JP7823641B2 - Management system and management method - Google Patents

Description

This disclosure relates to a management system and management method, and in particular to a management system and management method for managing secondary batteries.

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 voltage and current 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.

Japanese Patent Application Laid-Open No. 2022-147157

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 described in Patent Document 1 does not take rapid charging performance into consideration when ranking batteries. Therefore, using the technology described in Patent Document 1, it is difficult to appropriately evaluate the rapid charging performance of secondary batteries in line with customer needs.

This disclosure provides a management system and management method that can appropriately evaluate the rapid charging performance of secondary batteries in accordance with customer needs.

The management system disclosed herein 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 related to the manufacturing process for each battery cell that is a secondary battery, and a ranking unit that ranks each battery cell based on the estimated rapid charging performance.

The management method disclosed herein is a 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 related to the manufacturing process for each battery cell that is a secondary battery, and ranks each battery cell based on the estimated rapid charging performance.

This disclosure provides a management system and management method that can appropriately evaluate the rapid charging performance of secondary batteries in line with customer needs.

FIG. 1 illustrates a configuration of a management system according to a first embodiment. 1 is a flowchart illustrating a management method executed by the management system according to the first embodiment; FIG. 2 is a diagram illustrating manufacturing data according to the first embodiment. FIG. 10 is a diagram for explaining the processing of the ranking unit according to the first embodiment; FIG. 10 is a diagram illustrating an example of rank information according to the first embodiment.

The present embodiment will be described below with reference to the drawings. However, the present invention is not limited to the following embodiment. Also, for clarity of explanation, the following description and drawings have been simplified as appropriate.

(Embodiment 1)
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.

The management system 100 estimates the rapid charging performance of each battery cell using a pre-generated estimation model based on manufacturing data related to the manufacturing process for each battery cell, which is a secondary battery. The management system 100 also ranks each battery cell based on the estimated rapid charging performance. Details will be provided below.

The management system 100 has, as its main hardware components, a control unit 102, a memory unit 104, a communication unit 106, and an interface unit 108 (IF). The control unit 102, memory unit 104, communication unit 106, and interface unit 108 are interconnected via a data bus or the like. Note that if the management system 100 is implemented using multiple computers, each of the multiple computers may have the hardware configuration shown in FIG. 1.

The control unit 102 is a processor such as a CPU (Central Processing Unit). The control unit 102 functions as a calculation device that performs control processing, calculation 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 the function of storing control programs, calculation 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 the function of temporarily storing processing data, etc. The storage unit 104 may include a database. The storage unit 104 may also have multiple memories.

The communication unit 106 performs the processing necessary to communicate with other devices over 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, touch panel, or mouse, and an output device such as a display or speaker. The interface unit 108 may be configured so that the input device and output device are integrated, such as a touch screen (touch panel). The interface unit 108 accepts data input operations by the user (operator) and outputs information to the user.

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.

Each of the above-mentioned 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 can be realized by recording the necessary programs on any non-volatile recording medium and installing them as needed. Furthermore, each component is not limited to being realized by software programs, but may also be realized by any combination of hardware, firmware, and software. Furthermore, each component may be realized using a user-programmable integrated circuit, such as an FPGA (field-programmable gate array) or a microcomputer. In this case, this integrated circuit may be used to realize a program consisting of each of the above-mentioned components.

Figure 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.

Specifically, the estimation model generation unit 112 uses manufacturing data related to the manufacturing process used to manufacture the battery cell to generate an estimation model that estimates the rapid charge performance of the battery cell. For example, the estimation model generation unit 112 may estimate rapid charge time as the rapid charge performance. Furthermore, for example, the estimation model generation unit 112 may estimate high-rate resistance (the number of rapid charge operations allowed per day; hereinafter simply referred to as "rapid charge number") as the rapid charge performance. Note that the shorter the rapid charge time, the better the rapid charge performance. Also, the more rapid charge operations, the better the rapid charge performance. Note that the estimated rapid charge performance is not limited to rapid charge time and high-rate resistance.

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 rapid charging performance indicated in the manufacturing data as explanatory variables and rapid charging performance as the objective variable. For example, the estimation model generation unit 112 may generate an estimation model that estimates rapid charging time by multiple regression analysis with multiple factors that affect rapid charging time as explanatory variables and rapid charging time as the objective variable. Also, 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 high-rate resistance as explanatory variables and high-rate resistance as the objective variable.

Figure 3 is a diagram illustrating manufacturing data according to the first embodiment. The 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 the 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. The values indicated by the material data may vary for each material. Therefore, the material data may differ for each corresponding battery cell. In the example of Figure 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.

Process data is data determined during the manufacturing process of a battery cell. The process data is obtained during the manufacturing process of a 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. For example, the process data indicates the amount of coating on the positive electrode and the density of the positive electrode. For example, the process data indicates the amount of coating on the negative electrode and the density of the negative electrode. For example, the process data indicates the liquid volume (amount of electrolyte) and the opposing capacity ratio (ratio of the weight of the positive electrode to the negative electrode) in the process ("combination") after using a wound body or laminated body using a positive electrode, a negative electrode, and a separator. For example, the process data indicates the thickness of the battery cell.

In the example of Figure 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 Figure 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 for 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.

In the example of Figure 3, the items marked with a circle in the "High-rate resistance" section of the manufacturing data are factors that affect the high-rate resistance of the battery cell. Therefore, in the example of Figure 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 volume 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 for 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.

The management system 100 assesses the rapid charging performance of the battery cells (step S110). Specifically, the management system 100 performs the processes S120 to S140, described below, 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.

The estimation unit 130 estimates the rapid charge performance of the battery cell to be assessed (step S130). Specifically, the estimation unit 130 estimates the rapid charge performance of the battery cell to be assessed using an estimation model stored in the estimation model storage unit 114 based on the manufacturing data of the battery cell to be assessed. In other words, the estimation unit 130 estimates the rapid charge performance of each battery cell using a pre-generated estimation model based on manufacturing data related to the manufacturing process of each battery cell. The estimation unit 130 may estimate at least one of the rapid charge time and high-rate resistance of each battery cell as the rapid charge performance.

For example, the estimation unit 130 estimates the quick charge time of the battery cell being assessed using an estimation model for estimating quick charge time. Specifically, the estimation unit 130 substitutes the values of multiple factors that affect the quick charge time, which are included in the manufacturing data of the battery cell being 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.

Furthermore, for example, the estimation unit 130 estimates the high-rate resistance of the battery cell being assessed using an estimation model that estimates high-rate resistance (number of rapid charges). Specifically, the estimation unit 130 inputs the values of multiple factors that affect the high-rate resistance, which are included in the manufacturing data of the battery cell being 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 high-rate resistance (number of rapid charges).

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 processing of S130. Even more specifically, the ranking unit 140 ranks each battery cell so that the better the rapid charge performance, 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.

Figure 4 is a diagram illustrating the processing of the ranking unit 140 according to the first embodiment. Figure 4 shows an example of a ranking method. As indicated by the arrow RaA, the ranking unit 140 may rank each battery cell based on the rapid charge time estimated in the processing of S130. In this case, the ranking unit 140 ranks the battery cell with the shorter rapid charge time so that the battery cell is given a higher rank.

Specifically, the ranking unit 140 may assign the highest rank A when the rapid charge time estimated for the battery cell to be assessed is shorter than a predetermined threshold ThAa (when the rapid charge time is "short"). The ranking unit 140 may also assign the lowest rank C when the rapid charge time estimated for the battery cell to be assessed is equal to or greater than a predetermined threshold ThAb (when the rapid charge time is "long"), where ThAa < ThAb. The ranking unit 140 may also assign a rank B, which is lower than rank A but higher than rank C, when the rapid charge time estimated for the battery cell to be assessed is equal to or greater than threshold ThAa and shorter than threshold ThAb (when the rapid charge time is "medium").

Furthermore, as indicated by arrow RaB, the ranking unit 140 may rank each battery cell based on the high-rate resistance (number of times rapid charging has occurred) estimated in the processing of S130. In this case, the ranking unit 140 ranks the battery cells so that the more times they have been rapid charged, the higher their rank.

Specifically, the ranking unit 140 may assign the highest rank A when the estimated number of rapid charges for the battery cell being assessed is greater than a predetermined threshold ThBa (when the number of rapid charges is "high"). The ranking unit 140 may also assign the lowest rank C when the estimated number of rapid charges for the battery cell being assessed is equal to or less than a predetermined threshold ThBb (when the number of rapid 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 rapid charges for the battery cell being assessed is equal to or less than threshold ThBa and greater than threshold ThBb (when the number of rapid charges is "medium").

Furthermore, as indicated by the arrow RaC, the ranking unit 140 may rank each battery cell based on the rapid charge time and high-rate resistance (number of rapid charges) estimated in the processing of S130. In this case, the ranking unit 140 may rank the battery cells so that the shorter the rapid charge time and the more rapid charges they have, the higher the rank.

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

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 threshold value ThAb and whose estimated number of quick charges is greater than threshold value 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 threshold value ThAa and shorter than threshold value ThAb and whose estimated number of quick charges is equal to or less than threshold value ThBa and greater than threshold value ThBb. The ranking unit 140 may also assign rank C (rank C3) to battery cells whose estimated quick charge time is shorter than threshold value ThAa and whose estimated number of quick charges is less than threshold value ThBb. Ranks C1, C2, and C3 may be the same or different from each other.

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

Figure 5 is a diagram illustrating rank information according to the first embodiment. The rank information associates, for each battery cell, the battery cell's identification information, manufacturing data related to the battery cell, and a rank assigned to the battery cell. The rank information illustrated in Figure 5 is generated by the ranking method indicated by the arrow RaC in Figure 4. In the rank information illustrated in Figure 5, for example, battery cell #1, manufacturing data #1, and "Rank: A (rapid charge time: short, number of rapid charges: high)" 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: high)" are associated. Also, for example, battery cell #4, manufacturing data #4, and "Rank: E (rapid charge time: long, number of rapid charges: low)" are associated.

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 rapid 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 market conditions.

The management system 100 selects battery cells in accordance with the customer's request (step S150). Specifically, the rank display unit 144 performs processing to display the above-mentioned 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 battery cell performance. In other words, the request acquisition unit 150 acquires requests regarding the rank of the 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 and at a low price. The request acquisition unit 150 may acquire the customer's request, for example, by the user operating the interface unit 108.

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-ranking 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-ranking 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 illustrated 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 charging times regardless of the rapid charging time, the selection unit 160 may select rank A, rank B1, or rank C1, as illustrated by the arrow RaC in FIG. 4, depending on the customer's budget, etc. The selection unit 160 may also 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 customer requirements and operating the interface unit 108.

The management system 100 according to the first embodiment is configured to estimate the rapid charge performance of each battery cell based on manufacturing data and to rank each battery cell based on the estimated rapid charge performance. Therefore, the management system 100 according to the first embodiment can evaluate the battery cells according to their rank. In other words, it can appropriately evaluate the rapid charge performance of battery cells (secondary batteries) in line with the customer's needs. For example, if a customer desires battery cells with good rapid charge performance, it is possible to provide the customer with high-ranking battery cells. Furthermore, if the customer desires inexpensive battery cells, it is possible to provide the customer with low-ranking battery cells.

Furthermore, the quick charge performance of a battery cell can be determined based on the physical properties of each battery cell material, such as the physical properties of the active material and the physical properties of the electrodes, and the physical properties determined during the battery cell manufacturing process. However, there is variation in the physical properties of 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 according to the quick charge performance that it is estimated to actually achieve. In other words, each battery cell can be assigned a different rank depending on its estimated quick charge performance. This increases the likelihood that the battery cells will actually achieve their intended rapid charging performance when used.

Furthermore, the management system 100 according to the first embodiment may select battery cells of a rank that meets the customer's needs. This allows battery cells with rapid charging performance that meets the customer's needs to be selected.

Furthermore, the management system 100 according to the first embodiment is configured to estimate at least one of the rapid charge time and high-rate resistance of each battery cell as the rapid charge performance. Furthermore, when the rapid charge time is estimated, the management system 100 according to the first embodiment is configured to rank each battery cell based on the estimated rapid charge time. Furthermore, 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 rapid charge time and high-rate resistance.

In addition, in embodiment 1, the estimation model used to estimate quick charging performance is a multiple regression equation generated by multiple regression analysis using multiple factors that are indicated in the manufacturing data and affect quick charging performance as explanatory variables, and quick charging performance as the objective variable. This configuration allows the user to easily identify the factors that affect quick charging performance. In other words, the multiple regression equation can clarify the factors that affect 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 quick charging performance.

(Modification)
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.

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

Furthermore, when ranking is performed based on both rapid charge time and high-rate resistance, the ranking is not limited to the example shown by the arrows RaC in Figure 4. For example, rank B1 when the rapid charge time is "medium" and the number of rapid charges is "high" and rank B2 when the rapid charge time is "short" and the number of rapid charges is "medium" do not have to be the same. These ranks may be different depending on whether the rapid charge time or the number of rapid charges (high-rate resistance) is given more importance. The same applies to other rapid charge times and other numbers of rapid charges.

Furthermore, when rapid charge time is given greater importance than the number of rapid charge cycles (high-rate resistance), the ranking may be as follows. Note that, as with 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 when the rapid charge time is "short" and the number of rapid charge cycles is "medium," and a rank of C 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 when the rapid charge time is "medium" and the number of rapid charge cycles is "medium," and a rank of F when the rapid charge time is "medium" and the number of rapid charge cycles is "low." Furthermore, if the quick charge time is "long" and the number of quick charges is "high", the rank may be G; if the quick charge time is "long" and the number of quick charges is "medium", the rank may be H; and if the quick charge time is "long" and the number of quick charges is "low", the rank may be I.

The above-described program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored on 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 technology, CD-ROM, digital versatile disk (DVD), Blu-ray (registered trademark) disc 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.

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 (5)

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: 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: 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 . 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 . 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.