Battery electrolyte system simulation method, device and equipment and storage medium

Abstract

The invention provides a battery electrolyte system simulation method, device and equipment and a storage medium, and the method comprises the steps: calculating the size of each component in a battery electrolyte, and carrying out the coarse graining processing to obtain simulation parameters; setting a simulation box and a periodic boundary condition, and combining with energy minimization to construct an initial model of battery electrolyte coarse graining simulation; determining a potential energy function of the action among the components according to the distance, energy and ion valence of different particles in each component; performing energy minimization and pre-balance simulation on the initial model to obtain an optimization model; performing simulation according to the potential energy function of the action of each component and the simulation parameter to obtain a motion trail parameter of each particle in the battery electrolyte; and determining particle parameters based on the motion trail parameters of each particle in the battery electrolyte, and displaying the battery electrolyte system structure through visual software. By adopting the method, the simulation difficulty of the battery electrolyte system is reduced, the simulation range is expanded, and meanwhile, the simulation accuracy and the simulation efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of batteries, in particular to a battery electrolyte system simulation method, device, equipment and storage medium. BACKGROUND

[0002] Lithium ion batteries have become a supporting technology in key fields such as energy, information, transportation and medical treatment. As an important component of lithium ion batteries, electrolyte is closely related to the electrochemical performance of the battery. However, the electrolyte of lithium ion battery is complex, including lithium salt, organic solvent, flame retardant and other additives. For a long time, there is a lack of effective molecular level research methods, the micro interaction mechanism between different components is not clear, and each factor is coupled. It is extremely difficult to study the influence of a specific factor. Molecular dynamics simulation can explore the influence of each factor on the system properties at the microscale, predict system performance, study the micro mechanism of molecular interaction, and conduct research on extreme conditions, which helps to understand the related mechanism and optimize the experimental scheme. However, there is currently a lack of effective simulation methods suitable for lithium ion battery electrolyte systems.

[0003] Researchers often use all-atom molecular dynamics simulation to investigate the micro mechanism of lithium ion battery electrolyte system. All-atom molecular dynamics simulation takes atom as the basic unit, and the force field is a set of interatomic interaction functions, including non-bonding interactions between atoms (such as van der Waals interaction, Coulomb electrostatic interaction), bonding interactions (including interatomic chemical bond stretching potential, bond angle bending potential, dihedral angle torque potential, etc.). Taking dimethyl carbonate (C3H6O3) as an example, the all-atom modeling and simulation steps are as follows: 1. Describe the properties and positions of atoms in the molecule, such as identifying the atomic types, masses, and charges of three oxygen atoms, six hydrogen atoms, and three carbon atoms. 2. Describe the topological information of the system, such as describing five carbon-oxygen bonds, six carbon-hydrogen bonds, and seventeen bond angles. 3. Assign atomic types to atoms in the molecule according to chemical environment, and map elements to corresponding atomic types. 4. Assign different force field types to the topological structure of the molecule according to the newly mapped atomic types, and determine the types of bonds, bond angles, and dihedral angles. 5. Preliminary optimization of molecular conformation, eliminate incorrect structures that are difficult to optimize. 6. According to the experimental data, determine the concentration of each component, combine the optimized molecular models of different components into a simulation input file, determine the boundary conditions and simulation parameters, and use molecular dynamics simulation software to simulate and record and statistical data.

[0004] However, the simulation method has the following defects. First, the model construction is difficult. Specifically, in the all-atom simulation, there are many atom types and interactions. The molecular atom composition and topological structure need to be accurately determined, and then the chemical structure or environment defined by the all-atom force field is modeled, and finally the molecular structure is optimized and the wrong configuration is removed, resulting in great difficulty in model preparation and lack of universality. Second, the time and space scales are limited. Specifically, the all-atom simulation introduces more particle types and fine interactions, and the system relaxation is time-consuming, so it can only be studied at a very small time and space scale, and it is difficult to realize mesoscopic scale simulation, and it is difficult to carry out systematic research on the microstructure and dynamic properties of the electrolyte of the lithium ion battery. Third, the development level of the force field is limited. Specifically, the traditional force field function form is incomplete, the physical quantity is missing, the portability and prediction ability are poor, and the fitting results of some systems are difficult to extrapolate to other systems. Unconventional molecules are difficult to model, and the results of conventional molecules under different force field parameters differ significantly, and the accuracy of the simulation results and conclusions is low, and the simulation results of the lithium ion battery electrolyte system calculated by the output are difficult to effectively compare with the commonly used macroscopic performance. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a battery electrolyte system simulation method, device, equipment and storage medium, to reduce the simulation difficulty of the battery electrolyte system, expand the simulation range, and at the same time improve the simulation accuracy and simulation efficiency.

[0006] In a first aspect, the embodiments of the present application provide a battery electrolyte system simulation method, which comprises: The size of each component in the battery electrolyte is calculated, each component is subjected to coarse-grained processing, and the size of each component and the coarse-grained processing result are optimized to obtain simulation parameters; The simulation box and the periodic boundary condition are set, and the initial model of the coarse-grained simulation of the battery electrolyte is constructed according to the simulation box and the periodic boundary condition, combined with the energy minimization of the battery electrolyte system; The potential energy function of the interaction between each component is determined according to the particle distance and particle energy of different particles in each component, and the ion valence of each particle; The initial model is subjected to energy minimization operation and pre-equilibrium simulation, and the initial model is optimized to obtain an optimized model combined with the potential energy function of the interaction between each component; The motion trajectory parameters of each particle in the battery electrolyte are obtained by performing molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between each component and the simulation parameters; The particle parameters for characterizing the structure of the battery electrolyte system are determined based on the motion trajectory parameters of each particle in the battery electrolyte, and the structure of the battery electrolyte system is displayed by a visualization software based on the particle parameters.

[0007] Optionally, the size of each component in the battery electrolyte is calculated, each component is subjected to coarse-grained processing, and the size and coarse-grained processing result of each component are optimized to obtain simulation parameters, including: The size of the anion and cation in the lithium salt in the battery electrolyte is calculated by using Material Studio software; The lithium salt, solvent molecules and additives in the battery electrolyte are respectively coarse-grained into charged spherical particles, polar spherical particles or non-polar spherical particles by using Martini force field; The size of the anion and cation in the lithium salt in the battery electrolyte and the potential energy parameters of the charged spherical particles, the polar spherical particles and the non-polar spherical particles are optimized to obtain the simulation parameters.

[0008] Optionally, the initial model of the battery electrolyte coarse-grained simulation is constructed according to the simulation box and the periodic boundary condition, combined with the energy minimization of the battery electrolyte system, including: The concentration of each component of the battery electrolyte is determined according to the periodic boundary condition; Each component is randomly placed in the simulation box according to the concentration of each component, and the energy minimization is performed by using LAMMPS software combined with Soft potential to eliminate particle overlap, to obtain the initial model.

[0009] Optionally, the potential energy function of the interaction between each component is determined according to the particle distance and particle energy of different particles in each component, and the ionic valence of each particle, including: The Lennard-Jones potential between different particles of each component is determined according to the particle distance and particle energy of different particles in each component: The Coulum potential between ions is determined according to the ionic valence of each particle: The potential energy function is constructed according to the Lennard-Jones potential and the Coulum potential.

[0010] Optionally, the initial model is subjected to energy minimization operation and pre-equilibrium simulation, and the initial model is optimized to obtain an optimized model by using the potential energy function of the interaction between each component, including: The initial model is subjected to energy minimization operation and pre-equilibrium simulation by using LAMMPS software and soft repulsive potential, to obtain an initial structure in which each component in the initial model is uniformly dispersed; Based on the initial structure, the Soft potential is applied to the initial model for energy minimization in stages, and the model energy coefficient is gradually reduced to a preset threshold value, to ensure that the particles are uniformly dispersed and have no overlap, and the optimized model is obtained.

[0011] Optionally, the trajectory parameters of each particle in the battery electrolyte are obtained by performing molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between each component and the simulation parameters, including: The LAMMPS software is used to perform molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between each component and the simulation parameters. The system potential energy and the force of each particle are calculated based on the simulation results. The trajectory parameters of each particle are determined by using the Verlet-Velocity integral algorithm and the ensemble statistical algorithm according to the system potential energy and the force of each particle.

[0012] Optionally, the particle parameters include radial distribution function, structure factor, mean square displacement and diffusion coefficient.

[0013] In a second aspect, the embodiments of the present application provide a battery electrolyte system simulation device, the device comprising: A simulation parameter optimization module is configured to calculate the size of each component in the battery electrolyte, perform coarse-grained processing on each component, and optimize the size of each component and the coarse-grained processing result to obtain simulation parameters. A model construction module is configured to set a simulation box and a periodic boundary condition, and construct an initial model of the battery electrolyte coarse-grained simulation according to the simulation box and the periodic boundary condition in combination with the energy minimization of the battery electrolyte system. A potential energy function determination module is configured to determine the potential energy function of the interaction between each component according to the particle distance and particle energy of different particles in each component and the ion valence of each particle. A model optimization module is configured to perform energy minimization operation and pre-equilibrium simulation on the initial model, and optimize the initial model to obtain an optimized model in combination with the potential energy function of the interaction between each component. A trajectory parameter determination module is configured to obtain the trajectory parameters of each particle in the battery electrolyte by performing molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between each component and the simulation parameters. An electrolyte system simulation module is configured to determine particle parameters for characterizing the structure of the battery electrolyte system based on the trajectory parameters of each particle in the battery electrolyte, and display the structure of the battery electrolyte system by using a visualization software based on the particle parameters.

[0014] In a third aspect, the embodiments of the present application provide a computer device, comprising a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the computer device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the steps of the battery electrolyte system simulation method in any of the optional implementation manners of the first aspect.

[0015] In a fourth aspect, the embodiments of the present application provide a computer readable storage medium, the computer readable storage medium stores a computer program, when the computer program is run by a processor, the steps of the battery electrolyte system simulation method in any of the optional implementation manners of the first aspect are performed.

[0016] The technical solutions provided by the present application include but are not limited to the following beneficial effects: The present application first calculates the size of each component in the battery electrolyte, performs coarse-grained processing on each component, optimizes the size of each component and the coarse-grained processing result to obtain simulation parameters. The coarse-grained processing simplifies the complex molecular structure into fewer interaction sites, uses several atomic groups as simulation basic units, reduces the number of particles and the complexity of interaction in the simulation system, thereby reducing the calculation amount and improving the simulation efficiency. Optimizing the size of each component and the coarse-grained processing result makes the simulation parameters more consistent with the physical and chemical properties of the actual system, ensuring that the simulation can more accurately reflect the behavior and interaction of each component in the battery electrolyte.

[0017] Then, the simulation box and the periodic boundary condition are set, and according to the simulation box and the periodic boundary condition, an initial model of the battery electrolyte coarse-grained simulation is constructed in combination with the energy minimization of the battery electrolyte system. The periodic boundary condition can simulate an infinite system, avoiding the boundary effect caused by the simulation box boundary, making the simulation result closer to the real electrolyte system and improving the reliability of the simulation. And the initial model is constructed in combination with the energy minimization, the particle position is adjusted to make the system energy minimum, the particle overlapping phenomenon is eliminated, and the uniform dispersion of each component is ensured, providing a stable and reliable initial state for subsequent simulation, which is conducive to the smooth progress of the simulation and the accuracy of the result.

[0018] Then, a potential energy function of interaction between components is determined according to particle sizes of different particles in each component and interaction energy between particles, and ion valence of each particle. The potential energy function is determined by considering particle sizes, interaction energy and ion valence, which can comprehensively and accurately describe the interaction between particles in the battery electrolyte, including electrostatic interaction and short-range interaction, etc., and provide a reasonable mechanical basis for simulating the motion of particles and evolution of the system. A suitable potential energy function can more truly reflect the physical and chemical nature of the electrolyte system, so that the simulation results can accurately reflect the influence of the interaction between particles on the structure and properties of the system, and help to deeply understand the micro mechanism of the electrolyte system.

[0019] Then, an energy minimization operation and a pre-equilibrium simulation are performed on the initial model, and the initial model is optimized to obtain an optimized model by combining the potential energy function of interaction between components. The energy minimization operation and the pre-equilibrium simulation can further eliminate the unstable factors that may exist in the initial model, make the particle distribution more reasonable, and make the system more stable, thereby improving the reliability of the simulation results. In addition, the optimization combined with the potential energy function makes the model reach a more optimal state under the simulation conditions, and ensures that the behavior of the system in the simulation process conforms to the physical law, thereby providing a guarantee for accurately simulating the properties of the battery electrolyte system.

[0020] Then, a molecular dynamics simulation is performed on the optimized model according to the potential energy function of interaction between components and the simulation parameters to obtain the motion trajectory parameters of each particle in the battery electrolyte. Through the molecular dynamics simulation, the force and motion of the particles are calculated according to the potential energy function and the simulation parameters, and the motion trajectory parameters of each particle are obtained, which can dynamically display the motion process of the particles in the battery electrolyte, and provide detailed information for studying the transport properties and diffusion behavior of the electrolyte. In addition, the motion trajectory parameters contain information such as the position and velocity of the particles at different times, which helps to deeply understand the microstructure and dynamic properties of the electrolyte system, and provides a micro basis for explaining the macro experimental phenomena.

[0021] Finally, particle parameters for characterizing the structure of the battery electrolyte system are determined based on the motion trajectory parameters of each particle in the battery electrolyte. Based on the particle parameters, the structure of the battery electrolyte system is displayed through a visualization software, and the particle parameters such as radial distribution function, structure factor, mean square displacement and diffusion coefficient are calculated, which can quantitatively characterize the structure and dynamic characteristics of the battery electrolyte system from different angles, and provide a quantitative basis for studying the performance of the electrolyte. In addition, the structure of the electrolyte system is displayed by using the visualization software, which presents the complex simulation data in the form of intuitive graphics, facilitating researchers to observe and analyze, accelerating the research process, and promoting the understanding and optimization of the battery electrolyte system.

[0022] In summary, the above-mentioned scheme can reduce the simulation difficulty of the battery electrolyte system, expand the simulation range, and improve the simulation accuracy and efficiency.

[0023] In order to make the above objectives, characteristics and advantages of the present application more apparent, more comprehensible, hereinafter a preferred embodiment is specifically described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced as follows. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0025] Figure 1 A flow chart of a battery electrolyte system simulation method provided by the embodiment one of the present application is shown; Figure 2 A flow chart of a simulation parameter optimization method provided by the embodiment one of the present application is shown; Figure 3 A flow chart of an initial model construction method provided by the embodiment one of the present application is shown; Figure 4 A flow chart of a potential energy function determination method provided by the embodiment one of the present application is shown; Figure 5 A flow chart of an optimization model determination method provided by the embodiment one of the present application is shown; Figure 6 A flow chart of a motion trajectory parameter determination method provided by the embodiment one of the present application is shown; Figure 7 A structural schematic diagram of different particles of a battery electrolyte provided by the embodiment one of the present application is shown; Figure 8 A schematic diagram of a microstructure of an electrolyte provided by the embodiment one of the present application is shown; Figure 9 A schematic diagram of a lithium battery electrolyte characterization result provided by the embodiment one of the present application is shown; Figure 10 A structural schematic diagram of a battery electrolyte system simulation device provided by the embodiment two of the present application is shown; Figure 11 A structural schematic diagram of a computer device provided by the embodiment three of the present application is shown. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0027] Example 1 To facilitate understanding of this application, Figure 1 The flowchart of a battery electrolyte system simulation method provided in the first embodiment of the present invention is shown to describe the contents of the embodiment of the present application in detail.

[0028] See also Figure 1 As shown, Figure 1 A flowchart of a battery electrolyte system simulation method provided in Example 1 of the present invention is shown, wherein the method includes steps S101 to S106: S101: Calculate the size of each component in the battery electrolyte, perform coarse-graining processing on each component, and optimize the size of each component and the coarse-graining processing results to obtain simulation parameters.

[0029] Specifically, the electrolyte of a lithium-ion battery is a multi-component liquid mixture, primarily composed of lithium salts, solvent molecules, flame retardants, and other additives. As an electrolyte, lithium salts play a key role in ion conduction during the battery's charge and discharge processes.

[0030] Firstly, the size of anion and cation in lithium salt is calculated by Material Studio software, and the lithium salt is coarse-grained into charged spherical particles based on Martini force field and existing literature, and the size and potential energy parameters are optimized to more accurately simulate the diffusion and transport of ions in electrolyte. Secondly, the size of solvent molecules as ion transport medium is calculated by Avogadro software, and the solvent molecules are coarse-grained into polar spherical particles based on Martini force field, and the potential energy parameters of the solvent molecules are optimized according to the related literature to ensure that the model parameters obtained are consistent with the actual material conditions. The size of the flame retardant and other additives in the battery which play a role in improving safety and stability is calculated by Avogadro software, and they are coarse-grained into polar or non-polar spherical particles based on Martini force field, and the potential energy parameters of various additives in the simulation are optimized according to the parameters of the same family or similar additives in the related literature, so as to obtain simulation parameters consistent with the actual material system.

[0031] S102: Set the simulation box and the periodic boundary condition, and construct the initial model of the battery electrolyte coarse-grained simulation according to the energy minimization of the battery electrolyte system based on the simulation box and the periodic boundary condition.

[0032] Specifically, the length of the simulation box is set, and the periodic boundary condition is introduced in the system. The concentration of each component is calculated according to the actual conditions, and the above-mentioned components are randomly placed in the box by using the program written by Perl language to generate the read-in information file for LAMMPS software simulation. Since the randomly generated model inevitably has particle overlap, the Soft potential in the LAMMPS software is used to minimize the energy of the system to slowly push away the overlapping particles. The expression of Soft potential is:

[0033] wherein, is the energy coefficient, is the cut-off radius, is the ion distance. The system is set as a canonical ensemble (constant particle number, constant volume, constant temperature NVT), the system temperature is controlled by using Langevin heat bath, and the Soft potential is used to adjust the structure with a step length of to ensure that there is no overlap between particles, and each component is uniformly dispersed in the system. After the above optimization, the initial model of the lithium ion battery electrolyte coarse-grained simulation can be obtained.

[0034] The method for realizing the initial model of the random simulation can be constructed by using languages such as Fortran, C / C++, Python in addition to the Perl language construction program. The algorithm is not limited, and the final purpose is to obtain an initial structure with different structures.​

[0035] S103: Determine the potential energy function of the interaction between the components based on the particle distance and particle energy of different particles in each component and the ion valence of each particle.

[0036] Specifically, molecular dynamics simulations update the positions and velocities of particles based on the forces acting on them, which are determined by their interaction potential. Therefore, the choice of potential determines the evolution of the system over time. The set of mathematical functions and parameters that describe the interaction potentials of particles in a system is called the force field. In coarse-grained simulations, only the external field and the interaction between two particles are considered; the contribution of multi-body interactions involving three or more particles is not considered.

[0037] The interaction between particles is divided into three parts, which are composed of Coulum potential ( ) represents the electrostatic interaction, the Lennard-Jones potential ( ) represents the short-range interaction.

[0038] The Lennard-Jones potential energy between the components is calculated as follows:

[0039] Where, It is a particle and The distance between and are the energy parameter and the length parameter, is the cutoff radius. The potential function parameters between the components are calculated using the Lorentz-Berthelot mixing rule.

[0040] Energy parameters and length parameters The Lorentz-Berthelot mixing method is expressed as:

[0041] in, represents the length parameters of particles i and j, represents the length parameter of particle i and itself, represents the length parameter of particle j and itself, represents the energy parameters of particles i and j, represents the energy parameters of particle i and itself, Represents the length parameter of particle j and itself.

[0042] Coulum potential energy between ionic components The calculation formula is as follows:

[0043] Where, It is a particle and The distance between are the ionic valence states of particles i and j respectively, and e is the elementary charge = Cullen, is the dielectric constant of vacuum, is the relative dielectric constant of the solution. Depending on the organic solvent used in the lithium-ion battery electrolyte, the dielectric constant can be set to different values. The Coulum potential energy calculation is implemented using the PPPM (Particle-Particle Particle-Mesh) algorithm.

[0044] In addition to the PPPM algorithm, the Coulum potential energy calculation can be implemented using the Ewald summation algorithm or the PME algorithm.

[0045] S104: performing energy minimization operation and pre-equilibrium simulation on the initial model, and optimizing the initial model in combination with the potential energy function of the interaction between the components to obtain an optimized model.

[0046] Specifically, LAMMPS software and a soft repulsive potential were used to perform energy minimization and pre-equilibrium simulations to obtain an initial structure in which all components were uniformly dispersed. The soft potential was then removed and replaced with the coarse-grained force field optimized for lithium-ion battery electrolytes described in step S103. Molecular dynamics simulations were then performed using LAMMPS software and the simulation conditions set in step S102 to calculate the system potential energy and particle forces. The Verlet-Velocity integration algorithm was used to determine the particle positions at the next moment. The calculation results at each moment were then tallied until the system reached equilibrium, resulting in an optimized model.

[0047] S105: According to the potential energy function of the interaction between the components and the simulation parameters, a molecular dynamics simulation is performed on the optimization model to obtain the motion trajectory parameters of each particle in the battery electrolyte.

[0048] Specifically, molecular dynamics simulation is performed using LAMMPS software, the ensemble principle of step S102, and the potential energy function in S103 to calculate the system potential energy and particle forces. The particle position at the next moment is determined according to the Verlet-Velocity integration 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.

[0049] In addition to the Verlet-Velocity algorithm, the molecular dynamics kinematic integration algorithm can also use the Verlet and Frog-Leap algorithms.

[0050] S106: Determine particle parameters for characterizing the structure of the battery electrolyte system based on the motion trajectory parameters of each particle in the battery electrolyte, and display the structure of the battery electrolyte system based on each particle parameter through visualization software.

[0051] Specifically, the particle parameters include radial distribution function, structure factor, mean square displacement, and diffusion coefficient. A program is written using Fortran language, and based on the motion trajectory of each particle in the battery electrolyte, the structure factor of the lithium ion battery electrolyte in the statistical equilibrium state, the radial distribution function between each component, the intermediate scattering function, the mean square displacement of each component, and the diffusion coefficient are calculated, and the Ovito software is used to intuitively present the microstructure of the lithium ion battery electrolyte.

[0052] Radial distribution function is defined as:

[0053] wherein, represents the number of particles within the distance r to r+ in the spherical shell, is the distance between the particle pairs, is a parameter representing the resolution of the function, is the total number of particles in the system, is the volume of the system.

[0054] Structure factor is defined as:

[0055] wherein, is the total number of particles, is the imaginary unit, i is the imaginary unit, and represent the position vectors of the particles and , respectively, represents the ensemble average.

[0056] Mean square displacement is defined as:

[0057] wherein, the mean square displacement is a measure of the deviation of the instantaneous position of the particle from the initial position over time, is the position of the particle at time , is the initial position of the particle .

[0058] From the mean square displacement, the diffusion coefficient can be calculated by the Einstein diffusion formula :

[0059] Where t is time, is the number of particles, For particles exist The location at the moment, For particles The initial position of .

[0060] In addition to Fortran, simulation post-processing programs can also be written in Fortran, C / C++, Python, etc. The physical quantities represented are not limited to those listed here, but also include thermodynamic or kinetic quantities required in specific examples.

[0061] In an alternative embodiment, see Figure 2 As shown, Figure 2 A flowchart of a simulation parameter optimization method provided in a first embodiment of the present invention is shown, wherein the method includes calculating the size of each component in the battery electrolyte, performing coarse-graining processing on each component, and optimizing the size of each component and the coarse-graining processing results to obtain simulation parameters, including steps S201 to S203: S201: Calculate the sizes of anions and cations in the lithium salt in the battery electrolyte using Material Studio software.

[0062] Specifically, the calculations were performed using Material Studio. Material Studio is a powerful materials simulation software, equipped with advanced algorithms and models. It accurately calculates the sizes of anions and cations in lithium salts based on their molecular structure and relevant physical and chemical principles. This dimensional data serves as a crucial basis for subsequent processing and simulation.

[0063] S202: using a Martini force field to coarse-grain the lithium salt, solvent molecules and additives in the battery electrolyte into charged spherical particles, polar spherical particles or non-polar spherical particles.

[0064] Specifically, after obtaining the size of each component, in order to simplify the simulation process, improve computational efficiency, and retain the main physical and chemical properties of the system, the Martini force field is used to coarse-grain the components in the battery electrolyte. The Martini force field is a coarse-grained force field that can simplify complex molecular structures into fewer interaction sites. Specifically, the lithium salt in the battery electrolyte is coarse-grained into charged spherical particles because lithium salts will ionize into charged ions in solution; the solvent molecules are coarse-grained into polar spherical particles because many solvent molecules are polar; and the additives are coarse-grained into non-polar spherical particles, depending on the chemical properties of the additives. Through this coarse-graining process, the originally complex molecular system is converted into a relatively simple spherical particle system, greatly reducing the complexity of the calculation.

[0065] S203: Optimizing the sizes of anions and cations in the lithium salt in the battery electrolyte and the potential energy parameters of the charged spherical particles, the polar spherical particles, and the non-polar spherical particles to obtain the simulation parameters.

[0066] Specifically, the sizes of anions and cations in the lithium salt and the initial parameters of the coarse-grained charged spherical particles, polar spherical particles, and non-polar spherical particles obtained in the previous two steps may not be completely suitable for subsequent simulations. This is because these initial parameters may be based on some general models and assumptions, while the actual battery electrolyte system has its own unique properties. Therefore, these parameters need to be optimized. Specifically, the sizes of anions and cations in the lithium salt and the potential energy parameters of each spherical particle are adjusted and optimized. The potential energy parameters describe the interaction between particles and are crucial to the accuracy of the simulation results. By continuously adjusting these parameters to match them with actual experimental data or theoretical predictions, appropriate simulation parameters are eventually obtained. These optimized simulation parameters will provide a reliable basis for subsequent accurate simulation of the behavior of the battery electrolyte system.

[0067] Furthermore, in addition to Material Studio software, Avogadro software can be used to calculate the size of lithium-ion battery electrolyte components; in addition to the Martini force field, coarse-grained force fields such as OPLS-UA can also be used.

[0068] In an alternative embodiment, see Figure 3 As shown, Figure 3 A flowchart of an initial model construction method provided in the first embodiment of the present invention is shown, wherein the initial model for battery electrolyte coarse-grained simulation is constructed based on the simulation box and the periodic boundary conditions in combination with the energy minimization of the battery electrolyte system, including steps S301 to S303: S301: determining the concentration of each component of the battery electrolyte according to the periodic boundary condition.

[0069] Specifically, the periodic boundary condition is an important means for handling boundary effects in simulation, which makes the simulation system appear in an infinitely repeated environment. Under this setting, the spatial range and the overall properties of the system determined according to the periodic boundary condition are used to determine the concentration of each component of the battery electrolyte. The concentration of each component is a key parameter for describing the composition of the electrolyte system, which reflects the proportion of each component in the electrolyte. By accurately determining the concentration of each component, the actual battery electrolyte can be more realistically restored in subsequent simulation, laying a foundation for building an accurate initial model.

[0070] Further, the simulation dimension is three-dimensional, and the boundary condition is divided into fixed boundary condition, free boundary condition and periodic boundary condition. In order to reduce the influence of model "size effect" and control the calculation workload, the periodic boundary condition is adopted in the present application.

[0071] S302: randomly placing each component in the simulation box according to the concentration of each component, and performing energy minimization by using LAMMPS software combined with Soft potential to eliminate particle overlap, so as to obtain the initial model.

[0072] Specifically, after the concentration of each component is determined, the next step is to place each component into the simulation box. Here, a random placement method is adopted to distribute each component particle in the space defined by the simulation box. However, this random placement may result in particle overlap between particles, which is physically unreasonable and will negatively affect the accuracy of subsequent simulation. In order to solve this problem, LAMMPS software (a widely used software for molecular dynamics simulation) is used in combination with Soft potential to perform energy minimization operation. Soft potential is a potential energy function used to handle the interaction between particles, which can generate repulsive force when particles approach each other. By using LAMMPS software to execute energy minimization algorithm, the position of particles is constantly adjusted, so that the energy of the system gradually decreases. When the energy reaches a minimum value, the overlap between particles is eliminated, and each component particle reaches a relatively stable distribution state in the simulation box, and at this time the initial model required for battery electrolyte coarse-grained simulation is obtained. This initial model provides a reasonable starting point for subsequent in-depth study of various properties and behaviors of the battery electrolyte system.

[0073] In an optional embodiment, referring to Figure 4 , the concentration of each component of the battery electrolyte is determined according to the periodic boundary condition, and the concentration of each component is randomly placed in the simulation box according to the concentration of each component. Figure 4A flow chart of a method for determining a potential energy function according to an embodiment of the present application is shown. The potential energy function between components is determined according to the particle distance and particle energy of different particles in the components and the ion valence of each particle, and includes steps S401-S403: S401: determining the Lennard-Jones potential between different particles in the components according to the particle distance and particle energy of the different particles in the components.

[0074] Specifically, the Lennard-Jones potential is used to describe the interaction between neutral molecules or atoms, and is closely related to the particle distance and particle energy. In a battery electrolyte system, there is a short-range interaction between particles of different components, and this interaction changes with the distance between particles. When the particle distance is close, a strong repulsive force is generated due to the overlap of electron clouds; and when the distance is far, an attractive force is generated due to the van der Waals force between molecules. By the particle distance and particle energy of different particles in the components, the Lennard-Jones potential between different particles in the components can be calculated. After the potential is calculated, the short-range interaction between particles can be effectively simulated.

[0075] S402: determining the Coulum potential between ions according to the ion valence of each particle.

[0076] Specifically, in a battery electrolyte, many particles are charged, such as the anions and cations ionized from lithium salts. The charged particles interact with each other through electrostatic interaction, and the Coulum potential is used to describe this electrostatic interaction. According to the ion valence of each particle, the Coulum potential between ions can be determined.

[0077] S403: constructing the potential energy function according to the Lennard-Jones potential and the Coulum potential.

[0078] Specifically, after obtaining the Lennard-Jones potential and the Coulum potential, they can be combined to construct a complete potential energy function between components. This is because in a battery electrolyte system, the interaction between particles includes not only the short-range Lennard-Jones interaction, but also the long-range electrostatic Coulum interaction. This potential energy function comprehensively considers various interactions between particles, and can more accurately describe the interaction between components in a battery electrolyte system, providing a reliable basis for subsequent molecular dynamics simulation and system structure analysis.

[0079] In an optional embodiment, referring to Figure 5 shown, Figure 5A flow chart of a method for determining an optimized model is shown, wherein the method comprises steps S501-S502. S501: performing energy minimization operation and pre-equilibrium simulation on the initial model by using LAMMPS software and soft repulsive potential, to obtain an initial structure in which each component in the initial model is uniformly dispersed.

[0080] Specifically, the initial model is obtained by randomly placing particles of each component and performing preliminary processing, and at this time, the distribution of the particles may not be uniform, and even there may be local aggregation or overlapping. Such a state is not conducive to accurately simulating the properties of the electrolyte system in the subsequent step. Therefore, the professional molecular dynamics simulation software LAMMPS is used to perform energy minimization operation and pre-equilibrium simulation.

[0081] The soft repulsive potential is a special potential function, which generates a large repulsive force when the distance between particles is close, thereby avoiding the particles from being too close or overlapping. By using the LAMMPS software combined with the soft repulsive potential to process the initial model, the software will continuously adjust the position of the particles according to the potential function, so that the energy of the whole system gradually decreases. When the energy of the system reaches a relatively stable state, it means that the particles of each component are uniformly dispersed in the simulation box, and at this time, the initial structure in which each component in the initial model is uniformly dispersed is obtained. This initial structure is more stable and reasonable than the initial model, and lays a good foundation for further optimization.

[0082] S502: based on the initial structure, performing energy minimization on the initial model by applying Soft potential in stages, gradually reducing the model energy coefficient to a preset threshold, ensuring that the particles are uniformly dispersed and have no overlap, and obtaining the optimized model.

[0083] Specifically, although the initial structure in which each component is uniformly dispersed is obtained by step S501, it may not have reached the optimal state, and there may still be some subtle energy fluctuations or particle distribution that is not ideal in the system. In order to further optimize the model, based on this initial structure, the energy minimization is performed on the initial model by applying Soft potential in stages.

[0084] The Soft potential is also a potential energy function used to adjust the interaction between particles. By applying different strengths of the Soft potential at different stages, the positions of the particles and the energy of the system can be gradually adjusted. In this process, the model energy coefficient is gradually reduced to a preset threshold. The energy coefficient reflects the size of the system energy. By continuously reducing the energy coefficient, the energy of the system can gradually approach the minimum value. When the energy coefficient reaches the preset threshold, it means that the system has reached a relatively stable and low-energy state. At this time, the particles are uniformly dispersed and there is no overlap. The optimized model obtained is the optimized model. This optimized model can more accurately reflect the real structure and properties of the battery electrolyte system, providing a reliable basis for subsequent molecular dynamics simulation and analysis.

[0085] In an optional embodiment, referring to Figure 6 as shown, Figure 6 A flowchart of a motion trajectory parameter determination method provided by an embodiment of the present application is shown, wherein the motion trajectory parameters of each particle in the battery electrolyte are determined by performing molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between each component and the simulation parameters, including steps S601-S603: S601: Perform molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between each component and the simulation parameters by using LAMMPS software.

[0086] Specifically, the professional molecular dynamics simulation software LAMMPS is used to carry out simulation work. According to the given potential energy function and simulation parameters, the particles in the optimized model are dynamically simulated. During the simulation process, the software will constantly update the positions and velocities of the particles according to the interaction between the particles, so as to simulate the motion of the particles over time.

[0087] S602: Calculate the system potential energy and the force on the particles based on the simulation results.

[0088] Specifically, a series of simulation results will be generated as the molecular dynamics simulation progresses. Based on these results, two important physical quantities are calculated: the system potential energy and the force on the particles. The system potential energy is the sum of the interaction potential energy between all particles in the system, which reflects the energy state of the system. By summing the potential energy function of the interaction between each component in the entire system, the system potential energy can be obtained. The force on the particles is the force on each particle in the system due to the interaction with other particles. According to Newton's second law, force affects the motion state of the particle. By taking the gradient of the potential energy function, the force on each particle can be obtained.

[0089] S603: Determine the motion trajectory parameters of each particle by using the Verlet-Velocity integration algorithm and the ensemble summation method according to the system potential energy and the force on the particles.

[0090] Specifically, the Verlet-Velocity integration algorithm is a commonly used numerical integration method, which can predict the position and velocity of particles at the next time step according to the current position, velocity and force of the particles. By continuously iterating this process, the motion trajectory of the particles in the entire simulation process can be obtained. The ensemble statistical algorithm is based on statistical mechanics, which considers the relationship between the macroscopic properties and the microscopic state of the system, and modifies and analyzes the simulation results to ensure that the motion trajectory parameters obtained are more consistent with the actual physical conditions. By comprehensively using these two algorithms, the motion trajectory parameters of each particle in the battery electrolyte can be accurately determined, including the position and velocity of the particles at different times, which provides an important basis for further studying the structure and performance of the battery electrolyte.

[0091] In an alternative embodiment, the particle parameters include radial distribution functions, structure factors, mean square displacements, and diffusion coefficients.

[0092] Specifically, the radial distribution function is mainly used to describe the distribution of particles in space. The structure factor is an important parameter for studying the macroscopic structural properties of the system, which reflects the degree of aggregation and order of particles in space. The mean square displacement is used to measure the evolution of particles over time, and the deviation of the instantaneous position from the initial position. The diffusion coefficient is closely related to the mean square displacement, and it is a quantitative parameter for describing the speed of particle diffusion.

[0093] In order to more clearly and specifically describe the battery electrolyte system simulation method provided in the present application, a specific example of system simulation by the battery electrolyte system simulation method provided in the present application is provided. The first example specifically includes the following steps: Step 1: Determine the basic model of each component in the electrolyte. In this example, four main components of lithium-ion battery electrolyte are selected: 1 mol / L LiPF6, 1 mol / L LiPF6, 15 mol / L organic solvent molecule ethylene carbonate (EC) and a small amount of vinylene carbonate (VC) as an additive. The diameter of LiPF6 is 1.52, the diameter of PF6 is 5.82, the diameter of EC is 6.39, and the diameter of VC is 6.7. The basic unit of coarse-grained is selected, so the diameter of LiPF6 is 0.5, the diameter of PF6 is 1.5, the diameter of EC is 2.0, and the diameter of VC is 2.2. -1 + -1 - -1 + - + ​​​​​​​​​​​​​​- Diameter is 1.94 , EC and VC diameters are 2.13 With 2.23 See also Figure 7 As shown, Figure 7 The schematic diagram of the structure of different particles of a battery electrolyte provided by the first embodiment of the present invention is shown, wherein the Li + PF6 - The sizes of the components were calculated using MaterialStudio software, and the lithium salts, organic molecules, and additives were coarse-grained into polar or non-polar spherical particles based on the Martini force field.

[0094] Step 2: Determine the simulation conditions. Set the side length of the simulation box to , and introduce periodic boundary conditions into the system. Calculate the concentration of each component according to the actual conditions, where represents Li + and PF6 - The number of particles is 130, and the number of particles representing EC and VC are 1900 and 50 respectively. The above components are randomly placed in the box using a program written in Perl language to generate a read-in information file for LAMMPS software simulation. 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 apart. The system is set to a canonical ensemble (constant number of particles, constant volume, constant temperature NVT), and the system temperature is controlled using a Langevin heat bath, and its temperature is set to 、 and The energy coefficient of the first stage is set to ,conduct The structure adjustment process of the time step ensures that there is no overlap between particles; the energy coefficient of the second stage is set to ,conduct The simulation of the time step makes each component more evenly dispersed in the system. After the above optimization, the initial model of the coarse-grained simulation of the electrolyte can be obtained.

[0095] Step 3: Determine the potential energy function used to describe the interaction between the components. In this example, the interaction between particles is divided into two parts, which are represented by the Coulum potential ( ) represents the electrostatic interaction, the Lennard-Jones potential ( ) represents the short-range interaction. The interaction parameters between the components are shown in the following table:

[0096] Step 4: System relaxation: In step 2, LAMMPS software and soft repulsive potential were 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 was canceled and replaced with the coarse-grained force field optimized for the electrolyte as described in step 3. Molecular dynamics simulation was performed using LAMMPS software and the simulation conditions set in step 2. The simulation step size was set to , the simulation duration is 10 6 Time step. Simultaneously, the system potential energy and particle forces are calculated, and the particle position at the next moment is determined using the Verlet-Velocity integration algorithm. The calculation results at each moment are counted until the system reaches equilibrium.

[0097] Step 5: Core algorithm molecular dynamics simulation: Use LAMMPS software and the ensemble principle in step 2 and the potential energy function in step 3 to perform molecular dynamics simulation. The simulation step size is set to The simulation lasts for 106 time steps. The system's potential energy and particle forces are calculated, and the particle positions at the next moment are determined using the Verlet-Velocity integration algorithm, resulting in the three-dimensional coordinates of the molecular trajectory. Simultaneously, the system's evolution trajectory is sampled, and the coordinates and velocities of each particle in the system are output every 1000 steps, along with ensemble statistics.

[0098] Step 6: Data processing and visualization: see Figure 8 As shown, Figure 8 A schematic diagram of the microstructure of an electrolyte provided by the first embodiment of the present invention is shown, wherein the Ovito software can be used to intuitively present different temperatures ( 、 and ) and different ion concentrations ( 、 and ) electrolyte microstructure. Figure 9 As shown, Figure 9 A schematic diagram showing the characterization results of a lithium battery electrolyte provided by Example 1 of the present invention is shown, wherein a code written in Fortran is used to calculate different temperatures ( 、 and ), different ion concentrations ( 、 and ) and different qL / 2 Down( is the reciprocal space lattice vector, L is the length of the box side), the lithium ion structure factor , to characterize the degree of ion aggregation in space; use the code written in Fortran to calculate the different temperatures ( 、 and ), different ion concentrations ( , and ) and different r / The radial distribution function gLi+-Li+(r) of lithium ions was calculated to characterize the local distribution of components; using Fortran code, the mean square displacement of lithium ions , and ) at different ion concentrations ( , and ) at different times t was calculated to characterize the diffusion speed of ions in the electrolyte of lithium batteries. (k is the slope of the obtained MSD), to characterize the diffusion speed of ions in the electrolyte of lithium batteries.

[0099] A second specific example of system simulation by the battery electrolyte system simulation method provided in the present application is also provided. The second example specifically includes the following steps: Step one: determine the basic model of each component in the electrolyte. In the present example, four main components of the electrolyte of lithium ion batteries are selected: lithium salt is composed of Li + and PF6 - , 15 mol / L -1 of organic solvent molecules ethylene carbonate (EC) and a small amount of vinylene carbonate (VC) as an additive. In different systems, the concentration of lithium salt is selected as 0.5 mol / L -1 , 1 mol / L -1 and 1.5 mol / L -1 . The diameter of Li + is 1.52 , the diameter of PF6- is 5.82 , the diameter of EC is 6.39 , and the diameter of VC is 6.7 . The basic unit is selected to be coarse-grained , so the diameter of Li+ is 0.5 , the diameter of PF6 - is 1.94 , and the diameters of EC and VC are 2.13 and 2.23 , respectively. According to the calculation of the size of each component by the Material Studio software, the lithium salt, organic molecules and additives are coarse-grained into polar or non-polar spherical particles based on the Martini force field.

[0100] Step two: determine the simulation conditions. The side length of the simulation box is set to and periodic boundary conditions are introduced in the system. The concentrations of each component are calculated according to the actual conditions, in which Li + and PF6 - The particle numbers of Li+and PF6-are 65, 130 and 195, and the particle numbers of EC and VC are 1770 and 50, 1640 and 50, and 1510 and 50, respectively. A program written in Perl language is used to randomly place the above components in the box, to generate a read-in information file for LAMMPS software simulation, and to use the Soft potential in LAMMPS software to minimize the energy of the system, and to slowly push the overlapping particles apart.

[0101] The system is set as a canonical ensemble (constant particle number, constant volume, constant temperature NVT), and the temperature of the system is controlled by a Langevin heat bath, which is set to The energy coefficient in the first stage is set to be relatively weak , and the step size is to adjust the structure, so as to ensure that there is no overlap between the particles; the energy coefficient in the second stage is set to , and the step size is to simulate, so that the components are uniformly dispersed in the system. Through the above optimization, the initial model of the electrolyte coarse-grained simulation can be obtained.

[0102] Step three: determine the potential function for describing the interaction between components: in this example, only the external field and the two-body interaction are considered, and the contribution of three or more particles to the multi-body interaction is not considered. The interaction between particles is divided into two parts, the electrostatic interaction represented by the Coulum potential ( ) and the short-range interaction represented by the Lennard-Jones potential ( ). The potential function parameters between components are calculated by the Lorentz-Berthelot mixing rule. The interaction parameters between components are the same as in the first example.

[0103] Step four: system relaxation: in the second step, the energy minimization operation and the pre-equilibrium simulation are performed by using the LAMMPS software and the soft repulsive potential, to obtain the initial structure of the lithium battery electrolyte model with uniformly dispersed components. The Soft potential is cancelled, and the coarse-grained force field optimized for the electrolyte in the third step is used instead, and the molecular dynamics simulation is performed by using the LAMMPS software and the simulation conditions set in the second step. The simulation step size is set to , and the simulation time is 10 6 time steps. The system potential and the force on the particles are calculated during the simulation, the particle position at the next time is determined according to the Verlet-Velocity integral algorithm, and the calculation results at each time are counted until the system reaches an equilibrium state.

[0104] Step five: Core algorithm molecular dynamics simulation: using LAMMPS software and the ensemble principle in step two and the potential energy function in step three to perform molecular dynamics simulation, the simulation step is set to , and the simulation time is 10 6 time steps. Calculate the potential energy of the system and the force on the particles, determine the position of the particles at the next time according to the Verlet-Velocity integration algorithm, and obtain the three-dimensional coordinates of the molecular trajectory; At the same time, the system evolution trajectory is sampled, and the coordinates and velocities of each particle in the system are output at a frequency of once every 1000 steps, and the ensemble statistics is carried out.

[0105] Step six: data processing and visualization processing: as shown in Figure 8 , using Ovito software, the microstructure of the electrolyte at different temperatures can be intuitively presented. Figure 9 In it, the Fortran code is used to calculate the radial distribution function of lithium ions to represent the local composition distribution of ions. Figure 9 In it, the Fortran code is used to calculate the lithium ion structure factor to represent the degree of ion aggregation in space. Figure 9 In it, the Fortran code is used to calculate the mean square displacement of lithium ions to represent the diffusion speed of ions in the lithium battery electrolyte.

[0106] As can be seen, the coarse-grained model used in this application ignores molecular details that have little impact on system properties, and instead uses a few atomic groups as the basic units for modeling and simulation. For example, the coarse-grained process for surfactant molecules, such as alkylphenol polyoxyethylene ethers, integrates information about structural units along the chain, describing the molecule as a freely connected chain of coarse-grained particles. Each coarse-grained particle can represent several atoms, monomers, or even molecular fragments, and classical physical interactions are used to describe the interactions between molecules. Lennard-Jones particles are used to simulate anions and cations. Furthermore, to more accurately reflect the physicochemical properties of solvent molecules in the electrolyte, an explicit solvent method is employed to construct the electrolyte system model. In the model, Lennard-Jones particles are used to simulate organic solvent molecules, and the dielectric constant of the system is set based on the actual properties of the organic solvent molecules in the electrolyte. The explicit solvent method explicitly represents the interaction strength (such as van der Waals interactions and hydrophilic and hydrophobic interactions) and size of the solvent molecules, thereby accurately simulating the microstructure and evolution of the solvent molecules in the system, such as the formation of solvation shells. Furthermore, the model accurately simulates the dynamic properties of solvent molecules and the corresponding fluid dynamics. These features lay an important foundation for accurately simulating the complex interactions of ions, ions, and solvents in electrolyte systems, as well as the microstructure and dynamic properties of the corresponding systems. Furthermore, compared to the all-atom model, the coarse-grained model significantly reduces the computational effort and improves simulation efficiency.

[0107] In summary, using a coarse-grained model instead of an all-atom model ignores unnecessary details, thereby reducing the time required for the overall system evolution and extending the timescale of system simulation. Using a coarse-grained force field instead of an all-atom force field ignores interactions with less significant impact on the system and describes the primary interactions with classical physical potential energy. The potential function of coarse-grained simulation has a wider range of applicability, allowing for the simulation of unconventional molecular systems. It is also smoother than all-atom simulations, allowing for larger time steps. This method utilizes high-performance parallel computing to efficiently explore the parameter space, improving the efficiency and accuracy of the simulation. Finally, high-performance software for calculating physical parameters such as the structural factor was written in Fortran, enabling parallel analysis of the output structural data.

[0108] Example 2 The second embodiment of the present invention provides a battery electrolyte system simulation device, see Figure 10 As shown, Figure 10 The figure shows a schematic structural diagram of a battery electrolyte system simulation device provided in the second embodiment of the present invention, wherein the device comprises: The simulation parameter optimization module 1001 is configured to calculate the size of each component in the battery electrolyte, perform coarse-grained processing on each component, and optimize the size of each component and the coarse-grained processing result to obtain simulation parameters. The model construction module 1002 is configured to set a simulation box and a periodic boundary condition, and construct an initial model of the battery electrolyte coarse-grained simulation according to the simulation box and the periodic boundary condition, in combination with energy minimization of the battery electrolyte system. The potential function determination module 1003 is configured to determine a potential function of interaction between each component according to the particle distance and particle energy of different particles in each component, and the ion valence of each particle. The model optimization module 1004 is configured to perform energy minimization operation and pre-equilibrium simulation on the initial model, and optimize the initial model to obtain an optimized model in combination with the potential function of interaction between each component. The trajectory parameter determination module 1005 is configured to perform molecular dynamics simulation on the optimized model according to the potential function of interaction between each component and the simulation parameters to obtain the motion trajectory parameters of each particle in the battery electrolyte. The electrolyte system simulation module 1006 is configured to determine particle parameters for characterizing the structure of the battery electrolyte system based on the motion trajectory parameters of each particle in the battery electrolyte, and display the structure of the battery electrolyte system through visualization software based on the particle parameters.

[0109] Embodiment three Based on the same application concept, see Figure 11 as shown, Figure 11 The structure of the computer device provided by the embodiment three of the application is shown, wherein, as shown, Figure 11 The computer device 1100 provided by the embodiment three of the application includes: The processor 1101, the memory 1102 and the bus 1103, the memory 1102 stores machine readable instructions executable by the processor 1101, when the computer device 1100 runs, the processor 1101 and the memory 1102 communicate through the bus 1103, the machine readable instructions are executed by the processor 1101 to perform the steps of the battery electrolyte system simulation method shown in the above embodiment one.

[0110] Embodiment four Based on the same application concept, the computer readable storage medium provided by the embodiment of the application also stores the computer program, and the computer program is executed by the processor to perform the steps of the battery electrolyte system simulation method in any of the above embodiments.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and the device described above can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0112] The computer program product for simulating the battery electrolyte system provided in the embodiment of the application includes a computer readable storage medium storing program codes, the program codes include instructions for executing the method described in the foregoing method embodiment, and the specific implementation can be referred to the method embodiment, and will not be repeated here.

[0113] The device for simulating the battery electrolyte system provided in the embodiment of the application can be specific hardware on the equipment or software or firmware installed on the equipment. The device provided in the embodiment of the application has the same implementation principle and generated technical effects as the foregoing method embodiment, and for the brevity of description, the part not mentioned in the device embodiment can refer to the corresponding content in the foregoing method embodiment. Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, the device and the unit described above can refer to the corresponding process in the foregoing method embodiment, and will not be repeated here.

[0114] In the embodiments provided in the application, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or 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 units shown or discussed can be indirect coupling or communication connection through some communication interfaces, devices or units, and can be electrical, mechanical or other forms.

[0115] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0116] In addition, each functional unit in the embodiments provided in the application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0117] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0118] It should be noted that similar reference numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0119] Finally, it should be noted that the above-described embodiments are only specific implementations of the present application, which are used to illustrate the technical solutions of the present application, but not to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily think of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed by the present application, or make equivalent replacements to some technical features. These modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application. They should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A battery electrolyte system simulation method, characterized in that: The method comprises: Calculate the size of each component in the battery electrolyte, perform coarse-graining on each component, and optimize the size of each component and the coarse-graining results to obtain simulation parameters; Setting a simulation box and periodic boundary conditions, and constructing an initial model for battery electrolyte coarse-grained simulation based on the simulation box and the periodic boundary conditions and in combination with energy minimization of the battery electrolyte system; The potential energy function of the interaction between the components is determined based on the particle distance and particle energy of different particles in each component, as well as the ionic valence of each particle; Performing energy minimization and pre-equilibrium simulation on the initial model, and optimizing the initial model in combination with the potential energy function of the interaction between the components to obtain an optimized model; According to the potential energy function of the interaction between the components and the simulation parameters, molecular dynamics simulation is performed on the optimization model to obtain the motion trajectory parameters of each particle in the battery electrolyte; Based on the motion trajectory parameters of each particle in the battery electrolyte, particle parameters for characterizing the battery electrolyte system structure are determined, and the battery electrolyte system structure is displayed through visualization software based on the particle parameters.

2. The method according to claim 1, characterized in that The calculation of the size of each component in the battery electrolyte, the coarse-graining of each component, and the optimization of the size of each component and the coarse-graining results to obtain simulation parameters include: Calculating the sizes of anions and cations in the lithium salt in the battery electrolyte using Material Studio software; The lithium salt, solvent molecules and additives in the battery electrolyte are coarse-grained into charged spherical particles, polar spherical particles or non-polar spherical particles using a Martini force field; The simulation parameters are obtained by optimizing the sizes of anions and cations in the lithium salt in the battery electrolyte and the potential energy parameters of the charged spherical particles, the polar spherical particles and the non-polar spherical particles.

3. The method according to claim 1, characterized in that The initial model of the battery electrolyte coarse-grained simulation is constructed based on the simulation box and the periodic boundary conditions in combination with the energy minimization of the battery electrolyte system, including: Determining the concentration of each component of the battery electrolyte according to the periodic boundary conditions; The components were randomly placed in the simulation box according to their concentrations, and energy minimization was performed using LAMMPS software combined with Soft potential to eliminate particle overlap, thereby obtaining the initial model.

4. The method according to claim 1, wherein The potential energy function of the interaction between the components is determined based on the particle distance and particle energy of different particles in each component and the ion valence of each particle, including: The Lennard-Jones potential between different particles in each component is determined based on the particle distance and particle energy of different particles in each component: The Coulum potential between ions is determined according to the ionic valence of each particle: The potential energy function is constructed according to the Lennard-Jones potential and the Coulum potential.

5. The method according to claim 1, wherein The step of performing energy minimization and pre-equilibrium simulation on the initial model and optimizing the initial model in combination with the potential energy function of the interaction between the components to obtain an optimized model comprises: Performing energy minimization and pre-equilibrium simulation on the initial model using LAMMPS software and soft repulsive potential to obtain an initial structure in which the components in the initial model are uniformly dispersed; Based on the initial structure, soft potential is applied to the initial model in stages to minimize energy, and the model energy coefficient is gradually reduced to a preset threshold to ensure that the particles are evenly dispersed and have no overlap, thereby obtaining the optimized model.

6. The method according to claim 1, characterized in that The molecular dynamics simulation of the optimization model is performed based on the potential energy function of the interaction between the components and the simulation parameters to obtain the motion trajectory parameters of each particle in the battery electrolyte, including: Using LAMMPS software to perform molecular dynamics simulation on the optimized model according to the potential energy function of the interaction between the components and the simulation parameters; Calculate the system potential energy and particle forces based on the simulator results; According to the system potential energy and the force on the particles, the motion trajectory parameters of each particle are determined using the Verlet-Velocity integration algorithm and the ensemble statistics algorithm.

7. The method according to claim 1, characterized in that The particle parameters include radial distribution function, structure factor, mean square displacement and diffusion coefficient.

8. A battery electrolyte system simulation device, characterized in that: The device comprises: The simulation parameter optimization module is used to calculate the size of each component in the battery electrolyte, perform coarse-graining on each component, and optimize the size of each component and the coarse-graining results to obtain simulation parameters; A model construction module, used to set a simulation box and periodic boundary conditions, and to construct an initial model for battery electrolyte coarse-grained simulation based on the simulation box and the periodic boundary conditions in combination with energy minimization of the battery electrolyte system; A potential energy function determination module is used to determine the potential energy function of the interaction between the components based on the particle distance and particle energy of different particles in each component and the ion valence of each particle; A model optimization module is used to perform energy minimization and pre-equilibrium simulation on the initial model, and optimize the initial model by combining the potential energy function of the interaction between the components to obtain an optimized model; a trajectory parameter determination module, configured to perform molecular dynamics simulation on the optimization model based on the potential energy function of the interaction between the components and the simulation parameters to obtain the motion trajectory parameters of each particle in the battery electrolyte; The electrolyte system simulation module is used to determine the particle parameters used to characterize the battery electrolyte system structure based on the motion trajectory parameters of each particle in the battery electrolyte, and to display the battery electrolyte system structure through visualization software based on the particle parameters.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor and the memory communicate via the bus. When the machine-readable instructions are executed by the processor, the steps of the battery electrolyte system simulation method as described in any one of claims 1 to 7 are performed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the battery electrolyte system simulation method according to any one of claims 1 to 7.

Images