Lithium ion battery negative electrode microstructure modeling method, device, medium and equipment
By imaging and numerically reconstructing lithium-ion battery anode materials, a high-precision three-dimensional microstructure model is generated, which solves the problem of low simulation accuracy in existing technologies and achieves more efficient modeling and optimization of battery performance.
Patent Information
- Application Number
- CN202511615677.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-06
- Publication Date
- 2026-02-03
AI Technical Summary
Existing lithium-ion battery anode microstructure modeling methods cannot accurately describe the real microstructure and are difficult to reflect the pore distribution and particle contact characteristics of the material, resulting in low simulation accuracy.
By imaging lithium-ion battery anode materials to obtain structural information, multiple two-dimensional microstructure slice images are generated using numerical reconstruction technology, and then converted into three-dimensional microstructure models through three-dimensional Gaussian blurring. This method is applicable to different types of anode materials such as graphite, silicon-based materials, and metal oxides.
It improves the realism and computational efficiency of negative electrode microstructure modeling, reduces computational resource consumption, adapts to different application needs, and optimizes battery performance.
Smart Images
Figure CN121456936A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of lithium ion batteries, in particular to a lithium ion battery negative electrode microstructure modeling method, device, medium and equipment. BACKGROUND
[0002] Lithium ion batteries have been widely used in electric vehicles, energy storage systems and portable electronic devices due to their high energy density, long life and good cycle stability. The microstructure of the negative electrode material has an important influence on the performance of the battery, including its ion transport characteristics, electrochemical reaction uniformity and cycle stability. Therefore, accurate characterization and modeling of the negative electrode microstructure are key steps in optimizing battery design and improving battery performance.
[0003] However, the current negative electrode microstructure modeling method has some shortcomings. Statistical models cannot accurately describe the real microstructure and cannot reflect the real pore distribution and particle contact characteristics of the material; random generation models can simulate some structural characteristics, but lack experimental data support, resulting in low simulation accuracy, and thus it is difficult to accurately describe the lithium battery negative microstructure. SUMMARY
[0004] The present application provides a lithium ion battery negative electrode microstructure modeling method, device, medium and equipment to solve the above problems existing in the prior art, i.e. how to accurately describe the lithium battery negative microstructure in the prior art. The present application provides a lithium ion battery negative electrode microstructure modeling method, which comprises: Imaging the negative electrode material of the lithium ion battery to obtain the structural information of the negative electrode material; According to the structural information of the negative electrode material, the geometric features of the particles and pores in the structural information are extracted, and the geometric features extracted are subjected to morphological analysis to determine the particle morphology and pore distribution; In the reconstruction calculation domain, based on the particle morphology and pore distribution, each particle is subjected to coordinate transformation through a preset rotation axis and rotation angle, and an additive is randomly generated around the particle surface or the pore until a preset porosity is reached, and a numerical reconstruction technique is combined to generate multiple two-dimensional microstructure slice images reflecting the microstructure characteristics of the negative electrode material; The multiple two-dimensional microstructure slice images are subjected to three-dimensional Gaussian blur processing, and a visualization technique is combined to convert the two-dimensional microstructure slice images into a three-dimensional microstructure model for describing the microstructure of the negative electrode material.
[0005] Optionally, a high-resolution scanning electron microscope SEM is used to image the negative electrode material to obtain the structural information of the negative electrode material.
[0006] Optionally, the particles are in the shape of an "ellipsoid", and the particle surface is uniformly covered with a binder.
[0007] Optionally, the negative electrode material comprises graphite, silicon-based material or metal oxide negative electrode.
[0008] Optionally, internal transmission performance of the three-dimensional microstructure model is calculated and analyzed by a pore scale model (PSM) to optimize the particle distribution.
[0009] Optionally, in the two-dimensional microstructure slice image, a region with a pixel value less than or equal to a preset threshold value is an additive, and a region with a pixel value greater than the preset threshold value is a pore.
[0010] The present application provides a lithium ion battery negative electrode microstructure modeling device, comprising: An acquisition module is configured to image a negative electrode material of a lithium ion battery and acquire structural information of the negative electrode material. An extraction module is configured to extract geometric features of particles and pores in the structural information according to the structural information of the negative electrode material, perform morphological analysis on the extracted geometric features, and determine particle morphology and pore distribution. A two-dimensional microstructure slice image determination module is configured to perform coordinate transformation on each particle based on the particle morphology and the pore distribution by a preset rotation axis and rotation angle in a reconstruction calculation domain, and randomly generate an additive around the particle surface or the pore until a preset porosity is reached, and generate a plurality of two-dimensional microstructure slice images reflecting microstructure characteristics of the negative electrode material in combination with a numerical reconstruction technology. A three-dimensional microstructure model determination module is configured to perform three-dimensional Gaussian blur processing on the plurality of two-dimensional microstructure slice images, and convert the two-dimensional microstructure slice images into a three-dimensional microstructure model for describing the microstructure of the negative electrode material in combination with a visualization technology.
[0011] The present application provides a computer readable storage medium, the storage medium stores a computer program, the computer program is executed by a processor to realize the above-mentioned lithium ion battery negative electrode microstructure modeling method.
[0012] The present application provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executes the program to realize the above-mentioned lithium ion battery negative electrode microstructure modeling method.
[0013] Compared with the prior art, the present application has the following beneficial effects: the present application provides a lithium ion battery negative electrode microstructure modeling method, which can more accurately simulate the negative electrode microstructure and improve the authenticity of modeling by imaging the negative electrode material to obtain the structure information of the negative electrode material and combining the numerical reconstruction technology; the present application improves the calculation efficiency of numerical simulation and reduces the consumption of computing resources by generating three-dimensional structures under different deformation degrees through the numerical reconstruction technology; in addition, the present application is suitable for different types of negative electrode materials, such as graphite, silicon-based materials and metal oxide negative electrodes, and can be adjusted according to different application requirements, having high flexibility. BRIEF DESCRIPTION OF DRAWINGS
[0014] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.
[0015] Figure 1 A flowchart of a lithium ion battery negative electrode microstructure modeling method provided by an embodiment of the present application; Figure 2 A high-resolution SEM image of a negative electrode material provided by an embodiment of the present application; Figure 3 An XCT image of a negative electrode material provided by an embodiment of the present application; Figure 4 A numerical reconstruction process schematic diagram of a negative electrode material provided by an embodiment of the present application; Figure 5 A two-dimensional reconstruction diagram of a negative electrode microstructure obtained based on numerical reconstruction provided by an embodiment of the present application; Figure 6 A three-dimensional structure diagram of a negative electrode microstructure obtained based on numerical reconstruction and visualization technology provided by an embodiment of the present application; Figure 7 A microstructure comparison diagram of an optimized electrode provided by an embodiment of the present application; Among them, Figure 7 (a) of which is an experimental model, Figure 7 (b) of which is a numerical reconstruction model; Figure 8 A computer device schematic diagram of a lithium ion battery negative electrode microstructure modeling method provided by an embodiment of the present application. DETAILED DESCRIPTION
[0016] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the protection scope of the present application.
[0017] The technical solutions of the present application and how the technical solutions solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below in conjunction with the drawings.
[0018] Figure 1 is a flow chart of a lithium ion battery negative electrode microstructure modeling method provided by an embodiment of the present application, as shown in the embodiment, the lithium ion battery negative electrode microstructure modeling method comprises the following steps. Figure 1 S1: imaging the negative electrode material of the lithium ion battery to obtain the structure information of the negative electrode material.
[0019] Optionally, a high-resolution scanning electron microscope SEM is used to image the negative electrode material to obtain the structure information of the negative electrode material.
[0020] For example, a commercial lithium ion battery graphite negative electrode material such as graphite, silicon-based material and metal oxide negative electrode can be selected, and SEM imaging is used to obtain two-dimensional microstructure data; through observation of the SEM scanning image of the negative electrode material, it is found that the shape of a single active particle is similar to an “ellipsoid”. In numerical reconstruction, the active particle is assumed to be a randomly distributed “ellipsoid”. The negative electrode material is characterized by XCT technology, a three-dimensional experimental model of XCT scanning is obtained, and the XCT scanning image is processed by visualization software.
[0021] As shown in Figure 2 , it is a high-resolution SEM image of the negative electrode material, wherein, Figure 2 (a) to Figure 2 (c) of are the morphologies of the sample surface at different magnifications, Figure 2 (d) to Figure 2 (f) are the morphologies of the sample cross section at different magnifications. As shown in Figure 3 , the XCT scanning image is processed by visualization software, wherein the gray material is active material graphite, the black area is a pore, and the red material around the active material is an additive composed of conductive carbon black and a binder (carboxymethyl cellulose, butadiene rubber).
[0022] S2: According to the structural information of the negative electrode material, the geometric characteristics of the particles and pores in the structural information are extracted, and morphological analysis is performed on the extracted geometric characteristics to determine the particle morphology and pore distribution.
[0023] Exemplarily, the geometric characteristics of the particles, pores and binders can be extracted by using an image processing algorithm, and the geometric characteristics can include shape, size distribution and spatial distribution characteristics.
[0024] S3: In the reconstruction calculation domain, based on the particle morphology and pore distribution, coordinate transformation is performed on each particle by a preset rotation axis and rotation angle, and additives are randomly generated on the surface of the particle or around the pores until a preset porosity is reached. Combined with numerical reconstruction technology, a plurality of two-dimensional microstructure slice images reflecting the microstructure characteristics of the negative electrode material are generated.
[0025] Exemplarily, the active particle size is counted to obtain the size distribution of the two equatorial radii (major axis and medium axis) and the polar radius (minor axis) of the "ellipsoid". Under the premise of high consistency with the real microstructure, the negative electrode material reconstruction process in the embodiment is based on the following assumptions: the active particles are regular "ellipsoids". Generally, the reconstruction calculation domain is usually defined as a three-dimensional cube (or a two-dimensional rectangular region) with a preset size, and the size is related to the simulation or modeling accuracy requirement. A representative volume (Representative Volume Element, RVE) can be determined according to the actual image size of the negative electrode material obtained from XCT, SEM, etc. as the calculation domain. For example, if the image resolution is 0.1 μm / pixel and the imaging area is 500×500 pixels, the two-dimensional calculation domain can be defined as 50 μm × 50 μm. According to the representative volume (RVE) principle, the spatial size and resolution of the domain can be determined in the reconstruction calculation domain combined with the imaging results of the negative electrode material.
[0026] Exemplarily, the reconstruction calculation domain is a three-dimensional matrix, and all pixels are divided into three phases: active material, additive and pore; the center of the "ellipsoid" is randomly selected in the calculation domain; the probability density distribution of the "ellipsoid" position in each direction is uniform; the "ellipsoid" is allowed to overlap during the generation process; the two equatorial radii a and b and the polar radius c of each "ellipsoid" in the calculation domain are not fixed, and each radius is randomly selected within a preset size range; the additives are randomly distributed on the surface and around the active material.
[0027] As Figure 4The diagram illustrates the numerical reconstruction process of the anode material. During reconstruction, important parameters such as the size of the computational domain, the size of the active particles, and the porosity are used as input to the MATLAB code. Based on the aforementioned analysis of the actual microstructure of the anode material, the size of the reconstruction computational domain is determined. First, the shape of the "ellipsoid" is defined by random selection. A value is randomly selected from the maximum and minimum values of the two equatorial radii and polar radii of the preset "ellipsoid." Then, a coordinate is randomly selected within the computational domain as the center of the first "ellipsoid," i.e., the first active particle. Next, the coordinate system is rotated using a standard Euler angle transformation. The three rotation angles α, β, and γ are all arbitrarily selected between 0 and 60°, and the rotation matrix R is calculated. The pixels of the solid "ellipsoid" formed by the determined center and the three radii become solid, which is the generated first active particle. Following this method, the second active particle is generated, and this process is repeated until the volume ratio of all active particles reaches the preset size, i.e., the target porosity without additives.
[0028] Figure 5 The image shows a two-dimensional reconstruction of the anode microstructure. In the image, the slice size of the reconstructed model is 100×100 pixels, where the red phase represents active particles and the yellow phase represents binder.
[0029] S4: Perform three-dimensional Gaussian blur processing on multiple two-dimensional microstructure slice images, and combine with visualization technology to convert the two-dimensional microstructure slice images into a three-dimensional microstructure model for describing the microstructure of the negative electrode material.
[0030] like Figure 6 The image shows a three-dimensional structure diagram of the negative electrode microstructure. The size of the three-dimensional microstructure model is 100×100×40 pixels. The computational domain size of the model is 200×200×80 pixels. Figure 7 As shown, although the shape distribution of active particles varies slightly, Figure 7 The numerical reconstruction model shown in (a) is similar to Figure 7 The experimental model (b) shows a high degree of similarity, especially in the random position distribution characteristics of the active particles.
[0031] The following section uses graphite anode materials as an example to illustrate the process of generating the three-dimensional microstructure of the anode.
[0032] For example, the numerical reconstruction model of the anode material is divided into grid voxels described by multiple phase functions. Represents an arbitrary position vector in three-dimensional space (negative electrode reconstruction computational domain); I = 0, 1, 2, where "0" represents the porous phase, "1" represents the active particle, and "2" represents the solid additive.
[0033] The reconstruction process assumes that the direction parallel to the electrode thickness direction is along the Z axis, and the two directions perpendicular to the Z axis are parallel to the X and Y axes, respectively. The micro-morphology of the graphite negative electrode has obvious anisotropy in general, but is approximately isotropic in the X and Y directions, so two two-point correlation functions f1 and f2 need to be established.
[0034] The shape and position of the entire "ellipsoid" can be defined by six parameters. In addition to the two equatorial radii and in the single "ellipsoid" size parameter , three rotation angles , and are also included. The range of the three rotation angles is set to be between 0-60°. Since the single active particle distribution of the negative electrode material has directionality, when generating the active particle "ellipsoid", the rotation angle of the "ellipsoid", i.e. the Euler angle, needs to be set. Any rotation can be divided into three parts: rotating by an angle α around the axis, called A; rotating by an angle β around the axis (which becomes here), called B; and rotating by an angle γ around the axis (which becomes here), called C.
[0035] Since there is only one fixed coordinate system in the calculation domain of the reconstruction, coordinate transformation is needed after the "ellipsoid" is generated. The Euler angle describes how to transform from the original coordinate system to the final coordinate system step by step, and a coordinate transformation matrix is also needed to reverse from the final coordinate system to the original coordinate system.
[0036] The transformation matrix at each step is as follows: The coordinate system before rotation can be represented by the coordinate system after rotation : where the matrix R is the rotation matrix, i.e. R= .
[0037] In the numerical reconstruction process, in addition to the six limiting parameters of the "ellipsoid" active particles, the volume fractions of porosity, active material and additives, and the size of the calculation domain, etc. also need to be input. The numerical reconstruction method is used to randomly generate solid additives on the surface of the active particles. The area within a certain distance from the surface of the active particles gradually becomes a solid additive until the preset porosity is reached, i.e. the generation is stopped.
[0038] In the numerical reconstruction process, based on the generated Domain image without additives, first, the distance of each pixel in the image to the nearest non-zero pixel can be calculated by using the bwdist function. The non-zero region in the input image Domain is regarded as an active particle, so each pixel in the AA image represents its distance to the nearest particle. Then the input image is blurred using the imgaussfilt3 function. The AA image is blurred by Gaussian, and the result is stored in AAA to help smooth the image and reduce noise. Then a threshold sep is calculated according to the region sorting defined as "0" and "1", which is obtained by adding the proportion of additives to all solids. The position of the pixel value less than or equal to the threshold sep will be defined as "2", that is, the additive. The position of the pixel value greater than the threshold sep will be defined as "0", that is, the pore.
[0039] The cycle is generated until the preset porosity is reached, the numerical reconstruction process is ended, and the two-dimensional slice image of the graphite negative electrode microstructure is obtained; then, the visualization software is used to process the two-dimensional slice image of the graphite negative electrode microstructure, and the graphite negative electrode microstructure model is obtained.
[0040] Exemplarily, the negative electrode material numerical reconstruction algorithm is verified, and the experimental reconstruction model and the numerical reconstruction model are compared and analyzed, mainly in terms of pore distribution and active particle distribution.
[0041] Exemplarily, the three-dimensional experimental model obtained by XCT scanning and the numerical model in the thickness direction are compared in terms of porosity distribution. According to the comparison result, the numerical reconstruction code is optimized. Further verify the rationality and reliability of the numerical reconstruction method in describing the microstructure of the negative electrode material. Due to the irregular shape of the active particles, the volume of each active particle is converted into the diameter of a sphere, that is, the equivalent diameter of each particle is obtained and the size is counted, so as to quantitatively compare the microstructure similarity of the two models.
[0042] According to the comparison result, the numerical reconstruction code and the input parameters are constantly optimized until the comparison result presents high consistency. By controlling and adjusting the input parameters, graphite negative electrode three-dimensional microstructures with different volume proportions are generated. The internal transport performance of the negative electrode three-dimensional microstructure is calculated and analyzed by the pore scale model PSM, the pore distribution is optimized, the electrolyte wettability and electronic conductivity are improved, and the battery performance is optimized.
[0043] The method proposed in the application can be widely applied to different types of negative electrode materials, and can be adjusted according to different application requirements.
[0044] The lithium ion battery negative microstructure modeling method provided in the above one or more embodiments of the present specification is based on the same idea, and the present specification also provides a corresponding lithium ion battery negative microstructure modeling device, which comprises: An acquisition module is configured to image the negative material of the lithium ion battery and acquire structural information of the negative material. An extraction module is configured to extract geometric features of particles and pores in the structural information of the negative material according to the structural information of the negative material, perform morphological analysis on the extracted geometric features, and determine particle morphology and pore distribution. A two-dimensional microstructure slice image determination module is configured to perform coordinate transformation on each particle based on the particle morphology and the pore distribution by a preset rotation axis and a rotation angle in a reconstruction calculation domain, and randomly generate additives around the particle surface or the pores until a preset porosity is reached, and generate a plurality of two-dimensional microstructure slice images reflecting the microstructure characteristics of the negative material by combining a numerical reconstruction technique. A three-dimensional microstructure model determination module is configured to perform three-dimensional Gaussian blur processing on the plurality of two-dimensional microstructure slice images, and convert the two-dimensional microstructure slice images into a three-dimensional microstructure model for describing the microstructure of the negative material by combining a visualization technique.
[0045] The specific limitations of the lithium ion battery negative microstructure modeling device can be referred to the limitations of the lithium ion battery negative microstructure modeling method described above, which will not be repeated here. Each module in the above lithium ion battery negative microstructure modeling device can be realized by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each module.
[0046] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program can be used to execute the above-mentioned lithium ion battery negative microstructure modeling method.
[0047] The present application also provides Figure 8 The structure of the computer device is shown in the structure diagram of the computer device, as shown in Figure 8 As shown in the hardware level, the computer device comprises a processor, an internal bus, a network interface, a memory and a non-volatile memory, and of course can also comprise other hardware required by the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs to realize the lithium ion battery negative microstructure modeling method provided by the above embodiments.
[0048] The technical features of the above embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not contradict each other, they should be considered to be within the scope of the present application.
Claims
1. A method of modeling a lithium-ion battery negative microstructure, the method comprising: The method comprises the following steps: Imaging the negative electrode material of a lithium ion battery to obtain structural information of the negative electrode material; Extracting geometric features of particles and pores in the structural information of the negative electrode material, and performing morphological analysis on the extracted geometric features to determine particle morphology and pore distribution; In a reconstruction calculation domain, performing coordinate transformation on each particle based on the particle morphology and the pore distribution through a preset rotation axis and a rotation angle, and randomly generating additives around the particle surface or the pores until a preset porosity is reached, and combining a numerical reconstruction technique to generate multiple two-dimensional microstructure slice images reflecting microstructure characteristics of the negative electrode material; Performing three-dimensional Gaussian blur processing on the multiple two-dimensional microstructure slice images, and converting the two-dimensional microstructure slice images into a three-dimensional microstructure model for describing the microstructure of the negative electrode material through a visualization technique.
2. The method of modeling the microstructure of a lithium-ion battery anode of claim 1, wherein, The negative electrode material is imaged by a high-resolution scanning electron microscope (SEM) to obtain structural information of the negative electrode material.
3. The method of modeling the microstructure of a lithium-ion battery anode of claim 1, wherein, The particles are in the shape of "ellipsoids", and the particle surface is uniformly covered with a binder.
4. The method of modeling microstructure of a lithium-ion battery anode of claim 1, wherein, The negative electrode material includes graphite, silicon-based material or metal oxide negative electrode.
5. The method of modeling microstructure of a lithium-ion battery anode of claim 1, wherein, The internal transmission performance of the three-dimensional microstructure model is calculated and analyzed through a pore scale model (PSM) to optimize the particle distribution.
6. The method of modeling microstructure of a lithium-ion battery anode of claim 1, wherein, In the two-dimensional microstructure slice images, a region with a pixel value less than or equal to a preset threshold value is an additive, and a region with a pixel value greater than the preset threshold value is a pore.
7. A lithium-ion battery negative microstructure modeling apparatus, characterized by, The method comprises the following steps: An acquisition module is configured to image the negative electrode material of a lithium ion battery to obtain structural information of the negative electrode material; An extraction module is configured to extract geometric features of particles and pores in the structural information of the negative electrode material, and perform morphological analysis on the extracted geometric features to determine particle morphology and pore distribution; A two-dimensional microstructure slice image determination module is configured to perform coordinate transformation on each particle based on the particle morphology and the pore distribution through a preset rotation axis and a rotation angle in a reconstruction calculation domain, and randomly generate additives around the particle surface or the pores until a preset porosity is reached, and combine a numerical reconstruction technique to generate multiple two-dimensional microstructure slice images reflecting microstructure characteristics of the negative electrode material; A three-dimensional microstructure model determination module is configured to perform three-dimensional Gaussian blur processing on the multiple two-dimensional microstructure slice images, and convert the two-dimensional microstructure slice images into a three-dimensional microstructure model for describing the microstructure of the negative electrode material through a visualization technique.
8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is executed by the processor to implement the lithium ion battery negative electrode microstructure modeling method in any one of claims 1-6.
9. A computer device, comprising: The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the lithium ion battery negative electrode microstructure modeling method in any one of claims 1-6 when executing the program.