Battery material mixing ratio simulation optimization design method, system, device and medium

Abstract

The application relates to a battery material mixing ratio simulation optimization design method, system, device and medium, which comprises the following steps: obtaining experimental data of a material to be optimized, wherein the experimental data comprises the true density, particle size distribution and compaction density of the material to be optimized; describing the particle compaction process of the material to be optimized by a discrete element model; optimizing the discrete element model parameters by using a particle swarm optimization algorithm; generating discrete element models with different mixing ratios based on the optimized discrete element model parameters and calculating the compaction densities of the discrete element models to obtain the optimal mixing ratio of the material to be optimized. The particle size distribution of the material is optimized by combining the discrete element simulation and the particle swarm optimization algorithm, the optimal mixing ratio is determined, and the compaction density and energy density of the battery are improved.

Description

TECHNICAL FIELD

[0001] The application relates to a battery material mixing ratio simulation optimization design method, system, device and medium, and relates to the technical field of lithium ion batteries. BACKGROUND

[0002] With the continuous development of the new energy automobile industry, the performance of lithium ion batteries, especially the energy density, is required to be higher and higher. At present, the commonly used methods to improve the energy density include improving the material specific capacity and the compaction density. However, it is difficult to simply improve the specific capacity, involves modification and new process, and the process is complex and the cost is high. The method of improving the compaction density is a fast and effective method, and since the material cost does not increase much, it is the most mainstream method of manufacturers at present.

[0003] The method of improving the compaction density is to mix two particles with different particle sizes to realize the effect of filling the gaps between the particles of different sizes, thereby improving the compaction density of the material. For granular materials, the discrete element method is usually used for simulation calculation. The related research on lithium ion battery materials only uses the discrete element method to obtain a three-dimensional structure model of the pole piece for subsequent research.

[0004] At present, the best mixing ratio among multiple groups of materials is usually found by using the experimental trial-and-error method. Although this method has high reliability, the time and labor cost is high. Especially for three or more materials, since the search space increases exponentially with the number of types, the experimental cost further increases. Moreover, since the battery material particles are usually in the nanometer-micrometer scale, and in the material development stage, the corresponding parameters cannot be obtained from the database or literature of common mainstream discrete element simulation software, if the parameters of the simulation method are not correct, the reliability of the simulation result is low. SUMMARY

[0005] In view of the above problems, the application aims to provide a battery material mixing ratio simulation optimization design method, system, device and medium capable of obtaining an optimal mixing ratio, improving the compaction density and the energy density.

[0006] In order to achieve the above application purpose, the technical scheme adopted by the application is:

[0007] In the first aspect, the application provides a battery material mixing ratio simulation optimization design method, which comprises:

[0008] obtaining experimental data of the material to be optimized, wherein the experimental data includes the true density, particle size distribution and compaction density of the material to be optimized;

[0009] establishing an initial discrete element model of the particles of the material to be optimized;

[0010] optimizing the discrete element model parameters by using a particle swarm optimization algorithm.

[0011] Based on the optimized discrete element model parameters, the discrete element models of different mixing ratios are generated and the compaction densities are calculated to obtain the optimal mixing ratio of the material to be optimized.

[0012] Further, the initial discrete element model of the particles of the material to be optimized is established, including:

[0013] The proportion of each material in the material to be optimized under the corresponding particle size is calculated under the set mixing ratio;

[0014] A simulation space is created, and N particles are randomly generated in the simulation space according to the set mixing ratio;

[0015] It is judged whether each particle in the current simulation space is non-overlapping, and if there is particle overlapping, the simulation space size is increased;

[0016] It is judged whether the overall density of the current simulation space is less than a set value, and if not, the simulation space size is increased;

[0017] The above judgments are repeated until the conditions are met, and the initial discrete element model of the particles of the material to be optimized is generated.

[0018] Further, the discrete element model parameters include contact parameters between particles of the same material and contact parameters between particles of different materials, wherein the contact parameters between particles of the same material include friction coefficient, recovery coefficient, cohesive energy density, Young's modulus and Poisson's ratio; the contact parameters between particles of different materials include friction coefficients, recovery coefficients and cohesive energy densities of different materials.

[0019] Further, the contact parameters between particles of different materials are obtained by using a particle swarm optimization algorithm, including:

[0020] The search range of the discrete element model parameters to be optimized is set;

[0021] S particles are initialized;

[0022] The discrete element model parameters to be optimized are mapped to [0, 1] using linear normalization, i.e. the position of the particle corresponds to the contact force parameter;

[0023] The fitness value cost of each particle is calculated fit ;

[0024] The current fitness of each particle is compared with the historical fitness, and if there is no historical fitness or the current fitness is lower than the historical value, the historical fitness of the particle is updated to the current fitness;

[0025] The minimum value of each particle in the particle swarm is compared with the historical minimum value of the group, and if there is no group history or the current generation fitness is lower, the group historical best is updated to the current group best;

[0026] Update the velocity and position of each particle, if the particle position exceeds the range of 0-1, set the particle position to 0 or 1;

[0027] Check if the optimal parameter at this time meets the error standard, if it meets, take this parameter; if it does not meet, continue to update the trajectory until it meets.

[0028] Further, calculate the fitness value cost of each particle fit , including:

[0029] Normalize the position of the particle to the corresponding contact parameter;

[0030] Use the contact parameter to perform discrete element calculation, and the granularity of the discrete element model is consistent with the experimental value;

[0031] According to the pressure on the experimental data, take the corresponding density value p on the obtained compaction curve sim ;

[0032] The fitness value cost of the particle fit is the root mean square error of the calculated value p sim and the experimental value p exp

[0033]

[0034] Wherein, n is the algebra.

[0035] Further, based on the optimized discrete element model parameters, generate discrete element models of different mixing ratios and calculate their compaction densities to obtain the optimal mixing ratio of the material to be optimized, including:

[0036] Based on the moving of the flat plate, use the GH and SJKR contact force model to describe the interaction between particles, and calculate the compaction density curve of the material to be optimized;

[0037] Based on the compaction density curve, obtain the optimal mixing ratio that meets the conditions.

[0038] Further, if only two materials are mixed, obtain the optimal mixing ratio that meets the conditions, including: determining the mixing ratio to be calculated; generating an initial discrete element model under each mixing ratio; correcting the discrete element model using the optimized contact parameter, and calculating the compaction density based on the corrected discrete element model; plot the compaction density-mixing ratio graph to determine the position of the optimal ratio;

[0039] ​If the material type is greater than 2, the particle swarm optimization algorithm is used to optimize the mixing ratio, and the fitness value of the mixing ratio is calculated as follows: an initial discrete element model of the mixed particles with a set mixing ratio is generated; the discrete element model is corrected using the optimized contact parameters, and the compaction density is calculated based on the corrected discrete element model; according to the optimization target, the density under each pressure is weighted, and the mixing ratio corresponding to the particle with the lowest cost_mix in the particle swarm optimization algorithm is selected, which is the optimal mixing ratio of the optimization target.

[0040] In a second aspect, the present application also provides a battery material mixing ratio simulation optimization design system, comprising:

[0041] A data acquisition unit configured to acquire experimental data of the material to be optimized, wherein the experimental data includes the true density, particle size distribution and compaction density of the material to be optimized;

[0042] A discrete element initial construction unit configured to establish an initial discrete element model of the particles of the material to be optimized;

[0043] A model parameter optimization unit configured to optimize the discrete element model parameters using a particle swarm optimization algorithm;

[0044] An optimal mixing unit configured to generate discrete element models with different mixing ratios based on the optimized discrete element model parameters and calculate their compaction densities to obtain the optimal mixing ratio of the material to be optimized.

[0045] In a third aspect, the present application also provides an electronic device, which comprises at least a processor and a memory, and the memory stores a computer program, wherein the processor executes the computer program to implement the method.

[0046] In a fourth aspect, the present application also provides a computer storage medium storing computer readable instructions, which can be executed by a processor to implement the method.

[0047] The present application has the following characteristics due to the above technical solutions:

[0048] 1. The present application optimizes the particle size distribution of the material by discrete element simulation combined with a particle swarm optimization algorithm, determines the optimal mixing ratio, and further improves the compaction density and energy density of the battery.

[0049] 2. The present application obtains the parameters of the simulation model by a small amount of experimental data, which has high accuracy; at the same time, due to the use of the optimization algorithm, the simulation method can better play the advantage of high parallelism, which can significantly reduce the experimental cost and improve the material development efficiency.

[0050] 3、The whole discrete element simulation process of the application can also be coupled with other simulations (such as electrochemistry, finite element), and has high expansibility.

[0051] In conclusion, the application can be widely applied in the production of battery materials. BRIEF DESCRIPTION OF DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not intended to limit the scope of the application. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. In the drawings:

[0053] Figure 1 The method flowchart of the embodiment of the application.

[0054] Figure 2 The particle initial structure generation diagram of the embodiment of the application.

[0055] Figure 3 The compaction density simulation calculation process diagram of the embodiment of the application.

[0056] Figure 4 The compaction density experimental test result diagram of the embodiment of the application under different mixing ratios.

[0057] Figure 5 The electronic device structure diagram of the embodiment of the application. DETAILED DESCRIPTION

[0058] It should be understood that the terms used herein are for the purpose of describing particular example embodiments and are not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and the like are to be construed to be inclusive (i.e., to include both instances of open- and closed- ended conditions) unless otherwise indicated with certainty by context. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order in which they are described unless otherwise indicated with certainty by context. It is also to be understood that additional or alternative steps can be employed.

[0059] Although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms can be only used to distinguish one element, component, region, layer or section from another region, layer or section. Unless the context clearly indicates otherwise, terms such as "first", "second" and the like used herein do not imply a sequence or an order, but rather are used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of example embodiments.

[0060] For ease of description, spatial relative terms can be used herein to describe the relationship of one element or feature to another element or feature as shown in the drawings, such as "inner", "outer", "inside", "outside", "lower", "upper", etc. Such spatial relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the drawings.

[0061] The battery material mixing ratio simulation optimization design method, system, device and medium provided by the application comprise: obtaining experimental data of a material to be optimized, wherein the experimental data comprises true density, particle size distribution and compaction density of the material to be optimized; an initial discrete element model of particles of the material to be optimized is established; a particle swarm optimization algorithm is used to optimize the discrete element model parameters; based on the optimized discrete element model parameters, a discrete element model of different mixing ratios is generated and its compaction density is calculated to obtain an optimal mixing ratio of the material to be optimized. Therefore, the particle size distribution of the material is optimized by the discrete element simulation and the particle swarm optimization algorithm, the optimal mixing ratio is determined, and the compaction density and energy density of the battery are improved.

[0062] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be thoroughly understood, and the scope of the present application can be completely conveyed to those skilled in the art.

[0063] The present application describes the method process by taking three materials A, B and C as an example, but is not limited to the number of materials, and the type of mixed materials can be set according to actual needs. The mixed material of the present embodiment is represented as A a B b C c , wherein a, b and c are the proportions of materials A, B and C, respectively, and 0≤a, b, c≤1.

[0064] As Figure 1 indicated, the battery material mixing ratio simulation optimization design method provided by the embodiment comprises:

[0065] S1, obtaining experimental data of the material to be optimized, the experimental data comprising true density, particle size distribution and compacted density of the material to be optimized.

[0066] Specifically, the obtaining method of the experimental data is as follows:

[0067] The particle size distribution can be obtained by using the GB / T 19077-2016 particle size analysis laser diffraction method through a laser particle size analyzer to obtain the particle size data PSDa, PSDb and PSDc of the materials A, B and C.

[0068] The true density and the compacted density of the materials A, B and C can be obtained by using the GB / T 24533-2019 test method for lithium ion battery graphite negative electrode material to obtain the true density ρ A , ρ B , ρ C of the materials A, B and C and the compacted density of the materials A, B and C. Further, the compacted density of the mixed materials A a B b C c can also be obtained.

[0069] S2, as Figure 2 indicated, using a discrete element model to describe the particle compaction process of the material to be optimized, establishing an initial discrete element model of the particles of the material to be optimized, comprising:

[0070] S21, calculating the proportion PSDmix of each particle size of each material A, B and C under the mixing ratio a:b:c;

[0071] S22, initially creating a box with length X, width Y and height Z as a simulation space, and randomly generating N particles in the simulation space according to the mixing ratio, the position of each particle i being (x i ,y i ,z i ), the density being ρ i , and the radius being r i .

[0072] S23, judging whether each particle in the current box is non-overlapping, i.e. the coordinates of each particle i and particle j satisfy If there is particle overlap, increase the size of the box.

[0073] S24, judging whether the overall density of the current box (mass of particles in the box / volume of the box) is less than ρ0, if not, increase the size of the box.

[0074] S25, repeat the above steps S23 and S24 until the conditions are met, step S23 and S24 are to generate the initial structure of the appropriate conditions, prevent the initial structure from being too dense, and affect the subsequent anti-compaction method. Further, for ternary materials, p0 is generally between 2.0 and 2.5 g / cc, for iron-lithium system materials, it is between 1.3 and 1.8 g / cc, and for carbon-based materials, it is between 0.1 and 0.8 g / cc, and the number of particles N depends on the computing power, usually N is between 10000 and 200000, for example, but not limited to this.

[0075] S3, optimize the discrete element model parameters.

[0076] Material A a B b C c The contact force model parameters required to establish the discrete element model include the contact parameters between particles of the same material and the contact parameters between particles of different materials.

[0077] Further, the contact force model parameters between particles of the same material include the friction coefficients, the recovery coefficients, the cohesive energy densities, the Young's moduli, and the Poisson's ratios of A-A, B-B, and C-C. The contact force model parameters between particles of the same material can be obtained through the measured particle size and compaction experimental data of the individual materials.

[0078] Further, the contact parameters between particles of different materials include the friction coefficients, the recovery coefficients, and the cohesive energy densities of A-B, A-C, and B-C.

[0079] Further, the contact parameters between particles of different materials can be obtained by two methods:

[0080] The more common and simple method is to take the geometric mean, for example, the friction coefficient of the contact between A-B can be obtained by taking the geometric mean of the friction coefficients of A-A and B-B. Since the particle sizes, quantities, and contact areas of different particles are different, the contact coefficient between A-B obtained by this method deviates greatly from the actual value.

[0081] This embodiment is based on the obtained experimental compaction data of materials A a B b C c , and the particle swarm optimization is performed on the mixed contact parameters (friction coefficient, recovery coefficient, and cohesive energy density). In this embodiment, the particle swarm algorithm is used to adjust the contact parameters, so that the calculated value p sim of the compaction curve is as close as possible to the experimental value p exp . The specific process includes:

[0082] S31, set the search range of the simulation model parameters.

[0083] The GH+SJKR model used in this embodiment needs 5 parameters to describe the contact between each pair of objects, wherein the friction coefficient cof is set in the range of 0.1-0.7; the recovery coefficient cor is set in the range of 0.1-0.5; the cohesive energy density ced is set in the range of 1e3-1e7 pg um -1 us -2 ; the Young's modulus ym is set in the range of 1e7-5e8 KPa; and the Poisson's ratio pr is set in the range of 0.1-0.4.

[0084] S32, initialize S particles, each of which is randomly distributed in a 5-dimensional space, and the particle position in each dimension is within 0-1.

[0085] S33, use linear normalization to map the discrete element model parameters to be optimized to [0, 1], that is, the position of the particle corresponds to the 5 parameters of the contact force model.

[0086] S34, calculate the fitness value cost of each particle fit :

[0087] The position of the particle is denormalized to the corresponding contact parameter;

[0088] The contact parameter is used for discrete element calculation, and the granularity of the discrete element model is consistent with the experimental value;

[0089] According to the pressure on the experimental data, the corresponding density value p is taken on the obtained compaction curve sim .

[0090] The fitness value cost of the particle is fit , which is the root mean square error between the calculated value p sim and the experimental value p exp :

[0091]

[0092] In the formula, n is the algebra.

[0093] S35, compare the current fitness and the historical fitness of each particle, if there is no historical fitness, or the current fitness is lower than the historical value, update the historical fitness of the particle to the current fitness.

[0094] S36, compare the minimum value of each particle in the particle group and the group historical minimum value, if there is no group history, or the current generation fitness is lower, update the group historical best to the current group best.

[0095] S37, update the speed and position of each particle, the speed of particle i in the G+1 generation:

[0096]

[0097] wherein c1 and c2 are random numbers transformed in the range [0, 1]; is a non-negative number, called the inertia factor, v i,G , v i,G+1 are the velocities of the i-th particle in the G-th generation and the G+1-th generation, respectively, v RNG is a random velocity, p i,best , p i,G is the position of the i-th particle in the G-th generation, p *,best is the global best position.

[0098] S38, update the particle velocity and position, if the particle position exceeds the range of 0-1, set the particle position to 0 or 1. Check if the optimal parameter at this time meets the error standard, if yes, take the parameter; if not, continue, update the trajectory until it meets.

[0099] S4, generate particle discrete element models of different mixing ratios based on the optimized discrete element contact force model parameters, calculate the compaction density, and select the optimal mixing ratio that meets the conditions.

[0100] S41, as Figure 3 shown, simplify the compaction process as a flat plate compression, use Granular Hertzian (GH) and simplified Johnson-Kendall-Roberts (SJKR) contact force models to describe the interaction between particles, and the calculation process of the compaction density includes:

[0101] First, set a flat plate parallel to the horizontal direction at the top of the simulation box to move downward at a speed v, and record the plate position z and the force f in real time, wherein the simulation box is in a rectangular coordinate system xyz, and the horizontal direction is the xy plane direction. Convert z and f into density and pressure, which is the compaction density curve.

[0102] S42, obtain the optimal mixing ratio that meets the conditions based on the compaction density curve.

[0103] S421, if there are only two materials mixed, because the search space is small, a simple traversal calculation can be performed, and an optimal mixing ratio that meets the conditions can be obtained without using an optimization algorithm, the steps are as follows:

[0104] Determine the mixing ratio to be calculated, for example, when two positive electrode materials are mixed, the ratio can be set to 9:1, 8:2, 7:3, …, 1:9; when the positive electrode material is mixed with the conductive agent, the ratio can be set to 99.9:0.1, 99.7:0.3, …, 90:10, which can be set according to needs.

[0105] Generate the initial discrete element model of each particle under each mixing ratio.

[0106] Generate the discrete element model using the obtained contact parameters to calculate the compaction density.

[0107] Draw the compaction density-mixing ratio graph to determine the position of the optimal ratio, and the relationship between the compaction improvement and the mixing ratio can be intuitively observed through the graph drawing.

[0108] S422, if the number of materials is large, the search space increases exponentially with the number of materials, and the efficiency of the grid traversal search is low, so the particle swarm optimization algorithm can be used to optimize the mixing ratio, and the fitness value 〖cost〗_mix of the mixing ratio a, b and c is calculated by the particle swarm optimization algorithm.

[0109] Generate A a B b C c Initial discrete element model

[0110] Generate the discrete element model using the obtained contact parameters to calculate the compaction density.

[0111] According to the optimization target, set the weight of the density under each pressure, select the mixing ratio corresponding to the particle with the lowest 〖cost〗_mix in the particle swarm optimization algorithm, and the mixing ratio is the optimal mixing ratio of the optimization target.

[0112] For example, if the optimization target is to have a higher compaction density in the high pressure range, the fitness value 〖cost〗_mix calculation method can be set as:

[0113] 〖cost〗_mix=100-(1×ρ_150MPa+2×ρ_188MPa+3×ρ_226MPa)

[0114] Wherein, ρ_150MPa, ρ_188MPa, ρ_226MPa are the densities corresponding to the pressures of 150MPa, 188MPa and 226MPa on the compaction density curve, wherein 1, 2 and 3 in the formula are the weights, and the larger the number is, the greater the weight is, and this example is not limited to this.

[0115] The specific application of the battery material mixing ratio simulation optimization design method of the application will be described in detail in the following specific embodiment, and the optimal mixing ratio of ternary high-nickel materials A and B is calculated in this embodiment, and the process is as follows:

[0116] Obtain the particle size, true density and compaction curve of A and B;

[0117] Obtain the compaction curve of A:B mixed in a mass ratio of 7:3 to obtain the A-B contact parameters;

[0118] The contact force model parameters of A-A, B-B and A-B are fitted, and the contact parameters obtained are: the contact parameters of A material are: COF=0.55, COR=0.28, CED=68300 pg um -1 us -2 , YM=4.2e7 KPa, PR=0.12; the contact parameters of B material are: COF=0.26, COR=0.32, CED=298600 pg um -1 us -2 , YM=4.5e7 KPa, PR=0.21; the contact parameters between A and B are: COFmix=0.15, CORmix=0.55, CEDmix=435000 pg um -1 us -2

[0119] Based on the above contact force model parameters, the initial results of 1:9 to 9:1 are generated, and the compaction is calculated as shown in Table 1, and the calculation results are shown in Table 1, wherein the highest compaction is obtained by mixing 6:4.

[0120] Table 1

[0121]

[0122] Further, in order to illustrate the reliability of the method, the compaction density experiment under different mixing ratios is carried out to test the experimental compaction, and the comparison experiment and simulation results are shown in Table 2 and Figure 4 the highest error is <1%, and the trend is the same as the experiment, which illustrates that the method has reliability.

[0123] Table 2

[0124] Scale 1:9 2:8 3:7 4:6 5:5 6:4 7:3 8:2 9:1 Experimental results (g / cc) 3.54 3.57 3.63 3.65 3.66 3.67 3.62 3.57 3.48 Calculated error (%) 0.3 0.16 0.67 0.36 0.32 0.41 0.08 0.26 0.86

[0125] Embodiment Two: The above embodiment one provides a battery material mixing ratio simulation optimization design method, and correspondingly, the present embodiment provides a battery material mixing ratio simulation optimization design system. The system provided by the present embodiment can implement the battery material mixing ratio simulation optimization design method of embodiment one. The system can be implemented by software, hardware or a combination of software and hardware. For the convenience of description, the system is described as various units in the present embodiment. Of course, the functions of the units can be implemented in the same or multiple software and / or hardware. For example, the system can include integrated or separate functional modules or functional units to perform the corresponding steps in the methods of embodiment one. Since the system of the present embodiment is basically similar to the method embodiment, the description process of the present embodiment is relatively simple, and the related parts can be referred to the description of embodiment one. The embodiment of the battery material mixing ratio simulation optimization design system provided by the present application is only illustrative.

[0126] Specifically, the battery material mixing ratio simulation optimization design system provided by the present embodiment includes:

[0127] A data acquisition unit configured to acquire experimental data of the material to be optimized, wherein the experimental data includes true density, particle size distribution and compaction density of the material to be optimized;

[0128] A discrete element initial construction unit configured to establish an initial discrete element model of the particles of the material to be optimized;

[0129] A model parameter optimization unit configured to optimize the discrete element model parameters using a particle swarm optimization algorithm;

[0130] An optimal mixing unit configured to generate discrete element models of different mixing ratios based on the optimized discrete element model parameters and calculate their compaction densities to obtain the optimal mixing ratio of the material to be optimized.

[0131] Embodiment Three: The present embodiment provides an electronic device corresponding to the battery material mixing ratio simulation optimization design method provided by embodiment one. The electronic device can be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment one.

[0132] As Figure 5As shown, the electronic device includes a processor, a memory, a communication interface and a bus, the processor, the memory and the communication interface are connected through the bus to complete the communication between each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program executable on the processor, and the processor executes the computer program to perform the battery material mixing ratio simulation optimization design method provided in the first embodiment. Those skilled in the art can understand that Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computing device to which the scheme of the present application is applied. A specific computing device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In some implementations, the logical instructions in the memory described above can be implemented in the form of a software functional unit and sold or used as a standalone product, which can be stored in a computer readable storage medium. Based on this understanding, the technical scheme of the present application or the part that essentially contributes to the prior art or part of the technical scheme can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), an optical disk and various program code storage media.

[0134] In other implementations, the processor can be a central processing unit (CPU), a digital signal processor (DSP) and various types of general-purpose processors, which are not limited here.

[0135] Embodiment four: the battery material mixing ratio simulation optimization design method of the first embodiment can be specifically implemented as a computer program product, and the computer program product can include a computer readable storage medium having computer readable program instructions loaded thereon for executing the battery material mixing ratio simulation optimization design method described in the first embodiment.

[0136] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0137] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the description of the method embodiments. In the description of the present specification, the description referring to the terms "one embodiment", "some implementations", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one of the embodiments or examples of the present specification. The illustrative representation of the above terms in the present specification does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0138] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0139] These computer program instructions can also be stored in a computer readable storage medium capable of directing the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including instruction devices, which implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The functions specified in one or more flows and / or blocks

[0140] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0141] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A simulation optimization design method for the mixing ratio of battery materials, characterized in that, The method comprises the following steps: Obtain experimental data of the material to be optimized, wherein the experimental data comprises true density, particle size distribution and compaction density of the material to be optimized; Establish an initial discrete element model of the particles of the material to be optimized, comprising: calculating the proportion of each material in the material to be optimized at a corresponding particle size under a set mixing ratio; creating a simulation space and randomly generating N particles in the simulation space according to the set mixing ratio; judging whether each particle in the current simulation space is overlapped, and if there is any overlapping particle, increasing the size of the simulation space; judging whether the overall density of the current simulation space is less than a set value, and if not, increasing the size of the simulation space; repeating the above judgments until the conditions are met, and generating the initial discrete element model of the particles of the material to be optimized; Optimize the discrete element model parameters by using a particle swarm optimization algorithm; Generate discrete element models of different mixing ratios based on the optimized discrete element model parameters and calculate the compaction density to obtain the optimal mixing ratio of the material to be optimized, comprising: using GH and SJKR contact force models to describe the interaction between particles based on the moving plate, and calculating the compaction density curve of the material to be optimized; obtaining the optimal mixing ratio that meets the conditions based on the compaction density curve; If there are only two materials mixed, obtain the optimal mixing ratio that meets the conditions, comprising: determining the mixing ratio to be calculated; generating the initial discrete element model under each mixing ratio; correcting the discrete element model by using the optimized contact parameters, and calculating the compaction density based on the corrected discrete element model; drawing a compaction density-mixing ratio graph to determine the position of the optimal ratio; If the number of material types is greater than 2, use the particle swarm algorithm to optimize the mixing ratio, and the fitness value 〖cost〗_mix calculation method is: generate the initial discrete element model of the mixed particles under the set mixing ratio; correct the discrete element model by using the optimized contact parameters, and calculate the compaction density based on the corrected discrete element model; set weights for the densities under each pressure according to the optimization target, and select the mixing ratio corresponding to the particle with the lowest 〖cost〗_mix in the particle swarm optimization algorithm, which is the optimal mixing ratio of the optimization target. 2.The battery material mixing ratio simulation optimization design method according to claim 1, characterized in that, The discrete element model parameters include contact parameters between particles of the same material and contact parameters between particles of different materials, wherein the contact parameters between particles of the same material include friction coefficient, recovery coefficient, cohesive energy density, Young's modulus and Poisson's ratio; the contact parameters between particles of different materials include friction coefficients, recovery coefficients and cohesive energy densities of different materials. 3.The battery material mixing ratio simulation optimization design method according to claim 2, characterized in that, The contact parameters between particles of different materials are obtained by using a particle swarm optimization algorithm, comprising: Setting the search range of the discrete element model parameters to be optimized; Initializing S particles; Mapping the discrete element model parameters to be optimized to [0, 1] by using linear normalization, i.e. the position of the particle corresponds to the contact force parameter; calculating a fitness value for each particle ; Comparing the current fitness of each particle with the historical fitness, and if there is no historical fitness or the current fitness is lower than the historical value, updating the historical fitness of the particle to the current fitness; Comparing the minimum value of each particle in the particle swarm with the historical minimum value of the group, and if there is no historical group or the current generation fitness is lower, updating the historical best of the group to the current group best. Update the velocity and position of each particle, if the particle position exceeds the range of 0~1, set the particle position to 0 or 1; Check if the optimal parameter at this time has met the error standard, if yes, take this parameter; if not, continue, update the trajectory until it is satisfied. 4.The battery material mixing ratio simulation optimization design method according to claim 3, characterized in that, calculating a fitness value for each particle comprising: Normalize the position of the particle to the corresponding contact parameter; Use the contact parameter to perform discrete element calculation, and the granularity of the discrete element model is consistent with the experimental value; According to the pressure on the experimental data, the corresponding density value is taken on the obtained compaction curve ; fitness value of the particle i.e. the calculated value the root mean square error with the experimental value : wherein n is algebraic.

5. A system for implementing the method of simulating and optimizing the mixing ratio of battery materials according to any one of claims 1 to 4, characterized in that, Comprise: A data acquisition unit configured to acquire experimental data of a material to be optimized, wherein the experimental data includes true density, particle size distribution and compaction density of the material to be optimized; A discrete element initial construction unit configured to establish an initial discrete element model of particles of the material to be optimized; A model parameter optimization unit configured to optimize the discrete element model parameters by using a particle swarm optimization algorithm; An optimal mixing unit configured to generate discrete element models of different mixing ratios based on the optimized discrete element model parameters and calculate their compaction densities to obtain the optimal mixing ratio of the material to be optimized. 6.An electronic device comprising at least a processor and a memory having stored thereon a computer program, characterized in that, The processor executes the computer program to implement the method of any one of claims 1 to 4.

7. A computer storage medium, characterized in that The computer readable instructions stored thereon can be executed by the processor to implement the method of any one of claims 1 to 4.

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