Method for manufacturing negative electrode active material layer and method for manufacturing secondary battery

Abstract

The present invention relates to a manufacturing method of a negative electrode active material layer and a manufacturing method of a secondary battery. A manufacturing method of a negative electrode active material layer includes a step of preparing a negative electrode slurry by mixing a negative electrode active material, a solid electrolyte, a conductive additive, a binder, and a solvent; a step of obtaining a parameter of the negative electrode slurry using a dynamic viscoelasticity measuring device; a step of determining a quality of a coating film based on the obtained parameter; and a step of applying the negative electrode slurry that has been determined to be acceptable in the step of determining the quality of the coating film.

Description

Technical Field

[0001] This disclosure relates to a method for manufacturing a negative electrode active material layer contained in a secondary battery. Background Technology

[0002] In manufacturing electrodes for secondary batteries, an electrode mixture for forming an active material layer is prepared using active materials (solid powder), binders (paste), and solvents (liquid) as raw materials. Japanese Patent No. 7031259 (JP 7031259B) discloses a manufacturing method in which electrode coating is performed while keeping the thixotropic index value of the paste, measured by a viscometer, constant. Summary of the Invention

[0003] However, the thixotropic index value measured by a viscometer alone is insufficient to determine the quality of the coating film to be formed. That is, unsatisfactory aspects of the film may not become apparent until after the coating has actually been formed, resulting in wasted coating work.

[0004] Therefore, the purpose of this disclosure is to provide a method for manufacturing a negative electrode active material layer, which can effectively form a suitable coating film.

[0005] This specification discloses a method for manufacturing a negative electrode active material layer. The method includes: preparing a negative electrode slurry by mixing a negative electrode active material, a solid electrolyte, a conductive additive, a binder, and a solvent; obtaining parameters of the negative electrode slurry using a dynamic viscoelasticity measuring device; determining the quality of a coating film based on the obtained parameters; and applying the negative electrode slurry, which has been determined to be acceptable in the step of determining the quality of the coating film.

[0006] The parameters can be obtained by strain sweep measurement.

[0007] The strain scanning measurement can be the determination of the storage elastic modulus and loss elastic modulus within a strain range of 0.01% to 1000%.

[0008] This application also discloses a method for manufacturing a secondary battery. The method includes the steps of manufacturing the negative electrode active material layer.

[0009] According to this disclosure, physical property parameters are measured using a dynamic viscoelasticity measuring device while the negative electrode slurry is in its original state. This allows for the prediction and determination in advance of the quality of the coating film formed by applying the negative electrode slurry. As a result, wasteful coating work can be reduced, and a suitable coating film can be obtained efficiently. Attached Figure Description

[0010] The features, advantages, and technical and industrial significance of exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, wherein like symbols denote like elements, and wherein:

[0011] Figure 1 The layer structure of the all-solid-state battery 10 is shown. Detailed Implementation

[0012] 1. Secondary battery

[0013] Although this article describes all-solid-state batteries as one aspect of secondary batteries, secondary batteries are not limited to this; any battery can be a secondary battery as long as the negative electrode active material layer contains a solid electrolyte. Secondary batteries may also contain an electrolyte solution.

[0014] Figure 1 This is a schematic cross-sectional view illustrating an example of an all-solid-state battery. (See diagram below.) Figure 1 As shown, the all-solid-state battery 10 includes: a positive electrode active material layer 11 containing a positive electrode active material; a negative electrode active material layer 12 containing a negative electrode active material; a solid electrolyte layer 13 formed between the positive electrode active material layer 11 and the negative electrode active material layer 12; a positive electrode current collector layer 14 that collects current from the positive electrode active material layer 11; and a negative electrode current collector layer 15 that collects current from the negative electrode active material layer 12. The positive electrode active material layer 11 and the positive electrode current collector layer 14 can be collectively referred to as the positive electrode layer, and the negative electrode active material layer 12 and the negative electrode current collector layer 15 can be collectively referred to as the negative electrode layer.

[0015] The components of the all-solid-state battery 10 will be described below.

[0016] 1.1. Positive electrode active material layer

[0017] The positive electrode active material layer 11 is a layer containing positive electrode active material, and also contains solid electrolyte, conductive additives and binder.

[0018] The positive electrode active material can be any known active material. Examples include cobalt-based materials (LiCoO2, etc.), nickel-based materials (LiNiO2, etc.), manganese-based materials (LiMn2O4, Li2Mn2O3, etc.), iron phosphate-based materials (LiFePO4, Li2FeP2O7, etc.), NCA-based materials (compounds of nickel, cobalt, and aluminum), and NMC-based materials (compounds of nickel, manganese, and cobalt). More specific examples include LiNi 1 / 3Co 1 / 3 Mn 1 / 3 O2.

[0019] The surface of the positive electrode active material can be coated with an oxide layer, such as a lithium niobate layer, a lithium titanate layer, or a lithium phosphate layer.

[0020] Solid electrolytes are preferably inorganic solid electrolytes. This is because inorganic solid electrolytes have higher ionic conductivity and higher heat resistance compared to organic polymer electrolytes. Examples of inorganic solid electrolytes include sulfide solid electrolytes and oxide solid electrolytes.

[0021] Examples of sulfide solid electrolyte materials exhibiting lithium (Li) ion conductivity include Li₂S-P₂S₅, Li₂S-P₂S₅-LiI, Li₂S-P₂S₅-Li₂O, Li₂S-P₂S₅-Li₂O-LiI, Li₂S-SiS₂, Li₂S-SiS₂-LiI, Li₂S-SiS₂-LiBr, Li₂S-SiS₂-LiCl, Li₂S-SiS₂-B₂S₃-LiI, Li₂S-SiS₂-P₂S₅-LiI, Li₂S-B₂S₃, and Li₂S-P₂S₅-Z. m S n (where m and n are positive numbers, and Z is Ge, Zn, or Ga), Li2S-GeS2, Li2S-SiS2-Li3PO4, Li2S-SiS2-Li x MO y (Where x and y are positive numbers, and M is P, Si, Ge, B, Al, Ga, or In). The symbol "Li2S-P2S5" refers to a sulfide solid electrolyte material obtained using a raw material composition containing Li2S and P2S5, and this also applies to the other symbols listed above.

[0022] Examples of oxide solid electrolyte materials exhibiting Li ion conductivity include compounds with a NASICON-type structure. Examples of compounds with a NASICON-type structure include those composed of the general formula Li... 1+x Al x Ge 2-x The compound represented by (PO4)3 (0≤x≤2) (LAGP) and the compound represented by the general formula Li 1+x Al x Ti 2-x Compounds represented by (PO4)3 (0≤x≤2) (LATP). Other examples of oxide solid electrolyte materials include LiLaTiO2 (e.g., Li...). 0.34 La 0.51 TiO3), LiPON (e.g., Li 2.9 PO 3.3 N 0.46 LiLaZrO (e.g., Li7La3Zr2O) and LiLaZrO (e.g., Li7La3Zr2O) 12 ).

[0023] There are no particular restrictions on the adhesive, as long as it is chemically and electrically stable. Examples include fluorinated adhesives, such as polyvinylidene fluoride (PVDF) and polytetrafluoroethylene (PTFE); rubber-based adhesives, such as styrene-butadiene rubber (SBR); olefin-based adhesives, such as polypropylene (PP) and polyethylene (PE); and cellulose-based adhesives, such as carboxymethyl cellulose (CMC).

[0024] Examples of conductive additives include carbon materials such as carbon fiber, acetylene black, and Ketjen black, as well as metallic materials such as nickel, aluminum, and stainless steel.

[0025] The content of each component in the positive electrode active material layer 11 and the shape of the positive electrode active material layer 11 can be the same as in the prior art. In particular, from the viewpoint of promoting the formation of the all-solid-state battery 10, the positive electrode active material layer 11 is preferably in the form of a sheet. In this case, the thickness of the positive electrode active material layer 11 is preferably, for example, 0.1 μm or more and 1 mm or less, more preferably 1 μm or more and 150 μm or less.

[0026] 1.2. Negative electrode active material layer

[0027] The negative electrode active material layer 12 is a layer comprising at least a negative electrode active material, and may optionally comprise at least one of a solid electrolyte, a conductive additive, and a binder. The solid electrolyte, conductive additive, and binder may be the same as those used in the positive electrode active material layer 11.

[0028] There are no particular restrictions on the negative electrode active material. However, for lithium-ion batteries, examples of negative electrode active materials include carbon materials such as graphite and hard carbon, and various oxides such as lithium titanate (LTO), silicon (Si), Si alloys, lithium metal, and lithium alloys.

[0029] 1.3. Solid electrolyte layer

[0030] In this embodiment, the solid electrolyte layer 13 is a solid electrolyte layer disposed between the positive electrode active material layer 11 and the negative electrode active material layer 12. The solid electrolyte layer 13 contains at least a solid electrolyte. The solid electrolyte may be the same as the solid electrolyte described in the positive electrode active material layer 11.

[0031] 1.4. Current collector layer

[0032] The current collector layers are a positive current collector layer 14 that collects current from the positive electrode active material layer 11 and a negative current collector layer 15 that collects current from the negative electrode active material layer 12. Examples of materials that can be used for the positive electrode current collector layer 14 include stainless steel, aluminum, nickel, iron, titanium, and carbon. Examples of materials that can be used for the negative electrode current collector layer 15 include stainless steel, copper, nickel, and carbon.

[0033] 1.5. Battery casing

[0034] All-solid-state batteries may include a battery casing (not shown). The battery casing is a housing that holds various components and may be made of, for example, stainless steel.

[0035] 2. Manufacturing method of secondary batteries

[0036] The following description uses an all-solid-state battery as an example to illustrate the manufacturing method of a secondary battery. This specification also includes a method for manufacturing the negative electrode active material layer.

[0037] 2.1. Obtaining the correlation coefficient

[0038] In this disclosure, in order to obtain a suitable negative electrode active material layer, parameters that may contribute to obtaining a high-quality negative electrode active material layer and coefficients indicating their contribution are obtained from parameters representing the physical properties of the negative electrode slurry. For this purpose, machine learning techniques are used in this embodiment.

[0039] The process is as follows.

[0040] Preparation of negative electrode slurry

[0041] A negative electrode slurry is a composition (paste) used to form a negative electrode active material layer. Specifically, a negative electrode slurry is prepared by mixing and dispersing a mixture of a negative electrode active material, a solid electrolyte, conductive additives, a binder, and a solvent, and further mixing the mixture by stirring.

[0042] Data collection on the physical properties of negative electrode slurry

[0043] To evaluate the physical properties of the negative electrode slurry, a dynamic viscoelasticity measuring device (rheometer) was used to obtain parameters through various measurement methods, namely shear dependence (flow curve), stress-strain, strain sweep, and frequency sweep. Flow curves were obtained in both directions from high shear to low shear and from low shear to high shear.

[0044] Collection of evaluation data for coating

[0045] The prepared negative electrode slurry is applied to aluminum foil by a scraping method and then dried to form a negative electrode coating film.

[0046] Visually inspect the coating to determine its quality. Record the results as evaluation data. Coating quality can be determined based on whether the desired coating shape is achieved. Examples of "poor" quality include, but are not limited to, bumps, partial film loss (or localized thin areas), and coating streaks.

[0047] Machine Learning

[0048] In machine learning, a large amount of "physical property data of negative electrode slurry" and "evaluation data of coating film" are used for training. Correlation is then obtained for each parameter, and the learning model is selected based on the parameter showing the highest correlation.

[0049] While there are no particular restrictions on the specific machine learning methods used, a random forest method (open source) similar to a decision tree model is employed, selecting appropriate explanatory variables based on the correlation coefficient values ​​as the learning outcome output. The evaluation data of the coating is set as the target variable, and the physical property data (parameters) of the negative electrode slurry, measured by a dynamic viscoelasticity method, are set as explanatory variables. Machine learning is then performed using these data segments to extract parameters (explanatory variables) that exhibit a high correlation with the target variable.

[0050] Based on research conducted by the inventors using a large amount of data, it was found that "strain scanning" exhibits the highest correlation in the physical property data of negative electrode slurry. Therefore, it is preferable to use a machine learning model based on strain scanning.

[0051] Preferably, the energy storage elastic modulus and loss elastic modulus are measured within a strain range of 0.01 to 1000 (%).

[0052] 2.2. Manufacturing of Secondary Batteries

[0053] Formation of positive and negative electrode layers

[0054] Prepare a positive electrode active material and mix it with other materials (e.g., solid electrolyte, binder, and conductive additive) to obtain a positive electrode paste. Then, apply the obtained positive electrode paste to a layer serving as the positive electrode current collector layer to a predetermined thickness, and then dry it to form a positive electrode layer in which the positive electrode active material layer is laminated onto the positive electrode current collector layer.

[0055] Formation of solid electrolyte layer

[0056] A solid electrolyte material (e.g., a sulfide solid electrolyte) is prepared, and a material (e.g., a binder) is formulated and mixed with the solid electrolyte material to obtain a solid electrolyte paste. Subsequently, the obtained solid electrolyte paste is applied to the positive electrode active material layer of the positive electrode layer formed as described above to a predetermined thickness, and then dried to form a solid electrolyte layer.

[0057] As a result, a laminate was obtained in which the positive electrode active material layer and the solid electrolyte layer were laminated on the positive electrode current collector layer.

[0058] Formation of the negative electrode layer

[0059] In the formation of the negative electrode layer, a negative electrode slurry is first prepared as described above. Next, the physical property data (parameters) of the negative electrode slurry are obtained, and based on these parameters, a learning model selected in the manner described above is used to determine the quality of the negative electrode active material layer formed by applying the negative electrode slurry. Then, the negative electrode slurry determined to be "acceptable" in the above determination is applied to a layer serving as the negative electrode current collector layer to a predetermined thickness and dried. This yields a negative electrode active material layer laminated on the negative electrode current collector layer.

[0060] Manufacturing of secondary batteries

[0061] A laminate, in which the positive electrode active material layer and the solid electrolyte layer are laminated on the positive electrode current collector layer as described above, is placed on the negative electrode layer, wherein the solid electrolyte layer faces the negative electrode current collector layer, and the resulting stack is densified by pressing to form a secondary battery.

[0062] 3. Effects, etc.

[0063] According to the manufacturing method of this disclosure, a dynamic viscoelasticity measuring device is used to determine the physical property parameters. This allows for the prediction and determination in advance of the quality of the coating film formed by applying the negative electrode slurry. As a result, wasteful coating work can be reduced, and a suitable coating film can be obtained efficiently.

[0064] 4. Experimental Examples

[0065] 4.1. Preparation of negative electrode slurry and acquisition of its physical property data

[0066] Anode slurry was prepared using LTO-based anode active material, sulfide-based solid electrolyte, vapor-grown carbon fiber, PVDF-based binder, and butyl butyrate as raw materials. Specifically, these materials were mixed and dispersed using an ultrasonic disperser, and the resulting mixture was further mixed with stirring blades to prepare the anode slurry.

[0067] To evaluate the physical properties of the negative electrode slurry, a dynamic viscoelasticity measuring device (rheometer) was used to obtain parameters through various measurement methods, namely shear dependence (flow curve), stress-strain, strain scan, and frequency scan. Flow curves were obtained in both directions from high shear to low shear and from low shear to high shear.

[0068] 4.2. Collection of evaluation data for coating

[0069] The negative electrode slurry is applied to aluminum foil using a doctor blade coating method and dried on a hot plate at 100°C for 30 minutes to obtain a negative electrode coating film.

[0070] The obtained coating is evaluated by visual inspection as described above to determine its acceptability. Evaluation data is obtained by recording the number of acceptable and unacceptable cases.

[0071] 4.3. Machine Learning

[0072] A random forest method (open source) similar to a decision tree model was used to select appropriate explanatory variables based on the correlation coefficient values ​​as the learning outcome output. Evaluation data of the coating was set as the target variable, and the physical property data (parameters) of the negative electrode slurry measured by a dynamic viscoelasticity method were set as explanatory variables. Machine learning was performed using these data segments, and parameters (explanatory variables) exhibiting high correlation with the target variable were extracted using machine learning methods.

[0073] Table 1 shows the correlation coefficients of the machine learning models created using the parameters obtained by the dynamic viscoelasticity measurement method. The results are listed in descending order of correlation coefficient. Based on these results, the machine learning model created using strain scan measurement data showed the highest correlation coefficient of 0.53, and this model was selected.

[0074]

[0075] Table 2 shows the thixotropic index (TI) values, the NG (non-compliant) ratios predicted by a trained model (relative to a standard value), and the NG ratios actually determined by visual inspection of the coating properties (relative to a standard value). The standard value sets 1 as the allowable limit for the coating NG ratio used to determine the usability of the negative electrode slurry. Values ​​below 1 are determined to be acceptable, while values ​​above 1 are determined to be unacceptable. The results in Table 2 indicate that even within the TI value range, which is generally considered to indicate the differential dispersion state of the negative electrode slurry, there exist slurries that can be used with an NG ratio below 1. This suggests that prediction and determination based on TI values ​​are difficult, but such predictions are possible using machine learning models.

[0076] The TI value is commonly used as an indicator to evaluate fluids exhibiting thixotropic properties. In this case, viscosity data A (measured at 2 rpm) and viscosity data B (measured at 20 rpm) are obtained using a viscometer, and the TI value is calculated as A / B.

[0077]

[0078] Therefore, it was found that by using a machine learning model based on strain scan data, it is possible to predict in advance the quality of the negative electrode coating formed in subsequent coating processes from the physical property data of the slurry obtained during the negative electrode kneading process.

Claims

1. A method for manufacturing a negative electrode active material layer, the method comprising: The steps of preparing a negative electrode slurry by mixing a negative electrode active material, a solid electrolyte, a conductive additive, a binder, and a solvent; The steps for obtaining the parameters of the negative electrode slurry using a dynamic viscoelasticity measuring device; The steps to determine the coating quality based on the obtained parameters; as well as The step of applying the negative electrode slurry, which has been determined to be acceptable in the step of determining the quality of the coating film.

2. The method for manufacturing the negative electrode active material layer according to claim 1, wherein the parameters are obtained by strain scanning measurement.

3. The method for manufacturing the negative electrode active material layer according to claim 2, wherein the strain scanning measurement is the measurement of the storage elastic modulus and the loss elastic modulus within a strain range of 0.01% to 1000%.

4. A method for manufacturing a secondary battery, the method comprising the steps of manufacturing a negative electrode active material layer according to any one of claims 1 to 3.

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