Support Methods

Abstract

To propose an optimal value of an explanatory variable.SOLUTION: A support method includes: acquiring a distribution parameter A and initial data B (Step S1); and determining a plurality of candidate values of an explanatory variable (Step S4). The support method includes: calculating distributions of the candidate values (Step S6); and calculating attribution probabilities based on the first distribution, for each of the candidate values (Step S10). The support method includes determining, from among the candidate values, a proposal value based on the distributions of the candidate values and probability values, and notifying a user of the proposal value (Step S12, S14).SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present disclosure relates to an assistance method. [Background technology]

[0002] In recent years, various experiments are conducted for the manufacture of objects, etc. In such experiments, observed values ​​are obtained through experiments under conditions of parameter values ​​determined by a user. Then, the parameter values ​​and observed values ​​are used as design values ​​to manufacture objects, etc. Japanese Patent No. 6919770 (Patent Document 1) discloses a technology for searching for optimal parameter values ​​based on Bayesian optimization and notifying the user of the parameters. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6919770 Summary of the Invention [Problem to be solved by the invention]

[0004] Hereinafter, the parameter values ​​are also referred to as "explanatory variables", and the observed values ​​are also referred to as "target variables". Generally, there is a realistic appropriate range for the explanatory variables. However, in the invention described in Patent Document 1, there are cases where explanatory variables outside the appropriate range are proposed. Therefore, even if a user performs an experiment with explanatory variables outside the appropriate range, the optimal target variable cannot be obtained.

[0005] The present disclosure has been made to solve the above-mentioned problems, and its purpose is to propose optimal values ​​of explanatory variables. [Means for solving the problem]

[0006] (1) A support method according to the present disclosure is a method for supporting a search for explanatory variables. The support method includes obtaining a plurality of values ​​to be used as explanatory variables. The support method includes obtaining distribution parameters defining a first distribution of the explanatory variables. The support method includes determining a plurality of candidate values ​​for the explanatory variables. The support method includes calculating a variance of a second distribution of each of the plurality of candidate values ​​based on the plurality of values. The support method includes calculating a probability value for each of the plurality of candidate values ​​based on the first distribution. The support method includes determining a proposed value from the plurality of candidate values ​​based on the variance and the probability value of each of the plurality of candidate values, and notifying a user of the proposed value.

[0007] (2) The support method according to (1), wherein there are a plurality of types of explanatory variables, and calculating the probability value based on the first distribution includes calculating the probability value of each of the plurality of candidate values ​​based on information indicating a correlation between the plurality of types of explanatory variables.

[0008] (3) The method according to (1) or (2), further comprising: calculating a Mahalanobis distance for each of the plurality of candidate values ​​based on the distribution parameter. Notifying the user of the proposed value includes determining the proposed value based on the variance, the probability value, and the Mahalanobis distance for each of the plurality of candidate values.

[0009] (4) The support method according to any one of (1) to (3), wherein the explanatory variables are variables used in battery experiments. The support method further includes updating a prediction model that outputs a value as a response variable by inputting the explanatory variables, based on the explanatory variables indicated by the proposed values ​​and response variables corresponding to the explanatory variables. The explanatory variables are variables related to battery materials. The response variables are variables related to battery characteristics. Effect of the Invention

[0010] According to the present disclosure, it is possible to propose optimal explanatory variable values. [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram showing a support device, a display device, a prediction device, and the like of the present embodiment. [Diagram 2] FIG. 13 is a diagram illustrating an example of distribution parameters. [Diagram 3] FIG. 11 is a diagram illustrating an example of initial data. [Figure 4] FIG. 2 is a functional block diagram of the support device. [Diagram 5] FIG. 13 is a diagram showing candidate value combinations. [Figure 6] FIG. 13 is a diagram illustrating correlation coefficients of a correlation matrix. [Figure 7] 4 is a flowchart showing a process flow of the support device of the first embodiment. [Figure 8] 13 is a flowchart showing a process of updating a prediction model. [Figure 9] FIG. 2 is a diagram for explaining an effect of the support device of the first embodiment. [Figure 10] FIG. 2 shows the positive and negative electrodes of a test battery. [Figure 11] FIG. 4 is a diagram illustrating a simulation result of the first embodiment. [Figure 12] FIG. 4 is a diagram illustrating a simulation result of the first embodiment. [Figure 13] 13 is a flowchart showing a process flow of the support device of the second embodiment. [Figure 14] 11A and 11B are diagrams for explaining advantageous effects obtained by changing the threshold value Eth. [Figure 15] FIG. 11 is a diagram showing a simulation result of the second embodiment. [Figure 16] FIG. 11 is a diagram showing a simulation result of the second embodiment. [Figure 17] FIG. 13 is a functional block diagram of a support device according to a third embodiment. [Figure 18] FIG. 1 is a diagram showing an example of a multivariate normal distribution when there are two explanatory variables. [Figure 19] FIG. 2 is a diagram for explaining a first objective variable and a second objective variable. [Figure 20] 13 is a flowchart showing a process of the support device of the third embodiment. [Figure 21] FIG. 13 is a diagram illustrating a simulation result of the third embodiment. [Figure 22] FIG. 13 is a diagram showing an example of a predetermined distribution when there is one type of explanatory variable. [Figure 23] FIG. 1 is a diagram for explaining conversion from a log-normal distribution to a normal distribution. [Figure 24] FIG. 1 is a diagram illustrating an example of a discrete probability distribution. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the drawings, the same or corresponding parts are designated by the same reference characters and their description will not be repeated.

[0013] [First embodiment] 1 is a diagram showing a support device 100, a display device 200, and a prediction device 300 of the present embodiment. The support device 100 is a device for supporting a search for explanatory variables used in various experiments. The experiment of the present disclosure is an experiment for manufacturing a battery.

[0014] In the present disclosure, the explanatory variables are variables related to the battery materials. In the present disclosure, the variables related to the battery materials are the concentrations of the battery additives C1 and C2. In the present disclosure, the objective variables are variables related to the battery characteristics. In the present disclosure, the battery characteristics are the battery resistance value (IV resistance value).

[0015] The support device 100 and the prediction device 300 are, for example, computers such as a PC (personal computer), a tablet, and a smartphone. The display device 200 is connected to the support device 100. The display device 200 is an example of a notification device that notifies (displays) a proposed value output from the support device 100. The "proposed value" is a value of an explanatory variable proposed to a user.

[0016] A user inputs initial data (see FIG. 3) and distribution parameters (see FIG. 2) to the support device 100. The support device 100 determines a proposed value by executing a calculation (see FIG. 2). The support device 100 then outputs the proposed value to the display device 200. The display device 200 displays the proposed value from the support device 100.

[0017] In the example of FIG. 1, the support device 100 calculates a% as the concentration of additive C1 and b% as the concentration of additive C2. The a% and b% correspond to "suggested values." The display device 200 displays information based on the suggested values ​​calculated by the support device 100. The information in the example of FIG. 1 is "Please conduct an experiment to measure the resistance value of a battery with additive C1...a% and additive C2...b%."

[0018] The user visually checks the information displayed on the display device 200 to understand the proposed concentration value of the additive. Then, the user performs an experiment to measure the resistance value of the battery at the proposed concentration value. The experiment in this embodiment is a DCIR (Direct Current Internal Resistance) measurement. Then, the user inputs a combination of the concentration value proposed by the support device 100 and the resistance value obtained by the experiment at that concentration value to the prediction device 300 as teacher data.

[0019] The prediction device 300 holds a prediction model 350. The prediction device 300 is a device that predicts a target value based on the prediction model 350 and a value input by a user. For example, the prediction device 300 predicts a response variable (the IV resistance value of a battery) from an explanatory variable (the concentration of additives C1 and C2 of a battery) input by a user based on the prediction model 350. This prediction allows the user to obtain the IV resistance value of a battery without conducting an experiment.

[0020] Furthermore, the prediction device 300 may predict explanatory variables (concentrations of additives C1 and C2 in the battery) from a response variable (IV resistance value of the battery) input by a user based on the prediction model 350. This prediction allows the user to obtain the concentrations of additives C1 and C2 in the battery that result in a desired IV resistance value.

[0021] The prediction device 300 can learn (update) the prediction model 350 based on the input teacher data. This update improves the prediction accuracy by the prediction device 300. Note that, although the support device 100 and the prediction device 300 are shown as separate configurations in FIG. 1, the support device 100 and the prediction device 300 may be integrated together.

[0022] The objectives of the assistance device 100 of the present disclosure include the following first and second objectives. First, the first objective will be described. As described in FIG. 1, the prediction device 300 can perform the above prediction. The first objective of this embodiment is to "improve the prediction accuracy of the prediction device 300."

[0023] Next, the second objective will be described. For example, a user may search for explanatory variables for obtaining a desired range of an objective variable in an experiment. More specifically, this case is, for example, a case where a user detects explanatory variables (concentrations of additives C1 and C2) for manufacturing a battery with an objective variable (IV resistance value) in a desired range. In this case, it is preferable for the user to obtain an optimal objective variable (desired by the user) with as few experiments as possible. The second objective of the support device 100 of the present disclosure is "to suggest to the user explanatory variables for obtaining an objective variable in a desired range with as few experiments as possible."

[0024] The support device of the first embodiment and the second embodiment described below is a device for mainly achieving a first objective, and the support device of the third embodiment is a device for mainly achieving a second objective.

[0025] The support device 100 includes a CPU (Central Processing Unit) 181, a memory 182, and an interface (I / F) 183. The CPU 181 executes various processes. The interface 183 is a device for communicating with an external device of the support device 100. The external device is, for example, a display device 200.

[0026] The ROM stores programs executed by the CPU 181. The RAM can temporarily store data generated by the execution of the programs in the CPU 181 and data input via the interface 183. The RAM can function as a temporary data memory used as a working area.

[0027] The program stored in ROM 162 may be stored in a recording medium and distributed as a program product. The recording medium is a non-transitory medium from which the program recorded in the recording medium can be read by a computer. Alternatively, the program may be provided by an information provider as a so-called product program that can be downloaded via the Internet or the like. The support device 100 reads the program provided from a storage medium or the Internet or the like. The support device 100 stores the read program in a predetermined storage area (e.g., ROM). The CPU 181 executes the program to perform a process of calculating a proposed value.

[0028] [Distribution parameters and initial data] Next, the distribution parameters and initial data shown in Fig. 1 will be described. First, the distribution parameters will be described. The distribution parameter A is a parameter determined by the user. The distribution parameter A is a parameter that defines the first distribution of each of M (M is an integer equal to or greater than 1) types of explanatory variables. In this embodiment, the first distribution is a normal distribution. The distribution parameter A is a parameter for defining the normal distribution, and the distribution parameter A is a mean μ and a standard deviation σ.

[0029] FIG. 2 is a diagram showing an example of the distribution parameter A. In this embodiment, the number of types of explanatory variables is four (M=4). The four explanatory variables are configured by the concentrations of four types of additives. The four types of additives in the example of FIG. 2 are vinylene carbonate (VC), lithium bisoxalateborate (LiBOB), additive A, and additive B. Additive A and additive B are PF bond-containing lithium salt additives. Additive A and additive B differ in their decomposition in the electrolyte and their film formation behavior on the electrode.

[0030] In the example of Figure 2, the mean μs and standard deviation σs of each of the four explanatory variables are specified. For example, the mean μ1 of VC is 0.85, and the standard deviation σ1 of VC is 0.32. In addition, the normal distribution specified by the distribution parameter A in Figure 2 is a "multivariate normal distribution."

[0031] FIG. 3 is a diagram showing an example of initial data B. The initial data B is composed of N (N is an integer equal to or greater than 1) "initial data combinations". In the example of FIG. 3, N=18. The initial data combination is information indicating a combination of M types of explanatory variables and objective variables corresponding to the M types of explanatory variables. The objective variables corresponding to the M types of explanatory variables are variables derived in advance by a user through an experiment using the M types of explanatory variables. The objective variables corresponding to the M types of explanatory variables may be variables predicted in advance by inputting the M types of explanatory variables to the prediction device 300.

[0032] In Fig. 3, the information surrounded by a thick line is one initial data combination. That is, the initial data in Fig. 3 includes 18 initial data combinations. In the example of Fig. 3, for example, the objective variable (IV resistance value) of the explanatory variables that VC, LiBOB, additive A, and additive B are 0% is specified to be 111.7 mΩ. The values ​​of the 72 (=18 x 4) explanatory variables in Fig. 3 correspond to the "multiple values" of the present disclosure.

[0033] [Functional block diagram of the support device] 4 is a functional block diagram of the support device 100. The support device 100 includes an acquisition unit 102, a candidate unit 104, a distribution unit 106, a distance unit 108, a probability unit 110, and a decision unit 112. A threshold unit 114 is indicated by a dashed line, and this threshold unit 114 will be described in the second embodiment.

[0034] The acquiring unit 102 acquires distribution parameter A (see FIG. 2) and initial data B (see FIG. 3) input by a user. The distribution parameter A acquired by the acquiring unit 102 is output to the candidate unit 104 and the distance unit 108.

[0035] The candidate unit 104 calculates L (L is an integer equal to or greater than 2) candidate value combinations of M types of explanatory variables (four types of explanatory variables in this embodiment). Here, a "candidate value combination" is a combination of candidate values ​​of the M types of explanatory variables. Also, a "candidate value" is a value that is a candidate for the proposed value (see FIG. 1). In this embodiment, L=1000.

[0036] FIG. 5 is a diagram for explaining candidate value combinations. In the example of FIG. 5, the numbers of the candidate value combinations and four types of explanatory variables are shown. As described above, since L=1000, 1 to 1000 are specified as the numbers of the candidate value combinations. "..." in FIG. 5 indicates a candidate value calculated by the candidate unit 104. Also, information surrounded by a thick line in the example of FIG. 5 is one candidate value combination. That is, the candidate unit 104 calculates 1000 candidate value combinations and calculates 4000 (=4×1000) candidate values.

[0037] The candidate unit 104 calculates candidate values ​​using the distribution parameters. For example, the candidate unit 104 calculates the candidate values ​​by uniform random numbers in a numerical range based on the average μs and standard deviation σs corresponding to the explanatory variables in the distribution parameters. The numerical range is, for example, a range of μs-3σs or more and μs+3σs or less. For example, the candidate unit 104 calculates the explanatory variables for VC (see FIG. 2) by uniform random numbers in a numerical range of μ1-3σ1 or more and μ1+3σ1 or less. In this embodiment, the 1000 candidate value combinations S are output to the distribution unit 106, the distance unit 108, and the probability unit 110.

[0038] The distribution unit 106 calculates the variance σ of the second distribution of each of the L candidate value combinations from the candidate unit 104 based on the initial data B (see FIG. 3) from the acquisition unit 102. In this embodiment, the second distribution is a normal distribution. The variance σ is calculated for each of 1000 (L) candidate value combinations. The process of the distribution unit 106 will be described in detail below.

[0039] Furthermore, the dispersion unit 106 calculates the variance σ of the second distribution using a Gaussian process regression model. First, the dispersion unit 106 updates (learns) the Gaussian process regression model using initial data B (both explanatory variables and target variables). Then, the dispersion unit 106 calculates the variance σ using a kernel function in the updated Gaussian process regression model. The kernel function k(x n , x m ) is expressed, for example, by the following formula (1).

[0040]

number

[0041] Here, x on the left side of equation (1) n and x m indicates a vector consisting of four explanatory variables. Also, n and m indicate variables that satisfy 1≦n≦N (=18) and 1≦m≦N, respectively.

[0042] γ on the right side of equation (1) is a predetermined real number. γ is, for example, a real number in the range of 0<γ≦2. In this embodiment, γ=2. t on the right side of equation (1) indicates that the matrix is ​​a transposed matrix. σ(n, m) on the right side of equation (1) indicates Kronecker delta.

[0043] The θ0, θ1, θ2, and θ3 on the right side of the formula (1) are hyperparameters to be updated (optimized). The distribution unit 106 adjusts the hyperparameters θ0, θ1, θ2, and θ3 so that the probability expressed on the left side of the following formula (2) can best represent the initial data B.

[0044]

number

[0045] Here, X on the left side of equation (2) is x 1,..., x N In equation (2), y represents a vector consisting of the values ​​of N objective variables (IV resistance values) included in the initial data B. K represents a matrix whose (n, m) components are equation (1).

[0046] Also, one candidate value combination is x * When this is expressed as above, the distribution unit 106 calculates the distribution σ by the following formula (3).

[0047]

number

[0048] Here, K on the right side of equation (3) is the same as K in equation (2). * is expressed by equation (4).

[0049] As described above, the distribution unit 106 updates the Gaussian process regression model based on the formulas (1) and (2). Then, the distribution unit 106 calculates the variance σ for each of the L candidate value combinations using the updated Gaussian process regression model and the formulas (3) and (4). The calculated variance σ is output to the determination unit 112.

[0050] Next, a description will be given of the processing of the distance unit 108. The distance unit 108 calculates the Mahalanobis distance E for each combination of L candidate values ​​based on the distribution parameter A. In a certain aspect, the Mahalanobis distance E is the distance from the center of gravity of a data group consisting of the L candidate values.

[0051] The processing of the distance unit 108 will be described in detail below. First, the distance unit 108 sets a correlation matrix C between M explanatory variables defined by the distribution parameter A by a predetermined calculation. A method for calculating the correlation coefficient r constituting the correlation matrix C will be described below. The distance unit 108 calculates the correlation coefficient r by substituting the numerical value included in the initial data B into the following formula (5).

[0052]

number

[0053] Here, j and k in formula (5) respectively represent two explanatory variables for calculating the correlation coefficient r. The two explanatory variables are explanatory variables included in the initial data, such as the concentration of VC and the concentration of LiBOB. In addition, S in formula (5) jk indicates the covariance between explanatory variable j and explanatory variable k. j indicates the standard deviation of explanatory variable j. k indicates the standard deviation of explanatory variable k. N is the number of initial data combinations as described above. i is a variable and corresponds to the number in FIG. 3. j i indicates the value of the i-th explanatory variable j. k iindicates the numerical value of the i-th explanatory variable k. javg indicates the average value of N explanatory variables j. kavg indicates the average value of N explanatory variables k. javg indicates the average value of N explanatory variables j. The correlation matrix C corresponds to "information indicating correlations between multiple types of explanatory variables" in this disclosure.

[0054] As a modified example, the distance unit 108 may calculate the correlation coefficient r by substituting the numerical value of the distribution parameter A (see FIG. 2) into the formula (5). The distance unit 108 may substitute the average μs of two explanatory variables of the distribution parameter A into javg and kavg of the formula (5), respectively. Furthermore, the distance unit 108 may calculate the standard deviation σs of the two explanatory variables of the distribution parameter A into S of the formula (5), respectively. j and S k may be substituted for

[0055] FIG. 6 is a diagram showing correlation coefficients of the correlation matrix C. In the example of FIG. 6, for example, the correlation coefficient between VC and LiBOB is shown to be 0.44. The correlation matrix C is an M×M (4×4 in this embodiment) square matrix. Note that the correlation matrix C may be input by the user without being calculated.

[0056] If the distance unit 108 determines that there is no correlation between the explanatory variables, the correlation matrix C is set to a unit matrix. Various methods are applied to determine the presence or absence of correlation. For example, if the total value of the correlation coefficients is less than a predetermined value, the distance unit 108 determines that there is no correlation. Also, a configuration may be adopted in which the user can input information indicating the presence or absence of correlation to the support device 100. In the case where such a configuration is adopted, if the user inputs information indicating that there is no correlation, the distance unit 108 determines that there is no correlation.

[0057] The distance unit 108 calculates the Mahalanobis distance E for each of the L candidate combinations using the following equations (6) and (7).

[0058] Σ=DCD (6)

[0059]

number

[0060] Σ on the left side of equation (6) indicates a covariance matrix set for the explanatory variables. Matrix D on the right side of equation (6) is a diagonal matrix with σ1 to σ4 in FIG. 2 as diagonal components. Vector U on the right side of equation (7) is a vector with μ1 to μ4 in FIG. 2 as components. Vector X on the right side of equation (7) is a vector with four explanatory variables defined in the candidate combination as components. The Mahalanobis distance E calculated by distance unit 108 is output to probability unit 110 and decision unit 112.

[0061] Next, the processing of the probability unit 110 will be described. The probability unit 110 determines an attribute probability P for each of the L candidate value combinations. Here, the attribute probability P is a probability value based on the above-mentioned first distribution. In other words, the attribute probability P is a degree of closeness between the "centre of gravity of a data group assumed to be in a normal distribution (first distribution) defined by the distribution parameter A in FIG. 2" and the "candidate value combination corresponding to the attribute probability P." The attribute probability P corresponds to the "probability value" of the present disclosure. The processing of the probability unit 110 will be described in detail below.

[0062] In this embodiment, it is assumed that the explanatory variables follow a normal distribution (see FIG. 2). In this case, the Mahalanobis distance follows a chi-square distribution represented by the probability density function f(z) of the following formula (8). In addition, the probability unit 110 calculates the attribution probability P by the following formula (9).

[0063]

number

[0064] Here, γ() in equation (8) is a predetermined gamma function, and k in equation (8) is the degree of freedom. The probability value P calculated by the probability unit 110 is input to the determination unit 112.

[0065] As described above, the determination unit 112 receives the variance σ of each of the 1,000 candidate combinations from the dispersion unit 106, the Mahalanobis distance E of each of the 1,000 candidate combinations from the distance unit 108, and the membership probability P of each of the 1,000 candidate combinations from the probability unit 110.

[0066] The determining unit 112 calculates an evaluation value V for each of the 1000 candidate combinations based on the following formula (10).

[0067] Evaluation value V=σ a ×E b ×P c (10) Here, a, b, and c in formula (10) are predetermined real numbers. a, b, and c are, for example, real numbers in the range of -2 or more and 2 or less. In this embodiment, a, b, and c are 1. Therefore, the determination unit 112 calculates a candidate combination evaluation value V by multiplying the variance σ, the Mahalanobis distance E, and the attribution probability P calculated for one candidate combination. Then, the determination unit 112 calculates the evaluation value V of each of the 1000 candidate combinations (all evaluation values ​​V of the 1000 candidate combinations).

[0068] Then, the determination unit 112 determines the candidate combination with the maximum evaluation value among the evaluation values ​​for each of the 1000 candidate combinations as the proposed candidate value combination. The determination unit 112 notifies the user of the proposed candidate value combination (see the explanation of the display device 200 in FIG. 1). The proposed candidate value combination corresponds to the "proposed value" in the present disclosure.

[0069] [Support device processing flow] Fig. 7 is a flowchart showing the flow of processing of the support device 100. The processing of Fig. 7 is executed at predetermined intervals (for example, every second).

[0070] First, in step S1, the support device 100 judges whether or not the distribution parameter A and the initial data B have been input to the support device 100. If the distribution parameter A and the initial data B have not been input (NO in step S1), the process ends. If the distribution parameter A and the initial data B have been input (YES in step S1), the process proceeds to step S2.

[0071] Next, in step S2, the support device 100 learns a Gaussian process regression model using the initial data B (see the above formulas (1) and (2)). Next, in step S4, the support device 100 calculates L candidate value combinations (candidate values) based on the distribution parameter A.

[0072] Next, in step S6, the support device 100 calculates a variance σ for each of the L candidate value combinations based on the learned Gaussian process regression model (see formulas (3) and (4) above). Next, in step S8, the support device 100 calculates a Mahalanobis distance E for each of the L candidate value combinations based on the distribution parameter A (see formulas (5) to (7) above). Next, in step S10, the support device 100 calculates an attribution probability P for each of the L candidate value combinations based on the distribution parameter A (see formulas (8) and (9) above).

[0073] Next, in step S12, the support device 100 calculates an evaluation value V for each of the L candidate value combinations (see formula (10) above). Next, in step S14, the support device 100 outputs the candidate combination (candidate value) with the maximum evaluation value as a proposed candidate value combination (proposal value). The process in which the support device 100 outputs the proposed candidate value combination is also referred to as a "proposal process."

[0074] Also, the user may wish to obtain multiple proposed candidate value combinations. In this case, a DCIR measurement is performed using the proposed candidate value combination to measure an IV resistance value. Then, the user adds the proposed candidate value combination and the resistance value to initial data, and causes the support device 100 to execute a proposal process based on the initial data. The user can obtain multiple proposed candidate value combinations by causing the support device 100 to execute such a proposal process multiple times.

[0075] Fig. 8 is a flowchart showing updating of a prediction model. In step S20, the prediction device 300 updates the prediction model 350 based on the teacher data (proposed candidate value combinations and objective variables corresponding to the proposed candidate value combinations) input by the user. The processing in Fig. 8 may be included in the support method of this embodiment.

[0076] [Actions and Effects of the Support Device of this Embodiment] The support device 100 of this embodiment calculates an evaluation value V for each of the L candidate combinations. The evaluation value V is calculated using the variance σ and the attribution probability P. As described above, the variance σ is a value indicating the degree of variation in the normal distribution of the candidate value combination. Therefore, a candidate value combination with a large variance σ is a combination of explanatory variables that can be expected to produce experimental results (objective variables) that cover a wide range. Therefore, a configuration that calculates an evaluation value using only the variance σ (hereinafter referred to as the "comparative example configuration") is conceivable.

[0077] However, in such a configuration, a candidate value combination with an extremely large variance σ may be selected as the proposed candidate value combination, in which case a combination outside the appropriate range (unrealistic) may be selected.

[0078] Therefore, the support device 100 of the present embodiment determines the proposed value using not only the variance σ but also the attribution probability P. Specifically, the support device 100 calculates the evaluation value V based on the variance σ and the attribution probability P. As described above, the attribution probability P is a degree indicating the closeness to the center of gravity of the data group assumed to be in the normal distribution (first distribution) defined by the distribution parameter A in FIG. 2. Therefore, a candidate value combination with a large attribution probability P is a candidate value combination with a high probability in the normal distribution defined by the user, and therefore is a candidate value combination that tends to fall within the appropriate range. Therefore, a candidate value combination with a large evaluation value V calculated using the variance σ and the attribution probability P is a candidate value combination in a wide range while tending to fall within the appropriate range. Therefore, a candidate value combination with a large evaluation value V (proposed candidate value combination) is an optimal combination of explanatory variables.

[0079] Fig. 9 is a diagram for explaining the effect of the support device 100 of this embodiment. In the example of Fig. 9, the number M of types of explanatory variables is 3, that is, the diagram shows the first explanatory variable, the second explanatory variable, and the third explanatory variable.

[0080] In the example of Fig. 9, the proposed candidate value combinations by the configuration of the comparative example described above are shown by black circles, and the proposed candidate value combinations by the support device 100 of this embodiment are shown by white circles. Also, the hatched region R is a region defined by the above-mentioned multivariate normal distribution. Candidate value combinations with a large attribution probability P tend to be close to the center of gravity (center) of the region α.

[0081] In the configuration of the comparative example described above, a combination outside the appropriate range (unrealistic) may be selected, such as the proposed candidate value combination indicated by the black circle in Fig. 9. On the other hand, according to the support device 100 of the present embodiment, the proposed candidate value combination is a candidate value combination in a wide range while falling within the region α defined by the multivariate normal distribution (see the white circle in Fig. 9). Therefore, according to the support device 100 of the present embodiment, it is possible to propose optimal and scattered explanatory variable values ​​to the user.

[0082] In addition, in this embodiment, there are multiple types of explanatory variables (four types in this embodiment). The probability unit 110 performs calculations based on information indicating the correlation between multiple types of explanatory variables (the correlation matrix in FIG. 6 in this embodiment). Therefore, even if there are multiple types of explanatory variables, it is possible to calculate the attribution probability P that reflects the multiple types of explanatory variables.

[0083] In this embodiment, the determination unit 112 multiplies not only the variance σ and the membership probability P but also the Mahalanobis distance E to calculate the evaluation value V. As described above, the Mahalanobis distance E is the distance from the center of gravity of the data group of L candidate values. Therefore, the support device 100 of this embodiment can propose to the user a candidate value combination that is appropriately scattered (away from the center of gravity) while falling within an appropriate range.

[0084] 8 and the like, the prediction device 300 updates the prediction model 350 based on the teacher data (proposed candidate value combinations and objective variables corresponding to the proposed candidate value combinations) input by the user. Therefore, the support device 100 can also support the prediction device 300 in improving the prediction accuracy.

[0085] [Simulation results] Next, a simulation result showing that the support device 100 of this embodiment has an advantageous effect will be described. First, a test battery used in the simulation will be described. The positive electrode active material of the positive electrode of the test battery is Li(NiCoMn)O2. The conductive material of the positive electrode is acetylene black. The binder of the positive electrode is polyvinylidene difluoride (PVdF). The dispersion medium of the positive electrode is N-methyl-2-pyrrolidone (NMP). The positive electrode substrate of the positive electrode is an Al foil. A positive electrode slurry with a mass blending ratio of "positive electrode active material / conductive material / binder = 92.0 / 7.1 / 0.9" was prepared as the positive electrode solid content, and the positive electrode active material layer was formed by applying it to the surface (both front and back sides) of the positive electrode substrate and drying it, and the positive electrode active material layer was compressed. A positive electrode raw sheet was produced as described above.

[0086] Next, the negative electrode of the test battery will be described. The negative electrode active material of the negative electrode is graphite. The negative electrode binder is CMC (carboxymethyl cellulose) and SBR (styrene butadiene rubber). The negative electrode dispersion medium is water. The negative electrode substrate of the negative electrode is Cu foil. The mass blending ratio of the solid content of the negative electrode is "negative electrode active material / CMC / SBR=98.3 / 1.0 / 0.7". The negative electrode slurry was applied to the surface (both the front and back) of the negative electrode substrate and dried to form a negative electrode active material layer. The negative electrode active material layer was further compressed. The above steps produced a negative electrode roll.

[0087] Next, the electrolyte of the test battery will be explained. The volume ratio of the electrolyte solvents is EC / DMC / EMC=3 / 3 / 4. The supporting electrolyte is LiPF6 (lithium hexafluorophosphate) with a concentration of 0.88 mol / L. The additives of the electrolyte are VC, LiBOB, additive A, and additive B shown in Figure 2. The component ratio of the solvent is based on the volume at 25°C and 1 atm.

[0088] In addition, a member having a multi-layer structure including a PP (polypropylene) layer and a PE (polyethylene) layer was used as the separator of the test battery, and the separator of the test battery was manufactured by cutting the member into a predetermined planar size.

[0089] 10 is a diagram showing the positive electrode 210 and the negative electrode 220 used in the simulation. The positive electrode raw sheet is cut to produce the positive electrode 210. The negative electrode raw sheet is cut to produce the negative electrode 220.

[0090] The positive electrode 210 includes an Al tab 211 and a positive electrode active material layer 212. The positive electrode active material layer 212 has planar dimensions of 43 mm long by 29 mm wide. The Al tab 211 is ultrasonically bonded to the positive electrode active material layer 212. The Al tab 211 has planar dimensions of 7 mm long by 10 mm wide.

[0091] The negative electrode 220 includes a Cu tab 221 and a negative electrode active material layer 222. The negative electrode active material layer 222 has a planar dimension of 46 mm long by 30 mm wide. The Cu tab 221 is ultrasonically bonded to the positive electrode active material layer 212. The Cu tab 221 has a planar dimension of 8 mm long by 10 mm wide. Details of the manufacture of the positive electrode and negative electrode as shown in FIG. 10 are disclosed, for example, in Japanese Patent Publication No. 7153701.

[0092] Furthermore, an electrode body was formed by stacking the positive electrode, separator, and negative electrode in this order. The electrode body was inserted into an exterior body made of aluminum laminate film in a dry environment to form a member. The member was then dried at 100 degrees in a vacuum for 3 hours. The member was then cooled to 25 degrees, after which the above electrolyte was injected and the exterior body was welded and sealed. This produced a test battery.

[0093] The following aging process was performed on the test battery: the test battery was charged to 4.1 V at 400 mA, then discharged to 2.5 V at 400 mA, and held in an environment at 60°C for 12 hours. DCIR measurements were then performed by repeatedly charging and discharging the aged test battery to 3.7 V at 200 mA at 25°C and discharging it.

[0094] Fig. 11 is a diagram showing the simulation results. In the example of Fig. 11, the results of five comparative examples and the results of five examples are shown. In Fig. 11, the concentration of VC, the concentration of LiBOB, the concentration of additive A, the concentration of additive B, the variance σ, the attribution probability P, the Mahalanobis distance E, the evaluation value V, and the IV resistance value are shown for these 10 results.

[0095] First, an embodiment will be described. As described above, the support device 100 determines a proposed candidate value combination. Then, the user performs the above-mentioned DCIR measurement with the values ​​of multiple explanatory variables defined in the proposed candidate value combination to measure the IV resistance value (objective variable). Then, the above-mentioned proposal process is repeated four times to obtain the results of five embodiments.

[0096] Next, a comparative example will be described. In the comparative example, the user checks the initial data and determines five combinations of explanatory variables according to the user's intuition. Here, it is assumed that the user determines the five combinations of explanatory variables described in the comparative example of FIG. 11. It is also assumed that the variance σ, the attribution probability P, the Mahalanobis distance E, the evaluation value V, and the IV resistance value are calculated for the five combinations of explanatory variables in the comparative example.

[0097] As is clear from the description of Fig. 11, the variance σ of the embodiment is larger than the variance σ of the comparative example. Therefore, the degree of dispersion of the candidate value combination is larger in the embodiment. Therefore, the user can perform more efficient experiments, and as a result, the learning effect of the prediction model 350 (see Fig. 1) can be improved.

[0098] Next, a simulation result using root mean squared error (RMSE) for the accuracy of the prediction model 350 will be described. A method for calculating RMSE will be described below. Five sets of experimental data (combinations of four types of explanatory variables and a target variable) of the embodiment shown in FIG. 11 are set as a data set DS1 of the embodiment. Three sets of experimental data randomly extracted from the data set DS1 are combined with the initial data (see FIG. 3) to generate a learning data set Tr1 of the prediction model 350. The remaining two sets of experimental data of the data set DS1 are set as a data set Val for evaluating the prediction model 350.

[0099] A prediction model 350 was trained using a learning data set Tr1. In addition, in the training, explanatory variables were selected using a stepwise method. The trained prediction model 350 was used to predict a response variable. Then, the difference between the predicted response variable and the response variable measured by an experiment was calculated as RMSE. Such RMSE calculation was performed three times to calculate three RMSEs, and the average value of the three RMSEs was calculated. Similarly, the average value of the three RMSEs was calculated using the above-mentioned RMSE calculation method using five sets of experimental data of the comparative example shown in FIG. 11.

[0100] Fig. 12 is a diagram showing the average value of RMSE of the comparative example and the average value of RMSE of the example. In the example of Fig. 12, the average value of RMSE of the example is shown when the average value of RMSE of the comparative example is normalized to be 1. As is clear from Fig. 12, the combination of experimental variables proposed by the support device 100 of this embodiment can improve the prediction accuracy of the prediction model 350.

[0101] As a modification of the first embodiment, the evaluation value V may be calculated without using the Mahalanobis distance E. For example, the evaluation value V may be calculated by multiplying the variance σ and the membership probability P.

[0102] [Second embodiment] In the first embodiment, the configuration in which the evaluation value V is calculated using the Mahalanobis distance E has been described. In the second embodiment, the configuration in which the Mahalanobis distance E is used by another method will be described. The support device 100A of the second embodiment will be described with reference to FIG. 4.

[0103] The support device 100A of the second embodiment includes a threshold unit 114 indicated by a dashed line in Fig. 4. The threshold unit 114 changes a threshold Eth of the Mahalanobis distance E, and outputs the changed threshold Eth to the determination unit 112. The threshold unit 114 randomly changes the threshold Eth. For example, the threshold unit 114 holds a probability density function for the threshold Eth, which is a normal distribution, and determines the threshold Eth based on the probability density function.

[0104] Fig. 13 is a flowchart showing the flow of the process of the support device 100A of the second embodiment. In Fig. 13, step S12 and step S14 in Fig. 7 are replaced with step S12A and step S14A, and step S13A is added.

[0105] In step S12A, the determining unit 112 calculates an evaluation value V for each of the L candidate value combinations by the following equation (11).

[0106] V = σ × P (11) Next, in step S13A, the threshold unit 114 determines a threshold Eth based on a threshold probability density function, and outputs the determined threshold Eth to the determination unit 112. Then, in step S14A, the determination unit 112 selects a candidate value combination having a Mahalanobis distance greater than the threshold Eth determined in step S13A and a maximum evaluation value from among the L candidate value combinations. Then, the determination unit 112 outputs the selected candidate value combination as a proposed candidate value combination. Note that the maximum evaluation value is an example of the matter of "the evaluation value satisfies a criterion" in the present disclosure.

[0107] As described above, the support device 100A changes the threshold value Eth every time the above proposal process is executed. Fig. 14 is a diagram for explaining an advantageous effect of changing the threshold value Eth. Fig. 14 shows a case where the number of types of explanatory variables is two, that is, the explanatory variables are a first explanatory variable and a second explanatory variable. Fig. 14 also shows a contour corresponding to the threshold value Eth as a circle. Fig. 14 also shows the determined proposal candidate value combination G.

[0108] Fig. 14(A) is a diagram showing a case where a threshold value Eth for determining a first proposed candidate value combination is determined as 19. Fig. 14(B) is a diagram showing a case where a threshold value Eth for determining a second proposed candidate value combination is determined as 30. Fig. 14(C) is a diagram showing a case where a threshold value Eth for determining a third proposed candidate value combination is determined as 24.

[0109] 14(A) to 14(C), a proposal candidate value combination whose Mahalanobis distance E is greater than the threshold Eth is located outside the contour. Also, since the attribute probability P of the proposal candidate value combination is large, the proposal candidate value combination tends to be located near the contour (threshold Eth).

[0110] As shown in Fig. 14, the threshold value Eth is changed for each proposal process, so that the distance from the center of gravity of the proposed candidate value combination can be changed. As a result, the degree of dispersion of the proposed candidate value combination can be increased. The range of the threshold value Eth may be determined. This range is, for example, 10 or more and 30 or less.

[0111] Fig. 15 is a diagram showing the results of a simulation of this embodiment. Fig. 15 shows the results of the same simulation as that described in Fig. 11. As is clear from Fig. 11 and Fig. 15, in the second embodiment, the variance σ and the evaluation value V are greater than those in the first embodiment.

[0112] Fig. 16 is a diagram showing the RMSE of this embodiment. Fig. 16 shows the result of performing the same simulation as the simulation described in Fig. 12. As is clear from Fig. 12 and Fig. 16, the RMSE of the second embodiment is improved compared to the first embodiment.

[0113] As a modification of the second embodiment, the threshold value Eth may be a fixed value. For example, the threshold value Eth may be a fixed value of 5 or more. The threshold value Eth may be a fixed value of 40 or less. The threshold value Eth may be a fixed value of 10 or more and 30 or less.

[0114] <Third embodiment> In the first and second embodiments, the candidate unit 104 (see FIG. 4) is configured to calculate the candidate value by uniform random numbers. The method of determining the candidate value by the candidate unit 104 in the third embodiment is different from the method of determining the candidate value in the first and second embodiments.

[0115] Fig. 17 is a functional block diagram of a support device 100B according to the third embodiment. As shown in Fig. 17, in addition to a distribution parameter A and initial data B, a desired range C, which will be described later, is input to the support device 100B.

[0116] Next, a process of the candidate unit 104B will be described. The candidate unit 104B determines L candidate values ​​based on a probability value defined by a probability density function g(x) shown in the following equation (12).

[0117]

number

[0118] Here, Σ in formula (12) is shown in formula (6) above, and E is the Mahalanobis distance. FIG. 18 is a diagram showing an example of a multivariate normal distribution when there are two explanatory variables. This multivariate normal distribution is a distribution showing the probability density function g(x) of formula (12). FIG. 18(A) is a diagram showing a case where there is no correlation between explanatory variables, and FIG. 18(B) is a diagram showing a case where there is correlation between explanatory variables. The Z axis indicates the determination probability Q of the candidate value. The determination probability Q corresponds to the "determination probability of explanatory variables" in this disclosure. The L candidate value combinations determined by the candidate unit 104B are output to the distribution unit 106B.

[0119] Next, the desired range C will be described. As described above, the desired range C is the range of the objective variable desired by the user. In this embodiment, the desired range is defined for the IV resistance value. The desired range is, for example, a range of 106 mΩ to 108 mΩ. Initial data B and the desired range C are input to the dispersion unit 106B. The desired range C corresponds to the "range of the objective variable".

[0120] The dispersion unit 106B calculates the dispersion σ and the average value μ for each of the L candidate value combinations. First, the dispersion unit 106B defines a first objective variable and a second objective variable. Fig. 19 is a diagram for explaining the first objective variable and the second objective variable.

[0121] In the example of Fig. 19, an IV resistance value is shown as a first objective variable. In addition to the first objective variable, a second objective variable is also shown in the example of Fig. 19. The second objective variable is determined, for example, by the following formula (13). Second objective variable = log(first objective variable - median of desired range) (13) The median of the desired range in formula (13) is 107. The distribution unit 106B calculates the second objective variable for each of the N pieces of initial data, for example, by using formula (13).

[0122] The dispersion unit 106B updates the Gaussian process regression model using the above formulas (1) and (2). However, the objective variable (first objective variable or second objective variable) to which the desired range is applied is input to "y" in formula (2). Then, the dispersion unit 106B calculates the variance σ using the above formulas (3) and (4).

[0123] Furthermore, distribution section 106B calculates the average μ of the second distribution for each candidate value combination by the following equation (14).

[0124]

number

[0125] The average μ and variance σ for each candidate value combination calculated by the distribution unit 106B are input to the determination unit 112. The determination unit 112 calculates an evaluation value V based on the difference between the average μ and the variance σ. Specifically, the determination unit 112 calculates the evaluation value V based on the following formula (15). Note that in this embodiment, the above formula (10) for calculating the evaluation value V is not used.

[0126] Evaluation value V = μ-σ (15) Here, since a candidate point with a small second objective variable is preferable, a candidate point with a small evaluation value V in formula (15) is preferable. The determination unit 112 determines the candidate value combination with the smallest evaluation value V from among the L candidate value combinations as the proposed candidate value combination. Then, the determination unit 112 notifies the user of the proposed candidate value combination.

[0127] 20 is a flowchart showing the processing of the support device 100B of the third embodiment. First, in step S1C, the support device 100B judges whether the distribution parameter A, the initial data B, and the desired range C have been input to the support device 100. If the distribution parameter A, the initial data B, and the desired range C have not been input (NO in step S1C), the processing ends. If the distribution parameter A, the initial data B, and the desired range C have been input (YES in step S1C), the processing proceeds to step S2C.

[0128] Next, in step S2C, the assistance device 100 learns a Gaussian process regression model using the initial data B and the desired range C (see the above formulas (1) and (2)). As described above, the second objective variable is input to "y" in formula (2).

[0129] Next, in step S4C, the support device 100 determines L candidate value combinations based on a probability density function based on the predetermined distribution (multivariate normal distribution) of the above formula (12). Next, in step S6C, the support device 100B calculates the variance σ and the mean μ for each of the L candidate value combinations based on formulas (3), (4), and (14). Next, in step S30C, the support device 100B executes a user notification process.

[0130] Step S30C includes step S12C and step S14C. In step S12C, the support device 100B calculates an evaluation value V for each of the L candidate value combinations by the above formula (15). Next, in step S14C, the support device 100B selects a candidate value combination having the smallest evaluation value V from the L candidate value combinations. Then, the determination unit 112 outputs the selected candidate value combination as a proposed candidate value combination.

[0131] Fig. 21 shows the results of a simulation of the third embodiment. In the example of Fig. 21, the results of a comparative example and an example are shown. The comparative example is an example in which the candidate values ​​are determined by the uniform random numbers described above, and the example is an example in which the candidate values ​​are determined by the probability density function g(x) of equation (12).

[0132] As described above, the user performs an experiment using the proposed value proposed by the support device. The number of experiments in FIG. 21 is the number of experiments performed until the first objective variable falls within the desired range C. In the example of FIG. 21, the number of experiments in the comparative example is 18, whereas the number of experiments in the example is 16. Therefore, the number of experiments performed until the first objective variable falls within the desired range C is less in the example than in the comparative example, and therefore the burden of experimentation on the user can be reduced. Note that, in the example of FIG. 21, it is shown that the attribution probability P in the example is higher than the attribution probability P in the comparative example.

[0133] As described above, the support device 100B of the third embodiment acquires distribution parameters (see FIG. 2) from a user. The predetermined distribution (multivariate normal distribution) indicated by the distribution parameters specifies the determination probability Q of the explanatory variables (see FIG. 18). The support device 100B determines candidate values ​​based on the determination probability Q specified by the predetermined distribution. Therefore, the search range for candidate values ​​can be narrowed compared to the configuration of the comparative example in which candidate values ​​are determined by uniform random numbers. Therefore, the support device 100B can determine more appropriate candidate values.

[0134] In addition, the support device 100B calculates an evaluation value V based on the difference between the mean μ and the variance σ for each of the L candidate values. This improves the accuracy of proposing explanatory variables corresponding to the objective variables that belong to the user's desired range C. Specifically, the support device 100B can reduce the number of experiments shown in FIG.

[0135] In addition, in this embodiment, there are multiple types of explanatory variables (four types in this embodiment). The support device 100B determines L candidate value combinations using information indicating correlations between multiple types of explanatory variables (correlation matrix in FIG. 6 in this embodiment). Therefore, even if there are multiple types of explanatory variables, it is possible to determine L candidate values ​​that reflect the multiple types of explanatory variables.

[0136] In this embodiment, it is assumed that the explanatory variables follow a normal distribution. However, when it is assumed that the explanatory variables follow a predetermined distribution other than the normal distribution, the candidate values ​​are determined based on the other distribution. The other predetermined distribution is described below. FIG. 22 is an example of a predetermined distribution when there is one type of explanatory variable. Note that the vertical axis of FIG. 22 and FIG. 23 and FIG. 24 described later indicates the determination probability Q, and the horizontal axis indicates the explanatory variable.

[0137] The predetermined distribution may be a normal distribution as shown in FIG. 22(A). The predetermined distribution may be a log-normal distribution as shown in FIG. 22(B). For example, when the support device of the present embodiment is applied to an experiment in which a glass rod is dropped to measure the size of the broken pieces of the glass rod, a log-normal distribution may be applied as the predetermined distribution. The predetermined distribution may be a distribution having multiple peaks as shown in FIG. 22(C) (two peaks in the example of FIG. 22(C)). Although not shown in particular, when there are multiple types of explanatory variables, an integrated distribution obtained by integrating the multiple types of normal distributions may be applied as the predetermined distribution.

[0138] Fig. 23 is a diagram for explaining the conversion from the log-normal distribution in Fig. 22(B) to a normal distribution. As shown in Fig. 23, when the predetermined distribution is a log-normal distribution, the support device 100B may convert it to a normal distribution by converting it as y=logx. By converting it to a normal distribution in this way, the support device 100B can determine a proposed value by the above-mentioned method.

[0139] Fig. 24 is a diagram showing an example of a discrete probability distribution, in which (A) shows a binomial distribution, and (B) shows a Poisson distribution.

[0140] With regard to the above-mentioned embodiments and modified examples, it has been intended from the outset of the application that the configurations described in the embodiments may be appropriately combined, including combinations not mentioned in the specification, to the extent that no inconvenience or contradiction arises.

[0141] The embodiments disclosed herein should be considered to be illustrative and not restrictive in all respects. The scope of the present disclosure is defined by the claims, not the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0142] 100, 100A, 100B Support device, 102 Acquisition unit, 104 Candidate unit, 106 Dispersion unit, 108 Distance unit, 110 Probability unit, 112 Decision unit, 114 Threshold unit, 162 ROM, 182 Memory, 183 Interface, 200 Display device, 300 Prediction device, 350 Prediction model.

Claims

1. A method for assisting in a search for explanatory variables, comprising the steps of: obtaining a plurality of values ​​to be used as the explanatory variables; obtaining distribution parameters defining a first distribution of the explanatory variables; determining a plurality of candidate values ​​for the explanatory variables; calculating a variance of a second distribution for each of the plurality of candidate values ​​based on the plurality of values; calculating a probability value for each of the plurality of candidate values ​​based on the first distribution; calculating a Mahalanobis distance for each of the plurality of candidate values ​​based on the distribution parameters; Calculating an evaluation value for each of the plurality of candidate values ​​by the following formula: Evaluation value = σ a × E b × P c where, in the above formula, σ is the variance, E is the Mahalanobis distance, P is the probability value, and a, b, and c are predetermined real numbers. The support method further includes: determining, as a proposed value, the candidate value having the maximum evaluation value among the plurality of candidate values, and notifying the user of the proposed value.

2. There are multiple types of explanatory variables, The support method according to claim 1 , wherein calculating the probability value based on the first distribution includes calculating the probability value of each of the plurality of candidate values ​​based on information indicating a correlation between the plurality of types of explanatory variables.

3. The explanatory variables are variables used in battery experiments, The support method further includes updating a prediction model that outputs a value as a dependent variable when an explanatory variable is input, based on the explanatory variable indicated by the proposed value and a dependent variable corresponding to the explanatory variable; the explanatory variables are variables related to materials of the battery, The support method according to claim 1 , wherein the response variable is a variable related to a characteristic of the battery.

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