Electrode performance analysis device and method of operation thereof

Abstract

An electrode performance analysis device according to one embodiment disclosed in this document may include a first modeling unit that 3D models an initial electrode structure, an acquisition unit that acquires first parameters including at least one of particle size, porosity, or filling rate of an active material from each of a plurality of lattice cells in the initial electrode structure, a second modeling unit that 3D models a reference electrode structure based on the first parameters, and an analysis unit that analyzes the performance of the reference electrode structure.

Description

[Technical Field]

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2023-0037624, filed on March 22, 2023, and all contents disclosed in the documents of this Korean patent application are incorporated herein by reference. FIELD OF THE INVENTION The embodiments disclosed herein relate to an electrode performance analysis device and method of operation. [Background technology]

[0002] In recent years, research and development into secondary batteries has been actively pursued. Here, secondary batteries are batteries that can be charged and discharged, and include both conventional Ni / Cd batteries, Ni / MH batteries, and more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, and other batteries. Furthermore, because lithium-ion batteries can be manufactured to be compact and lightweight, they are used as power sources for mobile devices. In recent years, their range of use has expanded to include power sources for electric vehicles, and they are attracting attention as a next-generation energy storage medium.

[0003] Because the performance of secondary batteries is determined by many variables, research is being actively conducted into the development of technologies that can predict performance changes due to design parameter values ​​based on the structure of secondary batteries created to study changes in performance due to these variables, which are then realized in a virtual world on a computer using digital twin technology.

[0004] An example of such digital twin work is a method of 3D modeling an electrode structure based on a Representative Elementary Volume (REV), which can represent the characteristics of the actual electrode volume. Here, to increase the reliability of electrode performance analysis using a modeled electrode structure, the similarity between the modeled electrode structure and the actual electrode must be increased. To achieve this, the method of setting the REV for electrode structure modeling needs to be more systematic. Summary of the Invention [Problem to be solved by the invention]

[0005] The embodiments disclosed herein provide an electrode performance analysis device and its operating method that provides a systematic REV setting method that enables 3D modeling of electrode structures that are highly consistent with actual electrodes.

[0006] The embodiments disclosed herein provide an electrode performance analysis device and its operating method that provide a method for calculating geometric parameters capable of analyzing electrode performance from a 3D modeled electrode structure.

[0007] The technical problems of the embodiments disclosed in this document are not limited to the above-mentioned technical problems, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0008] An electrode performance analysis device according to one embodiment disclosed in this document may include a first modeling unit that 3D models an initial electrode structure, an acquisition unit that acquires first parameters including at least one of particle size, porosity, or filling rate of an active material from each of a plurality of lattice cells in the initial electrode structure, a second modeling unit that 3D models a reference electrode structure based on the first parameters, and an analysis unit that analyzes the performance of the reference electrode structure.

[0009] In the electrode performance analysis device according to one embodiment disclosed herein, the first modeling unit can 3D model the initial electrode structure using volume, particle size of the active material, and filling rate as variables.

[0010] In one embodiment of the electrode performance analysis device disclosed in this document, the second modeling unit can determine a Representative Elementary Volume (REV) based on the first parameter and perform 3D modeling of the reference electrode structure based on the REV.

[0011] In one embodiment of the electrode performance analysis device disclosed in this document, the second modeling unit can determine a reference lattice cell from among the plurality of lattice cells based on the first parameter, and determine the volume of the reference lattice cell as the REV.

[0012] In one embodiment of the electrode performance analysis device disclosed in this document, the second modeling unit identifies at least one lattice cell from the plurality of lattice cells that satisfies a specified condition, and determines the lattice cell having the smallest volume from the at least one lattice cell as the reference lattice cell, and the specified condition may include at least one of a first condition that the first parameter is within a specified range or a second condition that the first parameter includes a specified numerical value.

[0013] The electrode performance analysis device according to one embodiment disclosed herein may further include a verification unit that verifies the reliability of the reference electrode structure based on a D / P (Domain volume / Particle volume) value obtained by dividing the volume of the reference electrode structure by the volume of the active material in the reference electrode structure.

[0014] In one embodiment of the electrode performance analysis device disclosed in this document, the verification unit can verify that the reference electrode structure is reliable if the number of active material particles in the reference electrode structure is greater than the D / P value.

[0015] In one embodiment of the electrode performance analysis device disclosed herein, the analysis unit can analyze performance of the reference electrode structure in terms of at least one of electrical conductivity, ionic conductivity, or reaction area of ​​an active material.

[0016] An embodiment of a method for analyzing electrode performance disclosed herein may include the steps of: 3D modeling an initial electrode structure; obtaining a first parameter from each of a plurality of lattice cells in the initial electrode structure, the first parameter including at least one of particle size, porosity, or packing fraction of an active material; 3D modeling a reference electrode structure based on the first parameter; and analyzing the performance of the reference electrode structure.

[0017] In one embodiment of the electrode performance analysis method disclosed herein, the operation of 3D modeling the reference electrode structure may include an operation of determining a Representative Elementary Volume (REV) based on the first parameter, and an operation of 3D modeling the reference electrode structure based on the REV.

[0018] In one embodiment of the electrode performance analysis method disclosed herein, the operation of determining the REV may include an operation of determining a reference lattice cell from among the plurality of lattice cells based on the first parameter, and an operation of determining the volume of the reference lattice cell as the REV.

[0019] In one embodiment of the electrode performance analysis method disclosed herein, the operation of determining the reference lattice cell includes the operations of identifying at least one lattice cell from the plurality of lattice cells that satisfies a specified condition, and determining the lattice cell from the at least one lattice cell that has the smallest volume as the reference lattice cell, wherein the specified condition may include at least one of a first condition that the first parameter is within a specified range, or a second condition that the first parameter includes a specified numerical value.

[0020] The electrode performance analysis method according to one embodiment disclosed herein may further include verifying the reliability of the reference electrode structure based on a D / P (Domain volume / Particle volume) value obtained by dividing the volume of the reference electrode structure by the volume of the active material in the reference electrode structure.

[0021] In one embodiment of the electrode performance analysis method disclosed herein, the operation of verifying the reliability of the reference electrode structure may include an operation of verifying that the reference electrode structure is reliable if the number of active material particles in the reference electrode structure is greater than the D / P value.

[0022] In one embodiment of the electrode performance analysis method disclosed herein, analyzing the performance of the reference electrode structure may include analyzing the performance of the reference electrode structure in terms of at least one of electrical conductivity, ionic conductivity, or active material reaction area. [Effects of the Invention]

[0023] According to the embodiments disclosed herein, by systematically setting the REV using parameters obtained from multiple grid cells included in the initial electrode structure, it is possible to 3D model an electrode structure that is highly consistent with the actual electrode.

[0024] According to the embodiments disclosed herein, geometric parameters can be calculated from the 3D modeled electrode structure to analyze the performance of the electrode. In addition, this document can provide various other benefits that can be perceived directly or indirectly. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a block diagram of an electrode performance analysis device according to one embodiment. [Figure 2] FIG. 1 is a diagram showing an initial electrode structure 3D modeled by an electrode performance analysis device according to an embodiment. [Figure 3] 10 is a graph illustrating an example in which an electrode performance analysis device according to an embodiment determines a reference lattice cell from among a plurality of lattice cells for setting REV. [Figure 4]10 is a graph illustrating an example in which an electrode performance analysis device according to an embodiment determines a reference lattice cell from among a plurality of lattice cells for setting REV. [Figure 5] 10 is a graph illustrating an example in which an electrode performance analysis device according to an embodiment determines a reference lattice cell from among a plurality of lattice cells for setting REV. [Figure 6] FIG. 10 is a diagram showing a reference electrode structure 3D modeled based on the REV set by an electrode performance analysis device according to one embodiment. [Figure 7] 1 is an operation flowchart of an electrode performance analysis device according to an embodiment. [Figure 8] 1 is an operation flowchart of an electrode performance analysis device according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0026] Various embodiments of the present invention will now be described with reference to the accompanying drawings, although it should be understood that this is not intended to limit the invention to the particular embodiments, but rather to include various modifications, equivalents, and / or alternatives to the embodiments of the present invention.

[0027] The various embodiments and terms used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to include various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, like reference numerals may be used for like or related components. The singular form of a noun corresponding to an item may include one or more of the said item, unless the relevant context clearly dictates otherwise.

[0028] In this document, each phrase such as "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C" may include any one or all possible combinations of the items listed with that phrase. Terms such as "first," "second," "first," "second," "A," "B," "(a)," or "(b)" may be used simply to distinguish that element from other elements and do not limit that element in other respects (e.g., importance or order) unless specifically stated to the contrary.

[0029] In this document, when a (e.g., first) component is referred to as being "coupled," "coupled," or "connected" to another (e.g., second) component, with or without the terms "functionally" or "communicatively," or when a reference is made to "coupled" or "connected," this means that the component may be coupled to the other component directly (e.g., by wire), wirelessly, or through a third component.

[0030] According to one embodiment, a method according to various embodiments disclosed herein may be provided in a computer program product. The computer program product may be traded between a seller and a buyer as a commodity. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)) or distributed online (e.g., downloaded or uploaded) via an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be at least temporarily stored or temporarily generated on a machine-readable storage medium, such as the memory of a manufacturer's server, an application store server, or an intermediary server.

[0031] According to various embodiments, each of the aforementioned components (e.g., modules or programs) may include one or more entities, and some of the entities may be located separately in other components. According to various embodiments, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the respective components of the multiple components before the integration. According to various embodiments, operations performed by modules, programs, or other components may be performed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be performed in a different order, omitted, or one or more other operations may be added.

[0032] FIG. 1 is a block diagram of an electrode performance analysis device according to one embodiment. According to one embodiment, the electrode performance analysis device 100 may include a memory 110 and a processor 120. According to an embodiment, the electrode performance analysis device 100 shown in Fig. 1 may further include at least one component (e.g., a display, an input device, or an output device) other than the components shown in Fig. 1.

[0033] According to one embodiment, memory 110 may include volatile and / or non-volatile memory. In one embodiment, memory 110 can store data used by at least one component (e.g., processor 120) of electrode performance analysis apparatus 100. For example, the data can include software (or associated instructions), input data, or output data. In one embodiment, the instructions, when executed by processor 120, can cause electrode performance analysis apparatus 100 to perform the operations defined by the instructions.

[0034] In one embodiment, the memory 110 may include one or more pieces of software (eg, a first modeling unit 111, an acquisition unit 113, a second modeling unit 115, a verification unit 117, and / or an analysis unit 119).

[0035] In one embodiment, processor 120 may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0036] In one embodiment, the processor 120 can execute software (e.g., the first modeling unit 111, the acquisition unit 113, the second modeling unit 115, the verification unit 117, and / or the analysis unit 119), control at least one other component (e.g., a hardware or software component) of the electrode performance analysis device 100 coupled to the processor 120, and perform various data processing or calculations.

[0037] Below, with reference to Figures 2, 3, 4, 5, and 6, we will explain how the electrode performance analysis device 100 3D models an electrode structure via the first modeling unit 111, the acquisition unit 113, the second modeling unit 115, the verification unit 117, and / or the analysis unit 119, and analyzes the performance of the modeled electrode structure.

[0038] A secondary battery may be composed of a positive electrode layer, a negative electrode layer, a separator separating the positive electrode layer and the negative electrode layer, and a liquid electrolyte passing through the separator to electrically connect the positive electrode layer and the negative electrode layer, or may be composed of a positive electrode layer, a negative electrode layer, and a solid electrolyte layer located between the positive electrode layer and the negative electrode layer. Here, the positive electrode layer may include a positive electrode active material, a conducting material for increasing the conductivity of the positive electrode active material, and a binder for connecting the positive electrode active material and the conducting material. In addition, the negative electrode layer may include a negative electrode active material, a conducting material for increasing the conductivity of the negative electrode active material, and a binder for connecting the negative electrode active material and the conducting material.

[0039] The electrode structure described below may refer to a positive electrode layer or a negative electrode layer included in a secondary battery, and the electrode performance analysis device 100 can 3D model such an electrode structure in the virtual world to analyze the performance of the electrode.

[0040] Fig. 2 is a diagram showing an initial electrode structure 3D modeled by an electrode performance analysis device according to an embodiment. Figs. 3, 4, and 5 are graphs illustrating an example in which an electrode performance analysis device according to an embodiment determines a reference lattice cell from among a plurality of lattice cells for setting an REV. Fig. 6 is a diagram showing a reference electrode structure 3D modeled by an electrode performance analysis device according to an embodiment based on the set REV.

[0041] 2, the first modeling unit 111 may 3D model the initial electrode structure 210. Here, the initial electrode structure 210 may be a form in which a positive electrode layer or a negative electrode layer of a secondary battery including a positive electrode or a negative electrode active material, a conductive material, and a binder is realized in a virtual world.

[0042] According to one embodiment, the first modeling unit 111 may perform 3D modeling of the initial electrode structure 210 using variables such as volume, particle size of the active material, and porosity. Here, the particle size of the active material may include the median particle size D50 of the plurality of active materials included in the lattice cell. In addition, the porosity may refer to the proportion of voids other than the active material in the initial electrode structure 210. For example, the first modeling unit 111 may perform 3D modeling of the initial electrode structure 210 using a specified volume (e.g., 2.5*10 7 μm 3 ), a specified active material median particle size (e.g., 10 μm), and a specified porosity (e.g., 20%), an initial electrode structure 210 can be 3D modeled.

[0043] According to an embodiment, the acquiring unit 113 may acquire a first parameter including at least one of a particle size, a porosity, or a filling rate of the active material from each of a plurality of lattice cells in the initial electrode structure 210. For example, the plurality of lattice cells may be at least a portion of the initial electrode structure 210, and each of the plurality of lattice cells may have the same or different volumes.

[0044] According to an embodiment, the acquisition unit 113 may extract a plurality of lattice cells in the initial electrode structure 210 in a predetermined manner. According to one embodiment, the acquisition unit 113 may divide the initial electrode structure 210 into N equal parts (where N is a natural number) and extract lattice cells having a volume that is 1 / N of the initial electrode structure 210. For example, the acquisition unit 113 may extract the first lattice cells using a first method 220. Here, the first method 220 may be a method of dividing the initial electrode structure 210 into four equal parts and extracting lattice cells having a volume that is 1 / 4 of the initial electrode structure 210. As another example, the acquisition unit 113 may extract the second lattice cells using a second method 230. Here, the second method 230 may be a method of dividing the initial electrode structure 210 into nine equal parts and extracting lattice cells having a volume that is 1 / 9 of the initial electrode structure 210. In this way, the acquisition unit 113 may extract the first lattice cells, the second lattice cells, ..., the Nth lattice cells within the initial electrode structure 210 using the first method 220, the second method 230, ..., the Nth method.

[0045] However, the method by which the acquisition unit 113 extracts multiple lattice cells within the initial electrode structure 210 is not limited to the method described above, and the acquisition unit 113 can extract multiple lattice cells within the initial electrode structure 210 using various methods.

[0046] The acquiring unit 113 can acquire a first parameter including at least one of the particle size, porosity, and packing ratio of the active material from each of the extracted lattice cells. Here, the particle size of the active material can include the median particle size of the active materials included in the lattice cell. Furthermore, the porosity can refer to the proportion of voids other than the active material in the lattice cell, and the packing ratio can refer to the proportion of the active material in the lattice cell.

[0047] According to an embodiment, the second modeling unit 115 can perform 3D modeling of the reference electrode structure based on the first parameters acquired by the acquisition unit 113 .

[0048] According to an embodiment, the second modeling unit 115 may determine a representative elementary volume (REV) that can represent the characteristics of the actual electrode volume based on the first parameter, and may perform 3D modeling of the reference electrode structure based on the determined REV.

[0049] According to one embodiment, the second modeling unit 115 can determine a reference lattice cell from among the plurality of lattice cells extracted by the acquisition unit 113 based on the first parameter, and determine the volume of the determined reference lattice cell as REV.

[0050] According to an embodiment, the second modeling unit 115 may identify at least one lattice cell among the plurality of lattice cells that satisfies a specified condition. Here, the specified condition may include at least one of a first condition that a first parameter is within a specified range or a second condition that the first parameter includes a specified numerical value. For example, the specified range may be a range within an error of 5% from a theoretical value, and the specified numerical value may be the theoretical value.

[0051] Hereinafter, with reference to FIGS. 3, 4, and 5, an embodiment will be described in which the second modeling unit 115 identifies at least one lattice cell that satisfies a specified condition from among a plurality of lattice cells.

[0052] Referring to FIG. 3, a graph 300 can be seen which shows the median particle size of the active material in each of the plurality of lattice cells acquired by the acquisition unit 113 according to the volume of the lattice cell.

[0053] In graph 300, the particle size distribution (D1, D2, D3, D4, D5, or D6) of the active material may represent a distribution of intermediate particle sizes of the active material obtained from a lattice cell having a specific volume (e.g., V1, V2, V3, V4, V5, or V6). For example, particle size distribution D1 of the first active material may represent a distribution of intermediate particle sizes of the active material obtained by acquisition unit 113 from a lattice cell having a first volume V1 among the multiple lattice cells. In graph 300, particle size distribution D1 of the first active material may represent a distribution of intermediate particle sizes of the active material obtained from a lattice cell having a first volume V1. 1L The particle size distribution D1 of the first active material may have a maximum particle size D 1U which may be the maximum grain size of the intermediate grain size of the active material in the lattice cells having the first volume V1.

[0054] In the graph 300, T1 may represent a theoretical mean particle size of the active material. For example, T1 may be the mean particle size of the active material set by the first modeling unit 111 before the initial electrode structure 210 is 3D modeled. In this case, T1 may be the mean particle size of the active material of the initial electrode structure 210.

[0055] According to one embodiment, the second modeling unit 115 can identify at least one lattice cell from the plurality of lattice cells that satisfies a first condition, in which the particle size distribution of the active material is within a specified range (e.g., within a 5% error range from the theoretical active material median particle size T1). The second modeling unit 115 can identify a lattice cell from the plurality of lattice cells that has a fourth volume V4, a fifth volume V5, or a sixth volume V6 as satisfying the first condition based on the particle size distribution D4 of the fourth active material, the particle size distribution D5 of the fifth active material, and the particle size distribution D6 of the sixth active material being within a 5% error range from the theoretical active material median particle size T1.

[0056] Referring to FIG. 4, a graph 400 can be seen which shows the porosity of each of the plurality of lattice cells acquired by the acquisition unit 113 according to the volume of the lattice cell.

[0057] In the graph 400, the porosity distribution (P1, P2, P3, or P4) can indicate the distribution of porosity obtained from lattice cells having a specific volume (e.g., V3, V4, V5, or V6). For example, the first porosity distribution P1 can indicate the distribution of porosity obtained by the obtaining unit 113 from a lattice cell having a third volume V3 among the plurality of lattice cells. In the graph 400, the first porosity distribution P1 can indicate the distribution of porosity obtained from a lattice cell having a third volume V3 among the plurality of lattice cells. 1L The first porosity distribution P1 may have a maximum porosity P 1U which may be the maximum value of the porosity of the lattice cells having the third volume V3.

[0058] Also, in graph 400, for a lattice cell having a first volume V1 or a second volume V2, the volume of the lattice cell being less than or equal to the threshold volume confirms that the porosity is 0 or not obtained.

[0059] In the graph 400, T2 may represent a theoretical porosity. For example, T2 may be the porosity set by the first modeling unit 111 before the initial electrode structure 210 is 3D modeled. In this case, T2 may be the porosity of the initial electrode structure 210.

[0060] According to one embodiment, the second modeling unit 115 can identify at least one lattice cell among the plurality of lattice cells that satisfies a first condition, that is, that the porosity distribution is within a specified range (e.g., within a 5% error range from the theoretical porosity T2). The second modeling unit 115 can identify a lattice cell among the plurality of lattice cells that has a third volume V3, a fourth volume V4, a fifth volume V5, or a sixth volume V6 as satisfying the first condition, based on the fact that the first porosity distribution P1, the second porosity distribution P2, the third porosity distribution P3, and the fourth porosity distribution P4 are within a 5% error range from the theoretical porosity T2.

[0061] According to one embodiment, the second modeling unit 115 can identify at least one lattice cell among the plurality of lattice cells that satisfies a second condition, in which the porosity distribution includes a specified value (e.g., a theoretical porosity T2). Based on the fact that the first porosity distribution P1, the second porosity distribution P2, the third porosity distribution P3, and the fourth porosity distribution P4 include the theoretical porosity T2, the second modeling unit 115 can identify a lattice cell among the plurality of lattice cells that has a third volume V3, a fourth volume V4, a fifth volume V5, or a sixth volume V6 as satisfying the second condition.

[0062] Referring to FIG. 5, a graph 500 can be seen which shows the filling rate of each of the plurality of lattice cells acquired by the acquisition unit 113 according to the volume of the lattice cell.

[0063] In the graph 500, a filling ratio distribution (Q1, Q2, Q3, Q4, Q5, or Q6) may indicate a distribution of filling ratios obtained from lattice cells having a specific volume (e.g., V1, V2, V3, V4, V5, or V6). For example, the first filling ratio distribution Q1 may indicate a distribution of filling ratios obtained by the obtaining unit 113 from lattice cells having a first volume V1 among the plurality of lattice cells. In the graph 500, the first filling ratio distribution Q1 may indicate a distribution of filling ratios obtained from a lattice cell having a first volume V1 among the plurality of lattice cells. 1L The first filling ratio distribution Q1 may have a maximum filling ratio Q 1U which may be the maximum value of the filling factor of the lattice cells having the first volume V1.

[0064] In the graph 500, T3 may represent a theoretical filling rate. For example, T3 may be a filling rate set by the first modeling unit 111 before the initial electrode structure 210 is 3D modeled. In this case, T3 may be the filling rate of the initial electrode structure 210.

[0065] According to one embodiment, the second modeling unit 115 can identify at least one lattice cell among the plurality of lattice cells that satisfies a first condition, that is, that the packing fraction distribution is within a specified range (e.g., within a 5% error range from the theoretical packing fraction T3). The second modeling unit 115 can identify a lattice cell among the plurality of lattice cells that has the second volume V2, the third volume V3, the fourth volume V4, the fifth volume V5, or the sixth volume V6 as satisfying the first condition, based on the fact that the second packing fraction distribution Q2, the third packing fraction distribution Q3, the fourth packing fraction distribution Q4, the fifth packing fraction distribution Q5, and the sixth packing fraction distribution Q6 are within a 5% error range from the theoretical packing fraction T3.

[0066] According to an embodiment, the second modeling unit 115 can identify at least one lattice cell among the plurality of lattice cells whose filling rate distribution includes a specified value (e.g., a theoretical filling rate T3) and satisfies a second condition. Based on the fact that the second, third, fourth, fifth, and sixth filling rate distributions Q2, Q3, Q4, Q5, and Q6 include the theoretical filling rate T3, the second modeling unit 115 can identify a lattice cell among the plurality of lattice cells that has a second volume V2, a third volume V3, a fourth volume V4, a fifth volume V5, or a sixth volume V6 as satisfying the second condition.

[0067] According to one embodiment, the second modeling unit 115 may identify at least one lattice cell among the plurality of lattice cells, in which at least one of the particle size, porosity, or filling rate of the active material satisfies the first condition and / or the second condition, and may determine the lattice cell having the smallest volume among the at least one identified lattice cell as the reference lattice cell.

[0068] According to one embodiment, when the second modeling unit 115 determines the reference lattice cell based on the particle size of the active material, the second modeling unit 115 may determine the lattice cell having the smallest volume, a fourth volume V4, among the at least one identified lattice cell as the reference lattice cell, and may determine the fourth volume V4 as REV.

[0069] According to one embodiment, when the second modeling unit 115 determines the reference lattice cell based on the porosity, the second modeling unit 115 may determine the lattice cell having the third volume V3, which is the smallest volume, among the at least one identified lattice cell as the reference lattice cell, and may determine the third volume V3 as REV.

[0070] According to one embodiment, when the second modeling unit 115 determines the reference lattice cell based on the filling rate, the second modeling unit 115 may determine the lattice cell having the second volume V2, which is the smallest volume, among the at least one identified lattice cell as the reference lattice cell, and may determine the second volume V2 as REV.

[0071] According to one embodiment, when the second modeling unit 115 determines the reference lattice cell based on the particle size, porosity, and packing ratio of the active material, the second modeling unit 115 may identify a lattice cell having a fourth volume V4, a fifth volume V5, or a sixth volume V6 as at least one lattice cell. This is because the particle size, porosity, and packing ratio of the active material of the identified lattice cell satisfy the first condition and / or the second condition. The second modeling unit 115 may determine the lattice cell having the fourth volume V4, which is the smallest volume, of the at least one identified lattice cell as the reference lattice cell, and may determine the fourth volume V4 as REV.

[0072] 6, the second modeling unit 115 can 3D model the reference electrode structure (610, 620, 630, 640, 650, 660, 670, or 680) based on the determined REV. Here, the reference electrode structure (610, 620, 630, 640, 650, 660, 670, and 680) can have the same volume as the REV.

[0073] According to one embodiment, the second modeling unit 115 can 3D model the reference electrode structures (610, 620, 630, 640, 650, 660, 670, or 680) using variables such as the shape of the active material particles, the particle size of the active material, the manufacturing process, and / or the degree of electrode deterioration. For example, the first reference electrode structure 610, the third reference electrode structure 630, the fifth reference electrode structure 650, and the seventh reference electrode structure 670 may have a first active material particle shape and a first active material particle size, and the second reference electrode structure 620, the fourth reference electrode structure 640, the sixth reference electrode structure 660, and the eighth reference electrode structure 680 may have a second active material particle shape and a second active material particle size. Additionally, the first reference electrode structure 610, the second reference electrode structure 620, the fifth reference electrode structure 650, and the sixth reference electrode structure 660 may be 3D models of electrodes fabricated by a first manufacturing process, and the third reference electrode structure 630, the fourth reference electrode structure 640, the seventh reference electrode structure 670, and the eighth reference electrode structure 680 may be 3D models of electrodes fabricated by a second manufacturing process. Finally, the first reference electrode structure 610, the second reference electrode structure 620, the third reference electrode structure 630, and the fourth reference electrode structure 640 may be 3D models of initial electrodes, and the fifth reference electrode structure 650, the sixth reference electrode structure 660, the seventh reference electrode structure 670, and the eighth reference electrode structure 680 may be 3D models of electrodes that have degraded by a specified level.

[0074] According to one embodiment, the verification unit 117 can verify the reliability of the reference electrode structure based on a D / P (Domain volume / Particle volume) value obtained by dividing the volume of the reference electrode structure by the volume of the active material in the reference electrode structure. For example, the volume of the active material in the reference electrode structure can be calculated based on the median particle size of the active material in the reference electrode structure.

[0075] According to one embodiment, the verification unit 117 can verify the reliability of the reference electrode structure by comparing the number of active material particles in the reference electrode structure with the D / P value. For example, the verification unit 117 can verify that the reference electrode structure is reliable if the number of active material particles in the reference electrode structure is greater than the D / P value. As another example, the verification unit 117 can verify that the reference electrode structure is unreliable if the number of active material particles in the reference electrode structure is less than the D / P value.

[0076] According to one embodiment, the analysis unit 119 can analyze the performance of the reference electrode structure. According to one embodiment, the analysis unit 119 can also analyze the performance of the reference electrode structure if the verification unit 117 verifies the reference electrode structure as reliable.

[0077] According to one embodiment, the analysis unit 119 can analyze the performance of the reference electrode structure in terms of at least one of electrical conductivity, ionic conductivity, or reactive area of ​​the active material.

[0078] According to one embodiment, the analysis unit 119 can analyze the electrical conductivity performance of the reference electrode structure by calculating the filling rate of the conductive material and binder in the reference electrode structure, the dispersion of the conductive material and binder, or the resistance of the reference electrode structure. According to one embodiment, the analysis unit 119 can analyze the change in electrical conductivity depending on the shape of the active material particles, the particle size of the active material, the manufacturing process, and the degree of deterioration of the reference electrode structures (610, 620, 630, 640, 650, 660, 670, and 680) 3D modeled by the second modeling unit 115. For example, the analysis unit 119 can determine that the gradient of the filling rate of the conductive material and binder in the thickness direction of the electrode decreases when the drying process is omitted from the electrode manufacturing process. As another example, the analysis unit 119 can determine that the dispersion of the conductive material and binder improves when the particle size of the active material decreases. As another example, the analysis unit 119 can check the change in the resistance value of the reference electrode structure over time due to the electrode manufacturing process.

[0079] According to one embodiment, the analysis unit 119 can analyze the ionic conductivity performance of the reference electrode structure by calculating the shape, size, density, and orientation of voids in the reference electrode structure and the ionic tortuosity of the reference electrode structure. According to one embodiment, the analysis unit 119 can analyze the change in ionic conductivity depending on the shape, particle size, manufacturing process, and degradation level of the active material particles of the multiple reference electrode structures (610, 620, 630, 640, 650, 660, 670, and 680) 3D modeled by the second modeling unit 115. For example, the analysis unit 119 can confirm that the size of voids changes depending on the shape and particle size of the active material particles. As another example, the analysis unit 119 can confirm the difference in void density due to degradation of the reference electrode structure. As another example, the analysis unit 119 can confirm that the void density and orientation of the reference electrode structure affect the ionic tortuosity.

[0080] According to one embodiment, the analysis unit 119 can analyze the reaction area of ​​the active material of the reference electrode structure by calculating the specific surface area of ​​the active material and voids, the specific surface area of ​​the active material and binder, and the specific surface area of ​​the active material, voids, and binder of the reference electrode structure. According to one embodiment, the analysis unit 119 can analyze the change in the reaction area of ​​the active material depending on the shape of the active material particles, the particle size of the active material, the manufacturing process, and the degree of deterioration of the multiple reference electrode structures (610, 620, 630, 640, 650, 660, 670, and 680) 3D modeled by the second modeling unit 115. For example, the analysis unit 119 can confirm that the specific surface area decreases in the case of a deteriorated reference electrode structure.

[0081] 7 is a flowchart showing the operation of an electrode performance analysis device according to one embodiment. FIG. 7 will be explained using the configuration of FIG. 1 (for example, the electrode performance analysis device 100). The embodiment shown in Figure 7 is just one embodiment, and the order of steps according to various embodiments of the present invention may differ from that shown in Figure 7, and some steps shown in Figure 7 may be omitted, the order between steps may be changed, or steps may be merged. For example, in Figure 7, operation 720 may be omitted.

[0082] In operation 705, the electrode performance analysis apparatus 100 can 3D model an initial electrode structure. Here, the initial electrode structure may be a form in which a positive electrode layer or a negative electrode layer of a secondary battery including a positive electrode or a negative electrode active material, a conductive material, and a binder is realized in a virtual world.

[0083] According to one embodiment, the electrode performance analysis device 100 can perform 3D modeling of an initial electrode structure using variables such as volume, particle size of the active material, and porosity. Here, the particle size of the active material can include the median particle size D50 of the multiple active materials contained in the lattice cell. Furthermore, the porosity can refer to the proportion of voids other than the active material in the initial electrode structure. For example, the electrode performance analysis device 100 can perform 3D modeling of an initial electrode structure using a specified volume (e.g., 2.5*10 7 μm 3), a specified active material median particle size (e.g., 10 μm), and a specified porosity (e.g., 20%) can be 3D modeled.

[0084] In operation 710, the electrode performance analysis apparatus 100 may obtain a first parameter including at least one of a particle size, a porosity, or a packing fraction of the active material from each of a plurality of lattice cells in the initial electrode structure 3D modeled in operation 705. For example, the plurality of lattice cells may be at least a portion of the initial electrode structure, and each of the plurality of lattice cells may have the same or different volumes.

[0085] According to one embodiment, the electrode performance analysis apparatus 100 can extract a plurality of lattice cells in the initial electrode structure in a predetermined manner. According to one embodiment, the electrode performance analysis apparatus 100 can divide the initial electrode structure into N equal parts (where N is a natural number) and extract lattice cells having a volume that is 1 / N of the initial electrode structure. For example, the electrode performance analysis apparatus 100 can extract the first lattice cells using a first method. Here, the first method may be a method of dividing the initial electrode structure into four equal parts and extracting lattice cells having a volume that is 1 / 4 of the initial electrode structure. As another example, the electrode performance analysis apparatus 100 can extract the second lattice cells using a second method. Here, the second method may be a method of dividing the initial electrode structure into nine equal parts and extracting lattice cells having a volume that is 1 / 9 of the initial electrode structure. In this way, the electrode performance analysis apparatus 100 can extract the first lattice cells, the second lattice cells, ..., the Nth lattice cells within the initial electrode structure using the first method, the second method, ..., the Nth method.

[0086] However, the method by which the electrode performance analysis device 100 extracts multiple lattice cells within the initial electrode structure is not limited to the method described above, and the electrode performance analysis device 100 can extract multiple lattice cells within the initial electrode structure using various methods.

[0087] The electrode performance analysis device 100 can acquire a first parameter including at least one of the particle size, porosity, and filling rate of the active material from each of the extracted lattice cells. Here, the particle size of the active material can include the median particle size of the active materials included in the lattice cell. In addition, the porosity can refer to the proportion of voids other than the active material in the lattice cell, and the filling rate can refer to the proportion of the active material in the lattice cell.

[0088] In operation 715, the electrode performance analysis apparatus 100 may 3D model the reference electrode structure. According to one embodiment, the electrode performance analysis apparatus 100 may 3D model the reference electrode structure based on the first parameters obtained in operation 710. Operation 715, in which the electrode performance analysis apparatus 100 3D models the reference electrode structure, will be described in more detail with reference to Figure 8 below.

[0089] In operation 720, the electrode performance analysis apparatus 100 can verify the reliability of the reference electrode structure 3D modeled in operation 715. According to one embodiment, the electrode performance analysis apparatus 100 can verify the reliability of the reference electrode structure based on a D / P (Domain volume / Particle volume) value, which is the volume of the reference electrode structure divided by the volume of the active material in the reference electrode structure. For example, the volume of the active material in the reference electrode structure can be calculated based on the median particle size of the active material in the reference electrode structure.

[0090] According to one embodiment, the electrode performance analysis device 100 can compare the number of active material particles in a reference electrode structure with the D / P value to verify the reliability of the reference electrode structure. For example, if the number of active material particles in the reference electrode structure is greater than the D / P value, the electrode performance analysis device 100 can verify that the reference electrode structure is reliable. As another example, if the number of active material particles in the reference electrode structure is less than the D / P value, the electrode performance analysis device 100 can verify that the reference electrode structure is unreliable.

[0091] If the reference electrode structure is verified as unreliable in operation 720 (“Fail”), the electrode performance analysis apparatus 100 may perform operation 715 again.

[0092] If the reference electrode structure is verified as reliable in operation 720 (“Pass”), then in operation 725 the electrode performance analysis apparatus 100 may analyze the performance of the reference electrode structure.

[0093] According to one embodiment, the electrode performance analysis device 100 can analyze the performance of a reference electrode structure in terms of at least one of electrical conductivity, ionic conductivity, or reactive area of ​​an active material.

[0094] According to one embodiment, the electrode performance analysis device 100 can analyze the electrical conductivity performance of a reference electrode structure by calculating the filling rate of the conductive material and binder in the reference electrode structure, the dispersion of the conductive material and binder, or the resistance of the reference electrode structure. According to one embodiment, the electrode performance analysis device 100 can analyze the change in electrical conductivity depending on the shape of the active material particles, the particle size of the active material, the manufacturing process, and the degree of deterioration of the reference electrode structures 3D-modeled in operation 715. For example, the electrode performance analysis device 100 can determine that the gradient of the filling rate of the conductive material and binder in the thickness direction of the electrode decreases when the drying process is omitted from the electrode manufacturing process. As another example, the electrode performance analysis device 100 can determine that the dispersion of the conductive material and binder improves when the particle size of the active material decreases. As another example, the electrode performance analysis device 100 can determine the change in the resistance of the reference electrode structure depending on the electrode manufacturing process.

[0095] According to one embodiment, the electrode performance analysis device 100 can analyze the ionic conductivity performance of a reference electrode structure by calculating the shape, size, density, and orientation of voids in the reference electrode structure, as well as the ionic tortuosity of the reference electrode structure. According to one embodiment, the electrode performance analysis device 100 can analyze the change in ionic conductivity depending on the shape, particle size, manufacturing process, and degradation level of the active material particles in the multiple reference electrode structures 3D-modeled in operation 715. For example, the electrode performance analysis device 100 can confirm that the size of the voids changes depending on the shape and particle size of the active material particles. As another example, the electrode performance analysis device 100 can confirm the difference in void density due to degradation of the reference electrode structure. As another example, the electrode performance analysis device 100 can confirm that the void density and orientation of the reference electrode structure affect the ionic tortuosity.

[0096] According to one embodiment, the electrode performance analysis device 100 can analyze the reaction area of ​​the active material of a reference electrode structure by calculating the specific surface area of ​​the active material and voids, the specific surface area of ​​the active material and binder, and the specific surface area of ​​the active material, voids, and binder of the reference electrode structure. According to one embodiment, the electrode performance analysis device 100 can analyze the change over time of the reaction area of ​​the active material depending on the shape of the active material particles, the particle size of the active material, the manufacturing process, and the degree of degradation of the multiple reference electrode structures 3D modeled in operation 715. For example, the electrode performance analysis device 100 can confirm that the specific surface area value decreases in the case of a deteriorated reference electrode structure.

[0097] 8 is a flowchart showing the operation of an electrode performance analysis device according to one embodiment. FIG. 8 will be explained using the configuration of FIG. 1 (for example, the electrode performance analysis device 100). The embodiment shown in FIG. 8 is just one embodiment, and the order of steps according to various embodiments of the present invention may differ from that shown in FIG. 8, and some steps shown in FIG. 8 may be omitted, the order between steps may be changed, or steps may be merged.

[0098] In operation 805, the electrode performance analysis apparatus 100 can identify at least one lattice cell among the plurality of lattice cells that satisfies a specified condition. Here, the specified condition can include at least one of a first condition that a first parameter is within a specified range or a second condition that the first parameter includes a specified numerical value. For example, the specified range can be within a 5% error range from a theoretical value, and the specified numerical value can be the theoretical value.

[0099] In operation 810, the electrode performance analysis apparatus 100 may determine a reference lattice cell from among the at least one lattice cell. According to one embodiment, the electrode performance analysis apparatus 100 may determine a lattice cell having the smallest volume from among the at least one lattice cell as the reference lattice cell.

[0100] In operation 815, the electrode performance analysis apparatus 100 can determine the volume of the reference grid cell to be REV, which can represent the characteristics of the actual electrode volume. In operation 820, the electrode performance analysis apparatus 100 can 3D model the reference electrode structure based on the REV determined in operation 815. According to one embodiment, the electrode performance analysis apparatus 100 can 3D model the reference electrode structure using variables such as the shape of the active material particles, the particle size of the active material, the manufacturing process, and / or the degree of electrode degradation.

[0101] As used above, terms such as "comprise," "constitute," or "have" mean that the relevant element can be contained within the term, unless otherwise specified, and should be interpreted as meaning that other elements can be further included, rather than excluding other elements. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong, unless otherwise defined. Commonly used terms, such as dictionary-defined terms, should be interpreted to be consistent with the contextual meaning of the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly defined herein. [Explanation of symbols]

[0102] 100 Electrode performance analyzer 110 memory 111 First Modeling Section 113 Acquisition Department 115 Second Modeling Section 117 Verification Department 119 Analysis Department 120 processors 210 Initial electrode structure 610 First reference electrode structure 620 Second reference electrode structure 630 Third reference electrode structure 640 Fourth reference electrode structure 650 Fifth reference electrode structure 660 6th reference electrode structure 670 7th reference electrode structure 680 8th reference electrode structure

Claims

1. a first modeling unit that 3D models an initial electrode structure; an acquisition unit that acquires a first parameter including at least one of a particle size, a porosity, and a packing ratio of an active material from each of a plurality of lattice cells in the initial electrode structure; a second modeling unit that 3D models the reference electrode structure based on the first parameters; an analysis unit for analyzing the performance of the reference electrode structure; An electrode performance analysis device comprising:

2. The electrode performance analysis device according to claim 1 , wherein the first modeling unit performs 3D modeling of the initial electrode structure using a volume, a particle size of the active material, and a filling rate as variables.

3. The second modeling unit determining a representative elementary volume (REV) based on the first parameter; The electrode performance analysis device of claim 1 , further comprising 3D modeling the reference electrode structure based on the REV.

4. The second modeling unit determining a reference lattice cell from among the plurality of lattice cells based on the first parameter; 4. The electrode performance analysis device of claim 3, wherein the volume of the reference grid cell is determined as the REV.

5. The second modeling unit identifying at least one lattice cell from the plurality of lattice cells that satisfies a specified condition; determining a lattice cell having the smallest volume among the at least one lattice cell as the reference lattice cell; 5. The electrode performance analyzer according to claim 4, wherein the specified condition includes at least one of a first condition in which the first parameter is within a specified range, or a second condition in which the first parameter includes a specified numerical value.

6. 2. The electrode performance analysis device according to claim 1, further comprising a verification unit that verifies reliability of the reference electrode structure based on a D / P (Domain volume / Particle volume) value obtained by dividing the volume of the reference electrode structure by the volume of the active material in the reference electrode structure.

7. 7. The electrode performance analysis device according to claim 6, wherein the verification unit verifies that the reference electrode structure is reliable when the number of active material particles in the reference electrode structure is greater than the D / P value.

8. 2. The electrode performance analysis device of claim 1, wherein the analysis unit analyzes performance of the reference electrode structure in terms of at least one of electrical conductivity, ionic conductivity, or a reaction area of ​​an active material.

9. 3D modeling of an initial electrode structure; acquiring a first parameter from each of a plurality of lattice cells in the initial electrode structure, the first parameter including at least one of a particle size, a porosity, or a packing fraction of an active material; 3D modeling a reference electrode structure based on the first parameters; analyzing the performance of the reference electrode structure; A method for analyzing electrode performance, comprising:

10. The operation of 3D modeling the reference electrode structure includes: determining a representative elementary volume (REV) based on the first parameter; and 3D modeling the reference electrode structure based on the REV.

11. The operation of determining the REV is as follows: determining a reference lattice cell from among the plurality of lattice cells based on the first parameter; and determining the volume of the reference lattice cell as the REV.

12. The operation of determining the reference lattice cell includes: identifying at least one lattice cell from the plurality of lattice cells that satisfies a specified condition; determining a lattice cell having a smallest volume among the at least one lattice cell as the reference lattice cell; 12. The electrode performance analysis method of claim 11, wherein the specified condition includes at least one of a first condition in which the first parameter is within a specified range, or a second condition in which the first parameter includes a specified numerical value.

13. 10. The electrode performance analysis method according to claim 9, further comprising verifying reliability of the reference electrode structure based on a D / P (Domain volume / Particle volume) value obtained by dividing the volume of the reference electrode structure by the volume of the active material in the reference electrode structure.

14. 14. The electrode performance analysis method according to claim 13, wherein the operation of verifying the reliability of the reference electrode structure includes an operation of verifying that the reference electrode structure is reliable if the number of active material particles in the reference electrode structure is greater than the D / P value.

15. 10. The method of claim 9, wherein analyzing the performance of the reference electrode structure includes analyzing the performance of the reference electrode structure with respect to at least one of electrical conductivity, ionic conductivity, or active material reaction area.

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