Management system and management method

Abstract

To provide a management system capable of properly evaluating the performance of rapid charging of secondary batteries according to a request of customers.SOLUTION: A management system 100 has an estimation unit 130 and a ranking unit 140. The estimation unit 130 estimates a fast charging performance of each battery cell by using a pre-generated estimation model based on manufacturing data relevant to the manufacturing step of each battery cell as a secondary battery. The ranking unit 140 ranks each battery cell based on the estimated fast charging performance.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a management system and a management method, and more particularly to a management system and a management method for managing secondary batteries. [Background technology]

[0002] Patent Document 1 discloses a battery management system. The battery management system in Patent Document 1 evaluates the degree of deterioration of multiple batteries based on the voltages and currents of the multiple batteries charged and discharged by a power conversion device. The battery management system in Patent Document 1 also ranks each of the multiple batteries according to their degree of deterioration. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-147157 Summary of the Invention [Problem to be solved by the invention]

[0004] For customers of secondary batteries and products equipped with secondary batteries, rapid charging performance is an important factor in evaluating secondary batteries. However, the technology disclosed in Patent Document 1 does not take rapid charging performance into consideration when ranking secondary batteries. Therefore, the technology disclosed in Patent Document 1 makes it difficult to appropriately evaluate the rapid charging performance of secondary batteries in line with customer needs.

[0005] The present disclosure provides a management system and a management method that are capable of appropriately evaluating the rapid charging performance of a secondary battery in accordance with customer requests. [Means for solving the problem]

[0006] The management system according to the present disclosure is a management system for managing secondary batteries, and includes an estimation unit that estimates the rapid charging performance of each battery cell using a pre-generated estimation model based on manufacturing data relating to the manufacturing process of each battery cell that is a secondary battery, and a ranking unit that ranks each battery cell based on the estimated rapid charging performance.

[0007] The management method according to the present disclosure is a management method for managing secondary batteries, which estimates the rapid charging performance of each battery cell using a pre-generated estimation model based on manufacturing data relating to the manufacturing process of each battery cell that is a secondary battery, and ranks each battery cell based on the estimated rapid charging performance. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to provide a management system and a management method that are capable of appropriately evaluating the rapid charging performance of a secondary battery in accordance with customer requests. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a configuration of a management system according to a first embodiment. [Figure 2] 1 is a flowchart illustrating a management method executed by the management system according to the first embodiment. [Figure 3] FIG. 2 is a diagram illustrating manufacturing data according to the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining the processing of the ranking unit according to the first embodiment. [Figure 5] FIG. 10 is a diagram illustrating an example of rank information according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, the present embodiment will be described with reference to the drawings. However, the present invention is not limited to the following embodiment. In addition, the following description and drawings have been simplified as appropriate for clarity of explanation.

[0011] (Embodiment 1) FIG. 1 is a diagram illustrating a configuration of a management system 100 according to a first embodiment. The management system 100 according to the first embodiment is, for example, a computer such as a server. The management system 100 may be realized by, for example, cloud computing. The management system 100 may also be realized by a plurality of computers. In this case, a plurality of components of the management system 100, which will be described later, may be realized by physically different computers.

[0012] The management system 100 estimates the quick charge performance of each battery cell using a pre-generated estimation model based on manufacturing data relating to the manufacturing process of each battery cell, which is a secondary battery. The management system 100 also ranks each battery cell based on the estimated quick charge performance. Details will be described later.

[0013] The management system 100 has, as its main hardware components, a control unit 102, a storage unit 104, a communication unit 106, and an interface unit 108 (IF; Interface). The control unit 102, the storage unit 104, the communication unit 106, and the interface unit 108 are connected to each other via a data bus or the like. When the management system 100 is realized by multiple computers, each of the multiple computers may have the hardware configuration shown in FIG. 1.

[0014] The control unit 102 is a processor such as a CPU (Central Processing Unit). The control unit 102 functions as an arithmetic device that performs control processing, arithmetic processing, etc. The control unit 102 may have multiple processors. The storage unit 104 is a storage device such as a memory or a hard disk. The storage unit 104 is, for example, a ROM (Read Only Memory) or a RAM (Random Access Memory). The storage unit 104 has a function for storing control programs, arithmetic programs, etc. executed by the control unit 102. In other words, the storage unit 104 (memory) stores one or more instructions. The storage unit 104 also has a function for temporarily storing processing data, etc. The storage unit 104 may include a database. The storage unit 104 may have multiple memories.

[0015] The communication unit 106 performs processing necessary for communicating with other devices via a network. The communication unit 106 may include a communication port, a router, a firewall, etc. The interface unit 108 is, for example, a user interface (UI). The interface unit 108 has an input device such as a keyboard, a touch panel, or a mouse, and an output device such as a display or a speaker. The interface unit 108 may be configured such that the input device and the output device are integrated, for example, as in a touch screen (touch panel). The interface unit 108 accepts data input operations by a user (operator) and outputs information to the user.

[0016] The management system 100 according to the first embodiment also includes, as its components, an estimation model generation unit 112, an estimation model storage unit 114, a manufacturing data acquisition unit 120, and a manufacturing data storage unit 122. The management system 100 according to the first embodiment also includes, as its components, an estimation unit 130, a ranking unit 140, a rank storage unit 142, a rank display unit 144, a request acquisition unit 150, and a selection unit 160.

[0017] Each of the above-described components can be realized, for example, by executing a program under the control of the control unit 102. More specifically, each component can be realized by the control unit 102 executing a program (instructions) stored in the storage unit 104. Alternatively, each component may be realized by recording the necessary program on an arbitrary non-volatile recording medium and installing it as needed. Each component may not necessarily be realized by software using a program, but may also be realized by any combination of hardware, firmware, and software. Each component may also be realized using a user-programmable integrated circuit, such as an FPGA (field-programmable gate array) or a microcomputer. In this case, a program consisting of each of the above-described components may be realized using this integrated circuit.

[0018] 2 is a flowchart showing a management method executed by the management system 100 according to the first embodiment. The estimation model generation unit 112 of the management system 100 generates an estimation model (step S102). The generated estimation model is stored in the estimation model storage unit 114.

[0019] Specifically, the estimation model generation unit 112 generates an estimation model that estimates the quick charge performance of a battery cell using manufacturing data related to the manufacturing process when the battery cell is manufactured. For example, the estimation model generation unit 112 may estimate quick charge time as the quick charge performance. Furthermore, for example, the estimation model generation unit 112 may estimate high-rate resistance (the number of quick charge operations allowed per day: hereinafter simply referred to as "quick charge number") as the quick charge performance. Note that the shorter the quick charge time, the better the quick charge performance. Also, the more the number of quick charge operations, the better the quick charge performance. Note that the estimated quick charge performance is not limited to quick charge time and high-rate resistance.

[0020] More specifically, the estimation model generation unit 112 may generate an estimation model that is a multiple regression equation by multiple regression analysis with multiple factors that affect the rapid charging performance indicated in the manufacturing data as explanatory variables and the rapid charging performance as a response variable. For example, the estimation model generation unit 112 may generate an estimation model that estimates the rapid charging time by multiple regression analysis with multiple factors that affect the rapid charging time as explanatory variables and the rapid charging time as a response variable. Furthermore, for example, the estimation model generation unit 112 may generate an estimation model that estimates high-rate resistance by multiple regression analysis with multiple factors that affect the high-rate resistance as explanatory variables and the high-rate resistance as a response variable.

[0021] FIG. 3 is a diagram illustrating manufacturing data according to the first embodiment. Values ​​indicated by the manufacturing data may vary for each battery cell related to the manufacturing data. The manufacturing data includes material data and process data. The material data is data related to materials used in manufacturing the battery cell. The material data indicates the quality and specifications of the materials, such as the physical properties of the materials. The material data is obtained, for example, at the time of delivery of the materials, i.e., before the manufacturing process. Values ​​indicated by the material data may vary for each material. Therefore, the material data may vary for each corresponding battery cell. In the example of FIG. 3, the material data indicates the Li / M ratio of the positive electrode, the Ni / M ratio of the positive electrode, and the specific surface area of ​​the positive electrode. The material data also indicates the specific surface area of ​​the negative electrode. For example, the material data also indicates the air permeability of the separator and the coating amount of the separator. For example, the material data also indicates the additive ratio of the electrolyte.

[0022] The process data is data determined in the manufacturing process of the battery cell. The process data is obtained in the manufacturing process of the battery cell. The values ​​indicated by the process data may vary for each manufacturing process. Therefore, the process data may differ for each corresponding battery cell. The process data may indicate, for example, the coating amount of the positive electrode and the density of the positive electrode. Also, for example, the process data may indicate, for example, the coating amount of the negative electrode and the density of the negative electrode. Also, for example, the process data may indicate, for example, the liquid volume (amount of electrolyte) and the opposing capacity ratio (ratio of the basis weight of the positive electrode to the negative electrode) in a process ("combination") after using a wound body or a laminated body using a positive electrode, a negative electrode, and a separator. Also, for example, the process data may indicate the thickness of the battery cell.

[0023] In the example of FIG. 3, the items marked with a circle in the "rapid charge time" field of the manufacturing data are factors that affect the rapid charge time of the battery cell. Therefore, in the example of FIG. 3, the specific surface area of ​​the negative electrode, the separator coating amount, the negative electrode coating amount, the negative electrode density, and the opposing capacity ratio of the "combination" are factors that affect the rapid charge time. Therefore, the estimation model generation unit 112 performs multiple regression analysis using these multiple factors indicated in the manufacturing data of each of the multiple battery cells as explanatory variables and the rapid charge time of the corresponding battery cell as the objective variable. As a result, the estimation model generation unit 112 generates an estimation model that estimates the rapid charge time. It is assumed that the rapid charge time of the battery cell related to the manufacturing data used to generate the estimation model has been determined in advance.

[0024] In the example of FIG. 3, the items marked with a circle in the "high-rate resistance" field of the manufacturing data are factors that affect the high-rate resistance of the battery cell. Therefore, in the example of FIG. 3, the specific surface area of ​​the negative electrode, the coating amount of the positive electrode, the density of the positive electrode, the coating amount of the negative electrode, the density of the negative electrode, the liquid amount of the "combination," and the thickness of the battery cell are factors that affect the high-rate resistance. Therefore, the estimation model generation unit 112 performs multiple regression analysis using these multiple factors indicated in the manufacturing data of each of the multiple battery cells as explanatory variables and the high-rate resistance (number of fast charges) of the corresponding battery cell as the objective variable. As a result, the estimation model generation unit 112 generates an estimation model that estimates the high-rate resistance. It is assumed that the high-rate resistance of the battery cell related to the manufacturing data used to generate the estimation model has been determined in advance.

[0025] The management system 100 assesses the rapid charging performance of the battery cells (step S110). Specifically, the management system 100 performs the processes of S120 to S140, which will be described later, for each battery cell to be assessed. The manufacturing data acquisition unit 120 acquires manufacturing data for the battery cell to be assessed (step S120). The acquired manufacturing data is stored in the manufacturing data storage unit 122.

[0026] The estimation unit 130 estimates the quick charge performance of the battery cell to be assessed (step S130). Specifically, the estimation unit 130 estimates the quick charge performance of the battery cell to be assessed using an estimation model stored in the estimation model storage unit 114 based on manufacturing data of the battery cell to be assessed. That is, the estimation unit 130 estimates the quick charge performance of each battery cell using an estimation model generated in advance based on manufacturing data related to the manufacturing process of each battery cell. The estimation unit 130 may estimate at least one of the quick charge time and high-rate resistance of each battery cell as the quick charge performance.

[0027] For example, the estimation unit 130 estimates the quick charge time of the battery cell to be assessed using an estimation model for estimating the quick charge time. Specifically, the estimation unit 130 substitutes values ​​of multiple factors that affect the quick charge time and are included in the manufacturing data of the battery cell to be assessed as explanatory variables into a multiple regression equation, which is the estimation model. The estimation unit 130 obtains the value calculated by the multiple regression equation (objective variable) as an estimated value of the quick charge time.

[0028] Furthermore, for example, the estimation unit 130 estimates the high-rate resistance of the battery cell to be assessed using an estimation model that estimates high-rate resistance (number of rapid charge cycles). Specifically, the estimation unit 130 inputs values ​​of multiple factors that affect the high-rate resistance, which are included in the manufacturing data of the battery cell to be assessed, as explanatory variables into a multiple regression equation, which is the estimation model. The estimation unit 130 acquires the value (objective variable) calculated by the multiple regression equation as an estimated value of the high-rate resistance (number of rapid charge cycles).

[0029] The ranking unit 140 ranks the battery cells to be assessed (step S140). Specifically, the ranking unit 140 ranks each battery cell based on the rapid charge performance estimated in the process of S130. More specifically, the ranking unit 140 ranks each battery cell so that the better the rapid charge performance of the battery cell, the higher the rank. Furthermore, if the rapid charge time is estimated, the ranking unit 140 may rank each battery cell based on the estimated rapid charge time. Furthermore, if the high-rate resistance is estimated, the ranking unit 140 may rank each battery cell based on the estimated high-rate resistance. The ranking unit 140 may generate rank information indicating the rank assigned to each battery cell. The rank information is stored in the rank storage unit 142.

[0030] FIG. 4 is a diagram for explaining the processing of the ranking unit 140 according to Embodiment 1. FIG. 4 shows an example of a ranking method. As indicated by arrow RaA, the ranking unit 140 may perform ranking for each battery cell based on the rapid charging time estimated in the process of S130. In this case, the ranking unit 140 performs ranking such that a battery cell with a shorter rapid charging time has a higher rank.

[0031] Specifically, when the rapid charging time estimated for the battery cell to be evaluated is shorter than a predetermined threshold ThAa (when the rapid charging time is "short"), the ranking unit 140 may assign the highest rank A. Also, when the rapid charging time estimated for the battery cell to be evaluated is greater than or equal to a predetermined threshold ThAb (when the rapid charging time is "long"), the ranking unit 140 may assign the lowest rank C. Here, ThAa < ThAb. Further, when the rapid charging time estimated for the battery cell to be evaluated is greater than or equal to the threshold ThAa and shorter than the threshold ThAb (when the rapid charging time is "medium"), the ranking unit 140 may assign a rank B that is lower than rank A and higher than rank C.

[0032] Also, as indicated by arrow RaB, the ranking unit 140 may perform ranking for each battery cell based on the high rate tolerance (number of rapid charge cycles) estimated in the process of S130. In this case, the ranking unit 140 performs ranking such that a battery cell with a larger number of rapid charge cycles has a higher rank.

[0033] Specifically, the ranking unit 140 may assign the highest rank A when the estimated number of quick charges for the battery cell to be assessed is greater than a predetermined threshold ThBa (when the number of quick charges is "high"). The ranking unit 140 may also assign the lowest rank C when the estimated number of quick charges for the battery cell to be assessed is equal to or less than a predetermined threshold ThBb (when the number of quick charges is "low"), where ThBa>ThBb. The ranking unit 140 may also assign rank B, which is lower than rank A but higher than rank C, when the estimated number of quick charges for the battery cell to be assessed is equal to or less than the threshold ThBa and greater than the threshold ThBb (when the number of quick charges is "medium").

[0034] Furthermore, as indicated by the arrow RaC, the ranking unit 140 may rank each battery cell based on the quick charge time and high-rate resistance (number of quick charges) estimated in the process of S130. In this case, the ranking unit 140 may rank each battery cell so that the shorter the quick charge time and the more times the quick charge is performed, the higher the rank of the battery cell.

[0035] Specifically, the ranking unit 140 may assign the highest rank A to a battery cell whose estimated quick charge time is shorter than a threshold value ThAa and whose estimated number of quick charges is greater than a threshold value ThBa. The ranking unit 140 may also assign rank B (rank B1), the next highest rank after rank A, to a battery cell whose estimated quick charge time is equal to or greater than a threshold value ThAa and shorter than a threshold value ThAb and whose estimated number of quick charges is greater than a threshold value ThBa. The ranking unit 140 may also assign rank B (rank B2) to a battery cell whose estimated quick charge time is shorter than the threshold value ThAa and whose estimated number of quick charges is equal to or less than a threshold value ThBa and greater than a threshold value ThBb. Rank B1 and rank B2 may be the same or different ranks.

[0036] The ranking unit 140 may also assign rank C (rank C1), the next highest rank after rank B, to battery cells whose estimated quick charge time is equal to or greater than a threshold ThAb and whose estimated number of quick charges is greater than a threshold ThBa. The ranking unit 140 may also assign rank C (rank C2) to battery cells whose estimated quick charge time is equal to or greater than a threshold ThAa and shorter than a threshold ThAb and whose estimated number of quick charges is equal to or less than a threshold ThBa and greater than a threshold ThBb. The ranking unit 140 may also assign rank C (rank C3) to battery cells whose estimated quick charge time is shorter than a threshold ThAa and whose estimated number of quick charges is less than a threshold ThBb. Rank C1, rank C2, and rank C3 may be the same or different ranks.

[0037] The ranking unit 140 may also assign rank D (rank D1), which is the next highest rank after rank C, to battery cells whose estimated quick charge time is equal to or greater than a threshold ThAb and whose estimated number of quick charges is equal to or less than a threshold ThBa and greater than a threshold ThBb. The ranking unit 140 may also assign rank D (rank D2) to battery cells whose estimated quick charge time is equal to or greater than a threshold ThAa and shorter than a threshold ThAb and whose estimated number of quick charges is equal to or less than a threshold ThBb. Rank D1 and rank D2 may be the same rank or different ranks. The ranking unit 140 may also assign rank E, which is the lowest rank, to battery cells whose estimated quick charge time is equal to or greater than a threshold ThAb and whose estimated number of quick charges is equal to or less than a threshold ThBb.

[0038] FIG. 5 is a diagram illustrating rank information according to the first embodiment. The rank information associates, for each battery cell, battery cell identification information, manufacturing data related to the battery cell, and a rank assigned to the battery cell. The rank information illustrated in FIG. 5 is generated by the ranking method indicated by the arrow RaC in FIG. 4. In the rank information illustrated in FIG. 5, for example, battery cell #1, manufacturing data #1, and "rank: A (rapid charge time: short, number of rapid charges: many)" are associated. Also, for example, battery cell #2, manufacturing data #2, and "rank: B2 (rapid charge time: short, number of rapid charges: medium)" are associated. Also, for example, battery cell #3, manufacturing data #3, and "rank: B1 (rapid charge time: medium, number of rapid charges: many)" are associated. Also, for example, battery cell #4, manufacturing data #4, and "rank: E (rapid charge time: long, number of rapid charges: few)" are associated.

[0039] Here, the price of the battery cell may correspond to the rank of the battery cell. In other words, the higher the rank of the battery cell, the higher the price may be set. In other words, the better the quick charging performance of the battery cell, the higher the price may be set. Note that the correspondence between the rank of the battery cell and the price of the battery cell can be set appropriately by the user depending on the market situation.

[0040] The management system 100 selects battery cells in accordance with a customer's request (step S150). Specifically, the rank display unit 144 performs processing for displaying the above-described rank information. For example, the rank display unit 144 causes the interface unit 108 to display the rank information. Furthermore, the request acquisition unit 150 acquires the customer's request for battery cells. Specifically, the request acquisition unit 150 acquires requests regarding the performance of battery cells. In other words, the request acquisition unit 150 acquires requests regarding the rank of battery cells. For example, the request acquisition unit 150 may acquire a request that the customer desires battery cells with a short quick charge time and a high number of quick charge cycles. Furthermore, for example, the request acquisition unit 150 may acquire a request that the customer desires battery cells with a short quick charge time but a medium number of quick charge cycles. Furthermore, for example, the request acquisition unit 150 may acquire a request that the customer desires battery cells with no particular emphasis on quick charge performance but at an affordable price. The request acquisition unit 150 may acquire the customer's request by, for example, the user operating the interface unit 108.

[0041] The selection unit 160 performs processing to select a battery cell of a rank that meets the customer's requirements. If the customer's requirements indicate a battery cell with good rapid charging performance, the selection unit 160 selects a high-rank battery cell (e.g., battery cell #1 in FIG. 5). On the other hand, if the customer's requirements indicate a low-priced battery cell, the selection unit 160 selects a low-rank battery cell (e.g., battery cell #4 in FIG. 5). For example, if the customer's requirements indicate a battery cell with a short rapid charging time regardless of the number of times it is charged, the selection unit 160 may select rank A, rank B2, or rank C3, as exemplified by the arrow RaC in FIG. 4, depending on the customer's budget, etc. For example, if the customer's requirements indicate a battery cell with a high number of times it is charged regardless of the rapid charging time, the selection unit 160 may select rank A, rank B1, or rank C1, as exemplified by the arrow RaC in FIG. 4, depending on the customer's budget, etc. The selection unit 160 may cause the interface unit 108 to display a screen that enables the user to select a battery cell. The user may select a battery cell by checking the rank information and the customer's request and operating the interface unit 108.

[0042] The management system 100 according to the first embodiment is configured to estimate the quick charge performance of each battery cell based on manufacturing data and to rank each battery cell based on the estimated quick charge performance. Therefore, the management system 100 according to the first embodiment can evaluate the battery cells according to the rank. In other words, the quick charge performance of battery cells (secondary batteries) can be appropriately evaluated in accordance with the customer's needs. For example, if a customer desires a battery cell with good quick charge performance, a high-ranked battery cell can be provided to the customer. Also, if a customer desires a cheap battery cell, a low-ranked battery cell can be provided to the customer.

[0043] Furthermore, the quick charge performance of a battery cell may be determined based on physical properties determined for each battery cell material, such as the physical properties of the active material and the physical properties of the electrodes, and on physical properties determined during the battery cell manufacturing process. However, there is variation in the physical properties determined for each battery cell material and the physical properties determined during the battery cell manufacturing process. Therefore, it is difficult to appropriately set the quick charge performance of a battery cell. Therefore, the quick charge performance of a battery cell at the time of shipment may be set lower than that determined from the above physical properties. As a result, depending on the usage conditions of the battery cell, the quick charge performance that can actually be achieved may not be achieved. In contrast, the management system 100 according to the first embodiment is configured to estimate the quick charge performance using manufacturing data related to the battery cell manufacturing process and rank each battery cell based on the estimated quick charge performance. Therefore, each battery cell can be ranked based on the quick charge performance that it is estimated to actually achieve. In other words, each battery cell can be assigned a different rank based on its estimated quick charge performance. This increases the likelihood that the battery cell will exhibit its actual rapid charging performance when used.

[0044] Furthermore, the management system 100 according to the first embodiment may select battery cells of a rank according to the customer's request, thereby making it possible to select battery cells with rapid charging performance that meets the customer's request.

[0045] Moreover, the management system 100 according to the first embodiment is configured to estimate at least one of the quick charge time and high-rate resistance of each battery cell as the quick charge performance. When the quick charge time is estimated, the management system 100 according to the first embodiment is configured to rank each battery cell based on the estimated quick charge time. When the high-rate resistance is estimated, the management system 100 according to the first embodiment is configured to rank each battery cell based on the estimated high-rate resistance. Therefore, each battery cell can be assigned a different rank depending on the quick charge time and high-rate resistance.

[0046] In addition, in the first embodiment, the estimation model used to estimate the quick charging performance is a multiple regression equation generated by multiple regression analysis using multiple factors that are indicated in the manufacturing data and that affect the quick charging performance as explanatory variables and the quick charging performance as the objective variable. This configuration allows the user to easily identify the factors that affect the quick charging performance. In other words, the multiple regression equation can clarify the factors that affect the quick charging performance. Furthermore, the coefficients of each term in the multiple regression equation can clarify the degree of influence of each factor that affects the quick charging performance.

[0047] (Variation) The present invention is not limited to the above-described embodiment, and modifications can be made as appropriate without departing from the spirit and scope of the present invention. For example, the estimation model is not limited to a multiple regression equation. The estimation model may be generated by a machine learning algorithm such as a neural network. Specifically, the estimation model may be generated by machine learning so as to input manufacturing data and output quick charging performance. However, an estimation model generated by a machine learning algorithm may not clearly identify factors that affect quick charging performance. Therefore, if it is desired to clearly identify factors that affect quick charging performance, it is better to generate an estimation model by multiple regression analysis.

[0048] Furthermore, in the above-described embodiment, the ranking unit 140 ranks the quick charge time into three stages, but this is not limited to such a configuration. The number of stages to be assigned may be arbitrary. Furthermore, it is not necessary to perform the ranking discretely as described in the above-described embodiment. The assigned rank may be expressed by a continuous number. The same applies to the high-rate resistance.

[0049] Furthermore, when ranking is performed based on both the rapid charge time and the high-rate resistance, the ranking is not limited to the example shown by the arrow RaC in Fig. 4. For example, rank B1 when the rapid charge time is "medium" and the number of rapid charge cycles is "high" and rank B2 when the rapid charge time is "short" and the number of rapid charge cycles is "medium" do not have to be the same. These ranks may be different depending on which is more important, the rapid charge time or the number of rapid charge cycles (high-rate resistance). The same applies to other rapid charge times and other numbers of rapid charge cycles.

[0050] Furthermore, when rapid charge time is given greater importance than the number of rapid charge cycles (high-rate resistance), ranking may be performed as follows. As in the above-described embodiment, in the following ranking example, the later the alphabetical order, the lower the rank. A rank may be assigned when the rapid charge time is "short" and the number of rapid charge cycles is "high," a rank of B may be assigned when the rapid charge time is "short" and the number of rapid charge cycles is "medium," and a rank of C may be assigned when the rapid charge time is "short" and the number of rapid charge cycles is "low." Furthermore, a rank of D may be assigned when the rapid charge time is "medium" and the number of rapid charge cycles is "high," a rank of E may be assigned when the rapid charge time is "medium" and the number of rapid charge cycles is "medium," and a rank of F may be assigned when the rapid charge time is "medium" and the number of rapid charge cycles is "low." In addition, the rank may be G when the quick charge time is "long" and the number of quick charges is "high," the rank may be H when the quick charge time is "long" and the number of quick charges is "medium," and the rank may be I when the quick charge time is "long" and the number of quick charges is "low."

[0051] The above-mentioned program includes a set of instructions (or software code) that, when loaded into a computer, causes the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals. [Explanation of symbols]

[0052] 100 Management system, 112 Estimation model generation unit, 114 Estimation model storage unit, 120 Manufacturing data acquisition unit, 122 Manufacturing data storage unit, 130 Estimation unit, 140 Ranking unit, 142 Rank storage unit, 144 Rank display unit, 150 Request acquisition unit, 160 Selection unit

Claims

1. A management system for managing a secondary battery, an estimation unit that estimates the rapid charging performance of each battery cell using an estimation model that is generated in advance based on manufacturing data related to a manufacturing process for each battery cell that is a secondary battery; a ranking unit that ranks each of the battery cells based on the estimated quick charge performance; A management system having:

2. a selection unit that performs processing to select the battery cells of a rank that meets the customer's requirements; The management system of claim 1 further comprising:

3. the estimation unit estimates at least one of a quick charge time and a high-rate resistance of each battery cell as the quick charge performance, The ranking unit When the quick charge time is estimated, the battery cells are ranked based on the estimated quick charge time; When the high-rate resistance is estimated, the battery cells are ranked based on the estimated high-rate resistance. The management system according to claim 1 .

4. the estimation model is a multiple regression equation generated by multiple regression analysis using a plurality of factors that are indicated in the manufacturing data and that affect the quick charging performance as explanatory variables and the quick charging performance as a response variable; The management system according to claim 1 .

5. A management method for managing a secondary battery, comprising: estimating the quick charging performance of each battery cell using an estimation model generated in advance based on manufacturing data relating to a manufacturing process for each battery cell that is a secondary battery; ranking each of the battery cells based on the estimated fast charging performance; Management method.

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