Solid-state battery composite electrode particle size compounding optimization method and device and computer equipment

Abstract

The invention relates to the technical field of batteries, in particular to a solid-state battery composite electrode particle size compounding optimization method and device and computer equipment. The solid-state battery composite electrode particle size compounding optimization method comprises the following steps: acquiring a first physical parameter set of electrolyte particles and a second physical parameter set of active substance particles in multiple groups of mixed particles; the multiple sets of mixed particles are compressed till the actual pressure intensity of the surfaces of the mixed particles reaches the preset pressure intensity threshold value of each set; searching for effective active substance particles from the electrolyte particles on the surface of each group of mixed particles to the surroundings, and obtaining a search target value corresponding to each mixed particle according to a search result; and analyzing the plurality of search target values to obtain an optimization interval of each physical parameter in the first physical parameter set and the second physical parameter set. The method adopts the statistical value of the search target value to determine the optimization interval of the physical parameters, and compared with the currently adopted experimental method, the method is rapid, simple and efficient.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a method, device and computer equipment for optimizing the particle size of composite electrodes for solid-state batteries. Background Art

[0002] The booming new energy vehicle sector has spurred the continuous development of solid-state batteries with high energy density and high safety. However, the solid-solid interface between solid electrolyte particles (also referred to as electrolyte particles) and active material particles is a major factor limiting solid-state battery performance. Small electrolyte particles can form more lithium ion transport pathways, resulting in better contact between the electrolyte particles and the active material, but the lithium ion transport pathways are complex and the electrode tortuosity increases. Large electrolyte particles provide more direct lithium ion transport pathways, thereby reducing tortuosity, but the relative contact area between large electrolyte particles and the active material is smaller, resulting in a poor solid-solid interface between the two. Therefore, gradient-structured electrodes with varying particle sizes are considered a compromise. However, composite electrodes involve numerous parameters, such as the electrolyte particle ratio, electrolyte particle size distribution, active particle size distribution, and the ratio of particles of different sizes. Using conventional experimental methods to explore the formulation is not only costly and time-consuming, but using multi-physics numerical simulation to identify the optimal electrode formulation is more complex and difficult to implement. Summary of the Invention

[0003] In view of this, the present invention provides a method, device and computer equipment for optimizing the particle size of solid-state battery composite electrodes to solve the problem that the current common experimental methods used to explore electrode formulations are not only costly but also time-consuming.

[0004] In the first aspect, the present invention provides a method for optimizing the particle size of solid-state battery composite electrodes, comprising the following steps: obtaining a first set of physical parameters of electrolyte particles and a second set of physical parameters of active material particles in multiple groups of mixed particles; compressing the multiple groups of mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold of each group; searching for effective active material particles starting from the electrolyte particles at the surface of each group of mixed particles to the surrounding area, and obtaining a search target value corresponding to each mixed particle based on the search results; analyzing multiple search target values to obtain the optimization range of each physical parameter in the first physical parameter set and the second physical parameter set.

[0005] The present invention provides a solid-state battery composite electrode particle size compounding optimization method. After compressing multiple groups of mixed particles, the method searches for effective active material particles starting from the electrolyte particles at the surface of each group of mixed particles and moving toward the surrounding area. The search target value corresponding to each mixed particle is obtained based on the search results, and the multiple search target values are analyzed to obtain the optimized range of each physical parameter in the first physical parameter set and the second physical parameter set. The present invention uses statistical values of the search target values to determine the optimized range of the physical parameters. Compared with the currently used experimental methods, the method is fast, simple, and efficient, which will help improve the efficiency of composite solid-state battery electrode formulation design and accelerate the technological iteration of solid-state batteries.

[0006] In some optional embodiments, a search is performed for effective active material particles starting from the electrolyte particles at the surface of each group of mixed particles to the surrounding areas, and obtaining a search target value corresponding to each mixed particle based on the search results includes the following steps: searching for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding areas, and recording the transfer process parameters of lithium ions during the search; determining multiple search initial values based on multiple transfer process parameters obtained by multiple searches of the current mixed particle; obtaining the total amount of effective active material particles obtained by multiple searches of the current mixed particle; and calculating the ratio of the sum of the search initial values to the total amount of effective active material particles to obtain the search target value corresponding to the current mixed particle.

[0007] Therefore, the search target value can be determined according to the transfer process parameters of lithium ions during the search process, thereby meeting the needs of particle size compounding.

[0008] In some optional embodiments, a search is performed for effective active material particles starting from the electrolyte particles at the surface of each group of mixed particles and in the surrounding areas, and a search target value corresponding to each mixed particle is obtained based on the search results: a search is performed for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle and in the surrounding areas, and the total amount of effective active material particles obtained by searching the current mixed particle multiple times is obtained; the total amount of active material particles in the current mixed particle is obtained; the ratio of the total amount of effective active material particles to the total amount of active material particles is calculated to obtain the search target value corresponding to the current mixed particle.

[0009] Therefore, the ratio of the total amount of effective active material particles to the total amount of active material particles (i.e. the proportion of effective active material particles) is used as the search target value to meet the requirements of particle size compounding.

[0010] In an optional embodiment, a search for effective active material particles is performed from the electrolyte particle at the surface of the current mixed particle to the surrounding area, and recording the transfer process parameters of lithium ions during the search process includes the following steps: obtaining any electrolyte particle i at the surface of the current mixed particle, and taking electrolyte particle i as the current electrolyte particle; performing a current search from the current electrolyte particle to the surrounding area to obtain the current effective active material particle; wherein the current search is to search for the next particle from the current electrolyte particle to the surrounding area until it contacts any active material particle j, and taking active material particle j as the current effective active material particle; obtaining the transfer process parameters of lithium ions in the current search; obtaining the next electrolyte particle at the same surface of the current mixed particle, and taking the next electrolyte particle as the new current electrolyte particle, and returning to the step of performing a current search from the current electrolyte particle to the surrounding area to obtain the current effective active material particle, until all electrolyte particles at the surface of the current mixed particle are traversed.

[0011] This is because the transmission path of lithium ions in solid-state batteries is along the solid electrolyte in contact with it, so a search starts from the solid electrolyte at the surface of the mixed particles and ends when it contacts the active particles.

[0012] In an optional embodiment, the transfer process parameter includes at least one of the following: transfer distance, and the number of interfaces passed by the transfer.

[0013] Therefore, different transfer process parameters can be adopted according to different concerns when compounding the composite electrode particle size.

[0014] In an optional embodiment, when the transfer process parameter is the transfer distance, the search initial value is the tortuosity; determining multiple search initial values based on multiple transfer process parameters obtained by multiple searches of the current mixed particles includes the following steps: when the current effective active material particle is obtained by the current search starting from the current electrolyte particle and conducting the current search to the surrounding area, obtaining the current distance traveled by the lithium ion from the current electrolyte particle to the current effective active material particle; obtaining a preset distance array, wherein the distance array includes multiple effective active material particles, and the shortest distance corresponding to each effective active material particle for the lithium ion from the electrolyte particle to the corresponding effective active material particle; updating the distance array using the current distance and the preset first state transfer equation, and obtaining the shortest distance traveled by the lithium ion from the current electrolyte particle to the current effective active material particle in the updated distance array; obtaining the vertical coordinate of the current effective active material particle; calculating the ratio of the shortest distance traveled by the lithium ion from the current electrolyte particle to the current effective active material particle to the vertical coordinate of the current effective active material particle to obtain the tortuosity corresponding to the current effective active material particle.

[0015] In this way, the tortuosity corresponding to each active substance particle can be obtained when searching for mixed particles.

[0016] In an optional embodiment, when the transfer process parameter is the number of interfaces, the search initial value is the minimum number of interfaces; determining multiple search initial values based on multiple transfer process parameters obtained by multiple searches of the same mixed particle includes the following steps: when the current effective active material particle is obtained by the current search starting from the current electrolyte particle and conducting the surrounding current search, the current number of interfaces through which the lithium ions starting from the current electrolyte particle are transferred to the current effective active material particle is obtained; a preset interface array is obtained, wherein the interface array includes multiple effective active material particles, and the minimum number of interfaces through which the lithium ions starting from the electrolyte particle are transferred to the corresponding effective active material particle corresponding to each effective active material particle; the interface array is updated using the current number of interfaces and the preset second state transfer equation; and the minimum number of interfaces corresponding to the current effective active material particle is obtained in the updated distance array.

[0017] This allows the minimum number of interfaces corresponding to each effective active material particle to be obtained when searching for mixed particles.

[0018] In some optional embodiments, obtaining the total amount of effective active material particles obtained by multiple searches of the current mixed particle includes the following steps: after the current search is performed starting from the current electrolyte particle to the surrounding area to obtain the current effective active material particles, obtaining a first array, wherein the first array includes all active material particles in the current mixed particle, and whether each active material particle has been searched; judging whether the current effective active material particle has been searched based on the first array; when the current effective active material particle has been searched, keeping the first array unchanged; when the current effective active material particle has not been searched, updating the status of the current effective active material particle in the first array to be searched; after traversing all electrolyte particles at the same surface of the current mixed particle, calculating the total amount of effective active material particles in the current mixed particle based on the first array.

[0019] This can prevent the effective active material particles from being repeatedly counted during the search process, thereby ensuring the accuracy of the total amount of effective active material particles.

[0020] In an optional embodiment, searching for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding area includes: using a preset breadth-first algorithm to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding area; or; using a preset depth-first algorithm to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding area.

[0021] This allows a comprehensive search for the current mixed particles.

[0022] In an optional embodiment, the method for optimizing the particle size compounding of solid-state battery composite electrodes further includes the following steps: obtaining the size requirements of the box; laminating the electrolyte particles and the active material particles according to a plurality of first physical parameters and a plurality of second physical parameters to generate a plurality of groups of mixed particles that meet the size requirements; using the discrete element method to simulate the interactions in each group of mixed particles separately and adjust the positions of the electrolyte particles and / or active material particles in each group of mixed particles separately until each group of mixed particles is stable.

[0023] This allows for rapid generation of mixed particles that meet the requirements.

[0024] In the second aspect, the present invention also provides a solid-state battery composite electrode particle size compounding optimization device, including an acquisition module, a compression module, a search target value determination module and an analysis module; the acquisition module is used to obtain a first physical parameter set of electrolyte particles and a second physical parameter set of active material particles in multiple groups of mixed particles; the compression module is used to compress multiple mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold; the search target value determination module is used to search for effective active material particles starting from the electrolyte particles at the surface of each mixed particle to the surrounding area, and obtain the search target value corresponding to each mixed particle according to the search result; the analysis module is used to analyze multiple search target values to obtain the optimization range of each physical parameter in the first physical parameter set and the second physical parameter set.

[0025] In a third aspect, the present invention also provides a computer device comprising a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the solid-state battery composite electrode particle size optimization method of the first aspect or any corresponding embodiment thereof.

[0026] In a fourth aspect, the present invention also provides a computer-readable storage medium having computer instructions stored thereon, and the computer instructions are used to enable a computer to execute the solid-state battery composite electrode particle size compounding optimization method of the above-mentioned first aspect or any corresponding embodiment thereof.

[0027] In a fifth aspect, the present invention also provides a computer program product comprising computer instructions for enabling a computer to execute the solid-state battery composite electrode particle size optimization method of the first aspect or any corresponding embodiment thereof.

[0028] The solid-state battery composite electrode particle size optimization method, device and computer equipment provided by the present invention, after compressing multiple groups of mixed particles, searches for effective active material particles starting from the electrolyte particles at the surface of each group of mixed particles to the surrounding area, obtains the search target value corresponding to each mixed particle based on the search results, and then analyzes the multiple search target values to obtain the optimization range of each physical parameter in the first physical parameter set and the second physical parameter set. Compared with the currently used experimental methods, the present invention uses the statistical value of the search target value to determine the optimization range of the physical parameter. Compared with the currently used experimental methods, it is fast, simple and efficient, which will help improve the efficiency of composite solid-state battery electrode formula design and accelerate the technological iteration of solid-state batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 This is a flow chart of a method for optimizing particle size of composite electrodes for solid-state batteries according to an embodiment of the present invention;

[0031] Figure 2 It is a schematic diagram of a two-dimensional matrix composed of randomly generated active material particles and solid electrolyte particles;

[0032] Figure 3 This is a flow chart of another method for optimizing particle size of composite electrodes for solid-state batteries according to an embodiment of the present invention;

[0033] Figure 4 is a schematic diagram of the search path and the effective active particles and the inert active particles;

[0034] Figure 5 is a coordinate system of random particles and a schematic diagram of an effective path;

[0035] Figure 6 This is a flow chart of another method for optimizing the particle size of composite electrodes for solid-state batteries according to an embodiment of the present invention;

[0036] Figure 7 is a graph showing the change in the effective active particle ratio and average tortuosity with the solid electrolyte particle ratio in Example 1;

[0037] Figure 8 is a graph showing the effective active particle ratio and average tortuosity in Example 2 as a function of the particle size ratio of the solid electrolyte particles to the active particles;

[0038] Figure 9 is a graph showing the effective active particle ratio and the average number of interfaces in Example 2 as a function of the particle size ratio of the solid electrolyte particles to the active particles;

[0039] Figure 10 2 is a structural block diagram of a device for optimizing particle size of composite electrodes for solid-state batteries according to an embodiment of the present invention;

[0040] Figure 11 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0042] According to an embodiment of the present invention, an embodiment of a method for optimizing the particle size compounding of composite electrodes for solid-state batteries is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0043] This embodiment provides a method for optimizing the particle size of composite electrodes for solid-state batteries, which can be used in computer equipment. Figure 1 Flowchart of the solid-state battery composite electrode particle size optimization method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0044] Step S101: obtaining a first set of physical parameters of electrolyte particles and a second set of physical parameters of active material particles in a plurality of groups of mixed particles.

[0045] Specifically, the electrolyte particles include, but are not limited to, sulfide electrolytes, oxide electrolytes, halide electrolytes, sulfur-halide electrolytes, oxygen-halide electrolytes, etc. The first set of physical parameters of the electrolyte particles includes, but is not limited to, density of the electrolyte particles, radius of the electrolyte particles, stiffness of the electrolyte particles, damping coefficient of the electrolyte particles, Young's modulus of the electrolyte particles, etc.

[0046] Active material particles include, but are not limited to, NCM, lithium iron phosphate, lithium manganese oxide, lithium cobalt oxide, silicon, graphite, and the like. Second physical parameters of the active material particles include, but are not limited to, density of the active material particles, radius of the active material particles, stiffness of the active material particles, damping coefficient of the active material particles, and Young's modulus of the active material particles.

[0047] Step S102: compressing the multiple groups of mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold value for each group.

[0048] In this embodiment, when compressing multiple groups of mixed particles, the preset pressure thresholds for each group of mixed particles may be the same or different.

[0049] Step S103: Starting from the electrolyte particles on the surface of each group of mixed particles, a search for effective active material particles is performed in the surrounding area, and a search target value corresponding to each mixed particle is obtained according to the search results.

[0050] In this embodiment, effective active material particles are defined as active material particles to which lithium ions can be transferred from the solid electrolyte particles on the surface of the mixed particles, and inert active material particles are defined as active material particles to which lithium ions cannot be directly transferred from the solid electrolyte on the surface.

[0051] Step S104: Analyze the multiple search target values to obtain the optimization range of each physical parameter in the first physical parameter set and the second physical parameter set.

[0052] The solid-state battery composite electrode particle size optimization method provided in this embodiment, after compressing multiple groups of mixed particles, searches for effective active material particles starting from the electrolyte particles at the surface of each group of mixed particles to the surrounding area, obtains the search target value corresponding to each mixed particle based on the search results, and then analyzes the multiple search target values to obtain the optimization range of each physical parameter in the first physical parameter set and the second physical parameter set. Compared with the currently used experimental methods, the present invention uses the statistical value of the search target value to determine the optimization range of the physical parameter. It is fast, simple, and efficient, which will help improve the efficiency of composite solid-state battery electrode formulation design and accelerate the technological iteration of solid-state batteries.

[0053] This embodiment provides a method for optimizing the particle size of composite electrodes for solid-state batteries, which can be used in computer equipment. Figure 2 FIG. 1 is a flow chart of another solid-state battery composite electrode particle size optimization method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0054] Step S201: obtaining a plurality of first physical parameter sets of electrolyte particles and a plurality of second physical parameter sets of active material particles.

[0055] Step S202: Obtain the size requirements of the box.

[0056] Specifically, the box can be two-dimensional or three-dimensional. For example, if it is a two-dimensional box, the box size is: L×W=10mm×100μm; if it is a three-dimensional box, the box size is: L×W×H=10mm×10mm×100μm.

[0057] Step S203: According to the plurality of first physical parameters and the plurality of second physical parameters, the electrolyte particles and the active material particles are laminated to generate a plurality of groups of mixed particles that meet size requirements.

[0058] Step S204: using the discrete element method to simulate the interactions in each group of mixed particles and adjust the positions of the electrolyte particles and / or active material particles in each group of mixed particles until each group of mixed particles is stable.

[0059] Specifically, when using the discrete element method to simulate the interaction in each mixed particle, the contact model of the particles includes but is not limited to a linear model, a Hertz contact model, a burger contact model, etc., and the gravitational acceleration is set to 9.8 N / kg.

[0060] Figure 3 is a schematic diagram of a two-dimensional matrix composed of randomly generated active material particles and solid electrolyte particles, such as Figure 3 As shown, mixed particles are generated inside a rectangular box. The mixed particles can be spherical particles. The box is surrounded by walls. The upper wall is defined as the surface of the composite electrode, and the lower wall is defined as the current collector side.

[0061] Step S205: controlling a wall of the box to move at a preset speed.

[0062] In an optional embodiment, the upper wall is forced to move downward at a speed of 0.1 m / s to compress the mixed particles.

[0063] Step S206: Acquire the actual pressure on the wall.

[0064] Step S207: Determine whether the actual pressure reaches the preset pressure threshold. If so, proceed to step S208; otherwise, return to step S206.

[0065] The compression ends when the particles are compacted and the upper wall is subjected to a certain pressure. For example, the pressure threshold may be 300 MPa.

[0066] Step S208: When the actual pressure reaches the pressure threshold, the wall is controlled to stop moving.

[0067] In other words, after the mixed particles are generated, they are compressed to ensure good contact within the mixed particles, simulating the compaction process of solid-state battery electrodes. After compression, the wall is fixed to achieve self-equilibrium within the mixed particles. The above steps S203 to S206 can better simulate the actual compression process.

[0068] Step S209: obtaining any electrolyte particle i on the surface of the current mixed particle, and taking the electrolyte particle i as the current electrolyte particle.

[0069] Step S210: Starting from the current electrolyte particle, a current search is performed to obtain the current valid active material particle. The current search is to search for the next particle from the current electrolyte particle to the surrounding area until it contacts any active material particle j, and the active material particle j is taken as the current valid active material particle.

[0070] Specifically, after the mixed particles are compressed, in order to simulate the process of lithium ion transmission from the electrode surface to the inside of the electrode, a search is started from each solid electrolyte particle on the surface of the composite electrode according to the lithium ion transmission rules and particle contact rules. The search methods include but are not limited to depth-first search, breadth-first search, etc.

[0071] Unlike traditional lithium-ion batteries that use electrolytes, the transmission path of lithium ions in solid-state batteries is along the solid electrolyte in contact with it. Therefore, a search starts from the solid electrolyte at the surface and ends when it contacts the active particles.

[0072] like Figure 4 As shown in the figure, effective active material particles are defined as active material particles that can be transferred by lithium ions from the solid electrolyte particles on the surface, and inert active material particles are defined as active material particles that cannot be directly transferred by the solid electrolyte on the surface. In addition, the effective active material particle ratio is defined as the ratio of effective active material particles to the total active material particles.

[0073] Step S211: obtaining the transfer process parameters of lithium ions in the current search, and obtaining the search initial value corresponding to the current valid active material particles according to the transfer process parameters.

[0074] Specifically, the transfer process parameters include at least one of the following: transfer distance, and the number of interfaces passed through by the transfer.

[0075] In order to explain step S211 more clearly, in this embodiment, the tortuosity is defined as the ratio of the distance traveled by lithium ions from the surface solid electrolyte particles to the active material particles to the vertical coordinate of the active material particles. Figure 5As shown, an active material particle with coordinates of (x, y) needs to pass through two electrolyte particles with a radius of r from a certain solid electrolyte particle on the surface to this point, so the tortuosity at the particle is 4r / y. Therefore, the average tortuosity is defined as the ratio of the sum of the tortuosity at each effective active particle to the number of effective active particles. The transmission rate of lithium ions in the solid electrolyte particle bulk and the solid-solid interface is different. In order to measure the role of the interface effect, in this embodiment, the number of interfaces is defined as the number of interfaces passed by the lithium ions from the electrolyte particles on the surface to the active material particles. Therefore, the average number of interfaces is defined as the ratio of the sum of the interfaces at each effective active particle to the number of effective active particles.

[0076] In an optional embodiment, when the transfer process parameter is the transfer distance, the search initial value is the tortuosity. Obtaining the search initial value corresponding to the current effective active material particles according to the transfer process parameter includes the following steps A1 to A5.

[0077] Step A1: When a current effective active material particle is obtained by performing a current search from a current electrolyte particle to its surroundings, a current distance traveled by lithium ions from the current electrolyte particle to the current effective active material particle is obtained.

[0078] Step A2: Obtain a preset distance array, wherein the distance array includes a plurality of effective active material particles and the shortest distance corresponding to each effective active material particle for lithium ions to be transferred from the electrolyte particle to the corresponding effective active material particle.

[0079] Step A3: Update the distance array using the current distance and the preset first state transfer equation, and obtain the shortest distance traveled by the lithium ions from the current electrolyte particle to the current effective active material particle in the updated distance array.

[0080] For example, if lithium ions are transferred from particle i to particle j, the first state transfer equation of the distance of particle j is:

[0081] distance(j)=min[distance(j),diatance(i)+r i ]

[0082] Step A4: Obtain the vertical coordinate of the current effective active material particle.

[0083] Step A5: Calculate the ratio of the shortest distance traveled by lithium ions from the current electrolyte particle to the current effective active material particle to the ordinate of the current effective active material particle to obtain the tortuosity corresponding to the current effective active material particle.

[0084] In an optional embodiment, when the transfer process parameter is the number of interfaces, the search initial value is the minimum number of interfaces; obtaining the search initial value corresponding to the current effective active material particles according to the transfer process parameter includes the following steps B1 to B5.

[0085] Step B1: When a current effective active material particle is obtained by performing a current search starting from the current electrolyte particle and moving toward the surrounding area, the current number of interfaces through which lithium ions from the current electrolyte particle are transferred to the current effective active material particle is obtained.

[0086] Step B2: obtaining a preset interface array, wherein the interface array includes a plurality of effective active material particles and a minimum number of interfaces corresponding to each effective active material particle through which lithium ions starting from the electrolyte particles are transferred to the corresponding effective active material particles.

[0087] Step B3: Update the interface array using the current number of interfaces and the preset second state transfer equation.

[0088] For example, if lithium ions are transferred from particle i to particle j, the second state transfer equation at the interface of particle j is:

[0089] interface(j)=min[interface(i)+1,interface(j)]

[0090] Step B4: Obtain the minimum number of interfaces corresponding to the current valid active material particles in the updated distance array.

[0091] Step S212: Obtain the next electrolyte particle at the same surface of the current mixed particle, and use the next electrolyte particle as the new current electrolyte particle, and return to the step of searching around the current electrolyte particle to obtain the current valid active material particle until all electrolyte particles at the surface of the current mixed particle are traversed.

[0092] Through the above steps S209 to S212, multiple search target values can be obtained by performing multiple searches on the current mixed particles.

[0093] Step S213: obtaining the total amount of effective active material particles obtained by performing multiple searches on the current mixed particles.

[0094] Step S214: Calculate the ratio of the sum of the search initial values to the total amount of effective active material particles to obtain the search target value corresponding to the current mixed particles.

[0095] Specifically, when the search initial value is tortuosity, the ratio of the sum of multiple tortuosities obtained by performing multiple searches on the current mixed particles to the total amount of effective active substance particles is calculated to obtain the average tortuosity.

[0096] When the search initial value is the minimum interface number, the ratio of the sum of multiple minimum interface numbers obtained by multiple searches for the current mixed particles to the total amount of effective active material particles is calculated to obtain the average interface number.

[0097] This embodiment uses the discrete element method to randomly generate mixed particles composed of solid electrolytes and active materials to simulate the real mixing process, and abstracts the real physical process into statistics. By establishing reasonable search targets and using search algorithms, it finds the influence of various factors on the particle size compounding of solid-state battery composite electrodes, and provides a practical method for the optimization range of physical parameters during particle size compounding.

[0098] This embodiment provides a method for optimizing the particle size of composite electrodes for solid-state batteries, which can be used in computer equipment. Figure 6 FIG. 1 is a flow chart of another method for optimizing the particle size of composite electrodes for solid-state batteries according to an embodiment of the present invention. Figure 6 As shown, the process includes the following steps:

[0099] Step S601: obtaining a first set of physical parameters of electrolyte particles and a second set of physical parameters of active material particles in a plurality of groups of mixed particles.

[0100] Step S602: compressing the multiple groups of mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold value for each group.

[0101] Step S603: obtaining any electrolyte particle i on the surface of the current mixed particle, and taking the electrolyte particle i as the current electrolyte particle.

[0102] Step S604: Starting from the current electrolyte particle, a current search is performed in the surrounding area to obtain the current valid active material particle; wherein the current search is to search for the next particle in the surrounding area from the current electrolyte particle until it contacts any active material particle j, and the active material particle j is taken as the current valid active material particle.

[0103] Step S605: obtaining a first array, wherein the first array includes all active material particles in the mixed particles and whether each active material particle has been searched.

[0104] For example, the first array may be a Boolean array.

[0105] Step S606: determining whether the currently valid active material particles have been searched according to the first array.

[0106] Step S607: when the currently valid active material particles have been searched, the first array is kept unchanged; when the currently valid active material particles have not been searched, the status of the currently valid active material particles in the first array is updated to be searched.

[0107] This embodiment can prevent the effective active material particles from being repeatedly counted during the search process by setting the first array.

[0108] Step S608: obtaining the transfer process parameters of lithium ions in the current search, and obtaining the search initial value corresponding to the current valid active material particles according to the transfer process parameters.

[0109] Step S608 of this embodiment is the same as step S211 of the previous embodiment and will not be described again here.

[0110] Step S609: Obtain the next electrolyte particle at the same surface of the mixed particle, and use the next electrolyte particle as the new current electrolyte particle, and return to the step of searching from the current electrolyte particle to obtain the current valid active material particle until all electrolyte particles at the same surface of the mixed particle are traversed.

[0111] Through the above steps S603 to S609, multiple search target values can be obtained by performing multiple searches on the current mixed particles.

[0112] Step S610: Calculate the total amount of effective active material particles in the current mixed particles according to the first array.

[0113] Step S611: Calculate the ratio of the sum of the search initial values to the total amount of effective active material particles to obtain the search target value of the mixed particles. Step S611 of this embodiment is the same as step S214 of the previous embodiment and will not be repeated here.

[0114] Step S612: Obtain the total amount of active material particles in the current mixed particles.

[0115] Step S613: Calculate the ratio of the total amount of effective active material particles to the total amount of active material particles to obtain the effective active material particle ratio, and use the effective active material particle ratio as the search target value corresponding to the current mixed particles.

[0116] The solid-state battery composite electrode particle size optimization method provided in this embodiment uses the proportion of effective active material particles, average tortuosity and average number of interfaces as search target values, thereby enabling a comprehensive evaluation of mixed particles.

[0117] In order to more clearly illustrate the method for optimizing the particle size combination of solid-state battery composite electrodes according to an embodiment of the present invention, two specific examples are given.

[0118] Example 1:

[0119] A method for designing particle size combination of composite electrodes for solid-state batteries, comprising:

[0120] (S1) Randomly generate solid electrolyte particles and active material particles, wherein the particle size of the solid electrolyte is equal to the particle size of the active material particles, and the size of the box is: L×W=10 mm×100 μm.

[0121] The electrolyte particles are LPSC and the active material particles are NCM811. The radius of the electrolyte particles and the active material particles is set to 2.3 μm, the volume fraction of the electrolyte particles is set to 10%, and the density of the electrolyte is set to 1.87 g / cm 3 , the density of the active material is set to 4.85 g / cm 3 The gravitational acceleration is set to 9.8 N / kg, the damping coefficient is set to 0.7, the contact model is set to a linear model, and the normal stiffness and tangential stiffness are both set to 1×10 7 N / m.

[0122] (S2) A downward velocity of 0.1 m / s is applied to the upper wall of the box to compact the particles, and the stopping condition of the compression is set to a pressure of 300 MPa on the upper wall.

[0123] (S3) A breadth-first search is performed starting from each electrolyte particle on the electrode surface. For particles of a single size, the average interface and average tortuosity are equivalent, so only the proportion of active material particles and the average tortuosity are recorded during the search.

[0124] (S4) Changing the volume percentage of the electrolyte particles to 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, and 60% and then repeating the process from (S1) to (S3).

[0125] (S5) Visualize the search values of the proportions of different electrolyte particles.

[0126] Through the above simulation process, we can get the effect of electrolyte particle ratio on the composite electrode, and the results are as follows: Figure 7As shown in the figure, as the proportion of electrolyte particles increases, the proportion of effective active particles gradually increases and approaches a maximum of 100%, while the average tortuosity first increases and then decreases. The increase in the former can be attributed to the fact that the proportion of electrolyte particles brings more lithium ion transmission pathways, thereby reducing the number of inert active particles; the latter's initial increase trend can be attributed to the fact that the increase in the proportion of electrolyte particles makes the lithium ion transmission pathway more diverse, thereby increasing the tortuosity, and the subsequent decrease trend can be attributed to the fact that the increase in the proportion of electrolyte particles causes more serious agglomeration of electrolyte particles, thereby shortening the lithium ion transmission path.

[0127] In addition, it can be found that the changes in both the effective active particle ratio and the average tortuosity are not simply linear, but have a sharp inflection point. Therefore, the optimization range of the solid electrolyte particle ratio experiment with a single particle size can be narrowed to near the inflection point, that is, 45%-55%.

[0128] Example 2:

[0129] (S1) Randomly generate solid electrolyte particles and active material particles, wherein the particle size of the solid electrolyte is equal to the particle size of the active material particles, and the size of the box is: L×W=10 mm×100 μm.

[0130] The electrolyte particles are LPSC and the active material particles are silicon. The radius of the active material particles is set to 2.3 μm, and the radius of the electrolyte particles is set to 0.6 times the radius of the active material particles. The volume fraction of the electrolyte particles is set to 50%, and the density of the electrolyte is set to 1.87 g / cm 3 , the density of the active material is set to 2.32 g / cm 3 The gravitational acceleration is set to 9.8 N / kg, the damping coefficient is set to 0.65, the contact model is set to a linear model, and the normal stiffness and tangential stiffness are both set to 1×10 7 N / m.

[0131] (S2) A downward velocity of 0.1 m / s is applied to the upper wall of the box to compact the particles, and the stopping condition of the compression is set to a pressure of 200 MPa on the upper wall.

[0132] (S3) performing a breadth-first search starting from each electrolyte particle on the electrode surface. During the search, the proportion of effective active material particles, the average tortuosity, and the average number of interfaces are recorded.

[0133] (S4) The radius of the electrolyte particles is changed to 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, or 2.0 times the radius of the active material particles, and then the process from (S1) to (S3) is repeated.

[0134] (S5) Visualize the search values of the proportions of different electrolyte particles.

[0135] Through the above simulation process, we can get the effect of electrolyte particle size on solid-state battery composite electrode under fixed loading conditions. The results are as follows: Figure 8 and Figure 9 As shown in the figure, as the electrolyte particle size increases, the effective active particle ratio, average tortuosity, and average interface all gradually decrease. The decrease in the effective active particle ratio can be attributed to the fact that larger electrolyte particles make the contact between particles insufficient, causing more active particles to become inert particles; while the decreasing trend of average tortuosity and average interface can be attributed to: (1) fixed loading, the number of electrolyte particles with larger particle size is smaller, resulting in fewer interfaces; (2) large particles make the lithium ion transmission path more direct, making the lithium ion transmission path shorter.

[0136] In addition, it can be found that the decrease in the effective active particle ratio is faster than the average tortuosity and the average number of interfaces. Therefore, the intersection of the two curves can be used as the experimental optimization range of the particle size ratio.

[0137] In this embodiment, a solid-state battery composite electrode particle size compounding optimization device is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be repeated here. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0138] This embodiment provides a device for optimizing the particle size of composite electrodes for solid-state batteries. Figure 10 The system shown includes an acquisition module 1001 , a compression module 1002 , a search target value determination module 1003 and an analysis module 1004 .

[0139] The acquisition module 1001 is configured to acquire a first set of physical parameters of electrolyte particles and a second set of physical parameters of active material particles in a plurality of groups of mixed particles.

[0140] The compression module 1002 is configured to compress the plurality of mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold.

[0141] The search target value determination module 1003 is used to search for effective active material particles starting from the electrolyte particles on the surface of each mixed particle to the surrounding area, and obtain the search target value corresponding to each mixed particle according to the search results.

[0142] The analysis module 1004 is configured to analyze the multiple search target values to obtain an optimization range of each physical parameter in the first physical parameter set and the second physical parameter set.

[0143] In some optional embodiments, the search target value determination module 1003 includes a transfer process parameter acquisition unit, a search initial value determination unit, an effective active particle total amount acquisition unit, and a first search target value calculation unit. The transfer process parameter acquisition unit is configured to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle and extending to the surrounding area, recording the transfer process parameters of lithium ions during the search process; the search initial value determination unit is configured to determine multiple search initial values based on multiple transfer process parameters obtained by multiple searches of the current mixed particle; the effective active particle total amount acquisition unit is configured to obtain the total amount of effective active material particles obtained by multiple searches of the current mixed particle; and the first search target value calculation unit is configured to calculate the ratio of the sum of the search initial values to the total amount of effective active material particles to obtain the search target value corresponding to the current mixed particle.

[0144] In some optional embodiments, the search target value determination module 1003 includes an effective active particle total amount acquisition unit, an active particle total amount acquisition unit, and a second search target value calculation unit. The effective active particle total amount acquisition unit is configured to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle and extending toward the surrounding area, obtaining the total amount of effective active material particles obtained by multiple searches of the current mixed particle; the active particle total amount acquisition unit is configured to obtain the total amount of active material particles in the current mixed particle; and the second search target value calculation unit is configured to calculate the ratio of the total amount of effective active material particles to the total amount of active material particles to obtain the search target value corresponding to the current mixed particle.

[0145] In some optional embodiments, the transfer process parameter acquisition unit is specifically used to: acquire any electrolyte particle i at the surface of the current mixed particle, and use electrolyte particle i as the current electrolyte particle; start the current search from the current electrolyte particle to the surrounding area to obtain the current valid active material particle; wherein the current search is to search for the next particle from the current electrolyte particle to the surrounding area until it contacts any active material particle j, and use active material particle j as the current valid active material particle; acquire the transfer process parameters of lithium ions in the current search; acquire the next electrolyte particle at the same surface of the current mixed particle, and use the next electrolyte particle as the new current electrolyte particle, and return to the step of performing the current search from the current electrolyte particle to the surrounding area to obtain the current valid active material particle, until all electrolyte particles on the surface of the current mixed particle are traversed.

[0146] In some optional implementations, the transfer process parameters include at least one of the following: transfer distance, and the number of interfaces passed by the transfer.

[0147] In some optional embodiments, when the transfer process parameter is the transfer distance, the search initial value is the tortuosity, and the search initial value determination unit is specifically used to: when the current search is performed from the current electrolyte particle to the surrounding area to obtain the current effective active material particle, obtain the current distance traveled by the lithium ion starting from the current electrolyte particle to the current effective active material particle; obtain a preset distance array, wherein the distance array includes multiple effective active material particles, and the shortest distance corresponding to each effective active material particle for the lithium ion starting from the electrolyte particle to the corresponding effective active material particle; update the distance array using the current distance and the preset first state transfer equation, and obtain the shortest distance traveled by the lithium ion starting from the current electrolyte particle to the current effective active material particle in the updated distance array; obtain the vertical coordinate of the current effective active material particle; calculate the ratio of the shortest distance traveled by the lithium ion starting from the current electrolyte particle to the current effective active material particle to the vertical coordinate of the current effective active material particle, and obtain the tortuosity corresponding to the current effective active material particle.

[0148] In some optional embodiments, when the transfer process parameter is the number of interfaces, the search initial value is the minimum number of interfaces, and the search initial value determination unit is specifically used to: when the current search starts from the current electrolyte particle and the surrounding area to obtain the current effective active material particles, obtain the current number of interfaces through which the lithium ions starting from the current electrolyte particle are transferred to the current effective active material particles; obtain a preset interface array, wherein the interface array includes multiple effective active material particles, and the minimum number of interfaces through which the lithium ions starting from the electrolyte particle are transferred to the corresponding effective active material particles corresponding to each effective active material particle; update the interface array using the current number of interfaces and the preset second state transfer equation; obtain the minimum number of interfaces corresponding to the current effective active material particles in the updated distance array.

[0149] In some optional embodiments, the effective active particle total amount acquisition unit is specifically used to: after performing the current search starting from the current electrolyte particle to the surrounding area to obtain the current effective active material particles, obtain a first array, wherein the first array includes all active material particles in the current mixed particle, and whether each active material particle has been searched; determine whether the current effective active material particle has been searched based on the first array; when the current effective active material particle has been searched, keep the first array unchanged; when the current effective active material particle has not been searched, update the status of the current effective active material particle in the first array to be searched; after traversing all electrolyte particles on the same surface of the current mixed particle, calculate the total amount of effective active material particles in the current mixed particle based on the first array.

[0150] In some optional embodiments, the transfer process parameter acquisition unit is specifically used to: use a preset breadth-first algorithm to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding area; or; use a preset depth-first algorithm to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding area.

[0151] In some optional embodiments, the solid-state battery composite electrode particle size optimization device further includes a mixed particle generation module. The mixed particle generation module is specifically configured to: obtain the size requirements of the box; generate multiple groups of mixed particles that meet the size requirements by laminating the electrolyte particles and the active material particles according to the multiple first physical parameters and the multiple second physical parameters; and simulate the interactions in each group of mixed particles using the discrete element method and adjust the positions of the electrolyte particles and / or active material particles in each group of mixed particles until each group of mixed particles is stable.

[0152] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0153] The solid-state battery composite electrode particle size compounding optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0154] The embodiment of the present invention also provides a computer device having the above Figure 10 The solid-state battery composite electrode particle size compounding optimization device shown.

[0155] See also Figure 11 , Figure 11is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 11 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 11 A processor 10 is taken as an example.

[0156] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0157] The memory 20 stores instructions that can be executed by at least one processor 10, so as to enable at least one processor 10 to execute the method shown in the above embodiment.

[0158] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 11 The bus connection is taken as an example.

[0161] The input device 30 can receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touch pad, an indicator stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 can include a display device, an auxiliary lighting device (e.g., an LED), and a tactile feedback device (e.g., a vibration motor). The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0162] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0163] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0164] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for optimizing the particle size of solid-state battery composite electrodes, characterized in that: include: Obtaining a first set of physical parameters of electrolyte particles and a second set of physical parameters of active material particles in the plurality of groups of mixed particles; compressing the plurality of groups of mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold value of each group; Starting from the electrolyte particles on the surface of each group of mixed particles, searching for effective active material particles in the surrounding area, and obtaining a search target value corresponding to each mixed particle according to the search results; Analyze the multiple search target values to obtain the optimization range of each physical parameter in the first physical parameter set and the second physical parameter set.

2. The method according to claim 1, characterized in that The search for effective active material particles is performed starting from the electrolyte particles on the surface of each group of mixed particles and extending to the surrounding area, and the search target value corresponding to each mixed particle is obtained according to the search results, including: Search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particles and recording the parameters of the lithium ion transfer process during the search; determining a plurality of search initial values according to a plurality of transfer process parameters obtained by performing a plurality of searches on the current mixed particles; Obtaining a total amount of the effective active material particles obtained by performing multiple searches on the current mixed particles; The ratio of the sum of the search initial values to the total amount of the effective active material particles is calculated to obtain a search target value corresponding to the current mixed particles.

3. The method according to claim 1, characterized in that The effective active material particles are searched from the electrolyte particles on the surface of each group of mixed particles to the surrounding area, and the search target value corresponding to each mixed particle is obtained according to the search results: Searching for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle and moving toward the surrounding area, and obtaining a total amount of the effective active material particles obtained by searching the current mixed particle multiple times; Obtaining the total amount of active material particles in the current mixed particles; The ratio of the total amount of the effective active material particles to the total amount of the active material particles is calculated to obtain a search target value corresponding to the current mixed particles.

4. The method according to claim 2, characterized in that The effective active material particles are searched from the electrolyte particles at the surface of the current mixed particles to the surrounding areas, and the lithium ion transfer process parameters recorded during the search process include: Obtain any electrolyte particle i on the surface of the current mixed particle, and use the electrolyte particle i as the current electrolyte particle; Starting from the current electrolyte particle, a current search is performed in the surrounding area to obtain a current valid active material particle; wherein the current search is to search for the next particle in the surrounding area from the current electrolyte particle until it contacts any active material particle j, and the active material particle j is used as the current valid active material particle; Get the transfer process parameters of lithium ions in the current search; The next electrolyte particle is obtained at the same surface of the current mixed particle, and the next electrolyte particle is used as the new current electrolyte particle, and the step of searching the surrounding area from the current electrolyte particle to obtain the current effective active material particle is returned until all electrolyte particles at the surface of the current mixed particle are traversed.

5. The method according to claim 2, characterized in that The transfer process parameters include at least one of the following: transfer distance, and the number of interfaces passed through by the transfer.

6. The method according to claim 4, characterized in that When the transfer process parameter is the transfer distance, the search initial value is tortuosity; and determining multiple search initial values based on multiple transfer process parameters obtained by performing multiple searches on the current mixed particles comprises: When a current effective active material particle is obtained by performing a current search starting from the current electrolyte particle and moving toward the surrounding area, a current distance traveled by lithium ions from the current electrolyte particle to the current effective active material particle is obtained; Obtaining a preset distance array, wherein the distance array includes a plurality of the effective active material particles and the shortest distance corresponding to each of the effective active material particles for lithium ions to be transferred from the electrolyte particles to the corresponding effective active material particles; The distance array is updated using the current distance and a preset first state transfer equation, and the shortest distance traveled by lithium ions from the current electrolyte particle to the current effective active material particle is obtained in the updated distance array; Obtaining the vertical coordinate of the currently effective active substance particle; The ratio of the shortest distance traveled by lithium ions from the current electrolyte particle to the current effective active material particle to the ordinate of the current effective active material particle is calculated to obtain the tortuosity corresponding to the current effective active material particle.

7. The method according to claim 4, characterized in that When the transfer process parameter is the number of interfaces, the search initial value is the minimum number of interfaces; Determining multiple search initial values based on multiple transfer process parameters obtained by multiple searches for the same mixed particle includes: When a current effective active material particle is obtained by performing a current search starting from the current electrolyte particle and moving toward the surrounding area, a current number of interfaces through which lithium ions starting from the current electrolyte particle are transferred to the current effective active material particle is obtained; Obtaining a preset interface array, wherein the interface array includes a plurality of effective active material particles and a minimum number of interfaces corresponding to each effective active material particle through which lithium ions originating from the electrolyte particles are transferred to the corresponding effective active material particles; Updating the interface array using the current number of interfaces and a preset second state transfer equation; The minimum number of interfaces corresponding to the current valid active material particles is obtained in the updated distance array.

8. The method according to claim 4, characterized in that The obtaining of the total amount of the effective active material particles obtained by performing multiple searches on the current mixed particles includes: After performing a current search starting from the current electrolyte particle and moving toward the surrounding area to obtain a current valid active material particle, obtaining a first array, wherein the first array includes all active material particles in the current mixed particle and whether each active material particle has been searched; determining, based on the first array, whether the currently valid active material particles have been searched; When the currently valid active material particles have been searched, keeping the first array unchanged; When the currently valid active material particles have not been searched, updating the status of the currently valid active material particles in the first array to be searched; After traversing all electrolyte particles on the same surface of the current mixed particle, the total amount of the effective active material particles in the current mixed particle is calculated according to the first array.

9. The method according to claim 2 or 3, characterized in that The searching for effective active material particles starting from the electrolyte particles at the surface of the current mixed particle to the surrounding area includes: Using the preset breadth-first algorithm, effective active material particles are searched from the electrolyte particles at the surface of the current mixed particle to the surrounding area; or; A preset depth-first algorithm is used to search for effective active material particles starting from the electrolyte particles at the surface of the current mixed particles and moving toward the surrounding areas.

10. The method according to claim 1, characterized in that Also includes: Get the box size requirements; According to a plurality of the first physical parameters and a plurality of the second physical parameters, the electrolyte particles and the active material particles are laminated to generate a plurality of groups of the mixed particles meeting the size requirements; The discrete element method is used to simulate the interactions in each group of the mixed particles and the positions of the electrolyte particles and / or the active material particles in each group of the mixed particles are adjusted until each group of the mixed particles is stable.

11. A device for optimizing particle size of composite electrodes for solid-state batteries, characterized in that: include: an acquisition module, configured to acquire a first set of physical parameters of electrolyte particles and a second set of physical parameters of active material particles in the plurality of groups of mixed particles; a compression module, configured to compress the plurality of mixed particles until the actual pressure on the surface of the mixed particles reaches a preset pressure threshold; A search target value determination module is used to search for effective active material particles starting from the electrolyte particles on the surface of each mixed particle and toward the surrounding area, and obtain a search target value corresponding to each mixed particle according to the search results; An analysis module is used to analyze the multiple search target values to obtain an optimization range of each physical parameter in the first physical parameter set and the second physical parameter set.

12. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the solid-state battery composite electrode particle size optimization method according to any one of claims 1 to 10 by executing the computer instructions.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the solid-state battery composite electrode particle size optimization method according to any one of claims 1 to 10.

14. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the solid-state battery composite electrode particle size optimization method according to any one of claims 1 to 10.

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