Prediction method and manufacturing method of all-solid-state battery

Abstract

To provide a predictive method for evaluating the performance of an all-solid-state battery before assembly.SOLUTION: A prediction method for predicting the performance of an all-solid-state battery includes a preparation step of preparing a negative electrode active material, an analysis step of performing XRD analysis on the negative electrode active material to obtain physical property information of the negative electrode active material, and a prediction step of predicting a charging resistance value of an all-solid-state battery using the negative electrode active material based on the physical property information.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to prediction methods and methods for manufacturing all-solid-state batteries. [Background technology]

[0002] Information-related devices and communication devices such as personal computers, video cameras, and mobile phones are becoming widespread. In addition, from the perspective of reducing the burden on the environment, automobiles using motors such as electric vehicles are becoming more widespread. Accordingly, various studies are being conducted on the batteries used as the power source for these devices.

[0003] For example, Patent Document 1 discloses a state-of-charge measuring device that includes a voltage detection means for detecting the voltage of a secondary battery, a current detection means for detecting the current of the secondary battery, and a measurement means for measuring the state of charge of the secondary battery, and that calculates the resistance of the secondary battery using the voltage detected by the voltage detection means and the current detected by the current detection means, and measures the state of charge using the resistance. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2013-142649 A Summary of the Invention [Problem to be solved by the invention]

[0005] Here, it is assumed that the inspection (battery performance evaluation) to determine whether the battery (all-solid-state battery) is a good product or not will be performed on the finished battery (battery after assembly). On the other hand, if the inspection is performed after the battery is assembled, the materials and parts used in the battery that is determined to be a non-good product will be lost, and there is a risk that battery productivity will decrease.

[0006] The present disclosure has been made in consideration of the above-mentioned circumstances, and has as its main object to provide a prediction method capable of evaluating the performance of an all-solid-state battery before assembly. [Means for solving the problem]

[0007] [1] A method for predicting the performance of an all-solid-state battery, the method comprising: a preparation step of preparing a negative electrode active material; an analysis step of performing an XRD analysis on the negative electrode active material to obtain physical property information of the negative electrode active material; and a prediction step of predicting a charging resistance value of an all-solid-state battery using the negative electrode active material based on the physical property information.

[0008] [2] The negative electrode active material is Li 4 Ti 5 O 12 The prediction method according to [1], wherein the negative electrode active material has a crystalline phase, and in the XRD step, a degree of crystallinity of the crystalline phase in the negative electrode active material is obtained as the physical property information.

[0009] [3] The prediction method according to [1] or [2], wherein in the prediction step, the prediction is made based on a trained model that has previously been machine-learned to describe a relationship between the physical property information of the negative electrode active material and a charging resistance value of an all-solid-state battery, and on the physical property information acquired in the analysis step.

[0010] [4] A method for producing an all-solid-state battery having a positive electrode active material layer, a negative electrode active material layer, and a solid electrolyte layer disposed between the positive electrode active material layer and the negative electrode active material layer, the method comprising: a first step of performing the prediction method according to any one of [1] to [3]; and a second step of forming the negative electrode active material layer using the negative electrode active material predicted in the first step to have the charging resistance value be equal to or less than a threshold value.

[0011] [5] The negative electrode active material for which the charging resistance value is predicted to be equal to or less than the threshold value is Li 4 Ti 5 O 12 The method for producing an all-solid-state battery according to [4], wherein the battery has a crystalline phase, and the degree of crystallinity of the crystalline phase is 98.4% or more. Effect of the Invention

[0012] The present disclosure has an effect of enabling the performance of an all-solid-state battery to be evaluated before assembly. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a schematic cross-sectional view illustrating an all-solid-state battery according to the present disclosure. [Diagram 2] 1 is a graph showing the results of machine learning in an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] The prediction method and the manufacturing method of the all-solid-state battery according to the present disclosure will be described in detail below.

[0015] A. Forecasting Method The prediction method in the present disclosure is a method for predicting the performance of an all-solid-state battery. In the prediction method in the present disclosure, first, a negative electrode active material is prepared (preparation step). Next, an XRD analysis is performed on the negative electrode active material to obtain physical property information of the negative electrode active material (analysis step). Then, based on the physical property information, a charging resistance value of an all-solid-state battery using the negative electrode active material is predicted (prediction step).

[0016] According to the present disclosure, the performance of an all-solid-state battery can be predicted based on the physical property information of the negative electrode active material obtained by XRD analysis, and therefore the performance of the all-solid-state battery can be evaluated before assembly. In this specification, the term "battery performance" means "charging resistance" unless otherwise specified.

[0017] As described above, it is assumed that the performance of the assembled battery will be evaluated, and if the battery is determined to be non-defective, the materials and parts used in the battery will be lost in production. In contrast, the prediction method disclosed herein predicts the performance of an all-solid-state battery from the physical properties of the material (negative electrode active material) of the all-solid-state battery. In other words, it is possible to determine whether the battery to be manufactured is non-defective or not at a stage before the battery is assembled. Therefore, by using a negative electrode active material that is predicted to show good performance in the all-solid-state battery, it is possible to manufacture the all-solid-state battery with reduced production loss, and improve productivity.

[0018] 1. Preparation process The preparation step in the present disclosure is a step of preparing a negative electrode active material.

[0019] The negative electrode active material is preferably crystalline. "Crystalline" means having at least one crystal phase. The negative electrode active material may be a single-phase material having one crystal phase, or a multi-phase material having two or more crystal phases. In particular, the negative electrode active material is preferably a material having a Li 4 Ti 5 O 12 It is preferred that it has a crystalline phase.

[0020] The negative electrode active material is, for example, Li 2 TiO 3 , Li 4 Ti 5 O 12 and Li 2 Ti 2 O 5 Lithium titanate (LTO-based negative electrode active material); and TiNb 2 O 7 and Ti 2 Nb 10 O 29 Examples of niobium titanate composite oxides (NTO-based negative electrode active materials) include:

[0021] The shape of the negative electrode active material is, for example, particulate. The negative electrode active material may be a primary particle, or a secondary particle formed by agglomeration of a plurality of primary particles. The average particle size (D 50) is, for example, 10 nm or more and 50 μm or less. 50 ) refers to the cumulative 50% particle size in the volume-based particle size distribution measured by a laser diffraction particle size distribution analyzer. 50 The average particle size (D) may be the average particle size of primary particles or the average particle size of secondary particles. 50 ) refers to the cumulative 50% particle size in the volume-based particle size distribution measured with a laser diffraction particle size analyzer.

[0022] 2.Analysis process The analyzing step in the present disclosure is a step of performing an XRD analysis on the negative electrode active material to obtain physical property information of the negative electrode active material.

[0023] The XRD analysis can be a conventionally known method. Examples of physical property information obtained from the XRD analysis include peak information such as peak positions, peak intensities at specific positions, and peak intensity ratios between multiple peaks, and the degree of crystallinity obtained from the peak information. The degree of crystallinity can be Li 4 Ti 5 O 12 The crystallinity of (LTO) can be mentioned.

[0024] Here, the crystallinity (crystallinity of LTO) can be calculated, for example, by the following formula. Crystallinity (%) = (LTO peak area / total peak area) x 100

[0025] 3. Prediction process The prediction step in the present disclosure is a step of predicting a charging resistance value of an all-solid-state battery using the negative electrode active material, based on the physical property information.

[0026] The prediction is preferably performed, for example, based on a trained model that has previously been machine-learned on the relationship between the physical property information of the negative electrode active material and the charging resistance value of the all-solid-state battery, and the physical property information acquired in the analysis step. Machine learning is usually performed before the preparation step.

[0027] Although the algorithm of the machine learning is not particularly limited, it is preferable that the algorithm is supervised learning. For example, the supervised learning may be LightGBM (Light Gradient Boosting Machine). Note that the method of supervised learning in the present disclosure is regression analysis such as multiple regression analysis and short regression analysis.

[0028] In addition, in the machine learning, it is preferable to fix manufacturing parameters other than the physical property information of the negative electrode active material, such as the type of positive electrode active material, the type of solid electrolyte, the composition of the positive electrode active material layer, and the composition of the solid electrolyte layer. This is because the charging resistance value can be predicted more accurately. In addition, it is preferable that the physical property information of the negative electrode active material in the machine learning is one type. In other words, it is preferable that the machine learning method is a short-term regression analysis.

[0029] All-solid-state batteries are described in "B. Manufacturing method of all-solid-state batteries."

[0030] B. Manufacturing method of all-solid-state batteries The manufacturing method of the all-solid-state battery in the present disclosure includes a first step of performing the above-mentioned prediction method, and a second step of forming the negative electrode active material layer using the negative electrode active material predicted in the first step to have the charging resistance value be equal to or less than a threshold value.

[0031] In the present disclosure, a negative electrode active material layer is formed using a negative electrode active material that is predicted in the above-mentioned prediction method to have a charging resistance value of the all-solid-state battery equal to or less than a threshold value. In other words, a negative electrode active material that is predicted in advance to have a good charging resistance value of the all-solid-state battery is used. Therefore, the rate of good products in the finished all-solid-state battery can be increased, and the productivity of the all-solid-state battery is improved.

[0032] 1.First step The first step in the present disclosure is to carry out the above-mentioned prediction method. The prediction method is the same as that described in "A. Prediction method", so the description thereof will be omitted here.

[0033] 2.Second process The second step in the present disclosure is a step of forming the negative electrode active material layer using the negative electrode active material predicted in the first step to have the charging resistance value equal to or less than a threshold value.

[0034] For the negative electrode active materials for which the charging resistance value was predicted to be below the threshold, Li 4 Ti 5 O 12 It is preferable that the crystalline phase has a crystallinity of, for example, 98.4% or more, or may be 98.6% or more, or may be 98.8% or more. On the other hand, the crystallinity of the crystalline phase is, for example, less than 100%, or may be 99.5% or less, or may be 99.0% or less.

[0035] The threshold value can be appropriately adjusted depending on the desired performance of the all-solid-state battery. The threshold value is, for example, 3.0 mΩ or less, and may be 2.5 mΩ or less. On the other hand, the threshold value is, for example, 2.0 mΩ or more.

[0036] The method for forming the negative electrode active material layer may be a conventionally known method, for example, a method in which a negative electrode slurry containing at least a negative electrode active material predicted to have a charging resistance value equal to or less than a threshold value is applied to a negative electrode current collector and then dried.

[0037] The negative electrode slurry contains at least the above-mentioned negative electrode active material. The negative electrode slurry may further contain at least one of a binder, a conductive agent, and a solid electrolyte. Examples of the binder include rubber-based binders such as butylene rubber (BR) and fluoride-based binders such as polyvinylidene fluoride (PVdF). Examples of the conductive agent include carbon materials, metal particles, and conductive polymers. Examples of the carbon material include particulate carbon materials such as carbon fiber and Ketjen Black (KB), and fibrous carbon materials such as carbon fiber, carbon nanotubes (CNT), and carbon nanofibers (CNF). Examples of the solid electrolyte include oxide solid electrolytes and sulfide solid electrolytes. The negative electrode slurry can be prepared by adding the above-mentioned materials to a solvent such as butyl butyrate and mixing them. It is preferable that the conditions such as the type and ratio of each of the above-mentioned materials in the negative electrode slurry are the conditions set in the above-mentioned machine learning.

[0038] The negative electrode current collector can be made of a conventionally known material such as Cu foil, Ni foil, or SUS foil.

[0039] 3.Other processes The method for producing an all-solid-state battery in the present disclosure typically includes a cathode active material layer forming step of forming a cathode active material layer, a solid electrolyte layer forming step of forming a solid electrolyte layer, and a lamination step of obtaining a laminate having the anode active material layer, the solid electrolyte layer, and the cathode active material layer.

[0040] The manufacturing conditions such as the material and composition of the positive electrode active material layer and the solid electrolyte are not particularly limited, but are preferably the conditions set in the above machine learning. In addition, the method of forming the positive electrode active material and the method of forming the solid electrolyte are not particularly limited, and can be a conventionally known method.

[0041] 4.All-solid-state battery Fig. 1 is a schematic cross-sectional view illustrating an all-solid-state battery according to the present disclosure. The all-solid-state battery 10 shown in Fig. 1 has a positive electrode active material layer 1, a negative electrode active material layer 2, and a solid electrolyte layer 3 disposed between the positive electrode active material layer 1 and the negative electrode active material layer 2, and typically has a positive electrode current collector 4 that collects electrons from the positive electrode active material layer 1, and a negative electrode current collector 5 that collects electrons from the negative electrode active material layer 2.

[0042] The all-solid-state battery in the present disclosure is preferably a Li-ion secondary battery. In addition, the use of the all-solid-state battery includes, for example, a power source for vehicles such as a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), an electric vehicle (BEV), a gasoline vehicle, and a diesel vehicle. In particular, it is preferable to use the battery as a driving power source for a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), or an electric vehicle (BEV). In addition, the battery in the present disclosure may be used as a power source for a moving body other than a vehicle (for example, a train, a ship, or an aircraft), or may be used as a power source for an electric product such as an information processing device.

[0043] The present disclosure is not limited to the above-described embodiments. The above-described embodiments are merely examples, and anything that has substantially the same configuration as the technical idea described in the claims of the present disclosure and exhibits similar effects is included in the technical scope of the present disclosure. EXAMPLES

[0044] [Experimental Example] We produced several all-solid-state batteries with different manufacturing parameters, which will be described later. We also obtained charging resistance values ​​(evaluation data) for the several all-solid-state batteries we produced. Based on the obtained data, we obtained the correlation between each manufacturing parameter and the charging resistance value using the machine learning method, which will be described later.

[0045] <Obtaining manufacturing parameters> As a manufacturing parameter, the negative electrode active material (Li 4 Ti 5 O 12For the negative electrode active material having a crystalline phase (LTO negative electrode active material), the Ti content (%) was obtained by ICP-MS inductively coupled plasma mass spectrometry, and the Li content (%) was obtained by XRD analysis. 4 Ti 5 O 12 The crystallinity was also obtained. In addition, the temperature at which the ionic conductivity of the sulfide solid electrolyte used in the negative electrode material was measured was also obtained.

[0046] <Production of all-solid-state batteries> The negative electrode active material (LTO particles) and the sulfide solid electrolyte (Li 2 SP 2 S 5 A sulfide-based solid electrolyte (VGCF), a conductive agent (VGCF), and a binder (BR-based binder) were added to a solvent (butyl butyrate) and mixed using an ultrasonic dispersion device. This produced a negative electrode slurry. The negative electrode slurry was applied onto a negative electrode current collector foil (Ni foil) using a blade method. This was then dried on a hot plate at 100°C for 30 minutes. This produced a negative electrode having a negative electrode current collector and a negative electrode active material layer.

[0047] Positive electrode active material (NCA-based active material) and sulfide solid electrolyte (Li 2 SP 2 S 5 A PVDF-based sulfide solid electrolyte, a conductive agent (VGCF), and a binder (PVdF-based binder) were added to a solvent (butyl butyrate) and mixed using an ultrasonic dispersion device. This resulted in a positive electrode slurry. The positive electrode slurry was applied onto a positive electrode current collector foil (Al foil) using a blade method. This was then dried on a hot plate at 100°C for 30 minutes. This resulted in a positive electrode having a positive electrode current collector and a positive electrode active material layer.

[0048] Sulfide solid electrolyte (Li 2 SP 2 S 5A PVDF-based sulfide solid electrolyte and a binder (PVdF-based binder) were added to a solvent (butyl butyrate) and mixed using an ultrasonic dispersion device. This produced a solid electrolyte slurry. The solid electrolyte slurry was applied onto a release foil (Al foil) using a blade method, and dried on a hot plate at 100°C for 30 minutes. This produced a transfer member having a release foil and a solid electrolyte layer.

[0049] The negative electrode and the transfer member are laminated so that the negative electrode active material layer and the solid electrolyte layer are in contact with each other, and a pressure of 1 ton / cm 2 After pressing, the Al foil of the transfer member was removed to obtain a negative electrode with a solid electrolyte layer. Next, the positive electrode and the negative electrode were laminated so that the positive electrode active material layer and the solid electrolyte layer were in contact with each other, and the positive electrode was pressed with a pressure of 3 ton / cm. 2 The laminate was pressed at 100° C. This resulted in a laminate having a positive electrode active material layer, a solid electrolyte layer, and a negative electrode active material layer. A positive electrode current collector (Al foil), a negative electrode current collector (Cu foil), and a tab having a welding tape were ultrasonically bonded to the laminate, and the whole was sealed with an aluminum laminate film. This resulted in the production of a sulfide all-solid-state battery.

[0050] <Acquisition of evaluation data> The sulfide solid-state battery was subjected to one charge-discharge cycle, and then charged again to an SOC of 80%. It was then charged for 10 seconds with a current equivalent to 3C, and the charging resistance value was obtained as evaluation data.

[0051] <Machine learning> Using the acquired manufacturing parameters and evaluation data (charging resistance value), the manufacturing parameters were set as explanatory variables, and the evaluation data was set as the objective variable, and explanatory variables highly correlated with the objective variable (manufacturing parameters that contribute highly to charging resistance value) were derived using machine learning techniques. The LightGBM method, which is a supervised learning method, was used as the machine learning method. In addition, a simple regression model was used for supervised learning. The SHAP (SHapley Additive exPlanations) method was used to calculate the degree of contribution. The results are shown in Table 1 and Figure 2.

[0052] [Table 1]

[0053] As shown in Table 1 and Figures 2(a) to (c), the crystallinity (Li 4 Ti 5 O 12 The degree of crystallinity) had the highest correlation with the charging resistance value, while other physical properties of the negative electrode active material (Ti content) and information on the sulfide solid electrolyte, which is another material (temperature at which ionic conductivity is measured), did not show any correlation with the charging resistance value. 4 Ti 5 O 12 If the degree of crystallinity is 98.4% or more, it is predicted that the resulting all-solid-state battery will exhibit a charging resistance value of 3.0 mΩ or less, and it is predicted that an all-solid-state battery with good performance can be manufactured.

[0054] This indicates that the charging resistance value of the all-solid-state battery to be manufactured can be predicted by performing XRD analysis of the negative electrode active material in the upstream process of the all-solid-state battery manufacturing process. This suggests that the prediction method disclosed herein can predict the performance of the all-solid-state battery without assembling the all-solid-state battery, and can evaluate whether the all-solid-state battery to be manufactured is a good product or not before assembling the all-solid-state battery. [Explanation of symbols]

[0055] 1...Cathode active material layer 2...Negative electrode active material layer 3...Solid electrolyte layer 4...Positive electrode current collector 5...Negative electrode current collector 10...All-solid-state battery

Claims

1. A method for predicting the performance of an all-solid-state battery, comprising: A preparation step of preparing a negative electrode active material; An analysis step of performing an XRD analysis on the negative electrode active material to obtain physical property information of the negative electrode active material; and a prediction step of predicting a charging resistance value of an all-solid-state battery using the negative electrode active material based on the physical property information.

2. The negative electrode active material is Li ４ Ti ５ O １２ having a crystalline phase, The prediction method according to claim 1 , wherein in the XRD step, a degree of crystallinity of the crystalline phase in the negative electrode active material is acquired as the physical property information.

3. 2. The prediction method according to claim 1, wherein in the prediction step, the prediction is made based on a trained model that has previously been machine-learned to describe a relationship between the physical property information of the negative electrode active material and a charging resistance value of the all-solid-state battery, and on the physical property information acquired in the analysis step.

4. A method for producing an all-solid-state battery having a positive electrode active material layer, a negative electrode active material layer, and a solid electrolyte layer disposed between the positive electrode active material layer and the negative electrode active material layer, comprising: A first step of carrying out a prediction method according to any one of claims 1 to 3; and a second step of forming the negative electrode active material layer using the negative electrode active material predicted in the first step to have a charging resistance value equal to or less than a threshold value.

5. The negative electrode active material for which the charging resistance value is predicted to be equal to or less than the threshold value is Li ４ Ti ５ O １２ The method for producing an all-solid-state battery according to claim 4 , wherein the all-solid-state battery has a crystal phase, and the crystallinity of the crystal phase is 98.4% or more.

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