Electronic device, and method for modeling active material particles included in coated and rolled electrode structure thereof

Abstract

Provided is a method for modeling active material particles included in a coated electrode structure of an electronic device, comprising the steps of: acquiring an image related to a cross-section of at least one active material particle; calculating the curvature of the at least one active material particle on the basis of the image; checking a comparison result between the curvature and a threshold value; and modeling, on the basis of the comparison result and the particle size distribution of the at least one active material particle, the at least one active material particle included in a coated electrode structure.

Description

Method for modeling active material particles included in electronic devices and their coated and rolled electrode structures

[0001] This application claims the benefit of priority to Republic of Korea Patent Application No. 10-2024-0052239, dated April 18, 2024, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure relates to a method for modeling active material particles included in an electronic device and its coated and rolled electrode structures.

[0003] Modeling active material particles within battery electrode structures is essential for performance optimization and design innovation in electrochemical storage devices. Active material particles are key components that drive electrochemical reactions in battery electrodes, and their physical and chemical properties directly impact the battery's energy density, power density, cycle life, and stability. Modeling allows for precise design and prediction of particle size, shape, and distribution, enabling optimization of electrode structures to achieve performance close to theoretical maximum capacity. Furthermore, by simulating electrical connectivity between particles, lithium ion diffusion paths, and electron conduction paths, design guidelines can be derived to improve battery charging speed and cycle life.

[0004] Furthermore, active material particle modeling can play a crucial role in improving battery safety and reliability. Most thermal and chemical instability issues that can arise during battery operation are caused by the non-uniform reactions of active material particles. Precise particle-level modeling can predict and manage these non-uniform reactions in advance, thereby enhancing battery safety and reducing the risk of unexpected accidents. This is essential for the safe and efficient use of batteries in diverse fields, including electric vehicles, renewable energy storage systems, and portable electronic devices. Therefore, the development and application of highly advanced modeling techniques is becoming a key element for the sustainable advancement of battery technology.

[0005] However, the conventional active material particle modeling method has the disadvantage of not being able to accurately simulate the shape of the active material in the coating process stage during the electrode structure formation process, and not being able to simulate the breakage of secondary particles and the resulting filling phenomenon of packing particles in the rolling process stage.

[0006] The disclosed embodiments provide a method for modeling active material particles included in an electronic device and its coated electrode structure. Specifically, the purpose is to accurately simulate the shape of the active material during the coating process during the electrode structure formation process, and to simulate the breakage of secondary particles and the resulting filling phenomenon of packing particles during the rolling process.

[0007] The technical tasks to be achieved by this embodiment are not limited to the technical tasks described above, and other technical tasks can be inferred from the following embodiments.

[0008] One aspect of the present disclosure provides a method for modeling active material particles included in a coated electrode structure of an electronic device, the method comprising: obtaining an image related to a cross-section of at least one active material particle; calculating a curve of the at least one active material particle based on the image; confirming a comparison result of the curve and a threshold; and modeling the at least one active material particle included in the coated electrode structure based on the comparison result and a particle size distribution of the at least one active material particle.

[0009] In one embodiment of the present disclosure, the step of modeling at least one active material particle may include a modeling method including the steps of: calculating a number of vertices based on the curve and a first parameter when the comparison result indicates that the curve exceeds the threshold; confirming a three-dimensional shape of the at least one active material particle based on the number of vertices; and modeling the at least one active material particle based on the three-dimensional shape and the particle size distribution.

[0010] In addition, in one embodiment of the present disclosure, the step of identifying the three-dimensional shape may include a modeling method including a step of sampling vertices corresponding to the number of vertices on a virtual sphere; and a step of identifying the three-dimensional shape based on the vertices.

[0011] In addition, in one embodiment of the present disclosure, the step of modeling the at least one active material particle may include a modeling method including: calculating a number of vertices based on the curve and a second parameter when the comparison result indicates that the curve is less than or equal to the threshold; confirming a three-dimensional shape of the at least one active material particle based on the number of vertices; increasing roughness of the three-dimensional shape by adjusting vertices of the three-dimensional shape while maintaining an aspect ratio of the at least one active material particle confirmed based on the image; and modeling the active material particle based on the three-dimensional shape with increased roughness and the particle size distribution.

[0012] Additionally, in one embodiment of the present disclosure, the aspect ratio may include a modeling method in which the aspect ratio is determined by calculating an aspect ratio of an active material particle corresponding to at least one quantile in the particle size distribution among a plurality of active material particles.

[0013] Additionally, in one embodiment of the present disclosure, the step of calculating the curve may include a modeling method including a step of calculating an average of curves of a plurality of active material particles as the curve of the active material particle based on the image.

[0014] In addition, in one embodiment of the present disclosure, the step of calculating the curve may include a modeling method including the steps of: calculating, based on the image, a maximum diameter corresponding to a circle of the largest size included in a boundary corresponding to the cross-section of the at least one active material particle; calculating a diameter of each circle that touches at least a portion of the boundary corresponding to the cross-section; and calculating the curve by dividing an average of the diameters of each circle that touches at least a portion of the boundary corresponding to the cross-section by the maximum diameter.

[0015] Additionally, in one embodiment of the present disclosure, the step of modeling the at least one active material particle may include a modeling method including a step of modeling a plurality of the at least one active material particles to have a three-dimensional shape simulated based on the comparison result and to have at least one size value confirmed based on the particle size distribution.

[0016] Additionally, in one embodiment of the present disclosure, the step of obtaining the image may include a modeling method including a step of obtaining the image by photographing the collector coated with the active material particles using a scanning electron microscope (SEM).

[0017] In addition, in one embodiment of the present disclosure, the modeling method may further include a step of checking a particle size distribution of the active material particles measured by the particle size analyzer by injecting the at least one active material particle into the particle size analyzer in the first claim.

[0018] Additionally, in one embodiment of the present disclosure, a modeling method may be included, further comprising a step of modeling an electrode structure including at least one active material particle.

[0019] Another aspect of the present disclosure provides an electronic device for modeling active material particles included in an electrode structure, comprising: a processor; and a memory storing one or more instructions, wherein the processor is configured to perform the one or more instructions to: obtain an image related to a cross-section of at least one active material particle; calculate a curve of the at least one active material particle based on the image; determine a comparison result of the curve and a threshold; and model the at least one active material particle included in a coated electrode structure based on the comparison result and a particle size distribution of the at least one active material particle.

[0020] In addition, another aspect of the present disclosure provides a method for modeling cracking of rolled secondary particles and filling of packed particles in an electronic device, the method comprising: obtaining an image for an electrode structure including at least one secondary particle; identifying, based on the image, a distribution of a virtual ellipsoid diameter for at least one space related to a void included in the electrode structure; modeling, based on the distribution, at least one packing particle generated due to cracking of the secondary particle by a rolling process, on at least a portion of the space; and modeling, based on a volume of the packing particle, a void caused by cracking within the secondary particle.

[0021] In one embodiment of the present disclosure, the step of verifying the distribution may include a modeling method including the step of verifying each virtual maximum ellipsoid included in each of the gaps; and the step of verifying a distribution for the diameter of each of the maximum ellipsoids as the distribution.

[0022] In one embodiment of the present disclosure, the step of modeling the packing particle on at least a portion of the void may include a modeling method including: identifying information about a virtual ellipsoid corresponding to at least one quantile on the distribution; identifying a shape of the packing particle based on the information about the virtual ellipsoid; and modeling the packing particle having the identified shape of the packing particle, the packing particle having at least one size corresponding to a diameter of the virtual ellipsoid corresponding to the quantile, on at least a portion of the void.

[0023] In one embodiment of the present disclosure, the step of modeling the packing particles on at least a portion of the pores may include a modeling method including a step of modeling the packing particles such that the amount of the packing particles proportionally increases as the electrode structure gets closer to the surface in contact with the separator.

[0024] In one embodiment of the present disclosure, the step of modeling a void due to cracking on the secondary particle may include a modeling method including a step of modeling the void on the secondary particle such that the volume of the void on the secondary particle corresponds to the volume of the packing particle.

[0025] In one embodiment of the present disclosure, the step of modeling a void due to cracking on the secondary particle may include a modeling method including a step of modeling the void such that the secondary particle includes a fiber-shaped void.

[0026] In one embodiment of the present disclosure, the step of acquiring the image may include a modeling method including: identifying a model that simulates an electrode structure in which a slurry is coated on a current collector; and, based on the model, acquiring the image of the electrode structure before the rolling process is performed.

[0027] Another aspect of the present disclosure provides an electronic device for modeling active material particles included in an electrode structure, comprising: a processor; and a memory storing one or more instructions, wherein the processor is configured to perform the one or more instructions to: acquire an image for an electrode structure including at least one secondary particle; determine, based on the image, a distribution of a virtual ellipsoid diameter for at least one pore included in the electrode structure; and model, based on the distribution, at least one packing particle generated by cracking of the secondary particle by a rolling process, on at least a portion of the pores; and model, based on a volume of the packing particle, a pore caused by cracking on the secondary particle.

[0028] Another aspect of the present disclosure may provide a computer-readable, non-transitory recording medium having recorded thereon a program for executing the above-described modeling method on a computer.

[0029] Specific details of other embodiments are included in the detailed description and drawings.

[0030] According to the proposed embodiment, one or more of the following effects can be expected.

[0031] According to the embodiments of the present specification, the shape of the active material can be accurately simulated in the coating process step during the electrode structure formation process.

[0032] In addition, according to the embodiments of the present specification, the phenomenon of secondary particle breakage and subsequent packing particle filling can be simulated in the rolling process step.

[0033] Additionally, according to the embodiments of the present specification, performance optimization and design innovation of electrochemical storage devices can be achieved through modeling of active material particles.

[0034] The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

[0035] FIG. 1 is a diagram showing the relationship between an electronic device, an analysis device, and a microscope according to one embodiment.

[0036] FIG. 2 is a flowchart illustrating a method for modeling active material particles included in a coated electrode structure according to one embodiment.

[0037] Figures 3a and 3b are examples of images according to one embodiment and outline images of each active material particle according to the images.

[0038] Figure 4 is a diagram showing a curve calculation method according to one embodiment.

[0039] FIG. 5 is a flowchart illustrating a method for modeling active material particles included in an electrode structure according to one embodiment.

[0040] FIG. 6 is an example diagram of a modeled electrode structure according to one embodiment.

[0041] Figure 7 is an example of an image of a rolled electrode structure in which cracking of secondary particles and subsequent filling of packing particles have occurred.

[0042] FIG. 8 is a flowchart illustrating a method for modeling the cracking of secondary particles and the resulting packing particle filling phenomenon according to a rolling process, according to one embodiment.

[0043] FIG. 9 is an example diagram of creating a virtual ellipsoid in at least some of the gaps included in an image according to one embodiment.

[0044] FIG. 10 is an exemplary diagram of an electrode structure modeling the cracking of secondary particles and the filling phenomenon of packed particles according to one embodiment.

[0045] Fig. 11 is a block diagram of an electronic device according to one embodiment.

[0046] The terms used in the examples have been selected from widely used, current terms, taking into account the functions of the present disclosure. However, these terms may vary depending on the intentions of those skilled in the art, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, in which case their meanings will be described in detail in the relevant description. Therefore, the terms used in this disclosure should not be defined simply as names, but rather based on the meanings of the terms and the overall content of the present disclosure.

[0047] When a part of a specification is said to "include" a component, this does not mean that it excludes other components, but rather that it may include other components, unless otherwise stated.

[0048] The expression "at least one of a, b, and c" described throughout the specification may encompass 'a alone', 'b alone', 'c alone', 'a and b', 'a and c', 'b and c', or 'all of a, b, and c'.

[0049] The "terminal" mentioned below may be implemented as a computer or portable terminal that can connect to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, laptop, etc. equipped with a web browser, and the portable terminal may include, for example, a wireless communication device that guarantees portability and mobility, and may include all types of handheld-based wireless communication devices such as communication-based terminals such as IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution), smartphones, tablet PCs, etc.

[0050] Below, embodiments of the present disclosure are described in detail with reference to the attached drawings so that those skilled in the art can easily implement the present disclosure. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described herein.

[0051] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings.

[0052] FIG. 1 is a diagram showing the relationship between an electronic device, an analysis device, and a microscope according to one embodiment.

[0053] Referring to FIG. 1, the electronic device (100) can operate in conjunction with the analysis equipment (200) and the microscope (300). Meanwhile, FIG. 1 only illustrates components related to the present embodiment. Therefore, those skilled in the art will understand that, in addition to the components illustrated in FIG. 1, other general-purpose components may be included.

[0054] The electronic device (100) may include a memory and a processor capable of performing the following modeling methods, as will be described later. In some embodiments, the electronic device (100) may operate in conjunction with at least one of the analysis equipment (200) and the microscope (300). The term "operating in conjunction" herein may not necessarily mean that the devices are directly connected and operating. For example, the term "operating in conjunction" may mean that the electronic device (100) can receive and perform calculations on information acquired through the analysis equipment (200) and the microscope (300).

[0055] As the analysis equipment (200), various analysis equipment capable of analyzing the particle size of active material particles may be used. For example, various analysis equipment such as a laser particle size analyzer, a dynamic light scattering equipment, a suspension particle size analyzer, or a powder characteristic analyzer may be used, but is not limited to the equipment listed above, and any equipment capable of analyzing the particle size of active material particles may be used.

[0056] In addition, in the case of the microscope (300), according to one embodiment, it may be a scanning electron microscope (SEM), but is not limited thereto, and as will be described later, any microscope capable of confirming the cross-section of active material particles may be used.

[0057] Based on such an electronic device (100), a method for modeling the shape of an active material in a coating process step during the electrode structure formation process and a method for modeling the cracking of secondary particles and the resulting filling phenomenon of packing particles in a rolling process step are described below.

[0058] First, with reference to Fig. 2, a method for modeling active material particles included in a coated electrode structure will be described.

[0059] FIG. 2 is a flowchart illustrating a method for modeling active material particles included in a coated electrode structure according to one embodiment.

[0060] Referring to FIG. 2, in step S210, the electronic device (100) can obtain an image related to a cross-section of at least one active material particle. In step S220, the electronic device (100) can calculate a curve of at least one active material particle based on the image. In step S230, the electronic device (100) can check a comparison result of the curve and a threshold. In step S240, the electronic device (100) can model at least one active material particle included in the coated electrode structure based on the comparison result and the particle size distribution of the at least one active material particle. Each step will be described in more detail below.

[0061] According to one embodiment, the electronic device (100) can check the particle size distribution (PSD) of active material particles. The PSD may be a distribution checked by analyzing active material particles using analysis equipment (200), and based on the PSD, the size of active material particles corresponding to each quantile such as D10, D50, D90, Dmin, and Dmax can be checked, and the shape of the active material particles corresponding to each quantile can also be checked.

[0062] In addition, the electronic device (100) can confirm an image of a cross-section of the active material particles confirmed by photographing a current collector coated with active material particles through a microscope (300). According to one embodiment, the active material particles herein may be coated on the current collector in a state that includes only one type of particle as an active material and a binder. In other words, the image may be obtained by coating a slurry containing only one type of particle as an active material and a binder on the current collector, rather than a slurry in which two or more types of active materials and a binder are mixed, and then photographing the cross-section thereof through a microscope (300). Of course, the present invention is not limited thereto. Refer to FIGS. 3A and 3B to examine an example of such an image.

[0063] Figures 3a and 3b are examples of images according to one embodiment and outline images of each active material particle according to the images.

[0064] Referring to Fig. 3a, an example of a microscopic image of a current collector coated with active material particles can be seen. Referring to Fig. 3b, an outline image showing the boundaries of each active material particle, which can be identified by calculating the image, can be seen.

[0065] Thereafter, the electronic device (100) can calculate the curvature of at least one active material particle based on the image or the contour image calculated therefrom. The curvature can be calculated as a numerical value indicating how close the bending shape of the active material particle is to a curve, and can be calculated in various ways. For example, the curvature used in the present disclosure can refer to Roundness, one of various indices used to evaluate the shape of a particle in the field of materials engineering. Refer to FIG. 4 to examine an example of a method for calculating the curvature when using the Roundness index.

[0066] Figure 4 is a diagram showing a curve calculation method according to one embodiment.

[0067] Referring to Fig. 4, the curvature of the active material particle can be calculated based on the boundary line of the particle. Specifically, among each portion of the boundary line, virtual circles that are convex from the inside of the boundary to the outside, which are tangent to the boundary line, can be drawn, and their radii (411, 412, 413, and 414) can be calculated. In addition, at the same time, the largest virtual circle inscribed within the boundary line can be drawn, and its radius (410) can be calculated. Thereafter, the curvature can be calculated based on the ratio of the average of the radii of the virtual circles that are tangent to the boundary line, which is a convex curve from the inside of the boundary line, to the radius of the largest virtual circle. This process can be expressed by the following mathematical equation 1.

[0068]

[0069] In the formula, ∑r i is the sum of the radii of the imaginary circles that touch the boundary line, which are convex curves outward among each part of the boundary line, and r max represents the radius of a virtual circle of maximum size, and N can represent the number of virtual circles that touch the boundary line, which is a curve that is convex outward among each part of the boundary line. If this type of curve is calculated according to the calculation method, the closer the value is to 1, the more likely it is that the active material particles will have a curved shape without an angular shape.

[0070] According to one embodiment, the electronic device (100) can identify a curve value that can represent the active material based on each curve calculated for each active material particle. For example, the electronic device (100) can identify the average of the curves calculated for each particle corresponding to the active material. Alternatively, the electronic device (100) can identify a curve value for an active material particle having a median size identified based on the PSD. In addition, the electronic device (100) can identify a curve value that can represent the active material in various ways.

[0071] Thereafter, the electronic device (100) can compare the curve and the threshold, and confirm the comparison result of the curve and the threshold. Here, the threshold is a hyperparameter and can have various values ​​depending on the design, but can have a value such as 0.5 or 0.6 that can distinguish between circles and ellipses. At this time, the three-dimensional shape of the active material particle can be confirmed differently depending on whether the curve exceeds or is below the threshold. First, the case where the curve exceeds the threshold will be described below.

[0072] According to one embodiment, the electronic device (100) may calculate the number of vertices based on the curve and the first parameter when the comparison result indicates that the degree of curvature exceeds a threshold. Here, the first parameter may have a characteristic that causes the number of vertices to be calculated to be larger when calculated together with the curve, compared to a second parameter corresponding to a case where the degree of curvature is less than or equal to the threshold. For example, at least some of the first coefficients and the first intercepts included in the first parameter may be larger than at least some of the second coefficients and the second intercepts included in the second parameter, but is not limited thereto. According to one embodiment, the number of vertices may be calculated based on the curve and the first parameter as in Mathematical Expression 2 below.

[0073]

[0074] Thereafter, the electronic device (100) can confirm the three-dimensional shape of at least one active material particle based on the number of vertices. Specifically, as the number of vertices is confirmed, the electronic device (100) can sample as many vertices as the number of confirmed vertices on a virtual sphere. Here, the virtual sphere can have an arbitrary radius and an arbitrary number of points that can be sampled on the surface. At this time, the electronic device (100) can randomly sample as many vertices as the number of vertices on the surface of the virtual sphere. For example, if the number of vertices is 29, 29 vertices can be randomly sampled on the surface of the virtual sphere. As will be described later, in cases where the degree of curvature exceeds a threshold, the number of vertices is calculated to be relatively larger than in cases where the degree of curvature is below the threshold, and thus, the shape of the active material particle can be derived to be relatively close to a circle.

[0075] Next, we will explain the case where the curve is below the critical value.

[0076] According to one embodiment, the electronic device (100) may calculate the number of vertices based on the curve and the second parameter if the comparison result indicates that the curve is below a threshold value. Here, as described above, the second parameter may have a characteristic that causes the number of vertices to be calculated to be smaller than the first parameter. According to one embodiment, the number of vertices may be calculated based on the curve and the second parameter as in Mathematical Expression 3 below.

[0077]

[0078] The electronic device (100) can identify the three-dimensional shape of at least one active material particle based on the number of vertices. The identification of the three-dimensional shape here may be performed using a vertex sampling method based on the aforementioned virtual sphere. Furthermore, as described above, since the number of vertices is smaller than when the degree of curvature exceeds a threshold, the three-dimensional shape derived as a result of sampling vertices on the surface of the virtual sphere may be relatively far from a circle and may be sharp.

[0079] Thereafter, the electronic device (100) can increase the roughness of the three-dimensional shape by adjusting the vertices of the three-dimensional shape identified in this manner. Specifically, the electronic device (100) can randomly adjust the vertices of the three-dimensional shape while maintaining the aspect ratio of at least one active material particle identified based on the image. For example, assuming that one of the aspect ratios of the active material particles identified based on the image is 1:2, the electronic device (100) can randomly adjust the vertices while maintaining the aspect ratio of the three-dimensional shape at 1:2, thereby making the shape of the active material particle more pointed, i.e., increasing the roughness.

[0080] In one embodiment, the aspect ratio that serves as a criterion for such an increase in roughness can be identified based on the aspect ratio of active material particles having a size corresponding to at least one quantile in the particle size distribution among the active material particles. For example, the aforementioned criterion aspect ratio can be identified based on the aspect ratio of active material particles having a size corresponding to D50, i.e., the median, in the PSD. That is, an active material particle having a size corresponding to the median can be found in the image and its aspect ratio can be identified. Alternatively, the aspect ratio that serves as a criterion for an increase in roughness can be identified based on the average of the aspect ratios of at least one active material particle in the image. That is, any value that can represent the aspect ratio of the active material particle can be used.

[0081] Thereafter, the electronic device (100) can model the active material particles to have a three-dimensional shape confirmed based on the comparison result of the curve and the threshold, and to have a size confirmed based on the PSD. In other words, the active material particles can be modeled to have the same three-dimensional shape but have various sizes while following the PSD. For example, active material particles having respective sizes corresponding to D10, D50, D90, Dmin, and Dmax can be modeled. In addition, as an additional example, the number of active material particles having a size corresponding to D50 can be the largest, followed by D10 and D90, and the number of active material particles having a size corresponding to Dmin and Dmax can be the smallest.

[0082] To see this series of processes at a glance, refer to Figure 5.

[0083] FIG. 5 is a flowchart illustrating a method for modeling active material particles included in an electrode structure according to one embodiment.

[0084] Referring to FIG. 5, the electronic device (100) can receive an image in step S500. Then, the electronic device (100) can calculate a curve and an aspect ratio in steps S510 and S520, respectively. Thereafter, the electronic device (100) compares the curve and a threshold in step S511, and if the curve exceeds the threshold, calculates the number of vertices based on a first parameter in step S512-1, and if the curve is below the threshold, calculates the number of vertices based on a second parameter in step S512-2. In step S513-1 or step S513-2, the electronic device (100) samples a corresponding number of vertices on a virtual sphere based on the number of vertices confirmed, and if the degree of curve is below a threshold, a process of providing roughness based on the PSD input in step S530 and the aspect ratio value calculated in step S520 is additionally performed in step S514. Thereafter, in step S515, the electronic device (100) confirms the size of the active material particle to be modeled based on the PSD, and based on the size confirmed in this way and the shape of the active material particle confirmed through the aforementioned process, the active material particle can be modeled in step S516.

[0085] As an additional embodiment, it is also possible to model a single active material to have various three-dimensional shapes. For example, by sampling the vertices multiple times with different random seeds for the calculated number of vertices, or by performing roughening multiple times with different random seeds, various three-dimensional shapes can be confirmed. Alternatively, the above process can be performed by dividing the active material particles according to each quantile according to the PSD, calculating the curvature for the active material particles according to the quantile, and comparing the curvature with a threshold value for the active material particles classified by the quantile. For example, active material particles classified as D40-D60 may have a curvature above the threshold value, while active material particles classified as D5-D25 may have a curvature below the threshold value. In such cases, the active material particles may be modeled using three-dimensional shapes derived differently according to the curvature value, as in the above process.

[0086] When modeling of at least one active material particle is completed according to the above-described embodiment, the electronic device (100) can model an electrode structure including at least one active material particle. For example, the electronic device (100) can model an electrode structure by stacking or filling the modeled at least one active material particle. According to one embodiment, the electrode structure may include various types of active material particles. In such a case, the electrode structure may be modeled by randomly stacking the active material particles after modeling each type of active material particle. To examine an example of an electrode structure modeled in this manner, reference will be made to FIG. 6.

[0087] FIG. 6 is an example diagram of a modeled electrode structure according to one embodiment.

[0088] Referring to FIG. 6, an example of a three-dimensional modeling (610) of an electrode structure in which various types of active material particles are stacked and a cross-section (620) thereof can be seen. In the three-dimensional modeling (610) and the cross-section (620), it can be seen that the first particles (611 and 621) are relatively round, the second particles (612 and 622) are less round, and the third particles (613 and 623) are relatively much rougher and more jagged. In one embodiment, the first particles (611 and 621) have a high degree of curvature and an aspect ratio close to 1, and the second particles (612 and 622) have a lower degree of curvature than the first particles and an aspect ratio not close to 1. For the third particle (613 and 623), the curvature is the lowest of the three, and the aspect ratio can differ the most from 1.

[0089] Hereinafter, a method for modeling the cracking of secondary particles and the resulting packing particle filling phenomenon during the rolling process will be described. For example, the secondary particles may be particles formed by agglomeration of multiple single particles, or may be included in a cathode structure, but is not limited thereto. First, to explain the aforementioned cracking of secondary particles and the resulting packing particle filling phenomenon, reference will be made to Figure 7.

[0090] Figure 7 is an example of an image of a rolled electrode structure in which cracking of secondary particles and subsequent filling of packing particles have occurred.

[0091] Referring to FIG. 7, it can be confirmed that at least some of the large-sized secondary particles generated by aggregating primary particles are cracked (710) during the rolling process, and that small, crumb-like packing particles are filled in the pores (720). Since the rolling process applies pressure to the electrode structure using a roller, as can be confirmed in the drawing, the secondary particles are cracked by the pressure (710), and the packing particles (720) generated by the cracking can be filled in the pores. In addition, throughout FIG. 7, it can be confirmed that the surface on the separator side is more likely to receive greater pressure, resulting in more packing particles being filled in the pores on the separator side.

[0092] Refer to Fig. 8 to explain a method for modeling the cracking of secondary particles and the resulting packing particle filling phenomenon as shown in Fig. 7.

[0093] FIG. 8 is a flowchart illustrating a method for modeling the cracking of secondary particles and the resulting packing particle filling phenomenon according to a rolling process, according to one embodiment.

[0094] Referring to FIG. 8, in step S810, the electronic device (100) may acquire an image of an electrode structure including at least one secondary particle. In step S820, the electronic device (100) may, based on the image, determine a distribution of a virtual ellipsoid diameter for at least one space associated with a void included in the electrode structure. In step S830, the electronic device (100) may, based on the distribution, model at least one packing particle generated due to cracking of the secondary particle by the rolling process, on at least a portion of the space. In step S840, the electronic device (100) may model cracking within the secondary particle based on the volume of the packing particle. Each step will be described in more detail below.

[0095] First, the electronic device (100) can acquire an image of an electrode structure including at least one secondary particle. In one embodiment, such an image may be obtained by photographing an electrode structure with a microscope such as an SEM before the rolling process and on which the slurry is actually coated on the current collector, or may be obtained from a model that simulates the electrode structure with the slurry coated on the current collector before the rolling process. Alternatively, the image may be obtained from a model that simulates an electrode structure that has been subjected to a rolling process, but has not yet simulated cracking and packing phenomena. In one embodiment, the model that simulates the electrode structure may be a model that is confirmed based on a method of modeling the shape of an active material in the coating process step during the electrode structure formation process described above, and the image described above may be obtained by extracting an enlarged image of a cross-section from such a model.

[0096] Thereafter, the electronic device (100) can identify at least one space corresponding to a void included in the electrode structure based on the image. Here, the void may refer to an empty space between each particle that is not filled with particles. By such a void, at least one space can be displayed on the image. In addition, the electronic device (100) can identify each virtual maximum ellipsoid that can be included in each void. Refer to FIG. 9 for a description of such a virtual ellipsoid.

[0097] FIG. 9 is an example diagram of creating a virtual ellipsoid in at least some of the gaps included in an image according to one embodiment.

[0098] Referring to Fig. 9, virtual maximum ellipsoids (910, 920, and 930) can be identified. For convenience of explanation, Fig. 9 shows virtual ellipsoids generated for only a portion of the space, but in reality, virtual ellipsoids may be generated for all spaces.

[0099] Thereafter, the electronic device (100) can determine the distribution of the diameters of each maximum ellipsoid. The distribution of the diameters of each maximum ellipsoid can be determined by calculating in a manner similar to the manner used to calculate the PSD. Based on the distribution of the diameters of each maximum ellipsoid, the electronic device (100) can model at least one packing particle generated by cracking of secondary particles by the rolling process, on at least a portion of the space.

[0100] Specifically, the electronic device (100) can identify information about a virtual ellipsoid corresponding to at least one quantile on a distribution of the diameter of the maximum ellipsoid. Here, the quantile may be various quantiles, but may be a representative quantile such as a median, for example. The electronic device (100) can identify the shape of the packing particle based on the information about the virtual ellipsoid. Here, according to one embodiment, the process of identifying the shape of the packing particle may be similar to the process of identifying the shape of the active material in the method of modeling the shape of the active material in the coating process step of the electrode structure formation process described above. For example, the information about the virtual ellipsoid corresponding to the quantile may include the curvature and aspect ratio of the ellipsoid, and the shape of the active material may be identified through a process such as comparing the curvature with a threshold value, sampling the vertices accordingly, and, in some cases, providing roughness. Additionally, similar to the example of the method for modeling the shape of the active material in the coating process step during the electrode structure formation process described above, the packing particles may have various shapes, and for example, may have different shapes depending on the atmosphere.

[0101] Alternatively, rather than a distribution of the diameters of the largest ellipsoids, the shape of the packing particles can be identified based on the average value of the diameters of the largest ellipsoids and the information about the virtual ellipsoids that have diameter values ​​most similar to the average value.

[0102] Of course, other methods for determining the shape of a packing particle are also possible. For example, the shape of a packing particle could be determined by randomly adjusting the representative vertices of the virtual ellipsoid while maintaining the aspect ratio of the virtual ellipsoid. The process for determining the shape of a packing particle is not limited to the above-described method, and any conventionally known shape determination process can be applied.

[0103] Thereafter, the electronic device (100) can model packing particles having at least one size corresponding to the diameter of the virtual ellipsoid corresponding to the aforementioned atmosphere and having the shape of the identified packing particles on at least a portion of the space. Here, according to one embodiment, the process of identifying the size may also be similar to the process of identifying the size of the active material particles in the method of modeling the shape of the active material in the coating process step of the electrode structure forming process described above.

[0104] In one embodiment, when modeling packing particles on at least a portion of a space, it is possible to model by inserting packing particles into randomly selected spaces among each space, or by inserting packing particles into a number of spaces corresponding to a specific ratio or into spaces of a certain size or larger. Alternatively, it is also possible to model by inserting packing particles into spaces selected based on a specific algorithm.

[0105] According to one embodiment, the electronic device (100) may model the packing particles so that the amount of the packing particles increases proportionally as the electrode structure gets closer to the surface in contact with the separator. For example, it may be possible to model such that the probability of packing particles being inserted increases as the space gets closer to the surface in contact with the separator, or such that the closer the space gets to the surface in contact with the separator, the more packing particles are inserted, but the present invention is not limited thereto.

[0106] Thereafter, the electronic device (100) may model cracking within the secondary particle. In one embodiment, the electronic device (100) may model cracking such that the volume of the empty space due to the modeled cracking within the secondary particle corresponds to the volume of the packing particle. As a more specific example, the electronic device (100) may model such that the volume of the empty space due to the modeled cracking on the secondary particle is equal to the volume of the packing particle, or may calculate and model the volume of the empty space due to the cracking based on the density of the packing particle, but is not limited thereto. In addition, in one embodiment, the electronic device (100) may model such that the secondary particle includes a fiber-shaped empty space, but is not limited thereto.

[0107] Additionally, the electronic device (100) can model cracking even for packing particles. In one embodiment, the packing particles may also have fiber-shaped voids due to cracking.

[0108] As described above, the volume of the packing particles and the volume of the empty space due to cracking are proportional to each other, but errors may occur when calculating in this manner. A process for correcting such errors may also be performed, and according to one embodiment, the electronic device (100) may compare the volume of the packing particles and the volume of the empty space and recursively adjust the volume of the packing particles or the volume of the empty space so that the two volumes correspond appropriately.

[0109] Additionally, it may be possible to recursively modify the volume of the packing particles to ensure that it corresponds appropriately to the loading set in relation to the design of the electrode structure and the PSD described above.

[0110] Refer to Fig. 10 to examine an example of an electrode structure that models the cracking of secondary particles and the filling of packing particles according to the rolling process as described above.

[0111] FIG. 10 is an exemplary diagram of an electrode structure modeling the cracking of secondary particles and the filling phenomenon of packed particles according to one embodiment.

[0112] Referring to Fig. 10, it can be confirmed that the packing particles (1010) are largely packed on the surface close to the separator, and each particle includes a fiber-shaped void space. In addition, it can be confirmed that the secondary particles (1020) are cracked, and each particle also includes a fiber-shaped void space. In addition, it can be confirmed that the particles other than the secondary particles do not experience cracking.

[0113] Figure 11 illustrates a block diagram of an electronic device according to one embodiment.

[0114] According to one embodiment, the electronic device (100) may include a memory (101) and a processor (102). The electronic device (100) illustrated in FIG. 11 only illustrates components related to the present embodiment. Therefore, those skilled in the art will understand that, in addition to the components illustrated in FIG. 11, other general-purpose components may be included. In one embodiment, the processor (102) may be included in a controller.

[0115] The processor (102) can control the overall operation of the electronic device (100) and process data and signals. The processor (102) can be composed of at least one hardware unit. In addition, the processor (102) can operate by one or more software modules generated by executing program codes stored in the memory (101). The processor (102) can include a memory, and the processor (102) can control the overall operation of the electronic device (100) and process data and signals by executing program codes stored in the memory.

[0116] The processor (102) may be configured to acquire an image related to a cross-section of at least one active material particle by performing one or more instructions, calculate a curve of at least one active material particle based on the image, check a comparison result of the curve and a threshold, and model at least one active material particle included in a coated electrode structure based on the comparison result and a particle size distribution of the at least one active material particle.

[0117] Alternatively, the processor (102) may be configured to acquire an image for an electrode structure including at least one secondary particle by performing one or more instructions, determine a distribution of virtual ellipsoid diameters for at least one void included in the electrode structure based on the image, model at least one packing particle generated due to cracking of the secondary particle by the rolling process on at least a portion of the void, and model the void due to cracking on the secondary particle based on a volume of the packing particle.

[0118] Depending on the embodiment, the electronic device (100) may additionally include a transceiver for performing wired / wireless communication. The electronic device (100) may communicate with an external electronic device using the transceiver. The external electronic device may be a terminal or a server. In addition, the communication technologies used by the transceiver may include GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth (Bluetooth), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc.

[0119] The electronic device according to the above-described embodiments may include a processor, a memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with an external device, a user interface device such as a touch panel, a key, a button, etc. The methods implemented as software modules or algorithms may be stored on a computer-readable recording medium as computer-readable codes or program instructions executable on the processor. Here, the computer-readable recording medium includes a magnetic storage medium (e.g., read-only memory (ROM), random-access memory (RAM), floppy disk, hard disk, etc.) and an optical reading medium (e.g., CD-ROM, DVD: Digital Versatile Disc)). The computer-readable recording medium may be distributed to computer systems connected to a network, so that the computer-readable code may be stored and executed in a distributed manner. The medium may be readable by a computer, stored in a memory, and executed by a processor.

[0120] The present embodiment may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various hardware and / or software configurations that perform specific functions. For example, the embodiment may employ direct circuit configurations such as memory, processing, logic, look-up tables, etc., which may perform various functions under the control of one or more microprocessors or other control devices. Similarly, the present embodiment may be implemented in a programming or scripting language such as C, C++, Java, assembler, etc., including various algorithms implemented as a combination of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms that execute on one or more processors. Furthermore, the present embodiment may employ conventional techniques for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism," "element," "means," and "composition" can be used broadly and are not limited to mechanical or physical structures. These terms can also encompass a series of software routines, such as those associated with a processor.

[0121] The above-described embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. A method for modeling active material particles included in a coated electrode structure of an electronic device, A step of acquiring an image related to a cross-section of at least one active material particle; A step of calculating a curve of at least one active material particle based on the image; A step of checking the comparison results of the above curve and threshold; and A modeling method comprising a step of modeling at least one active material particle included in a coated electrode structure based on the comparison result and the particle size distribution of the at least one active material particle.

2. In paragraph 1, The step of modeling at least one active material particle comprises: If the above comparison result indicates that the curve exceeds the threshold, A step of calculating the number of vertices based on the above curve and the first parameter; A step of confirming the three-dimensional shape of at least one active material particle based on the number of vertices; and A modeling method comprising a step of modeling at least one active material particle based on the three-dimensional shape and the particle size distribution.

3. In paragraph 2, The step of confirming the above three-dimensional shape is: A step of sampling vertices corresponding to the number of vertices on a virtual sphere; and A modeling method comprising a step of confirming the three-dimensional shape based on the above vertices.

4. In paragraph 1, The step of modeling at least one active material particle comprises: If the above comparison result indicates that the curve is below the threshold, A step of calculating the number of vertices based on the above curve and the second parameter; A step of confirming the three-dimensional shape of at least one active material particle based on the number of vertices; A step of increasing the roughness of the three-dimensional shape by adjusting the vertices of the three-dimensional shape while maintaining the aspect ratio of at least one active material particle identified based on the image; and A modeling method comprising a step of modeling the active material particles based on the three-dimensional shape with increased roughness and the particle size distribution.

5. In paragraph 4, A modeling method in which the above aspect ratio is confirmed by calculating the aspect ratio of an active material particle corresponding to at least one quantile in the particle size distribution among a plurality of active material particles.

6. In paragraph 1, The steps for calculating the above curve are: A modeling method, comprising a step of calculating an average of curves of a plurality of active material particles based on the above image as the curve of the active material particle.

7. In paragraph 1, The steps for calculating the above curve are: Based on the image, calculating a maximum diameter corresponding to a circle having a maximum size included in a boundary corresponding to the cross-section of the at least one active material particle; A step of calculating the diameter of each circle that touches at least a portion of the boundaries corresponding to the above cross-section; and A modeling method comprising a step of calculating the curve by dividing the average of the diameters of each circle that touches at least a portion of the boundaries corresponding to the cross-section by the maximum diameter.

8. In paragraph 1, The step of modeling at least one active material particle comprises: A modeling method comprising a step of modeling a plurality of at least one active material particles to have a three-dimensional shape simulated based on the comparison results and to have at least one size value confirmed based on the particle size distribution.

9. In paragraph 1, The steps of obtaining the above image are: A modeling method comprising a step of obtaining the image by photographing the entire collector coated with the active material particles using a scanning electron microscope (SEM).

10. In paragraph 1, A modeling method further comprising a step of checking the particle size distribution of the active material particles measured by the particle size analyzer by injecting at least one active material particle into the particle size analyzer.

11. In paragraph 1, A modeling method further comprising the step of modeling an electrode structure comprising at least one active material particle.

12. An electronic device that models active material particles included in an electrode structure, processor; and Contains memory that stores one or more instructions, An electronic device, wherein the processor is configured to obtain an image related to a cross-section of at least one active material particle by performing the one or more instructions, calculate a curve of the at least one active material particle based on the image, check a comparison result of the curve and a threshold, and model the at least one active material particle included in a coated electrode structure based on the comparison result and a particle size distribution of the at least one active material particle.

13. In a method for modeling cracking of rolled secondary particles and filling of packed particles in an electronic device, A step of acquiring an image for an electrode structure including at least one secondary particle; Based on the image, a step of confirming the distribution of a virtual ellipsoid diameter for at least one space related to a void included in the electrode structure; Based on the above distribution, a step of modeling at least one packing particle generated by cracking of the secondary particle by the rolling process on at least a part of the space; A modeling method comprising a step of modeling cracking within the secondary particle based on the volume of the packing particle.

14. In paragraph 13, The steps to check the above distribution are: A step of identifying each virtual maximum ellipsoid included in each of the above spaces; and A modeling method comprising a step of confirming the distribution of the diameter of each of the above maximum ellipsoids as the above distribution.

15. In paragraph 13, The step of modeling the above packing particles on at least a portion of the above space is: A step of checking information about a virtual ellipsoid corresponding to at least one quantile on the above distribution; A step of confirming the shape of the packing particle based on information about the virtual ellipsoid; and A modeling method comprising the step of modeling the packing particle having the shape of the identified packing particle and having at least one size corresponding to the diameter of the virtual ellipsoid corresponding to the atmosphere, on at least a portion of the space.

16. In paragraph 13, The step of modeling the above packing particles on at least a portion of the above space is: A modeling method comprising a step of modeling the packing particles so that the amount of the packing particles increases proportionally as the electrode structure gets closer to the surface in contact with the separator.

17. In paragraph 13, The step of modeling the cracking-induced voids on the above secondary particles is: A modeling method comprising the step of modeling a void on the secondary particle so that the volume of the void on the secondary particle corresponds to the volume of the packing particle.

18. In paragraph 13, The step of modeling the cracking-induced voids on the above secondary particles is: A modeling method comprising a step of modeling the pores so that the secondary particles include pores in the form of fibers.

19. In paragraph 13, The steps of obtaining the above image are: A step of verifying a model simulating an electrode structure in which a slurry is coated on a current collector; and A modeling method, comprising a step of obtaining an image of the electrode structure before the rolling process is performed, based on the above model.

20. An electronic device that models active material particles included in an electrode structure, processor; and Contains memory that stores one or more instructions, An electronic device, wherein the processor is configured to obtain an image of an electrode structure including at least one secondary particle by performing the one or more instructions, and, based on the image, determine a distribution of virtual ellipsoid diameters for at least one space associated with at least one void included in the electrode structure, and, based on the distribution, model at least one packing particle generated by cracking of the secondary particle by a rolling process on at least a portion of the void, and, based on a volume of the packing particle, model the void caused by cracking on the secondary particle.

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