Method and device for determining powder compaction density of energy storage battery, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a powder compaction density determination method and device of an energy storage battery, electronic equipment and a storage medium. The method comprises the following steps: acquiring sample granularity data corresponding to a sample positive electrode material; constructing a grading model according to the sample material type of the sample positive electrode material and the sample granularity data corresponding to the sample positive electrode material; based on material information corresponding to a to-be-estimated positive electrode material and a grading model, estimating a grading proportion corresponding to the positive electrode material; and determining the powder compaction density of the sample positive electrode material according to the grading proportion. According to the scheme, the powder compaction density determination efficiency can be improved, and meanwhile, the cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device, electronic device and storage medium for determining the powder compaction density of an energy storage battery. Background Art

[0002] Lithium batteries are widely used in production and life due to their high energy density and environmental protection. At present, lithium batteries mostly use mixed materials of different particle sizes as positive electrode materials. For example, the commonly used positive electrode material lithium iron phosphate is obtained by mixing lithium iron phosphate of different particle sizes according to a certain grading ratio, but it has the shortcoming of low energy density.

[0003] The compaction density of the positive electrode material directly affects the energy density of the battery. The compaction density is related to the densest packing of the positive electrode material. If the densest packing of the positive electrode material can be achieved, the compaction density of the positive electrode material can be increased, thereby improving the electrochemical performance of the positive electrode material. At present, it is usually determined by experiments whether the positive electrode material has achieved the densest packing, but the experimental method is inefficient and costly. Summary of the invention

[0004] The embodiments of the present application provide a method, device, electronic device and storage medium for determining the compacted density of powder of an energy storage battery, which can improve the efficiency of determining the compacted density of powder while reducing the cost.

[0005] In a first aspect, an embodiment of the present application provides a method for determining a compacted density of a powder, comprising:

[0006] Obtaining sample particle size data corresponding to the sample positive electrode material;

[0007] Constructing a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material;

[0008] Based on the material information and grading model corresponding to the positive electrode material to be estimated, estimating the grading ratio corresponding to the positive electrode material;

[0009] The powder compaction density of the sample positive electrode material is determined according to the grading ratio.

[0010] Optionally, in some embodiments of the present application, constructing a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material includes:

[0011] extracting a sample particle size distribution of the sample positive electrode material from the sample particle size data;

[0012] A grading model is constructed according to the sample material type, sample particle size distribution and a preset close packing model of the sample positive electrode material.

[0013] Optionally, in some embodiments of the present application, constructing a gradation model according to the sample material type, sample particle size distribution and a preset close packing model of the sample positive electrode material includes:

[0014] Determining a target model from a plurality of preset close-packed models according to a sample material type of the sample positive electrode material;

[0015] configuring model parameters of the target model;

[0016] The target model is adjusted according to the sample particle size distribution of the sample positive electrode material and the model parameters of the target model to obtain a gradation model corresponding to the sample positive electrode material.

[0017] Optionally, in some embodiments of the present application, adjusting the target model according to the sample particle size distribution of the sample positive electrode material and the model parameters of the target model to obtain a gradation model corresponding to the sample positive electrode material includes:

[0018] Determining a reference gradation corresponding to the target model according to the model parameters of the target model;

[0019] comparing a sample particle size distribution of the sample positive electrode material with a reference gradation;

[0020] The target model is adjusted according to the comparison result to obtain a gradation model corresponding to the sample positive electrode material.

[0021] Optionally, in some embodiments of the present application, configuring the model parameters of the target model includes:

[0022] Determining the porosity corresponding to the sample positive electrode material;

[0023] The model parameters of the target model are configured according to the relationship between the porosity and the hexagonal closest packing ratio.

[0024] Optionally, in some embodiments of the present application, estimating the grading ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the grading model includes:

[0025] Obtaining material information corresponding to the positive electrode material to be estimated;

[0026] Determine the material type and particle size distribution of the positive material according to the material information;

[0027] According to the material type, particle size distribution and grading model, the grading ratio corresponding to the positive electrode material is estimated.

[0028] Optionally, in some embodiments of the present application, estimating the grading ratio corresponding to the positive electrode material according to the material type, particle size distribution and grading model includes:

[0029] Determining the characteristic particle size and distribution index corresponding to the grading model;

[0030] According to the material type, particle size distribution, characteristic particle size and distribution index, the grading ratio corresponding to the positive electrode material is estimated.

[0031] In a second aspect, an embodiment of the present application further provides a device for determining the compacted density of powder of an energy storage battery, comprising:

[0032] An acquisition module, used to acquire sample particle size data corresponding to the sample positive electrode material;

[0033] A construction module, used to construct a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material;

[0034] An estimation module, used for estimating the gradation ratio corresponding to the positive electrode material to be estimated based on the material information corresponding to the positive electrode material to be estimated and the gradation model;

[0035] A determination module is used to determine the powder compaction density of the sample positive electrode material according to the grading ratio.

[0036] Correspondingly, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the steps of any of the above methods when executing the program.

[0037] The present application also provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0038] The embodiment of the present application provides a method, device, electronic device and storage medium for determining the powder compaction density of an energy storage battery. After obtaining the sample particle size data corresponding to the sample positive electrode material, a gradation model is constructed according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material. Then, based on the material information corresponding to the positive electrode material to be estimated and the gradation model, the gradation ratio corresponding to the positive electrode material is estimated. Finally, the powder compaction density of the sample positive electrode material is determined according to the gradation ratio. The scheme for determining the powder compaction density provided by the present application uses the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material to construct a gradation model. Subsequently, the gradation ratio of the positive electrode material can be estimated by the gradation model to determine the powder compaction density. Compared with determining the gradation ratio of the positive electrode material by experimental testing, the efficiency of determining the gradation ratio is greatly improved, and the experimental cost is saved. Thus, the efficiency of determining the powder compaction density is improved, while reducing the cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 It is a schematic flow chart of a method for determining the powder compaction density of an energy storage battery provided in an embodiment of the present application;

[0041] Figure 2 It is a schematic diagram of the structure of a device for determining the powder compaction density of an energy storage battery provided in an embodiment of the present application;

[0042] Figure 3 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0044] Embodiments of the present application provide a method for determining the compacted density of powder, an electronic device, and a storage medium.

[0045] Among them, the method for determining the powder compaction density of the energy storage battery can be specifically applied in a terminal, and the terminal can include a tablet computer or a personal computer (PC). The terminal can establish a wired or wireless connection with a server. The server can include an independently running server or a distributed server, or a server cluster composed of multiple servers.

[0046] It should be noted that the order of description of the following embodiments is not intended to limit the priority order of the embodiments.

[0047] A method for determining the powder compaction density of an energy storage battery comprises: obtaining sample particle size data corresponding to a sample positive electrode material; constructing a gradation model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material; estimating the gradation ratio corresponding to the positive electrode material based on material information corresponding to the positive electrode material to be estimated and the gradation model; and determining the powder compaction density of the positive electrode material according to the gradation ratio.

[0048] See also Figure 1 , Figure 1 A schematic diagram of a process flow of a method for determining the compacted density of energy storage batteries provided in an embodiment of the present application. The specific process flow of the method for determining the compacted density of energy storage batteries may be as follows:

[0049] 101. Obtain sample particle size data corresponding to the sample positive electrode material.

[0050] Sample cathode material refers to a small portion taken from a batch of cathode materials for various tests and analyses to understand the properties and performance of the entire batch of materials. In the field of battery manufacturing, cathode materials generally refer to cathode active materials used in chemical power sources such as lithium-ion batteries. Common cathode materials include lithium iron phosphate (LFP), nickel cobalt manganese (NCM), nickel cobalt aluminum (NCA), etc. The particle size data corresponding to the sample cathode material refers to the size distribution information of particles of different particle sizes in the sample. These data are crucial to understanding the physical properties, processing performance and final application of the material. Particle size data generally include:

[0051] Particle size distribution: describes the proportion of particles of different sizes in a material, usually expressed as mass percentage or volume percentage. D10, D50, D90: represent the particle sizes corresponding to the cumulative distribution of 10%, 50%, and 90%, respectively. D50 is the median particle size and is also the most common particle size.

[0052] Average particle size: The weighted average of the particle sizes of all particles.

[0053] For example, take 100 grams of NCM811 material as a sample (i.e., sample positive electrode material). Put 10 grams of the sample into a laser particle size analyzer. Measure and obtain particle size distribution data, such as: 10% below 5μm, 30% between 5-10μm, 40% between 10-20μm, and 20% above 20μm.

[0054] 102. Construct a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material.

[0055] Particle Size Distribution (PSD) refers to the distribution of particles of different sizes in a material. In powder materials or granular materials, gradation describes the proportional relationship of particles of various sizes.

[0056] For example, determine the chemical composition and physical properties of the sample positive electrode material, such as LFP, NCM, NCA, etc. Use screening or laser particle size analysis and other techniques to obtain the particle size distribution data of the sample positive electrode material. Then, organize the particle size distribution data into a format that can be used for model analysis, usually including the particle mass or volume percentage of each particle size interval. Based on the material properties and particle size distribution data, select a suitable grading model. Common models include the Rosin-Rammler model, the Gaudin-Schuhmann model, the Horsfield model, etc. It should be noted that the theoretical basis of the selected model matches the material properties. For example, the spherical particle stacking model is suitable for particles that are close to spherical. Identify the key parameters required for the selected model, which include characteristic particle size, distribution index, maximum particle size, and minimum particle size. Optionally, in some embodiments of the present application, the grading model can be expressed using a mathematical formula.

[0057] Optionally, in some embodiments of the present application, the step of “constructing a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material” may specifically include:

[0058] extracting a sample particle size distribution of a sample positive electrode material from the sample particle size data;

[0059] A grading model is constructed according to the sample material type, sample particle size distribution and a preset close packing model of the sample positive electrode material.

[0060] For example, use screening, laser particle size analyzer or other particle size analysis techniques to perform particle size analysis on the sample cathode material. Collect the particle mass or volume percentage of each particle size interval (such as <10μm, 10-20μm, 20-30μm, 30-40μm, etc.). According to the material type (such as LFP, NCM, NCA, etc.) and characteristics of the sample cathode material, select a suitable dense packing model, such as the Horsfield model. According to the theory of the Horsfield model or other selected models, determine the required model parameters, such as characteristic particle size, distribution index, etc. The sorted particle size distribution data is applied to the grading model to calculate the matching ratio of particles of different particle sizes. Next, the Horsfield model is adjusted according to the sample particle size distribution of the sample cathode material to obtain the grading model.

[0061] Optionally, in some embodiments of the present application, the step of “constructing a gradation model according to the sample material type, sample particle size distribution and a preset close packing model of the sample positive electrode material” may specifically include:

[0062] determining a target model from a plurality of preset close-packed models according to a sample material type of the sample positive electrode material;

[0063] Configure model parameters of the target model;

[0064] The target model is adjusted according to the sample particle size distribution of the sample positive electrode material and the model parameters of the target model to obtain a gradation model corresponding to the sample positive electrode material.

[0065] Determine the parameters required for the selected model, which may include characteristic particle size, distribution index, maximum particle size, minimum particle size, etc.

[0066] For example, the model parameters of the target model are configured using the known properties of the sample material or through experimental data. Specifically, if the model requires a distribution index, the distribution index can be determined by fitting the experimental data. The model parameters are adjusted according to the sample particle size distribution data to ensure that the model can accurately reflect the actual stacking behavior of the sample material. Such as adjusting parameters such as characteristic particle size and distribution index. If there is a difference between the model prediction and the experimental data, the model parameters are further adjusted and the model is optimized to improve the accuracy of the prediction. After multiple iterations and verifications, the final grading model is determined, which can predict the stacking density and compaction density of the positive electrode material.

[0067] Optionally, in some embodiments of the present application, the step of “configuring model parameters of the target model” may specifically include:

[0068] Determine the porosity corresponding to the sample cathode material;

[0069] According to the relationship between the porosity and the hexagonal closest packing ratio, the model parameters of the target model are configured.

[0070] Porosity is the ratio of the volume of pores or voids in a material to the total volume of the material. It is an important parameter to measure the porosity of a material. Porosity has an important influence on many physical and chemical properties of the material, including its mechanical strength, thermal conductivity, electrical conductivity, adsorption capacity, and the electrochemical properties of electrode materials in battery technology.

[0071] Hexagonal Close Packing (HCP) ratio refers to the proportion of particles arranged in hexagonal close packing in the stacking structure of granular materials. Hexagonal close packing is an ideal particle stacking method, in which each particle is surrounded by six other particles, forming an efficient space-filling structure.

[0072] In hexagonal close packing, particles are arranged in a specific geometry to maximize space utilization and minimize porosity. This stacking is common in nature for atomic arrangements in metal crystals and is an important concept in powder metallurgy and particle materials science.

[0073] Characteristics of hexagonal close packing: High packing density: Hexagonal close packing can reach the highest value of theoretical packing density of granular materials, that is, 74% volume fraction; Ordered structure: Particles are arranged in order in hexagonal close packing, and the center of each particle forms a hexagonal pattern; Symmetry: Hexagonal close packing has a high degree of symmetry, and each particle is surrounded by six other particles at equal distances.

[0074] In actual granular materials, it is difficult to achieve perfect hexagonal close packing due to factors such as the irregularity of particle shape, surface roughness, and particle size distribution. Therefore, the hexagonal close packing ratio is usually used as a theoretical reference value to evaluate the compactness of actual particle packing.

[0075] Determine the relationship between porosity and the hexagonal closest packing fraction (x), such as the Smith relation, which is an empirical relationship that describes the relationship between porosity (∈) and particle packing structure. ∈ = 1-x where x is the hexagonal closest packing fraction. Use the relationship between porosity and hexagonal closest packing fraction to configure model parameters. For example, if the model requires a parameter related to packing density, you can use porosity directly to determine or adjust that parameter.

[0076] Optionally, in some embodiments of the present application, the step of “adjusting the target model according to the sample particle size distribution of the sample positive electrode material and the model parameters of the target model to obtain a gradation model corresponding to the sample positive electrode material” may specifically include:

[0077] According to the model parameters of the target model, the reference gradation corresponding to the target model is determined;

[0078] Comparing the sample particle size distribution of the sample cathode material with the reference gradation;

[0079] The target model is adjusted according to the comparison results to obtain the gradation model corresponding to the sample positive electrode material.

[0080] For example, model parameters can be determined according to the target model (such as the Horsfield model), and these model parameters include characteristic particle size, distribution index, etc. Using these model parameters, an ideal reference gradation is calculated. The actual particle size distribution data of the sample positive electrode material is obtained by experimental methods (such as screening or laser particle size analysis). The actual particle size distribution data is compared with the calculated reference gradation to find the difference between the two. These differences may be reflected in the proportion of particles in certain particle size intervals or the shape of the overall particle size distribution. According to the comparison results, the parameters of the target model are adjusted to make the gradation predicted by the model closer to the actual particle size distribution, such as changing the distribution index, adjusting the boundary of the particle size interval, or modifying the model to include additional physical properties (such as particle shape, surface characteristics, etc.). After multiple iterations and verifications, the final gradation model is determined. Specifically, an ideal reference gradation is calculated and then compared with the actual particle size distribution. It is found that the actual proportion of fine particles (<10μm) is low. Therefore, the model parameters are adjusted to increase the proportion of fine particles and optimize the gradation model. After several iterations and experimental verifications, the final gradation model is obtained.

[0081] 103. Based on the material information and grading model corresponding to the positive electrode material to be estimated, estimate the grading ratio corresponding to the positive electrode material.

[0082] For example, when actually estimating the grading ratio of the positive electrode material, the particle size distribution data of the positive electrode material to be estimated can be input into the constructed grading model. The model is used to calculate the grading ratio of the positive electrode material to be estimated. Specifically, if the particle size distribution data of the positive electrode material is known to be as follows:

[0083] Particle size <10μm: 15%

[0084] 10μm≤Particle size<20μm:35%

[0085] 20μm≤Particle size<30μm:30%

[0086] Particle size ≥30μm: 20%

[0087] The particle size distribution data is directly input into the grading model. The model calculates the grading ratio based on the input particle size distribution data, such as particles smaller than 10μm account for 15%; particles between 10μm and 20μm account for 35%; particles between 20μm and 30μm account for 30%; particles greater than or equal to 30μm account for 20%.

[0088] Optionally, in some embodiments of the present application, the step of “estimating the grading ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the grading model” may specifically include:

[0089] Obtaining material information corresponding to the positive electrode material to be estimated;

[0090] Determine the material type and particle size distribution of the genuine material based on the material information;

[0091] According to the material type, particle size distribution and grading model, estimate the corresponding grading ratio of the positive electrode material.

[0092] For example, specifically, collect information such as the chemical composition, crystal structure, and morphology of the positive electrode material to be estimated. According to the chemical composition and physical properties, determine the specific type of positive electrode material, such as LFP, NCM, NCA, etc. Use screening, laser particle size analyzer and other technologies to obtain the particle size distribution data of the positive electrode material, including the mass or volume percentage of particles in different particle size ranges. Input the particle size distribution data of the positive electrode material into the grading model, and use the model to calculate the matching ratio of particles of different particle sizes. The grading model will provide the grading ratio of the positive electrode material, that is, the mass or volume ratio of particles in different particle size ranges.

[0093] Optionally, in some embodiments of the present application, the step of “estimating the grading ratio corresponding to the positive electrode material according to the material type, particle size distribution and grading model” may specifically include:

[0094] Determine the characteristic particle size and distribution index corresponding to the grading model;

[0095] Estimate the corresponding grading ratio of the positive electrode material based on the material type, particle size distribution, characteristic particle size and distribution index.

[0096] Characteristic particle size (D n ) is a parameter in the grading model, which represents the center particle size of the particle distribution, usually the particle size corresponding to the cumulative distribution function (CDF) reaching a certain value (such as 50%). The distribution index (n) is a parameter that describes the width of the particle distribution, which affects the degree of dispersion of the particle size. In the Rosin-Rammler model, the larger the distribution index, the more concentrated the particle distribution. Using the determined characteristic particle size and distribution index, the particle size distribution data is input into the grading model to predict the matching ratio of particles of different particle sizes.

[0097] 104. Determine the powder compaction density of the positive electrode material according to the grading ratio.

[0098] For example, the gradation ratio of the positive electrode material is determined based on the constructed gradation model. Then, the theoretical compaction density is calculated using the gradation ratio and the true density of the material.

[0099] It should be noted that in some embodiments of the present application, according to Horsfield's dense packing theory, for a system of discrete particles with multiple particle sizes, when the gaps between the largest particles can be filled by the second largest particle size, and the gaps between the second particle size can be filled by the third particle size, and so on, the highest packing efficiency can be achieved. Based on this theory, the packing rate of particles of different sizes can be obtained, and the compaction density corresponding to LFP can be calculated (the default LFP true density is 3.6g / cm 3 ).

[0100]

[0101] Horsfield's dense packing theory is an ideal model that represents the densest packing of smooth spheres. In reality, the surface of LFP particles is rough and has moisture, and the sphericity varies. The particle size is normally distributed at each particle size and is not relatively discrete. It can be considered that the actual LFP particle packing is a combination of cubic packing (cubic sparsest packing), hexagonal (orthorhombic packing), and rhombohedral packing (cubic densest packing). The score is complex and difficult to determine, but it can be determined based on our single particle grading compaction of 2.30g / cm 3 , it can be estimated that

[0102]

[0103]

[0104] This actual compaction result is highly consistent with the current secondary and tertiary grading results (the current secondary grading compaction is 2.5, and the tertiary grading is 2.55).

[0105] It should be noted that according to the Smith relationship, ε is the porosity, and x is the hexagonal closest packing ratio. According to the above relationship and the actual packing ratio of the primary particles, the primary ball compaction is 2.3, the true density is 3.6, then the packing ratio is 0.639, the porosity is 0.361, and the result is: x = 0.5320. Similarly, the actual x of the secondary stacking is 0.7621. The actual tertiary stacking is: x = 0.8515. The above is the actual data of our current three generations of materials, and the data fitting is as follows:

[0106] x(n)=0.1886*log(n+2.207)+0.6481. Where n is the number of gradations, and x is the proportion of hexagonal closest packing. It can be concluded that the fourth-level gradation x=0.9081, and the fifth-level is 0.9495. It can be obtained that the estimated actual compaction of the fourth-level particles is 2.594g / cm3, and the fifth-level particles is 2.626g / cm3. According to the close packing model, the same-sized balls are first stacked, and then the secondary particles are stuffed into the gaps, and then the gaps are stuffed three times, and so on. These particles can be named E, J, K, etc.

[0107] The embodiment of the present application provides a method for determining the powder compaction density of an energy storage battery. After obtaining the sample particle size data corresponding to the sample positive electrode material, a gradation model is constructed according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material. Then, based on the material information corresponding to the positive electrode material to be estimated and the gradation model, the gradation ratio corresponding to the positive electrode material is estimated. Finally, the powder compaction density of the sample positive electrode material is determined according to the gradation ratio. The scheme for determining the powder compaction density provided by the present application uses the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material to construct a gradation model. Subsequently, the gradation ratio of the positive electrode material can be estimated by the gradation model to determine the powder compaction density. Compared with determining the gradation ratio of the positive electrode material by experimental testing, the efficiency of determining the gradation ratio is greatly improved, and the experimental cost is saved, thereby improving the efficiency of determining the powder compaction density and reducing the cost.

[0108] In order to facilitate better implementation of the method for determining the compacted density of the energy storage battery of the embodiment of the present application, the embodiment of the present application also provides a device for determining the compacted density of the powder (hereinafter referred to as the determination device) based on the above. The meanings of the terms are the same as those in the above method for determining the compacted density of the powder, and the specific implementation details can refer to the description in the method embodiment.

[0109] See also Figure 2 , Figure 2 A schematic diagram of the structure of a device for determining the compacted density of a powder of an energy storage battery provided in an embodiment of the present application, wherein the device for determining the compacted density of a powder may include an acquisition module 201, a construction module 202, an estimation module 203, and a determination module 204, which may be specifically as follows:

[0110] The acquisition module 201 is used to acquire sample particle size data corresponding to the sample positive electrode material.

[0111] The construction module 202 is used to construct a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material.

[0112] The estimation module 203 is used to estimate the gradation ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the gradation model.

[0113] The determination module 204 is used to determine the powder compaction density of the sample positive electrode material according to the grading ratio.

[0114] The embodiment of the present application provides a device for determining the powder compaction density of an energy storage battery. After the acquisition module 201 acquires the sample particle size data corresponding to the sample positive electrode material, the construction module 202 constructs a gradation model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material. Then, the estimation module 203 estimates the gradation ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the gradation model. Finally, the determination module 204 determines the powder compaction density of the sample positive electrode material according to the gradation ratio. The scheme for determining the powder compaction density provided by the present application uses the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material to construct a gradation model. Subsequently, the gradation ratio of the positive electrode material can be estimated by the gradation model to determine the powder compaction density. Compared with determining the gradation ratio of the positive electrode material by experimental testing, the efficiency of determining the gradation ratio is greatly improved, and the experimental cost is saved. Thus, the efficiency of determining the powder compaction density is improved, while reducing the cost.

[0115] In addition, the present application also provides an electronic device, such as Figure 3 As shown, it shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0116] The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that Figure 3 The electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0117] The processor 301 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. By running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0118] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and powder compaction density determination by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0119] The electronic device also includes a power supply 303 for supplying power to each component. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so that the power management system can manage charging, discharging, power consumption and other functions. The power supply 303 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0120] The electronic device may further include an input unit 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0121] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302, thereby realizing various functions, as follows:

[0122] Obtain sample particle size data corresponding to the sample positive electrode material; construct a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material; estimate the grading ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the grading model; determine the powder compaction density of the positive electrode material according to the grading ratio.

[0123] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0124] After obtaining the sample particle size data corresponding to the sample positive electrode material, the embodiment of the present application constructs a gradation model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material. Then, based on the material information corresponding to the positive electrode material to be estimated and the gradation model, the gradation ratio corresponding to the positive electrode material is estimated. Finally, the powder compaction density of the sample positive electrode material is determined according to the gradation ratio. The scheme for determining the powder compaction density provided by the present application constructs a gradation model using the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material. Subsequently, the gradation ratio of the positive electrode material can be estimated by the gradation model to determine the powder compaction density. Compared with determining the gradation ratio of the positive electrode material by experimental testing, the efficiency of determining the gradation ratio is greatly improved, and the experimental cost is saved, thereby improving the efficiency of determining the powder compaction density and reducing the cost.

[0125] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0126] To this end, an embodiment of the present application provides a storage medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any one of the powder compaction density determination methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0127] Obtain sample particle size data corresponding to the sample positive electrode material; construct a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material; estimate the grading ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the grading model; determine the powder compaction density of the positive electrode material according to the grading ratio.

[0128] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0129] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0130] Since the instructions stored in the storage medium can execute the steps in any method for determining the powder compaction density of an energy storage battery provided in the embodiments of the present application, the beneficial effects that can be achieved by any method for determining the powder compaction density provided in the embodiments of the present application can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0131] The above is a detailed introduction to a method for determining the powder compaction density of an energy storage battery, a device electronic device, and a storage medium provided in an embodiment of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for determining the compacted density of powder of an energy storage battery, characterized in that: include: Obtaining sample particle size data corresponding to the sample positive electrode material; Constructing a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material; Based on the material information and grading model corresponding to the positive electrode material to be estimated, estimating the grading ratio corresponding to the positive electrode material; The powder compaction density of the sample positive electrode material is determined according to the grading ratio.

2. The method for determining the compacted density of powder according to claim 1, characterized in that: The step of constructing a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material comprises: extracting a sample particle size distribution of the sample positive electrode material from the sample particle size data; A grading model is constructed according to the sample material type, sample particle size distribution and a preset close packing model of the sample positive electrode material.

3. The method for determining the compacted density of powder according to claim 2, characterized in that: The step of constructing a gradation model according to the sample material type, sample particle size distribution and a preset close packing model of the sample positive electrode material comprises: Determining a target model from a plurality of preset close-packed models according to a sample material type of the sample positive electrode material; configuring model parameters of the target model; The target model is adjusted according to the sample particle size distribution of the sample positive electrode material and the model parameters of the target model to obtain a gradation model corresponding to the sample positive electrode material.

4. The method for determining the compacted density of powder according to claim 3, characterized in that: The target model is adjusted according to the sample particle size distribution of the sample positive electrode material and the model parameters of the target model to obtain a gradation model corresponding to the sample positive electrode material, including: Determining a reference gradation corresponding to the target model according to the model parameters of the target model; comparing a sample particle size distribution of the sample positive electrode material with a reference gradation; The target model is adjusted according to the comparison result to obtain a gradation model corresponding to the sample positive electrode material.

5. The method for determining the compacted density of powder according to claim 3, characterized in that: The configuring of the model parameters of the target model includes: Determining the porosity corresponding to the sample positive electrode material; The model parameters of the target model are configured according to the relationship between the porosity and the hexagonal closest packing ratio.

6. The method for determining the compacted density of powder according to any one of claims 1 to 5, characterized in that: The estimating the grading ratio corresponding to the positive electrode material based on the material information corresponding to the positive electrode material to be estimated and the grading model includes: Obtaining material information corresponding to the positive electrode material to be estimated; Determine the material type and particle size distribution of the positive material according to the material information; According to the material type, particle size distribution and grading model, the grading ratio corresponding to the positive electrode material is estimated.

7. The method for determining the compacted density of powder according to claim 6, characterized in that: The estimating the grading ratio corresponding to the positive electrode material according to the material type, particle size distribution and grading model includes: Determining the characteristic particle size and distribution index corresponding to the grading model; According to the material type, particle size distribution, characteristic particle size and distribution index, the grading ratio corresponding to the positive electrode material is estimated.

8. A device for determining the compacted density of powder of an energy storage battery, characterized in that: include: An acquisition module, used to acquire sample particle size data corresponding to the sample positive electrode material; A construction module, used to construct a grading model according to the sample material type of the sample positive electrode material and the sample particle size data corresponding to the sample positive electrode material; An estimation module, used for estimating the gradation ratio corresponding to the positive electrode material to be estimated based on the material information corresponding to the positive electrode material to be estimated and the gradation model; A determination module is used to determine the powder compaction density of the sample positive electrode material according to the grading ratio.

9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method for determining the powder compaction density of the energy storage battery as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, wherein when the computer program is executed by a processor, the steps of the method for determining the powder compaction density of an energy storage battery as described in any one of claims 1 to 7 are implemented.

Images