CN117350088B - Method, device, storage medium and equipment for generating simulation grid of battery pole piece - Google Patents
Method, device, storage medium and equipment for generating simulation grid of battery pole piece Download PDFInfo
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- 238000004088 simulation Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 36
- 239000002245 particle Substances 0.000 claims abstract description 157
- 230000001172 regenerating effect Effects 0.000 claims abstract description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 8
- 238000010586 diagram Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 5
- 239000007772 electrode material Substances 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 2
- 238000005094 computer simulation Methods 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 229910052744 lithium Inorganic materials 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000001878 scanning electron micrograph Methods 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012938 design process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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Abstract
The invention discloses a method, a device, a storage medium and equipment for generating a simulation grid of a battery pole piece, wherein the method comprises the following steps: acquiring at least one electrode tangent plane graph, and identifying the geometric characteristics and distribution rules of particles in the tangent plane graph; classifying the identified particles into different morphology categories, extracting key parameters of geometric features of the particles according to the morphology categories to which the particles belong, and regenerating a particle model; generating an electrode microstructure model according to the size distribution rule of the identified particles based on the particle model; and generating a grid file based on the generated microstructure model. The method greatly simplifies the process of manual modeling, reduces the difficulty of battery electrode simulation of non-simulation professionals, improves the efficiency of battery electrode simulation, and ensures the quality of the generated grid file through repeated cycle judgment and addition of limiting conditions in the process.
Description
Technical Field
The invention relates to the field of simulation, in particular to a simulation grid generation method of a lithium ion battery.
Background
In the design process of lithium batteries, electrochemical characteristics of the lithium batteries are evaluated in advance, so that it is very important to design batteries meeting the requirements. The current mainstream electrochemical model includes: three-dimensional models, mesoscale models, particle stacking models, etc., which require acquisition of geometric characteristic information of various material particles inside the electrode sheet prior to calculation, to predict electrochemical performance of the battery through simulation. How to generate the simulation grid of the battery pole piece has important influence on the simulation result.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides a method for generating the simulation grid of the battery pole piece, which provides a basis for accurately predicting the electrochemical performance of the battery.
In order to achieve the above object, in one aspect, the present application provides a method for generating a simulation grid of a battery pole piece, including the following steps:
acquiring at least one electrode tangent plane graph, and identifying the geometric characteristics and distribution rules of particles in the tangent plane graph;
classifying the identified particles into different morphology categories, extracting key parameters of geometric features of the particles according to the morphology categories to which the particles belong, and regenerating a particle model;
and generating an electrode microstructure model according to the size distribution rule of the identified particles based on the particle model.
And generating a grid file based on the generated electrode microstructure model.
According to the method, particles in the section view of the electron microscope are identified, the particles are classified according to the morphology features, the corresponding geometrical feature key parameters are extracted according to the morphology categories after classification, and a particle model is generated through the key parameters.
The particles in the original tangent plane graph generate a simplified particle model by the method, and an electrode microstructure model is generated according to the size and quantity distribution rule of the electrode material particles in the tangent plane graph. The modeling process of the electrode plate is greatly simplified, and after the section diagram is identified, a high-quality grid file can be automatically generated, so that the difficulty of battery electrode simulation of non-simulation professionals is reduced, and the efficiency of battery electrode simulation is improved.
As a further improvement, the step of obtaining a slice section of at least one sheet of electrode material, identifying geometric features and distribution rules of particles in the slice section, further comprises the steps of:
acquiring a plurality of electrode section diagrams of the same electrode section or different sections;
identifying the geometric characteristics and distribution rules of particles in each section;
taking the median value of the geometric features of the same particle in different section views;
the total particle distribution rule in all section views is used as the reference.
By acquiring a plurality of section views, the error of particle identification in part of the section views can be corrected, and the accuracy of identification is ensured. For example, when the grain shape in one tangent plane graph is distorted and the boundary is blurred, the correction can be performed by the same grain geometric feature value in the other graph, for example, taking the intermediate value of the two or taking the value of a clearer shape. In addition, more accurate particle distribution information is further obtained by acquiring the particle size distribution conditions in different section views. Therefore, after the multi-Zhang Qiemian graph is obtained, more particle geometric characteristic information and particle size distribution information can be obtained, and the accuracy of subsequent simulation modeling can be improved.
As a further improvement, the step classifies the identified particles into different morphology categories, extracts key parameters of geometric characteristics of the particles according to the morphology categories to which the particles belong, regenerates a particle model, and further comprises the following steps:
acquiring geometric characteristic information of the identification particles;
based on the geometric characteristic information of the particles, classifying the particles into different morphology classifications by an image recognition algorithm.
As a further improvement, the morphology categories include circular, elliptical, rectangular, etc., and the morphology categories are preset according to the particle-based morphology features.
Since the particles in the sectional view generally have a shape close to a circle, an ellipse or a rectangle, classifying the particles with irregular boundaries facilitates the generation of a particle model again in the shape of a circle, an ellipse or a rectangle. The process of generating the model can be greatly simplified, and the simulation difficulty and the consumption of calculation resources are greatly reduced.
As a further improvement, based on the particle model, according to the size distribution rule of the identified particles, an electrode microstructure model is generated, further comprising the steps of:
and generating an electrode microstructure model according to the distribution rule of particles in the electrode slice based on the generated particle model.
If the generation of the electrode microstructure model does not meet the given condition, repeating the steps to regenerate the electrode microstructure model.
After the electrode microstructure model is generated, the distribution of the particle model is similar to that of the electrode slice in reality as far as possible, so that the subsequent simulation result is more accurate, and when the generated model is obviously different from the real slice, the electrode microstructure model is regenerated.
As a further improvement, a method of generating a battery pole piece simulation grid, the given conditions comprising:
the particle models are mutually separated, and the conditions of tangent boundary, intersecting or mutual overlapping do not exist.
In general, when particle models in the generated electrode microstructure model are separated from each other, a back-and-forth calculation can be performed. Of course, more conditions can be set so that the generated model is more similar to the actual situation.
As a further improvement, the step of generating a mesh file based on the generated electrode microstructure model further comprises the steps of:
generating at least one of a structured grid or an unstructured grid, and gridding the geometric model;
regenerating the structured grid when one included angle in the structured grid cell is less than 50 ° or one of the edges has a length greater than twice the length of the other edge;
an unstructured grid is regenerated when one included angle within an unstructured grid cell is less than 20 ° or one of the edges has a length greater than twice the length of the other edge.
The evaluation criteria for grid generation are not unique, and can be set as described above, or new angles and aspect ratios can be set as desired, even if other constraints are supplemented.
In a second aspect of the present application, an apparatus for generating a battery pole piece simulation grid, includes:
the acquisition unit acquires at least one electrode section graph and identifies the geometric characteristics and distribution rules of particles in the section graph;
the classification unit classifies the identified particles into different morphology categories, and extracts key parameters of geometric characteristics of the particles according to the morphology categories to which the particles belong to and regenerates a particle model;
the generation unit is used for generating an electrode microstructure model according to the size distribution rule of the identification particles based on the particle model;
and a grid unit for generating a grid file based on the generated electrode microstructure model.
In a third aspect of the present application, a computer storage medium is provided, configured to store network platform generated data, and a program for processing the network platform generated data;
the program, when read and executed by the processor, performs the method of generating a battery pole piece simulation grid described above.
In a fourth aspect of the present application, there is provided an electronic device, comprising: and the processor is used for storing a program for processing the data generated by the network platform, and the program, when being read and executed by the processor, executes the method for generating the battery pole piece simulation grid.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular description of preferred embodiments, as illustrated in the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of generating a simulation grid of a battery pole piece according to the present invention;
FIG. 2 is a cut-away view of a different material of the present invention;
FIG. 3a is a schematic representation of a particle model of the present invention when particles are classified as circular;
FIG. 3b is a schematic representation of a particle model of the present invention when the particles are categorized as ellipses;
fig. 3c is a schematic view of a particle model of the present invention when the particles are categorized as rectangular.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
in one aspect, the present invention provides a method for generating a simulation grid of a battery pole piece, as shown in fig. 1, including the following steps:
step 110, at least one electrode tangent plane graph is obtained first, and the geometric features and distribution rule of particles in the tangent plane graph are identified. For example, an SEM image of the electrode section as shown in fig. 2 is obtained, and particles with different shapes and sizes in the section image are identified through an algorithm, so that the geometric characteristics and the size distribution rule of the particles are obtained.
In addition to SEM images of electrode slices, other images that can reflect geometric characteristics of the electrode slice particles can be used in the method.
For obtaining the section view, the method further comprises the following steps:
acquiring a plurality of electrode section diagrams of the same electrode section or different sections;
identifying geometric features and distribution features of particles in each section;
taking the median value of the geometric features of the same particle in different section views;
based on the total particle distribution rule in all section views;
since particles in the section view may be in unclear shape, boundary, deformation and the like due to shooting problems, the problems can be solved by inputting and identifying a plurality of section views shot at different times for the same section. Specifically, the same particles in the multiple tangent planes are identified for multiple times, clear geometric characteristic information of the particles is selected as a result, and when deformation and other problems exist, the median value of the geometric characteristic information obtained by multiple times of identification of the same particles can be calculated as the identification result. Of course, the person skilled in the art can also correct the geometrical information of the particles in other well known ways.
In order to obtain a more accurate distribution rule of the particle size inside the electrode, a plurality of section images of different section surfaces can be identified, and finally, for example, the total particle distribution rule in the plurality of images can be adopted, so that the obtained result is more accurate.
And 120, classifying the identified particles into different morphology categories, and extracting key parameters of geometric characteristics of the particles according to the morphology categories to which the particles belong to so as to regenerate a particle model.
For the classification mode of the particles, the specific steps are as follows:
acquiring geometric characteristic information of the identification particles;
based on the geometric characteristic information of the particles, classifying the particles into different morphology classifications by an image recognition algorithm.
After the geometrical characteristic information of the particles in the tangent plane graph is obtained, the particles are classified into different morphology classifications by an image recognition algorithm based on the geometrical characteristic information of the particles.
Since the particle morphology features of electrode materials are typically nearly spherical, ellipsoidal, and rectangular in shape, these shapes can be categorized into different morphology categories. The purpose of classification is to generate a particle model with more regular boundaries and simpler boundaries based on the basic morphology of the particles identified in the electrode slice diagram, for example, if the shape of a certain particle in the original electrode slice is irregular and round-like, then the picture identification software classifies the particle model into a round type, and regenerates the corresponding round particle model according to the morphology type to which the particle model belongs.
In particular, existing mature image recognition algorithms, such as those in OpenCV visual recognition libraries using open sources, may be employed. Such as circular recognition with circular hough transform (Circular Hough Transform, CHT), random hough transform (Randomized Hough Transform, RHT), random circle detection (RandomizedCircle detection, RCD), etc., and the algorithm for recognizing ellipses may be ellipse detection (Ellipse Detection), etc. The invention directly uses a mature counting algorithm to identify which morphology a specific particle belongs to, and then analyzes the identification result to obtain data and quantity of different geometric feature morphologies.
In special cases, when a certain particle has a morphology between a standard circle and an ellipse, multiple identification can be performed, and in the case of a certain shape, such as a circle, which occupies most of the identification results, the particle is directly classified into the circle, and in the case of an ellipse which occupies most of the identification results, the particle is directly classified into the ellipse.
And extracting corresponding geometric characteristic key parameters according to the shape category after classification, and generating a particle model according to the shape category.
As shown in FIG. 3a, when a particle is classified as circular, the particle model is regenerated and expressed in a coordinate system asWherein->The coordinates of each point of the circular boundary can be bit as center coordinates>All points meeting the above formula +.>The connection is a circle on a rectangular coordinate system.
When a particle is classified as an ellipse, as in FIG. 3b, it can be expressed in a coordinate system asOr->. The coordinates of each point of the oval border can be seen as +.>All points meeting the above formula +.>The connection is an ellipse on a rectangular coordinate system. The ellipse may be with the major axis in the x-axis direction or with the major axis in the y-axis direction, all of which may have two expressions.
When a particle is identified as rectangular, the corresponding particle model is directly expressed in terms of length and width, length l and width t, as shown in fig. 3 c.
In the microstructure model, the position distribution of the particle model may have a certain rule, for example, the particle distribution in the original slice diagram may be arranged according to the particle distribution mode, or may be random distribution, but the size and the number of the particle model should be distributed in accordance with the original identification image.
It can be seen that by obtaining this raw particle key geometrical feature information, such as radius of the circle, length of the long and short sides of the ellipse, length and width of the rectangle, a corresponding more simplified particle model is regenerated. The simulation results output based on these particle models are still accurate. For simulation modeling, such a way of categorizing and regenerating the particle model can greatly simplify the modeling process and simulation calculations.
And 130, generating an electrode microstructure model according to the size distribution rule of the identified particles based on the particle model.
The method comprises the following specific steps:
and generating an electrode microstructure model according to the distribution rule of particles in the electrode slice based on the generated particle model.
If the generated grain models of the electrode microstructure models are mutually overlapped, and the boundaries are tangent or intersected, the generated microstructure models are not satisfied with the actual situation, and the electrode microstructure models need to be regenerated. Until the particles within the generated model are separated from each other.
After the electrode microstructure model is generated, the distribution of the particle model is similar to that of the electrode slice in reality as far as possible, so that the subsequent simulation result is more accurate, and when the generated model is obviously different from the real slice, the electrode microstructure model is regenerated. The particle distribution may be a normal distribution or a weber distribution. In this embodiment, the default is normal distribution.
And 140, generating a grid file based on the generated electrode microstructure model.
The method specifically comprises the following steps:
generating at least one of a structured grid or an unstructured grid, and gridding the geometric model;
regenerating the structured grid when one included angle in the structured grid cell is less than 50 ° or one of the sides is longer than twice the other side;
an unstructured grid is regenerated when one included angle within an unstructured grid cell is less than 20 ° or one of the sides is longer than twice the other side.
The application also discloses a device for generating the battery pole piece simulation grid, which comprises:
the acquisition unit acquires at least one electrode section graph and identifies the geometric characteristics and distribution rules of particles in the section graph;
the classification unit classifies the identified particles into different morphology categories, and extracts key parameters of geometric characteristics of the particles according to the morphology categories to which the particles belong to and regenerates a particle model;
the generation unit is used for generating an electrode microstructure model according to the size distribution rule of the identification particles based on the particle model;
and a grid unit for generating a grid file based on the generated electrode microstructure model.
The application also provides a computer storage medium for storing the network platform generated data and a program for processing the network platform generated data;
the program, when read and executed by the processor, performs the method of generating a battery pole piece simulation grid described above.
The application also provides an electronic device, comprising: and the processor is used for storing a program for processing the data generated by the network platform, and the program, when being read and executed by the processor, executes the method for generating the battery pole piece simulation grid.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (9)
1. A method of generating a battery pole piece simulation grid, comprising the steps of:
acquiring at least one electrode tangent plane graph, and identifying the geometric characteristics and distribution rules of particles in the tangent plane graph;
classifying the identified particles into different morphology categories, extracting key parameters of geometric features of the particles according to the morphology categories to which the particles belong, and regenerating a particle model;
generating an electrode microstructure model according to the size distribution rule of the identified particles based on the particle model;
generating a grid file based on the generated electrode microstructure model;
the step of generating a mesh file based on the generated electrode microstructure model, further comprising the steps of:
generating at least one of a structured grid or an unstructured grid, and gridding the geometric model;
regenerating the structured grid when one included angle in the structured grid cell is less than 50 ° or one of the edges has a length greater than twice the length of the other edge;
an unstructured grid is regenerated when one included angle within an unstructured grid cell is less than 20 ° or one of the edges has a length greater than twice the length of the other edge.
2. The method of generating a simulation grid of a battery pole piece of claim 1, wherein the step of obtaining a slice section of at least one sheet of electrode material, identifying geometric features and distribution rules of particles in the slice section, further comprises the steps of:
acquiring a plurality of electrode section diagrams of the same electrode section or different sections;
identifying the geometric characteristics and distribution rules of particles in each section;
taking the median value of the geometric features of the same particle in different section views;
the total particle distribution rule in all section views is used as the reference.
3. The method for generating a simulation grid of a battery pole piece according to claim 1, wherein the step classifies the identified particles into different morphology categories, extracts key parameters of geometric features of the particles according to the morphology categories to which the particles belong, regenerates the particle model, and further comprises the steps of:
acquiring geometric characteristic information of the identification particles;
based on the geometric characteristic information of the particles, classifying the particles into different morphology categories through an image recognition algorithm.
4. A method of generating a battery pole piece simulation grid according to claim 3, wherein the morphology categories include circular, elliptical and rectangular, the morphology categories being preset based on the morphology characteristics of the particles.
5. The method of generating a simulation grid for a battery pole piece of claim 1, wherein the step of generating an electrode microstructure model based on a particle model according to a size distribution rule of the identified particles, further comprises the steps of:
generating an electrode microstructure model according to the distribution rule of particles in the electrode slice based on the particle model;
if the generation of the electrode microstructure model does not meet the given condition, repeating the steps to regenerate the electrode microstructure model.
6. A method of generating a battery pole piece simulation grid according to claim 5, wherein the given conditions comprise:
the particle models are mutually separated, and the conditions of tangent boundary, intersecting or mutual overlapping do not exist.
7. An apparatus for generating a battery pole piece simulation grid, comprising:
the acquisition unit acquires at least one electrode section graph and identifies the geometric characteristics and distribution rules of particles in the section graph;
the classification unit classifies the identified particles into different morphology categories, and extracts key parameters of geometric characteristics of the particles according to the morphology categories to which the particles belong to and regenerates a particle model;
the generation unit is used for generating an electrode microstructure model according to the size distribution rule of the identification particles based on the particle model;
a grid unit for generating a grid file based on the generated electrode microstructure model;
the step of generating a mesh file based on the generated electrode microstructure model, further comprising the steps of:
generating at least one of a structured grid or an unstructured grid, and gridding the geometric model;
regenerating the structured grid when one included angle in the structured grid cell is less than 50 ° or one of the edges has a length greater than twice the length of the other edge;
an unstructured grid is regenerated when one included angle within an unstructured grid cell is less than 20 ° or one of the edges has a length greater than twice the length of the other edge.
8. A computer storage medium for storing network platform generated data and a program for processing the network platform generated data; it is characterized in that the method comprises the steps of,
the program, when read and executed by a processor, performs a method of generating a battery pole piece simulation grid using one of claims 1 to 6.
9. An electronic device, comprising: a processor; a memory for storing a program for processing network platform generated data, which when read and executed by the processor, performs the method of generating a battery pole piece simulation grid as claimed in any one of claims 1 to 6.
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| CN114139371A (en) * | 2021-11-29 | 2022-03-04 | 华中科技大学 | Multiphase and multi-scale modeling method and system for lithium ion battery electrode |
| CN114564866A (en) * | 2022-03-02 | 2022-05-31 | 中国电力科学研究院有限公司 | Thermal simulation meshing method |
| CN116933587A (en) * | 2023-07-14 | 2023-10-24 | 宁波大学 | Lithium battery electrode simulation analysis method and electronic equipment |
| CN117132822A (en) * | 2023-08-29 | 2023-11-28 | 瑞昌中建材光电材料有限公司 | Laminated cell of cadmium telluride perovskite and manufacturing method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114139371A (en) * | 2021-11-29 | 2022-03-04 | 华中科技大学 | Multiphase and multi-scale modeling method and system for lithium ion battery electrode |
| CN114564866A (en) * | 2022-03-02 | 2022-05-31 | 中国电力科学研究院有限公司 | Thermal simulation meshing method |
| CN116933587A (en) * | 2023-07-14 | 2023-10-24 | 宁波大学 | Lithium battery electrode simulation analysis method and electronic equipment |
| CN117132822A (en) * | 2023-08-29 | 2023-11-28 | 瑞昌中建材光电材料有限公司 | Laminated cell of cadmium telluride perovskite and manufacturing method thereof |
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