Electrolyte activity prediction method and system, iron-chromium flow battery and electronic device

Abstract

The present application relates to the technical field of liquid flow battery energy storage, and provides an electrolyte activity prediction method and system, an iron-chromium liquid flow battery and an electronic device. The method comprises: obtaining a solvent composition of an electrolyte; determining a first parameter representing the intrinsic activity of a complex ion with a target metal ion as a central ion in the electrolyte through a complex model with the target metal ion as the central ion and wave function information constructed based on the solvent composition; determining a second parameter representing the conversion difficulty of the complex ion through the interconversion of different complex forms of ions existing in the electrolyte determined based on the solvent composition; and determining a third parameter representing the diffusion performance of the target metal ion in the electrolyte through an explicit-implicit hybrid solvent model of the electrolyte constructed based on the solvent composition. The present application is used to solve the defect of high experimental investment cost caused by the analysis of electrolyte activity by means of a large number of instruments in the prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of liquid flow battery energy storage, and particularly relates to an electrolyte activity prediction method and system, an iron-chromium liquid flow battery and an electronic device. BACKGROUND

[0002] The electrolyte mainly plays the roles of electrochemical reaction and conduction in the battery, and thus the performance of the electrolyte has a huge impact on the performance of the battery.

[0003] Taking an iron-chromium redox flow battery as an example, the iron-chromium liquid flow battery realizes the mutual conversion between chemical energy and electrical energy through the redox reaction of Cr 3+ / Cr 2+ and Fe 2+ / Fe 3+ pairs.

[0004] However, due to the poor reactivity of the chromium pair, the existence of a hydrogen evolution competition reaction in the electrolyte and other problems, the energy efficiency of the iron-chromium liquid flow battery is difficult to make a substantial breakthrough. Therefore, the optimization of the electrolyte composition is a key point to improve the activity of the electrolyte, but in the prior art, a large number of instruments are needed to analyze the activity of the electrolyte, which increases the experimental investment cost. SUMMARY

[0005] The present application provides an electrolyte activity prediction method and system, an iron-chromium liquid flow battery and an electronic device, to solve the defect of high experimental investment cost caused by the need for a large number of instruments to analyze the activity of the electrolyte in the prior art.

[0006] The present application provides an electrolyte activity prediction method, comprising:

[0007] obtaining a solvent composition of an electrolyte;

[0008] determining a first parameter representing the intrinsic activity of a complex ion with a target metal ion as a central ion in the electrolyte based on a complex model with the target metal ion as the central ion and wave function information constructed based on the solvent composition;

[0009] determining a second parameter representing the conversion difficulty of the complex ion based on the interconversion of different complex forms of ions existing in the electrolyte determined based on the solvent composition;

[0010] determining a third parameter representing the diffusion performance of the target metal ion in the electrolyte based on an explicit-implicit hybrid solvent model of the electrolyte constructed based on the solvent composition.

[0011] The electrolyte activity prediction method according to the present application, the first parameter representing the intrinsic activity of the complex ion with the target metal ion as the central ion in the electrolyte is determined based on the complex model with the target metal ion as the central ion constructed based on the solvent composition and the wave function information, comprising:

[0012] Based on the solvent composition, according to the coordination configuration characteristics of the target metal ion in the solvent, an initial model is established with the solvent molecules in the electrolyte as ligands;

[0013] Based on the composition of the solvent molecules, the charge carrying amount of the initial model is set, and the spin multiplicity test is performed to determine the molecular orbital electron ground state distribution in the complex ion;

[0014] Based on the molecular orbital electron ground state distribution, the initial model is optimized by B3LYP functional combined with def2-TZVP calculation basis set and DFT-D3 dispersion correction to obtain the complex model;

[0015] The self-consistent field calculation is performed by using wB97M-V hybrid functional combined with def2-TZVP calculation basis set, and the implicit solvent model is used to simulate the model in the solvent environment to obtain the wave function information;

[0016] Based on the complex model and the wave function information, the first parameter is determined.

[0017] The electrolyte activity prediction method according to the present application, the structure optimization of the initial model to obtain the complex model, comprising:

[0018] The frequency calculation is performed on the optimized initial model to determine whether the optimized initial model is in a metastable state;

[0019] When it is determined that the optimized initial model is not in the metastable state, the optimized initial model is taken as the complex model.

[0020] The electrolyte activity prediction method according to the present application, the first parameter includes the structure characteristics, the surface electrostatic potential distribution and the molecular polarization index of the complex model;

[0021] Wherein, based on the complex model and the wave function information, the surface electrostatic potential distribution and the molecular polarization index are determined, comprising:

[0022] Based on the complex model, the nuclear charge number, position and complex model surface area of the atom in the complex model are determined;

[0023] Based on the wave function information, the spatial electron density distribution of the complex model is determined;

[0024] determining the surface electrostatic potential distribution based on the nuclear charge number of the atom, the position, and the spatial electron density distribution;

[0025] determining the molecular polarizability index based on the complex model surface area and the surface electrostatic potential distribution.

[0026] According to the electrolyte activity prediction method, the second parameter includes a conversion energy barrier and an interaction strength change;

[0027] wherein the conversion energy barrier is determined by interconversion between different complex forms of ions existing in the electrolyte based on the solvent composition, including:

[0028] determining interconversion between different complex forms of ions existing in the electrolyte based on the solvent composition by transition state analysis;

[0029] determining a transition state energy based on interconversion between different complex forms of ions existing in the electrolyte;

[0030] determining the conversion energy barrier based on the transition state energy and the initial state energy before conversion;

[0031] determining the interaction strength change based on interconversion between different complex forms of ions existing in the electrolyte, including:

[0032] determining spatial electron density distribution and electron density gradient of the complex ions during conversion based on interconversion between different complex forms of ions existing in the electrolyte;

[0033] determining an interaction region indicator function value based on the spatial electron density distribution and the electron density gradient;

[0034] determining the interaction strength change based on a change in the interaction region indicator function value.

[0035] According to the electrolyte activity prediction method, the third parameter includes a radial solvent distribution rule and ion diffusion performance of the target metal ion;

[0036] wherein the radial solvent distribution rule is determined by the explicit-implicit hybrid solvent model of the electrolyte, including:

[0037] performing first-principle molecular dynamics simulation based on the explicit-implicit hybrid solvent model of the electrolyte to determine a molecular motion trajectory file;

[0038] determine the radial solvent distribution rule centered on the target metal ion by counting the frequency of the distance between the target metal ion and the solvent molecule in the electrolyte explicit-implicit hybrid solvent model based on the molecular motion trajectory file;

[0039] determine the ion diffusion performance by the electrolyte explicit-implicit hybrid solvent model, including:

[0040] determine the number of atoms and the atomic position at different times in the electrolyte explicit-implicit hybrid solvent model based on the molecular motion trajectory file;

[0041] determine the mean square displacement curve for characterizing the ion diffusion performance of the target metal ion based on the number of atoms and the atomic position at different times.

[0042] The application also provides an electrolyte activity prediction system, including:

[0043] an acquisition module configured to acquire the solvent composition of the electrolyte;

[0044] a first processing module configured to determine a first parameter for characterizing the intrinsic activity of the complex ion centered on the target metal ion in the electrolyte by a complex model of the ion centered on the target metal ion and wave function information constructed based on the solvent composition;

[0045] a second processing module configured to determine a second parameter for characterizing the conversion difficulty of the complex ion by the inter-ion conversion of different complex forms existing in the electrolyte determined based on the solvent composition;

[0046] a third processing module configured to determine a third parameter for characterizing the diffusion performance of the target metal ion in the electrolyte by an electrolyte explicit-implicit hybrid solvent model constructed based on the solvent composition.

[0047] The application also provides an iron-chromium flow battery for predicting the activity of chromium electrolyte by using any of the electrolyte activity prediction methods described above.

[0048] The application also provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the electrolyte activity prediction method described above when executing the program.

[0049] The application also provides a non-transitory computer readable storage medium having a computer program stored thereon, and the computer program is executable on a processor to implement the electrolyte activity prediction method described above.

[0050] The electrolyte activity prediction method, system, iron-chromium liquid flow battery and electronic equipment provided by the application, through obtaining the solvent composition of the electrolyte; then respectively through the complex model with the target metal ion as the central ion and the wave function information constructed based on the solvent composition, determining the first parameter representing the intrinsic activity of the complex ion with the target metal ion as the central ion in the electrolyte; through the interconversion of different complex forms of ions existing in the electrolyte determined based on the solvent composition, determining the second parameter representing the conversion difficulty of the complex ion; through the explicit-implicit hybrid solvent model of the electrolyte constructed based on the solvent composition, determining the third parameter representing the diffusion performance of the target metal ion in the electrolyte. The rapid prediction of the activity of the electrolyte is realized through calculation, so as to provide a trial and error space for the preparation of the electrolyte, and greatly reduce the experimental investment cost; at the same time, the connection between the macroscopic activity of the electrolyte and the microscopic activity of the complex ion is established, so as to reveal the microscopic behavior mechanism of the complex ion in the electrolyte, and provide theoretical guidance for the research and development of the electrolyte. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0052] Figure 1 It is a flowchart of an electrolyte activity prediction method provided by an embodiment of the application;

[0053] Figure 2 It is a flowchart of determining the complex model with the target metal ion as the central ion and the wave function information provided by an embodiment of the application;

[0054] Figure 3 It is a structure diagram of a complex model provided by an embodiment of the application;

[0055] Figure 4 It is a relationship diagram of the surface electrostatic potential distribution and the molecular polarization index of the complex ion in an example electrolyte provided by an embodiment of the application;

[0056] Figure 5 It is an energy barrier diagram of a key conversion process in an example electrolyte provided by an embodiment of the application;

[0057] Figure 6 It is a statistical diagram of the radial distribution of water molecules with chromium ions as the center in an example electrolyte provided by an embodiment of the application;

[0058] Figure 7The mean square displacement diagram of chromium ions in different solvent environments is provided by the embodiment of the present application.

[0059] Figure 8 The flowchart for electrolyte activity prediction by the electrolyte activity prediction method provided by the embodiment of the present application is shown.

[0060] Figure 9 The structural diagram of the electrolyte activity prediction system provided by the embodiment of the present application is shown.

[0061] Figure 10 The structural diagram of the electronic device provided by the present application is shown. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0063] The present application will be described below with reference to the drawings. Figures 1 to 8 The electrolyte activity prediction method provided by the present application is described below, which can be executed by software and / or hardware in a computer, a tablet computer, a mobile phone and other electronic devices, as shown in the following figure. Figure 1 The method comprises the following steps:

[0064] 101. Obtain the solvent composition of the electrolyte.

[0065] It can be understood that the electrolyte contains impurities, and in order to improve the accuracy of the activity prediction of the electrolyte, the solvent composition of the electrolyte needs to be determined first.

[0066] It should be noted that the electrolyte activity prediction method provided by the embodiment of the present application is suitable for metal ions forming complex due to solvation effect in solution.

[0067] Specifically, the specific process of the electrolyte activity prediction by the electrolyte activity prediction method provided by the embodiment of the present application is described below by taking the Cr3Cl+HCl electrolyte containing impurities (impurity ions are sodium ions and magnesium ions) as an example.

[0068] 102. Determine the first parameter representing the intrinsic activity of the complex ion with the target metal ion as the central ion in the electrolyte by the complex model with the target metal ion as the central ion and the wave function information constructed based on the solvent composition.

[0069] In this embodiment, the first parameter is determined by considering the solvent composition in the chromium electrolyte, the complex model with chromium as the central ion, and the wave function information based on the solvation effect, so as to predict the intrinsic activity of the Cr3Cl+HCl electrolyte containing impurities.

[0070] Further, the complex model with the target metal ion as the central ion and the wave function information can be obtained through the flow as shown in the figure, and the specific steps 102 include the following sub-steps: Figure 2

[0071] 1021. Based on the solvent composition, an initial model is established with the solvent molecules in the electrolyte as ligands according to the coordination configuration characteristics of the target metal ion in the solvent.

[0072] Specifically, in the Cr3Cl+HCl electrolyte containing impurities, according to the hexa-coordination configuration characteristics of the chromium ion, the trivalent chromium ion forms a complex with water molecules and chloride ions in the hydrochloric acid solution, and the coordination configuration usually has three kinds, namely Cr(H2O)4Cl2 + , Cr(H2O)5Cl 2+ , and Cr(H2O)6 3+ .

[0073] 1022. Based on the composition of the solvent molecules, the charge amount carried by the initial model is set, and the spin multiplicity test is performed to determine the molecular orbital electron ground state distribution in the complex ion.

[0074] Specifically, since the trivalent chromium ion is simulated and the initial model containing chloride ions in part of the coordination configuration is considered, the initial model can be set to carry charges of +1, +2, and +3, and the spin multiplicity test is obtained. The spin multiplicity is 4.

[0075] 1023. Based on the molecular orbital electron ground state distribution, the initial model is optimized by B3LYP functional combining def2-TZVP calculation basis set and DFT-D3 dispersion correction to obtain the complex model.

[0076] Specifically, in order to ensure that the initial model after structure optimization is not in a metastable state. In an embodiment of the present application, in sub-step 1023, the optimized initial model is also subjected to frequency calculation to determine whether the optimized initial model is in a metastable state.

[0077] When it is determined that the optimized initial model is not in a metastable state, the optimized initial model is taken as the complex model.

[0078] In this embodiment, by performing frequency calculation on the optimized initial model, it can be detected whether the model structure has imaginary frequency, so as to ensure that the optimized initial model is not in a metastable state, and then the complex model as shown in the figure can be obtained. Figure 3 ​The complexation model shown.

[0079] 1024. Self-consistent field calculations were performed using wB97M-V hybrid functionals combined with def2-TZVP basis sets, and the implicit solvent model was used to simulate the model in a solvent environment to obtain wavefunction information.

[0080] Furthermore, the structural characteristics of the complex ions can reflect the stability of the electrolyte, and the surface electrostatic potential distribution V(r) can reflect the electrochemical performance and molecular polarity of the complex ions, thereby reflecting the activity of the iron-chromium flow battery.

[0081] Therefore, in one embodiment of the present invention, the first parameter includes the structural features of the complexation model, the surface electrostatic potential distribution, and the molecular polarization index;

[0082] Among these, the surface electrostatic potential distribution and molecular polarization index are determined based on the complexation model and wave function information, including:

[0083] Based on the complexation model, the nuclear charge number, position, and surface area of ​​the atoms in the complexation model are determined.

[0084] Based on wave function information, the spatial electron density distribution of the complexation model is determined;

[0085] The surface electrostatic potential distribution is determined based on the nuclear charge number, position, and spatial electron density distribution of atoms.

[0086] Based on the surface area and surface electrostatic potential distribution of the complexation model, the molecular polarization index is determined.

[0087] Specifically, the structural features of the complexation model can include the coordination bond length and bond angle between the target metal ion and the ligand. In a Cr3Cl+HCl electrolyte containing impurities, the structural features of the complexation model calculated based on the complexation model and wavefunction information are shown in Table 1.

[0088] Table 1 Structural characteristics of different complexation models

[0089]

[0090] As shown in Table 1, chromium ions form an octahedral coordination configuration with the ligands. Furthermore, the introduction of chloride ions leads to distortion of the octahedral configuration and reduced stability. The bond angles reveal that Cr(H₂O)₅Cl... 2+ The distortion was greatest in Cr(H2O)5Cl, indicating that during the reaction process, Cr(H2O)5Cl... 2+ Under the influence of an electric field, it is easier to lose ligands, which facilitates subsequent reactions.

[0091] Meanwhile, the surface electrostatic potential distribution can be calculated using formula (1):

[0092]

[0093] Among them, Z A R is the nuclear charge number of atom A. A Let r' be the position of atom A, ρ(r') be the electron density at r', and r' be the integral variable of the spatial electron density. From a modeling perspective, the complex model is built up from individual atoms; therefore, "atom" here is a collective term.

[0094] Furthermore, based on the surface electrostatic potential distribution, the molecular polarization index (MPI), used to quantitatively measure the polarization characteristics of the complexed ions, can be calculated. Specifically, it can be calculated using formula (2):

[0095]

[0096] Where A is the surface area of ​​the complexation model.

[0097] Understandably, formula (2) is applicable to calculating the molecular polarization index of neutral molecules. However, since most complexation models involve charged ions, the molecular polarization index can be further calculated using the improved and optimized formula (3).

[0098]

[0099] in, This represents the average electrostatic potential of the molecular surface.

[0100] In one specific embodiment Figure 4 The presence of Cr(H2O)4Cl2 in a Cr3Cl+HCl electrolyte containing impurities is given. + Cr(H2O)5Cl 2+ Cr(H2O)6 3+ The surface electrostatic potential distribution and molecular polarization index of the three complex ions are shown. It can be seen that Cr(H₂O)₅Cl 2+ It has the strongest polarity and will promote the formation of an electric double layer on the electrode surface during charging, thereby enhancing the reactivity.

[0101] 103. By determining the inter-ion conversion of different complex forms present in the electrolyte based on the solvent composition, a second parameter characterizing the conversion difficulty of complex ions is determined.

[0102] Specifically, the conversion between ions in different complex forms present in the electrolyte can affect the difficulty of the complex ions converting from active ions to inactive ions. When active ions are converted into inactive ions, the battery will become inactive.

[0103] It is understandable that the reaction energy barrier is a parameter indicating the difficulty of reaction transformation, and the strength of the interaction between ions can affect the reaction energy barrier.

[0104] Therefore, in one embodiment of the present invention, the second parameter includes the transformation energy barrier and the interaction strength variation;

[0105] The conversion energy barrier is determined by the inter-ion conversion of different complex forms present in the electrolyte, based on the solvent composition. This includes:

[0106] Based on solvent composition, transition state analysis was used to determine the inter-ion transformations of different complexation forms present in the electrolyte;

[0107] The transition state energy was determined based on the inter-ion transformations of different complexation forms present in the electrolyte.

[0108] The conversion energy barrier is determined based on the transition state energy and the initial state energy before the conversion;

[0109] Based on the interion transformation of different complexation forms present in the electrolyte, the changes in interaction strength are determined, including:

[0110] Based on the inter-ion conversion of different complex forms present in the electrolyte, the spatial electron density distribution and electron density gradient of complex ions during the conversion process were determined.

[0111] The values ​​of the indicator function for the interaction region are determined based on the spatial electron density distribution and electron density gradient.

[0112] The change in interaction strength is determined based on the change in the indicator function value of the interaction region.

[0113] Specifically, based on the inter-ion transformations of different complex forms present in the electrolyte, transition state analysis can be used to calculate the transformation energy barrier and changes in interaction strength. For example, the inter-ion transformations of complex ions can be calculated using the NEB-TS transition state calculation method, and the reaction pathway can be verified based on intrinsic reaction coordinates.

[0114] More specifically, the reaction energy barrier E obtained through transition state calculations b The degree of difficulty in characterizing the transformation can be specifically calculated using (4):

[0115] E b =E TS -E IS (4)

[0116] Among them, E TS E is the transition state energy. IS The initial energy is given.

[0117] In one specific embodiment Figure 5Energy barrier diagrams of key conversion processes in Cr3Cl+HCl electrolyte containing impurities are presented and compared with those for electrolytes containing impurity ions. It is evident that the introduction of sodium and magnesium ions lowers the conversion energy barrier and promotes the conversion of the active ion Cr(H2O)5Cl. 2+ To the inactive ion Cr(H2O)6 3+ The transformation indicates that the introduction of metal impurity ions accelerates the deactivation of the electrolyte.

[0118] Furthermore, changes in interaction intensity can be characterized using the Interaction Region Indicator Function (IRI), which can reveal the core mechanism in the transformation process. The formula is as follows:

[0119]

[0120] Where ρ(r) is the spatial electron density distribution, Let be the electron density gradient, and 'a' be a standard parameter, typically taken as 1.1.

[0121] In one specific embodiment, the change in the indicator function of the interaction region during the conversion process of the impurity-containing Cr3Cl+HCl electrolyte indicates that the electrostatic attraction of metal impurity ions to chloride ions is the main reason for the reduction of the conversion energy barrier.

[0122] 104. By constructing an explicit-implicit hybrid solvent model of the electrolyte based on solvent composition, a third parameter characterizing the diffusion performance of target metal ions in the electrolyte is determined.

[0123] Specifically, taking the iron-chromium redox flow battery as an example, the better the diffusion of chromium ions in the electrolyte, the more favorable it is for the rapid migration of chromium ions in the electrochemical reaction. Therefore, by determining the third parameter characterizing the diffusion performance of the target metal ions in the electrolyte through an explicit-implicit hybrid solvent model based on the solvent composition, the conductivity of the electrolyte can be predicted.

[0124] More specifically, in chromium electrolytes, the radial solvent distribution pattern centered on chromium ions affects the ion diffusion performance of chromium ions, and therefore can be used to reflect the ion diffusion performance of chromium ions in chromium electrolytes.

[0125] Based on this, in one embodiment of the present invention, the third parameter includes the radial solvent distribution pattern and the ion diffusion performance of the target metal ions.

[0126] Among them, the radial solvent distribution law was determined by using an explicit-implicit hybrid solvent model of the electrolyte, including:

[0127] Based on the electrolyte explicit-implicit hybrid solvent model, first-principles molecular dynamics simulations were performed to determine the molecular motion trajectory file;

[0128] Based on molecular motion trajectory files, the radial solvent distribution pattern centered on the target metal ion is determined by statistically analyzing the frequency of the distance between the target metal ion and solvent molecules in the explicit-implicit hybrid solvent model of the electrolyte.

[0129] Ion diffusion performance was determined using an explicit-implicit hybrid solvent model of the electrolyte, including:

[0130] Based on molecular motion trajectory files, the number of atoms and the positions of atoms at different times in the explicit-implicit hybrid solvent model of the electrolyte are determined.

[0131] Based on the number of atoms and the atomic positions at different times, the mean square displacement curves used to characterize the ion diffusion performance of the target metal ions are determined.

[0132] Specifically, based on the actual composition ratio of the electrolyte, an explicit-implicit hybrid solvent model of the electrolyte is established, and first-principles molecular dynamics simulations are performed to obtain the radial solvent distribution law centered on the target metal ion and the ion diffusion performance of the target metal ion.

[0133] The radial solvent distribution pattern is derived from the frequency of occurrence of the distance between the target metal ions and solvent molecules in the electrolyte model, based on the molecular motion trajectory file.

[0134] In one specific embodiment Figure 6 The statistical distribution of water molecules centered on chromium ions in a Cr3Cl+HCl electrolyte containing impurities is presented. Figure 6 As can be seen, the characteristic peaks are located at 0.2 nm and 0.4 nm, respectively, indicating the distribution pattern of water molecules. The diffusion performance of chromium ions is characterized by the mean square displacement (MSD) curve, and the formula is as follows:

[0135]

[0136] Where N is the number of diffusing atoms, r j (t0) represents the atomic position at time t0, r j (t0+t) represents the atomic position after time t.

[0137] Figure 7 The mean square displacement diagrams of chromium ions under different solvent environments are presented. It can be seen that in an electrolyte free of impurities, chromium ions exhibit good diffusivity, which is beneficial for their rapid migration in electrochemical reactions. However, in an electrolyte containing metal impurity ions, the diffusion of chromium ions is inhibited, indicating that impurity ions weaken the kinetic properties of chromium ions, and the electrolyte conductivity also decreases accordingly.

[0138] In summary, this invention provides a method for simulating and predicting electrolyte activity using density functional theory calculations. The electrolyte activity prediction method provided by this invention maps the macroscopic activity of the electrolyte to the activity of microscopic complexed ions, taking into account the influence of additives, impurity ions, etc. From a microscopic perspective, such as... Figure 8 As shown, the activity of chromium electrolyte was evaluated from several aspects, including the intrinsic activity of complexed ions, the transformation law of different active complexed ions, and the diffusion performance of complexed ions. This method reduces the cost of experimental research, shortens the electrolyte development cycle, and provides theoretical guidance for electrolyte preparation.

[0139] It is understood that steps 102 to 104 provided in the above embodiments do not represent a substantial order, but are only used to conveniently describe several aspects for predicting electrolytes. Therefore, the order of these steps can be arbitrarily adjusted.

[0140] Based on the same general inventive concept, this invention also protects an electrolyte activity prediction system. The electrolyte activity prediction system provided by this invention will be described below. The electrolyte activity prediction system described below can be referred to in correspondence with the electrolyte activity prediction method described above.

[0141] Figure 9 This is a schematic diagram of the electrolyte activity prediction system provided by the present invention. Figure 9 As shown, it includes: an acquisition module 910, a first processing module 920, a second processing module 930, and a third processing module 940; wherein,

[0142] The acquisition module 910 is used to acquire the solvent composition of the electrolyte;

[0143] The first processing module 920 is used to determine a first parameter characterizing the intrinsic activity of the complex ions with the target metal ion as the center ion in the electrolyte by using a complexation model and wave function information constructed based on the solvent composition with the target metal ion as the center ion.

[0144] The second processing module 930 is used to determine a second parameter characterizing the difficulty of complex ion conversion by the inter-ion conversion of different complex forms present in the electrolyte based on the solvent composition.

[0145] The third processing module 940 is used to determine a third parameter characterizing the diffusion performance of target metal ions in the electrolyte by using an electrolyte explicit-implicit hybrid solvent model constructed based on the solvent composition.

[0146] The electrolyte activity prediction system provided in this invention obtains the solvent composition of the electrolyte; then, using a complexation model and wavefunction information based on the solvent composition and centered on the target metal ion, it determines a first parameter characterizing the intrinsic activity of the complexed ions in the electrolyte; a second parameter characterizing the transformation difficulty of the complexed ions is determined by the inter-ion transformation of different complexation forms in the electrolyte based on the solvent composition; and a third parameter characterizing the diffusion performance of chromium ions in the electrolyte is determined by an explicit-implicit hybrid solvent model based on the solvent composition. This system enables rapid prediction of electrolyte activity through calculation, providing a trial-and-error space for electrolyte preparation and significantly reducing experimental costs. Simultaneously, it establishes a macroscopic relationship between electrolyte activity and microscopic complexed ion activity, revealing the microscopic behavioral mechanism of complexed ions in the electrolyte and providing theoretical guidance for electrolyte development.

[0147] Optionally, the first processing module 920 is specifically used for:

[0148] Based on the solvent composition and the coordination configuration of the target metal ion in the solvent, an initial model is established using solvent molecules in the electrolyte as ligands. Based on the composition of the solvent molecules, the charge carried by the initial model is set, and spin multiplicity is tested to determine the ground state distribution of molecular orbitals in the complexed ion. Based on the ground state distribution of molecular orbitals, the initial model is structurally optimized using B3LYP functionals combined with def2-TZVP computational basis sets and DFT-D3 dispersion correction to obtain the complexed model. Self-consistent field calculations are performed using wB97M-V hybrid functionals combined with def2-TZVP computational basis sets, and an implicit solvent model is used to simulate the model in the solvent environment to obtain wavefunction information. Based on the complexed model and wavefunction information, the first parameter is determined.

[0149] Optionally, the first processing module 920 is more specifically used for:

[0150] Frequency calculations are performed on the optimized initial model to determine whether it is in a metastable state. If it is determined that the optimized initial model is not in a metastable state, it is used as the complexation model.

[0151] Optionally, the first parameter includes the structural features of the complexation model, the surface electrostatic potential distribution, and the molecular polarization index;

[0152] The first processing module 920 is also specifically used for:

[0153] Based on the complexation model and wave function information, the nuclear charge number, position, and surface area of ​​the atoms in the complexation model are determined; based on the wave function information, the spatial electron density distribution of the complexation model is determined; based on the nuclear charge number, position, and spatial electron density distribution of the atoms, the surface electrostatic potential distribution is determined; based on the surface area and surface electrostatic potential distribution of the complexation model, the molecular polarization index is determined.

[0154] Optionally, the second parameter includes the conversion energy barrier and the change in interaction strength;

[0155] The second processing module 930 is specifically used for:

[0156] Based on solvent composition, transition state analysis was used to determine the inter-ion transformations of different complex forms in the electrolyte; based on the inter-ion transformations of different complex forms in the electrolyte, the transition state energy was determined; based on the transition state energy and the initial state energy before transformation, the transformation energy barrier was determined; and based on the inter-ion transformations of different complex forms in the electrolyte, the spatial electron density distribution and electron density gradient of the complex ions during the transformation process were determined; based on the spatial electron density distribution and electron density gradient, the indicator function value of the interaction region was determined; and based on the change in the indicator function value of the interaction region, the change in interaction strength was determined.

[0157] Optionally, the third parameter includes the radial solvent distribution pattern and the ion diffusion performance of the target metal ions;

[0158] The third processing module 940 is specifically used for:

[0159] Based on the electrolyte implicit hybrid solvent model, first-principles molecular dynamics simulations were performed to determine the molecular trajectory file. Based on the molecular trajectory file, the radial solvent distribution law centered on the target metal ion was determined by statistically analyzing the frequency of the distance between the target metal ion and solvent molecules in the electrolyte implicit hybrid solvent model. Furthermore, based on the molecular trajectory file, the number of atoms and the atomic positions at different times in the electrolyte implicit hybrid solvent model were determined, and the mean square displacement curve used to characterize the ion diffusion performance of the target metal ion was determined.

[0160] Based on the same general inventive concept, the present invention also protects an iron-chromium flow battery that uses the electrolyte activity prediction method provided in any of the above embodiments to predict the activity of a chromium electrolyte.

[0161] Specifically, unlike other batteries, the iron-chromium redox flow battery's reactants reside within the electrolyte, while the electrodes serve only as the sites of the electrochemical reactions. The electrolyte is stored separately in a storage tank, achieving a high degree of decoupling between battery power and capacity, thus allowing for flexible design based on the operating environment. Furthermore, the abundant reserves of iron and chromium, the raw materials for redox reactions, significantly reduce construction costs. The low toxicity of iron and low-valence chromium ions facilitates electrolyte recovery and recycling. Therefore, the iron-chromium flow battery is widely recognized as the most promising electrochemical energy storage technology and has been extensively researched and applied in the construction of large-scale energy storage systems.

[0162] Figure 10 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 10 As shown, the electronic device may include a processor 110, a communication interface 120, a memory 130, and a communication bus 140, wherein the processor 110, the communication interface 120, and the memory 130 communicate with each other via the communication bus 140. The processor 110 can call logic instructions in the memory 130 to execute an electrolyte activity prediction method, which includes: obtaining the solvent composition of the electrolyte; determining a first parameter characterizing the intrinsic activity of complex ions centered on the target metal ion in the electrolyte by using a complexation model and wave function information constructed based on the solvent composition and centered on the target metal ion; determining a second parameter characterizing the conversion difficulty of the complex ions by the inter-ion conversion of different complexation forms present in the electrolyte determined based on the solvent composition; and determining a third parameter characterizing the diffusion performance of the target metal ion in the electrolyte by using an explicit-implicit hybrid solvent model constructed based on the solvent composition.

[0163] Furthermore, the logical instructions in the aforementioned memory 130 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the electrolyte activity prediction method provided by the above methods. The method includes: obtaining the solvent composition of the electrolyte; determining a first parameter characterizing the intrinsic activity of complex ions with the target metal ion as the central ion in the electrolyte by using a complexation model and wave function information constructed based on the solvent composition with the target metal ion as the central ion; determining a second parameter characterizing the transformation difficulty of the complex ions by the inter-ion transformation of different complexation forms present in the electrolyte determined based on the solvent composition; and determining a third parameter characterizing the diffusion performance of the target metal ion in the electrolyte by using an explicit-implicit hybrid solvent model of the electrolyte constructed based on the solvent composition.

[0165] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the electrolyte activity prediction method provided by the above methods. The method includes: obtaining the solvent composition of the electrolyte; determining a first parameter characterizing the intrinsic activity of complexed ions with the target metal ion as the central ion in the electrolyte by using a complexation model and wave function information constructed based on the solvent composition with the target metal ion as the central ion; determining a second parameter characterizing the conversion difficulty of the complexed ions by the inter-ion conversion of different complexation forms present in the electrolyte determined based on the solvent composition; and determining a third parameter characterizing the diffusion performance of the target metal ion in the electrolyte by using an explicit-implicit hybrid solvent model of the electrolyte constructed based on the solvent composition.

[0166] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0167] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting electrolyte activity, characterized in that, include: To obtain the solvent composition of the electrolyte; By using a complexation model and wave function information based on the solvent composition and centered on the target metal ion, a first parameter characterizing the intrinsic activity of the complexed ion centered on the target metal ion in the electrolyte is determined. A second parameter characterizing the difficulty of transformation of the complexed ions is determined by the inter-ion transformation of different complexed forms present in the electrolyte based on the solvent composition. A third parameter characterizing the diffusion performance of the target metal ions in the electrolyte is determined by constructing an electrolyte implicit hybrid solvent model based on the solvent composition.

2. The method for predicting electrolyte activity according to claim 1, characterized in that, The determination of the first parameter characterizing the intrinsic activity of the complexed ions centered on the target metal ion in the electrolyte, based on the complexation model constructed with the target metal ion as the central ion and wavefunction information constructed based on the solvent composition, includes: Based on the solvent composition and according to the coordination configuration characteristics of the target metal ion in the solvent, an initial model is established using solvent molecules in the electrolyte as ligands. Based on the composition of the solvent molecules, the charge carried by the initial model is set, and a spin multiplicity test is performed to determine the ground state distribution of molecular orbital electrons in the complex ion. Based on the molecular orbital electronic ground state distribution, the initial model was structurally optimized by combining the B3LYP functional with def2-TZVP basis set calculation and DFT-D3 dispersion correction to obtain the complexation model. Self-consistent field calculations were performed using a wB97M-V hybrid functional combined with a def2-TZVP computational basis set, and an implicit solvent model was used to simulate the model in a solvent environment to obtain the wavefunction information. The first parameter is determined based on the complexation model and the wave function information.

3. The method for predicting electrolyte activity according to claim 2, characterized in that, The step of structural optimization of the initial model to obtain the complexed model includes: Frequency calculations are performed on the optimized initial model to determine whether the optimized initial model is in a metastable state. If it is determined that the optimized initial model is not in the metastable state, the optimized initial model is used as the complexation model.

4. The method for predicting electrolyte activity according to claim 3, characterized in that, The first parameter includes the structural features of the complexation model, the surface electrostatic potential distribution, and the molecular polarization index; The determination of the surface electrostatic potential distribution and the molecular polarization index based on the complexation model and the wave function information includes: Based on the complexation model, the nuclear charge number, position, and surface area of ​​the atoms in the complexation model are determined. Based on the wave function information, the spatial electron density distribution of the complexation model is determined; The surface electrostatic potential distribution is determined based on the nuclear charge number of the atoms, their positions, and the spatial electron density distribution. The molecular polarization index is determined based on the surface area of ​​the complexation model and the surface electrostatic potential distribution.

5. The method for predicting electrolyte activity according to claim 1, characterized in that, The second parameter includes the conversion energy barrier and the change in interaction strength; The determination of the conversion energy barrier, based on the inter-ion conversion of different complex forms present in the electrolyte determined by the solvent composition, includes: Based on the solvent composition, transition state analysis was used to determine the inter-ion transformations of different complexation forms present in the electrolyte; The transition state energy is determined based on the inter-ion transformations of different complexation forms present in the electrolyte. The transformation energy barrier is determined based on the transition state energy and the initial state energy before transformation; The change in the interaction strength is determined based on the interion transformation of different complexation forms present in the electrolyte, including: Based on the inter-ion conversion of different complex forms present in the electrolyte, the spatial electron density distribution and electron density gradient of the complex ions during the conversion process are determined; Based on the spatial electron density distribution and the electron density gradient, the value of the interaction region indicator function is determined; The change in interaction intensity is determined based on the change in the indicator function value of the interaction region.

6. The method for predicting electrolyte activity according to claim 1, characterized in that, The third parameter includes the radial solvent distribution pattern and the ion diffusion performance of the target metal ions; The radial solvent distribution pattern is determined using the electrolyte implicit hybrid solvent model, including: Based on the electrolyte explicit-implicit hybrid solvent model, first-principles molecular dynamics simulations were performed to determine the molecular motion trajectory file. Based on the molecular motion trajectory file, the radial solvent distribution pattern centered on the target metal ion is determined by statistically analyzing the frequency of the distance between the target metal ion and solvent molecules in the electrolyte implicit hybrid solvent model. The ion diffusion performance was determined using the explicit-implicit hybrid solvent model of the electrolyte, including: Based on the molecular motion trajectory file, the number of atoms and the positions of atoms at different times in the electrolyte explicit-implicit hybrid solvent model are determined. Based on the number of atoms and the atomic positions at different times, a mean square displacement curve is determined to characterize the ion diffusion performance of the target metal ion.

7. An electrolyte activity prediction system, characterized in that, include: The acquisition module is used to acquire the solvent composition of the electrolyte; The first processing module is used to determine a first parameter characterizing the intrinsic activity of the complexed ions with the target metal ion as the central ion in the electrolyte by using a complexation model and wave function information constructed based on the solvent composition with the target metal ion as the central ion. The second processing module is used to determine a second parameter characterizing the conversion difficulty of the complexed ions by the inter-ion conversion of different complexed forms present in the electrolyte based on the solvent composition. The third processing module is used to determine a third parameter characterizing the diffusion performance of the target metal ions in the electrolyte by constructing an electrolyte explicit-implicit hybrid solvent model based on the solvent composition.

8. A flow battery made of iron-chromium redox fluid, characterized in that, The activity prediction of chromium electrolyte is performed using the electrolyte activity prediction method as described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the electrolyte activity prediction method as described in any one of claims 1 to 6.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the electrolyte activity prediction method as described in any one of claims 1 to 6.

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