Coarse graining molecular dynamics simulation method in ion transmission process

Abstract

The invention relates to a coarse graining molecular dynamics simulation method for an ion transmission process, which is applied to an electrode-electrolyte system of a lithium ion battery and comprises the following steps: determining a basic model of the electrode-electrolyte system; determining simulation conditions of the electrode-electrolyte system based on the basic model so as to establish an initial model of the electrode-electrolyte system; determining a potential energy function used for describing the action among the components in the electrode-electrolyte system; determining system relaxation of an electrode-electrolyte system according to the initial model and the potential energy function; based on the system relaxation, respectively simulating the lithium ion transmission process in the charging and discharging process of the lithium ion battery; therefore, the simulation efficiency and accuracy of the lithium ion transmission process of the electrode-electrolyte system of the lithium ion battery are improved.

Description

Technical Field

[0001] The present application relates to the technical field of lithium-ion batteries, and in particular to a coarse-grained molecular dynamics simulation method for an ion transport process. Background Art

[0002] Lithium-ion batteries have become a key supporting technology in the fields of energy, information, transportation, and medical care. When lithium-ion batteries are charging and discharging, the lithium ion transmission process includes its diffusion in the electrolyte, as well as its embedding and extraction in the positive and negative active materials. During the transmission process, the microscopic mechanism of the interaction between lithium ions and different components is still unclear, and various factors are coupled with each other, making it extremely difficult to study the effects of specific factors. Molecular dynamics simulation focuses on exploring the effects of various factors on the properties of the system at a microscopic scale. It can not only predict the performance of the system, but also study the microscopic mechanism of the interaction between molecules in the system, or conduct research under extreme conditions, which helps researchers understand the relevant mechanisms and optimize experimental plans and paths.

[0003] In traditional all-atom simulations, each component in the electrodes, electrolytes, and electrode-electrolyte interfaces of lithium-ion batteries is modeled with atoms as the basic unit, and includes complex interactions between atoms. All-atom simulations have the following problems: First, the model preparation process for all-atom simulations is complex and difficult, and it is necessary to describe the properties and positions of each atom of each component and obtain the corresponding topological structure; second, each force field can only describe a specific type of system, and the model has poor mobility, which hinders high-throughput calculations and screening; finally, complex interactions make the force field parameters poorly describe many unconventional molecules, which reduces the accuracy of calculations. Summary of the invention

[0004] Based on this, it is necessary to provide a coarse-grained molecular dynamics simulation method, apparatus, computer equipment, computer-readable storage medium and computer program product for ion transport processes that can improve simulation efficiency and accuracy in order to address the above technical problems.

[0005] In a first aspect, the present application provides a coarse-grained molecular dynamics simulation method for an ion transport process, which is applied to a lithium-ion battery electrode-electrolyte system, and the method comprises:

[0006] Determine the basic model of the electrode-electrolyte system;

[0007] Determining simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system;

[0008] Determine the potential energy function used to describe the interaction between the components in the electrode-electrolyte system;

[0009] Determining a system relaxation of an electrode-electrolyte system according to the initial model and the potential energy function;

[0010] Based on the system relaxation, the lithium ion transport process during the charging and discharging process of the lithium ion battery is simulated respectively.

[0011] In one embodiment, the components of the electrode-electrolyte system include: a current collector, an active material of the positive electrode, an active material of the negative electrode, an electrode-electrolyte interface, a solvent molecule of the electrolyte, a lithium salt, a flame retardant and other additives;

[0012] Wherein, the basic model of determining the electrode-electrolyte system includes:

[0013] Use Material Studio software or Avogadro software to calculate the size of each component in the electrode-electrolyte system of lithium-ion batteries, and model the molecules and ions in the electrode-electrolyte system based on the coarse-grained rules of the coarse-grained force field.

[0014] In one embodiment, the use of Material Studio software or Avogadro software to calculate the size of each component in the lithium-ion battery electrode-electrolyte system, and modeling the molecules and ions in the electrode-electrolyte system based on the coarse-grained rule of the coarse-grained force field, includes:

[0015] Use coarse-grained particles tightly packed into a two-dimensional plane to describe the current collector;

[0016] The structure of the active materials of the positive and negative electrodes is described by using coarse-grained particles tightly packed into a parallel multilayer structure, wherein the interlayer spacing of the active materials of the positive and negative electrodes is calculated using Material Studio software or Avogadro software;

[0017] Using a two-dimensional plane of coarse-grained particles to construct an electrode-electrolyte interface film, wherein the thickness of the electrode-electrolyte interface film is calculated using Material Studio software or Avogadro software;

[0018] Lithium salt, solvent molecules, flame retardant molecules and other additive molecules are coarse-grained into charged, polar or non-polar spherical particles respectively.

[0019] In one embodiment, the determining the simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system includes:

[0020] Use Langevin or Nose-Hoover heating baths to control the temperature of the electrode-electrolyte system;

[0021] The base model was optimized using the LAMMPS pair_style soft command or the LAMMPSminimize command based on the Polak-Ribiere gradient descent algorithm.

[0022] In one embodiment, the determining of the potential energy function for describing the interaction between components in the electrode-electrolyte system comprises:

[0023] Determine the Lennard-Jones potential energy function between the components in the electrode-electrolyte system;

[0024] Determine the Coulum potential energy function between ionic components in the electrode-electrolyte system.

[0025] In one embodiment, determining the system relaxation of the electrode-electrolyte system according to the initial model and the potential energy function comprises:

[0026] The equations of motion are solved using the Verlet-Velocity algorithm.

[0027] In one embodiment, based on the system relaxation, the lithium ion transport process during the charging and discharging process of the lithium ion battery is simulated, including:

[0028] During the charging process of lithium-ion batteries, the positive electrode of the battery and its current collector are uniformly positively charged, and the negative electrode of the battery and its current collector are uniformly negatively charged;

[0029] During the discharge process of lithium-ion batteries, the positive electrode of the battery and its current collector are uniformly negatively charged, and the negative electrode of the battery and its current collector are uniformly positively charged;

[0030] The positive electrode of the battery and the negative electrode of the battery have opposite electrical properties and equal charges.

[0031] In one embodiment, after simulating the lithium ion transport process during the charging and discharging process of the lithium ion battery based on the system relaxation, the method further includes:

[0032] Data processing and visualization of the simulation of lithium-ion transport processes.

[0033] In a second aspect, the present application also provides a coarse-grained molecular dynamics simulation device for an ion transport process, comprising:

[0034] Basic model module, used to determine the basic model of the electrode-electrolyte system;

[0035] An initial model module, used to determine the simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system;

[0036] Potential energy function module, used to determine the potential energy function used to describe the interaction between components in the electrode-electrolyte system;

[0037] A system relaxation module, for determining a system relaxation of an electrode-electrolyte system according to the initial model and the potential energy function;

[0038] The charge and discharge simulation module is used to simulate the lithium ion transmission process during the charge and discharge process of the lithium ion battery based on the system relaxation.

[0039] In a third aspect, the present application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the coarse-grained molecular dynamics simulation method of the ion transport process provided in any of the above embodiments.

[0040] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a coarse-grained molecular dynamics simulation method for an ion transport process as provided in any of the above embodiments.

[0041] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the coarse-grained molecular dynamics simulation method of the ion transport process provided in any of the above embodiments.

[0042] The coarse-grained molecular dynamics simulation method, device, computer equipment, computer-readable storage medium and computer program product of the above-mentioned ion transport process use a coarse-grained model to replace the all-atom model, and through the reasonable simplification of molecular details, shorten the time required for simulation and improve the simulation efficiency. The coarse-grained force field is used to replace the all-atom force field, ignoring the interactions that have little effect on the system, and using classical physical potential energy to describe the main interactions, so that the model has a wider range of applications and can simulate various types of molecular systems. At the same time, the potential function of the coarse-grained simulation is smoother than that of the all-atom force field, and a larger time step can be used. In addition, high-performance parallel computing is used to simulate complex system parameters, which improves the efficiency and accuracy of the simulation. Finally, the analysis program is written in Fortran language to analyze the output structural data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of a model of a lithium-ion battery electrode-electrolyte system in one embodiment;

[0045] Figure 1A for Figure 1 A colorless image of

[0046] Figure 2 A schematic diagram of a process of a coarse-grained molecular dynamics simulation method for an ion transport process in one embodiment;

[0047] Figure 3a This is a structural diagram of a lithium-ion battery before charging or discharging;

[0048] Figure 3A for Figure 3a A colorless image of

[0049] Figure 3b This is the structural diagram of a fully charged lithium battery at a voltage of 4.10V;

[0050] Figure 3B for Figure 3b A colorless image of

[0051] Figure 3c This is the simulated structure diagram of the lithium battery after it is fully discharged when the working voltage is 4.10V;

[0052] Figure 3C for Figure 3c A colorless image of

[0053] Figure 3d This is a simulated structural diagram of a lithium battery in a 0.41V low voltage protection state;

[0054] Figure 3D for Figure 3d A colorless image of

[0055] Figure 4 The structure diagram of the lithium-ion battery after charging;

[0056] Figure 4A for Figure 4 A colorless image of

[0057] Figure 5 It is the radial distribution function between different components in the electrolyte, after the lithium battery is charged, and after the lithium battery is discharged;

[0058] Figure 6 It is one of the kinetic data of the electrode-electrolyte system of lithium-ion batteries;

[0059] Figure 7 This is the second kinetic data of the lithium-ion battery electrode-electrolyte system. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] Electrodes and electrolytes are key components of lithium-ion batteries. During the charge and discharge process of lithium-ion batteries, lithium ions are repeatedly embedded in and out of the electrode material, and enter the electrolyte through the electrode-electrolyte interface. The transfer process of lithium ions between electrodes and electrolytes is one of the core processes of battery operation, involving multiple aspects such as ion transport and charge transfer, including diffusion, migration, polarization, etc. The transfer process of lithium ions between electrodes and electrolytes is an extremely complex and multi-level process. When studying the lithium ion transport process, it is necessary to comprehensively consider the interaction of various physical and chemical mechanisms in order to thoroughly understand and control the performance and life of the battery. At present, the academic community lacks efficient molecular-level simulation research methods for the lithium ion transfer process in the electrode-electrolyte system, and the microscopic mechanism of action in the lithium ion transfer process is still unclear.

[0062] In previous studies, researchers usually use molecular dynamics simulations of all-atom models to examine the microscopic mechanisms of the system. The all-atom molecular dynamics simulation method uses atoms as the basic unit, and the force field used is usually a set of interatomic interaction functions, including non-bonded interactions between atoms, bonded interactions, and constraints. Non-bonded interactions include van der Waals interactions and Coulomb electrostatic interactions. Bonded interactions include the stretching potential energy of chemical bonds between atoms, the bending potential energy of bond angles, and the torsion potential energy of dihedral angles. For example, in the all-atom model, dimethyl carbonate (DMC) is constructed as a molecule composed of carbon, oxygen, and hydrogen atoms.

[0063] The specific steps of the all-atom modeling and simulation process are as follows:

[0064] (1) Describing the properties and positions of atoms in a molecule: For example, for dimethyl carbonate, it is necessary to describe the atomic types of the three oxygen atoms, six hydrogen atoms, and three carbon atoms as well as their atomic properties such as mass and charge.

[0065] (2) Describing the topological information in the system: For example, for dimethyl carbonate, it is necessary to describe multiple carbon-oxygen bonds and carbon-hydrogen bonds, as well as more bond angles. When the topological structure becomes complex, the complexity of the simulation increases exponentially.

[0066] (3) Assigning atomic types to atoms in the molecule according to different chemical environments: In step (1), usually only the element of the atom is given, but the all-atom force field usually distinguishes atoms in different chemical environments, so it is necessary to map the element to the atomic type.

[0067] (4) Assign different force field types to the topological structures in the molecule: According to the newly mapped atom types, assign the types of topological information such as bonds, bond angles, and dihedral angles.

[0068] (5) Preliminary optimization of molecular conformation: Preliminary optimization of the molecular structure to eliminate erroneous structures that are difficult to optimize.

[0069] (6) According to the experimental data, the concentration of each component is determined, and the optimized molecular models of different components are combined into a simulation input file. After determining the boundary conditions and simulation parameters, the molecular dynamics simulation software is used to simulate and record and statistically analyze the simulation data.

[0070] As mentioned above, all-atom simulation has many defects, such as difficult model construction, complex force field and poor accuracy, poor system generalization ability, and time-consuming system convergence. Specifically,

[0071] (1) The model construction of the all-atom molecular dynamics simulation method is difficult. There are many types of atoms and atomic interactions in the all-atom simulation. In the all-atom molecular dynamics simulation method, each molecule needs to accurately determine its atomic composition and topological structure, and then model it according to the chemical structure or chemical environment defined by the all-atom force field, and finally optimize the molecular structure to eliminate the erroneous configuration that is difficult to optimize. Therefore, the model setting process is difficult.

[0072] (2) The time and space scales that can be investigated by the all-atom molecular dynamics simulation method are extremely limited. The all-atom simulation introduces more particle types and more sophisticated interactions. The relaxation of the system is extremely time-consuming and can only be carried out at extremely small time and space scales, and it is impossible to achieve simulation research at the mesoscopic scale. Due to the small time and space scales of the simulation, the all-atom molecular dynamics simulation method is difficult to achieve a systematic study of the microstructure and dynamic properties of lithium-ion battery electrolytes.

[0073] (3) The development level of force fields in all-atom molecular dynamics simulation methods is limited. Due to incomplete function forms and the lack of many physical quantities, the portability and predictive power of traditional force fields are poor, and the fitting results on a certain system are difficult to extrapolate to other systems. Some unconventional molecules are difficult to model. Even for conventional molecules, there are significant differences in the research results obtained under different force field parameter conditions, and the accuracy of simulation results and conclusions is low. The simulation results of lithium-ion battery electrolyte systems calculated and output by all-atom molecular dynamics simulation methods are difficult to effectively compare with macroscopic performance.

[0074] In order to overcome the above problems, in an embodiment of the present application, a coarse-grained molecular dynamics simulation method for studying the ion transport process of a lithium-ion battery electrode-electrolyte system is provided. The coarse-grained model described in the embodiment of the present application ignores the molecular details that have little effect on the properties of the system, and uses several atomic groups as the basic units of simulation for modeling and simulation. For example, when the model describes solvent molecules and additive molecules such as flame retardants, it ignores the specific molecular structure information and describes them as spherical coarse-grained particles. Each coarse-grained particle can represent several atoms, several monomers or even molecular fragments, and uses classical physical interactions to describe the interactions between molecules. For example Figure 1 As shown, Lennard-Jones particles can be used to simulate anions, cations and solvent molecules, and their dielectric constants can be set according to the organic solvent used in the electrolyte. The positive and negative current collectors of lithium-ion batteries are stacked by coarse-grained particles, and the positive electrode (Cathode, C) and negative electrode (Anode, A) active materials are multilayer boards stacked by coarse-grained particles. At the same time, the electrode-electrolyte interface between the electrode active material and the electrolyte solution is stacked by coarse-grained particles. This interface only allows lithium ions to pass through, and does not allow anions and solvent molecules in the electrolyte to pass through. This model can effectively simulate the interaction between components, the fluid mechanics and solvation effects of the solvent, and reduce the number of system particles, thereby reducing the amount of calculation.

[0075] The present application embodiment proposes a coarse-grained molecular dynamics simulation method, which aims to solve the technical problems existing in the study of ion transport in the electrode-electrolyte system of lithium-ion batteries. This method can deeply study the transport mechanism of lithium ions between the electrode and the electrolyte.

[0076] The coarse-grained molecular dynamics simulation method of the ion transport process provided in the embodiments of the present application can be applied to the electrode-electrolyte system of a lithium-ion battery.

[0077] In an exemplary embodiment, Figure 2 As shown, a coarse-grained molecular dynamics simulation method for ion transport process is provided, and the method is applied to the lithium ion transport process of the lithium ion battery electrode-electrolyte system as an example for explanation, including the following S102 to S114. Among them:

[0078] S102, determine the basic model of the electrode-electrolyte system.

[0079] For example, Material Studio software or Avogadro software can be used to calculate the size of each component in the lithium-ion battery electrode-electrolyte system, and the molecules and ions in the electrode-electrolyte system can be modeled based on the coarse-grained rules of coarse-grained force fields such as Martini force field or OPLS-UA force field.

[0080] The current collector is an important component in lithium-ion battery cells. Its main function is to carry the active substances of the positive and negative electrodes, and to collect the current generated by the chemical reactions of the active substances to form a larger current for external output, thereby completing the conversion process of chemical energy into electrical energy.

[0081] Exemplarily, in the basic model, the current collector is a two-dimensional plane formed by tightly packed coarse-grained particles.

[0082] The active material of the positive electrode is usually lithium cobalt oxide with a hexagonal layered structure (the corresponding interlayer spacing is 0.468nm) or a ternary material, and the active material of the negative electrode is generally graphite with a hexagonal or rhombus layered structure (the corresponding interlayer spacing is 0.3354nm) and other materials. The common feature of these materials is that they have a lamellar structure.

[0083] For example, in the basic model, coarse-grained particles are tightly packed into a parallel multilayer structure to describe the structure of the active materials of the positive and negative electrodes. In the process of establishing the basic model, the interlayer spacing of lithium cobalt oxide and graphite can be calculated using Material Studio software or Avogadro software, and the interlayer spacing of lithium cobalt oxide and graphite in the basic model is set to 3 ~ 4Å based on this, and each layer of the layer structure is composed of N×N spherical particles.

[0084] Among them, in order to improve the calculation efficiency, the topological structure, position and local charge distribution of the atoms or molecules of the components in the layer can be ignored, and several atomic or molecular groups can be coarse-grained into single spherical particles, and their physical and chemical properties can be described using coarse-grained force fields. According to the actual simulation needs, the number of spherical particles and layers of each layer of the active materials of the positive and negative electrodes can be set, and the setting of the periodic boundary conditions of the simulation box can be achieved by having the system particles leave the box from one end and enter the box from the other end. In this way, the layered structure of the active materials of the positive and negative electrodes can be simulated more accurately, and the transport of ions can be better studied during the simulation of the charge and discharge process of lithium-ion batteries.

[0085] For example, in the basic model, the electrode-electrolyte interface (SEI or CEI) membrane between the electrode active material and the electrolyte solution is also constructed using a two-dimensional plane stacked with coarse-grained particles. The interface is perpendicular to the electrode active material and parallel to the current collector, allowing only lithium ions to pass through. In the process of establishing the basic model, the thickness of the electrode-electrolyte interface membrane can be calculated using MaterialStudio software or Avogadro software, and the size of the SEI or CEI membrane can be set according to the size of the vertical interface of the layered structure of the electrode active material, that is, the SEI or CEI membrane is composed of N×N spherical particles laid flat along a structure perpendicular to the electrode layer, and its area is equal to the size of the vertical interface.

[0086] Among them, in order to ensure the selective permeability of SEI or CEI membrane to lithium ions, the interaction between lithium ions and SEI or CEI membrane can be ignored, and Lenard-Jones potential is used to describe the van der Waals interaction and volume exclusion effect between anions, solvent molecules, flame retardants and other additives and SEI or CEI membrane. In this way, it is ensured that lithium ions can pass through SEI or CEI membrane to transfer between electrode and electrolyte, while other particles cannot cross the membrane.

[0087] Exemplarily, in the process of establishing the basic model, Material Studio software or Avogadro software can be used, and based on the coarse-graining rules of the coarse-graining force field, lithium salts, solvent molecules, flame retardant molecules and other additive molecules can be coarse-grained into charged, polar or non-polar spherical particles.

[0088] That is, the electrode structure design provided in the embodiments of the present application simulates the electrode current collector as a single-layer plate stacked by coarse-grained particles, the negative electrode active material and the positive electrode active material are simulated as multi-layer parallel plates stacked by coarse-grained particles, the electrode-electrolyte interface is simulated as a single-layer plate perpendicular to the current collector, and lithium salts, solvent molecules, flame retardants and other additives are simulated as spherical particles.

[0089] S104, determining simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system.

[0090] Exemplarily, a Langevin heating bath or a Nose-Hoover heating bath is used to control the temperature of the electrode-electrolyte system, and the basic model is optimized using a LAMMPS pair_style soft command or a LAMMPSminimize command based on a Polak-Ribiere gradient descent algorithm.

[0091] The side length of the simulation box is set, and periodic boundary conditions are introduced in the x- and y-axis directions, while the z-axis direction is set as a non-periodic boundary condition.

[0092] For example, the concentration of each component is calculated according to the actual conditions, and the above components are placed in the box using a program written in Perl language, and a read-in information file for LAMMPS software simulation is generated. For example, since the randomly generated model cannot avoid particle overlap, the Soft potential in the LAMMPS software is used to minimize the energy of the system and slowly push the overlapping particles away. The expression of the Soft potential is:

[0093]

[0094] Where A is the energy coefficient, r cis the cutoff radius. The system is set as a canonical ensemble (constant number of particles, constant volume, constant temperature NVT), and the system temperature is controlled by a Langevin heat bath or a Nose-Hoover heat bath. The Soft potential is run for 10 5 The structure is adjusted by the step size to ensure that there is no overlap between particles and that each component is dispersed more evenly in the system. After the above optimization, the initial model of the lithium-ion battery electrode-electrolyte system will be obtained.

[0095] That is, the simulation dimension of the embodiment of the present application is set to three dimensions, and the boundary conditions adopt periodic boundary conditions. The ensemble used for the simulation is a canonical ensemble, and the system temperature is controlled by a Langevin heat bath or a Nose-Hoover heat bath. In the process of model construction, a molecular dynamics simulation based on Soft potential is implemented to ensure the uniform distribution of each component in the model.

[0096] S106, determining a potential energy function for describing the interaction between components in the electrode-electrolyte system.

[0097] Molecular dynamics simulation updates the position and velocity of particles according to the forces they are subjected to, and the forces on the particles are determined by the interaction potential function. Therefore, the choice of potential function determines the evolution trajectory of the system over time. A series of mathematical functions and parameters that describe the interaction potential of particles in the system is called a force field. The potential of coarse-grained particles can be set according to the Martini force field. In the coarse-grained model, only the external field and the interaction between two bodies can be considered, and the contribution of multi-body interactions of three or more particles is not considered. The interaction between particles is divided into two parts: short-range interaction represented by Lennard-Jones potential (ULJ) and electrostatic interaction represented by Coulum potential (UCoul). The formula for calculating the Lennard-Jones potential energy between components is as follows:

[0098]

[0099] In the formula, r ij is the distance between particles i and j, ϵ and σ are the and The energy and length parameter between c is the cutoff radius. The potential function parameters between the components are calculated by the Lorentz-Berthelot mixing law. The Lorentz-Berthelot mixing law is expressed as:

[0100] ;

[0101] In the formula, the subscripts i and j represent different types of particles. The Coulum potential energy between ionic components is calculated as follows:

[0102]

[0103] In the formula, q i ,q j are the valence states of ions i and j respectively, and e is the elementary charge Coulomb, ε 0 is the vacuum dielectric constant, ε r is the relative dielectric constant of the solution. Depending on the different organic solvents used in the lithium-ion battery electrolyte, the dielectric constant can be set to different values.

[0104] Exemplarily, the Coulum potential energy calculation is implemented using a PPPM (Particle-Particle Particle-Mesh) algorithm, an Ewald summation algorithm, or a PME algorithm.

[0105] That is, the coarse-grained simulation force field for lithium-ion battery electrolyte in the embodiment of the present application is composed of the following three classical physical potential energies: electrostatic interaction represented by Coulum potential, short-range interaction represented by Lennard-Jones potential, and polymer particle bonding interaction represented by FENE potential. The potential function parameters between the components are calculated by the Lorentz-Berthelot mixing rule. In addition, Lennard-Jones particles are used to simulate anions and cations. At the same time, the system uses Lennard-Jones particles as solvent molecules, and sets different dielectric constants according to the type of solvent molecules.

[0106] S108, determining the system relaxation of the electrode-electrolyte system according to the initial model and the potential energy function.

[0107] The system relaxation of the coarse-grained simulation of lithium-ion battery electrolyte based on molecular dynamics uses the Verlet-Velocity algorithm to solve the equations of motion.

[0108] Among them, LAMMPS software and soft repulsive potential energy are used to perform energy minimization operations and pre-equilibrium simulations to obtain an initial structure in which each component is uniformly dispersed. After that, the coarse-grained force field optimized for lithium-ion battery electrolyte described in S104 is used, and molecular dynamics simulations are performed using LAMMPS software and the simulation conditions set in S104 to calculate the system potential energy and particle forces, determine the particle position at the next moment according to the Verlet-Velocity integration algorithm, and count the calculation results at each moment until the electrolyte part of the system reaches a state of equilibrium.

[0109] In addition, the method of realizing the initial model of random configuration can be constructed by using languages ​​such as Fortran, Python, etc. in addition to the Perl language. The algorithm used is not limited, and the ultimate goal is to obtain initial structures with different structures.

[0110] S110, simulating the lithium ion transport process during the charging process of the lithium ion battery based on system relaxation.

[0111] The charging and discharging process of lithium-ion batteries is achieved by setting the positive and negative charges carried by the active materials of the current collector and the electrode, wherein the electrodes include a positive electrode and a negative electrode, and the positive electrode and the negative electrode have opposite electrical properties and equal charges.

[0112] Lithium-ion battery charging process:

[0113] During the charging process of a lithium-ion battery, electrons flow from the power source into the negative electrode of the battery, causing the negative electrode to carry a negative charge. In the positive electrode, lithium ions lose electrons, escape from the positive electrode, and are embedded in the negative electrode active material through the electrode-electrolyte interface and the electrolyte. This process causes the positive electrode to carry a positive charge. In the simulation method of the embodiment of the present application, in order to describe the charging process, the current collector and active material of the negative electrode can be made to carry a negative charge, and the current collector and active material of the positive electrode can be made to carry a positive charge, so that the lithium ions move toward the negative electrode under the action of electrostatics, and finally embed into the negative electrode active material. Molecular dynamics simulation can be performed using the LAMMPS software and the ensemble principle in S104 and the potential energy function in S106 to calculate the system potential energy and particle force, and determine the particle position at the next moment according to the Verlet-Velocity integration algorithm or the Verlet and Frog-Leap algorithm to obtain the three-dimensional coordinates of the molecular trajectory; at the same time, sampling is performed along the system evolution trajectory at set intervals, and ensemble statistics are performed.

[0114] S112, simulating the lithium ion transport process during the discharge process of the lithium ion battery based on system relaxation.

[0115] The charging and discharging process of lithium-ion batteries is achieved by setting the positive and negative charges carried by the active materials of the current collector and the electrode, wherein the electrodes include a positive electrode and a negative electrode, and the positive electrode and the negative electrode have opposite electrical properties and equal charges.

[0116] Lithium-ion battery discharge process:

[0117] Similar to the charging process, in the simulation method of the embodiment of the present application, the negative electrode current collector and the negative electrode material will be positively charged during the discharge process, and the positive electrode current collector and the active material will be negatively charged, so that the lithium ions move toward the positive electrode under the action of electrostatics and are finally embedded in the positive electrode active material. During the simulation process, the potential energy function described in S106 can be used to perform molecular dynamics simulation, record the three-dimensional coordinates of the molecular trajectory, and perform ensemble statistics.

[0118] In the simulation method of the embodiment of the present application, multiple cycles of charge and discharge processes can be added according to actual simulation needs, that is, the embodiment of the present application can simulate and study the charge and discharge cycle of the lithium battery electrode-electrolyte model based on S110 and S112. In addition, in addition to using the set charge command to make the positive and negative electrodes carry charges of the same amount and opposite electrical properties, you can also use fixefield to make the model have a uniform electric field to simulate the electrostatic effect between the positive and negative electrodes during charging and discharging.

[0119] S114, performing data processing and visualization on the simulation of the lithium ion transfer process.

[0120] Use Fortran, Python or C / C++ to write programs to calculate the structure factor, radial distribution function between components, embedding number and charge and discharge rate of lithium-ion battery electrolyte in statistical equilibrium state, and use Ovito software to intuitively present the microstructure of lithium-ion battery electrolyte.

[0121] The radial distribution function is defined as:

[0122]

[0123] Where r is the distance between the particle pairs, Δr is a parameter representing the resolution of the function, N is the total number of particles in the system, and V is the volume of the system. The negative electrode embedding number is defined as the change in the number of lithium ions embedded in the negative electrode active material over time, defined as:

[0124]

[0125] Among them, M Li + (t) is the number of lithium ions whose z coordinate is less than the solid electrolyte interface at time t. Similarly, the positive electrode embedding number N can be defined as C , is the number of lithium ions whose coordinates in the z direction are greater than the positive electrode electrolyte interface. The charging time of a lithium battery can be defined as the time required for lithium ions to enter the negative electrode active material from the electrolyte solution and reach equilibrium. At the same time, the movement speed of lithium ions can be defined as the distance from the center of mass of the electrolyte solution to the center of mass of the negative electrode active material divided by the average charging time of lithium ions. Similarly, the discharge time of lithium ions can be defined as the time required for lithium ions to escape from the negative electrode active material until they enter the positive electrode material and reach equilibrium. In this case, the movement speed of lithium ions is the distance from the center of mass of the negative electrode active material to the center of mass of the electrolyte solution divided by the average discharge time of lithium ions.

[0126] In the embodiments of the present application, the physical quantities characterized are not limited to those listed currently, but also include the thermodynamic or kinetic quantities required in the specific examples. In addition, various parameters of the lithium-ion battery electrolyte model are calculated as needed. In the MD simulation, physical quantities such as radial distribution function and structure factor are used to analyze the properties of the local structure and macrostructure of the system, and the number of positive and negative electrode embeddings, charge and discharge time, ion migration speed, etc. are used to characterize the kinetic information of each component.

[0127] In an exemplary embodiment, a simulation case of the ion transport process of a lithium-ion battery electrode-electrolyte at different voltages is provided, referring to Figure 2 , the electrode-electrolyte simulation system of lithium-ion battery is calculated by molecular dynamics using the following method:

[0128] (1) Determine the basic model of the electrode-electrolyte system including:

[0129] The electrolyte of lithium-ion batteries is composed of three main components: 1 mol / L Li + With 1 mol / L PF 6 - , 15 mol / L organic solvent molecule ethylene carbonate (DMC). + The diameter is 1.52Å, PF 6 - The diameter is 5.82Å, and the DMC diameter is 8.69Å. The basic unit of coarse-grained σ = 3Å is selected, so Li + The diameter is 0.5σ, PF 6 - The diameter is 1.94σ, and the diameter of DMC is 2.86σ. The sizes of each component are calculated by Material Studio software, and the lithium salt, solvent molecules and additives are coarse-grained into polar or non-polar spherical particles based on the Martini force field. Figure 1 As shown, the electrode current collector is simulated as a single-layer plate stacked by coarse-grained particles, the negative electrode active material (Anode, A) and the positive electrode active material (Cathode, C) are simulated as multi-layer parallel plates stacked by coarse-grained particles, the electrode-electrolyte interface is simulated as a single-layer plate parallel to the current collector, and the lithium salt and solvent molecules are simulated as spherical particles.

[0130] (2) Determine the simulation conditions of the electrode-electrolyte system based on the basic model to establish the initial model of the electrode-electrolyte system, including:

[0131] Set the side length of the simulation box to L x = 11σ, L y = 11σ and L z= 33σ, and periodic boundary conditions are introduced in the x and y directions. Since the two ends of the simulation box in the z direction are battery electrodes and current collectors, the periodic boundary conditions in the Lammps software are set to fixed. At the same time, the kspace_modify slab command is used to set the calculation mode of long-range electrostatic effects on non-periodic boundaries. The concentrations of each component are calculated according to the actual conditions, where represents Li + and PF 6 - The number of particles representing DMC is 20, and the number of particles representing DMC is 300. The current collector, positive and negative active materials and electrode-electrolyte interface in the lithium-ion battery are placed according to the above rules. The components in the electrolyte are randomly placed between the electrode-electrolyte interfaces of the positive and negative electrodes using a program written in Perl language, and finally a read-in information file is generated for LAMMPS software simulation. Since the randomly generated model cannot avoid particle overlap, the Soft potential in the LAMMPS software is required to minimize the energy of the system and slowly push the overlapping particles apart. The system is set to a canonical ensemble (constant number of particles, constant volume, constant temperature NVT), and a Langevin heat bath is used to control the system temperature, and the temperature is set to T = 1.0. The energy coefficient of the first stage is set to A = 1.0, and 10 5 The structural adjustment process of the time step ensures that there is no overlap between particles; the energy coefficient of the second stage is set to A = 1.0 and is performed for 10 6 The simulation of the time step makes each component dispersed more evenly in the system. After the above optimization, the initial model of the electrolyte part in the lithium battery can be obtained.

[0132] (3) The potential energy function used to describe the interaction between the components in the electrode-electrolyte system includes:

[0133] The interaction between particles is divided into two parts, which are composed of the Coulum potential ( ) represents the electrostatic interaction, the Lennard-Jones potential ( ) represents the short-range interaction. + and PF 6 - There is no short-range interaction between the two main salt particles and the electrode-electrolyte interface, and the mass (m = 1) and radius (σ = 1) of all other particles, and the energy parameter of the short-range interaction is 𝜖 = 1.0.

[0134] (4) Determine the system relaxation of the electrode-electrolyte system based on the initial model and potential energy function, including:

[0135] First, we can use LAMMPS software and soft repulsive potential to perform energy minimization and pre-equilibrium simulation to obtain the initial structure of the lithium battery electrolyte model with uniform dispersion of each component. Then, we use the coarse-grained force field optimized for the electrolyte described in the third step, and use LAMMPS software and the simulation conditions set in the second step to perform molecular dynamics simulation. The simulation step size is set to τ = 0.002, and the simulation time is 10 6 Time step. While simulating, the system potential energy and particle forces are calculated, the particle position at the next moment is determined according to the Verlet-Velocity integration algorithm, and the calculation results at each moment are counted until the system reaches equilibrium.

[0136] (5) Based on system relaxation, the simulation of lithium ion transport during the charging process of lithium ion batteries includes:

[0137] Using the set charge command in LAMMPS, the positive charge can be evenly distributed between the positive electrode and the current collector, and the negative charge can be evenly distributed between the negative electrode and its current collector. This charge distribution shows that the positive and negative electrodes have opposite electrical properties and equal charges. In the simulation, the battery voltage can be set to 0.41V, 2.05V, 4.10V, and 8.20V, respectively. Relatively speaking, the charge of each coarse-grained particle in the current collector and the active material is e = 0.136, 1.36, and 2.72. In this example, let U 0 = 4.10 V. Molecular dynamics simulations were performed using the LAMMPS software and the ensemble principle in the second step and the potential energy function in the third step. The simulation step was set to τ = 0.002 and the simulation time was 10 6 Time step. Calculate the system potential energy and particle force, determine the particle position at the next moment according to the Verlet-Velocity integration algorithm, and obtain the three-dimensional coordinates of the molecular trajectory; at the same time, sample the system evolution trajectory, output the coordinates and velocity of each particle in the system at a frequency of once every 1000 steps, and perform ensemble statistics.

[0138] (6) Based on system relaxation, the lithium ion transport process during the discharge of lithium-ion batteries is simulated, including:

[0139] Use the set charge command of LAMMPS to reverse the electrical properties of the positive and negative electrodes and the current collector of the battery. At this time, the positive electrode is negatively charged and the negative electrode is positively charged, while the charge remains unchanged. Use the LAMMPS software and the ensemble principle in the second step and the potential energy function in the third step to perform molecular dynamics simulation. The simulation step size is set to τ = 0.002 and the simulation time is 10 6 time step to obtain the three-dimensional coordinates of the molecular trajectory; at the same time, the system evolution trajectory is sampled, and the coordinates and velocity of each particle in the system are output at a frequency of once every 1000 steps, and ensemble statistics are performed.

[0140] (7) Data processing and visualization of the simulation of lithium ion transport process include:

[0141] As shown in Figure 3, using Ovito software, the microstructure of the electrolyte at different temperatures can be intuitively presented. Figure 4 This is the structure diagram of a lithium-ion battery after charging. Figure 4 middle, Figure 4 aThe electrolyte concentration is ρ / ρ 0 = 0.1; Figure 4 bThe electrolyte concentration is ρ / ρ 0 = 1.0; Figure 4 cThe electrolyte concentration is ρ / ρ 0 = 5.0; Figure 4 dThe electrolyte concentration is ρ / ρ 0 = 10.0, and a program written in Fortran was used to calculate the radial distribution function of lithium ions to characterize the ion composition distribution. Figure 5 is the radial distribution function between different components in the electrolyte, after the lithium battery is charged, and after the lithium battery is discharged. Figure 5 middle, Figure 5 a: radial distribution function between Li+ and negative electrode; Figure 5 b: radial distribution function between Li+ and the positive electrode; Figure 5 c: radial distribution function between Li+ and solvent molecules; Figure 5 d: Radial distribution function between Li+ and acid radical ions. The mean square displacement of lithium ions is calculated using a program written in Fortran to characterize the diffusion rate of ions in the electrolyte of lithium batteries. Figure 6 It is one of the kinetic data of the electrode-electrolyte system of lithium-ion batteries; Figure 6 a is the curve of the change of lithium ion negative electrode insertion number over time at different voltages. Figure 6 b is the charging time of lithium-ion batteries at different voltages and the diffusion rate of lithium ions during the charging process; Figure 6 c is the curve of the change of lithium ion negative electrode insertion number over time under different electrolyte concentrations. Figure 6 d is the charging time of lithium-ion batteries under different electrolyte concentrations and the diffusion rate of lithium ions during the charging process.

[0142] In an exemplary embodiment, a simulation case of the ion transport process between the electrode and the electrolyte of a lithium-ion battery under different lithium salt concentrations is provided, referring to Figure 2 The lithium-ion battery electrolyte simulation system is subjected to molecular dynamics calculation by the following method:

[0143] (1) Determine the basic model of the electrode-electrolyte system including:

[0144] The electrolyte contains three main components: lithium salt + With PF 6 - The composition is 15 mol / L of organic solvent molecule dimethyl carbonate (DMC). In different systems, the concentrations of lithium salts are selected as 0.1 mol / L, 0.5 mol / L, 1.0 mol / L, 2.0 mol / L, 5.0 mol / L and 10.0 mol / L. + The diameter is 1.52Å, PF 6 - The diameter is 5.82Å and the DMC diameter is 8.69Å. Select the basic unit of coarse-grained , so Li + The diameter is 0.5σ, PF 6 - The diameter is 1.94σ, and the diameter of DMC is 2.86σ. The size of each component is calculated by Material Studio software, and the lithium salt, organic molecules and additives are coarse-grained into polar or non-polar spherical particles based on the Martini force field. Figure 1 As shown, the electrode current collector is simulated as a single-layer plate stacked by coarse-grained particles, the negative electrode active material (Anode, A) and the positive electrode active material (Cathode, C) are simulated as multi-layer parallel plates stacked by coarse-grained particles, the electrode-electrolyte interface is simulated as a single-layer plate parallel to the current collector, and the lithium salt and solvent molecules are simulated as spherical particles.

[0145] (2) Determine the simulation conditions of the electrode-electrolyte system based on the basic model to establish the initial model of the electrode-electrolyte system, including:

[0146] Set the side length of the simulation box to L x = 11σ, L y = 11σ and L z = 33σ, and introduce periodic boundary conditions in the x and y directions. Since the two ends of the simulation box are battery electrodes and current collectors in the z direction, the periodic boundary conditions are set to fixed. At the same time, the kspace_modify slab command is used to set the calculation mode of long-range electrostatic effects on non-periodic boundaries. The concentrations of each component are calculated according to the actual conditions, among which Li + and PF 6 - The number of particles of ρ is 2, 10, 20, 40, 100 and 200, and the number of particles of DMC is 896, 880, 860, 820, 700 and 500 respectively. 0 = 1.0 mol / L, corresponding to Li + and PF 6 -The number of particles is 20. The above components are randomly placed in the box using a program written in Perl language, and the read-in information file for LAMMPS software simulation is generated. The Soft potential in LAMMPS software is used to minimize the energy of the system and slowly push the overlapping particles apart.

[0147] The system was set as a canonical ensemble (constant number of particles, constant volume, constant temperature NVT), and the system temperature was controlled by a Langevin heat bath, which was set to T = 1.0. In the first stage, a weaker energy coefficient A = 30 was set for 10 5 The structure is adjusted in the simulation of the time step to ensure that there is no overlap between particles; the energy coefficient of the second stage is set to A = 60, and 10 6 The step size of the simulation is 200 μm, which makes each component dispersed more evenly in the system. After the above optimization, the initial model of the coarse-grained simulation of the electrolyte can be obtained.

[0148] (3) The potential energy function used to describe the interaction between the components in the electrode-electrolyte system includes:

[0149] We can only consider the interaction between the external field and the two bodies, and ignore the contribution of the multi-body interaction of three or more particles. The interaction between particles is divided into two parts, which are composed of the Coulum potential (U Coul ) represents the electrostatic interaction, the Lennard-Jones potential (U LJ ) represents the short-range interaction. The potential function parameters between the components are calculated by the Lorentz-Berthelot mixing rule. The interaction parameters between the components are the same as those in Example 1.

[0150] (4) Determine the system relaxation of the electrode-electrolyte system based on the initial model and potential energy function, including:

[0151] In the second step, LAMMPS software and soft repulsive potential can be used to perform energy minimization and pre-equilibrium simulation to obtain the initial structure of the lithium battery electrolyte model with uniform dispersion of each component. The soft potential is cancelled and replaced with the coarse-grained force field optimized for the electrolyte described in the third step. Molecular dynamics simulation is performed using LAMMPS software and the simulation conditions set in the second step. The simulation step size is set to τ = 0.002 and the simulation time is 10 6 Time step. While simulating, the system potential energy and particle forces are calculated, the particle position at the next moment is determined according to the Verlet-Velocity integration algorithm, and the calculation results at each moment are counted until the system reaches equilibrium.

[0152] (5) Based on system relaxation, the simulation of lithium ion transport during the charging process of lithium ion batteries includes:

[0153] By using the set charge command in LAMMPS, the positive electrode and its current collector can be made to have a uniform positive charge, and the negative electrode and its current collector can be made to have a uniform negative charge. This charge distribution shows that the positive and negative electrodes have opposite electrical properties and equal charges. In the simulation, the charge of each current collector and coarse-grained particle in the active material can be set to e = 1.36. Molecular dynamics simulations were performed using the LAMMPS software and the ensemble principle in the second step and the potential energy function in the third step. The simulation step size was set to τ = 0.002 and the simulation time was 10 6 Time step. Calculate the system potential energy and particle force, determine the particle position at the next moment according to the Verlet-Velocity integration algorithm, and obtain the three-dimensional coordinates of the molecular trajectory; at the same time, sample the system evolution trajectory, output the coordinates and velocity of each particle in the system at a frequency of once every 1000 steps, and perform ensemble statistics.

[0154] (6) Based on system relaxation, the lithium ion transport process during the discharge of lithium-ion batteries is simulated, including:

[0155] The set charge command of LAMMPS is used to reverse the electrical properties of the positive and negative current collectors of the battery, while the charge remains unchanged. Molecular dynamics simulation is performed using LAMMPS software and the ensemble principle in the second step and the potential energy function in the third step. The simulation step size is set to τ = 0.002 and the simulation time is 10 6 time step to obtain the three-dimensional coordinates of the molecular trajectory; at the same time, the system evolution trajectory is sampled, and the coordinates and velocity of each particle in the system are output at a frequency of once every 1000 steps, and ensemble statistics are performed.

[0156] (7) Data processing and visualization of the simulation of lithium ion transport process include:

[0157] The Ovito software can be used to visually present the microstructure of the electrolyte at different temperatures. The Fortran code is used to calculate the radial distribution function of lithium ions to characterize the distribution of ion components. Figure 5 In this paper, a code written in Fortran is used to calculate the mean square displacement of lithium ions to characterize the diffusion rate of ions in the electrolyte of lithium batteries. Figure 7 This is the second kinetic data of the lithium-ion battery electrode-electrolyte system. Figure 7 a is the curve of the change of lithium ion positive electrode insertion number over time at different voltages. Figure 7 b is the discharge time of lithium-ion batteries at different voltages and the diffusion rate of lithium ions during the discharge process; Figure 7 c is the curve of the change of lithium ion positive electrode insertion number over time under different electrolyte concentrations, Figure 7d is the discharge time of lithium-ion batteries under different electrolyte concentrations and the diffusion rate of lithium ions during the discharge process.

[0158] It can be seen from the above exemplary embodiments that the embodiments of the present application can simulate different lithium battery electrolyte systems according to different temperatures and lithium salt concentrations.

[0159] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0160] Based on the same inventive concept, the embodiment of the present application also provides a coarse-grained molecular dynamics simulation device for ion transport process for implementing the coarse-grained molecular dynamics simulation method for ion transport process involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more coarse-grained molecular dynamics simulation devices for ion transport process provided below can refer to the limitations of the coarse-grained molecular dynamics simulation method for ion transport process above, and will not be repeated here.

[0161] In an exemplary embodiment, a coarse-grained molecular dynamics simulation device for an ion transport process is provided, comprising:

[0162] Basic model module, used to determine the basic model of the electrode-electrolyte system;

[0163] An initial model module, used to determine the simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system;

[0164] Potential energy function module, used to determine the potential energy function used to describe the interaction between components in the electrode-electrolyte system;

[0165] System relaxation module, used to determine the system relaxation of the electrode-electrolyte system based on the initial model and potential energy function;

[0166] The charge and discharge simulation module is used to simulate the lithium ion transport process during the charge and discharge process of lithium-ion batteries based on system relaxation.

[0167] Each module in the above-mentioned coarse-grained molecular dynamics simulation device for ion transport process can be implemented in whole or in part by software, hardware and their combination. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0168] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, a coarse-grained molecular dynamics simulation method of an ion transport process as provided in any of the above embodiments is implemented.

[0169] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the coarse-grained molecular dynamics simulation method of the ion transport process provided in any of the above embodiments is implemented.

[0170] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the coarse-grained molecular dynamics simulation method for the ion transport process provided in any of the above embodiments.

[0171] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0172] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0173] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A coarse-grained molecular dynamics simulation method for ion transport process, characterized in that: Applied to a lithium-ion battery electrode-electrolyte system, the method comprises: Determine the basic model of the electrode-electrolyte system; Determining simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system; Determine the potential energy function used to describe the interaction between the components in the electrode-electrolyte system; Determining a system relaxation of an electrode-electrolyte system according to the initial model and the potential energy function; Based on the system relaxation, the lithium ion transport process during the charging and discharging process of the lithium ion battery is simulated respectively.

2. The method according to claim 1, characterized in that: The components of the electrode-electrolyte system include: current collector, active material of the positive electrode, active material of the negative electrode, electrode-electrolyte interface, solvent molecules of the electrolyte, lithium salt, flame retardant and other additives; among them, The basic model for determining the electrode-electrolyte system comprises: Use Material Studio software or Avogadro software to calculate the size of each component in the electrode-electrolyte system of lithium-ion batteries, and model the molecules and ions in the electrode-electrolyte system based on the coarse-grained rules of the coarse-grained force field.

3. The method according to claim 2, characterized in that The use of Material Studio software or Avogadro software to calculate the size of each component in the lithium-ion battery electrode-electrolyte system, and modeling of molecules and ions in the electrode-electrolyte system based on the coarse-grained rule of the coarse-grained force field, includes: Use coarse-grained particles tightly packed into a two-dimensional plane to describe the current collector; The structure of the active materials of the positive and negative electrodes is described by using coarse-grained particles tightly packed into a parallel multilayer structure, wherein the interlayer spacing of the active materials of the positive and negative electrodes is calculated using Material Studio software or Avogadro software; Use a two-dimensional plane of coarse-grained particles to construct an electrode-electrolyte interface film, wherein the thickness of the electrode-electrolyte interface film is calculated using MaterialStudio software or Avogadro software; Lithium salt, solvent molecules, flame retardant molecules and other additive molecules are coarse-grained into charged, polar or non-polar spherical particles respectively.

4. The method according to claim 1, characterized in that: Determining the simulation conditions of the electrode-electrolyte system based on the basic model to establish an initial model of the electrode-electrolyte system includes: Use Langevin or Nose-Hoover heating baths to control the temperature of the electrode-electrolyte system; The base model was optimized using the LAMMPS pair_style soft command or the LAMMPSminimize command based on the Polak-Ribiere gradient descent algorithm.

5. The method according to claim 1, characterized in that The determining of the potential energy function used to describe the interaction between components in the electrode-electrolyte system comprises: Determine the Lennard-Jones potential energy function between the components in the electrode-electrolyte system; Determine the Coulum potential energy function between ionic components in the electrode-electrolyte system.

6. The method according to claim 1, characterized in that Determining the system relaxation of the electrode-electrolyte system according to the initial model and the potential energy function comprises: The equations of motion are solved using the Verlet-Velocity algorithm.

7. The method according to claim 1, characterized in that Based on the system relaxation, the lithium ion transmission process during the charging and discharging process of the lithium ion battery is simulated, including: During the charging process of lithium-ion batteries, the positive electrode of the battery and its current collector are uniformly positively charged, and the negative electrode of the battery and its current collector are uniformly negatively charged; During the discharge process of lithium-ion batteries, the positive electrode of the battery and its current collector are uniformly negatively charged, and the negative electrode of the battery and its current collector are uniformly positively charged; The positive electrode of the battery and the negative electrode of the battery have opposite electrical properties and equal charges.

8. The method according to claim 1, characterized in that After simulating the lithium ion transmission process during the charging and discharging process of the lithium ion battery based on the system relaxation, the method further includes: Data processing and visualization of the simulation of lithium-ion transport processes.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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