Simulation method of graphite material and related device

Abstract

The application discloses a simulation method of a graphite material and related devices, and comprises the following steps: obtaining first material data and first test data of a first graphite material, wherein the first test data is obtained by testing the first graphite material; obtaining a first simulation model, and performing first simulation processing on the first graphite material according to the first simulation model and the first material data to obtain first simulation data; performing second simulation processing on the first graphite material according to the first material data, the first simulation model and the first simulation data to obtain second simulation data; adjusting the first simulation model according to the first test data and the second simulation data; obtaining second material data of a to-be-tested graphite material, and performing third simulation processing on the to-be-tested graphite material according to the first simulation model and the second material data to obtain target test data.

Description

Technical Field

[0001] This invention relates to the field of electrical digital data processing technology related to simulation, and in particular to a simulation method and related apparatus for graphite materials. Background Technology

[0002] In existing simulations of graphite materials, the common approach is to establish a relationship equation between the simulation target data and the simulation parameters, and then optimize this relationship equation through iterative solutions. The graphite material is then simulated based on the optimized relationship equation to obtain the final simulation target data. However, this simulation method suffers from issues such as the need to improve optimization efficiency and the reliability of simulation results. Summary of the Invention

[0003] To address the aforementioned issues, this application provides a simulation method and related apparatus for graphite materials. Adopting the solution of this application is beneficial to improving the optimization efficiency of existing simulation models for graphite materials and the reliability of simulation results.

[0004] In a first aspect, embodiments of this application provide a simulation method for graphite materials, comprising: acquiring first material data and first experimental data of a first graphite material, wherein the first experimental data is data obtained by conducting experiments on the first graphite material; acquiring a first simulation model, and performing a first simulation processing on the first graphite material based on the first simulation model and the first material data to obtain first simulation data; performing a second simulation processing on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain second simulation data; adjusting the first simulation model based on the first experimental data and the second simulation data; acquiring second material data of a graphite material to be tested, and performing a third simulation processing on the graphite material to be tested based on the first simulation model and the second material data to obtain target experimental data.

[0005] As can be seen from the embodiments of this application, the simulation model can be corrected and improved during the simulation test of graphite materials by using experimental data and simulation data, thereby improving the optimization efficiency of the simulation model. At the same time, the simulation model can be adjusted according to the real experimental data of graphite materials, thereby improving the credibility of the simulation results output by the simulation model.

[0006] In conjunction with the first aspect, in one possible embodiment, the first simulation data includes a first particle space, the second simulation data includes a simulated static expansion rate, and the second simulation processing includes static expansion simulation; the second simulation data is obtained by performing a second simulation processing on the first graphite material based on the first material data, the first simulation model, and the first simulation data, including: setting the static time of the first graphite material; performing a static expansion simulation on the first graphite material based on the first material data, the first simulation model, the first particle space, and the static time to obtain a second particle space of the first graphite material after static expansion, wherein the second particle space characterizes the volume of the first graphite material after static expansion; and determining the simulated static expansion rate of the first graphite material based on the first particle space and the second particle space.

[0007] As can be seen in the embodiments of this application, by performing static expansion simulation based on the simulation model, the complex physical behavior is transformed into a controllable simulation step, which can obtain various parameters related to the volume expansion of graphite materials during the static stage, so that relevant researchers can refer to improve the reference value of the simulation results. At the same time, the output simulation data also provides a clear simulation output quantity for subsequent comparison with experimental data and optimization of simulation parameters.

[0008] In conjunction with the first aspect, in one possible embodiment, the first experimental data includes the experimental static expansion rate; adjusting the first simulation model based on the first experimental data and the second simulation data includes: if the deviation between the experimental static expansion rate and the simulated static expansion rate is not less than a first preset deviation value, then using the static duration as a variable, establishing a third-order polynomial response surface characterizing the relationship between the static duration and the simulated static expansion rate; constructing a first loss function based on the ratio of the absolute value of the difference between the simulated static expansion rate and the experimental static expansion rate to the experimental static expansion rate; updating the static duration through Bayesian inference until the first loss function is less than a preset threshold or the number of updates to the static duration is not less than a preset number; and adjusting the first simulation model based on the latest static duration.

[0009] As can be seen, in the embodiments of this application, by parameterizing the resting time and constructing a response surface model and loss function, and then using Bayesian inference to perform automated and guided parameter optimization, the manual trial and error is replaced. This can quickly and objectively determine the optimal simulation parameters, significantly reduce the number of manual interventions and simulation calculations required for calibration, and greatly improve the efficiency of simulation model construction and optimization while ensuring model accuracy.

[0010] In conjunction with the first aspect, in one possible embodiment, the method further includes: setting a first expansion coefficient for a single graphite particle of the first graphite material; performing a full-fill expansion simulation on the first graphite material based on the first material data, the first simulation model, the second particle space, and the first expansion coefficient to obtain a third particle space, wherein the third particle space characterizes the volume of the first graphite material after full-fill expansion; and determining a first simulated full-fill expansion rate of the first graphite material based on the second particle space and the third particle space.

[0011] As can be seen, in the embodiments of this application, by introducing and setting the expansion coefficient of a single particle, the volume expansion of the graphite electrode during the charging and discharging process is efficiently simulated in discrete element simulation. The complex electrochemical-mechanical coupling process is simplified into a mechanical simulation based on particle-scale expansion. Thus, the overall expansion rate of the graphite electrode in the fully charged state can be predicted using a calibrated high-precision mechanical model, providing a key simulation prediction index for evaluating and screening low-expansion graphite materials.

[0012] In conjunction with the first aspect, in one possible embodiment, the first experimental data further includes the experimental full-fill expansion rate, and the method further includes: if the deviation between the experimental full-fill expansion rate and the first simulated full-fill expansion rate is not less than a second preset deviation value, then obtaining a plurality of second expansion coefficients; performing full-fill expansion simulation on the first graphite material according to the first material data, the first simulation model, the second particle space, and the plurality of second expansion coefficients respectively, to obtain a plurality of fourth particle spaces corresponding to the plurality of second expansion coefficients, wherein the fourth particle space characterizes the volume of the first graphite material after full-fill expansion according to the corresponding second expansion coefficient; determining the second simulated full-fill expansion rate corresponding to the plurality of second expansion coefficients according to the second particle space and the plurality of fourth particle spaces; determining the deviation value between the plurality of second simulated full-fill expansion rates and the experimental full-fill expansion rate; adjusting the first simulation model according to the second expansion coefficient corresponding to the second simulated full-fill expansion rate with the lowest deviation value from the experimental full-fill expansion rate.

[0013] As can be seen, in this embodiment of the application, by setting multiple sets of expansion coefficients for parallel simulation and systematically comparing the simulation results with experimental data, the particle-scale parameters that best reflect the actual expansion behavior of the material can be quickly and objectively identified. This replaces the traditional approach of relying on a single empirical value or blind trial and error. The optimal parameters can be locked through a planned multi-condition simulation analysis, which significantly improves the efficiency and reliability of model parameter calibration and ensures the accuracy of the final full-fill expansion rate prediction.

[0014] In conjunction with the first aspect, in one possible embodiment, the method further includes: setting contact parameters for the first graphite material; obtaining a second simulation model; performing a roll pressing simulation on the first graphite material based on the first material data, the second simulation model, and the contact parameters to obtain the simulated compaction density of the first graphite material; and adjusting the second simulation model based on the simulated compaction density to obtain the first simulation model.

[0015] As can be seen from the embodiments of this application, by setting key contact parameters and running roll compaction simulation, a quantifiable simulated compaction density is obtained as a calibration index. Then, through a systematic optimization method, the optimal contact parameters are inverted and determined, ultimately completing the precise adjustment from a general second simulation model to a first simulation model customized for a specific material. This lays a high-precision model foundation for subsequent prediction simulations of other simulation data, avoiding the accumulation of subsequent simulation deviations due to inaccurate model parameters.

[0016] In conjunction with the first aspect, in one possible embodiment, the contact parameters include the coefficient of restitution, the static friction coefficient, and the rolling friction coefficient. Adjusting the second simulation model based on the simulated compaction density to obtain the first simulation model includes: establishing a relationship model between the simulated compaction density of the first graphite material and the contact parameters, using the coefficient of restitution, the static friction coefficient, and the rolling friction coefficient as variables; constructing a second loss function based on the ratio of the absolute value of the difference between the simulated compaction density and the experimental compaction density to the experimental compaction density; using Bayesian optimization on the relationship model to determine the optimal coefficient of restitution, the optimal static friction coefficient, and the optimal rolling friction coefficient corresponding to the minimum of the second loss function; and adjusting the second simulation model based on the optimal coefficient of restitution, the optimal static friction coefficient, and the optimal rolling friction coefficient to obtain the first simulation model.

[0017] As can be seen, in this embodiment of the application, by associating microscopic contact parameters with macroscopic compaction density, constructing a surrogate model and loss function, and then applying Bayesian optimization, an intelligent optimization algorithm, the automated, efficient, and high-precision calibration of key but difficult-to-measure contact parameters in the discrete element simulation model is achieved. This avoids the subjectivity and inefficiency of traditional trial-and-error methods. Through a data-driven scientific approach, the simulation model is ensured to have reliable predictive capabilities from the initial rolling stage, thereby improving the credibility of the simulation results.

[0018] By implementing the methods in the above-described embodiments, it can be seen that using experimental and simulation data to correct and improve the simulation model during the simulation testing of graphite materials enhances the optimization efficiency and reliability of the simulation model. Static rolling and expansion simulations based on the simulation model provide key simulation prediction indicators for evaluating and screening low-expansion graphite materials. Adjusting the simulation model by setting simulation parameters and comparing simulation results with experimental data improves the efficiency and reliability of model parameter calibration, ensuring the accuracy of the final full-fill expansion rate prediction.

[0019] Secondly, embodiments of this application provide a simulation device for graphite materials, the device comprising:

[0020] The acquisition unit is used to acquire first material data and first test data of the first graphite material, wherein the first test data is data obtained by testing the first graphite material;

[0021] Obtain the first simulation model, and perform the first simulation processing on the first graphite material based on the first simulation model and the first material data to obtain the first simulation data;

[0022] The simulation unit is used to perform a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain the second simulation data;

[0023] An adjustment unit is used to adjust the first simulation model based on the first experimental data and the second simulation data.

[0024] The simulation unit is also used to acquire the second material data of the graphite material to be tested, and to perform a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain the target test data.

[0025] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, a communication interface, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, and one or more instructions being adapted to be loaded by the processor and to execute part or all of the methods of the first aspect and / or the second aspect.

[0026] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform part or all of the methods of the first aspect and / or the second aspect.

[0027] Fifthly, this application provides a computer program product that, when read and executed by a computer, causes the computer to perform part or all of the methods of the first aspect and / or the second aspect.

[0028] It is understood that the beneficial effects of the embodiments of the second to fifth aspects can be referred to the beneficial effects of the method of the first aspect, and will not be repeated here. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram illustrating an application scenario of a simulation method for graphite materials provided in an embodiment of this application.

[0031] Figure 2 A flowchart illustrating a simulation method for graphite materials provided in this application embodiment;

[0032] Figure 3 This is a schematic diagram of the morphology of a first graphite material before roll forming, provided in an embodiment of this application.

[0033] Figure 4 This is a schematic diagram of the morphology of a first graphite material after roll forming, provided in an embodiment of this application.

[0034] Figure 5 This is a schematic diagram of the morphology of a first graphite material after static expansion, provided in an embodiment of this application.

[0035] Figure 6 A flowchart illustrating another simulation method for graphite materials provided in this application embodiment;

[0036] Figure 7 A flowchart illustrating another simulation method for graphite materials provided in this application embodiment;

[0037] Figure 8 This is a schematic diagram of the structure of a simulation device for graphite materials provided in an embodiment of this application;

[0038] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0039] Explanation of the reference numerals: 100: Application scenario; 101: Simulation terminal; 102: Experimental terminal; 800: Simulation device for graphite materials; 801: Acquisition unit; 802: Simulation unit; 803: Adjustment unit; 900: Electronic equipment; 901: Memory; 902: Processor; 903: Communication interface; 904: Bus. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0041] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0042] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0043] The embodiments of this application will now be described with reference to the accompanying drawings.

[0044] Please see Figure 1 , Figure 1 This is a schematic diagram of an application scenario for a simulation method for graphite materials provided in an embodiment of this application. The application scenario 100 includes a simulation terminal 101 and an experimental terminal 102. The experimental terminal is used to collect and record experimental data (such as expansion rate, density, etc.) of the graphite material and send the experimental data to the simulation terminal 101 so that the simulation terminal can perform simulation and adjust the simulation model based on the experimental data sent by the experimental terminal 102.

[0045] In this embodiment of the application, the simulation terminal 101 acquires the first material data and the first test data of the first graphite material. The first test data is the data obtained by conducting experiments on the first graphite material.

[0046] The first material data specifically includes the true density and shear modulus of the graphite particles, as well as other data related to the material properties of the first graphite material. In addition, to ensure the proper execution of subsequent simulation processing, the simulation terminal 101 also needs to acquire other data related to simulation conditions, such as particle container, density, and shear modulus.

[0047] The data, such as the first material data and the first test data, can be sent from the test terminal 102 to the simulation terminal 101, or obtained by the operator through input to the simulation terminal 101, or by the simulation terminal 101 itself.

[0048] The simulation terminal 101 acquires the first simulation model and performs a first simulation process on the first graphite material based on the first simulation model and the first material data to obtain the first simulation data.

[0049] The first simulation model here is specifically a simulation model in discrete element simulation software, used to implement various simulation processes for graphite materials.

[0050] The simulation terminal 101 performs a first simulation process on the first graphite material based on the acquired first simulation model and first material data. The simulation process specifically includes rolling simulation, static expansion simulation, full filling simulation, etc. The first simulation data is the simulation result obtained based on the first simulation process.

[0051] Based on the first material data, the first simulation model, and the first simulation data, the first graphite material is subjected to a second simulation process to obtain the second simulation data;

[0052] The simulation terminal 101 adjusts the first simulation model based on the first experimental data and the second simulation data.

[0053] After obtaining the first experimental data and the second simulation data, the simulation terminal 101 adjusts the first simulation model based on the first experimental data and the second simulation data.

[0054] It should be noted that the second simulation data here is obtained based on the second simulation processing; while the first experimental data also includes real experimental data corresponding to the second simulation data obtained through real experiments corresponding to the second simulation processing.

[0055] Based on this simulation terminal 101, the first simulation model can be adjusted using the first experimental data and the second simulation data, thereby improving the reliability and accuracy of the first simulation model and other model performance.

[0056] The simulation terminal 101 acquires the second material data of the graphite material to be tested, and performs a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain the target test data.

[0057] After the simulation terminal 101 completes the construction and parameter calibration of the first simulation model, the prediction accuracy of the model is further improved. At this time, the simulation terminal 101 acquires the second material data of the graphite material to be tested and performs predictive simulation, i.e., the third simulation processing. The third simulation processing includes, but is not limited to, rolling simulation, static expansion simulation, and full-fill expansion simulation. After the simulation is completed, the model outputs the various performance data of the graphite material to be tested under the simulated conditions, i.e., the target test data.

[0058] Furthermore, the above simulation process includes, but is not limited to, various simulation experiments on graphite electrodes made of the first graphite material. Based on this, the solution can also be applied to the simulation of other similar materials, such as electrodes made of lithium iron phosphate particles.

[0059] As can be seen from the embodiments of this application, the simulation model can be corrected and improved during the simulation test of graphite materials by using experimental data and simulation data, thereby improving the optimization efficiency of the simulation model. At the same time, the simulation model can be adjusted according to the real experimental data of graphite materials, thereby improving the credibility of the simulation results output by the simulation model.

[0060] Example 1: Please refer to Figure 2 , Figure 2 This application provides a flowchart illustrating a simulation method for graphite materials, which can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 2 As shown, it includes steps S201-S205.

[0061] S201: The simulation terminal acquires the first material data and the first test data of the first graphite material. The first test data is the data obtained by conducting experiments on the first graphite material.

[0062] Specifically, the first material data here includes data characterizing the material properties, testing environment, simulation parameters, etc. of the first graphite material; the first experimental data here is the data obtained from real experiments on the first graphite material.

[0063] S202: The simulation terminal acquires the first simulation model and performs the first simulation processing on the first graphite material based on the first simulation model and the first material data to obtain the first simulation data.

[0064] Specifically, the first simulation model here is the simulation model in the discrete element simulation software; further, the first simulation model here also includes the iterative simulation model, which is optimized and iterated based on the simulation data obtained from the simulation processing and the corresponding experimental data.

[0065] S203: The simulation terminal performs a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain the second simulation data.

[0066] Optionally, the first simulation data includes a first particle space, the second simulation data includes a simulated static expansion rate, and the second simulation processing includes static expansion simulation; the second simulation data is obtained by performing a second simulation processing on the first graphite material based on the first material data, the first simulation model, and the first simulation data, including: setting the static time of the first graphite material; performing a static expansion simulation on the first graphite material based on the first material data, the first simulation model, the first particle space, and the static time to obtain the second particle space of the first graphite material after static expansion, the second particle space representing the volume of the first graphite material after static expansion; and determining the simulated static expansion rate of the first graphite material based on the first particle space and the second particle space.

[0067] Specifically, in this embodiment, the process of obtaining the second simulation data is explained, wherein the second simulation processing specifically refers to static expansion simulation. Static expansion simulation specifically requires obtaining the expansion rate of the first graphite material after rolling and post-processing.

[0068] See, for example Figure 3 , Figure 3 This is a schematic diagram of the morphology of a first graphite material before roll forming, provided in an embodiment of this application. It can be seen that the initial morphology of the first graphite material in the simulation model is a relatively fluffy morphology.

[0069] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the morphology of a first graphite material after roll forming, as provided in an embodiment of this application. The first graphite material is transformed into a more compact form under the influence of roll forming.

[0070] Before performing a static expansion simulation, a key simulation parameter needs to be set first—the static duration t. r This duration represents the simulated time required for the graphite particle system to reach volume equilibrium through its own interactions after the external roller pressure is removed.

[0071] Next, based on the acquired first material data (such as particle size distribution, mechanical property parameters, etc.) and the pre-established or adjusted first simulation model, and with the input of the first particle space V1 (characterizing the volume occupied by graphite particles after roller compaction), a static expansion simulation calculation is performed. In the simulation, the simulation terminal will calculate the static expansion based on the set static time t. rThe relaxation and rebound process of the particles is dynamically simulated. After the simulation is completed, the spatial volume occupied by the graphite particle system at this moment can be extracted and denoted as the second particle space V2. This volume represents the volume of the first graphite material after static expansion and stabilization.

[0072] For example, please see Figure 5 , Figure 5 This is a schematic diagram of the morphology of a first graphite material after static expansion provided in an embodiment of this application. The first graphite material, after static expansion, recovers to an intermediate morphology between the state before and after the rolling process.

[0073] Finally, based on the first particle space V1 and the second particle space V2, the simulated static expansion rate α of the first graphite material is determined by calculating the volume change rate of both. sim The calculation of the simulated static expansion rate satisfies the following formula (1).

[0074] (1)

[0075] Where α sim To simulate the static expansion rate, V1 represents the first particle space, and V2 represents the second particle space.

[0076] As can be seen in the embodiments of this application, by performing static expansion simulation based on the simulation model, the complex physical behavior is transformed into a controllable simulation step, which can obtain various parameters related to the volume expansion of graphite materials during the static stage, so that relevant researchers can refer to improve the reference value of the simulation results. At the same time, the output simulation data also provides a clear simulation output quantity for subsequent comparison with experimental data and optimization of simulation parameters.

[0077] S204: The simulation terminal adjusts the first simulation model based on the first test data and the second simulation data.

[0078] Optionally, the first experimental data includes the experimental static expansion rate; adjusting the first simulation model based on the first experimental data and the second simulation data includes: if the deviation between the experimental static expansion rate and the simulated static expansion rate is not less than a first preset deviation value, then using the static duration as a variable, establishing a third-order polynomial response surface characterizing the relationship between the static duration and the simulated static expansion rate; constructing a first loss function based on the ratio of the absolute value of the difference between the simulated static expansion rate and the experimental static expansion rate to the experimental static expansion rate; updating the static duration through Bayesian inference until the first loss function is less than a preset threshold or the number of times the static duration is updated is not less than a preset number; and adjusting the first simulation model based on the latest static duration.

[0079] Specifically, in the embodiments of this application, the specific optimization process of adjusting the first simulation model based on experimental data is described. This process is aimed at parameter calibration in the static expansion simulation stage.

[0080] When the experimental static expansion rate α is obtained... exp The simulated static expansion rate α obtained from simulation calculations sim Next, the deviation between the two is calculated. If the deviation is not less than a first preset deviation value (e.g., 5%), it indicates that the resting time t used in the current simulation is... r It needs optimization.

[0081] First, set the resting time t r Let t be a variable, and establish a third-order polynomial response surface as a surrogate model to approximately describe the resting time t. r Compared with the simulated static expansion rate α sim The functional relationship between them is such that the third-order polynomial response surface satisfies the following equation (2).

[0082] (2)

[0083] Where, α sim To simulate the static expansion rate; θ n To update the coefficients (n=1, 2, 3…); t r This refers to the settling time.

[0084] The response surface can initially be obtained from a small number of sample points (e.g., four different resting times t selected using Latin hypercube sampling). r Values ​​and their corresponding simulated static expansion rate α sim The results were obtained by fitting.

[0085] Secondly, a first loss function L1 is constructed based on the ratio of the absolute value of the difference between the simulated static expansion rate and the experimental static expansion rate to the experimental static expansion rate, which is used to quantify the difference between the simulation and the experiment. This first loss function L1 satisfies the following equation (3).

[0086] (3)

[0087] Where L1 is the first loss function; α sim k α is the simulated static expansion rate obtained in the k-th iteration; exp To test the static expansion rate.

[0088] Subsequently, the iterative optimization process is initiated. Using the Bayesian inference method, based on the current loss function value and response surface model, the most promising next sampling point that can reduce the loss function is predicted. The specific update direction can be made according to the following formula (4).

[0089] (4)

[0090] Where ε kγ is the relative error of the k-th iteration; γ is the adaptive step size; (dα) sim ^ t r / dt r )|(t) r k The third-order polynomial response surface shown in equation (2) at t r =t r k Calculate the derivative at this point; t r k This is the resting time for the k-th iteration. This process continues automatically until the stopping condition is met: the first loss function L1 drops below a preset threshold (e.g., 0.5%), or the number of iterations reaches a preset maximum (e.g., 6 times).

[0091] Finally, the optimal settling time obtained after optimization convergence is used as the calibrated parameter and updated in the corresponding settings of the first simulation model. Thus, the simulation accuracy of the model regarding the settling expansion phase is specifically improved.

[0092] As can be seen, in the embodiments of this application, by parameterizing the resting time and constructing a response surface model and loss function, and then using Bayesian inference to perform automated and guided parameter optimization, the manual trial and error is replaced. This can quickly and objectively determine the optimal simulation parameters, significantly reduce the number of manual interventions and simulation calculations required for calibration, and greatly improve the efficiency of simulation model construction and optimization while ensuring model accuracy.

[0093] S205: The simulation terminal acquires the second material data of the graphite material to be tested, and performs a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain the target test data.

[0094] Specifically, after completing the construction and parameter calibration of the first simulation model, a third simulation process can be performed on the graphite material under test based on the second material data of the graphite material under test, and the performance data of the graphite material under test under simulation conditions, i.e., the target test data, can be output.

[0095] For example, please refer to Table 1, which is a comparison of simulation results of different graphite materials before and after adjustment according to the embodiments of this application.

[0096] Taking graphite material No. 1 as an example, the measured full-fill expansion rate of this graphite material is 24.42%, while the simulated expansion rate obtained by the simulation model after optimization through the above steps is 23.89%, and the original model is 22.96%. The deviation between the simulation data output by the optimized simulation model and the experimental data is 2.17%, while the deviation between the simulation data output by the original model and the experimental data is 5.98%.

[0097] It can be seen that the deviation between the simulation data and the experimental data obtained by the simulation model optimized through the above steps is smaller than the deviation between the simulation data and the experimental data obtained by the original model, thereby improving the accuracy of various simulation results obtained in subsequent simulations.

[0098] Table 1

[0099]

[0100] Example 2: The above-mentioned application embodiment provides a specific simulation method for graphite materials by adjusting the simulation model through static simulation. Based on this, in the case that the simulation processing of the first graphite material also includes rolling simulation and full-fill expansion simulation, this application embodiment also provides a more detailed simulation method for graphite materials.

[0101] Please see Figure 6 , Figure 6 A flowchart illustrating another simulation method for graphite materials provided in this application embodiment is shown, which can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 6 As shown, steps S601-S607 are included.

[0102] S601: The simulation terminal acquires the first material data and the first test data of the first graphite material. The first test data is the data obtained by conducting experiments on the first graphite material.

[0103] S602: The simulation terminal sets the contact parameters of the first graphite material; obtains the second simulation model; and performs a roll pressing simulation on the first graphite material based on the first material data, the second simulation model, and the contact parameters to obtain the simulated compaction density of the first graphite material.

[0104] Specifically, in the embodiments of this application, the first simulation process is roll forming simulation. Before performing the first simulation process, the simulation model needs to be adjusted for roll forming simulation.

[0105] First, the key contact parameters of the first graphite material in the discrete element simulation need to be set. These parameters include the coefficient of restitution e and the coefficient of static friction μ. s and rolling friction coefficient μ r These properties collectively define the collision and friction behavior between graphite particles and between particles and the container wall, and are the fundamental mechanical properties that affect particle accumulation and compaction behavior during the rolling process.

[0106] Next, a second simulation model is obtained, which is not calibrated with the second simulation data. This model contains the basic framework of discrete element simulation, such as solver settings, particle generation methods (e.g., zero-gravity static filling), particle constitutive models (e.g., the Hertz-Mindlin model and the Hyper Spring constitutive model for characterizing plasticity), and basic geometric and material properties (e.g., particle true density, container properties, etc.), but does not yet include validated, precise contact parameters for a specific graphite material.

[0107] In other words, the second simulation model is the first simulation model before the parameters of the simulated compaction density were adjusted. In subsequent steps, the second simulation model is adjusted based on the simulated compaction density to obtain the first simulation model. Specifically, the second simulation model is obtained through adjustments based on earlier simulations and corresponding experimental data, or it is directly generated based on the original model.

[0108] S603: The simulation terminal adjusts the second simulation model according to the simulated compaction density to obtain the first simulation model.

[0109] Specifically, based on the first material data (e.g., graphite particle size distribution measured by a Malvern laser particle size analyzer), the second simulation model, and the set contact parameters, a rolling process simulation of the first graphite material is performed. In the simulation, the upper pressure plate is simulated to press down at a certain rate, and the load and particle bed height changes are recorded. After the simulation is completed, the simulated compaction density ρ of the first graphite material is calculated through post-processing. sim Its calculation formula satisfies the following equation (5).

[0110] (5)

[0111] Where ρ sim The simulation is for compaction density; M is the total mass of the first graphite material particles; R1 is the container radius (e.g., 100 mm) during the roll pressing simulation; H(t) represents the height change function of the particles, and its specific value is automatically recorded by the monitoring unit set in the roll pressing simulation.

[0112] Then, the model adjustment phase begins. The calculated simulated compaction density ρ is then... sim The test compaction density ρ obtained through physical testing exp A comparison is then performed. If a significant difference exists (e.g., the relative error exceeds a preset threshold such as 1%), it indicates that the currently set contact parameters fail to accurately reflect the actual mechanical properties of the graphite material. Furthermore, the compaction density ρ is simulated. sim and test compaction density ρ expThe second simulation model is adjusted to obtain the optimized first simulation model. This first simulation model will serve as a reliable basis for subsequent simulations such as static expansion and full-fill expansion.

[0113] As can be seen from the embodiments of this application, by setting key contact parameters and running roll compaction simulation, a quantifiable simulated compaction density is obtained as a calibration index. Then, through a systematic optimization method, the optimal contact parameters are inverted and determined, ultimately completing the precise adjustment from a general second simulation model to a first simulation model customized for a specific material. This lays a high-precision model foundation for subsequent prediction simulations of other simulation data, avoiding the accumulation of subsequent simulation deviations due to inaccurate model parameters.

[0114] Optionally, the contact parameters include the coefficient of restitution, the static friction coefficient, and the rolling friction coefficient. The process of adjusting the second simulation model to obtain the first simulation model based on the simulated compaction density includes: establishing a relationship model between the simulated compaction density of the first graphite material and the contact parameters, using the coefficient of restitution, the static friction coefficient, and the rolling friction coefficient as variables; constructing a second loss function based on the ratio of the absolute value of the difference between the simulated compaction density and the experimental compaction density to the experimental compaction density; using Bayesian optimization on the relationship model to determine the optimal coefficient of restitution, the optimal static friction coefficient, and the optimal rolling friction coefficient corresponding to the minimum of the second loss function; and adjusting the second simulation model based on the optimal coefficient of restitution, the optimal static friction coefficient, and the optimal rolling friction coefficient to obtain the first simulation model.

[0115] Specifically, in the embodiments of this application, the specific process of adjusting the second simulation model by optimizing contact parameters is described.

[0116] First, identify the core contact parameters to be optimized, namely the coefficient of restitution e and the static friction coefficient μ. s and rolling friction coefficient μ r These three parameters are defined as design variables, forming a three-dimensional parameter space θ=[e,μ]. s ,μ r ] T .

[0117] Next, a relational model is established to characterize the simulated compaction density ρ. sim The mapping relationship between the above contact parameter θ. For example, the relationship shown in equation (5). It should be noted that the coefficient of restitution e and the static friction coefficient μ s and rolling friction coefficient μ r There is a functional relationship between the particle height change function and the particle height change function.

[0118] Since discrete element method (DEM) simulation is a computationally expensive and complex process, directly using it as the objective function in optimization is inefficient. Therefore, this relational model can also be a surrogate model, such as a model based on Gaussian process regression. Its construction process involves selecting a series of representative sampling points in the three-dimensional parameter space (e.g., using Sobol sequences or Latin hypercube sampling), and running a roll compaction simulation on θ for each combination of sampling points to obtain the corresponding simulated compaction density ρ. sim Then, these input and output data are used to train a proxy model, enabling it to quickly predict the simulated compaction density ρ under any given three-dimensional parameter space θ. sim approximation.

[0119] Then, a second loss function L2 is constructed based on the ratio of the absolute value of the difference between the simulated compaction density and the experimental compaction density to the experimental compaction density. This function is used to measure the difference between the simulation results and the experimental results, and satisfies the following equation (6).

[0120] (6)

[0121] Where L2 is the second loss function; ρ sim For simulating compacted density; ρ exp To test the compaction density.

[0122] Then, the Bayesian optimization process is initiated. First, the uncertainty of the entire parameter space and the objective function value are modeled using the current surrogate model (Gaussian process).

[0123] Then, the next most promising evaluation point is determined based on a sampling function (e.g., expected improvement), which balances "exploitation" (sampling in areas where the model predicts well) and "exploration" (sampling in areas where the model has high uncertainty).

[0124] Next, run an actual discrete element simulation at this point to calculate the loss function value;

[0125] Finally, the surrogate model is updated with the new data. This process is repeated until convergence conditions are met, such as the loss function value falling below a preset threshold (e.g., 1%), or the maximum number of iterations is reached. Ultimately, the optimization algorithm outputs the set of parameters that minimizes the second loss function, namely the optimal recovery coefficient, the optimal static friction coefficient, and the optimal rolling friction coefficient.

[0126] Finally, the optimized combination of contact parameters is updated in the material property definition of the second simulation model. After this calibration step, the simulated compaction density output by the model when simulating the rolling behavior of a specific graphite material is in high agreement with the experimental value, thus completing the adjustment to the high-fidelity first simulation model.

[0127] As can be seen, in this embodiment of the application, by associating microscopic contact parameters with macroscopic compaction density, constructing a surrogate model and loss function, and then applying Bayesian optimization, an intelligent optimization algorithm, the automated, efficient, and high-precision calibration of key but difficult-to-measure contact parameters in the discrete element simulation model is achieved. This avoids the subjectivity and inefficiency of traditional trial-and-error methods. Through a data-driven scientific approach, the simulation model is ensured to have reliable predictive capabilities from the initial rolling stage, thereby improving the credibility of the simulation results.

[0128] S604: The simulation terminal acquires the first simulation model and performs the first simulation processing on the first graphite material based on the first simulation model and the first material data to obtain the first simulation data.

[0129] S605: The simulation terminal performs a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain the second simulation data.

[0130] S606: The simulation terminal adjusts the first simulation model based on the first test data and the second simulation data.

[0131] S607: The simulation terminal acquires the second material data of the graphite material to be tested, and performs a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain the target test data.

[0132] For detailed explanations of steps S601 and S604-S607, please refer to the explanations and related content of steps S201-S205, which will not be repeated here. It should be noted that the second simulation process specifically includes static expansion simulation.

[0133] Optionally, the method further includes: setting a first expansion coefficient for a single graphite particle of the first graphite material; performing a full-fill expansion simulation of the first graphite material based on the first material data, the first simulation model, the second particle space, and the first expansion coefficient to obtain a third particle space, wherein the third particle space characterizes the volume of the first graphite material after full-fill expansion; and determining a first simulated full-fill expansion rate of the first graphite material based on the second particle space and the third particle space.

[0134] Specifically, in this embodiment, after performing static expansion simulation on the first graphite material, it is also necessary to perform full-fill expansion simulation to obtain the full-fill expansion rate. This process is performed after completing the static expansion simulation and obtaining a stable second particle space V2.

[0135] First, the initial expansion coefficient β of each individual graphite particle needs to be set. This coefficient is used to define the expansion ratio of each graphite particle in the simulation when simulating lithium-ion intercalation (i.e., the fully charged state).

[0136] Next, the simulation is based on the first material data (such as particle size distribution), the calibrated first simulation model, and the second particle space V2 after static expansion and stabilization. In the pre-simulation processing, within the space of V1, a discrete particle system with the same number of particles as the statically stabilized state is regenerated according to the particle size distribution. Then, the first expansion coefficient β is assigned to each graphite particle in the system. After completing the settings, a full-fill expansion simulation is run. During the simulation, all particles expand synchronously according to their expansion coefficients and rearrange and compress under the contact mechanics between particles, ultimately reaching a new mechanical equilibrium state.

[0137] After the simulation is completed, the volume of space occupied by the entire particle system at this time is extracted through post-processing and denoted as the space of the third particle as V3. This volume represents the final volume of the first graphite material in the simulated fully filled state.

[0138] Finally, based on the volume of the second particle space V2 after static expansion and the volume of the third particle space V3 after full expansion, the first simulated full-fill expansion rate γ of the first graphite material is determined by calculating the volume change rate between the two. sim The specific calculation formula satisfies the following equation (7).

[0139] (7)

[0140] As can be seen, in the embodiments of this application, by introducing and setting the expansion coefficient of a single particle, the volume expansion of the graphite electrode during the charging and discharging process is efficiently simulated in discrete element simulation. The complex electrochemical-mechanical coupling process is simplified into a mechanical simulation based on particle-scale expansion. Thus, the overall expansion rate of the graphite electrode in the fully charged state can be predicted using a calibrated high-precision mechanical model, providing a key simulation prediction index for evaluating and screening low-expansion graphite materials.

[0141] Optionally, the first experimental data also includes the experimental full-fill expansion rate, and the method further includes: if the deviation between the experimental full-fill expansion rate and the first simulated full-fill expansion rate is not less than a second preset deviation value, then obtaining multiple second expansion coefficients; performing full-fill expansion simulation on the first graphite material according to the first material data, the first simulation model, the second particle space, and the multiple second expansion coefficients respectively, to obtain multiple fourth particle spaces corresponding to the multiple second expansion coefficients, wherein the fourth particle space characterizes the volume of the first graphite material after full-fill expansion according to the corresponding second expansion coefficient; determining the second simulated full-fill expansion rate corresponding to the multiple second expansion coefficients according to the second particle space and the multiple fourth particle spaces; determining the deviation value between the multiple second simulated full-fill expansion rates and the experimental full-fill expansion rate; adjusting the first simulation model according to the second expansion coefficient corresponding to the second simulated full-fill expansion rate with the lowest deviation value from the experimental full-fill expansion rate.

[0142] Specifically, in the embodiments of this application, the process of calibrating the single particle expansion coefficient, a key parameter, is explained when the simulated full-fill expansion rate deviates significantly from the experimental value.

[0143] When the calculated first simulated full-fill expansion ratio γ sim The experimentally measured full-fill expansion rate γ exp If the deviation between the two values ​​is not less than the second preset deviation value (e.g., 5%), it indicates that the initially set first expansion coefficient β may be inaccurate and needs to be calibrated.

[0144] First, obtain multiple different second expansion coefficients (denoted as β). i (i=1,2…), forming a set of candidate parameters. These coefficient values ​​can be generated within a reasonable empirical range (e.g., the range of expansion coefficients estimated based on material properties) through equally spaced sampling, random sampling, or experimental design methods (such as DOE).

[0145] Next, multiple sets of parallel full-fill expansion simulations were performed. For each second expansion coefficient β... i All simulations are based on the same foundation: the first material data, the calibrated first simulation model, and the second particle space V2 after static expansion stabilization. In the simulation, β... i Assign the volume to the particle system and run a full-fill expansion simulation. After each simulation, extract the final volume of the corresponding particle system, denoted as the fourth particle space V4. i This volume characterizes the volume using the expansion coefficient β i At that time, the volume of the first graphite material was simulated after being fully filled.

[0146] Then, each set of simulation results is processed. This is based on the second particle space V2 and the corresponding fourth particle space V4. i The expansion coefficient β is calculated according to the formula. i The corresponding second simulated full-fill expansion rate γ sim i .

[0147] Subsequently, the accuracy of all simulation results was evaluated. The full-fill expansion rate γ for each second simulation was calculated. sim i Compared with the experimental full-fill expansion rate γ exp The deviation value.

[0148] Finally, the minimum value among all deviations is identified, which is the simulation result that best matches the experimental results. The second expansion coefficient that produced this result is determined as the optimal particle expansion coefficient. The parameter settings for the full-fill expansion simulation in the first simulation model are updated to this optimal value, thereby completing the accurate calibration of the model's predictive ability during the full-fill expansion stage.

[0149] As can be seen, in this embodiment of the application, by setting multiple sets of expansion coefficients for parallel simulation and systematically comparing the simulation results with experimental data, the particle-scale parameters that best reflect the actual expansion behavior of the material can be quickly and objectively identified. This replaces the traditional approach of relying on a single empirical value or blind trial and error. The optimal parameters can be locked through a planned multi-condition simulation analysis, which significantly improves the efficiency and reliability of model parameter calibration and ensures the accuracy of the final full-fill expansion rate prediction.

[0150] Data such as simulated compaction density, simulated static expansion rate, and simulated full-fill expansion rate can not only be used as input data for the simulation model, but can also be directly output to the product end that requires this data. At the same time, considering the objectivity of test data and unavoidable human measurement errors and singularities in test data acquisition, by flowing the simulation model result data into the test end, the objectivity and authenticity of the test data can be guaranteed to a great extent, and the accuracy of the simulation model can also be further corrected.

[0151] Example 3: The above-mentioned application embodiments mainly focus on describing the simulation process of graphite materials. Based on this, this application embodiment also provides another simulation method for graphite materials in terms of adjusting and verifying the experimental data of graphite materials.

[0152] Please see Figure 7 , Figure 7 A flowchart illustrating another simulation method for graphite materials provided in this application embodiment is shown, which can be based on... Figure 1 The application scenario 100 shown is implemented as follows: Figure 7 As shown, steps S701-706 are included.

[0153] S701: The simulation terminal acquires the first material data and the first test data of the first graphite material. The first test data is the data obtained by conducting experiments on the first graphite material.

[0154] S702: The simulation terminal acquires the first simulation model and performs the first simulation processing on the first graphite material based on the first simulation model and the first material data to obtain the first simulation data.

[0155] S703: The simulation terminal performs a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain the second simulation data.

[0156] S704: The simulation terminal calculates the first credibility score of the first test data and determines that the first credibility score is not lower than the preset score. The credibility of the first test data is positively correlated with the first credibility score.

[0157] Specifically, in this step, before adjusting the first simulation model based on the first experimental data, the feasibility of the first experimental data needs to be verified. If the first experimental data fails the credibility verification, it proves that the data may seriously exceed the theoretical numerical range of the experimental results of the first graphite material and is not credible. Therefore, the first simulation model cannot be adjusted based on the first experimental data.

[0158] Optionally, the number of iterations of the first simulation model is determined; if the number of iterations is not greater than a first preset number, the theoretical maximum and theoretical minimum values ​​of the first experimental data are determined based on the first material data; the reliable data range of the first experimental data is determined based on the theoretical maximum and theoretical minimum values ​​of the first experimental data; and the first reliability score of the first experimental data is calculated based on the first experimental data and the reliable data range.

[0159] Specifically, this application provides an explanation of a specific process for evaluating the reliability of experimental data during simulation iteration. This process aims to pre-verify the input experimental data before simulation parameter inversion to exclude obviously abnormal data.

[0160] First, record the total number of iterations performed to calibrate the first simulation model. If this number of iterations does not exceed a preset upper limit (i.e., a first preset number, such as 30), the reliability of the first simulation model is low, and therefore, the corresponding model adjustment cannot be implemented using the first simulation model at this time. Under this premise, initiate a reliability check on the first experimental data used to guide the simulation.

[0161] Next, the theoretical maximum and minimum values ​​of the first experimental data are determined based on the first material data. The first material data includes the basic physical properties of graphite (such as true density, Young's modulus, and particle size distribution). Based on these inherent properties, the theoretical limits of the relevant experimental indicators can be calculated using physical or empirical models. For example, for compaction density, its theoretical maximum value cannot exceed the true density of graphite; for expansion rate, its reasonable range can be estimated based on the material's structural characteristics and known electrochemical expansion theories.

[0162] Then, based on the calculated theoretical maximum and minimum values, a reliable data range is determined. For example, this range can be defined as [theoretical minimum × (1 - buffer coefficient), theoretical maximum × (1 + buffer coefficient)], where the buffer coefficient is used to accommodate reasonable measurement fluctuations.

[0163] Next, a credibility score is calculated. The actual measured first experimental data is compared with the aforementioned credibility data range. If the experimental data falls within the credibility range, a higher base score (e.g., 100 points) is assigned; if it falls outside the range, points are deducted proportionally based on its relative deviation, resulting in the first credibility score. The lower the score, the more questionable the experimental data.

[0164] Furthermore, if the initial confidence score falls below a certain acceptable threshold, an additional data review process can be triggered. This includes checking the experimental environment (temperature and humidity), equipment calibration status, performing parallel sample retests according to standard operating procedures, and calculating the coefficient of variation for multiple sets of data to determine repeatability. Ultimately, based on the review results, a decision is made on whether to adopt the corrected experimental data or mark it as an anomaly and exclude it from this round of simulation calibration.

[0165] As can be seen, in this embodiment, a pre-emptive data quality filtering mechanism is constructed by combining the simulation iteration state with the theoretical range verification of experimental data based on material physical properties. This method can identify and process potential abnormal experimental data before the simulation begins, preventing unreliable inputs from causing miscalibration of simulation parameters or divergence in the optimization process. It forms an important quality control node in the test-simulation bidirectional process, ensuring the accuracy of the input data for the final expansion rate prediction model.

[0166] Optionally, if the number of iterations of the first simulation model is greater than a first preset number, the method further includes: acquiring third material data and second experimental data of the second graphite material; wherein, the second graphite material is the graphite material simulated before the first graphite material, and the second experimental data is data obtained from experiments on the second graphite material; performing simulation processing on the second graphite material according to the first simulation model and the third material data to obtain second simulation data; calculating the historical simulation deviation value of the second graphite material according to the second experimental data and the second simulation data; calculating the simulation deviation value of the first graphite material according to the first experimental data and the first simulation data; determining the first confidence score of the first experimental data based on the historical simulation deviation value and data simulation, wherein the first confidence score is positively correlated with the similarity between the historical deviation value and the data deviation value.

[0167] Specifically, in this embodiment, an alternative intelligent process for assessing the reliability of current experimental data when the number of model calibration iterations is large is explained. This method uses the "experience" of historical materials as a reference benchmark to determine whether the current input data may contain anomalies.

[0168] First, the trigger condition is that the number of iterations of the first simulation model is greater than the first preset number (e.g., 30 times).

[0169] Next, the simulation terminal acquires the third material data and the second experimental data of the second graphite material. The second graphite material is a type of graphite material from a historical batch that has already undergone simulation modeling. Its third material data (such as particle size distribution) and second experimental data (such as historically measured compaction density, expansion rate, etc.) have been stored in the database.

[0170] Then, a retrospective verification of historical data is performed. Using the first simulation model currently being calibrated (including its current parameter settings), combined with the third material data, a complete simulation process (including rolling, static expansion, full-fill expansion, etc.) is performed again on the second graphite material to obtain a set of second simulation data for the second graphite material.

[0171] Subsequently, the historical simulation deviation value was calculated: based on the second experimental data (historical experimental values) and the second simulation data obtained from this retrospective simulation, the deviation between the two was calculated. This deviation reflects the accuracy of the current first simulation model when simulating historically known materials.

[0172] Current simulation deviation value: The deviation between the first experimental data (current experimental value) and the first simulation data obtained for the first graphite material is calculated. This deviation reflects the degree of agreement between the current model and the current input experimental data when simulating the new material.

[0173] Finally, the first confidence score is determined based on these two deviation values. If the current first simulation model is still accurate in predicting the historical material (the second graphite material) (i.e., the historical simulation deviation value is small), but the prediction of the current new material (the first graphite material) deviates greatly from the current experimental data (i.e., the current simulation deviation value is large), then it is very likely that the current input first experimental data itself is abnormal, rather than the model completely failing.

[0174] Therefore, the design of the first confidence score is positively correlated with the "similarity" between these two deviation values. Specifically, the statistical distance (such as Euclidean distance or relative difference) between the historical simulation deviation value and the current simulation deviation value can be calculated. The smaller the distance, the more consistent the deviation pattern of the current data is with historical experience, and the higher the score; the larger the distance, the more "abnormal" the deviation of the current data is, and the lower its confidence score.

[0175] As can be seen, in this embodiment, by using the historical performance of the established simulation model as a reference, the root cause of the current modeling difficulties is intelligently diagnosed. When the number of iterations is large, the simulation model has basic credibility. Therefore, by comparing the model's performance differences for historical and current materials, potential problematic data inputs can be effectively identified, improving the efficiency of model adjustment while also enhancing the dynamic monitoring and evaluation capabilities of experimental data quality.

[0176] Optionally, if the number of iterations is greater than the second preset number of iterations, the method further includes: determining the data range of the first experimental data based on the second simulation data; and calculating the first rationality score of the first experimental data based on the first experimental data and the data range.

[0177] Specifically, in the embodiments of this application, another supplementary process for evaluating the reasonableness of experimental data is explained.

[0178] First, the trigger condition is that the number of iterations is greater than a higher second preset number (e.g., 50 times).

[0179] Next, the data range of the first experimental data is determined based on the second simulation data. Here, the numerical range generated based on the first simulation data represents the fluctuation range corresponding to the second simulation data generated by the first simulation model.

[0180] Then, a first reasonableness score for the first experimental data is calculated based on the first experimental data and the data range. The scoring logic focuses on the position of the current data point in the historical distribution.

[0181] For example, the calculation method involves calculating the relative distance between the current experimental value and the center point of the data range, and normalizing it into a score. The closer the distance, the more credible the data, and the higher the first reasonableness score. Conversely, if the data falls on the edge of the range or outside of it, the credibility is lower.

[0182] As can be seen from the embodiments of this application, when the number of iterations of the simulation model is high, the credibility of the first experimental data is evaluated by the reasonable data range of the experimental data generated by the simulation model, which further improves the efficiency of model adjustment.

[0183] S705: The simulation terminal adjusts the first simulation model based on the first test data and the second simulation data.

[0184] S706: The simulation terminal acquires the second material data of the graphite material to be tested, and performs a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain the target test data.

[0185] For detailed explanations of steps S701-S703, S705, and S706, please refer to the descriptions and related content of steps S201-S205, which will not be repeated here.

[0186] By implementing the methods in the above-described embodiments, it can be seen that using experimental and simulation data to correct and improve the simulation model during the simulation testing of graphite materials enhances the optimization efficiency and reliability of the simulation model. Static rolling and expansion simulations based on the simulation model provide key simulation prediction indicators for evaluating and screening low-expansion graphite materials. Adjusting the simulation model by setting simulation parameters and comparing simulation results with experimental data improves the efficiency and reliability of model parameter calibration, ensuring the accuracy of the final full-fill expansion rate prediction.

[0187] Based on the description of the above configuration method embodiments, this application also provides a graphite material simulation device 800, which can operate in... Figure 1 The simulation terminal 101 shown contains a computer program (including program code) and is used to execute... Figure 2 , Figure 6 and Figure 7 The method shown. See also Figure 8 , Figure 8 This is a schematic diagram of the structure of a graphite material simulation device provided in an embodiment of this application. The graphite material simulation device 800 includes:

[0188] The acquisition unit 801 is used to acquire first material data and first test data of the first graphite material, wherein the first test data is data obtained by conducting tests on the first graphite material;

[0189] Obtain the first simulation model, and perform the first simulation processing on the first graphite material based on the first simulation model and the first material data to obtain the first simulation data;

[0190] Simulation unit 802 is used to perform a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain second simulation data;

[0191] The adjustment unit 803 is used to adjust the first simulation model based on the first experimental data and the second simulation data.

[0192] The simulation unit 802 is also used to acquire the second material data of the graphite material to be tested, and to perform a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain the target test data.

[0193] In one possible embodiment, the first simulation data includes a first particle space, the second simulation data includes a simulated static expansion rate, and the second simulation processing includes static expansion simulation. In obtaining the second simulation data by performing a second simulation processing on the first graphite material based on the first material data, the first simulation model, and the first simulation data, the simulation unit 802 is further specifically configured to: set the static time of the first graphite material; perform a static expansion simulation on the first graphite material based on the first material data, the first simulation model, the first particle space, and the static time to obtain a second particle space of the first graphite material after static expansion, the second particle space representing the volume of the first graphite material after static expansion; and determine the simulated static expansion rate of the first graphite material based on the first particle space and the second particle space.

[0194] In one possible embodiment, the first experimental data includes the experimental static expansion rate; in adjusting the first simulation model based on the first experimental data and the second simulation data, the adjustment unit 803 is further specifically used for: if the deviation between the experimental static expansion rate and the simulated static expansion rate is not less than a first preset deviation value, then using the static duration as a variable, establishing a third-order polynomial response surface characterizing the relationship between the static duration and the simulated static expansion rate; constructing a first loss function based on the ratio of the absolute value of the difference between the simulated static expansion rate and the experimental static expansion rate to the experimental static expansion rate; updating the static duration through Bayesian inference until the first loss function is less than a preset threshold or the number of times the static duration is updated is not less than a preset number; and adjusting the first simulation model based on the latest static duration.

[0195] In one possible embodiment, the simulation unit 802 is further specifically used to: set a first expansion coefficient for a single graphite particle of the first graphite material; perform a full-fill expansion simulation of the first graphite material based on the first material data, the first simulation model, the second particle space, and the first expansion coefficient to obtain a third particle space, wherein the third particle space characterizes the volume of the first graphite material after full-fill expansion; and determine a first simulated full-fill expansion rate of the first graphite material based on the second particle space and the third particle space.

[0196] In one possible embodiment, the first experimental data further includes the experimental full-fill expansion rate, and the adjustment unit 803 is further specifically used for: if the deviation between the experimental full-fill expansion rate and the first simulated full-fill expansion rate is not less than a second preset deviation value, then obtaining a plurality of second expansion coefficients; performing full-fill expansion simulation on the first graphite material according to the first material data, the first simulation model, the second particle space, and the plurality of second expansion coefficients respectively, to obtain a plurality of fourth particle spaces corresponding to the plurality of second expansion coefficients, wherein the fourth particle space characterizes the volume of the first graphite material after full-fill expansion according to the corresponding second expansion coefficients; determining the second simulated full-fill expansion rate corresponding to the plurality of second expansion coefficients according to the second particle space and the plurality of fourth particle spaces; determining the deviation value between the plurality of second simulated full-fill expansion rates and the experimental full-fill expansion rate; and adjusting the first simulation model according to the second expansion coefficient corresponding to the second simulated full-fill expansion rate with the lowest deviation value from the experimental full-fill expansion rate.

[0197] In one possible embodiment, the adjustment unit 803 is further specifically used for: setting the contact parameters of the first graphite material; obtaining a second simulation model; performing a roll pressing simulation on the first graphite material based on the first material data, the second simulation model, and the contact parameters to obtain the simulated compaction density of the first graphite material; and adjusting the second simulation model based on the simulated compaction density to obtain the first simulation model.

[0198] In one possible embodiment, the contact parameters include the coefficient of restitution, the static friction coefficient, and the rolling friction coefficient. Regarding adjusting the second simulation model to obtain the first simulation model based on the simulated compaction density, the adjustment unit 803 is further specifically used to: establish a relationship model between the simulated compaction density of the first graphite material and the contact parameters, using the coefficient of restitution, the static friction coefficient, and the rolling friction coefficient as variables; construct a second loss function based on the ratio of the absolute value of the difference between the simulated compaction density and the experimental compaction density to the experimental compaction density; use Bayesian optimization on the relationship model to determine the optimal coefficient of restitution, the optimal static friction coefficient, and the optimal rolling friction coefficient corresponding to the minimum of the second loss function; and adjust the second simulation model based on the optimal coefficient of restitution, the optimal static friction coefficient, and the optimal rolling friction coefficient to obtain the first simulation model.

[0199] Based on the description of the above method and device embodiments, please refer to... Figure 9 , Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 9 The electronic device 900 shown (specifically, the electronic device 900 may be a computer device, Figure 1 The simulation terminal 101 shown includes a memory 901, a processor 902, a communication interface 903, and a bus 904. The memory 901, processor 902, and communication interface 903 are interconnected via the bus 904.

[0200] The memory 901 may be a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM).

[0201] The memory 901 can store programs. When the program code stored in the memory 901 is executed by the processor 902, the processor 902 and the communication interface 903 are used to execute the various steps of the simulation method for graphite materials according to the embodiments of this application.

[0202] The processor 902 may be a general-purpose central processing unit (CPU), microcontroller, application-specific integrated circuit (ASIC), graphics processing unit (GPU), or one or more integrated circuits, used to execute relevant programs to achieve the functions required by the units in the electronic device 900 of this application embodiment, or to execute the graphite material simulation method of this application method embodiment.

[0203] The processor 902 can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of the graphite material simulation method of this application can be completed by the integrated logic circuits in the hardware of the processor 902 or by software instructions. The processor 902 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microcontroller or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory 901. The processor 902 reads the information in the memory 901 and, in conjunction with its hardware, performs the functions required by the units included in the electronic device 900 of this application embodiment, or performs the simulation method of graphite material of this application method embodiment.

[0204] The communication interface 903 uses transceiver devices, such as, but not limited to, transceivers, to enable communication between the electronic device 900 and other devices or communication networks. For example, data can be acquired through the communication interface 903.

[0205] Bus 904 may include a pathway for transmitting information between various components of electronic device 900 (e.g., memory 901, processor 902, communication interface 903).

[0206] It should be noted that, although Figure 9 The illustrated electronic device 900 only shows a memory 901, a processor 902, and a communication interface 903. However, those skilled in the art should understand that in specific implementations, the electronic device 900 may also include other devices necessary for normal operation. Furthermore, depending on specific needs, those skilled in the art should understand that the electronic device 900 may also include hardware devices for implementing other additional functions. Moreover, those skilled in the art should understand that the electronic device 900 may only include the devices necessary for implementing the embodiments of this application, and may not necessarily include... Figure 9 All the devices shown.

[0207] This application also provides a chip, which includes a processor and a data interface. The processor reads instructions stored in a memory through the data interface to implement the simulation method for graphite materials.

[0208] Optionally, as one implementation, the chip may further include a memory storing instructions, and the processor is used to execute the instructions stored in the memory. When the instructions are executed, the processor is used to execute the simulation method for the graphite material.

[0209] This application also provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform one or more steps of any of the above methods.

[0210] This application also provides a computer program product containing instructions. When the computer program product is run on a computer or processor, it causes the computer or processor to perform one or more steps of any of the methods described above.

[0211] Those skilled in the art will appreciate that the functionality described in conjunction with the various illustrative logic blocks, modules, and algorithmic steps disclosed herein can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality described by the various illustrative logic blocks, modules, and steps can be stored or transmitted as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may comprise a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or a communication medium that includes any medium facilitating the transfer of a computer program from one place to another (e.g., based on a communication protocol). In this way, the computer-readable medium may substantially correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. The data storage medium may be any available medium accessible by one or more computers or one or more processors to retrieve instructions, code, and / or data structures for implementing the techniques described in this application. A computer program product may comprise a computer-readable medium.

[0212] By way of example and not limitation, such computer-readable storage media may include RAM, ROM, EEPROM, CD-ROM or other optical disc storage devices, magnetic disk storage devices or other magnetic storage devices, flash memory, or any other media that can be used to store desired program code in the form of instructions or data structures and is accessible by a computer. Furthermore, any connection is properly referred to as computer-readable media. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of media. However, it should be understood that the computer-readable storage media and data storage media do not include connections, carrier waves, signals, or other temporary media, but are specifically addressed to non-temporary tangible storage media. As used herein, disks and optical discs include compact optical discs (CDs), laser optical discs, optical discs, digital versatile optical discs (DVDs), and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs reproduce data optically using lasers. The combination of the above items should also be included in the scope of computer-readable media.

[0213] Instructions can be executed by one or more processors, such as digital signal processors (DSPs), general-purpose microcontrollers, application-specific integrated circuits (ASICs), field-programmable arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Therefore, the term "processor" as used herein can refer to any of the foregoing structures or any other structures suitable for implementing the techniques described herein. Furthermore, in some aspects, the functionality described in the various illustrative logic blocks, modules, and steps described herein can be provided within dedicated hardware and / or software modules configured for encoding and decoding, or incorporated into combined codecs. Moreover, the techniques can be fully implemented within one or more circuit or logic elements.

[0214] The technology of this application can be implemented in a wide variety of devices or apparatuses, including wireless handheld devices, integrated circuits (ICs), or a set of ICs (e.g., chipsets). The various components, modules, or units described in this application are intended to emphasize functional aspects of the apparatus for performing the disclosed technology, but do not necessarily need to be implemented by different hardware units. In fact, as described above, the various units can be combined with suitable software and / or firmware within coded hardware units, or provided via interoperable hardware units (containing one or more processors as described above).

[0215] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the specific descriptions of the corresponding steps in the foregoing method embodiments, and will not be repeated here.

[0216] It should be understood that in the description of this application, unless otherwise stated, " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B can represent A or B; where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of this application, the terms "first," "second," etc., are used in the embodiments of this application to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or execution order, and the terms "first," "second," etc., do not necessarily imply difference. In this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or explanation. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0217] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection shown or discussed between each other may be indirect coupling or communication connection through some interfaces, apparatuses, or units, and may be electrical, mechanical, or other forms.

[0218] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0219] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid state disks (SSDs).

[0220] The above description is merely a specific implementation of the embodiments of this application, but the protection scope of the embodiments of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the embodiments of this application should be covered within the protection scope of the embodiments of this application. Therefore, the protection scope of the embodiments of this application should be determined by the protection scope of the claims.

[0221] The device embodiments described above are merely illustrative. The units and modules described as separate components may or may not be physically separate. Furthermore, some or all of the units and modules can be selected to achieve the purpose of this embodiment, depending on actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0222] The above description is only a specific embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A simulation method for graphite materials, characterized in that, The method includes: Obtain first material data and first test data of the first graphite material. The first test data is the data obtained by testing the first graphite material. The first test data includes the static expansion rate of the test. A first simulation model is obtained, and the first simulation data is obtained by performing a first simulation process on the first graphite material based on the first simulation model and the first material data; wherein, the first simulation process includes rolling simulation, and the first simulation data includes the first particle space; The first graphite material is subjected to a second simulation process based on the first material data, the first simulation model, and the first simulation data to obtain second simulation data; wherein, the second simulation process includes static expansion simulation, and the second simulation data includes simulated static expansion rate; Adjusting the first simulation model based on the first experimental data and the second simulation data specifically includes: if the deviation between the experimental static expansion rate and the simulated static expansion rate is not less than a first preset deviation value, then using the static duration as a variable, establishing a third-order polynomial response surface characterizing the relationship between the static duration and the simulated static expansion rate; constructing a first loss function based on the ratio of the absolute value of the difference between the simulated static expansion rate and the experimental static expansion rate to the experimental static expansion rate; updating the static duration through Bayesian inference until the first loss function is less than a preset threshold or the number of updates to the static duration is not less than a preset number; and adjusting the first simulation model based on the latest static duration. Second material data of the graphite material to be tested is obtained, and third simulation processing is performed on the graphite material to be tested based on the first simulation model and the second material data to obtain target test data; wherein, the third simulation processing includes rolling simulation, static expansion simulation and full-fill expansion simulation, and the target test data is the performance data of the graphite material to be tested under simulated conditions output by the first simulation model.

2. The method according to claim 1, characterized in that, The step of performing a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain second simulation data includes: Set the settling time of the first graphite material; Based on the first material data, the first simulation model, the first particle space, and the settling time, a settling expansion simulation is performed on the first graphite material to obtain the second particle space of the first graphite material after settling expansion. The second particle space represents the volume of the first graphite material after settling expansion. The simulated static expansion rate of the first graphite material is determined based on the first particle space and the second particle space.

3. The method according to claim 2, characterized in that, The method further includes: Set the first expansion coefficient of a single graphite particle of the first graphite material; Based on the first material data, the first simulation model, the second particle space, and the first expansion coefficient, a full-fill expansion simulation of the first graphite material is performed to obtain the third particle space, which represents the volume of the first graphite material after full-fill expansion. The first simulated full-fill expansion rate of the first graphite material is determined based on the second particle space and the third particle space.

4. The method according to claim 3, characterized in that, The first test data also includes the test full-charge expansion rate, and the method further includes; If the deviation between the experimental full-fill expansion rate and the first simulated full-fill expansion rate is not less than the second preset deviation value, then multiple second expansion coefficients are obtained; Based on the first material data, the first simulation model, the second particle space, and the plurality of second expansion coefficients, a full-fill expansion simulation is performed on the first graphite material to obtain a plurality of fourth particle spaces corresponding to the plurality of second expansion coefficients. The fourth particle space represents the volume of the first graphite material after full-fill expansion according to the corresponding second expansion coefficient. Based on the second particle space and the plurality of fourth particle spaces, determine the second simulated full-fill expansion rate corresponding to the plurality of second expansion coefficients; Determine the deviation between the plurality of second simulated full-charge expansion rates and the experimental full-charge expansion rates; The first simulation model is adjusted according to the second expansion coefficient corresponding to the second simulated full-charge expansion rate that has the lowest deviation from the experimental full-charge expansion rate.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Set the contact parameters of the first graphite material; Obtain the second simulation model; The simulated compaction density of the first graphite material is obtained by performing a roll pressing simulation based on the first material data, the second simulation model, and the contact parameters. The first simulation model is obtained by adjusting the second simulation model based on the simulated compaction density.

6. The method according to claim 5, characterized in that, The contact parameters include the coefficient of restitution, the coefficient of static friction, and the coefficient of rolling friction; the step of adjusting the second simulation model according to the simulated compaction density to obtain the first simulation model includes: Using the coefficient of recovery, the coefficient of static friction, and the coefficient of rolling friction as variables, a model is established to show the relationship between the simulated compaction density of the first graphite material and the contact parameters. A second loss function is constructed by dividing the absolute value of the difference between the simulated compaction density and the experimental compaction density by the ratio of the experimental compaction density. Bayesian optimization is used to determine the optimal recovery coefficient, optimal static friction coefficient, and optimal rolling friction coefficient corresponding to the minimum of the second loss function in the relationship model. The first simulation model is obtained by adjusting the second simulation model based on the optimal recovery coefficient, the optimal static friction coefficient, and the optimal rolling friction coefficient.

7. A simulation device for graphite materials, characterized in that, The device includes: The acquisition unit is used to acquire first material data and first test data of the first graphite material. The first test data is data obtained by testing the first graphite material and includes the static expansion rate of the test. A first simulation model is obtained, and the first simulation data is obtained by performing a first simulation process on the first graphite material based on the first simulation model and the first material data; wherein, the first simulation process includes rolling simulation, and the first simulation data includes the first particle space; The simulation unit is used to perform a second simulation process on the first graphite material based on the first material data, the first simulation model, and the first simulation data to obtain second simulation data; wherein, the second simulation process includes static expansion simulation, and the second simulation data includes the simulated static expansion rate; The adjustment unit is used to adjust the first simulation model based on the first experimental data and the second simulation data. Specifically, it includes: if the deviation between the experimental static expansion rate and the simulated static expansion rate is not less than a first preset deviation value, then using the static duration as a variable, establishing a third-order polynomial response surface characterizing the relationship between the static duration and the simulated static expansion rate; constructing a first loss function based on the ratio of the absolute value of the difference between the simulated static expansion rate and the experimental static expansion rate to the experimental static expansion rate; updating the static duration through Bayesian inference until the first loss function is less than a preset threshold or the number of updates to the static duration is not less than a preset number; and adjusting the first simulation model based on the latest static duration. The simulation unit is also used to acquire second material data of the graphite material to be tested, and to perform a third simulation process on the graphite material to be tested based on the first simulation model and the second material data to obtain target test data; wherein, the third simulation process includes rolling simulation, static expansion simulation and full-fill expansion simulation, and the target test data is the performance data of the graphite material to be tested under the simulation conditions output by the first simulation model.

8. An electronic device, characterized in that, The method includes a processor, a memory, a communication interface, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs including instructions for performing the steps of the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program for electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of claims 1-6.

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