIG. 11 is an electrical configuration diagram of a battery cell handling device according to a fourth embodiment of the present invention. [Figure 13] FIG. 11 is a functional block diagram of the inside of a CPU in a battery cell handling device according to a fourth embodiment of the present invention. [Figure 14] FIG. 11 is a process flow diagram of a battery cell handling device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] <First embodiment> Hereinafter, an embodiment of the present invention will be described with reference to FIGS.

[0026] <Electrical configuration of the battery cell process control device> As shown in FIG. 1, the battery cell handling device 10 of this embodiment is configured to include a CPU (Central Processing Unit) 100, a ROM (Read Only Memory) 200, a RAM (Random Access Memory) 300, a judgment model storage unit 400, a non-defective product judgment unit 500, and an interface unit 600.

[0027] The CPU 100 controls the overall processing of the battery cell handling device 10 in accordance with a control program pre-stored in the ROM 200 . In particular, in this embodiment, based on various data that has been obtained up to now at each step in the manufacturing and management processes, a regularity between good and defective battery cells in the manufacturing and management processes is derived from data obtained by measurement, etc., and based on this regularity, singular points between good and defective products are extracted, and a judgment model is created.

[0028] The ROM 200 is a read-only non-volatile memory, and stores the control programs, initial parameters, etc., as described above.

[0029] The RAM 300 is a writable and readable memory, and examples of such memory include a dynamic RAM (DRAM) and a static RAM (SRAM). The RAM 300 temporarily stores data and the like obtained during the control processing of the CPU 100.

[0030] The judgment model storage unit 400 is a storage unit that stores the judgment model generated by the CPU 100. The judgment model to be stored in the judgment model storage unit 400 may be stored in the above-mentioned RAM 300. However, by providing an independent judgment model storage unit 400 for storing the judgment model as in this embodiment, it is possible to avoid a delay in the entire calculation process using the judgment model due to a delay in reading out the judgment model, even if the RAM 300 is being used for another process, for example.

[0031] The pass / fail judgment unit 500 uses the data of the battery cell in each process as input data and performs calculation processing using the judgment model stored in the judgment model storage unit 400 to judge whether the battery cell is pass or fail. Here, the battery cell data refers to data obtained during the manufacturing process, that is, data measured at each step of the battery cell manufacturing process and data obtained for production management, and includes, for example, the battery capacity, energy density, shape, size, material of the electrode plate, thickness of the electrode plate, length of the electrode plate, material of the electrode active material layer and the mixing ratio of the materials used, presence or absence of a binder and the type of binder, thickness of the electrode active material layer, size of the uncoated part of the electrode active material layer, type of separator, stacking structure of the electrode plate and separator (number of windings in the case of a wound type, number of layers in the case of a stack ... Examples of the material include the number of layers in a battery if any), type of electrolyte, composition ratio of the electrolyte, presence or absence of additives such as flame retardants and the type and concentration of the additives, drying time, drying temperature, material of the current collector, structure of the current collector, joining method and joining structure of the current collector and electrode plate, injection time of the electrolyte, injection method of the electrolyte, time required for impregnation and temperature during impregnation, formation charging time, formation charging voltage value, temperature during formation charging, battery voltage during charge / discharge test, battery temperature, discharge time, charging time, charging voltage, battery capacity, production lot, weather (humidity) at the time of production, time during production, name of person in charge of production, and name of manufacturer of each material. Furthermore, information and measurement data relating to the battery cells are linked to a common serial number (production number) and stored and managed for each battery cell to be manufactured, for example, in a data server on a network. Moreover, information and data necessary for judgment using the judgment model are written to the RAM 300 from the data server, and judgment processing using the judgment model is performed based on this written information (data). It is also possible to configure the processing of the non-defective product judgment unit 500 to be performed by the CPU 100 without providing a separate non-defective product judgment unit 500. However, providing a separate non-defective product judgment unit 500 has the advantage that the non-defective product judgment processing by the non-defective product judgment unit 500 can be executed in parallel with the processing by the CPU 100.

[0032] The interface unit 600 exchanges data with various devices connected to the network via the network. For example, when the data storage unit 1000 that stores various data obtained so far in each process is a server or the like on the network, the battery cell handling device 10 according to the present embodiment exchanges data stored in the data storage unit 1000 via the interface unit 600.

[0033] <Function blocks inside the CPU> As shown in FIG. 2, the CPU 100 of the battery cell handling device 10 according to the present embodiment includes a regular information extraction unit 101, a regular information storage unit 102, an outlier extraction unit 103, a conditional expression calculation unit 104, and a determination model generation unit 105.

[0034] The regular information extraction unit 101 extracts regular information regarding good or defective battery cells from a plurality of past data regarding good or defective battery cells in each process from the externally provided data storage unit 1000, for example. Specifically, for example, using machine learning, the regularity between each data regarding good or defective battery cells may be executed by analyzing the relationship of the data. As a specific example, a method of graphing the relationship between the battery voltage, battery temperature, battery capacity and time during charging or discharging of each battery cell can be mentioned. Furthermore, after classifying the battery cells by production lot or by the type of electrolyte applied, reclassification processing is performed based on information that allows further classification. It should be noted that it is also possible by a computer to input necessary data and graph various relationships based on the input data, and it is also possible by a computer to further classify the graph by information such as the production lot of the battery cells or the type of materials used, as long as a link is established between the information regarding the battery cells and the target graph. In particular, in the present embodiment, it is important to link the obtained graph with information indicating whether the battery cell is good or defective. Also, because unexpected commonalities can be found, it is a good idea to create graphs or distribution charts showing the relationships between pieces of information that do not appear to be particularly related.

[0035] The regularity information storage unit 102 stores regularity information between each piece of data relating to non-defective or defective battery cells extracted by the regularity information extraction unit 101 . In this embodiment, the regularity information storage unit 102 is a component of the CPU 100. However, the regularity information storage unit 102 may be provided on a network as, for example, a data server.

[0036] The singularity extraction unit 103 inputs regularity information between each piece of data relating to good and defective battery cells from the regularity information storage unit 102, and extracts common points between the good and defective battery cells, and singular points between the good and defective battery cells. Specifically, for example, by comparing several pieces of graphed information, common points between good battery cells, common points between defective battery cells, and differences between good and defective battery cells are extracted, and elements that can be singular points that make it possible to distinguish between good and defective battery cells are found. In other words, even if there is a difference between good and defective battery cells, if the difference is not common to good battery cells, the difference cannot be used to distinguish between good and defective battery cells. Also, if the common points between good battery cells are also common to defective battery cells, it is not possible to distinguish between good and defective battery cells. For this reason, singular points that make it possible to distinguish between good and defective battery cells are extracted from the common points between good battery cells, common points between defective battery cells, and differences between good and defective battery cells.

[0037] The conditional expression calculation unit 104 calculates a conditional expression capable of distinguishing between a good battery cell and a defective battery cell, based on the singular points extracted by the singular point extraction unit 103. Specifically, data required for calculating the extracted singular points are specified, an equation capable of calculating the singular points is calculated using the data, and a numerical value related to the singular points obtained from the calculated equation is prepared in the form of a conditional equation for determining whether the product is good or defective. Here, the number of conditional equations prepared is not limited to one, and if multiple singular points are found, an equation for calculating the singular points is calculated using data required for each singular point, and conditional equations based on the calculated equations are prepared.

[0038] The judgment model generating unit 105 verifies the validity of the conditional expressions calculated by the conditional expression calculating unit 104, and generates a judgment model. Specifically, for multiple battery cells that have already been determined as pass / fail based on the conditional formula calculated by the conditional formula calculation unit 104, information required for determining whether the battery cells are pass / fail is applied to the conditional formula to verify whether it is possible to correctly determine whether the battery cells are pass / fail. If multiple conditional formulas are calculated, verification is performed for each conditional formula. If the verification results in a correct determination of whether a battery cell is good or defective (this can be the case if all battery cells used for verifying the judgment model can be correctly determined to be good or defective, or if 95% can be correctly determined to be good or defective), then a model that uses the conditional equation to determine whether a battery cell is good or defective is determined to be the judgment model for that process. When there are multiple conditional expressions verified, the one with the highest probability of determining that the product is a good product is selected as a result of the verification, and the model for determining whether the product is good or defective is determined as the judgment model for the process. Note that a plurality of judgment models may be applied to the process. For example, if the verification result shows that a product can be judged as a good product with a similarly high probability using a plurality of conditional expressions, or if the probability of judging a product as a good product in the verification result exceeds an arbitrarily determined pass line probability (e.g., 85% or more can be judged as good products), a model for judging a product as good or defective using each of the plurality of conditional expressions may be used as the judgment model. A plurality of determination models for which the information necessary for determination is different will be prepared respectively. Compared with the case of one determination model, although it may become redundant because the determination of non-defective products or defective products will be performed many times, for example, when a determination model that can correctly determine 95% of non-defective products and defective products is adopted, it is expected that the remaining 5% that could not be correctly determined by this determination model can be correctly determined as non-defective products or defective products by another determination model. Also, when making a determination with a plurality of determination models, when the determination results of X (an integer of 2 or more smaller than N) out of the determinations of N (an integer of 3 or more) determination models match, it is possible to handle non-defective products or defective products with higher accuracy by proceeding with the handling as non-defective products or defective products according to the result. Also, prioritize in descending order of the probability of correctly determining non-defective products and defective products, apply the result of the determination model with the highest priority first, and within the error range of the boundary point between non-defective products and defective products in this determination model, adopt the result of the determination model with the next priority, etc., and it may be possible to make a determination in multiple stages. Then, the determined determination model is output to the non-defective product determination unit 500 of the battery cell handling device 10. Note that, based on the singular points extracted by a personal computer or the like, it is not limited to modeling the determination model. For example, by deep learning (deep learning), singular points are automatically extracted from the input information (data), a conditional expression based on the singular points is calculated, and the determination model may be created by verifying the conditional expression.

[0039] <CPU Processing> Using FIG. 3, the generation process of the determination model in the CPU 100 according to the present embodiment will be described.

[0040] The regular information extraction unit 101 inputs, from the data storage unit 1000, past data regarding defective battery cells that have been acquired so far in the target process (step S110).

[0041] Next, the regularity information extraction unit 101 extracts regularity information between each piece of data relating to non-defective or defective battery cells from the multiple pieces of past data relating to defective battery cells input from the data storage unit 1000 (step S120).

[0042] The singularity extraction unit 103 inputs regularity information between each piece of data relating to good and defective battery cells from the regularity information storage unit 102, and extracts common points between the good and defective battery cells and singular points between the good and defective battery cells, for example, by using graphs obtained by classifying the input data (step S130).

[0043] The conditional expression calculation unit 104 calculates a conditional expression capable of distinguishing between a good battery cell and a defective battery cell, based on the singular points extracted by the singular point extraction unit 103 (step S140).

[0044] The judgment model generating unit 105 generates a judgment model based on the conditional expression calculated by the conditional expression calculating unit 104 (step S150).

[0045] In addition, based on the condition equation calculated by the condition equation calculation unit 104, the judgment model generation unit 105 applies information necessary for judging whether a battery cell is pass / fail to the condition equation for multiple battery cells that have already been judged as pass / fail, and verifies whether it is possible to correctly judge whether the battery cell is pass / fail (step S160).

[0046] When the judgment model generation unit 105 determines, as a result of its investigation, that the conditional expression does not allow correct judgment as to whether the battery cell is a pass or fail product ("NO" in step S160), it returns the process to step S120.

[0047] On the other hand, when the judgment model generation unit 105 determines, as a result of its consideration, that the conditional expression can correctly determine whether a battery cell is good or defective ("YES" in step S160), it determines that judgment model as the judgment model for the process (step S170).

[0048] <Handling of battery cell handling equipment> The processing in the battery cell handling device 10 according to this embodiment will be described with reference to FIG.

[0049] The CPU 100 inputs data obtained by performing processing in a target process from the data storage unit 1000, which is, for example, a server on a network, via the interface unit 600 (step S210).

[0050] The non-defective product determination unit 500 performs calculation processing using the determination model stored in the determination model storage unit 400, with the data of the battery cell in each process as input data (step S220).

[0051] The quality determination unit 500 determines whether the battery cell is quality-defective or defective based on the results of the arithmetic processing (step S230).

[0052] The determination result is output, for example, from the interface unit 600 via the CPU 100, and is used to control a functional unit that performs a process (post-processing step) of separating non-defective and defective battery cells (step S240).

[0053] As described above, the battery cell handling device 10 of this embodiment uses information (data) acquired at each process for the battery cell and extracts from this information characteristic points found in good battery cells, characteristic points found in defective battery cells, and peculiar points found between good and defective battery cells. It then performs calculations on the extracted characteristic points and peculiar points and various information (data) obtained in the actual processes to generate a judgment model for determining whether a battery cell is good or defective, thereby making it possible to appropriately judge whether a battery cell is good or defective at each process for the battery cell.

[0054] Furthermore, by deducing the regularity between good and defective battery cells at each process from data obtained by measurement, etc., and extracting the singular points between good and defective battery cells, and appropriately determining whether the battery cells are good or defective at each process, it is possible, for example, to shorten the lead time in the manufacturing process, to make appropriate decisions about reusing battery cells in the reuse process, and to realize appropriate maintenance management of battery cells in the maintenance management process.

[0055] <Second embodiment> Hereinafter, an embodiment of the present invention will be described with reference to FIG. 5 to FIG. In this embodiment, the charge / discharge process is illustrated as an example of a battery cell production process.

[0056] <Electrical configuration of the battery cell handling device> As shown in FIG. 5, the battery cell handling device 20 of this embodiment is configured to include a CPU (Central Processing Unit) 110, a ROM (Read Only Memory) 200, a RAM (Random Access Memory) 300, a judgment model storage unit 410, a non-defective product judgment unit 510, an interface unit 600, and an aging judgment unit 700. In addition, since the components having the same reference numerals as those in the first embodiment have the same functions, detailed description thereof will be omitted.

[0057] The CPU 110 controls the overall processing of the battery cell handling device 10 in accordance with a control program for the charge and discharge process that is pre-stored in the ROM 200 . In particular, in this embodiment, based on various data obtained up to now during the charging and discharging process, a regularity between good and bad battery cells during the charging and discharging process is derived from data obtained by measurement, etc., and based on this regularity, singular points between good and bad products are extracted, and a judgment model is generated.

[0058] The judgment model storage unit 410 is a storage unit that stores the judgment model generated by the CPU 110.

[0059] The pass / fail determination unit 510 receives battery cell data in a charge / discharge process as input data, and performs calculation processing using the determination model stored in the determination model storage unit 410 to determine whether the battery cell is pass / fail. Here, examples of the battery cell data include the battery voltage, battery temperature, discharge time, charge time, charge voltage, and the like in the charge and discharge process.

[0060] If the battery cell is determined to be non-defective, the aging determining unit 700 determines whether or not the battery cell has been aged. It should be noted that aging here refers to a process of determining whether a battery cell is truly good or defective based on whether an abnormality has occurred due to an internal short circuit or the like, that is, whether the battery cell has a greater amount of self-discharge compared to a good battery cell. This determination process is performed by charging the battery cell to a certain extent and leaving it for a long period of time, for example, one week or one month.

[0061] Moreover, as shown in FIG. 6, the CPU 110 includes a regularity information extraction unit 111, a regularity information storage unit 112, a singularity extraction unit 113, a conditional expression calculation unit 114, and a judgment model generation unit 115.

[0062] The regularity information extraction unit 111 extracts regularity information between each piece of data relating to good and defective battery cells from a plurality of past data relating to good and defective battery cells in a charge and discharge process from an external data storage unit 1000, for example.

[0063] The regularity information storage unit 112 stores regularity information between each piece of data relating to non-defective or defective battery cells extracted by the regularity information extraction unit 111 .

[0064] The singularity extraction unit 113 inputs regularity information between each piece of data relating to good and defective battery cells from the regularity information storage unit 112, and extracts common points between the good and defective battery cells, and singular points between the good and defective battery cells.

[0065] The conditional expression calculation section 114 calculates a conditional expression capable of distinguishing between a good battery cell and a defective battery cell, based on the singular points extracted by the singular point extraction section 113.

[0066] The judgment model generating unit 115 verifies the validity of the conditional expressions calculated by the conditional expression calculating unit 114, and generates a judgment model.

[0067] <Handling of battery cell handling equipment> The processing in the battery cell handling device 10 according to this embodiment will be described with reference to FIG.

[0068] The CPU 110 inputs data obtained by performing processing in the charge / discharge process from the data storage unit 1000, which is, for example, a server on a network, via the interface unit 600 (step S310). In this embodiment, there is one judgment model, and the data required for judgment using the judgment model will be described as data of the battery cell in the charge / discharge process.

[0069] The non-defective product determination unit 510 performs calculation processing using the determination model stored in the determination model storage unit 410, with the data of the battery cell in the charge / discharge process as input data (step S320). This allows obtaining information on the charge voltage, discharge voltage and capacity obtained during the charge and discharge process, and obtaining the charge and discharge characteristics from the relationship between the charge voltage, discharge voltage and capacity.

[0070] The non-defective product determination unit 510 determines whether or not the battery cell can be determined to be non-defective based on the result of the calculation using the determination model (step S330). Here, the determination of whether the battery cell is a non-defective or defective product is made based on whether the result of calculation using the determination model is within a range for determining that the battery cell is a non-defective product.

[0071] If the battery cell is determined to be non-defective ("NO" in step S330), it is removed from the original production line without proceeding to the next processing step and disposed of as a defective product (e.g., transported to a waste storage facility) (step S340).

[0072] On the other hand, if the battery cell is determined to be a non-defective product by the non-defective product judgment unit 510 ("YES" in step S330), the non-defective product judgment unit 510 then judges whether or not aging processing is necessary (step S350). Here, the determination of whether or not aging processing is necessary is made based on, for example, whether or not the result of the determination by the quality determination unit 510 in step S340 is that the battery cell is determined to be a quality product, but is near the limit point for distinguishing between quality and defective battery cells. Specifically, the judgment is made based on whether or not the boundary point between a good and bad battery cell is within a predetermined error range.

[0073] Therefore, if the charge / discharge characteristics of the battery cell are judged to be good within a predetermined error range of the boundary point between good and defective battery cells, for example, within a range of the boundary point + (or -) 15%, ("NO" in step S350), the good product judgment unit 510 determines that normal aging processing is necessary and makes a judgment to perform normal aging processing (step S370).

[0074] On the other hand, if the charge / discharge characteristics of the battery cell are judged to be good because they are outside a predetermined error range of the boundary point between good and defective products, for example, the boundary point + (or -) 15%, ("YES" in step S350), the good product judgment unit 510 determines that normal aging processing is unnecessary and makes a decision not to perform the aging processing (step S360).

[0075] As described above, according to this embodiment, the pass / fail judgment unit 510 does not judge whether to perform normal aging processing on all pass / fail battery cells, but judges that normal aging processing is necessary if the charge / discharge characteristics of the battery cell are judged to be pass / fail within a predetermined error range of the boundary point between pass / fail battery cells, for example, within the range of the boundary point + (or -) 15%, and judges that normal aging processing is unnecessary if the charge / discharge characteristics of the battery cell are judged to be pass / fail outside the predetermined error range of the boundary point between pass / fail battery cells, for example, within the range of the boundary point + (or -) 15%. Therefore, by applying the judgment model to the charging and discharging process of the battery cell, the production efficiency of the battery cell can be improved.

[0076] <Modification> In this embodiment, if the battery cell is determined to be non-defective ("NO" in step S330) in determining whether the battery cell is definable based on the results of calculations using the determination model in Fig. 7, the battery cell is discarded (step S340). However, even if the battery cell is determined to be non-defective, it may be determined whether aging processing is necessary. For example, if the battery cell is determined to be defective within the error range of the boundary point between good and defective battery cells as described above, it may be determined that normal aging processing is necessary, and if the charge / discharge characteristics of the battery cell are outside a specified error range of the boundary point between good and defective battery cells and the battery cell is determined to be defective, it may be discarded.

[0077] Specifically, as shown in FIG. 8, the non-defective product determination unit 510 determines whether or not the battery cell can be determined to be non-defective, based on the result of calculation using the determination model (step S330). If the non-defective product determination unit 510 determines that the battery cell is not a non-defective product ("NO" in step S330), it determines whether or not aging processing is required (step S380). Here, if the charge / discharge characteristics of the battery cell are judged to be defective within a predetermined error range of the boundary point between good and defective battery cells, for example, within the range of the boundary point + (or -) 15% ("NO" in step S380), the quality determination unit 510 determines that normal aging processing is necessary and makes a decision to perform normal aging processing (step S390).

[0078] On the other hand, if the quality determination unit 510 determines that the charge / discharge characteristics of the battery cell are defective because they are outside a predetermined error range of the boundary point between good and defective products, for example, + (or -) 15% of the boundary point ("YES" in step S350), the battery cell is removed from the original production line without proceeding to the next processing step, and is discarded as a defective product (e.g., transported to a waste storage facility) (step S340).

[0079] In this way, by performing normal aging processing on battery cells that are judged to be good or bad within the error range of the boundary point between good and bad battery cells, the aging processing can be omitted for battery cells that are judged not to require aging processing, and the aging processing can be performed on the other battery cells. Therefore, it is possible to reduce the number of battery cells to be aged and to more reliably separate non-defective and defective battery cells. Furthermore, by feeding back the results of this aging process into the creation of a judgment model and creating a judgment model that can reliably determine whether a battery cell is good or bad (or updating the created judgment model), the number of battery cells that fall within the range of the boundary point between good and bad battery cells can be reduced, and a judgment model can be constructed that can more accurately determine whether a battery cell is good or bad.

[0080] In addition to feeding back the results of the aging process to the creation of the judgment model to update the judgment model, if the results of the aging process are extracted as regularity information or singularity information caused by raw material lots in the extraction of regularity information about good or defective battery cells using machine learning in the creation of the judgment model (S120, S130 in FIG. 3), the regularity information or singularity information may be provided (feedback) to a stage prior to the production process, such as reviewing the delivery standards of raw materials, rather than feeding back the results of the aging process to the creation of the judgment model. In this way, in addition to feeding back new judgment results in the battery cell handling process to which the judgment model is applied to the update (creation) of the judgment model, regularity information or singularity information may be output according to the nature of the extracted regularity or singularity, and used at a more appropriate stage in the battery cell handling process, which is expected to improve the performance and quality of the battery.

[0081] In the present embodiment, the data required for judgment using the judgment model is data of the battery cell in the charge / discharge process, and an example has been described in which the aging process is selectively performed. However, when the data required for judgment using the judgment model is other than data obtained during the charge / discharge process, for example, data obtained during a process prior to the charge / discharge process, the judgment as to whether the product is good or bad may be made at a stage prior to the charge / discharge process, that is, at the stage when all the data that can be used for judgment using the judgment model is available. It is preferable to link battery cells that are determined to be defective with a serial number or other identification given by a barcode attached to the battery cell, so that the battery cell can be recognized as a defective product that does not require any further processing. By doing this, battery cells that are identified as defective when their barcodes are read in subsequent processes can be managed so that they can be removed from the production line at the appropriate time.

[0082] Further, only battery cells that have been determined to be non-defective (only battery cells that have not been determined to be defective) may be subjected to subsequent charge / discharge processes, aging processes, and the like. In this way, a CPU or the like manages whether or not the data required for the judgment model is available, and when the data required for judgment using the judgment model is available, the judgment model judges whether the battery cell is pass or fail, and by linking the judgment result of the judgment model's pass or fail judgment to information that can identify the battery cell on the production line, the following advantages are obtained. The manufacturing process for battery cells consists of a plurality of steps, and in one or more of these steps, a step of performing a pass / fail judgment on battery cells without using the judgment model of this embodiment and eliminating defective products is included. If the results of the pass / fail judgment on battery cells using the judgment model described above, which is performed in parallel with the pass / fail judgment on battery cells without using the judgment model, are linked to information that can identify the battery cell, then when making a pass / fail judgment on battery cells without using the judgment model, the information that can identify the battery cell to be judged is read, and the judgment results of the pass / fail judgment on battery cells using the judgment model described above are confirmed. By recognizing the information that the judgment result of the pass / fail judgment on battery cells using the judgment model described above is a defective battery cell, it is possible to eliminate the defective battery cell without making a pass / fail judgment on battery cells without using the judgment model.

[0083] <Third embodiment> Hereinafter, an embodiment of the present invention will be described with reference to FIGS. In this embodiment, a reuse (reuse determination) process is exemplified as a process.

[0084] <Electrical configuration of the battery cell handling device> As shown in FIG. 8, the battery cell handling device 30 of this embodiment is configured to include a CPU (Central Processing Unit) 120, a ROM (Read Only Memory) 200, a RAM (Random Access Memory) 300, a judgment model storage unit 420, a non-defective product judgment unit 520, an interface unit 600, and a reuse judgment unit 800. In addition, since the components having the same reference numerals as those in the first and second embodiments have the same functions, detailed description thereof will be omitted.

[0085] The CPU 120 controls the overall processing of the battery cell handling apparatus 10 in accordance with a control program for the reuse process that is pre-stored in the ROM 200 . In particular, in this embodiment, based on various data obtained in the production process or the reuse process, which consists of processes including charging and discharging processes in which recovered battery cells are charged and discharged, and measuring the capacity when fully charged, a regularity between good and bad battery cells in the reuse process is derived from data obtained by measurements, etc., and singular points between good and bad products are extracted based on this regularity, and a judgment model is generated.

[0086] The judgment model storage unit 420 is a storage unit that stores the judgment model generated by the CPU 120.

[0087] The pass / fail judgment unit 520 uses as input data battery cell data obtained in the production process or the reuse process, which consists of processes including a charge / discharge process in which recovered battery cells are charged / discharged and capacity measurement at full charge, and performs calculations using the judgment model stored in the judgment model storage unit 420 to judge whether the battery cell is pass or defective. Examples of battery cell data include battery temperature, battery voltage, battery capacity, number of charge / discharge cycles, number of years of use, whether or not the battery has been unused for a long period of time, number of errors, time when the error occurred, and information regarding the content of the error.

[0088] When the battery cell is determined to be non-defective, the reuse determination unit 800 determines whether or not both the full charge characteristic and the temperature characteristic are within a range that allows reuse of the battery cell. Here, reuse means using collected battery cells and energy storage systems again in their original form. It is required that charging up to the desired capacity be ensured even if the charging capacity at full charge decreases due to deterioration from the time of delivery, and that the temperature of the battery cells does not increase due to charging and discharging. In the following, we will particularly explain the case where a judgment model is used that can classify battery cells into good and bad based on the relationship between the charge / discharge voltage and capacity of the battery cell (charge / discharge characteristics), the capacity value when fully charged (full charge characteristics), and the relationship between the charge / discharge voltage and the temperature change of the battery cell (temperature characteristics).

[0089] As shown in FIG. 9, the CPU 120 includes a regularity information extraction unit 121, a regularity information storage unit 122, a singularity extraction unit 123, a conditional expression calculation unit 124, and a judgment model generation unit 125.

[0090] The regularity information extraction unit 121 extracts regularity information between each piece of data relating to good or defective battery cells from multiple past data relating to good or defective battery cells obtained, for example, from an external data storage unit 1000 in a production process or a reuse process consisting of processes including a charge / discharge process for charging / discharging recovered battery cells and a capacity measurement at full charge.

[0091] The regularity information storage unit 122 stores regularity information between each piece of data relating to non-defective or defective battery cells extracted by the regularity information extraction unit 121 .

[0092] The singularity extraction unit 123 inputs regularity information between each piece of data relating to good and defective battery cells from the regularity information storage unit 122, and extracts common points between the good and defective battery cells and singular points between the good and defective battery cells.

[0093] The conditional expression calculation section 124 calculates a conditional expression capable of distinguishing between a good battery cell and a defective battery cell, based on the singular points extracted by the singular point extraction section 123.

[0094] The judgment model generating unit 125 verifies the validity of the conditional expressions calculated by the conditional expression calculating unit 124, and generates a judgment model.

[0095] <Handling of battery cell handling equipment> The processing in the battery cell handling device 30 according to this embodiment will be described with reference to FIG.

[0096] The CPU 120 inputs data obtained during the production process or the reuse process, which includes processes such as charging and discharging processes for charging and discharging recovered battery cells and measuring the capacity when fully charged, from the data storage unit 1000, which is, for example, a data server, via the interface unit 600 (step S410).

[0097] The non-defective product determination unit 520 performs calculation processing using the determination model stored in the determination model storage unit 420, as input data for the battery cell data obtained in the production process or the reuse process, which includes a charge / discharge process for charging / discharging recovered battery cells and a process for measuring the capacity when fully charged (step S420). This makes it possible to obtain the relationship between the charge / discharge voltage and capacity of the battery cell (charge / discharge characteristics), the capacity value at full charge (full charge characteristics), and the relationship between the charge / discharge voltage and the temperature change of the battery cell (temperature characteristics).

[0098] The non-defective product determination unit 520 determines whether or not the battery cell can be determined to be non-defective based on the result of the calculation using the determination model (step S430). Here, the determination of whether the battery cell is a pass or fail is made, for example, based on whether the result of calculation using a determination model based on the charge and discharge characteristics is within a range for determining that the battery cell is a pass or fail.

[0099] If the battery cell is determined to be non-defective ("NO" in step S430), it is removed from the original production line without proceeding to the next processing step and disposed of as a defective product (e.g., transported to a waste storage facility) (step S440).

[0100] On the other hand, if the non-defective product determination unit 520 determines that the battery cell is non-defective ("YES" in step S430), the reuse determination unit 800 then determines whether or not the battery cell is reusable (step S450). Here, the determination as to whether or not the battery can be reused is made based on, for example, full charge characteristics and temperature characteristics.

[0101] If the reuse determination unit 800 determines that at least one of the full charge characteristics or temperature characteristics of the battery cell is outside the range for which it is determined that the battery cell can be reused ("NO" in step S450), the battery cell is discarded as a defective product that cannot be reused (step S440).

[0102] On the other hand, if the reuse determination unit 800 determines that both the full charge characteristic and the temperature characteristic are within the range in which reuse is possible ("YES" in step S450), the battery cell is reused and its state is managed (step S460).

[0103] As described above, according to this embodiment, the reuse determination unit 800 determines whether or not a battery cell determined to be a good product by the good product determination unit 520 is reusable based on whether or not at least one of the full charge characteristics or temperature characteristics of the battery cell is within a range for which the battery cell is determined to be reusable. Therefore, by applying the judgment model to the battery cell reuse process, the battery cells can be reused efficiently and appropriately.

[0104] <Fourth embodiment> Hereinafter, an embodiment of the present invention will be described with reference to FIGS. In this embodiment, as an example of a process, a monitoring process for monitoring the behavior of a battery cell mounted in an electricity storage system and in operation is illustrated.

[0105] <Electrical configuration of the battery cell handling device> As shown in FIG. 11 , the battery cell handling device 40 of this embodiment is configured to include a CPU (Central Processing Unit) 130, a ROM (Read Only Memory) 200, a RAM (Random Access Memory) 300, a judgment model storage unit 430, a non-defective product judgment unit 530, an interface unit 600, and a life prediction unit 900. Note that components having the same reference numerals as those in the first, second and third embodiments have similar functions, and therefore detailed descriptions thereof will be omitted.

[0106] The CPU 130 controls the overall processing of the battery cell handling device 10 in accordance with a control program for the monitoring process that is pre-stored in the ROM 200 . In particular, in this embodiment, based on various data obtained in the production process or monitoring process, a regularity between good and defective battery cells in the monitoring process is derived from data obtained by measurement, etc., and singular points between good and defective products are extracted based on this regularity, and a judgment model is generated.

[0107] The judgment model storage unit 430 is a storage element that stores the judgment model generated by the CPU 130.

[0108] The pass / fail judgment unit 530 uses battery cell data obtained in the production process or monitoring process as input data and performs calculations using the judgment model stored in the judgment model storage unit 430 to judge whether the battery cell is pass or defective. Examples of battery cell data include battery temperature, battery voltage, battery capacity, number of charge / discharge cycles, number of years of use, whether or not the battery has been unused for a long period of time, number of errors, time when the error occurred, and information regarding the content of the error.

[0109] When a battery cell is determined to be non-defective, the life prediction unit 900 predicts the life of the battery cell. It should be noted that the life prediction is made here when the battery voltage and battery temperature of the battery cell measured in the monitoring process are within the range of voltage values ​​and temperatures that cannot be measured in the case of a normal battery cell, and for any battery cell that is determined to be unable to be used normally, a warning is issued to inform the user that continued use is prohibited. This can be determined in the actual monitoring process by checking whether the battery voltage and battery temperature of the battery cell being monitored are within normal ranges. In this embodiment, even if the battery voltage and battery temperature of the monitored battery cell are judged to be normal, a judgment model is created based on the measured data to determine whether the battery cell is nearing the end of its life (the battery cell is deteriorating) and a judgment is made through calculations. In the following, a determination model capable of determining deterioration using information related to the battery voltage and battery temperature of the battery cell measured in the process of monitoring the battery cell will be described as an example.

[0110] As shown in FIG. 12, the CPU 130 includes a regularity information extraction unit 131, a regularity information storage unit 132, a singularity extraction unit 133, a conditional expression calculation unit 134, and a judgment model generation unit 135.

[0111] The regularity information extraction unit 131 extracts regularity information between each piece of data relating to good and defective battery cells from multiple past data relating to good and defective battery cells obtained during a production process or monitoring process from an external data storage unit 1000, for example.

[0112] The regularity information storage unit 132 stores regularity information between each piece of data relating to non-defective or defective battery cells extracted by the regularity information extraction unit 131 .

[0113] The singularity extraction unit 133 inputs regularity information between each piece of data relating to good and defective battery cells from the regularity information storage unit 132, and extracts common points between the good and defective battery cells and singular points between the good and defective battery cells.

[0114] The conditional expression calculation section 134 calculates a conditional expression capable of distinguishing between a good battery cell and a defective battery cell, based on the singular points extracted by the singular point extraction section 133.

[0115] The judgment model generating unit 135 verifies the validity of the conditional expressions calculated by the conditional expression calculating unit 134, and generates a judgment model.

[0116] <Handling of battery cell handling equipment> The processing in the battery cell handling device 40 according to this embodiment will be described with reference to FIG.

[0117] The CPU 130 inputs data, such as the battery voltage and battery temperature of the battery cells, obtained in the production process or monitoring process from the data storage unit 1000, which is, for example, a data server, via the interface unit 600 (step S510).

[0118] The non-defective product judgment unit 530 performs calculation processing using the judgment model stored in the judgment model storage unit 420, using as input data battery cell data, such as the battery voltage and battery temperature, obtained in the production process or monitoring process (step S520). This makes it possible to obtain the relationship between the charge / discharge voltage of the battery cell and temperature change (temperature characteristics).

[0119] The non-defective product determination unit 530 determines whether or not the battery cell can be determined to be non-defective (normal) based on the result of the calculation using the determination model (step S530). Here, the determination of whether the battery cell is a pass or fail is made, for example, based on whether the result of calculation using a determination model based on temperature characteristics is within a range for determining that the battery cell is a pass or fail.

[0120] If the battery cell is determined to be not of good quality (normal) ("NO" in step S530), it is determined that there is some abnormality in the battery cell, and the user of the power storage system in which the battery cell is installed is notified of the abnormality in the battery cell (step S540). At this time, for safety reasons, the operation of the power storage system itself may be prohibited by remote control.

[0121] On the other hand, if the non-defective product determination unit 520 determines that the battery cell is non-defective (normal) ("YES" in step S530), the life prediction unit 900 then determines whether the battery cell has deteriorated (step S550). Here, whether or not the battery cell has deteriorated can be determined based on the calculation results obtained by calculating the measured battery voltage and battery temperature using a determination model.

[0122] If the life prediction unit 900 determines that the battery cell shows signs of deterioration and that the battery cell has a short life ("NO" in step S550), it warns the user of this and encourages the user to take measures such as strengthening monitoring of the battery cell and replacing the battery cell in advance to prevent the battery cell from rapidly deteriorating and becoming unable to function adequately in the event of a power outage or the like (step S570).

[0123] On the other hand, if it is determined that the battery cell does not show any signs of deterioration ("YES" in step S550), the battery cell continues to be monitored without issuing any particular warning to the user (step S560).

[0124] As described above, according to this embodiment, the life prediction unit 900 determines whether or not the battery voltage and battery temperature of the battery cell being monitored in the monitoring process, among the battery cells that have been determined to be pass-quality by the pass-quality determination unit 530, are within normal ranges.

[0125] In addition, the lifespan of the battery cell in question will be predicted, and if the battery cell has a short lifespan, a warning will be issued to the user and monitoring of the battery cell in question will be strengthened, encouraging the user to take measures such as replacing the battery cell in advance to prevent the battery cell from rapidly deteriorating and being unable to function adequately in the event of a power outage, etc. Therefore, by applying the judgment model to the battery cell monitoring process, the battery cells can be monitored more precisely.

[0126] In the above embodiment, a judgment model is created and various selections are made using this judgment model (selection of good or defective products, selection of whether or not to reuse, selection of the degree of monitoring), but the present invention is not limited to this.

[0127] For example, as described above, the number of judgment models to be created may not be one but multiple.

[0128] For example, when multiple judgment models, each of which requires different information for judgment, are created, they may be used as appropriate, and after a processing step in which judgment can be made, judgment may be made using each judgment model, and selection processing may be performed as appropriate each time.

[0129] Furthermore, for example, if a selection process is performed by determining whether a battery cell is good or defective using a plurality of judgment models and this is performed for each appropriate processing step, defective battery cells can be removed as appropriate in each processing step, and processing efficiency can be improved without performing unnecessary processing on defective battery cells.

[0130] Also, for example, in a case where one judgment model can be used to determine whether a battery cell is defective with a 90% probability, and another judgment model using information different from the information required by the first judgment model can be used to determine whether the remaining 10% of battery cells are defective with a probability of about 90%, after performing a selection process for good or defective battery cells using the first judgment model, selection process for good or defective battery cells can be performed using the other judgment model for battery cells that could not be determined to be defective. By preparing different judgment models and performing selection processing in two or more stages in this way, it is possible to extract defective battery cells with greater accuracy, thereby making the processing of defective battery cells more efficient.

[0131] The battery cell sorting method can be recorded on a computer-readable recording medium, and the program recorded on this recording medium can be read into and executed by a battery cell handling device or the like, thereby realizing the battery cell handling device of the present invention. The computer system referred to here includes the OS and hardware such as peripheral devices.

[0132] In addition, if the "computer system" utilizes a WWW (World Wide Web) system, it also includes the homepage providing environment (or display environment). Furthermore, the above program may be transmitted from a computer system in which the program is stored in a storage device or the like to another computer system via a transmission medium, or by a transmission wave in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.

[0133] The program may be for realizing part of the functions described above. Furthermore, the above-mentioned functions may be realized in combination with a program already recorded in the computer system, that is, a so-called differential file (differential program).

[0134] Although an embodiment of the present invention has been described in detail above with reference to the drawings, the specific configuration is not limited to this embodiment, and designs that do not deviate from the gist of the present invention are also included. [Explanation of symbols]

[0135] 10. Battery cell handling device 20. Battery cell handling device 30: Battery cell handling device 40: Battery cell handling device 100;CPU 101; Regularity information extraction part 102;Regularity information storage unit 103;Singularity extraction part 104: Conditional expression calculation section 105: Decision model generation unit 110;CPU 111; Regularity information extraction part 112;Regularity information storage unit 113;Singularity extraction part 114: Conditional expression calculation section 115: Decision model generation unit 120;CPU 121; Regularity information extraction part 122;Regularity information storage unit 123;Singularity extraction part 124: Conditional expression calculation section 125: Decision model generation unit 130;CPU 131; Regularity information extraction part 132;Regularity information storage unit 133;Singularity extraction part 134; Conditional expression calculation section 135: Decision model generation unit 200;ROM 300;RAM 400: Decision model storage unit 410: Decision model storage unit 420: Decision model storage unit 430: Decision model storage unit 500; Good product judgment department 510;Good product judgment department 520;Good product judgment department 530;Good product judgment department 600;Interface section 700; Aging judgment section 800; Reuse Judgment Department 900; Life expectancy prediction section 1000; Data storage unit

Claims

1. A step of creating a determination model capable of distinguishing between a good battery cell and a defective battery cell by using data obtained in each process of handling the battery cell, the process including a plurality of processes; In each of the steps, acquiring various data for each battery cell; a step of using data necessary for judging the battery cell using the judgment model as input data from among the data acquired in each step, and judging whether the battery cell is a good product or a defective product using the judgment model; a step of treating the battery cells determined as non-defective and the battery cells determined as defective differently according to the results of the determination; Including, One of the steps is a charge / discharge step, A battery cell sorting method characterized in that, in the step of using the judgment model to judge whether the battery cell is good or bad, if the battery cell is judged to be good, the method further comprises a step of determining whether or not a process is necessary to charge the battery cell and leave it for a predetermined period of time to check the amount of self-discharge.

2. 2. The battery cell sorting method according to claim 1, further comprising a step of determining whether the battery cell is defective or not using the judgment model, in which, when the battery cell is judged to be defective, it is necessary to charge the battery cell and leave it for a predetermined period of time to observe its self-discharge amount, in order to determine whether the battery cell should be removed from the production line.

3. 3. The battery cell sorting method according to claim 1, wherein the judgment model is created by extracting regularity information between each piece of data relating to the good or defective battery cells from a plurality of past data relating to the good or defective battery cells, extracting singular points of the good and defective battery cells from common points for the good or defective battery cells based on the extracted regularity information, and using data obtained in each process to create a conditional equation that can distinguish between the good battery cells and the defective battery cells based on the singular points.

4. 4. The battery cell sorting method according to claim 1, wherein the data obtained in each step is data obtained during the manufacturing process of the battery cells.

5. a judgment model generation unit that generates a judgment model capable of distinguishing between a good battery cell and a defective battery cell by using data obtained in each process of handling the battery cell, the process including a plurality of processes; a data acquisition unit that acquires various data for each battery cell in each of the steps; a quality determination unit that uses data necessary for determining the battery cell using the determination model as input data from among the data acquired in each of the steps, and performs quality determination or defective determination of the battery cell using the determination model; a battery cell handling unit that handles the battery cells determined to be non-defective and the battery cells determined to be defective differently according to the results of the battery cell handling; Including, One of the steps is a charge / discharge step, a battery cell handling device further comprising an abandoned processing judgment unit that judges whether or not a process is necessary for charging a battery cell that has been judged to be a good product by the good product judgment unit and leaving it for a predetermined period of time to check its self-discharge amount.

6. 6. The battery cell handling device according to claim 5, wherein the leaving process determination unit determines whether or not a process of leaving a battery cell determined to be defective by the non-defective product determination unit for a predetermined period of time is required in order to charge the battery cell and check its self-discharge amount, and the battery cell determined as not requiring the leaving process as a result of the determination is excluded from the production line.

7. A regularity information extraction unit that extracts regularity information between each piece of data related to the battery cells of the good products or the defective products from a plurality of past data related to the battery cells of the good products or the defective products in each of the processes; a storage unit that stores the regularity information between the data regarding the non-defective or defective battery cells extracted by the regularity information extraction unit; a singularity extraction unit that receives the regularity information between the data relating to the non-defective or defective battery cells from the storage unit and extracts common points between the non-defective or defective battery cells and singular points between the non-defective and defective battery cells; a conditional expression calculation unit that calculates a conditional expression capable of discriminating between the non-defective battery cell and the defective battery cell based on the singular point; a judgment model generation unit that verifies the validity of the conditional expression calculated by the conditional expression calculation unit and generates the judgment model; 7. The battery cell handling device according to claim 5 or 6, further comprising:

8. 8. A battery cell handling device according to claim 5, wherein the data is data obtained during the manufacturing process of the battery cells.

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