Lithium ion battery polycrystal / single crystal positive electrode material multi-physics field coupling modeling and failure analysis method

Abstract

A lithium ion battery polycrystal / single crystal positive electrode material multi-physics field coupling modeling and failure analysis method comprises the steps that a three-dimensional positive electrode particle structure model is constructed, a polycrystal structure is generated by randomly filling spherical boundaries with non-overlapped spherical crystal grains, and single crystals are complete spheres; importing the model into a finite element simulation platform, and setting a multi-physics field coupling interface; establishing a diffusion, migration and electrochemical reaction kinetic model; establishing a lithium concentration-strain-stress coupling relation and solving the lithium concentration-strain-stress coupling relation; setting a failure criterion based on stress distribution to identify a damaged area; and outputting a simulation result and carrying out structure-performance correlation analysis. The method can reveal the evolution behavior difference of the polycrystalline positive electrode and the monocrystalline positive electrode in the electrochemical-mechanical process, provides reliable theoretical and simulation support for electrode material design, optimization and life evaluation, and provides powerful technical support for optimization design of the high-performance lithium ion battery positive electrode material.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium - ion batteries, in particular to a multi - physical - field coupling modeling and failure analysis method for polycrystalline / single - crystal cathode materials of lithium - ion batteries. Background Art

[0002] Due to their high energy density, long cycle life and environmental friendliness, lithium - ion batteries have been widely used in electric vehicles, portable electronic devices and energy storage fields. Among them, as the key carrier for battery energy storage and release, the structural design and evolution behavior of cathode materials have a decisive impact on battery performance. Currently, layered oxides (such as NCM, LCO) or olivine - structured materials (such as LFP) are widely used as cathode materials for lithium - ion batteries. In practical applications, these materials can be prepared in single - crystal or polycrystalline forms. In contrast, polycrystalline cathodes dominate in industry due to their ease of synthesis and cost advantages, but their complex grain - boundary structures and grain - size distributions often lead to discontinuous lithium - ion migration paths, stress concentration and local failure; while single - crystal materials, although having stronger structural integrity, also have certain limitations in rate performance and volume - strain coordination.

[0003] Existing research mostly analyzes the degradation mechanism of cathode materials through experimental methods or simplified models, and it is often difficult to simultaneously consider the coupling effects of multiple physical processes such as crystal structure, electrochemical reaction, lithium diffusion behavior and stress evolution, and thus unable to accurately predict the cracks and failure paths caused by grain - boundary blockage and stress concentration in polycrystalline cathodes. In addition, most existing modeling methods use ideal particle morphologies (such as spherical, cubic) for approximate modeling, ignoring the influence of grain - size distribution, orientation difference and inter - connection in the real structure on transport behavior, which limits the modeling accuracy and prediction ability. Therefore, there is an urgent need to propose a modeling and simulation method based on the actual microstructure and with multi - physical - field coupling ability to systematically reveal the differences in the evolution behavior of polycrystalline and single - crystal cathode materials during the electrochemical - mechanical process, and provide theoretical basis and simulation tool support for electrode material design, structure optimization and life improvement.

[0004] It should be noted that the information disclosed in the above background art section is only used for understanding the background of the present application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The main object of the present invention is to overcome the defects existing in the above - mentioned background art, and provide a multi - physical - field coupling modeling and failure analysis method for polycrystalline / single - crystal cathode materials of lithium - ion batteries.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A multi-physics field coupling modeling and failure analysis method for polycrystalline / single crystal positive electrode materials of lithium-ion batteries, comprising the following steps: S1. Construct a three-dimensional cathode particle structure model, where the polycrystalline structure is generated by randomly filling multiple non-overlapping spherical grains within a spherical boundary, and the single crystal structure is constructed as a complete sphere; S2. The structural model is imported into the finite element simulation platform, and a multi-physics coupling interface for lithium ion diffusion model, electrolyte migration model, interfacial electrochemical reaction model, and solid mechanics model is set; S3. Establish models for lithium-ion diffusion kinetics within cathode particles, ion migration in the electrolyte, and interfacial electrochemical reaction kinetics. S4. Establish the lithium concentration-strain-stress coupling relationship and solve the concentration field and stress field simultaneously; S5. Set failure criteria based on stress distribution and identify potential structural damage areas; S6. Output the multi-physics coupling simulation results of the cathode material, such as electrochemical performance curves, stress cloud maps, lithium concentration distribution maps, and crack prediction results, and perform structure-performance correlation analysis of the cathode material.

[0007] Furthermore, in step S1: The polycrystalline structure is constructed by randomly generating spherical grains that obey a normal distribution, and dynamically excluding grains outside the boundary and overlapping with each other during the filling process until the total filling volume reaches a preset ratio and the generation is terminated.

[0008] Furthermore, in step S3: The lithium ion diffusion model is described by Fick's second law, and the diffusion coefficient is set independently for different grains; The electrolyte migration model uses the Nernst-Planck equation to couple the concentration gradient and electric field migration terms; The interfacial electrochemical reaction model establishes a relationship between current density and interfacial overpotential based on the Butler-Volmer kinetic equation.

[0009] Furthermore, in step S4: The concentration-strain coupling relationship is established by a linear interpolation function, mapping the concentration change to volume strain; The stress field is solved by the solid mechanics module using the concentration-strain relationship, and the stress distribution is calculated using the linear elastic constitutive model.

[0010] Furthermore, in step S5: The failure criterion includes a von-Mises stress threshold criterion or a principal strain threshold criterion to determine the fracture initiation risk area at the grain boundary or inside the grain.

[0011] Furthermore, in step S6: The output results include voltage-capacity curves, von-Mises stress cloud maps, lithium concentration distribution heat maps, and crack location prediction maps, and quantify performance differences by comparing simulation results of polycrystalline and single crystal structures.

[0012] Furthermore, the structural modeling and multi-physics coupling process enables batch parameter input and control through modular code and parameter configuration files, enabling sensitivity analysis and parallel simulation of multiple models. The simulation process supports sensitivity analysis of boundary conditions, material parameters, and geometric inputs.

[0013] Furthermore, the positive electrode material includes NCM and LCO materials with polycrystalline and single crystal structures, and supports expansion to ellipsoidal particles, Voronoi structures or actual morphologies reconstructed based on CT images.

[0014] Furthermore, the method is applied to electrode life prediction, crack evolution simulation and structural optimization design, and long-term performance degradation is evaluated through multi-cycle loading and damage accumulation variable statistics.

[0015] A computer program product includes a computer program, which, when executed by a processor, implements the multi-physics field coupling modeling and failure analysis method for polycrystalline / single crystal positive electrode materials of lithium-ion batteries.

[0016] The present invention has the following beneficial effects: The multi-physics field coupling modeling and failure analysis method for lithium-ion battery polycrystalline / single crystal positive electrode materials provided by the present invention addresses the problem that existing modeling methods are difficult to simultaneously consider the coupling effects of multiple physical processes and ignore the real structural characteristics. By randomly filling non-overlapping spherical grains within the spherical boundary to construct a polycrystalline structure and constructing a single crystal structure with a complete sphere, the model is imported into the finite element simulation platform to set up a multi-physics field coupling interface, establish lithium ion diffusion, ion migration, electrochemical reaction kinetics models and lithium concentration-strain-stress coupling relationship, jointly solve the concentration field and stress field, set failure criteria based on stress distribution to identify potential damage areas and It outputs multi-physics field coupling simulation results and conducts structure-performance correlation analysis, and can realize multi-physics field coupling modeling and simulation based on actual microstructure, systematically revealing the differences in the evolutionary behaviors of polycrystalline and single-crystalline positive electrode materials in the electrochemical-mechanical process, and accurately revealing the evolutionary relationship between positive electrode structure and performance. It solves the problem that existing methods cannot accurately predict the cracks and failure paths caused by grain boundary blocking and stress concentration in polycrystalline positive electrodes, and provides a theoretical basis and repeatable and scalable full-process integrated simulation tool support for electrode material design, structural optimization, cycle life assessment, reliability analysis and performance prediction, which has high engineering application value.

[0017] Other beneficial effects of the embodiments of the present invention will be further described below. Description of the Drawings

[0018] Figure 1 This is the overall flowchart of the multi-physics field coupling modeling and failure analysis method for the polycrystalline / single-crystalline cathode material of the lithium-ion battery of the present invention.

[0019] Figure 2 This is the three-dimensional cathode particle structure model constructed in the embodiment of the present invention. The polycrystalline particle model is on the left, and the single-crystalline particle model is on the right.

[0020] Figure 3 This is the deformation distribution diagram of the polycrystalline (upper) and single-crystalline (lower) cathodes in different states of charge in the embodiment of the present invention, showing the deformation evolution behavior on the particle surface during the charge and discharge process.

[0021] Figure 4 This is the von-Mises stress distribution nephogram of the polycrystalline and single-crystalline cathodes in different states in the embodiment of the present invention, used to identify potential stress concentration regions and their changing trends.

[0022] Figure 5 This is the average strain histogram of the polycrystalline and single-crystalline cathodes in different cycling states in the embodiment of the present invention. Detailed Embodiment

[0023] The following makes a detailed description of the embodiments of the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope of the present invention and its applications.

[0024] Aiming at the problem that the existing modeling methods cannot simultaneously characterize the structural characteristics, electrochemical behavior and mechanical failure evolution of the cathode material, the present invention provides a multi-physics field coupling modeling and failure analysis method for polycrystalline and single-crystalline cathodes of lithium-ion batteries, which is applicable to the structural optimization of cathode materials, cycle life assessment and reliability analysis, and provides a strong technical support for the optimized design of high-performance cathode materials.

[0025] Refer to Figure 1 , the embodiment of the present invention provides a multi-physics field coupling modeling and failure analysis method for polycrystalline / single-crystalline cathode materials of lithium-ion batteries, including the following steps: Step S1. Construct a three-dimensional cathode particle structure model, wherein the polycrystalline structure is generated by randomly filling a plurality of non-overlapping spherical grains within a spherical boundary, and the single-crystalline structure is constructed as a complete sphere.

[0026] Specifically, in some embodiments, the construction of the polycrystalline structure is realized by randomly generating spherical grains that follow a normal distribution, and during the filling process, the grains outside the boundary and overlapping with each other are dynamically excluded until the total filling volume reaches a preset ratio and then the generation is terminated.

[0027] Step S2. Import the structural model into a finite element simulation platform, and set up a multi-physics field coupling interface for a lithium ion diffusion model, an electrolyte migration model, an interface electrochemical reaction model, and a solid mechanics model.

[0028] Step S3. Establishing a lithium ion diffusion kinetic model inside the positive electrode particles, an ion migration model in the electrolyte, and an interfacial electrochemical reaction kinetic model.

[0029] Specifically, in some embodiments, the lithium ion diffusion model is described using Fick's second law, and the diffusion coefficient is set independently for different grains; the electrolyte migration model uses the Nernst-Planck equation to couple the concentration gradient and the electric field migration term; the interface electrochemical reaction model establishes the relationship between current density and interface overpotential based on the Butler-Volmer kinetic equation.

[0030] Step S4: Establishing the lithium concentration-strain-stress coupling relationship and solving the concentration field and stress field jointly.

[0031] Specifically, in some embodiments, the concentration-strain coupling relationship is established through a linear interpolation function, and the concentration change is mapped to volume strain; the stress field is solved by combining the concentration-strain relationship through a solid mechanics module, and the stress distribution is calculated using a linear elastic constitutive model.

[0032] Step S5. Set failure criteria based on stress distribution and identify potential structural damage areas. Specifically, in some embodiments, the failure criterion includes a von-Mises stress threshold criterion or a principal strain threshold criterion to determine the fracture initiation risk area at the grain boundary or inside the grain.

[0033] Step S6. Output the multi-physics coupling simulation results of the cathode material, such as electrochemical performance curves, stress cloud maps, lithium concentration distribution heat maps, and crack prediction results, and perform a structure-performance correlation analysis of the cathode material.

[0034] Specifically, in some embodiments, the output results include a voltage-capacity curve, a von-Mises stress cloud map, a lithium concentration distribution heat map, and a crack location prediction map, and the performance difference is quantified by comparing the simulation results of polycrystalline and single crystal structures.

[0035] In some embodiments, the structural modeling and multiphysics coupling process utilizes modular code and parameter configuration files to enable batch parameter input and control, enabling sensitivity analysis and parallel simulation of multiple models. Simulations support sensitivity analysis for boundary conditions, material parameters, and geometric inputs.

[0036] In some embodiments, the cathode material includes NCM, LCO and other materials with polycrystalline and single crystal structures, and supports expansion to ellipsoidal particles, Voronoi structure or actual morphology reconstructed based on CT images.

[0037] In some embodiments, the multi-physics field coupling modeling and failure analysis method of lithium-ion battery polycrystalline / single crystal positive electrode materials can be applied to electrode life prediction, crack evolution simulation and structural optimization design, and long-term performance degradation can be evaluated through multi-cycle loading and damage accumulation variable statistics.

[0038] The multi-physics field coupled modeling and failure analysis method for polycrystalline / single-crystalline positive electrode materials for lithium-ion batteries of the present invention can accurately reveal the evolutionary relationship between positive electrode structure and performance by explicitly modeling the polycrystalline structure, jointly solving the coupled physical fields, and introducing failure criteria to identify high-risk areas. It is suitable for various engineering tasks such as lithium-ion battery design, degradation analysis and performance prediction.

[0039] The following further describes specific embodiments of the present invention and experimental verification.

[0040] A multi-physics field coupling modeling and failure analysis method for lithium-ion battery cathode materials mainly includes the following steps: a. constructing a three-dimensional cathode structure model, wherein the cathode structure includes a polycrystalline structure and a single crystal structure; b. importing the structure model into a finite element simulation platform to establish a physical field calculation domain; c. establishing a lithium ion diffusion model, an electrolyte migration model, and an interface electrochemical reaction model within the cathode structure; d. establishing a concentration-strain-stress coupling relationship and jointly solving it to form a stress field; e. setting failure criteria to identify potential structural damage areas; f. outputting simulation results such as electrochemical performance, stress distribution, and concentration heat map, and performing structure-performance correlation analysis. Figure 1 This is a modeling flow chart of the method of the present invention, showing the key steps in multi-physics field simulation of polycrystalline and single crystal positive electrodes, including geometric modeling, physical modeling, stress and strain calculation and failure analysis.

[0041] The specific implementation process includes the following steps: Step 1: Construct a three-dimensional positive electrode particle structure model, generating single-crystal and polycrystalline geometric models. The polycrystalline structure model is achieved by randomly filling multiple small spheres within a spherical envelope. The sphere radius follows a set normal distribution, and their spatial distribution is controlled by excluding overlapping and out-of-bounds areas. The filling process sets a total sphere volume limit based on the target porosity or volume fraction, and terminates the generation process. The single-crystal structure model uses complete spheres as the building block, with a continuous structure and no grain boundaries, for performance comparison with the polycrystalline structure.

[0042] Step 2: Import the constructed positive electrode geometry model into a finite element simulation platform for physical field simulation. Multi-physics coupling analysis includes basic battery components, charge transfer, material diffusion, and solid mechanics behavior.

[0043] Step 3: Establish a lithium ion diffusion model inside the positive electrode particles and use Fick's second law to describe the concentration evolution behavior. The diffusion coefficient can be assigned to single crystal particles and polycrystalline grains according to the material type.

[0044] Step 4: Establish an ion migration model in the electrolyte, use the Nernst-Planck equation to couple the diffusion term and the migration term, and calculate the ion flux distribution.

[0045] Step 5: Establish an interfacial electrochemical reaction model and calculate the current density and reaction rate based on the Butler-Volmer equation.

[0046] In step 6, the charge transfer model is coupled with the mass transport model using the Electrode Surface Coupling interface.

[0047] Step 7: Establish the coupling relationship between lithium concentration and volume strain and define the concentration-induced strain function.

[0048] Step 8: Introduce the Solid Mechanics module to realize concentration-stress coupling and solve the stress field through the linear elastic constitutive relationship.

[0049] Step 9: Calculate the equivalent stress based on the Von-Mises criterion and identify local high-risk areas.

[0050] Step 10: Use failure criteria to determine whether the material damage limit has been reached and whether there is a risk of fracture at the grain boundary or inside the crystal.

[0051] Preferably, the construction of the polycrystalline structure in step 1 can be automatically generated using MATLAB code to control the size distribution and position of the random spheres.

[0052] Preferably, after each new sphere is generated, the code calculates the distance to the existing sphere and excludes the case where the distance is less than the sum of the radii of the two spheres to avoid overlap.

[0053] Preferably, the cycle process can be terminated according to a preset maximum total volume of filled spheres.

[0054] Preferably, the boundary conditions of various physical quantities involved in steps 3 to 5 can be set according to actual experiments, for example, a constant current or constant voltage boundary is applied to the electrode end, and the particle surface is set as a flux boundary.

[0055] Preferably, in steps 6 to 9, a concentration heat map, a stress nephogram, and a current density distribution map can be output to visualize the evolution process of multi-physical behaviors.

[0056] Preferably, the method supports comparing the performance differences of different structural models under the same working conditions, such as voltage-capacity curves, maximum stress values, and crack initiation positions.

[0057] Preferably, the modeling framework can be extended to other structural types of cathode materials, including but not limited to NCM, NCA, LFP, etc.

[0058] Preferably, the method can calibrate model parameters by combining experimental data to improve the accuracy of simulation results.

[0059] Preferably, the failure criterion can include the maximum principal stress criterion, the critical strain criterion, or a fracture judgment criterion based on the energy release rate.

[0060] Preferably, the coupling behavior between the stress concentration position and the lithium-ion concentration gradient in the method can be used to reveal the internal relationship between polarization and structural failure.

[0061] Preferably, parameters in the electrochemical reaction model, such as exchange current density, transference number, reaction activation energy, etc., can all be set as spatially varying functions.

[0062] Preferably, after the model solution is completed, the cyclic performance prediction results can be output to assist in cathode design and life assessment.

[0063] Preferably, the method is applicable to cathode modeling of all-solid-state batteries, liquid electrolyte batteries, and gel electrolyte battery systems.

[0064] Example: The implementation of the present invention is mainly composed of four core modules: polycrystalline / single-crystal structure modeling, finite element multi-physics field solution, failure criterion evaluation, and electrochemical-mechanical behavior comparative analysis.

[0065] In a preferred embodiment of the present invention, geometric modeling is mainly completed by relying on MATLAB programming in cooperation with the COMSOL Multiphysics modeling interface, which can accurately control particle generation rules, particle size distribution, spatial arrangement, and the distinction of structural types (polycrystalline or single-crystalline).

[0066] First, a spherical envelope domain is created as the carrier of the polycrystalline structure, and its radius R big is set to the approximate scale of the target cathode particles, for example, taking 15 μm.

[0067] Inside the envelope domain, multiple spherical sub-structures are filled in sequence to represent grains, and the radius r of the sphere sphere is randomly generated according to the set normal distribution, and its mean value rmean represents the average grain size (e.g., 1.5 μm), and the standard deviation r sigma represents the width of the particle size distribution (e.g., 0.7 μm).

[0068] To ensure the geometric rationality and physical authenticity of the structure, before generating each new small sphere, it is necessary to judge whether its center coordinates are located within the outer envelope, and at the same time calculate its center distance from all the generated spheres.

[0069] If the shortest center distance is less than the sum of the radii of the currently generated sphere and the existing spheres, it is regarded as overlapping, and the current sphere is discarded and regenerated randomly.

[0070] This process ensures that the filled spheres are geometrically independent of each other through double restrictions (boundary range + non-overlapping) and constitutes a typical approximation of polycrystalline grains.

[0071] Whenever a new small sphere is successfully added, its center position and radius information are automatically recorded, and the current total filling volume is updated.

[0072] The current filling volume is statistically calculated in real time from the sum of the volumes of the generated spheres, and the calculation formula is , represents the radius of the i-th grain, until the filling volume fraction V f meets the set threshold n (e.g., 40%) or the maximum number of sphere limit (e.g., 1×10 6 pieces) until it stops.

[0073] After the filling is completed, the position and size information of all the spheres are recorded, and with the help of the COMSOL LiveLink interface, the geometric information is batch imported into the COMSOL modeling platform.

[0074] In COMSOL, each sphere is constructed as an independent geometric entity, and they are combined into a unified polycrystalline domain through Boolean operations or set operations.

[0075] During the modeling process, to improve the solution accuracy and mesh generation efficiency, all the spheres are default set as "structural bodies" to participate in entity recognition, and local mesh scale control is independently applied in the subsequent mesh control.

[0076] For the single crystal structure modeling, it is simplified to a complete sphere structural body, and its radius is set to the equivalent radius under the condition of equal volume with the polycrystalline model (equivalent radius , is the total volume of the polycrystalline structure).

[0077] This single crystal structure does not contain internal interfaces and there is no grain boundary intersection, and it is used as a control model to evaluate the modulation effect of the polycrystalline structure on the electrochemical-mechanical behavior.

[0078] In a preferred embodiment, all the small sphere generation parameters are set using a standardized input interface, supporting batch adjustment and multi-group comparative modeling.

[0079] To enhance grain diversity, the present invention can also be extended to support the replacement of non-spherical grains, such as ellipsoids, Voronoi polyhedra, etc., to construct a more complex multi-grain structure model.

[0080] To better control the statistical distribution and spatial correlation of different particle sizes, the modeling process also supports the introduction of a regulation function to control the probability density of sphere generation, such as exponential decay distribution or truncated normal distribution.

[0081] During the spatial layout process, the generation algorithm adopts a one-by-one update mechanism instead of a parallel filling strategy to avoid premature regional saturation resulting in insufficient overall structure density.

[0082] After the polycrystal modeling is completed, the structure is exported in the.mphbin format supported by COMSOL, retaining all geometries and their parameter attributes for subsequent coupled physical field simulation.

[0083] To further improve the modeling efficiency and parameter reproducibility, the present invention realizes modular encapsulation of the modeling code, and all key control parameters can be uniformly read in through a JSON or EXCEL configuration file.

[0084] This modeling module can be seamlessly connected with the multi-physical field simulation module to form a continuous process from geometry generation to solution setting, suitable for large-scale model batch construction and parallel simulation analysis.

[0085] In an alternative solution, visual results can be synchronously generated during the modeling process, including three-dimensional structure preview diagrams, particle distribution density diagrams, and particle size distribution statistical diagrams, for quickly evaluating the model construction effect.

[0086] The above three-dimensional structure model is finally divided into several geometric domains in the COMSOL platform, and it supports setting independent material properties, diffusion coefficients, or mechanical moduli for each small sphere to simulate grain heterogeneity.

[0087] To achieve the compatibility of multi-scale modeling, the modeling parameter scale designed by the present invention is adjustable, covering the modeling requirements of the cathode structure from sub-micron particles (0.1μm) to hundred-micron-scale structures (100μm).

[0088] After the above modeling is completed, the "mesh module" can be directly called in the COMSOL platform for automatic mesh generation, or a "free tetrahedron + local refinement" combination strategy can be specified for complex geometries.

[0089] Among them, for the area near the grain boundary, a specific local mesh scale factor (such as 0.1 times the average sphere diameter) can be set to enhance the physical accuracy of this area.

[0090] To ensure the structural comparability and simulation accuracy consistency among different models, a target range (such as no less than 2e5 cells) is uniformly set for the number of mesh cells in all models to improve the stability of the solution.

[0091] If it is necessary to further distinguish the boundary and center positions of each grain in the polycrystalline model, independent identification labels (such as "grain_1", "grain_2") can be set for each sphere, which are used to apply differential parameters or boundary conditions subsequently.

[0092] Without special settings, by default, all polycrystalline grains are specified with the same material parameters, but the optional modeling strategy of "consistent within grains and variable between grains" is retained to facilitate the study of the regulation effect of the inter-grain performance distribution on the overall response.

[0093] In the model saving stage, all structural information, parameter information, and particle identification are synchronously written into the model property tree, supporting subsequent import / export and version iteration.

[0094] The three-dimensional geometric modeling process provided by the present invention has the advantages of fast structure generation, controllable morphology, and traceable parameters, and is applicable to the diverse simulation requirements of the cathode structure of lithium-ion batteries.

[0095] In the next stage, around the above-mentioned constructed structural model, the electrochemical-mechanical coupling modeling process and failure analysis strategy in a multi-physical field environment will be introduced.

[0096] In the COMSOL platform, first, physical interfaces are configured for the polycrystalline structure and the single-crystalline structure respectively, including the diffusion module (Transport of Diluted Species), the solid mechanics module (Solid Mechanics), the electrochemistry module (Electrochemistry), and the interface coupling module (Electrode Surface Coupling).

[0097] Among them, the diffusion module is used to simulate the migration behavior of lithium ions inside the cathode particles, and the control equation of the concentration field changing with time is established by using Fick's second law.

[0098] To achieve a more realistic simulation, the present invention supports setting different diffusion coefficients between different grains to characterize the microheterogeneity in the actual material.

[0099] Regarding the diffusion boundary conditions, the particle surface is set as a constant flux boundary, and the boundary value is calculated according to the battery working current density.

[0100] In the Solid Mechanics module, define the elastic parameters of the cathode material, such as Young's modulus and Poisson's ratio, and activate the strain-concentration coupling term.

[0101] Specifically, the strain Including mechanical strain and chemical strain Two parts, where the chemical strain is defined by the concentration dependence: , where β is the expansion coefficient, which can be determined based on the experimental value (e.g. 2.8×10 -3 )set up; is the initial concentration, usually 0, is the current lithium concentration.

[0102] The electrochemical reaction is partially realized by the Butler-Volmer equation, which takes into account the current density changes caused by the redox reaction at the electrode interface.

[0103] Current density The mathematical expression is: ,in is the exchange current density, is the anode transfer coefficient, is the cathode transfer coefficient, is the Faraday constant, is the interface overpotential, is the gas constant, is the thermodynamic temperature.

[0104] The exchange current density is further defined as: , is the surface lithium concentration, is the electrolyte concentration, is the reaction rate constant.

[0105] In the coupled interface section, the current density is applied as a source term to the governing equations for ion migration and stress through the Electrode Surface Coupling interface.

[0106] In terms of solver settings, the present invention adopts a multi-step solution strategy, first calculating the steady-state initial distribution and then entering the time domain dynamic evolution process.

[0107] In order to improve the stability and convergence efficiency of the solution, the time step, residual tolerance and nonlinear iterator are optimized item by item.

[0108] During the simulation, the following result variables are gradually derived: lithium concentration distribution, von-Mises stress contour, volume strain distribution, and reaction current density distribution.

[0109] In the result analysis stage, the post-processing module analyzes the stress concentration areas inside the particles and further counts the positions of the maximum principal stress and the maximum shear stress.

[0110] To identify potential structural failure areas, the von-Mises criterion and the maximum principal strain criterion are introduced, corresponding to the brittle fracture and plastic deformation failure mechanisms respectively.

[0111] If the von-Mises stress (material fracture strength) in a certain calculation area, it is judged as the failure initiation point.

[0112] By comparing the stress distribution and strain accumulation behavior under different crystal structures, it is found that due to the grain boundary constraint effect in polycrystalline cathodes, earlier and more concentrated crack initiation areas often appear.

[0113] While the single crystal structure has a more uniform strain distribution, but there may be a risk of overall instability under extreme state of charge.

[0114] Based on the simulation data, the average strain values under different states (such as 4.3V and 4.6V charging states, 2.6V discharging state) are extracted, and a bar chart is plotted for comparison.

[0115] Furthermore, by defining the slice cross-section, the stress transfer path and concentration behavior on different grain contact interfaces are analyzed.

[0116] This method supports the rapid identification of weak structural positions and provides a quantitative basis for subsequent structural optimization.

[0117] To verify the reliability of the model, the simulated prediction results can be compared with the SEM morphology after cycling, the stress release curve or the change of charge-discharge voltage platform of the actual battery.

[0118] In addition, parametric sensitivity analysis of input conditions such as different diffusion coefficients and reaction kinetic parameters can also be carried out based on this model.

[0119] Under different parameter perturbations, the robustness and physical rationality of the model prediction results are evaluated.

[0120] If it is necessary to evaluate the cycle life and the evolution trend of structural damage, multiple cycle periods can be repeatedly loaded on the current basis, and damage accumulation variables can be set for statistics.

[0121] The model output data supports being exported in CSV, VTP, VTU formats, which can be used for in-depth visualization analysis on platforms such as Paraview and MATLAB.

[0122] In terms of model deployment, this method supports the parallel operation of multiple groups of models and can be used to construct a database of cathode material structures for machine learning-assisted material design.

[0123] Experimental verification results: Figure 2 For the three-dimensional positive electrode particle structure model constructed in the present invention, the polycrystalline particle model is on the left and the single-crystalline particle model is on the right, showing significant differences in structural continuity and the number of grain boundaries between the two. Figure 3 It is the deformation distribution diagram of polycrystalline (upper) and single-crystalline (lower) positive electrodes under different charge states, showing the deformation evolution behavior on the particle surface during the charge and discharge process. Figure 4 It is the von-Mises stress distribution nephogram of polycrystalline and single-crystalline positive electrodes in different states, which can identify potential stress concentration areas and their change trends. Figure 5 It is the average strain histogram of polycrystalline and single-crystalline positive electrodes in different cycle states, quantifying the response differences between the two structures.

[0124] In summary, the present invention proposes a multi-physics field coupling modeling and failure analysis method for polycrystalline and single-crystalline positive electrodes of lithium-ion batteries. The entire simulation system realizes the full-process integration from geometric construction, electrochemical behavior description to failure risk analysis, and has high repeatability, expandability and engineering application value. The present invention can not only reveal the evolution mechanism of single-crystalline and polycrystalline structures in terms of lithium-ion diffusion, volume expansion and stress response, but also provides strong technical support for the optimization design of high-performance positive electrode materials.

[0125] The embodiment of the present invention also provides a storage medium for storing a computer program, which when executed, at least executes the method as described above.

[0126] The embodiment of the present invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein, the processor is used to execute the computer program to at least execute the method as described above.

[0127] The embodiment of the present invention also provides a processor, which executes a computer program and at least executes the method as described above.

[0128] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but not limited to, these and any other suitable types of memories.

[0129] In several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0130] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in the embodiments of the present invention can be all integrated in a processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in a unit; the above integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.

[0132] Those of ordinary skill in the art will understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0133] Alternatively, if the above integrated units of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. And the foregoing storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0134] The methods disclosed in several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0135] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0136] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0137] The above content is a further detailed description of the present invention in combination with specific preferred implementation manners. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those skilled in the technical field to which the present invention belongs, without departing from the concept of the present invention, several equivalent substitutions or obvious variations can be made, and as long as the performance or use is the same, they should all be regarded as falling within the protection scope of the present invention.

Claims

1. A multi-physical field coupling modeling and failure analysis method for polycrystalline / single crystal cathode materials of lithium-ion batteries, characterized in that, It includes the following steps: S1. Construct a three-dimensional positive electrode particle structure model, where the polycrystalline structure is generated by randomly filling multiple non-overlapping spherical grains within a spherical boundary, and the single-crystalline structure is constructed as a complete sphere; S2. Import the structure model into a finite element simulation platform, and set the multi-physics coupling interfaces of the lithium-ion diffusion model, electrolyte migration model, interfacial electrochemical reaction model, and solid mechanics model; S3. Establish a lithium-ion diffusion kinetics model inside the positive electrode particles, an ion migration model in the electrolyte, and an interfacial electrochemical reaction kinetics model; S4. Establish a lithium concentration-strain-stress coupling relationship, and simultaneously solve the concentration field and the stress field; S5. Set a failure criterion based on the stress distribution to identify potential structural damage regions; S6. Output the multi-physics coupling simulation results of the positive electrode material, and conduct a structure-property correlation analysis of the positive electrode material.

2. The multi-physical field coupling modeling and failure analysis method according to claim 1, wherein In step S1: The construction of the polycrystalline structure is achieved by randomly generating spherical grains that follow a normal distribution, and dynamically excluding grains outside the boundary and overlapping grains during the filling process until the total filling volume reaches a preset ratio and then terminating the generation.

3. The multi-physical field coupling modeling and failure analysis method according to claim 1, characterized in that In step S3: The lithium-ion diffusion model is described by Fick's second law, and diffusion coefficients are independently set for different grains; The electrolyte migration model couples the concentration gradient and the electric field migration term using the Nernst-Planck equation; The interfacial electrochemical reaction model establishes the correlation between the current density and the interfacial overpotential based on the Butler-Volmer kinetic equation.

4. The multi-physical field coupling modeling and failure analysis method according to claim 1, characterized in that In step S4: The concentration-strain coupling relationship is established through a linear interpolation function, mapping the concentration change to the volumetric strain; The stress field is solved by simultaneously considering the concentration-strain relationship through the solid mechanics module, and the linear elastic constitutive model is used to calculate the stress distribution.

5. The multi-physical field coupling modeling and failure analysis method according to claim 1, characterized in that In step S5: The failure criterion includes the von-Mises stress threshold criterion or the principal strain threshold criterion to judge the fracture initiation risk regions at grain boundaries or inside grains.

6. The multi-physical field coupling modeling and failure analysis method according to claim 1, characterized in that In step S6: The output results include the voltage-capacity curve, von-Mises stress nephogram, lithium concentration distribution heat map, and crack position prediction map, and the performance differences are quantified by comparing the simulation results of polycrystalline and single-crystalline structures.

7. The multi-physics coupling modeling and failure analysis method according to claim 1, characterized in that: The structure modeling and multi-physics coupling process are realized through modular codes and parameter configuration files to achieve batch parameter input and control, and realize sensitivity analysis and parallel simulation of multiple groups of models.

8. The multi-physics coupling modeling and failure analysis method according to claim 1, characterized in that: The positive electrode material includes NCM and LCO materials with polycrystalline and single-crystalline structures, and supports extension to ellipsoidal particles, Voronoi structures, or actual morphologies reconstructed based on CT images.

9. The multi-physics coupling modeling and failure analysis method according to any one of claims 1 to 8, characterized in that: The method is further applied to electrode life prediction, crack evolution simulation, and structural optimization design, and the long-term performance degradation is evaluated through multi-cycle loading and statistical evaluation of damage accumulation variables.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the multi-physics field coupling modeling and failure analysis method for the polycrystalline / single-crystalline cathode material of a lithium-ion battery as described in any one of claims 1 to 9.

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