Molecular dynamics-based simulation method for soc change of electrolyte of iron-chromium flow battery

Abstract

The application discloses a simulation method for SOC change of an electrolyte of an iron-chromium liquid flow battery based on molecular dynamics, which establishes an electrolyte model of the iron-chromium liquid flow battery under different SOCs, and constructs a molecular dynamics model of the electrolyte of the iron-chromium liquid flow battery under different SOCs at a specific temperature. The reliability of the model is demonstrated from aspects of a diffusion coefficient, a coordination number, viscosity and density, and the model can effectively obtain the composition relationship between molecules and ions in the electrolyte, predict the electrochemical properties of the electrolyte, and further optimize the electrolyte composition, which is beneficial to improving the power, capacity and stability of the iron-chromium liquid flow battery. The simulation method based on the molecular dynamics is used to calculate and analyze the changes of physical properties and chemical properties of the electrolyte when the electrolyte changes with the SOC, and the influence of the SOC on the physical and chemical properties of the electrolyte is accurately explored, which has guiding significance for optimizing the electrolyte of the iron-chromium liquid flow battery and improving the performance of the iron-chromium liquid flow battery.

Description

Technical Field

[0001] This invention relates to the field of flow battery simulation, specifically to a method for simulating the state of charge (SOC) changes of the electrolyte in an iron-chromium flow battery based on molecular dynamics. Background Technology

[0002] Flow batteries, with their outstanding advantages such as high safety, long cycle life, and flexible design, are widely recognized as the most suitable energy storage component for building large-scale energy storage systems, and have made significant progress in commercial applications. Among them, iron-chromium flow batteries, as the earliest type of flow battery technology, use inexpensive metals iron and chromium as active materials, and are expected to become a cost-effective energy storage device.

[0003] Electrolyte is the core material for storing energy in iron-chromium redox flow batteries, and the reaction characteristics of the electrode / electrolyte interface directly affect and determine the battery's performance. State of charge (SOC) is a key indicator of battery capacity, which is greatly affected by changes in the physical and chemical properties of the electrolyte. By establishing a simulation model of the iron-chromium redox flow battery electrolyte, the compositional relationships between molecules and ions in the electrolyte can be effectively obtained, the electrochemical properties of the electrolyte can be predicted, and the electrolyte composition can be optimized, which will help improve the power, capacity, and stability of iron-chromium redox flow batteries.

[0004] Molecular dynamics methods, based on classical Newton's equations of motion and the interaction potentials between atoms, numerically integrate the Newtonian equations of motion for the velocity and forces acting on each particle, providing relatively accurate approximate expressions of the force and thermal properties, as well as the dynamic laws, of microscopic systems. Molecular dynamics simulations are characterized by low experimental cost, high safety, and the ability to perform experiments that are difficult or impossible under normal conditions. They can effectively explore the complex relationships between molecules and ions in the electrolyte of iron-chromium redox flow batteries and accurately calculate various properties and parameters of the electrolyte at different states of charge (SOC). However, current simulations of iron-chromium redox flow battery electrolytes are limited, and corresponding molecular dynamics simulation models have not yet been established.

[0005] Therefore, researchers in this field are dedicated to developing a method for simulating and analyzing the SOC changes of electrolytes in iron-chromium flow batteries based on molecular dynamics. Summary of the Invention

[0006] Given the limited existing research on molecular dynamics simulations of iron-chromium redox flow battery electrolytes, this invention aims to overcome the shortcomings of existing technologies by providing a molecular dynamics-based simulation method for SOC changes in iron-chromium redox flow battery electrolytes. This simulation method establishes models of iron-chromium redox flow battery electrolytes under different SOCs, constructing molecular dynamics models of electrolytes with different SOCs at specific temperatures. The reliability of the model is demonstrated through diffusion coefficients, coordination numbers, viscosity, and density. This method effectively obtains the compositional relationships between molecules and ions in the electrolyte, predicts the electrochemical properties of the electrolyte, and optimizes the electrolyte composition, thereby improving the power, capacity, and stability of iron-chromium redox flow batteries. By calculating and analyzing the changes in the physical and chemical properties of the electrolyte with varying SOCs through molecular dynamics simulations, the influence of SOCs on the various physicochemical properties of the electrolyte is accurately explored. This provides guidance for subsequent optimization of iron-chromium redox flow battery electrolytes and improvement of iron-chromium redox flow battery performance.

[0007] To achieve the above technical effects, the following technical solution is adopted:

[0008] A molecular dynamics-based method for simulating the state of charge (SOC) changes of the electrolyte in an iron-chromium flow battery includes the following steps:

[0009] Step S1: Establish a molecular dynamics model of the electrolyte in an iron-chromium redox flow battery based on molecular dynamics. The electrolyte should contain ferrous ions (Fe). 2+ ferric ions Fe 3+ Divalent chromium ions Cr 2+ Trivalent chromium ions (Cr) 3+ chloride ions Cl - and hydrogen ions H + The solvent is water (H2O).

[0010] Ferrous ions (Fe) were constructed using Packmol software. 2+ ), trivalent iron ions (Fe 3+ ), divalent chromium ions (Cr 2+ ), trivalent chromium ions (Cr 3+ ), water molecules (H2O), hydrogen ions (H+) + ), chloride ions (Cl) - )Model;

[0011] When constructing the aqueous solution models of ferrous ions, ferric ions, divalent chromium ions, and trivalent chromium ions, the distance constraint tolerance was set to 2 Å, and the atom type was selected as pdb format.

[0012] Step S2: Based on the studied iron-chromium redox flow battery electrolyte, select appropriate model potential energy parameters and distribute them in the system particles. The model potential energy parameters are selected from one or more of the following: REBO, ReaxFF, AMBER IOD, TIP4P, and TIP4P-EW potential functions.

[0013] Step S3: Construct a structure containing Fe 2+ and Fe 3+ Cr 3+ and Cr 2+ A molecular simulation system of electrolytes of hydrochloric acid (HCl) and water, in which Fe 2+ Fe 3+ and Cr 3+ Cr 2+ The quantity is set according to different SOCs;

[0014] The amounts of ferrous ions, ferric ions, divalent chromium ions, and trivalent chromium ions in the electrolyte are determined based on the SOC, and a molecular dynamics-based electrolyte model for an iron-chromium flow battery is constructed using the model obtained in step S1 through the MOLTEMPLATE software.

[0015] Step S4: Set the ensemble and boundary conditions for the molecular dynamics simulation;

[0016] Step S5: Perform isochoric ensemble NVT equilibrium simulation on the iron-chromium redox flow battery electrolyte molecular dynamics simulation system at high temperature and output the result file. Then, set the temperature under isothermal and isobaric ensemble NPT and perform simulation with energy and density balance as the goal, and output the result file.

[0017] Step S6: Calculate the self-diffusion coefficient of each system in the isothermal and isobaric ensemble NPT based on the results file, and calculate the viscosity of the electrolyte system in the microcanonical ensemble NVE;

[0018] Step S7: The atomic trajectories are processed using the Visual Molecular Dynamics (VMD) visualization software to calculate the radial distribution and coordination of ions and water atoms;

[0019] Furthermore, in step S1, the molecular dynamics model of the iron-chromium flow battery electrolyte is established using PACKMOL and MOLTEMPLATE software. The data file contains the side length of the simulated cube, the initial coordinates of the atoms, and the bond angles and dihedral angles between the atoms.

[0020] Furthermore, in step S3, the SOC is selected as 0%, 25%, 50%, 75%, and 100%, and Fe is set... 2+ and Fe 3+ When the quantity ratio is 100:0, the SOC is 0%.

[0021] Specifically: when SOC is 0%, the ratio of ferrous ions to ferric ions is 100:0, and the ratio of ferrous ions to chromium ions is 0:100; when SOC is 25%, the ratio of ferrous ions to ferric ions is 75:25, and the ratio of ferrous ions to chromium ions is 25:75; when SOC is 50%, the ratio of ferrous ions to ferric ions is 50:50, and the ratio of ferrous ions to chromium ions is 50:50; when SOC is 75%, the ratio of ferrous ions to ferric ions is 25:75, and the ratio of ferrous ions to chromium ions is 75:25; when SOC is 100%, the ratio of ferrous ions to ferric ions is 0:100, and the ratio of ferrous ions to chromium ions is 100:0.

[0022] Furthermore, in step S4, the molecular dynamics simulation calculations are performed using LAMMPS software; in step S5, the isochoric ensemble NVT equilibrium simulation of the iron-chromium redox flow battery electrolyte molecular dynamics simulation system at high temperature employs three-dimensional periodic boundary conditions with a time step of 0.1 fs. The water molecules are modeled using the TIP4P-EW force field, and the bond angles and bond lengths of the water molecules are fixed using the fix shake command; the long-range forces in the isochoric ensemble NVT equilibrium simulation are solved using the PPPM method; the equilibrium temperature is 1000K, and the equilibrium time is 10 ps.

[0023] Furthermore, in step S5, the isothermal and isobaric ensemble NPT uses a Noose-Hoover thermostat and a pressure regulator to anneal the system. The target temperature of the system is set to 330K and equilibration is performed for 200 ps. The temperature damping parameter is 100 fs, the pressure damping parameter is 100 fs, and the density of the system is output.

[0024] Furthermore, in step S5, the self-diffusion coefficient of each system is calculated under isothermal and isobaric ensemble NPT using the Eistein method, with an equilibrium temperature of 330K and an equilibrium time of 40ps. Three-dimensional periodic boundary conditions are adopted, with a time step of 0.01 fs. The water molecules are modeled using the TIP4P-EW force field model, and the bond angles and bond lengths of the water molecules are fixed using the fix shake command.

[0025] Furthermore, in step S6, the viscosity of the electrolyte system is calculated in the microcanonical ensemble NVE using the Green-Kubo method, with an equilibrium temperature of 330K, an equilibrium time of 1000ps, a three-dimensional periodic boundary condition, and a time step of 0.1 fs. The water molecules are modeled using the TIP4P-EW force field model, and the bond angles and bond lengths of the water molecules are fixed using the fix shake command.

[0026] Furthermore, the formula for calculating the self-diffusion coefficient in step S6 is as follows:

[0027] ;

[0028] in, is the position vector of the particle at time t; D is the particle self-diffusion coefficient; t is the simulation time.

[0029] Furthermore, the viscosity calculation formula for the electrolyte system in step S6 is as follows:

[0030] ;

[0031] in, Boltzmann's constant, To simulate the model volume, The off-diagonal components of the stress tensor t is the viscosity of the electrolyte system, T is the simulated temperature of the electrolyte, t0 is the simulation start time, and t is the total simulation time.

[0032] Furthermore, the formula for calculating the coordination situation in step S7 is as follows:

[0033] ;

[0034] Where n is the coordination number, The number density of particles, The radius of the central particle, The radial distribution function of a specified particle relative to the central particle is calculated as follows:

[0035] ;

[0036] in, The total number of molecules, For the total simulation time, For the set distance difference, for The number of molecules between; The radius of the central particle, The number density of particles.

[0037] Based on any of the simulation methods described, the model can be verified for accuracy and feasibility by experimentally measuring density and viscosity.

[0038] This simulation method establishes electrolyte models for iron-chromium redox flow batteries under different state of charge (SOC) conditions, constructing molecular dynamics models of electrolytes with different SOCs at specific temperatures. The reliability of the models is demonstrated through diffusion coefficients, coordination numbers, viscosity, and density. This method effectively obtains the compositional relationships between molecules and ions in the electrolyte, predicts the electrochemical properties of the electrolyte, and optimizes the electrolyte composition, which is beneficial for improving the power, capacity, and stability of iron-chromium redox flow batteries. By calculating and analyzing the changes in the physical and chemical properties of the electrolyte with varying SOC based on molecular dynamics simulations, the influence of SOC on various physicochemical properties of the electrolyte is accurately explored. This provides guidance for subsequent optimization of iron-chromium redox flow battery electrolytes and improvement of iron-chromium redox flow battery performance. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. The drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0040] Figure 1 This is a simulation model diagram of the electrolyte at 330K with a SOC of 0% in Example 3 of the present invention;

[0041] Figure 2 This is a comparison chart of the simulated density and actual density of the electrolyte at 0% SOC at 330K in Example 3 of the present invention.

[0042] Figure 3 This is a comparison chart of the simulated viscosity and actual viscosity of the electrolyte at 0% SOC at 330K in Example 3 of the present invention.

[0043] Figure 4 This is a graph showing the change in MSD of chromium ions at 330K with SOC in Example 3 of the present invention;

[0044] Figure 5 This is a graph showing the change in iron ion MSD with SOC at 330K in Example 3 of the present invention.

[0045] Figure 6 This is a graph showing the change in MSD of hydrogen ions at 330K with SOC in Example 3 of the present invention;

[0046] Figure 7 This is a graph showing the change in chloride ion MSD with SOC at 330K in Example 3 of the present invention;

[0047] Figure 8 This is a graph showing the changes in viscosity and density with SOC at 330K in Example 3 of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0051] Example 1:

[0052] Step S1: Constructing the electrolyte for an iron-chromium flow battery based on molecular dynamics, incorporating ferrous ions (Fe2+) into the electrolyte. 2+ ), trivalent iron ions (Fe 3+ ), divalent chromium ions (Cr 2+ ), trivalent chromium ions (Cr 3+ ), chloride ions (Cl) - ), hydrogen ions (H) + The models for ionic force fields and water molecule (H2O) are used, where the ionic force field is described using the OPLS-AA all-atomic force field and the water molecule force field is described using the TIP4P-EW force field.

[0053] Step S2: The software PACKMOL was used to construct aqueous models of ferrous ions, ferric ions, divalent chromium ions, and trivalent chromium ions. The software MOLTEMPLATE was used to construct a molecular dynamics model of the electrolyte in the iron-chromium flow battery. The model size was 60 Å × 60 Å × 60 Å (x × y × z). The total number of ferrous ions and ferric ions was controlled to be 100, and the total number of divalent chromium ions and trivalent chromium ions was controlled to be 100.

[0054] Specifically: when SOC is 0%, the ratio of ferrous ions to ferric ions is 100:0, and the ratio of ferrous ions to chromium ions is 0:100; when SOC is 25%, the ratio of ferrous ions to ferric ions is 75:25, and the ratio of ferrous ions to chromium ions is 25:75; when SOC is 50%, the ratio of ferrous ions to ferric ions is 50:50, and the ratio of ferrous ions to chromium ions is 50:50; when SOC is 75%, the ratio of ferrous ions to ferric ions is 25:75, and the ratio of ferrous ions to chromium ions is 75:25; when SOC is 100%, the ratio of ferrous ions to ferric ions is 0:100, and the ratio of ferrous ions to chromium ions is 100:0.

[0055] Step S3: Use the LAMMPS (Large-scale Atomic / Molecular Massively ParallelSimulator) open-source software package to perform molecular dynamics simulation calculations on the constructed iron-chromium flow battery electrolyte model.

[0056] Step S4: Three-dimensional periodic boundary conditions are used for all molecular dynamics simulations of the model.

[0057] Step S5: Long-range forces are solved using the PPPM method.

[0058] Step S6: The fix shake command fixes the bond angles and bond lengths of water molecules.

[0059] Step S7: Perform a preliminary simulation in an isochoric ensemble (NVT) with a time step of 0.1 fs, a temperature of 1000 K, and a total running time of 10 ps.

[0060] Step S8: Anneal the system to the target temperature of 330K in an isothermal-isobaric (NPT) ensemble using a Noose-Hoover thermostat and a pressure regulator, with a time step of 0.1 fs, and perform equilibration for 200 ps. The temperature damping parameter is 100 fs, the pressure damping parameter is 100 fs, and the density of the system is output.

[0061] Step S9: Calculate the self-diffusion coefficient of each system under isothermal and isobaric ensemble (NPT) with a time step of 0.01 fs, using the Eistein method, with an equilibrium temperature of 330 K, an equilibrium time of 40 ps, ​​and sampling every 0.1 ps.

[0062] Step S10: Calculate the viscosity of the electrolyte system in the microcanonical ensemble (NVE) with a time step of 0.1 fs, using the Green-Kubo method, an equilibrium temperature of 330 K, an equilibrium time of 1000 ps, ​​and sampling every 4 ps to perform the analysis.

[0063] Step S11: The atomic trajectory is processed using VMD (Visual Molecular Dynamics) visualization software.

[0064] The formula for calculating the self-diffusion coefficient is as follows:

[0065] ;

[0066] in, is the position vector of the particle at time t; D is the particle self-diffusion coefficient; t is the simulation time.

[0067] The formula for calculating the viscosity of an electrolyte system is as follows:

[0068] ;

[0069] in, Boltzmann's constant, To simulate the model volume, The off-diagonal components of the stress tensor t is the viscosity of the electrolyte system, T is the simulated temperature of the electrolyte, t0 is the simulation start time, and t is the total simulation time.

[0070] The formula for calculating coordination is as follows:

[0071] ;

[0072] Where n is the coordination number, The number density of particles, The radius of the central particle, The radial distribution function of a specified particle relative to the central particle is calculated as follows:

[0073] ;

[0074] in, The total number of molecules, For the total simulation time, For the set distance difference, for The number of molecules between; The radius of the central particle, The number density of particles.

[0075] Step S12: Prepare electrolytes with different SOCs according to the different ion ratios in the above simulation. Control the total concentration of iron in the electrolyte to be 1 mol / L, the concentration of chromium to be 1 mol / L, and the total concentration of hydrochloric acid to be 3 mol / L. Use an Ubbelohde viscometer and a densitometer to measure the viscosity and density of the electrolyte. The temperature is controlled at 330K throughout the measurement process.

[0076] Simulation results are as follows Figure 1-8 As shown, Figure 1 The diagram shows the electrolyte simulation model at 330K with a SOC of 0% in Example 3 of the present invention. It can be seen that the ions are evenly distributed in the model and the model is completely filled. Figure 2 This is a comparison chart of the simulated density and actual density of the electrolyte at 0% SOC at 330K in Example 3 of the present invention. The results show that the simulated density is almost the same as the actual measured density, and the model has a good predictive effect on the density of the electrolyte. Figure 3 This is a comparison chart of the simulated viscosity and actual viscosity of the electrolyte at 0% SOC at 330K in Example 3 of the present invention. The results show that the simulated viscosity is almost the same as the actual measured viscosity, and the model has a good predictive effect on the viscosity of the electrolyte. Figure 4 The graph shows the change in MSD of chromium ions at 330K with SOC in Example 3 of this invention. The results indicate that the activity of chromium ions is not affected by the change in SOC. Figure 5 The graph shows the change of iron ion MSD with SOC at 330K in Example 3 of this invention. The results show that iron ions have the highest activity at SOC of 75% and the lowest activity at SOC of 100%. Figure 6 This is a graph showing the change of hydrogen ion SOC with temperature at 330K in Example 3 of the present invention. The results show that hydrogen ions have the highest activity when the SOC is 75% and the lowest activity when the SOC is 0%. Figure 7 The graph shows the change of chloride ion SOC with temperature at 330K in Example 3 of the present invention. The results show that chloride ions have the highest activity when the SOC is 50% and the lowest activity when the SOC is 100%. Figure 8 The graph shows the changes in viscosity and density at 330K with SOC in Example 3 of this invention. The results show that the viscosity decreases first and then increases as the SOC increases from 0% to 100%, reaching its minimum at 50% SOC. Similarly, the density increases first and then decreases as the SOC increases from 0% to 100%, reaching its maximum at 50% SOC.

[0077] In summary, this invention constructs a molecular dynamics model for the electrolyte of an iron-chromium flow battery and demonstrates the reliability of the model in terms of coordination number, diffusion coefficient, density, and viscosity. The model analyzes the influence of SOC changes on the electrolyte's physical properties, more realistically reflecting the regulatory mechanism of SOC changes on the interactions of ions within the electrolyte. This provides guidance for subsequent optimization of iron-chromium flow battery electrolyte formulations and improvement of iron-chromium flow battery performance.

[0078] Therefore, those skilled in the art will recognize that although embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Therefore, the scope of the present invention should be understood and recognized as covering all such other variations or modifications.

Claims

1. A method for simulating the state of charge (SOC) change of electrolyte in an iron-chromium redox flow battery based on molecular dynamics, characterized in that, The method includes the following steps: Step S1: Establishing a molecular dynamics model of the electrolyte of the iron-chromium flow battery based on molecular dynamics, the electrolyte should contain ferrous ions Fe 2+ , ferric ions Fe 3+ , divalent chromium ions Cr 2+ , trivalent chromium ions Cr 3+ , chloride ions Cl - and hydrogen ions H + , and the solvent is water H2O; Step S2: Based on the studied iron-chromium redox flow battery electrolyte, select appropriate model potential energy parameters and distribute them in the system particles. The model potential energy parameters are selected from one or more of the following: REBO, ReaxFF, AMBER IOD, TIP4P, and TIP4P-EW potential functions. Step S3: Constructing electrolyte molecular simulation system containing Fe 2+ and Fe 3+ , Cr 3+ and Cr 2+ , hydrochloric acid HCl and water, wherein the number of Fe 2 + , Fe 3+ and Cr 3+ , Cr 2+ is set according to different SOC respectively; Step S4: Set the ensemble and boundary conditions for the molecular dynamics simulation; Step S5: Perform isochoric ensemble NVT equilibrium simulation on the iron-chromium redox flow battery electrolyte molecular dynamics simulation system at high temperature and output the result file. Then, set the temperature under isothermal and isobaric ensemble NPT and perform simulation with energy and density balance as the goal, and output the result file. Step S6: Calculate the self-diffusion coefficient of each system in the isothermal and isobaric ensemble NPT based on the results file, and calculate the viscosity of the electrolyte system in the microcanonical ensemble NVE; Step S7: The atomic trajectories are processed using the Visual Molecular Dynamics (VMD) visualization software to calculate the radial distribution and coordination of ions and water atoms.

2. The method for simulating the SOC change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, In step S1, the molecular dynamics model of the iron-chromium redox flow battery electrolyte is established using PACKMOL and MOLTEMPLATE software. The data file contains the side length of the simulated cube, the initial coordinates of the atoms, and the bond angles and dihedral angles between the atoms.

3. The method for simulating the SOC change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, In step S3, the SOC is selected as 0%, 25%, 50%, 75%, and 100%, and Fe is set. 2+ and Fe 3+ When the quantity ratio is 100:0, the SOC is 0%.

4. The method for simulating the state of charge (SOC) change of an iron-chromium flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, In step S4, the molecular dynamics simulation calculations were performed using LAMMPS software. In step S5, the isochoric ensemble NVT equilibrium simulation of the iron-chromium redox flow battery electrolyte system at high temperature was performed using three-dimensional periodic boundary conditions with a time step of 0.1 fs. The water molecules were modeled using the TIP4P-EW force field, and the bond angles and bond lengths of the water molecules were fixed using the fix shake command. In the isochoric ensemble NVT equilibrium simulation, the long-range forces were solved using the PPPM method. The equilibrium temperature was 1000 K, and the equilibrium time was 10 ps.

5. The method for simulating the state of charge (SOC) change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, In step S5, the isothermal and isobaric ensemble NPT uses a Noose-Hoover thermostat and pressure regulator to anneal the system. The target temperature is set to 330K and equilibration is performed for 200ps. The temperature damping parameter is 100 fs, the pressure damping parameter is 100 fs, and the density of the system is output.

6. The method for simulating the state of charge (SOC) change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, In step S5, the self-diffusion coefficient of each system is calculated under isothermal and isobaric ensemble NPT using the Eistein method, with an equilibrium temperature of 330K and an equilibrium time of 40ps. Three-dimensional periodic boundary conditions are used with a time step of 0.01 fs. The water molecules are modeled using the TIP4P-EW force field model, and the bond angles and bond lengths of the water molecules are fixed using the fix shake command.

7. The method for simulating the state of charge (SOC) change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, In step S6, the viscosity of the electrolyte system is calculated in the microcanonical ensemble NVE using the Green-Kubo method. The equilibrium temperature is 330K, the equilibrium time is 1000ps, a three-dimensional periodic boundary condition is used, and the time step is 0.1fs. The water molecules are modeled using the TIP4P-EW force field model, and the bond angles and bond lengths of the water molecules are fixed using the fix shake command.

8. The method for simulating the SOC change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, The formula for calculating the self-diffusion coefficient in step S6 is as follows: ； in, is the position vector of the particle at time t; D is the particle self-diffusion coefficient; t is the simulation time.

9. The method for simulating the SOC change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, The viscosity calculation formula for the electrolyte system in step S6 is as follows: ； in, Boltzmann's constant, To simulate the model volume, The off-diagonal components of the stress tensor t is the viscosity of the electrolyte system, T is the simulated temperature of the electrolyte, t0 is the simulation start time, and t is the total simulation time.

10. The method for simulating the SOC change of an iron-chromium redox flow battery electrolyte based on molecular dynamics as described in claim 1, characterized in that, The formula for calculating coordination in step S7 is as follows: ； Where n is the coordination number, The number density of particles, The radius of the central particle, The radial distribution function of a specified particle relative to the central particle is calculated as follows: ； in, The total number of molecules, For the total simulation time, For the set distance difference, for The number of molecules between; The radius of the central particle, The number density of particles.

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