Management system and management method

Abstract

To provide a management system and a management method for improving accuracy of estimating deterioration of a battery cell on the basis of manufacturing data for each battery cell.SOLUTION: A management system 100 has an estimation unit 130 and a ranking unit 140. The estimation unit 130 estimates, on the basis of manufacturing data related to a manufacturing process of a battery cell which is a secondary battery, high-rate resistance and load-bearing performance of each battery cell. The ranking unit 140 ranks, on the basis of the high-rate resistance and the load-bearing performance, each battery cell.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a management system and a management method, and particularly to a management system and a management method for managing secondary batteries.

Background Art

[0002] Patent Document 1 discloses a method of calculating a side reaction current value from manufacturing data, cell voltage, and environmental temperature, and estimating the degree of deterioration of a secondary battery.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Since the prior art does not consider the manufacturing data for each battery cell, there is a problem that an error occurs in estimating the deterioration of the battery cell.

[0005] The present disclosure provides a management system and a management method for improving the estimation accuracy of the deterioration of battery cells based on the manufacturing data for each battery cell.

Means for Solving the Problems

[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 high-rate tolerance and load resistance performance for each battery cell based on manufacturing data related to the manufacturing process of the battery cells that are secondary batteries, and a ranking unit that ranks each battery cell based on the high-rate performance and the load resistance performance.

[0007] The management method according to the present disclosure is a management method for managing a secondary battery. Based on manufacturing data related to the manufacturing process of battery cells, which are secondary batteries, the high-rate tolerance and load-bearing performance for each battery cell are estimated, and ranking is performed for each battery cell based on the high-rate performance and the load-bearing performance.

Effect of the Invention

[0008] According to the present disclosure, it is possible to provide a management system and a management method that improve the estimation accuracy of the deterioration of battery cells based on manufacturing data for each battery cell.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

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Figure 5

Modes for Carrying Out the Invention

[0010] Hereinafter, this embodiment will be described with reference to the drawings. However, the present invention is not limited to the following embodiments. Also, for clarity of explanation, the following description and drawings are simplified as appropriate.

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

[0012] The management system 100 estimates the swelling performance of each battery cell based on the manufacturing data regarding the manufacturing process of each battery cell, which is a secondary battery. More specifically, the management system estimates the high-rate resistance and load-bearing performance of each battery cell. For example, the lower the high-rate resistance, the easier it is for the battery cell to swell, and the smaller the load-bearing capacity, the easier it is for the battery cell to swell. Further, the management system 100 ranks each battery cell based on the estimated high-rate resistance and load-bearing performance. Details will be described later. The battery cell may be mounted on a vehicle.

[0013] As a main hardware configuration, the management system 100 includes 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 a plurality of computers, each of the plurality of 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 has a function as an arithmetic unit that performs control processing, arithmetic processing, and the like. Note that the control unit 102 may have a plurality of 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, and the like executed by the control unit 102. That is, the storage unit 104 (memory) stores one or more instructions. Further, the storage unit 104 has a function for temporarily storing processing data and the like. The storage unit 104 may include a database. Also, the storage unit 104 may have a plurality of 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, and the like. 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 an input device and an output device are integrated, such as a touch screen (touch panel). The interface unit 108 receives an operation of inputting data by a user (operator) and outputs information to the user.

[0016] Further, the management system 100 according to Embodiment 1 includes, as components, an estimation model generation unit 112, an estimation model storage unit 114, a production data acquisition unit 120, and a production data storage unit 122. Further, the management system 100 according to Embodiment 1 includes, as 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 causing a program to be executed under the control of the control unit 102. More specifically, each component can be realized by the control unit 102 executing a program (instruction) stored in the storage unit 104. Alternatively, necessary programs may be recorded on an arbitrary non-volatile recording medium and installed as needed to realize each component. Also, each component is not limited to being realized by software based on a program, and may be realized by any combination of hardware, firmware, and software, etc. Further, each component may be realized using a user-programmable integrated circuit such as, for example, an FPGA (field-programmable gate array) or a microcomputer. In this case, a program composed of the above-described components may be realized using this integrated circuit.

[0018] FIG. 2 is a flowchart showing a management method executed by the management system 100 according to Embodiment 1. 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 for estimating the swelling performance of the battery cell using manufacturing data related to the manufacturing process when manufacturing the battery cell. The estimation model generation unit 112 estimates the high-rate tolerance (the allowable number of rapid charge cycles per day: hereinafter simply referred to as "rapid charge cycles") as the swelling performance. For example, the higher the rapid charge cycles, the less likely it is to swell, and the better the high-rate tolerance. Also, the estimation model generation unit 112 estimates the load resistance performance (the load resistance of the battery cell) as the swelling performance. The load resistance performance may represent the load that the battery cell can withstand without swelling (deforming). For example, the greater the load resistance, the less likely the battery cell is to swell, and the better the load resistance performance.

[0020] More specifically, the estimation model generation unit 112 may generate an estimation model for estimating high-rate tolerance by performing multiple regression analysis with a plurality of factors affecting high-rate tolerance as explanatory variables and high-rate tolerance as the target variable. The estimation model generation unit 112 may generate an estimation model for estimating high-rate tolerance by performing multiple regression analysis with a plurality of factors affecting load-bearing performance as explanatory variables and high-rate tolerance as the target variable.

[0021] FIG. 3 is a diagram illustrating manufacturing data according to Embodiment 1. The values shown in the manufacturing data may differ 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 the manufacture of the battery cell. The material data indicates the quality and specifications of the materials, such as the physical property values of the materials. The material data is obtained, for example, at the time of material receipt, that is, before the manufacturing process. The values shown in the material data may vary for each material. Therefore, the material data may differ 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. Further, for example, the material data indicates the air permeability of the separator and the coating amount of the separator. Further, for example, the material data indicates the additive ratio of the electrolytic solution.

[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 shown in the process data may vary for each manufacturing process. Therefore, the process data may differ for each corresponding battery cell. The process data indicates, for example, the coating amount of the positive electrode (electrode material) and the density of the positive electrode (electrode material). Further, for example, the process data indicates the coating amount of the negative electrode (electrode material) and the density of the negative electrode (electrode material). Further, for example, the process data indicates the liquid amount (amount of electrolytic solution) and the facing capacity ratio (ratio of the weights of the positive electrode and the negative electrode) in the process ( "combination") after using a wound body or a laminate using the positive electrode, the negative electrode, and the separator. Further, for example, the process data indicates the thickness of the battery cell.

[0023] Here, in the example of FIG. 3, the items marked with circles in the "load-bearing capacity" of the manufacturing data are factors that affect the load-bearing capacity performance of the battery cell. Therefore, in the example of FIG. 3, 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, and the thickness of the battery cell are factors that affect the load-bearing capacity performance. Therefore, the estimation model generation unit 112 performs multiple regression analysis using these multiple factors shown in the manufacturing data of each of the plurality of battery cells as explanatory variables and the load-bearing capacity performance of the corresponding battery cell as the objective variable. Thereby, the estimation model generation unit 112 generates an estimation model for estimating the load-bearing capacity performance. It is assumed that the load-bearing capacity performance of the battery cell regarding the manufacturing data used in the generation of the estimation model is already known in advance.

[0024] Also, in the example of FIG. 3, the items marked with circles in the "high-rate tolerance" of the manufacturing data are factors that affect the high-rate tolerance 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 tolerance. Therefore, the estimation model generation unit 112 performs multiple regression analysis using these multiple factors shown in the manufacturing data of each of the plurality of battery cells as explanatory variables and the high-rate tolerance (number of rapid charge cycles) of the corresponding battery cell as the objective variable. Thereby, the estimation model generation unit 112 generates an estimation model for estimating the high-rate tolerance. It is assumed that the high-rate tolerance of the battery cell regarding the manufacturing data used in the generation of the estimation model is already known in advance.

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

[0026] The estimation unit 130 estimates the high-rate tolerance and load-bearing performance of the battery cell to be inspected (step S130). Specifically, the estimation unit 130 estimates the swelling performance of the battery cell to be inspected using the estimation model stored in the estimation model storage unit 114 based on the manufacturing data of the battery cell to be inspected. That is, the estimation unit 130 estimates the high-rate tolerance and load-bearing performance of each battery cell using the pre-generated estimation model based on the manufacturing data related to the manufacturing process of each battery cell.

[0027] For example, the estimation unit 130 estimates the load-bearing performance of the battery cell to be inspected using the estimation model for estimating the load-bearing performance. Specifically, the estimation unit 130 substitutes the values of a plurality of factors that affect the load-bearing performance included in the manufacturing data of the battery cell to be inspected as explanatory variables into the multiple regression equation, which is the estimation model. The estimation unit 130 obtains the value (objective variable) calculated by the multiple regression equation as the estimated value of the load-bearing performance (e.g., load-bearing).

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

[0029] The ranking unit 140 ranks the battery cells to be evaluated (step S140). Specifically, the ranking unit 140 ranks each battery cell based on the swelling performance estimated in the process of S130, that is, the high-rate tolerance and the load resistance performance. More specifically, the ranking unit 140 performs ranking such that the higher the load resistance performance of the battery cell, the higher the rank. Also, the ranking unit 140 performs ranking such that the higher the estimated high-rate tolerance of the battery cell, the higher the rank. 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 process of the ranking unit 140 according to the first embodiment. The ranking unit 140 ranks each battery cell based on the load resistance performance (load resistance) and the high-rate tolerance (number of rapid charge cycles) estimated in the process of S130. In this case, the ranking unit 140 may perform ranking such that the higher the load resistance and the more the number of rapid charge cycles of the battery cell, the higher the rank.

[0031] Specifically, the ranking unit 140 may assign the highest rank A to a battery cell whose estimated load resistance is greater than the threshold ThAa and whose estimated number of rapid charge cycles is greater than the threshold ThBa. Also, the ranking unit 140 may assign the rank B (rank B1), which is the next highest rank after rank A, to a battery cell whose estimated load resistance is less than or equal to the threshold ThAa and greater than the threshold ThAb and whose estimated number of rapid charge cycles is greater than the threshold ThBa. The threshold ThAb is smaller than the threshold ThAa. Also, the ranking unit 140 may assign rank B (rank B2) to a battery cell whose estimated load resistance is greater than the threshold ThAa and whose estimated number of rapid charge cycles is less than or equal to the threshold ThBa and greater than the threshold ThBb. The threshold ThBb is smaller than the threshold ThBa. Note that rank B1 and rank B2 may be the same rank as each other or may be different ranks from each other.

[0032] In addition, the ranking unit 140 may assign rank C (rank C1), which is the next highest rank after rank B, to a battery cell whose estimated load-bearing capacity is equal to or less than the threshold ThAb and whose estimated number of rapid charge cycles is greater than the threshold ThBa. Further, the ranking unit 140 may assign rank C (rank C2) to a battery cell whose estimated load-bearing capacity is less than or equal to the threshold ThAa and greater than the threshold ThAb, and whose estimated number of rapid charge cycles is less than or equal to the threshold ThBa and greater than the threshold ThBb. Further, the ranking unit 140 may assign rank C (rank C3) to a battery cell whose estimated load-bearing capacity is greater than the threshold ThAa and whose estimated number of rapid charge cycles is less than or equal to the threshold ThBb. Note that rank C1, rank C2, and rank C3 may be the same rank as each other or different ranks from each other.

[0033] In addition, the ranking unit 140 may assign rank D (rank D1), which is the next highest rank after rank C, to a battery cell whose estimated load-bearing capacity is equal to or less than the threshold ThAb and whose estimated number of rapid charge cycles is less than or equal to the threshold ThBa and greater than the threshold ThBb. Further, the ranking unit 140 may assign rank D (rank D2) to a battery cell whose estimated load-bearing capacity is less than or equal to the threshold ThAa and greater than the threshold ThAb, and whose estimated number of rapid charge cycles is less than or equal to the threshold ThBb. Note that rank D1 and rank D2 may be the same rank as each other or different ranks from each other. Further, the ranking unit 140 may assign the lowest rank E to a battery cell whose estimated load-bearing capacity is equal to or less than the threshold ThAb and whose estimated number of rapid charge cycles is less than or equal to the threshold ThBb.

[0034] FIG. 5 is a diagram illustrating rank information according to Embodiment 1. The rank information associates, for each battery cell, identification information of the battery cell, manufacturing data related to the battery cell, and a rank given to the battery cell. In the rank information illustrated in FIG. 5, for example, battery cell #1, manufacturing data #1, and "Rank: A (high load resistance, many rapid charge cycles)" are associated with each other. Also, for example, battery cell #2, manufacturing data #2, and "Rank: B2 (high load resistance, medium rapid charge cycles)" are associated with each other. Also, for example, battery cell #3, manufacturing data #3, and "Rank: B1 (medium load resistance, many rapid charge cycles)" are associated with each other. Also, for example, battery cell #4, manufacturing data #4, and "Rank: E (low load resistance, few rapid charge cycles)" are associated with each other.

[0035] Here, the price of the battery cell may correspond to the rank of the battery cell. That is, a higher price may be set for a battery cell with a higher rank. In other words, a higher price may be set for a battery cell with better swelling performance. Note that the correspondence between the rank of the battery cell and the price of the battery cell can be appropriately set by the user according to the market situation. Also, the price of a vehicle equipped with a battery cell having good swelling performance may be set high.

[0036] The management system 100 selects a battery cell according to the customer's request (step S150). Specifically, the rank display unit 144 performs processing for displaying the rank information described above. For example, the rank display unit 144 causes the interface unit 108 to display the rank information. Also, the request acquisition unit 150 acquires the customer's request for the battery cell. Specifically, the request acquisition unit 150 acquires the request regarding the performance of the battery cell. In other words, the request acquisition unit 150 acquires the request regarding the rank of the battery cell. For example, the request acquisition unit 150 may acquire a request that the customer desires a battery cell with a large load-bearing capacity and a large number of rapid charge times. Also, for example, the request acquisition unit 150 may acquire a request that the customer desires a battery cell with a short load-bearing capacity but a medium number of rapid charge times. Also, for example, the request acquisition unit 150 may acquire a request that the customer desires a battery cell with no particular emphasis on swelling performance but a low price. The request acquisition unit 150 may acquire the customer's request, for example, when the user operates the interface unit 108.

[0037] The selection unit 160 performs processing for selecting a battery cell of a rank according to the customer's request. When the customer's request indicates a battery cell with good swelling performance, the selection unit 160 selects a high-rank battery cell (for example, battery cell #1 in FIG. 5). On the other hand, when the customer's request indicates a battery cell with a low price, the selection unit 160 selects a low-rank battery cell (for example, battery cell #4 in FIG. 5). Also, for example, when the customer's request indicates a battery cell with a large load-bearing capacity regardless of the number of charge times, the selection unit 160 may select rank A, rank B2, or rank C3 illustrated in FIG. 4 according to the customer's budget, etc. Also, for example, when the customer's request indicates a battery cell with a large number of charge times regardless of the load-bearing capacity, the selection unit 160 may select rank A, rank B1, or rank C1 according to the customer's budget, etc. Note that the selection unit 160 may cause the interface unit 108 to display a screen for the user to select a battery cell. The user may select a battery cell by operating the interface unit 108 after confirming the rank information and the customer's request.

[0038] The swelling of the battery cell can be controlled within the pack according to the total discharge capacity of the battery cell. Therefore, the total discharge capacity of the battery cell may be controlled according to the swelling performance of each battery cell.

[0039] Embodiment 1 can improve the estimation accuracy of the degradation of the battery cell based on the manufacturing data for each battery cell. Further, different from the prior art, Embodiment 1 can evaluate the battery cell in the initial state before being mounted on the vehicle.

[0040] The management system 100 according to Embodiment 1 is configured to estimate the swelling performance for each battery cell based on the manufacturing data and rank each battery cell based on the estimated swelling performance. Therefore, the management system 100 according to Embodiment 1 can evaluate the battery cell according to the rank. In other words, the swelling performance of the battery cell (secondary battery) can be appropriately evaluated in accordance with the customer's desire. For example, when the customer desires a battery cell with good swelling performance, a battery cell with a high rank can be provided to the customer. Also, when the customer desires an inexpensive battery cell, a battery cell with a low rank can be provided to the customer. That is, by providing a battery cell with good swelling performance to a user who charges the battery cell a large number of times, the quality of the battery cell until EOL (End of Life) can be ensured. Also, a battery cell with relatively low swelling performance can be provided at low cost to a user who charges the battery cell a small number of times.

[0041] In addition, the swelling performance of a battery cell can be determined according to physical property values determined for each material of the battery cell, such as physical property values of the active material and physical property values of the electrode, and physical property values determined in the manufacturing process of the battery cell. However, there are variations in the physical property values determined for each material of the battery cell and the physical property values determined in the manufacturing process of the battery cell. Therefore, it is difficult to appropriately evaluate the swelling performance of the battery cell. Therefore, the swelling performance at the time of shipment of the battery cell may be evaluated lower than that determined from the above physical property values. As a result, depending on the usage situation of the battery cell, there is a possibility that the charge-discharge performance (e.g., rapid charging performance) that can actually be exhibited may not be exhibited. On the other hand, the management system 100 according to Embodiment 1 is configured to estimate the swelling performance using manufacturing data related to the manufacturing process of the battery cell, and perform ranking for each battery cell based on the estimated swelling performance. Therefore, ranking can be performed according to the swelling performance estimated for each battery cell. In other words, different ranks can be assigned to each battery cell according to the estimated swelling performance. Thereby, when using the battery cell, the possibility of exhibiting the charge-discharge performance that can actually be exhibited can be increased.

[0042] In addition, the management system 100 according to Embodiment 1 may select a battery cell of a rank according to the customer's request. Thereby, a battery cell with a swelling performance according to the customer's request can be selected.

[0043] In addition, the management system 100 according to Embodiment 1 is configured to estimate the load resistance and high rate tolerance for each battery cell as the swelling performance. And the management system 100 according to Embodiment 1 is configured to perform ranking for each battery cell based on the estimated load resistance and high rate tolerance. Therefore, different ranks can be assigned to each battery cell according to the load resistance and high rate tolerance.

[0044] In Embodiment 1, the estimation model used for estimating the expansion performance is a multiple regression equation generated by multiple regression analysis in which a plurality of factors shown in the production data and affecting the expansion performance are explanatory variables and the expansion performance is the target variable. With such a configuration, the user can easily recognize what factors affect the expansion performance. In other words, the factors affecting the expansion performance can be clarified by the multiple regression equation. Furthermore, the degree of influence of each factor on the expansion performance can be clarified by the coefficient of each term in the multiple regression equation.

[0045] (Modification example) Note that the present invention is not limited to the above-described embodiments and can be appropriately modified without departing from the gist. For example, the estimation model is not limited to the 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 output the expansion performance with the production data as the input. Note that in the estimation model generated by the machine learning algorithm, the factors affecting the expansion performance may not be clarified. Therefore, when it is desired to clarify the factors affecting the expansion performance, it is better to generate the estimation model by multiple regression analysis.

[0046] In the above-described embodiment, the ranking unit 140 ranks the load-bearing performance in three levels of ranks, but the configuration is not limited to this. The number of levels of the ranks to be given is arbitrary. Also, as described in the above-described embodiment, it is not necessary to perform ranking discretely. The ranks to be given may be represented by continuous numerical values. This also applies to the high-rate resistance.

[0047] Also, when ranking based on both load-bearing performance and high-rate tolerance, it is not limited to the configuration of ranking as shown in the example of Fig. 4. For example, Rank B1 when the load-bearing is "medium" and the number of rapid charge cycles is "many", and Rank B2 when the load-bearing is "large" and the number of rapid charge cycles is "medium" do not have to be the same. These ranks may be made different depending on which of the load-bearing and the number of rapid charge cycles (high-rate tolerance) is emphasized more. The same applies to other load-bearings and other numbers of rapid charge cycles.

[0048] Also, when emphasizing the load-bearing more than the number of rapid charge cycles (high-rate tolerance), ranking may be performed as follows. Similar to the above-described embodiments, in the following examples of ranks, the later the alphabetical order, the lower the rank. The rank when the load-bearing is "large" and the number of rapid charge cycles is "many" may be A, the rank when the load-bearing is "large" and the number of rapid charge cycles is "medium" may be B, and the rank when the load-bearing is "large" and the number of rapid charge cycles is "few" may be C. Also, the rank when the load-bearing is "medium" and the number of rapid charge cycles is "many" may be D, the rank when the load-bearing is "medium" and the number of rapid charge cycles is "medium" may be E, and the rank when the load-bearing is "medium" and the number of rapid charge cycles is "few" may be F. Also, the rank when the load-bearing is "small" and the number of rapid charge cycles is "many" may be G, the rank when the load-bearing is "small" and the number of rapid charge cycles is "medium" may be H, and the rank when the load-bearing is "small" and the number of rapid charge cycles is "few" may be I.

[0049] The above program includes a set of instructions (or software code) for causing a computer to perform one or more functions described in the embodiments when loaded into the computer. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, a computer-readable medium or a tangible storage medium includes 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 (registered trademark) disk, or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage, or other magnetic storage devices. The program may be transmitted on a transitory computer-readable medium or a communication medium. By way of example and not limitation, a transitory computer-readable medium or a communication medium includes electrical, optical, acoustic, or other forms of propagated signals.

Description of Reference Numerals

[0050] 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 ··· Requirement acquisition unit, 160 ··· Selection unit

Claims

1. A management system for managing a secondary battery, comprising: an estimation unit that estimates the high-rate resistance and load-bearing performance for each battery cell based on manufacturing data related to the manufacturing process of the battery cells that are secondary batteries; a ranking unit that ranks each battery cell based on the high-rate resistance and the load-bearing performance; The management system.

2. The estimation unit uses a multiple regression equation generated by multiple regression analysis with a plurality of factors shown in the manufacturing data and affecting the high-rate resistance as first explanatory variables and the high-rate resistance as the target variable to estimate the high-rate resistance. The management system according to Claim 1.

3. The estimation unit uses a multiple regression equation generated by multiple regression analysis with a plurality of factors shown in the manufacturing data and affecting the load-bearing performance as second explanatory variables and the load-bearing performance as the target variable to estimate the load-bearing performance. The management system according to Claim 2.

4. The first explanatory variables and the second explanatory variables include the coating amount of the electrode material, the density of the electrode material, and the thickness of the battery cell. The management system according to Claim 3.

5. A management method for managing a secondary battery, comprising: estimating the high-rate resistance and load-bearing for each battery cell based on manufacturing data related to the manufacturing process of the battery cells that are secondary batteries; ranking each battery cell based on the high-rate resistance and the load-bearing; The management method.

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