Molecular dynamics-based iron-chromium flow battery electrolyte SOC change simulation method

Abstract

The invention discloses an iron-chromium flow battery electrolyte SOC change simulation method based on molecular dynamics, and the simulation method comprises the steps: building an iron-chromium flow battery electrolyte model under different SOCs, and constructing a molecular dynamics model of the iron-chromium flow battery electrolyte under different SOCs at a specific temperature; the reliability of the model is demonstrated from the aspects of diffusion coefficient, coordination number, viscosity, density and the like, the composition relationship between molecules and ions in the electrolyte can be effectively obtained, the electrochemical property of the electrolyte can be predicted, the composition of the electrolyte is further optimized, the power, capacity and stability of the iron-chromium flow battery can be improved, and the service life of the iron-chromium flow battery can be prolonged. The change of physical properties and chemical properties of the electrolyte along with the change of SOC is calculated and analyzed based on molecular dynamics simulation, the influence of SOC on the physical and chemical properties of the electrolyte is accurately explored, and the method has guiding significance on subsequent optimization of the electrolyte of the iron-chromium flow battery and improvement of the performance of the iron-chromium flow battery.

Description

Technical Field

[0001] The invention relates to the field of liquid flow battery simulation, and in particular to a method for simulating SOC changes of an electrolyte of an iron-chromium liquid flow battery based on molecular dynamics. Background Art

[0002] Liquid flow batteries are recognized by the industry as the most suitable energy storage component for building large-scale energy storage systems due to their outstanding advantages such as high safety, long cycle life, and flexible design, and have made significant progress in commercial applications. Among them, iron-chromium liquid flow batteries, as the earliest liquid flow battery technology, use low-cost metal iron and chromium as active materials, and are expected to become cost-effective energy storage equipment.

[0003] The electrolyte is the core material for storing energy in iron-chromium flow batteries, and the reaction characteristics of the electrode / electrolyte interface directly affect and determine the performance of the battery. SOC is a key indicator for measuring battery capacity and is greatly affected by changes in the physical and chemical properties of the electrolyte. By establishing an iron-chromium flow battery electrolyte simulation model, the composition relationship between molecules and ions in the electrolyte can be effectively obtained, the electrochemical properties of the electrolyte can be predicted, and then the electrolyte composition can be optimized, which will be beneficial to improving the power, capacity and stability of the iron-chromium flow battery.

[0004] The molecular dynamics method is based on the classical Newtonian equations of motion and the interaction potential between atoms. It numerically integrates the Newtonian equations of motion for the velocity and force of each particle, and has a relatively accurate approximate expression of the force and thermal properties of the microscopic system, as well as the laws of kinetics. Molecular dynamics simulation has the characteristics of low experimental cost, high safety, and the ability to conduct experiments that are difficult or impossible to conduct under normal conditions. It can effectively explore the complex relationship between molecules and ions in the electrolyte of iron-chromium flow batteries, and accurately calculate the various properties and parameters of the electrolyte at different SOCs. Moreover, there are currently few simulations of iron-chromium flow battery electrolytes, and the corresponding molecular dynamics simulation model has not been established.

[0005] Therefore, researchers in this field are committed to developing a molecular dynamics-based simulation and analysis method for the SOC variation properties of iron-chromium liquid flow battery electrolytes. Summary of the invention

[0006] In view of the fact that there are few studies on molecular dynamics simulation of iron-chromium flow battery electrolyte, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a method for simulating SOC changes of iron-chromium flow battery electrolyte based on molecular dynamics. The simulation method establishes an iron-chromium flow battery electrolyte model under different SOCs, constructs a molecular dynamics model of the iron-chromium flow battery electrolyte with different SOCs at a specific temperature, and demonstrates the reliability of the model from the aspects of diffusion coefficient, coordination number, viscosity and density. It can effectively obtain the structural relationship between molecules and ions in the electrolyte, predict the electrochemical properties of the electrolyte, and then optimize the electrolyte composition, which is beneficial to improving the power, capacity and stability of the iron-chromium flow battery. By calculating and analyzing the changes in physical and chemical properties of the electrolyte as the SOC changes based on molecular dynamics simulation, the influence of SOC on the physicochemical properties of the electrolyte is accurately explored, which has guiding significance for the subsequent optimization of the iron-chromium flow battery electrolyte and improving the performance of the iron-chromium flow battery.

[0007] In order to achieve the above technical effects, the following technical solutions are adopted: The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics includes the following steps: Step S1: Establish 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 ion Fe 3+ , divalent chromium ion Cr 2+ , trivalent chromium ion Cr 3+ , chloride ion Cl - and hydrogen ions H + , the solvent is water H2O; Packmol software was used to construct the ferrous ion (Fe 2+ ), ferric ions (Fe 3+ ), divalent chromium ions (Cr 2+ ), trivalent chromium ion (Cr 3+ ), water molecules (H2O), hydrogen ions (H + ), chloride ion (Cl - )Model; When constructing the models of ferrous ion, ferric ion, divalent chromium ion and trivalent chromium ion aqueous solution, the distance constraint tolerance was selected as 2 Å and the atom type was selected as pdb format.

[0008] Step S2: based on the iron-chromium liquid flow battery electrolyte under study, selecting appropriate model potential energy parameters to be distributed in the system particles, wherein the model potential energy parameters are selected from one or more of REBO, ReaxFF, AMBER IOD, TIP4P and TIP4P-EW potential functions; Step S3: Constructing Fe 2+ and Fe3+ Cr 3+ and Cr 2+ , hydrochloric acid HCl and water electrolyte molecular simulation system, in which Fe 2+ , Fe 3+ and Cr 3+ Cr 2+ The number is set according to different SOCs; Determine the amount of ferrous ions, ferric ions, divalent chromium ions and trivalent chromium ions in the electrolyte according to the SOC, and use the model obtained in step S1 to construct a molecular dynamics-based iron-chromium liquid flow battery electrolyte model using MOLTEMPLATE software; Step S4: Setting the ensemble and boundary conditions for molecular dynamics simulation; Step S5: performing an isochoric ensemble NVT equilibrium simulation on the iron-chromium liquid flow battery electrolyte molecular dynamics simulation system at high temperature and outputting a result file, and then setting the temperature under the isothermal and isobaric ensemble NPT, performing a simulation with energy and density balance as the goal, and outputting a result file; Step S6: Calculate the self-diffusion coefficient of each system in the isothermal and isobaric ensemble NPT according to the result file, and calculate the viscosity of the electrolyte system in the microcanonical ensemble NVE; Step S7: The atomic trajectory is processed using Visual Molecular Dynamics visualization software VMD to calculate the atomic radial distribution and coordination of ions and water; Furthermore, in step S1, the molecular dynamics model of the electrolyte of the iron-chromium liquid flow battery is established using PACKMOL and MOLTEMPLATE software, and the data file contains the side length of the simulated cube box, the initial coordinates of the atoms, and the bond angles and dihedral angles between the atoms.

[0009] Furthermore, in step S3, the SOC is selected at 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%.

[0010] Specifically: when the SOC is 0%, the ratio of divalent ferrous ions to trivalent ferrous ions is 100:0, and the ratio of divalent chromium ions to trivalent chromium ions is 0:100; when the SOC is 25%, the ratio of divalent ferrous ions to trivalent ferrous ions is 75:25, and the ratio of divalent chromium ions to trivalent chromium ions is 25:75; when the SOC is 50%, the ratio of divalent ferrous ions to trivalent ferrous ions is 50:50, and the ratio of divalent chromium ions to trivalent chromium ions is 50:50; when the SOC is 75%, the ratio of divalent ferrous ions to trivalent ferrous ions is 25:75, and the ratio of divalent chromium ions to trivalent chromium ions is 75:25; when the SOC is 100%, the ratio of divalent ferrous ions to trivalent ferrous ions is 0:100, and the ratio of divalent chromium ions to trivalent chromium ions is 100:0.

[0011] Furthermore, the molecular dynamics simulation calculation in step S4 is performed using LAMMPS software; in step S5, the isochoric ensemble NVT equilibrium simulation of the iron-chromium liquid flow battery electrolyte molecular dynamics simulation system at high temperature adopts three-dimensional periodic boundary conditions, the time step is 0.1 fs, the water molecules adopt the TIP4P-EW force field model, and the fix shake command is used to fix the bond angle and bond length of the water molecules; the long-range forces in the isochoric ensemble NVT equilibrium simulation are solved by the PPPM method; the equilibrium temperature is 1000K, and the equilibrium time is 10ps.

[0012] Furthermore, in step S5, the target temperature of the isothermal and isobaric ensemble NPT annealed using a Noose-Hoover thermostat and a barostat is set to 330 K and balanced 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.

[0013] Furthermore, in step S5, the self-diffusion coefficient of each system is calculated under the isothermal and isobaric ensemble NPT, using the Eistein method, with an equilibrium temperature of 330 K and an equilibrium time of 40 ps; a three-dimensional periodic boundary condition is adopted, with a time step of 0.01 fs, and the TIP4P-EW force field model is adopted for water molecules, and the fix shake command is used to fix the bond angle and bond length of the water molecules.

[0014] Furthermore, in step S6, the viscosity of the electrolyte system is calculated in the microcanonical ensemble NVE, the Green-Kubo method is used, the equilibrium temperature is 330K, the equilibrium time is 1000ps, three-dimensional periodic boundary conditions are used, the time step is 0.1 fs, the TIP4P-EW force field model is used for water molecules, and the fix shake command is used to fix the bond angle and bond length of the water molecules.

[0015] Furthermore, the self-diffusion coefficient calculation formula 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.

[0016] Furthermore, the viscosity calculation formula of the electrolyte system in step S6 is as follows: ; in, is the Boltzmann constant, To simulate the model volume, are the off-diagonal components of the stress tensor, is the viscosity of the electrolyte system, T is the electrolyte simulation temperature, t0 is the simulation start time, and t is the total simulation time.

[0017] Furthermore, the coordination calculation formula in step S7 is as follows: ; Where n is the coordination number, is the number density of particles, is the central particle radius, is the radial distribution function of the specified particle to the central particle, and its calculation formula is as follows: ; in, is the total number of molecules, is the total simulation time, is the set distance difference, for The number of molecules between is the radius of the central particle, is the number density of particles.

[0018] Based on any of the simulation methods described above, the model can be verified for accuracy and feasibility by experimentally measuring density and viscosity.

[0019] This simulation method establishes an iron-chromium flow battery electrolyte model under different SOCs, and constructs a molecular dynamics model of the iron-chromium flow battery electrolyte at different SOCs at a specific temperature. The reliability of the model is demonstrated from the aspects of diffusion coefficient, coordination number, viscosity and density. It can effectively obtain the composition relationship between molecules and ions in the electrolyte, predict the electrochemical properties of the electrolyte, and then optimize the electrolyte composition, which is beneficial to improving the power, capacity and stability of the iron-chromium flow battery. By calculating and analyzing the changes in the physical and chemical properties of the electrolyte as the SOC changes based on molecular dynamics simulation, the influence of SOC on the physicochemical properties of the electrolyte is accurately explored, which has guiding significance for the subsequent optimization of the iron-chromium flow battery electrolyte and improving the performance of the iron-chromium flow battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. The drawings in the following description are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0021] Figure 1 This is a simulation model diagram of the electrolyte when the SOC is 0% at 330K according to an embodiment of the present invention; Figure 2 This is a comparison diagram of the simulated density and the actual density of the electrolyte when the SOC is 0% at 330K in an embodiment of the present invention; Figure 3 This is a comparison diagram of the simulated viscosity and the actual viscosity of the electrolyte when the SOC is 0% at 330K in the embodiment of the present invention; Figure 4 This is a graph showing the variation of the MSD of chromium ions with SOC at 330K in an embodiment of the present invention; Figure 5 This is a graph showing the change of MSD of iron ions with SOC at 330K in an embodiment of the present invention; Figure 6 This is a graph showing the variation of hydrogen ion MSD with SOC at 330K in an embodiment of the present invention; Figure 7 This is a graph showing the change of chloride ion MSD with SOC at 330K in an embodiment of the present invention; Figure 8 This is a graph showing the change of viscosity and density with SOC at 330K for an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0025] Embodiment 1:

[0026] Step S1: Constructing the ferrous ion (Fe) in the electrolyte of the iron-chromium flow battery based on molecular dynamics 2+ ), ferric ions (Fe 3+ ), divalent chromium ions (Cr 2+ ), trivalent chromium ion (Cr 3+ ), chloride ion (Cl - ), hydrogen ion (H + ) and water molecule (H2O) models, where the ion force field is described by the OPLS-AA all-atom force field and the water molecule force field is described by the TIP4P-EW force field.

[0027] Step S2: The software PACKMOL is used to construct aqueous solution models of ferrous ions, ferric ions, divalent chromium ions and trivalent chromium ions. The software MOLTEMPLATE is used to construct a molecular dynamics model of the iron-chromium liquid flow battery electrolyte. The size of the model is 60Å×60Å×60Å (x×y×z). The total number of ferrous ions and ferric ions is controlled to be 100, and the total number of divalent chromium ions and trivalent chromium ions is controlled to be 100.

[0028] Specifically: when the SOC is 0%, the ratio of divalent ferrous ions to trivalent ferrous ions is 100:0, and the ratio of divalent chromium ions to trivalent chromium ions is 0:100; when the SOC is 25%, the ratio of divalent ferrous ions to trivalent ferrous ions is 75:25, and the ratio of divalent chromium ions to trivalent chromium ions is 25:75; when the SOC is 50%, the ratio of divalent ferrous ions to trivalent ferrous ions is 50:50, and the ratio of divalent chromium ions to trivalent chromium ions is 50:50; when the SOC is 75%, the ratio of divalent ferrous ions to trivalent ferrous ions is 25:75, and the ratio of divalent chromium ions to trivalent chromium ions is 75:25; when the SOC is 100%, the ratio of divalent ferrous ions to trivalent ferrous ions is 0:100, and the ratio of divalent chromium ions to trivalent chromium ions is 100:0.

[0029] 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 liquid flow battery electrolyte model.

[0030] Step S4: Three-dimensional periodic boundary conditions are used when the model is subjected to molecular dynamics simulation.

[0031] Step S5: The long-range forces are solved by the PPPM method.

[0032] Step S6: The fix shake command fixes the bond angle and bond length of the water molecules.

[0033] Step S7: A preliminary simulation was performed 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.

[0034] Step S8: Anneal to the target temperature of 330 K using a Noose-Hoover thermostat and barostat in an isothermal and isobaric (NPT) ensemble, with a time step of 0.1 fs, an equilibrium of 200 ps, ​​a temperature damping parameter of 100 fs, a pressure damping parameter of 100 fs, and output of the density of the system.

[0035] Step S9: Calculate the self-diffusion coefficient of each system in the isothermal and isobaric ensemble (NPT) with a time step of 0.01 fs, using the Eistein method, an equilibrium temperature of 330 K, an equilibrium time of 40 ps, ​​and sampling every 0.1 ps.

[0036] 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 analysis.

[0037] Step S11: The atomic trajectory is processed using VMD (Visual Molecular Dynamics) visualization software.

[0038] The self-diffusion coefficient is calculated 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.

[0039] The viscosity of the electrolyte system is calculated as follows: ; in, is the Boltzmann constant, To simulate the model volume, are the off-diagonal components of the stress tensor, is the viscosity of the electrolyte system, T is the electrolyte simulation temperature, t0 is the simulation start time, and t is the total simulation time.

[0040] The coordination calculation formula is as follows: ; Where n is the coordination number, is the number density of particles, is the radius of the central particle, is the radial distribution function of the specified particle to the central particle, and its calculation formula is as follows: ; in, is the total number of molecules, is the total simulation time, is the set distance difference, for The number of molecules between is the radius of the central particle, is the number density of particles.

[0041] Step S12: Prepare electrolytes with different SOCs according to the different ion ratios in the above simulation, control the total iron concentration of the electrolyte to 1 mol / L, the chromium concentration to 1 mol / L, and the total hydrochloric acid concentration to 3 mol / L, use Ubbelohde viscometer and densitometer to measure the viscosity and density of the electrolyte, and control the temperature of the entire measurement process to 330K.

[0042] The simulation results are as follows Figure 1-8 As shown, Figure 1 This is a diagram of the electrolyte simulation model when the SOC is 0% at 330K in an embodiment of the present invention. It can be seen that the ions in the model are evenly distributed and the model is completely filled; Figure 2 This is a comparison diagram of the simulated density and the actual density of the electrolyte when the SOC is 0% at 330K in the embodiment of the present invention. The results show that the simulated density is almost the same as the actual measured density. The model has a good prediction effect on the density of the electrolyte; Figure 3 This is a comparison diagram of the simulated viscosity and the actual viscosity of the electrolyte when the SOC is 0% at 330K in the embodiment of the present invention. The simulated viscosity is almost the same as the actual measured viscosity. The model has a good prediction effect on the viscosity of the electrolyte. Figure 4 This is a graph showing the variation of the MSD of chromium ions with SOC at 330K in an embodiment of the present invention. The results show that the activity of chromium ions is not affected by the change of SOC. Figure 5 The MSD of iron ions at 330K varies with SOC in the embodiment of the present invention. The results show that the activity of iron ions is highest when the SOC is 75%, and the activity is lowest when the SOC is 100%. Figure 6 This is a graph showing the variation of hydrogen ion SOC with temperature at 330K in an embodiment of the present invention. The results show that the hydrogen ion activity is highest when the SOC is 75%, and lowest when the SOC is 0%; Figure 7 This is a graph showing the change of chloride ion SOC with temperature at 330K in an embodiment 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 This is a graph showing how the viscosity and density change with SOC at 330K in an embodiment of the present invention. The results show that the viscosity first decreases and then increases as the SOC increases from 0% to 100%, and is the smallest when the SOC is 50%. The density first increases and then decreases as the SOC increases from 0% to 100%, and is the largest when the SOC is 50%.

[0043] In summary, the present invention constructs a molecular dynamics model of the iron-chromium flow battery electrolyte and demonstrates the reliability of the model from the aspects of coordination number, diffusion coefficient, density, viscosity, etc. The model analyzes the influence of SOC changes on the physical parameters of the electrolyte, and more realistically reflects the regulation mechanism of SOC changes on the interaction of ions inside the electrolyte, which has guiding significance for the subsequent optimization of the iron-chromium flow battery electrolyte formula and the improvement of the performance of the iron-chromium flow battery.

[0044] At this point, those skilled in the art recognize that, although the embodiments of the present invention have been shown and described in detail herein, many other variations or modifications that conform to the principles of the present invention can still be directly determined or derived based on the contents disclosed in the present invention without departing from the spirit and scope of the present 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 SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics, characterized in that: The method comprises the following steps: Step S1: Establish 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 ion Fe 3+ , divalent chromium ion Cr 2+ , trivalent chromium ion Cr 3+ , chloride ion Cl - and hydrogen ions H + , the solvent is water H2O; Step S2: based on the iron-chromium liquid flow battery electrolyte under study, selecting appropriate model potential energy parameters to be distributed in the system particles, wherein the model potential energy parameters are selected from one or more of REBO, ReaxFF, AMBER IOD, TIP4P and TIP4P-EW potential functions; Step S3: Constructing Fe 2+ and Fe 3+ Cr 3+ and Cr 2+ , hydrochloric acid HCl and water electrolyte molecular simulation system, in which Fe 2 + , Fe 3+ and Cr 3+ Cr 2+ The number is set according to different SOCs; Step S4: Setting the ensemble and boundary conditions for molecular dynamics simulation; Step S5: performing an isochoric ensemble NVT equilibrium simulation on the iron-chromium liquid flow battery electrolyte molecular dynamics simulation system at high temperature and outputting a result file, and then setting the temperature under the isothermal and isobaric ensemble NPT, performing a simulation with energy and density balance as the goal, and outputting a result file; Step S6: Calculate the self-diffusion coefficient of each system in the isothermal and isobaric ensemble NPT according to the result file, and calculate the viscosity of the electrolyte system in the microcanonical ensemble NVE; Step S7: The atomic trajectory is processed using Visual Molecular Dynamics visualization software VMD to calculate the atomic radial distribution and coordination of ions and water.

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

3. The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: In step S3, the SOC is selected from 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 SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: The molecular dynamics simulation calculation in step S4 is performed using LAMMPS software; in step S5, the isochoric ensemble NVT equilibrium simulation of the iron-chromium liquid flow battery electrolyte molecular dynamics simulation system at high temperature is performed using three-dimensional periodic boundary conditions, the time step is 0.1 fs, the water molecules adopt the TIP4P-EW force field model, and the fix shake command is used to fix the bond angle and bond length of the water molecules; the long-range forces in the isochoric ensemble NVT equilibrium simulation are solved by the PPPM method; the equilibrium temperature is 1000K, and the equilibrium time is 10ps.

5. The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: In step S5, the target temperature of the isothermal and isobaric ensemble NPT annealed using a Noose-Hoover thermostat and a barostat is set to 330 K and balanced for 200 ps, ​​with a temperature damping parameter of 100 fs, a pressure damping parameter of 100 fs, and the density of the system is output.

6. The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: In step S5, the self-diffusion coefficient of each system is calculated under the isothermal and isobaric ensemble NPT, using the Eistein method, with an equilibrium temperature of 330 K and an equilibrium time of 40 ps; a three-dimensional periodic boundary condition is adopted, with a time step of 0.01 fs, and the TIP4P-EW force field model is adopted for water molecules, and the fix shake command is used to fix the bond angle and bond length of the water molecules.

7. The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: In the step S6, the viscosity of the electrolyte system is calculated in the microcanonical ensemble NVE, the Green-Kubo method is used, the equilibrium temperature is 330K, the equilibrium time is 1000ps, the three-dimensional periodic boundary conditions are used, the time step is 0.1fs, the TIP4P-EW force field model is used for water molecules, and the fix shake command is used to fix the bond angle and bond length of the water molecules.

8. The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: The calculation formula of 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 SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: The viscosity calculation formula of the electrolyte system in step S6 is as follows: ； in, is the Boltzmann constant, To simulate the model volume, are the off-diagonal components of the stress tensor, is the viscosity of the electrolyte system, T is the electrolyte simulation temperature, t0 is the simulation start time, and t is the total simulation time.

10. The method for simulating SOC changes of electrolyte of iron-chromium flow battery based on molecular dynamics as claimed in claim 1, characterized in that: The calculation formula of the coordination situation in step S7 is as follows: ； Where n is the coordination number, is the number density of particles, is the radius of the central particle, is the radial distribution function of the specified particle to the central particle, and its calculation formula is as follows: ； in, is the total number of molecules, is the total simulation time, is the set distance difference, for The number of molecules between is the radius of the central particle, is the number density of particles.

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