Proposed device, proposed method, and method for producing electrode mixture and program

Abstract

To propose a formulation for electrode mixtures whose physical property values satisfy target values.SOLUTION: A proposal device having a control unit that proposes a formulation of an electrode mixture generates candidate formulations for an electrode mixture, predicts a physical property value of the electrode mixture on the basis of the candidate formulations, and outputs candidate formulations in which the physical property values satisfy target values.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present disclosure relates to a proposed device, a proposed method, a method for producing an electrode mixture, and a program. [Background technology]

[0002] There is a technology that uses a machine learning model to propose a blend that improves the properties of a substance. For example, Patent Document 1 discloses a substance search device that uses a machine learning model to specify a predicted range of the spectral properties of a specific substance that improves the evaluation value of the properties, and searches for a specific substance that has the predicted range of the spectral properties. Patent Document 2 discloses a prediction method that uses a machine learning technique to predict the properties of a paint after adjustment when a raw material for adjusting the properties of the paint is added to the paint before adjustment. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-149449 [Patent Document 2] International Publication No. 2022 / 131180 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the prior art does not suggest the formulation of an electrode mixture to be used in the preparation of an electrode.

[0005] The present disclosure provides a technique for proposing a formulation of a mixture for an electrode that satisfies target physical property values. [Means for solving the problem]

[0006] A proposal device according to a first aspect of the present disclosure is a proposal device having a control unit that proposes a formulation of a mixture for electrodes, wherein the control unit generates a candidate formulation for the mixture for electrodes, predicts physical property values ​​of the mixture for electrodes based on the candidate formulation, and outputs the candidate formulation in which the physical property values ​​satisfy target values.

[0007] According to the first aspect of the present disclosure, it is possible to propose a formulation of a mixture for an electrode in which physical property values ​​satisfy target values.

[0008] A second aspect of the present disclosure is a proposed device according to the first aspect, wherein the control unit predicts the physical property value based on a trained model.

[0009] A third aspect of the present disclosure is the proposed device according to the first or second aspect, wherein the electrode mixture is a mixture of an active material, a conductive member, and a binder.

[0010] A fourth aspect of the present disclosure is the proposed device according to the third aspect, wherein the binder comprises a polymer.

[0011] A fifth aspect of the present disclosure is the proposed device according to the fourth aspect, wherein the polymer comprises a fluororesin.

[0012] A sixth aspect of the present disclosure is a proposal device relating to any of the first to fifth aspects, wherein the control unit predicts two or more types of physical properties and outputs the candidate formulation in which all of the two or more types of physical properties satisfy target values.

[0013] In a proposal method according to a seventh aspect of the present disclosure, a control unit provided in a proposal device that proposes a formulation of a mixture for electrodes generates a candidate formulation for the mixture for electrodes, predicts physical property values ​​of the mixture for electrodes based on the candidate formulation, and outputs the candidate formulation in which the physical property values ​​satisfy target values.

[0014] A method for producing an electrode mixture according to an eighth aspect of the present disclosure includes a step of mixing an active material, a conductive material, and a binder according to a candidate formulation output by the proposed method according to the seventh aspect.

[0015] A program according to a ninth aspect of the present disclosure generates candidate formulations of a mixture for electrodes in a control unit of a proposal device that proposes a formulation of a mixture for electrodes, predicts physical property values ​​of the mixture for electrodes based on the candidate formulations, and outputs the candidate formulations in which the physical property values ​​satisfy target values. [Brief description of the drawings]

[0016] [Figure 1] 1A to 1C are diagrams illustrating an example of a manufacturing process for an electrode sheet. [Diagram 2] FIG. 2 is a block diagram showing an example of a hardware configuration of the proposed device. [Diagram 3] 13 is a flowchart showing an example of a processing procedure of a learning method. [Figure 4] FIG. 1 is a diagram showing an example of a model of viscosity change rate after one day. [Diagram 5] FIG. 1 is a diagram showing an example of a model of viscosity change rate after 7 days. [Figure 6] FIG. 2 is a diagram showing an example of a coating resistance model. [Figure 7] FIG. 1 is a diagram showing an example of a three-point bending strength model. [Figure 8] FIG. 1 is a diagram showing an example of a peel strength model. [Figure 9] 11 is a flowchart illustrating an example of a processing procedure of the proposed method. [Figure 10] FIG. 11 is a diagram illustrating an example of condition information. [Figure 11] FIG. 11 is a diagram illustrating an example of proposal information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, each embodiment will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0018] [Embodiment] An embodiment of the present disclosure is a proposal device that proposes a formulation of a mixture for an electrode. The mixture for an electrode in this embodiment is, for example, an electrode slurry.

[0019] The electrode slurry is a material used to prepare an electrode sheet for use in a secondary battery such as a lithium ion battery. The electrode slurry is a slurry containing an active material, a conductive assistant, and a binder. The binder may be a mixture of multiple types of binders.

[0020] For example, a lithium ion battery is manufactured by filling a container with a laminate of a separator sandwiched between an electrode sheet to be a positive electrode and an electrode sheet to be a negative electrode, injecting an electrolyte solution, and sealing the container. Hereinafter, the electrode sheet may be simply referred to as an "electrode." The electrode slurry in this embodiment may be used to prepare either an electrode sheet to be a positive electrode or an electrode sheet to be a negative electrode.

[0021] Fig. 1 is a diagram showing an example of the manufacturing process of an electrode sheet. As shown in Fig. 1, the manufacturing process of an electrode sheet can be broadly divided into a slurry preparation process and an electrode preparation process. In the slurry preparation process, an active material, a conductive assistant, and a binder are kneaded to prepare an electrode slurry. In the electrode preparation process, an electrode sheet is prepared using the electrode slurry prepared in the slurry preparation process.

[0022] In the slurry preparation process, first, the binder solution and the conductive assistant are put into a stirrer and kneaded. Next, the active material and the solvent are added to the stirrer and kneading is repeated again. For example, N-methylpyrrolidone (NMP) is used as the solvent. Then, the solvent is further put into the stirrer and kneaded.

[0023] In the electrode preparation process, first, the electrode slurry is applied to a current collector such as an aluminum foil. Next, the current collector coated with the electrode slurry is heat-treated and dried. The heat treatment temperature may be increased stepwise, for example, to 90°C, 110°C, and 120°C. The completed electrode sheet is cut into a shape according to the application and used to manufacture secondary batteries.

[0024] The composition of the electrode mixture greatly affects the physical properties of the electrode using the electrode mixture and the performance of the secondary battery using the electrode. Here, the composition of the electrode mixture includes the mixing ratio of the materials (i.e., the active material, the conductive additive, and the binder) and the mixing ratio of the binder.

[0025] Conventionally, the composition of an electrode mixture has been determined based on the designer's knowledge. For example, the designer first makes a hypothesis of a composition that satisfies the required specifications based on his or her own experience or knowledge. Next, the designer prototypes an electrode mixture with that composition and measures its physical properties through experiments. The designer may also prototype an electrode or secondary battery using the electrode mixture and measure its performance. The designer then repeats the prototypes and experiments until an electrode mixture that satisfies the required specifications is obtained.

[0026] In the present embodiment, the purpose is to propose a formulation of an electrode mixture that satisfies required specifications. In particular, in the present embodiment, by predicting physical property values ​​related to the electrode mixture, a formulation of an electrode mixture that satisfies target physical property values ​​is proposed without performing prototyping or experiments.

[0027] In this embodiment, one or more trained models are used to predict physical property values ​​related to the electrode mixture. The physical property values ​​related to the electrode mixture include physical property values ​​measured from the electrode mixture and physical property values ​​measured from an electrode using the electrode mixture.

[0028] The physical property values ​​measured from the electrode mixture include a physical property value indicating the slurry stability. Examples of the physical property values ​​indicating the slurry stability include a viscosity change rate, a rheology, and a sedimentation evaluation. In the present embodiment, as an example, a machine learning model for predicting the viscosity change rate after 1 day and the viscosity change rate after 7 days will be described.

[0029] The physical property values ​​measured from the electrodes include a physical property value indicating dispersibility. Examples of the physical property value indicating dispersibility include coating resistance and blackness. In the present embodiment, as an example, a machine learning model for predicting coating resistance will be described.

[0030] The physical property values ​​measured from the electrodes include a physical property value indicating flexibility. Examples of the physical property value indicating flexibility include a three-point bending test result and a mandrel test result. In the present embodiment, a machine learning model for predicting three-point bending strength will be described as an example.

[0031] The physical property values ​​measured from the electrodes include a physical property value indicating adhesion. Examples of the physical property value indicating adhesion include peel test results. In this embodiment, as an example, a machine learning model for predicting peel strength will be described.

[0032] According to one aspect, the present embodiment makes it possible to efficiently determine the composition of an electrode mixture that satisfies required specifications, thereby shortening the lead time in the development of an electrode mixture, an electrode, and a secondary battery.

[0033] <Hardware configuration> Fig. 2 is a block diagram showing an example of a hardware configuration of the proposed device 100 in this embodiment. As shown in Fig. 2, the proposed device 100 includes a processor 101, a memory 102, an auxiliary storage device 103, an operation device 104, a display device 105, a communication device 106, and a drive device 107. Each piece of hardware of the proposed device 100 is connected to each other via a bus 108.

[0034] The processor 101 includes various arithmetic devices such as a CPU (Central Processing Unit), etc. The processor 101 reads out various programs installed in the auxiliary storage device 103 onto the memory 102 and executes them.

[0035] The memory 102 has a main storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The processor 101 and the memory 102 form a so-called computer (hereinafter also referred to as a "control unit"), and the processor 101 executes various programs read onto the memory 102, whereby the computer realizes various functions.

[0036] The auxiliary storage device 103 (hereinafter also referred to as a “storage unit”) stores various programs and various data used when the processor 101 executes the various programs.

[0037] The operation device 104 is an operation device that allows a user of the proposal device 100 to perform various operations. The display device 105 is a display device that displays the results of various processes executed by the proposal device 100.

[0038] The communication unit 106 is a communication device for communicating with an external device via a communication network.

[0039] The drive device 107 is a device for setting the storage medium 109. The storage medium 109 here includes media that store information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, a magneto-optical disk, etc. The storage medium 109 may also include semiconductor memory that stores information electrically, such as a ROM, a flash memory, etc.

[0040] The various programs to be installed in the auxiliary storage device 103 are installed, for example, by setting the distributed storage medium 109 in the drive device 107 and reading out the various programs stored in the storage medium 109 by the drive device 107. Alternatively, the various programs to be installed in the auxiliary storage device 103 may be installed by being downloaded from a network via the communication device 106.

[0041] <Learning method flow> 3 is a flowchart showing an example of the flow of a learning method executed by the proposal device 100 in this embodiment. The learning method is a method for learning a prediction model used to propose an electrode mixture.

[0042] In step S1, the control unit of the proposed device 100 stores the learning data in the storage unit. The learning data is data used to learn a prediction model. The prediction model is a machine learning model for predicting physical properties related to the electrode mixture. The prediction models in this embodiment include, as examples, a viscosity change rate model after 1 day, a viscosity change rate model after 7 days, a coating resistance model, a three-point bending strength model, and a peel strength model.

[0043] The training data includes parameters that are explanatory variables of the prediction model and parameters that are objective variables of the prediction model. The training data can be collected by conducting experiments using electrode mixtures prepared with various blends. The training data may be collected from information published in papers, databases, etc.

[0044] The learning data in this embodiment has the following data items. (1) Material composition ratio (2) Binder Mixture Ratio (3) Solid content concentration (4)Initial viscosity (5) Basic properties of binders (6) Electrode information (7) Viscosity change rate after 1 day (8) Viscosity change rate after 7 days (9) Coating resistance (10) Three-point bending strength (11) Peel strength

[0045] (1) The material composition ratio further includes the following data items: (1-1) Weight ratio of active materials (1-2) Weight ratio of conductive additive (1-3) Weight ratio of binder

[0046] (2) The binder mixing ratio is the weight ratio of each type of binder used in preparing the electrode mixture. (3) The solid content concentration is the solid content concentration when the electrode mixture is an electrode slurry. (4) The initial viscosity is the viscosity measured immediately after preparing the electrode mixture.

[0047] (5) The basic physical properties of the binder further include the following data items. (5-1) Intrinsic viscosity (5-2) Elastic modulus (5-3) Polymerization method (5-4) Functional group modification (5-5) Mw / Mn (5-6) Solution viscosity (5-7) Denaturation amount (5-8) Branching Degree (5-9) Abnormal coupling degree (5-10) Random Rate

[0048] (6) The electrode information further includes the following data items: (6-1) Amount of slurry applied (6-2) Density before pressing (6-3) Density after pressing process

[0049] (7) The viscosity change rate after one day is the ratio of the initial viscosity to the viscosity measured after one day. (8) The viscosity change rate after seven days is the ratio of the initial viscosity to the viscosity measured after one day. (9) The coating resistance may be measured using a measuring device on an electrode sheet prepared using the electrode mixture. (10) The three-point bending strength may be measured by performing a three-point bending test on an electrode sheet prepared using the electrode mixture. (11) The peel strength may be measured by performing a peel test on an electrode sheet prepared using the electrode mixture.

[0050] (1) Material composition ratio, (2) binder compounding ratio, (3) solids concentration, (4) initial viscosity, (5) binder basic properties, and (6) electrode information are used as explanatory variables for the prediction model. (7) Viscosity change rate after 1 day, (8) Viscosity change rate after 7 days, (9) coating resistance, (10) 3-point bending strength, and (11) peel strength are used as objective variables for the prediction model.

[0051] In collecting the learning data, constraints on the composition of the electrode mixture or binder may be set based on the expected required specifications. While there are a huge number of combinations of electrode mixtures and binders, the combinations that can actually be used in the manufacture of secondary batteries are limited. By limiting the learning data in areas that are unlikely to be actually used, the amount of calculation required for learning can be reduced and the prediction accuracy can be improved.

[0052] In this embodiment, the active material and the conductive additive are fixed to one type. Two types of binders are mixed. One of the two types of binders is selected from a plurality of predetermined binder candidates. The number of binder candidates is not limited, but in this embodiment, 10 types are used as an example.

[0053] The other of the two types of binders is a binder having a polymer. An example of a polymer is a fluororesin. Hereinafter, a binder containing a fluororesin will be referred to as a "fluororesin binder."

[0054] The weight ratio of the active material is in the range of 96.0 to 98.0 wt %. The weight ratio of the conductive assistant is in the range of 1.0 to 3.0 wt %. The weight ratio of the binder is in the range of 1.0 to 3.0 wt %. The weight ratio of the fluororesin binder to the total binder is in the range of 0 to 20 wt %.

[0055] In step S2, the control unit of the proposed device 100 learns a one-day viscosity change rate model based on the learning data stored in the memory unit. The one-day viscosity change rate model is a machine learning model that predicts the one-day viscosity change rate of the electrode mixture. The control unit of the proposed device 100 stores the learned one-day viscosity change rate model in the memory unit.

[0056] Fig. 4 is a diagram showing an example of a one-day viscosity change rate model. As shown in Fig. 4, the one-day viscosity change rate model 300 inputs the material composition ratio, binder mixing ratio, initial viscosity, solid content concentration, and binder physical properties, and outputs the one-day viscosity change rate. The one-day viscosity change rate is classified into four classes: "A: 0-120%, " "B: 121-150%, " "C: 151-300%, " and "D: 301% or more".

[0057] The binder properties input to the one-day viscosity change rate model 300 are the intrinsic viscosity, elastic modulus, polymerization method, and the presence or absence of functional group modification, among the binder property items included in the learning data.

[0058] The one-day viscosity change rate model 300 includes a classification model 301 and a regression model 302. The classification model 301 receives the material composition ratio, binder mixing ratio, initial viscosity, solid content concentration, and binder physical properties as input, and classifies the one-day viscosity change rate into "300% or less" or "301% or more." The structure of the classification model 301 can be, for example, a random forest.

[0059] A formulation whose viscosity change rate after one day is classified as "300% or less" in classification model 301 is input to regression model 302. On the other hand, a formulation whose viscosity change rate after one day is classified as "301% or more" in classification model 301 is classified into the class "D: 301% or more."

[0060] The absolute value of the viscosity change rate after one day tends to be small because the time that has elapsed since the preparation of the electrode mixture is short. Therefore, there is little learning data in which the viscosity change rate after one day is 300% or more, and there is a risk of a decrease in prediction accuracy. The viscosity change rate after one day model 300 is configured to verify whether the formulation has a viscosity change rate after one day within a realistic range before making a prediction using the regression model 302.

[0061] The regression model 302 receives the material composition ratio, binder mixing ratio, initial viscosity, solid content concentration, and binder physical properties as input, and outputs the viscosity change rate after one day. The regression model 302 classifies the viscosity change rate after one day into "A: 0-120%, " "B: 121-150%, " and "C: 151-300%". The structure of the regression model 302 can be, for example, Gaussian process regression.

[0062] Returning to FIG. 3, in step S3, the control unit of the proposed device 100 learns a 7-day viscosity change rate model based on the learning data stored in the memory unit. The 7-day viscosity change rate model is a machine learning model that predicts the 7-day viscosity change rate of the electrode mixture. The control unit of the proposed device 100 stores the learned 7-day viscosity change rate model in the memory unit.

[0063] 5 is a diagram showing an example of a viscosity change rate model after 7 days. As shown in FIG. 5, the viscosity change rate model after 7 days 310 receives the material composition ratio, binder mixing ratio, initial viscosity, solid content concentration, and binder physical properties as inputs, and outputs the viscosity change rate after 7 days. The viscosity change rate after 7 days is classified into four classes: "A: 0-120%", "B: 121-150%, "C: 151-300%, "and "D: 301% or more". The structure of the viscosity change rate model after 7 days 310 can be, for example, Gaussian process regression.

[0064] The binder properties input to the 7-day viscosity change rate model 310 are, similarly to the 1-day viscosity change rate model 300, the intrinsic viscosity, elastic modulus, polymerization method, and the presence or absence of functional group modification, among the binder property items included in the learning data.

[0065] The 7-day viscosity change rate has a tendency to have a large absolute value because the time that has elapsed since the preparation of the electrode mixture is long. Therefore, unlike the 1-day viscosity change rate model 300, the 7-day viscosity change rate model 310 may be configured to directly predict the 7-day viscosity change rate for all blends.

[0066] Returning to FIG. 3, in step S4, the control unit of the proposed device 100 learns a coating resistance model using the learning data stored in the memory unit. The coating resistance model is a machine learning model that predicts the coating resistance of an electrode produced using the electrode mixture. The control unit of the proposed device 100 stores the learned coating resistance model in the memory unit.

[0067] Fig. 6 is a diagram showing an example of a coating resistance model. As shown in Fig. 6, the coating resistance model 320 receives the material composition ratio, the binder mixing ratio, the binder properties, and the electrode information as input, and outputs the coating resistance. The structure of the coating resistance model 320 can be, for example, Gaussian process regression.

[0068] The binder properties input to the coating resistor model 320 are the intrinsic viscosity, elastic modulus, polymerization method, and presence or absence of functional group modification among the various binder property items included in the learning data. The electrode information input to the coating resistor model 320 is the density before the pressing process among the various electrode information items included in the learning data.

[0069] Returning to FIG. 3, in step S5, the control unit of the proposed device 100 learns a three-point bending strength model using the learning data stored in the memory unit. The three-point bending strength model is a machine learning model that predicts the three-point bending strength of an electrode produced using the electrode mixture. The control unit of the proposed device 100 stores the learned three-point bending strength model in the memory unit.

[0070] Fig. 7 is a diagram showing an example of a three-point bending strength model. As shown in Fig. 7, the three-point bending strength model 330 receives the material composition ratio, the binder mixing ratio, the binder properties, and the electrode information as input, and outputs the three-point bending strength. The structure of the three-point bending strength model 330 can be, for example, Gaussian process regression.

[0071] The binder properties input to the three-point bending strength model 330 are the intrinsic viscosity, elastic modulus, Mw / Mn, solution viscosity, modification amount, branching degree, abnormal bonding degree, and random rate among the binder property items included in the learning data. Also, the electrode information input to the three-point bending strength model 330 is the slurry application amount, density before the pressing process, and density after the pressing process among the electrode information items included in the learning data.

[0072] Returning to FIG. 3, in step S6, the control unit of the proposed device 100 learns a peel strength model using the learning data stored in the storage unit. The peel strength model is a machine learning model that predicts the peel strength of an electrode produced using the electrode mixture. The control unit of the proposed device 100 stores the learned peel strength model in the storage unit.

[0073] Fig. 8 is a diagram showing an example of a peel strength model. As shown in Fig. 8, the peel strength model 340 receives the material composition ratio, the binder mixing ratio, the binder properties, and the electrode information as input, and outputs the peel strength. The structure of the peel strength model 340 can be, for example, Gaussian process regression.

[0074] The binder properties input to the peel strength model 340 are the intrinsic viscosity, elastic modulus, Mw / Mn, solution viscosity, modification amount, branching degree, abnormal bonding degree, and random rate among the binder property items included in the learning data. Also, the electrode information input to the peel strength model 340 is the slurry application amount, density before the pressing process, and density after the pressing process among the electrode information items included in the learning data.

[0075] <Proposal method flow> 7 is a flowchart showing an example of the flow of the proposal method executed by the proposal device 100 in this embodiment. The proposal method is a method for proposing a blend of a mixture for an electrode by using a trained prediction model.

[0076] In step S11, the control unit of the proposal device 100 accepts input of condition information. The condition information is information indicating prerequisites when proposing a formulation of an electrode mixture. The condition information can also be said to be information indicating required specifications for an electrode mixture or an electrode. The condition information may be input, for example, in CSV (Comma-Separated Values) format or a data format of a desired spreadsheet software.

[0077] Fig. 10 is a diagram showing an example of condition information. As shown in Fig. 10, the condition information includes a blending condition, a fixed condition, and a target value.

[0078] The blending conditions include an active material ratio, a conductive additive ratio, a binder ratio, a fluororesin binder ratio, and a solids concentration. Each blending condition includes a minimum value, a maximum value, and an interval. The minimum value, the maximum value, and the interval are used to generate candidate blends for the electrode mixture.

[0079] The fixed conditions include the amount of slurry applied, the density after the pressing process, the density before the pressing process, and the initial viscosity. The fixed conditions are used to specify values ​​to be set for the explanatory variables of each prediction model.

[0080] The target values ​​include peel strength, three-point bending strength, viscosity change rate after 1 day, viscosity change rate after 7 days, and coating resistance. The target values ​​are used to specify the values ​​that the physical property values ​​predicted by each prediction model should satisfy.

[0081] Returning to FIG. 9, the explanation will be given. In step S12, the control unit of the proposal device 100 generates candidate formulations for the electrode mixture according to the condition information received in step S11. Specifically, the control unit of the proposal device 100 first generates options for the formulation ratio of each material according to the formulation conditions included in the condition information. For example, if the formulation conditions for the active material ratio are set to a minimum of 97, a maximum of 98, and an interval of 1, the options for the active material are 97wt% or 98wt%. Also, for example, if the formulation conditions for the conductive assistant ratio are set to a minimum of 1, a maximum of 3, and an interval of 1, the options for the conductive assistant are 1wt%, 2wt%, and 3wt%.

[0082] Next, the control unit of the proposed device 100 extracts combinations in which the sum of the compounding ratios of each material is 100wt% from among all combinations of the compounding ratios of each material. For example, a combination in which the compounding ratio of the active material is 97wt%, the compounding ratio of the conductive additive is 1wt%, and the compounding ratio of the binder is 2wt% is extracted because the sum of the compounding ratios is 100wt%. Also, for example, a combination in which the compounding ratio of the active material is 98wt%, the compounding ratio of the conductive additive is 2wt%, and the compounding ratio of the binder is 2wt% is not extracted because the sum of the compounding ratios is 102wt%.

[0083] Next, the control unit of the proposal device 100 generates all combinations of candidate binder options, fluororesin binder ratio options, and solids concentration options for each extracted formulation, thereby generating candidate formulations for the electrode mixture.

[0084] In step S13, the control unit of the proposal device 100 calculates the binder properties to be set as explanatory variables of each prediction model for each candidate blend generated in step S12. In this embodiment, since two types of binders are mixed, the binder properties are calculated by taking a weighted average of the basic properties of each binder according to the binder blending ratio. The basic properties are the physical properties of the binder alone. The basic properties may be measured in advance and stored in the memory unit.

[0085] In the case of a mixture in which one type of binder is used without mixing, the basic physical properties of the binder can be used as is. In the case of a mixture in which three or more types of binders are mixed, the weighted average can be calculated according to the binder mixing ratio, as in the case of mixing two types of binders.

[0086] In step S14, the control unit of the proposal device 100 reads out from the storage unit the trained one-day viscosity change rate model 300. Next, the control unit of the proposal device 100 predicts the one-day viscosity change rate for each candidate blend based on the trained one-day viscosity change rate model 300.

[0087] Specifically, the control unit of the proposing device 100 inputs the material composition ratio, binder mixing ratio, initial viscosity, solids concentration, and binder physical properties (intrinsic viscosity, elastic modulus, polymerization method, and presence or absence of functional group modification) for each candidate formulation into a classification model 301 of a one-day viscosity change rate model 300. The classification model 301 classifies each candidate formulation into a class of "300% or less" or a class of "301% or more" for the viscosity change rate after one day.

[0088] The control unit of the proposal device 100 outputs the class "D: 301%~" as the predicted result of the viscosity change rate after one day for a candidate formulation whose viscosity change rate after one day has been classified into the class "301% or more" by the classification model 301.

[0089] The control unit of the proposal device 100 inputs the material composition ratio, binder mixing ratio, initial viscosity, solid content concentration, and binder physical properties (intrinsic viscosity, elastic modulus, polymerization method, and presence or absence of functional group modification) for the candidate formulation classified into the class of "300% or less" viscosity change rate after one day by the classification model 301 into the regression model 302 of the viscosity change rate after one day model 300. The regression model 302 outputs a predicted value of the viscosity change rate after one day for each candidate formulation. Then, the control unit of the proposal device 100 classifies the predicted value of the viscosity change rate after one day output from the regression model 302 into one of the classes of "A: 0 to 120%, " "B: 121 to 150%, " "C: 151 to 300%, " and outputs it as the predicted result of the viscosity change rate after one day.

[0090] In step S15, the control unit of the proposal device 100 reads out from the storage unit the trained 7-day viscosity change rate model 310. Next, the control unit of the proposal device 100 predicts the 7-day viscosity change rate for each candidate blend based on the trained 7-day viscosity change rate model 310.

[0091] Specifically, the control unit of the proposal device 100 inputs the material composition ratio, binder mixing ratio, initial viscosity, solid content concentration, and binder physical properties (intrinsic viscosity, elastic modulus, polymerization method, and presence or absence of functional group modification) for each candidate formulation into the 7-day viscosity change rate model 310. The 7-day viscosity change rate model 310 outputs a predicted value of the viscosity change rate after 7 days for each candidate formulation. Then, the control unit of the proposal device 100 classifies the predicted value of the viscosity change rate after 7 days output from the 7-day viscosity change rate model 310 into one of "A: 0-120%", "B: 121-150%, "C: 151-300%, "D: 301% or more", and outputs it as a predicted result of the viscosity change rate after 7 days.

[0092] In step S16, the control unit of the proposal device 100 reads out from the memory unit the trained coating resistance model 320. Next, the control unit of the proposal device 100 predicts the coating resistance for each candidate blend based on the trained coating resistance model 320.

[0093] Specifically, the control unit of the proposal device 100 inputs the material composition ratio, binder mixing ratio, binder physical properties (intrinsic viscosity, elastic modulus, polymerization method, and presence or absence of functional group modification) and electrode information (density before the pressing process) for each candidate formulation to the coating resistance model 320. The coating resistance model 320 outputs a predicted value of coating resistance for each candidate formulation. Then, the control unit of the proposal device 100 outputs the predicted value of coating resistance output from the coating resistance model 320 as a predicted result of coating resistance.

[0094] In step S17, the control unit of the proposal device 100 reads out the trained three-point bending strength model 330 from the storage unit. Next, the control unit of the proposal device 100 predicts the three-point bending strength for each candidate blend based on the trained three-point bending strength model 330.

[0095] Specifically, the control unit of the proposed device 100 inputs the material composition ratio, binder mixing ratio, binder physical properties (intrinsic viscosity, elastic modulus, Mw / Mn, solution viscosity, modification amount, branching degree, abnormal bonding degree, and random rate) and electrode information (slurry application amount, density before the pressing process, and density after the pressing process) for each candidate formulation to the three-point bending strength model 330. The three-point bending strength model 330 outputs a predicted value of the three-point bending strength for each candidate formulation. Then, the control unit of the proposed device 100 outputs the predicted value of the three-point bending strength output from the three-point bending strength model 330 as a predicted result of the three-point bending strength.

[0096] In step S18, the control unit of the proposing device 100 reads out from the storage unit the trained peel strength model 340. Next, the control unit of the proposing device 100 predicts the peel strength for each candidate blend based on the trained peel strength model 340.

[0097] Specifically, the control unit of the proposed device 100 inputs the material composition ratio, binder mixing ratio, binder physical properties (intrinsic viscosity, elastic modulus, Mw / Mn, solution viscosity, modification amount, branching degree, abnormal bonding degree, and random rate) and electrode information (slurry application amount, density before the pressing process, and density after the pressing process) for each candidate formulation into the peel strength model 340. The peel strength model 340 outputs a predicted value of peel strength for each candidate formulation. Then, the control unit of the proposed device 100 outputs the predicted value of peel strength output from the peel strength model 340 as the predicted result of peel strength.

[0098] In step S19, the control unit of the proposal device 100 extracts a candidate formulation in which the physical property values ​​of the electrode mixture predicted in steps S14 to S18 satisfy the target values ​​specified in the condition information from among the candidate formulations.Then, the control unit of the proposal device 100 outputs proposal information including the extracted candidate formulations.The proposal information may be output in, for example, CSV format or a data format of a desired spreadsheet software.

[0099] FIG. 11 is a diagram showing an example of the proposed information. As shown in FIG. 11, the proposed information includes a material composition ratio, a binder mixing ratio, a viscosity change rate after 1 day, a viscosity change rate after 7 days, a coating resistance, a three-point bending strength, and a peel strength. The material composition ratio includes an active material ratio, a conductive additive ratio, and a binder ratio. The binder mixing ratio includes a type and ratio of a binder, and a type and ratio of a fluororesin binder. The viscosity change rate after 1 day includes a predicted value, a standard deviation, and a label indicating a class. The viscosity change rate after 7 days includes a predicted value, a standard deviation, and a label indicating a class. The coating resistance, the three-point bending strength, and the peel strength each include a predicted value and a standard deviation.

[0100] The user of the proposed device 100 can manufacture an electrode mixture that satisfies the required specifications based on the proposed information output by the proposed device 100. Specifically, the user of the proposed device 100 can manufacture an electrode mixture by mixing an active material, a conductive assistant, and a binder according to a desired formulation among the formulations of the electrode mixture included in the proposed information. The electrode mixture manufactured in this manner is predicted to have physical property values ​​that satisfy the target values, and therefore is highly likely to be an electrode mixture that satisfies the required specifications.

[0101] <Modification> In this embodiment, a configuration has been described in which the viscosity change rate after 1 day, the viscosity change rate after 7 days, the coating resistance, the three-point bending strength, and the peel strength are predicted using a trained model, but these physical property values ​​are only examples, and the physical property values ​​predicted by the proposed device 100 are not limited to these. This embodiment may be configured to predict any physical property value as long as it is a physical property value of an electrode mixture or an electrode using the electrode mixture. In addition, it is not limited to a configuration in which multiple physical property values ​​are predicted, and it may be configured to predict one physical property value.

[0102] <Summary> As described above, according to one embodiment of the present disclosure, it is possible to propose a formulation of an electrode mixture whose physical property values ​​satisfy the target values. For example, the proposal device 100 predicts physical property values ​​related to the electrode mixture based on the candidate formulations of the electrode mixture, and outputs the candidate formulations whose physical property values ​​satisfy the target values. Therefore, according to one embodiment of the present disclosure, it is possible to propose a formulation of an electrode mixture whose physical property values ​​satisfy the target values.

[0103] The electrode mixture is a mixture containing an active material, a conductive member, and a binder, and the binder may contain a binder containing a polymer such as a fluororesin. Therefore, according to one embodiment of the present disclosure, a formulation of an electrode mixture containing a binder containing a fluororesin, an active material, and a conductive member can be proposed.

[0104] The proposal device 100 may predict two or more physical property values ​​of the electrode mixture and output a candidate formulation in which all of the two or more physical property values ​​satisfy the target values. Therefore, according to one embodiment of the present disclosure, it is possible to propose a formulation of an electrode mixture in which multiple physical property values ​​simultaneously satisfy the target values.

[0105] A user of the proposed device 100 may manufacture an electrode mixture by mixing an active material, a conductive material, and a binder according to a candidate composition included in the proposal information output from the proposed device 100. Since the candidate composition included in the proposal information is predicted to have physical property values ​​that satisfy target values, an electrode mixture manufactured according to the candidate composition is considered to satisfy required specifications. Therefore, according to one embodiment of the present disclosure, an electrode mixture, an electrode, or a secondary battery that satisfies required specifications can be manufactured with a short lead time.

[0106] [supplement] Each function of the above-described embodiments can be realized by one or more processing circuits. Here, the term "processing circuit" in this specification includes a processor programmed to execute each function by software, such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) implemented by an electronic circuit, and an ASIC (Application Specific Integrated Circuit), DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array), conventional circuit module, and other devices designed to execute each function described above.

[0107] Although the embodiments have been described above, it will be understood that various changes in form and details are possible without departing from the spirit and scope of the claims. [Explanation of symbols]

[0108] 100 Proposed device

Claims

1. A proposal device having a control unit that proposes a blend of an electrode mixture, The control unit is generating candidate formulations for the electrode mixture; Predicting physical properties of an electrode manufactured using the electrode mixture based on the trained model and the candidate formulation; Outputting the candidate formulation in which the physical property value satisfies the target value; The electrode mixture is a mixture containing an active material, a conductive material, and a plurality of binders, The trained model includes, as explanatory variables, physical property values ​​obtained by weighting the physical property values ​​of the plurality of binders according to the blending ratio of the plurality of binders included in the candidate blend. Proposed device.

2. The trained model includes the candidate formulation and the manufacturing conditions of the electrode as explanatory variables. The proposal device according to claim 1 .

3. The binder comprises a polymer. The proposal device according to claim 1 or 2.

4. The polymer includes a fluororesin. The proposal device according to claim 3 .

5. The control unit is Predicting two or more of the physical properties; outputting the candidate formulation in which all of the two or more physical property values ​​satisfy the target values; The proposal device according to claim 1 or 2.

6. A control unit of a proposal device for proposing a blend of an electrode mixture, generating candidate formulations for the electrode mixture; Predicting physical properties of an electrode manufactured using the electrode mixture based on the trained model and the candidate formulation; outputting the candidate formulation in which the physical property value satisfies the target value; Execute the process, The electrode mixture is a mixture containing an active material, a conductive material, and a plurality of binders, The trained model includes, as explanatory variables, physical property values ​​obtained by weighting the physical property values ​​of the plurality of binders according to the blending ratio of the plurality of binders included in the candidate blend. Proposal method.

7. A method for producing an electrode mixture, comprising the step of mixing an active material, a conductive material, and a plurality of binders according to a candidate mixture output by the proposal method according to claim 6.

8. A control unit of a proposal device for proposing a blend of an electrode mixture, generating candidate formulations for the electrode mixture; Predicting physical properties of an electrode manufactured using the electrode mixture based on the trained model and the candidate formulation; outputting the candidate formulation in which the physical property value satisfies the target value; Execute the process, The electrode mixture is a mixture containing an active material, a conductive material, and a plurality of binders, The trained model includes, as explanatory variables, physical property values ​​obtained by weighting the physical property values ​​of the plurality of binders according to the blending ratio of the plurality of binders included in the candidate blend. program.

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