Distribution characteristic evaluation method and device of battery electrode microstructure, and electronic equipment

Abstract

The invention discloses a distribution characteristic evaluation method and device for a microstructure of a battery electrode and electronic equipment. The method comprises the following steps: constructing a microscopic geometric model of the battery electrode; determining a target segmentation number; segmenting the microscopic geometric model into a target segmentation number of partial microscopic geometric models with the same volume; and determining a porous electrode non-uniformity index according to the overall porosity of the micro-geometric model and the local porosity of part of the micro-geometric model. The evaluation method provided by the invention can more accurately evaluate and compare the uniformity of different electrode designs on the microscopic level.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a method for evaluating the distribution characteristics of a battery electrode microstructure, a computer-readable storage medium, an electronic device, and a device for evaluating the distribution characteristics of a battery electrode microstructure. Background Art

[0002] Against the backdrop of the rapid development of lithium battery technology, the importance of high-fidelity electrode geometry models has become increasingly prominent. These models can accurately capture the microstructure of battery electrodes, including key parameters such as active material distribution, porosity, tortuosity, and pore size distribution, thereby enabling accurate prediction and optimization of battery performance on computers. With the continuous improvement of battery performance requirements, especially in terms of energy density, power density, and cycle stability, high-fidelity models have become a key tool for understanding the multi-physics transport mechanisms within batteries and guiding the design of electrode materials and structures.

[0003] In the current related technologies, a variety of methods are used to construct electrode geometric micromodels, such as virtual electrode microstructure generation technology based on spatial random modeling, and the use of random geometry tools to create various electrode microstructures on computers. The development of these technologies not only promotes a deeper understanding of the impact of battery electrode microstructure, but also provides a powerful optimization method for battery design, which helps to accelerate the research and development of new battery materials and structures, and meet the urgent needs of electric vehicles, mobile devices, and large-scale energy storage for high-performance batteries. With the increase in computing power and the continuous advancement of modeling technology, high-fidelity electrode geometry models will play a more critical role in the development of lithium batteries.

[0004] However, when constructing high-fidelity electrode geometric models, macroscopic property parameters are generally used for regulation and evaluation, such as porosity and tortuosity. These parameters describe the overall or statistical properties of electrode materials or geometric models, but cannot reflect the spatial distribution characteristics of electrode materials as porous materials, such as the size and uniformity of particles in different phases. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems in the related art to a certain extent. To this end, the first purpose of the present invention is to propose a method for evaluating the distribution characteristics of the microstructure of a battery electrode, construct a microscopic geometric model of the battery electrode, determine a target number of segmentations, segment the microscopic geometric model into a target number of segmented partial microscopic geometric models of the same volume, and determine the porous electrode non-uniformity index according to the overall porosity of the microscopic geometric model and the local porosity of the partial microscopic geometric model, thereby being able to more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level.

[0006] A second object of the present invention is to provide a computer-readable storage medium.

[0007] A third objective of the present invention is to provide an electronic device.

[0008] A fourth object of the present invention is to provide a device for evaluating the distribution characteristics of a battery electrode microstructure.

[0009] To achieve the above-mentioned purpose, the first aspect of the present invention proposes a method for evaluating the distribution characteristics of a battery electrode microstructure, the method comprising: constructing a microscopic geometric model of the battery electrode; determining a target number of divisions; dividing the microscopic geometric model into the target number of partial microscopic geometric models of the same volume; and determining a porous electrode inhomogeneity index based on the overall porosity of the microscopic geometric model and the local porosity of the partial microscopic geometric model.

[0010] According to the distribution characteristics evaluation method of the battery electrode microstructure of the embodiment of the present invention, a microscopic geometric model of the battery electrode is constructed, the target number of segmentations is determined, the microscopic geometric model is segmented into the target number of partial microscopic geometric models with the same volume, and the porous electrode non-uniformity index is determined according to the overall porosity of the microscopic geometric model and the local porosity of the partial microscopic geometric models. As a result, the method can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level.

[0011] In addition, the method for evaluating the distribution characteristics of the battery electrode microstructure according to the above embodiment of the present invention may also have the following additional technical features:

[0012] According to one embodiment of the present invention, determining the target segmentation number includes: calculating the porous electrode non-uniformity index under multiple segmentation numbers; determining a fitting curve based on the multiple segmentation numbers and the porous electrode non-uniformity index corresponding to each segmentation number; and determining the target segmentation number based on the fitting curve.

[0013] According to one embodiment of the present invention, the calculation of the porous electrode non-uniformity index under multiple division numbers includes: determining the overall porosity of the micro-geometric model and the local porosity of the part of the micro-geometric model divided according to the division number; determining the porous electrode non-uniformity index based on the overall porosity and the local porosity.

[0014] According to one embodiment of the present invention, the porous electrode non-uniformity index is determined in the following manner:

[0015]

[0016] Wherein, H is the inhomogeneity index of the porous electrode, φ is the overall porosity of the micro-geometric model, and φ iIt is the i-th local porosity after dividing the micro-geometric model into n partial micro-geometric models of the same volume, where n is the number of divisions.

[0017] According to one embodiment of the present invention, determining the target number of segmentations based on the fitting curve includes: determining the position on the fitting curve corresponding to when the rate of change of the porous electrode non-uniformity index reaches the target rate of change; determining whether the number of segmentations corresponding to the position is a natural number; if not, taking the natural number closest to the position as the target number of segmentations; if yes, taking the number of segmentations corresponding to the position as the target number of segmentations.

[0018] According to one embodiment of the present invention, the method of determining a fitting curve based on multiple segmentation numbers and the porous electrode non-uniformity index corresponding to each segmentation number includes: determining a fitting curve segment between two adjacent segmentation numbers based on the two adjacent segmentation numbers and the porous electrode non-uniformity index corresponding to the segmentation numbers to obtain at least one fitting curve segment; and synthesizing the at least one fitting curve segment to obtain the fitting curve.

[0019] According to one embodiment of the present invention, the micro-geometric model is a positive electrode micro-geometric model, and the method further comprises: combining the positive electrode micro-geometric model with the diaphragm and the negative electrode micro-geometric model, and performing electrochemical simulation through simulation software.

[0020] To achieve the above-mentioned purpose, a second aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating the distribution characteristics of a battery electrode microstructure.

[0021] According to the computer-readable storage medium of the embodiment of the present invention, by implementing the above-mentioned method for evaluating the distribution characteristics of the microstructure of a battery electrode when executed, the uniformity of different electrode designs at the microscopic level can be more accurately evaluated and compared.

[0022] To achieve the above-mentioned purpose, an electronic device is proposed in an embodiment of the third aspect of the present invention, comprising a memory, a processor and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned method for evaluating the distribution characteristics of the battery electrode microstructure is implemented.

[0023] The electronic device according to the embodiment of the present invention can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level by executing the above-mentioned method for evaluating the distribution characteristics of the battery electrode microstructure.

[0024] To achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a device for evaluating the distribution characteristics of a battery electrode microstructure, the device comprising: a construction module for constructing a microscopic geometric model of the battery electrode; a first determination module for determining a target number of segmentations; a division module for segmenting the microscopic geometric model into the target number of partial microscopic geometric models of the same volume; and a second determination module for determining a porous electrode inhomogeneity index based on the overall porosity of the microscopic geometric model and the local porosity of the partial microscopic geometric model.

[0025] According to the distribution characteristics evaluation device of the battery electrode microstructure of the embodiment of the present invention, the construction module is used to construct the micro-geometric model of the battery electrode, the first determination module is used to determine the target segmentation number, the division module is used to segment the micro-geometric model into the target segmentation number of partial micro-geometric models with the same volume, and the second determination module is used to determine the porous electrode non-uniformity index according to the overall porosity of the micro-geometric model and the local porosity of the partial micro-geometric model. As a result, the device can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart of a method for evaluating the distribution characteristics of a battery electrode microstructure according to an embodiment of the present invention;

[0028] Figure 2 is a schematic diagram of a fitting curve according to an embodiment of the present invention;

[0029] Figure 3 A flow chart of a method for evaluating the distribution characteristics of a battery electrode microstructure according to a specific example of the present invention;

[0030] Figure 4 is a block diagram of an electronic device according to an embodiment of the present invention;

[0031] Figure 5 Schematic diagram of a block diagram of a device for evaluating distribution characteristics of a battery electrode microstructure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0033] The following describes a method for evaluating the distribution characteristics of a battery electrode microstructure, a computer-readable storage medium, an electronic device, and a device for evaluating the distribution characteristics of a battery electrode microstructure, which are proposed in embodiments of the present invention, with reference to the accompanying drawings.

[0034] Figure 1 Flow chart of a method for evaluating distribution characteristics of a battery electrode microstructure according to an embodiment of the present invention.

[0035] like Figure 1 As shown, the method for evaluating the distribution characteristics of the battery electrode microstructure according to the embodiment of the present invention may include the following steps:

[0036] S1, construct a microgeometric model of the battery electrode.

[0037] S2, determine the target number of segmentations.

[0038] S3, dividing the micro-geometric model into a target number of partial micro-geometric models with the same volume.

[0039] S4, determining the inhomogeneity index of the porous electrode based on the global porosity of the microgeometric model and the local porosity of part of the microgeometric model.

[0040] Specifically, in order to accurately evaluate and compare the uniformity of different electrode designs at the microscopic level, a microscopic geometric model of the battery electrode can be first constructed. For example, multiple two-dimensional images (called slices or tomographic images) can be obtained from the battery through CT (Computed Tomography) scanning technology, and the acquired images can be processed, including denoising, contrast enhancement, binarization, etc., for analysis and reconstruction, so that the two-dimensional image data can be converted into a three-dimensional model, such as through image stacking, voxelization or three-dimensional reconstruction software, and a microscopic geometric model of the electrode can be generated using computer simulation software to ensure that the model can accurately reflect the microstructure of the electrode.

[0041] In addition, in one embodiment of the present invention, after obtaining a micro-geometric model, the micro-geometric model can be transformed (such as particle encryption, scaling, etc.) to obtain multiple micro-geometric models. For example, after transformation, four different battery electrode micro-geometric models are determined to compare the porous electrode non-uniformity indicators.

[0042] After constructing the micro-geometric model of the battery electrode, the target number of divisions can be determined. In other words, the optimal number of divisions is sought to evaluate the inhomogeneity of the electrode in the model. For example, multiple numbers of divisions can be taken, such as 4, 8, 12, 16, etc., and the target number of divisions can be determined from the multiple numbers of divisions. For example, the number of divisions can be started from a smaller number and gradually increased until the inhomogeneity index or performance index of the model is no longer significantly improved and is determined as the target number of divisions.

[0043] Thus, after determining the target number of cuts, the microgeometric model can be segmented into the target number of partial microgeometric models of the same volume, and the porous electrode inhomogeneity index can be determined according to the overall porosity of the microgeometric model and the local porosity of the partial microgeometric model. The microgeometric model is segmented into local areas of equal volume so as to more uniformly evaluate the inhomogeneity of the electrode. For example, the segmentation can be performed by using computer-aided design software or image processing software to ensure that the volume of each part is as equal as possible to avoid inhomogeneity evaluation errors caused by volume differences. When determining the inhomogeneity index of the porous electrode, the overall porosity of the microgeometric model can be calculated first, such as determining the overall porosity according to the ratio of the pore volume to the total volume of the electrode, wherein the total volume model of the electrode can be directly obtained from the three-dimensional data, and the pore volume can be achieved by distinguishing the solid part and the pore part in the model, which can be achieved by image processing or voxelization, wherein the pores and solids are assigned different values. The total pore volume can be obtained by accumulating the volumes of all voxels or spaces identified as pores. Likewise, the local porosity of some microgeometric models can be calculated.

[0044] After determining the overall porosity of the micro-geometric model and the local porosity of part of the micro-geometric model, the porous electrode inhomogeneity index can be determined according to the overall porosity of the micro-geometric model and the local porosity of part of the micro-geometric model. For example, the porous electrode inhomogeneity index is calculated by the following calculation formula (1), and the above calculation can be completed in Matlab. Alternatively, the porous electrode inhomogeneity index can be determined by a pre-set corresponding relationship, for example, the relationship between the overall porosity and multiple local porosities and the porous electrode inhomogeneity index is pre-determined, and after the overall porosity and multiple local porosities are determined, the porous electrode inhomogeneity index can be obtained by directly calling the corresponding relationship. Through this index, researchers can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level. For example, the smaller the inhomogeneity index, the more uniform the microstructure of the electrode; the larger the inhomogeneity index, the more inhomogeneous the microstructure of the electrode.

[0045] Therefore, an index to characterize the distribution of electrode microstructure was introduced. Through quantitative means, researchers can more objectively measure and compare the uniformity of different electrode designs at the microscopic level. And the selection of the number of segmentations is determined to ensure the consistency and systematicness of the evaluation process and improve the comparability of the results. And this standardized evaluation method provides a highly operational and repeatable scientific basis for the optimization of electrode geometry models, making electrode design and improvement work more efficient and targeted.

[0046] According to one embodiment of the present invention, determining a target segmentation number includes: calculating a porous electrode non-uniformity index under multiple segmentation numbers; determining a fitting curve according to the multiple segmentation numbers and the porous electrode non-uniformity index corresponding to each segmentation number; and determining the target segmentation number according to the fitting curve.

[0047] Specifically, when determining the target number of segmentations, the porous electrode inhomogeneity index under multiple segmentation numbers may be calculated first. For example, multiple segmentation numbers may include 4, 8, 12, 16, 20, etc., that is, the porous electrode inhomogeneity index under each segmentation number is calculated. After determining the porous electrode inhomogeneity index under each segmentation number, a fitting curve may be determined based on the multiple segmentation numbers and the porous electrode inhomogeneity index corresponding to each segmentation number. For example, by determining the segmentation number as the horizontal coordinate and the corresponding porous electrode inhomogeneity index as the vertical coordinate on the scatter plot, a curve fitting technique (such as polynomial fitting) is used to determine the best fitting curve, so that after determining the fitting curve, the target segmentation number is determined according to the fitting curve, that is, by observing the trend of the porous electrode inhomogeneity index changing with the segmentation number, the segmentation number that best reflects the electrode inhomogeneity is determined.

[0048] According to one embodiment of the present invention, the porous electrode non-uniformity index under multiple division numbers is calculated, including: determining the overall porosity of the micro-geometric model and the local porosity of part of the micro-geometric model divided according to the division number; determining the porous electrode non-uniformity index according to the overall porosity and the local porosity.

[0049] Specifically, when calculating the inhomogeneity index of the porous electrode under multiple segmentation numbers, the overall porosity of the microgeometric model and the local porosity of the part of the microgeometric model segmented according to the segmentation number can be determined. For example, the microgeometric model of the battery electrode is first obtained, such as a three-dimensional image of the electrode obtained by CT scanning or computer simulation, and the acquired CT image is processed using image processing software, including denoising, binarization, etc., to distinguish between electrode materials and pores. The image is segmented into different regions. One method is binarization, which divides the pixels in the image into two types: material and pores, usually based on a grayscale threshold. For each segmented pore region, its volume is calculated. If voxelized data is used, the pore volume can be estimated by accumulating the number of pore voxels. Similarly, this can be achieved by accumulating the number of material voxels when calculating the total volume of the electrode material. Therefore, the overall porosity can be determined based on the ratio of the pore volume in the electrode material to the total volume of the electrode, and the local local porosity can be determined based on the ratio of the void volume in the local area to the total volume of the local area. You can also use professional software to automate the above process, such as MATLAB, COMSOL Multiphysics, etc. These software provide tools for processing three-dimensional data and calculating volumes. In addition, in order to ensure the reliability of the results, it is necessary to repeat the experiment and calculation many times to evaluate the consistency of the results. In this way, the ratio of the pore volume in the electrode material to the total volume of the electrode can be accurately determined, and then the porosity can be calculated, which provides important parameters for the design and performance optimization of battery electrodes.

[0050] After determining the overall porosity and the local porosity, the porous electrode inhomogeneity index can be determined according to the overall porosity and the local porosity. For example, the porous electrode inhomogeneity index can be determined by a pre-set corresponding relationship, for example, the relationship between the overall porosity and the local porosity and the porous electrode inhomogeneity index is pre-determined, and after the overall porosity and the local porosity are determined, the porous electrode inhomogeneity index can be obtained by directly calling the corresponding relationship. Thus, after determining a segmentation number, the porous electrode inhomogeneity index corresponding to the segmentation number can be determined.

[0051] Further, according to one embodiment of the present invention, the porous electrode non-uniformity index is determined by the following method:

[0052]

[0053] Where H is the inhomogeneity index of the porous electrode, φ is the overall porosity of the microgeometric model, and φ i It is the i-th local porosity after the microgeometric model is divided into n partial microgeometric models of equal volume, where n is the number of divisions.

[0054] Specifically, when determining the porous electrode non-uniformity index, it can be determined by the above formula (1), that is, first determine a segmentation number n, and calculate the square of the difference between the i-th local porosity and the overall porosity of the micro-geometric model after the micro-geometric model is segmented into n parts of the micro-geometric model with the same volume, start calculating from the first local part until the n-th part is calculated, and perform cumulative summation of the porosity, divide the cumulative summation result by n-1 and take the square root, and finally divide it by the overall porosity of the micro-geometric model to obtain the porous electrode non-uniformity index corresponding to the segmentation number n. Adjust the segmentation number n, and calculate the corresponding porous electrode non-uniformity index according to the above formula (1).

[0055] In addition, in one embodiment of the present invention, after the micro-geometric model is divided into a target number of partial micro-geometric models of the same volume, when the porous electrode non-uniformity index is determined according to the overall porosity of the micro-geometric model and the local porosity of the partial micro-geometric model, it can also be determined according to the above formula (1), and the number of divisions n can be substituted as the target number of divisions, thereby determining the optimal porous electrode non-uniformity index.

[0056] According to an embodiment of the present invention, determining the target number of segmentations according to the fitting curve includes: determining the position corresponding to the porous electrode non-uniformity index change rate on the fitting curve when it reaches the target change rate; determining whether the number of segmentations corresponding to the position is a natural number; if not, taking the natural number closest to the position as the target number of segmentations; if so, taking the number of segmentations corresponding to the position as the target number of segmentations. The target change rate can be determined according to actual conditions.

[0057] Specifically, when determining the target number of segmentations based on the fitting curve, the position corresponding to when the rate of change of the porous electrode non-uniformity index on the fitting curve reaches the target rate of change can be determined. For example, the target rate of change is the rate of change corresponding to the inflection point of the fitting curve. The absolute value of the rate of change between the porous electrode non-uniformity index and the number of segmentations at the inflection point is the smallest. After determining the position, check whether the number of segmentations corresponding to the above position is a natural number. If it is a natural number, then this number of segmentations is the target number of segmentations. If the number of segmentations corresponding to the position is not a natural number, it is necessary to find the natural number closest to this position, such as rounding off or selecting the nearest integer. Therefore, the natural number determined in the above steps is used as the target number of segmentations. The value of the number of segmentations represents the optimal number of segmentations of the electrode model (target number of segmentations) for evaluating the non-uniformity of the electrode. For example, Figure 2 As shown, the number of position segmentations n corresponding to the lowest change rate of the porous electrode inhomogeneity index H on the fitting curve is 18.

[0058] According to one embodiment of the present invention, a fitting curve is determined based on multiple segmentation numbers and the porous electrode non-uniformity index corresponding to each segmentation number, including: determining a fitting curve segment between two adjacent segmentation numbers based on two adjacent segmentation numbers and the porous electrode non-uniformity index corresponding to the segmentation numbers to obtain at least one fitting curve segment; synthesizing at least one fitting curve segment to obtain a fitting curve.

[0059] Specifically, when determining the fitting curve according to multiple segmentation numbers and the porous electrode inhomogeneity index corresponding to each segmentation number, the fitting curve segment between two adjacent segmentation numbers can be determined according to the porous electrode inhomogeneity index corresponding to the segmentation numbers, and a fitting curve segment can be obtained. For multiple adjacent segmentation numbers and the porous electrode inhomogeneity index corresponding to the segmentation numbers, multiple fitting curve segments can be determined, and the multiple fitting curve segments are synthesized to obtain the fitting curve. For example, Figure 2 As shown, a, b, c and d are fitting curves corresponding to four different battery electrode micro-geometry models, and the porosity of the overall model is calculated to be 0.35, 0.38, 0.42 and 0.46 respectively. A fitting curve segment between 4 and 8 can be determined first, and a fitting curve segment between 8 and 12 can be determined, and a fitting curve segment between 12 and 16 can be determined, and then a fitting curve segment between 16 and 20 can be determined, so that multiple fitting curve segments can be synthesized to obtain the final fitting curve.

[0060] According to one embodiment of the present invention, the micro-geometric model is a positive electrode micro-geometric model, and the method for evaluating the distribution characteristics of the battery electrode microstructure also includes: combining the positive electrode micro-geometric model with the diaphragm and the negative electrode micro-geometric model, and performing electrochemical simulation through simulation software.

[0061] Specifically, the micro-geometry model is a positive electrode micro-geometry model, that is, a detailed three-dimensional representation of the positive electrode of the battery, which may include features such as the distribution of active materials, pore structure, and particle size. The separator is a key component in the battery, which is used to separate the positive and negative electrodes to prevent them from directly contacting and causing short circuits, while allowing lithium ions to pass through. The negative electrode micro-geometry model describes the microstructure of the negative electrode material, including active materials, porosity, and particle characteristics. The positive electrode micro-geometry model is combined with the separator and negative electrode micro-geometry models. For example, the positive electrode, separator, and negative electrode models are combined in the simulation software to form a complete battery cell model. This step ensures that all components are correctly geometrically related to each other and can be electrochemically simulated by the simulation software after combination. For example, electrochemical simulation software (such as COMSOL Multiphysics) is used to simulate the electrochemical reactions and ion transport of the battery during the charging and discharging process. The parameters required for the simulation can be set, including the operating conditions of the battery (such as current density, temperature, etc.), material properties (such as conductivity, ion diffusion rate, etc.). Electrochemical simulations are performed to track the diffusion of lithium ions in the positive and negative electrode materials, the charge transfer reactions, and the volume changes of the electrode materials. The simulation results can be used to analyze the inhomogeneities of different electrode geometry models, for example, to observe the lithium ion concentration distribution, potential distribution, and stress distribution of the electrode materials.

[0062] Therefore, the positive electrode lithium concentration distribution at different times can be simulated, and the simulation results of the positive electrode microgeometry reflect the inhomogeneous behavior of the electrode during operation, and the degree of inhomogeneous behavior of electrode geometric models with different inhomogeneities is different, that is, the porous electrode inhomogeneity index can describe the inhomogeneity of the electrode microgeometry.

[0063] Combine the following Figure 3 The evaluation method of the present invention will be described.

[0064] As a specific example, the method for evaluating the distribution characteristics of the battery electrode microstructure of the present invention may include the following steps:

[0065] S101, construct a micro-geometric model of battery electrodes.

[0066] S102, determining the overall porosity of the micro-geometric model and the local porosity of the part of the micro-geometric model divided according to a plurality of division numbers.

[0067] S103, determining a plurality of porous electrode non-uniformity indicators according to the overall porosity and the local porosity.

[0068] S104, determining a fitting curve segment between two adjacent segmentation numbers according to the two adjacent segmentation numbers and the porous electrode non-uniformity index corresponding to the segmentation numbers, so as to obtain at least one fitting curve segment.

[0069] S105: synthesize at least one fitting curve segment to obtain a fitting curve.

[0070] S106, determining the position on the fitting curve corresponding to when the change rate of the porous electrode non-uniformity index reaches the target change rate.

[0071] S107, determine whether the number of divisions corresponding to the position is a natural number. If yes, execute step S108; if no, execute step S111.

[0072] S108, taking the number of segmentations corresponding to the position as the target number of segmentations.

[0073] S109, dividing the micro-geometric model into a target number of partial micro-geometric models with the same volume.

[0074] S110, determining a porous electrode non-uniformity index according to the overall porosity of the micro-geometric model and the local porosity of a part of the micro-geometric model.

[0075] S111, taking the natural number closest to the position as the target segmentation number, and proceeding to step S109.

[0076] In summary, according to the distribution characteristics evaluation method of the battery electrode microstructure of the embodiment of the present invention, a microscopic geometric model of the battery electrode is constructed, the target number of segmentations is determined, the microscopic geometric model is segmented into the target number of partial microscopic geometric models with the same volume, and the porous electrode non-uniformity index is determined according to the overall porosity of the microscopic geometric model and the local porosity of the partial microscopic geometric model. As a result, the method can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level.

[0077] Corresponding to the above embodiment, the present invention also proposes a computer-readable storage medium.

[0078] The computer-readable storage medium of the embodiment of the present invention stores a program thereon, and when the program is executed by a processor, the above-mentioned method for evaluating the distribution characteristics of the battery electrode microstructure is implemented.

[0079] According to the computer-readable storage medium of the embodiment of the present invention, by executing the above-mentioned method for evaluating the distribution characteristics of the battery electrode microstructure, the uniformity of different electrode designs at the microscopic level can be more accurately evaluated and compared.

[0080] Corresponding to the above embodiment, the present invention further provides an electronic device.

[0081] like Figure 4As shown, the electronic device 200 of an embodiment of the present invention may include: a memory 210, a processor 220, and a program stored in the memory 210 and executable on the processor 220. When the processor 220 executes the program, the above-mentioned method for evaluating the distribution characteristics of the battery electrode microstructure is implemented.

[0082] The electronic device according to the embodiment of the present invention can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level by executing the above-mentioned method for evaluating the distribution characteristics of the battery electrode microstructure.

[0083] Corresponding to the above-mentioned embodiment, the present invention further proposes a device for evaluating the distribution characteristics of a battery electrode microstructure.

[0084] like Figure 5 As shown, the distribution characteristic evaluation device 100 of the battery electrode microstructure according to the embodiment of the present invention includes: a construction module 110 , a first determination module 120 , a division module 130 and a second determination module 140 .

[0085] Among them, the construction module 110 is used to construct a micro-geometric model of the battery electrode. The first determination module 120 is used to determine the target number of segmentations. The division module 130 is used to segment the micro-geometric model into a target number of partial micro-geometric models of the same volume. The second determination module 140 is used to determine the porous electrode inhomogeneity index based on the overall porosity of the micro-geometric model and the local porosity of the partial micro-geometric model.

[0086] According to one embodiment of the present invention, the first determination module 120 determines the target segmentation number, which is specifically used to: calculate the porous electrode non-uniformity index under multiple segmentation numbers; determine the fitting curve according to the multiple segmentation numbers and the porous electrode non-uniformity index corresponding to each segmentation number; determine the target segmentation number according to the fitting curve.

[0087] According to one embodiment of the present invention, the first determination module 120 calculates the porous electrode non-uniformity index under multiple segmentation numbers, and is specifically used to: determine the overall porosity of the micro-geometric model and the local porosity of part of the micro-geometric model after segmentation according to the segmentation number; determine the porous electrode non-uniformity index based on the overall porosity and the local porosity.

[0088] According to one embodiment of the present invention, the first determination module 120 determines the porous electrode non-uniformity index in the following manner:

[0089]

[0090] Where H is the inhomogeneity index of the porous electrode, φ is the overall porosity of the microgeometric model, and φ iIt is the i-th local porosity after the microgeometric model is divided into n partial microgeometric models of equal volume, where n is the number of divisions.

[0091] According to one embodiment of the present invention, the first determination module 120 determines the target number of segmentations based on the fitting curve, and is specifically used to: determine the position corresponding to the fitting curve when the rate of change of the porous electrode non-uniformity index reaches the target rate of change; determine whether the number of segmentations corresponding to the position is a natural number; if not, use the natural number closest to the position as the target number of segmentations; if so, use the number of segmentations corresponding to the position as the target number of segmentations.

[0092] According to one embodiment of the present invention, the first determination module 120 determines a fitting curve based on multiple segmentation numbers and the porous electrode non-uniformity index corresponding to each segmentation number, and is specifically used to: determine a fitting curve segment between two adjacent segmentation numbers based on the porous electrode non-uniformity index corresponding to the segmentation numbers to obtain at least one fitting curve segment; synthesize at least one fitting curve segment to obtain a fitting curve.

[0093] According to one embodiment of the present invention, the micro-geometric model is a positive electrode micro-geometric model, and the construction module 110 is also used to: combine the positive electrode micro-geometric model with the diaphragm and the negative electrode micro-geometric model, and perform electrochemical simulation through simulation software.

[0094] It should be noted that for details not disclosed in the device for evaluating the distribution characteristics of the battery electrode microstructure in the embodiment of the present invention, please refer to the details disclosed in the method for evaluating the distribution characteristics of the battery electrode microstructure in the embodiment of the present invention, and the details will not be repeated here.

[0095] According to the distribution characteristics evaluation device of the battery electrode microstructure of the embodiment of the present invention, the construction module is used to construct the micro-geometric model of the battery electrode, the first determination module is used to determine the target segmentation number, the division module is used to segment the micro-geometric model into the target segmentation number of partial micro-geometric models with the same volume, and the second determination module is used to determine the porous electrode non-uniformity index according to the overall porosity of the micro-geometric model and the local porosity of the partial micro-geometric model. As a result, the device can more accurately evaluate and compare the uniformity of different electrode designs at the microscopic level.

[0096] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0097] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0099] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0100] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0101] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the distribution characteristics of battery electrode microstructure, It is characterized in that The method comprises: constructing a micro-geometric model of the battery electrode; Determine the target number of segments; Segmenting the micro-geometric model into the target segmentation number of partial micro-geometric models with the same volume; The porous electrode non-uniformity index is determined according to the overall porosity of the micro-geometric model and the local porosity of the partial micro-geometric model.

2. The method for evaluating the distribution characteristics of the battery electrode microstructure according to claim 1, It is characterized in that Determining the target segmentation number includes: Calculating the porous electrode non-uniformity index under multiple segmentation numbers; Determining a fitting curve according to the plurality of segmentation numbers and the porous electrode non-uniformity index corresponding to each of the segmentation numbers; The target segmentation number is determined according to the fitting curve.

3. The method for evaluating the distribution characteristics of the battery electrode microstructure according to claim 2, It is characterized in that The calculating of the porous electrode non-uniformity index under multiple segmentation numbers includes: Determining the global porosity of the microgeometric model and the local porosity of the portion of the microgeometric model divided according to the number of divisions; The porous electrode non-uniformity index is determined according to the overall porosity and the local porosity.

4. The method for evaluating the distribution characteristics of the battery electrode microstructure according to claim 3, It is characterized in that The porous electrode non-uniformity index is determined by: Wherein, H is the inhomogeneity index of the porous electrode, φ is the overall porosity of the micro-geometric model, and φ i It is the i-th local porosity after dividing the micro-geometric model into n partial micro-geometric models of the same volume, where n is the number of divisions.

5. The method for evaluating the distribution characteristics of the battery electrode microstructure according to claim 2, It is characterized in that The step of determining the target segmentation number according to the fitting curve comprises: Determine the position on the fitting curve corresponding to when the rate of change of the porous electrode non-uniformity index reaches a target rate of change; Determine whether the number of divisions corresponding to the position is a natural number; If not, the natural number closest to the position is used as the target segmentation number; If so, the number of segmentations corresponding to the position is used as the target number of segmentations.

6. The method for evaluating the distribution characteristics of the battery electrode microstructure according to claim 2, It is characterized in that The step of determining a fitting curve according to a plurality of segmentation numbers and a porous electrode non-uniformity index corresponding to each of the segmentation numbers comprises: Determine a fitting curve segment between two adjacent segmentation numbers according to the two adjacent segmentation numbers and the porous electrode non-uniformity index corresponding to the segmentation numbers, so as to obtain at least one fitting curve segment; The at least one fitting curve segment is synthesized to obtain the fitting curve.

7. The method for evaluating the distribution characteristics of the battery electrode microstructure according to claim 1, It is characterized in that The micro-geometric model is a positive micro-geometric model, and the method further comprises: The positive electrode micro-geometry model is combined with the separator and the negative electrode micro-geometry model, and electrochemical simulation is performed through simulation software.

8. A computer-readable storage medium, It is characterized in that A program is stored thereon, and when the program is executed by a processor, the method for evaluating the distribution characteristics of the battery electrode microstructure according to any one of claims 1 to 7 is implemented.

9. An electronic device, It is characterized in that include: A memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for evaluating the distribution characteristics of the battery electrode microstructure according to any one of claims 1 to 7 is implemented.

10. A device for evaluating the distribution characteristics of a battery electrode microstructure, It is characterized in that The device comprises: A construction module, used to construct a micro-geometric model of the battery electrode; A first determination module is used to determine the number of target segmentations; A division module, used for dividing the micro-geometric model into the target number of divided partial micro-geometric models with the same volume; The second determination module is used to determine the porous electrode non-uniformity index according to the overall porosity of the micro-geometric model and the local porosity of the partial micro-geometric model.

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