Analysis device, analysis method, and storage medium

Abstract

Provided are an analysis device, an analysis method, and a storage medium that can analyze the state of an electrode provided in a battery with high precision. The analysis device of the embodiment is an analysis device of an active material layer of an electrode of a secondary battery, which includes: an acquisition unit that acquires image data that represents an active material in the active material layer after ion milling processing and a gap or a filler between the active materials by using a difference in brightness; and a comparison unit that compares a pattern of the difference in brightness between the image data in at least two different states of the active material layer.

Description

Technical Field

[0001] This invention relates to a parsing apparatus, a parsing method, and a storage medium. Background Technology

[0002] In the past, research on methods for analyzing various states of a battery has been continuously advancing. For example, methods for estimating the state of charge and state of degradation of a battery based on images of the positive electrode active material captured by a camera are known (see, for example, Japanese Patent Application Publication No. 2017-224405).

[0003] On the other hand, Digital Image Correlation (DIC) is known as a general method for measuring the strain and displacement of various materials. In DIC, a random pattern is generated by spraying a coating onto the surface of the material to be analyzed, and the random pattern is photographed by a camera to perform image analysis, thereby enabling the measurement of the material's strain and displacement. Summary of the Invention

[0004] The problem that the invention aims to solve

[0005] As a method for analyzing the state of electrodes disposed within a battery, conventional DIC (Distributed Injection Catalysis) is not suitable for analyzing electrodes in lithium-ion batteries, where the electrode thickness is only tens to hundreds of μm. Furthermore, in liquid lithium-ion batteries, voids exist on the electrode cross-section, which is also impregnated with electrolyte, making it difficult to form random patterns by spraying onto the electrode cross-section. Therefore, conventional DIC is not readily applicable for analyzing electrodes disposed within batteries.

[0006] The present invention was made in consideration of such circumstances, and one of its objectives is to provide an analytical apparatus, analytical method, and storage medium capable of resolving the state of electrodes disposed within a battery with high precision.

[0007] Solution for solving the problem

[0008] The parsing apparatus, parsing method, and storage medium of the present invention adopt the following structure.

[0009] (1) One aspect of the analytical apparatus of the present invention is an analytical apparatus for the active material layer of the electrode of a secondary battery, wherein the analytical apparatus comprises: an acquisition unit that acquires image data, the image data being image data representing the active material in the active material layer after ion milling and the voids or fillers between the active materials using brightness difference; and a comparison unit that compares the brightness difference patterns between the image data in at least two different states of the active material layer.

[0010] The solution in (2) is that the analytical device of the solution in (1) above further includes a calculation unit, which calculates at least one of the displacement of the active material, the change in the thickness of the active material layer, the strain distribution in the active material layer, and the displacement distribution of the active material in the active material layer based on the comparison result of the pattern of the brightness difference between the image data.

[0011] (3) is that in the analysis apparatus of the above-mentioned (1) or (2) schemes, the acquisition unit acquires the image data captured by the scanning electron microscope.

[0012] The solution in (4) is that in the analysis device of the solution in (1) or (2) above, the acquisition unit acquires the image data as a color image taken by a confocal microscope, and the analysis device also includes an image adjustment unit that grayscales the image data.

[0013] The solution in (5) is that in the analysis device of the solution in (1) or (2) above, the acquisition unit acquires the image data as a color image taken by a confocal microscope, and the analysis device further includes an image adjustment unit that adjusts any one of the hue, brightness and chroma of the image data according to the charging state of the secondary battery when the image data is acquired.

[0014] (6) Another aspect of the present invention is a method for analyzing the active material layer of an electrode in a secondary battery, wherein the method involves a computer performing the following processing: acquiring first image data, which is image data representing the active material within the active material layer and the voids or fillers between the active materials in a first state after ion milling using light and shadow differences; acquiring second image data, which is image data representing the active material within the active material layer and the voids or fillers between the active materials in a second state after processing the active material layer, which is different from the first state, using light and shadow differences; and comparing a pattern of light and shadow differences between the first image data and the second image data.

[0015] The solution in (7) is, in the analytical method of the solution in (6) above, further, based on the comparison result of the brightness difference pattern between the first image data and the second image data, at least one of the displacement of the active material, the change in the thickness of the active material layer, the strain distribution in the active material layer and the displacement distribution of the active material in the active material layer is calculated.

[0016] The solution (8) is to obtain the first image data and the second image data captured by a scanning electron microscope in the analysis method of the solution (6) or (7) above.

[0017] In the analysis method of the above-mentioned schemes (6) or (7), the scheme of (9) obtains the first image data and the second image data as color images captured by the confocal microscope, and further, converts the first image data and the second image data to grayscale.

[0018] In the analysis method of the above-mentioned schemes (6) or (7), the scheme of (10) obtains the first image data and the second image data as color images taken by the confocal microscope, and further adjusts any one of the hue, brightness and chroma of the first image data and the second image data according to the charging state of the secondary battery when the first image data and the second image data are obtained.

[0019] In the analytical method of any of the above schemes (6) to (10), scheme (11) further includes a process of processing the active material layer by ion milling.

[0020] (12) In another aspect of the present invention, the storage medium stores a program for analyzing the active material layer of the electrode of a secondary battery, wherein the program causes a computer to perform the following processing: acquiring first image data, which is image data representing the active material within the active material layer and the voids or fillers between the active materials in a first state of the active material layer after ion milling, using brightness and darkness difference; acquiring second image data, which is image data representing the active material within the active material layer and the voids or fillers between the active materials in a second state of the processed active material layer, different from the first state, using brightness and darkness difference; and comparing a pattern of brightness and darkness difference between the first image data and the second image data.

[0021] Invention Effects

[0022] According to the schemes (1) to (12) above, the state of the electrodes set in the battery can be analyzed with high precision. By using image data that represents the active material in the active material layer and the gaps or fillers between the active materials using light and dark differences, DIC can be applied to electrode cross-sections where it is difficult to form random patterns with conventional DIC spraying.

[0023] According to the schemes described in (4), (5), (9), and (10) above, it is possible to realize DIC using image data captured by a confocal microscope, which is a less expensive device compared to a scanning electron microscope. Unlike a scanning electron microscope, it is not necessary to place the electrodes of the object to be analyzed under vacuum conditions during imaging, thus making it applicable to liquid-based secondary batteries. Furthermore, by eliminating the influence of color changes caused by the charging state of the negative electrode, identification errors in DIC can be reduced. Attached Figure Description

[0024] Figure 1 This is a diagram illustrating an example of the structure of a parsing system including a parsing device according to an embodiment.

[0025] Figure 2 This is a flowchart illustrating an example of the processing flow of the parsing system in an implementation method.

[0026] Figure 3 This is a flowchart illustrating an example of the parsing process of the parsing apparatus in an embodiment.

[0027] Figure 4 This is an example of a microscope image of the negative electrode taken under different charging conditions according to the embodiment.

[0028] Figure 5 This is a diagram showing an example of the analysis results displayed on the display unit, representing an implementation method.

[0029] Figure 6 This is a diagram showing another example of the analysis results displayed on the display unit, representing the analysis results of the implementation method. Detailed Implementation

[0030] Hereinafter, with reference to the accompanying drawings, embodiments of the analytical apparatus, analytical method, and storage medium of the present invention will be described.

[0031] [Overall Structure]

[0032] Figure 1 This diagram illustrates an example of the structure of a analytical system S, including an analytical apparatus 1, according to an embodiment. The analytical system S performs analytical processing to determine the state of electrodes (positive and negative electrodes) disposed within a secondary battery. The analytical system S is, for example, installed in facilities for research and development of secondary batteries. The analytical system S includes, for example, an ion milling apparatus D1, a microscope D2, and the analytical apparatus 1.

[0033] The ion milling apparatus D1 performs ion milling on the electrode that is to be analyzed. The ion milling apparatus D1 performs ion milling, for example, by irradiating the surface of the electrode with an argon ion beam to grind or etch the surface of the electrode.

[0034] Microscope D2 captures cross-sectional images of the active material layer of the ion-milled electrode after processing by the ion milling apparatus D1. Microscope D2 is, for example, a confocal microscope (confocal laser microscope, confocal microscope) or a scanning electron microscope (SEM). Microscope D2 captures cross-sectional images of the active material layer of the ion-milled electrode in multiple states. For example, microscope D2 acquires first image data of the active material layer of the ion-milled electrode in a first state, and second image data of the active material layer of the ion-milled electrode in a second state different from the first state. The first state and the second state are, for example, states where the battery's state of charge is different. For example, the first state and the second state are states where the battery's state of charge (SOC) is different. Or, the first state and the second state are, for example, states where the electrode's load conditions are different. For example, the first state and the second state are states where the load applied to the electrode is different when the electrode is fixed inside the battery.

[0035] The analysis device 1 analyzes the state (structural behavior) of the electrode being analyzed based on the microscope image M captured by microscope D2. The analysis device 1 replaces the random patterns generated by spraying in conventional DIC with the light and dark patterns formed by the active material and the voids between the active materials, or by the active material and the filler material between the active materials, in the microscope image M of the electrode cross-section after ion milling. This allows the analysis to obtain the strain and displacement distribution within the electrode cross-section based on DIC.

[0036] [Analysis device]

[0037] The parsing device 1 includes, for example, a control unit 10, an input interface 20, a display unit 30, and a storage unit 40.

[0038] The control unit 10 controls the overall operation of the analysis device 1. The control unit 10 includes, for example, an acquisition unit 101, an image adjustment unit 102, a comparison unit 103, a calculation unit 104, and a display control unit 105. Each functional unit of the control unit 10 is implemented by executing programs (software) through a hardware processor (computer) such as a CPU (Central Processing Unit). Alternatively, each functional unit can be implemented using hardware (including circuitry) such as LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or GPU (Graphics Processing Unit), or through the coordinated operation of software and hardware. The program can be pre-saved in the storage unit 40 (a storage device equipped with a non-transitory storage medium), or stored in a removable storage medium such as a DVD or CD-ROM, and installed in the storage unit 40 by mounting the storage medium (non-transitory storage medium) onto a drive device.

[0039] The acquisition unit 101 acquires the microscope image M captured by the microscope D2. When the analysis device 1 and the microscope D2 are connected via a network, the acquisition unit 101 acquires the microscope image M from the microscope D2 via the network. Alternatively, the acquisition unit 101 may acquire the microscope image M from a removable storage medium storing the microscope image M via the input interface 20 based on the operator's operation.

[0040] The acquisition unit 101 acquires cross-sectional images of the active material layer of the electrode after ion milling in multiple states. For example, the acquisition unit 101 acquires first image data of the electrode after ion milling in a first state and second image data of the electrode after ion milling in a second state. That is, the acquisition unit 101 acquires image data representing the active material within the active material layer after ion milling, as well as the voids or fillers between the active materials, by means of brightness and darkness.

[0041] The image adjustment unit 102 performs a process to convert the microscope image M, which is a color image captured by the confocal microscope, into grayscale. Furthermore, the image adjustment unit 102 performs color adjustment processing on the microscope image M, which is a color image captured by the confocal microscope, to reflect the battery's charging state. Details of the processing by the image adjustment unit 102 will be described later. By performing such image adjustment, even when using a color image, the influence of color changes caused by the charging state, such as those of the negative electrode, can be eliminated, thereby reducing recognition errors in the DIC described later.

[0042] The comparison unit 103 compares the first image data with the second image data. The comparison unit 103 compares the brightness difference pattern (digital pattern) of the first image data with the brightness difference pattern (digital pattern) of the second image data, and outputs the comparison result. The comparison unit 103 performs the comparison between the brightness difference patterns of the first image data and the second image data, for example, using a DIC (Digital Image Comparison). That is, the comparison unit 103 compares the brightness difference patterns between image data from at least two different states of the active material layer. Details regarding the processing of the comparison unit 103 will be described later.

[0043] Based on the comparison results of DIC, the calculation unit 104 calculates the displacement of the active material contained in the active material layer of the electrode after ion milling and the change in the thickness of the active material layer. Furthermore, based on the calculated displacement of the active material and the change in the thickness of the active material layer, the calculation unit 104 calculates the strain distribution, displacement distribution, and stress distribution within the active material layer. In other words, based on the comparison results of the brightness difference patterns between image data, the calculation unit 104 calculates at least one of the displacement of the active material, the change in the thickness of the active material layer, the strain distribution within the active material layer, and the displacement distribution of the active material within the active material layer. Details regarding the processing of the calculation unit 104 will be described later.

[0044] The display control unit 105 controls the display content displayed by the display unit 30. The display control unit 105 causes the display unit 30 to display, for example, images showing the displacement of the active material, the change in the thickness of the active material layer, the strain distribution within the active material layer, the displacement distribution of the active material within the active material layer, and the stress distribution within the active material layer, as calculated by the calculation unit 104.

[0045] The input interface 20 accepts various input operations from the operator of the analysis device 1 and converts the received input operations into electrical signals, which are then output to the control unit 10. For example, the input interface 20 may include a mouse, keyboard, touch panel, etc. When the input interface 20 is a touch panel, it may also function as the display unit 30. Furthermore, the input interface 20 may include a device capable of connecting to a removable storage medium such as a USB (Universal Serial Bus) memory and reading data (microscope images M, etc.) stored on that removable storage medium.

[0046] Display unit 30 displays various information. For example, display unit 30 displays images representing the analysis results generated by control unit 10, and a GUI (Graphical User Interface) for accepting various input operations from the operator. Display unit 30 may be, for example, a monitor, a touch panel, etc.

[0047] The storage unit 40 stores microscope images M, etc., acquired by the acquisition unit 101. The storage unit 40 is, for example, an HDD (Hard Disk Drive), a flash memory, or RAM (Random Access Memory).

[0048] [Processing Flow]

[0049] Next, the processing flow of the analysis system S will be explained. An example will be given below, where the electrode of the object to be analyzed is a lithium-ion battery electrode, and the microscope D2 is a confocal microscope. Figure 2 This is a flowchart illustrating an example of the processing flow of the analysis system S in the implementation method. First, the electrode to be analyzed is subjected to ion milling by the ion milling apparatus D1 (step S101).

[0050] Next, microscope image M of the cross-section of the active material layer of the electrode after ion milling is obtained by microscope D2 (step S103). Microscope image D2 obtains, for example, a microscope image (first image data) of the active material layer of the electrode after ion milling in a first state, and a microscope image (second image data) of the active material layer of the electrode after ion milling in a second state.

[0051] Figure 4 This diagram illustrates an example of a microscopic image M of the negative electrode taken under different charging states according to an embodiment. Microscopic image M1 is an image of the negative electrode (graphite) taken in a first state with a SOC of 40%, and microscopic image M2 is an image of the same negative electrode taken in a second state with a SOC of 90%. Microscopic images M1 and M2 are color images taken using a confocal microscope. As shown, the hue of the active material in the image changes as the fill rate increases (i.e., the content of Li ions increases).

[0052] Next, the analysis device 1 performs analysis processing on the microscope image M captured by the microscope D2 (step S105). Figure 3 This is a flowchart illustrating an example of the parsing process of the parsing apparatus 1 in an embodiment.

[0053] First, the acquisition unit 101 of the control unit 10 acquires microscope image M1 (first image data) (step S201) and microscope image M2 (second image data) (step S203). When the analysis device 1 and microscope D2 are connected via a network, the acquisition unit 101 acquires microscope image M from microscope D2 via the network. Alternatively, the acquisition unit 101 may acquire microscope image M from a removable storage medium storing microscope image M via input interface 20 based on operator input.

[0054] Next, the image adjustment unit 102 of the control unit 10 performs a process to convert the microscope images M1 and M2, which are color images, to grayscale (step S205). In the grayscale conversion process, for example, the grayscale value is determined based on the RGB values ​​of each pixel of the microscope image. For example, the grayscale value Y is determined by the following formula (1) or formula (2). It should be noted that other grayscale transformation methods can also be used in the grayscale conversion process.

[0055] Y = (maximum value of RGB + minimum value of RGB) ÷ 2 ... Equation (1)

[0056] Y=0.299×R+0.587×G+0.114×B···Formula (2)

[0057] Figure 4 The grayscale image M_g1 shown is the image after converting microscope image M1 to grayscale, and the grayscale image M_g2 is the image after converting microscope image M2 to grayscale. In grayscale images M_g1 and M_g2, the active substances and the gaps between active substances are represented by the difference in brightness.

[0058] It should be noted that when the electrodes of an all-solid-state lithium-ion battery are the object of analysis, the grayscale image becomes an image representing the active material and the filler between the active materials (SE: Solid Electrolyte) through the difference in brightness and darkness. Furthermore, if the microscope image is a black-and-white image taken with a scanning electron microscope, the aforementioned grayscale processing can be omitted.

[0059] Next, the comparison unit 103 of the control unit 10 performs a comparison process using grayscale images M_g1 and M_g2 with DIC (step S207). In this comparison process, the image that will serve as the reference is segmented into small regions (reference images) called subsets. Image analysis is used to determine which region within the other image each subset is located in, and the displacement of the subset is calculated. For example, the correlation value between each subset defined in one image and each region (pixel group) in the other image is calculated, and based on the calculated correlation value, a correspondence between the positions of the subsets in the two images is established, and the displacement is calculated. The correlation value is calculated, for example, based on the brightness value of each pixel contained in the subset.

[0060] Next, the calculation unit 104 of the control unit 10 calculates the displacement of the active material based on the comparison result obtained by the comparison unit 103 (step S209). Additionally, the calculation unit 104 calculates the strain distribution and displacement distribution of the active material within the active material layer based on the comparison result obtained by the comparison unit 103 (step S211). Next, the display control unit 105 of the control unit 10 causes the display unit 30 to display a screen showing the analysis results (step S213).

[0061] Figure 5 This is a diagram of a resolution result screen P1, representing an example of the resolution results displayed on the display unit 30 according to an embodiment. Resolution result screen P1 appends resolution result information to a grayscale image M_g1 captured in the first state (SOC 40%). Resolution result screen P1 shows the displacement of the active material as it changes from the second state to the first state, based on a grayscale image M_g2 captured in the second state (SOC 90%). Resolution result screen P1 shows the battery structure including the current collector foil C1, the positive electrode P (ternary cathode material), the electrolyte layer E, the negative electrode N (graphite), and the current collector foil C2.

[0062] exist Figure 5 The analysis results shown in display panel V2 of screen P1 indicate that, compared to grayscale image M_g2 (second state), the position of one active material (particle 3) within the positive electrode P in grayscale image M_g1 (first state) has changed by a displacement of d = 0.271 μm. Additionally, display panel V1 shows that, compared to grayscale image M_g2 (second state), the distance between one active material (particle 2) within the positive electrode P and one active material (particle 1) within the negative electrode N in grayscale image M_g1 (first state) has changed by a change of L = 128.203 μm. Furthermore, display panel V3 shows that the layer thickness in the Y direction of the positive electrode P in grayscale image M_g1 (first state) is LY = 80.112 μm, and compared to grayscale image M_g2 (second state), the change rate is epsLY = +0.123%. Furthermore, as shown in display panel V4, the layer thickness in the Y direction of the negative electrode N in the grayscale image M_g1 (first state) is LY = 97.969 μm, and the change rate epsLY = +0.879% compared to the grayscale image M_g2 (second state). It should be noted that, based on operator instructions via input interface 20, active substances such as display displacement can also be specified in the analysis result screen P1.

[0063] Figure 6 This is a diagram of a resolution result screen P2, representing another example of the resolution results displayed on the display unit 30, illustrating an embodiment of the present invention. Resolution result screen P2 and... Figure 5The analysis result shown in screen P1 corresponds to the strain distribution within the active material layer. Figure 6 In the example shown, the strain distribution of each region of the electrode, superimposed on the grayscale image M_g1, is represented by a gradient color that corresponds to the strain [%] value. Display bar V5 of the analysis result screen P2 shows that the strain value at point 1 of the negative electrode N is eps1.1 = 1.316%. Display bar V6 shows that the strain value at point 2 of the negative electrode N is eps1.2 = 1.354%. Display bar V7 shows that the strain value at point 3 of the negative electrode N is eps1.3 = 0.859%. It should be noted that, based on operator instructions via input interface 20, the location for displaying strain values ​​can be specified in the analysis result screen P2. Furthermore, the direction of strain between any two points can be displayed in the analysis result screen P2. Additionally, based on information about strain distribution and strain direction, information such as stress distribution and stress direction can be displayed.

[0064] By displaying the analysis results screen as described above on the display unit 30, the operator can confirm the displacement of the active material, the change in the thickness of the active material layer, the strain distribution within the active material layer, and the displacement distribution of the active material within the active material layer. Therefore, Figure 2 and Figure 3 The process shown in the flowchart has ended.

[0065] (Modified Example)

[0066] The above embodiments describe a structure for grayscale processing of a microscope image M, which is a color image obtained by a confocal microscope. However, this grayscale processing can be substituted, or color adjustment processing reflecting the battery's state of charge can be performed in addition to the grayscale processing. That is, the image adjustment unit 102 of the control unit 10 adjusts any one of the hue, brightness, and chroma of the image data according to the state of charge of the secondary battery when the image data is acquired. For example, the image adjustment unit 102 performs color adjustment reflecting the state of charge (SOC) on the microscope images M1 and M2, which are color images, respectively, using the following formula (3).

[0067] [R', G', B'] = [(α×ΔSOC+1)×R, (β×ΔSOC+1)×G, (γ×ΔSOC+1)×B]···Equation (3)

[0068] In equation (3) above, [R', G', B'] are the adjusted colors, [R, G, B] are the colors of the microscope image M as the source image, ΔSOC is the variation of the charging state measured by the electrical meter, and α, β, γ are correction coefficients under unit ΔSOC and are values ​​set to match the material properties. For example, when adjusting the color of the microscope image M2 taken in the second state (SOC 90%) to match the microscope image M1 taken in the first state (SOC 40%), the above equation (3) is applied to each pixel of the microscope image M2, and the adjustment is set to [R', G', B'] = [(-0.68×50[%]+1)×R, (0.17×50[%]+1)×G, (1.20×50[%]+1)×B]. It should be noted that after this color adjustment, grayscale processing can also be further performed.

[0069] According to the embodiment described above, the system includes: an acquisition unit 101 that acquires image data, which represents the active material within the active material layer after ion milling and the voids or fillers between the active materials using brightness and darkness differences; and a comparison unit 103 that compares the brightness and darkness difference patterns between image data of at least two different states of the active material layer, thereby enabling high-precision analysis of the state of the electrodes disposed within the battery. By using image data representing the active material within the active material layer and the voids or fillers between the active materials using brightness and darkness differences, DIC can be applied to electrode cross-sections that are difficult to form with random patterns by spraying, as is the case with conventional DIC. Furthermore, DIC can be performed even when using image data captured by a confocal microscope, which is a less expensive device compared to a scanning electron microscope. Unlike a scanning electron microscope, the electrode to be analyzed does not need to be placed under vacuum conditions during imaging, thus making it applicable to liquid-based secondary batteries. In addition, by eliminating the influence of color changes caused by the charging state of the negative electrode, identification errors in DIC can be reduced.

[0070] The implementation methods described above can be performed as follows.

[0071] An analytical apparatus is provided for analyzing the active material layer of the electrodes in a secondary battery, wherein...

[0072] The analytical device includes:

[0073] Storage device, which stores a program; and

[0074] Hardware processor,

[0075] The hardware processor executes the program to perform the following processing:

[0076] The acquisition unit acquires image data, which is image data representing the active material within the active material layer after ion milling, and the voids or fillers between the active materials, using brightness and darkness differences; and

[0077] The comparison unit compares the pattern of brightness difference between image data in at least two different states of the active material layer.

[0078] The above description illustrates specific embodiments of the present invention, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. A device for analyzing the active material layer of an electrode in a secondary battery, wherein, The analytical device includes: The acquisition unit acquires image data, which is image data representing the active material within the active material layer after ion milling, and the voids or fillers between the active materials, using brightness and darkness differences; and The comparison unit compares patterns of brightness differences between image data from at least two different states of the active material layer. The acquisition unit acquires the image data, which is a color image, captured by a confocal microscope. The analysis device also includes an image adjustment unit that converts the image data to grayscale. The comparison unit compares the brightness difference patterns between the grayscale image data of at least two different states of the active material layer.

2. The analytical apparatus according to claim 1, wherein, It also includes a calculation unit that calculates at least one of the following based on the comparison results of the brightness difference patterns between the image data: the displacement of the active material, the change in the thickness of the active material layer, the strain distribution within the active material layer, and the displacement distribution of the active material within the active material layer.

3. The analytical apparatus according to claim 1 or 2, wherein, The acquisition unit acquires the image data captured by a scanning electron microscope.

4. The analytical apparatus according to claim 1 or 2, wherein, The image adjustment unit adjusts any one of the hue, brightness, and chroma of the image data according to the charging state of the secondary battery when the image data is acquired.

5. A method for analyzing the active material layer of an electrode in a secondary battery, wherein, The parsing method causes the computer to perform the following processing: First image data is obtained, which is image data representing the active material in the active material layer and the voids or fillers between the active materials in the first state of the active material layer after ion milling, using the difference in brightness and darkness. Obtain second image data, which is image data representing the active substances within the active substance layer and the gaps or fillers between the active substances in a second state different from the first state of the processed active substance layer using light and dark difference. The first image data and the second image data are color images captured by a confocal microscope, and the first image data and the second image data are converted to grayscale. as well as A pattern comparing the brightness difference between the first image data and the second image data that have been grayscaled.

6. The parsing method according to claim 5, wherein, Furthermore, based on the comparison results of the brightness difference pattern between the first image data and the second image data, at least one of the following is calculated: the displacement of the active material, the change in the thickness of the active material layer, the strain distribution within the active material layer, and the displacement distribution of the active material within the active material layer.

7. The analytical method according to claim 5 or 6, wherein, The first image data and the second image data were obtained by scanning electron microscopy.

8. The analytical method according to claim 5 or 6, wherein, Furthermore, based on the charging state of the secondary battery when the first image data and the second image data are acquired, any one of the hue, brightness, and chroma of the first image data and the second image data is adjusted.

9. The analytical method according to claim 5 or 6, wherein, It also includes a process for machining the active material layer by ion milling.

10. A storage medium storing a program for analyzing the active material layer of the electrodes of a secondary battery, wherein, The program causes the computer to perform the following processing: First image data is obtained, which is image data representing the active material in the active material layer and the voids or fillers between the active materials in the first state of the active material layer after ion milling, using the difference in brightness and darkness. Obtain second image data, which is image data representing the active substances within the active substance layer and the gaps or fillers between the active substances in a second state different from the first state of the processed active substance layer using light and dark difference. The first image data and the second image data are color images captured by a confocal microscope, and the first image data and the second image data are converted to grayscale. as well as A pattern comparing the brightness difference between the first image data and the second image data that have been grayscaled.

Images