Method and device for determining electrolyte concentration of flow battery

Abstract

The invention provides a method and a device for determining the electrolyte concentration of a flow battery. Fitting the relationship between the viscosity of the electrolyte and the concentration of the active substance, and determining the hydraulic radius of the active substance; determining the ionic conductivity of the active substance based on the inverse relation between the self-diffusion coefficient of the active substance and the viscosity of the electrolyte and the hydraulic radius; based on the material conservation theory and the ionic conductivity, establishing a time-varying model of the concentration of the active material in the positive and negative electrolyte of the flow battery; and determining current and battery energy efficiency parameters based on the active substance concentration, so as to determine the target concentration through the energy efficiency parameters. According to the scheme, the dynamic model of the active substance concentration change in the charging and discharging process is established, the battery state is monitored in real time according to the model, the electrolyte formula is optimized through performance evaluation, the optimal electrolyte active substance concentration is determined, the efficiency of electrolyte formula design and performance evaluation is improved, and the research and development and production cost is reduced.

Description

[0001] This application is a divisional application of an invention patent application with an application date of August 30, 2024, Chinese application number 202411209851.7, and invention name “A method and device for determining the concentration of electrolyte in a flow battery”. Technical Field

[0002] The present application relates to the field of liquid flow batteries, and in particular, to a method and device for determining the electrolyte concentration of a liquid flow battery. Background Art

[0003] The concentration of active substances in the electrolyte of flow batteries is an important factor affecting energy density, system efficiency and electrolyte utilization: when the concentration is too low, the amount of active substances carried in a unit volume of electrolyte is less, and the energy density of the flow battery is lower. When the concentration of active substances is higher, the energy density increases. However, high concentrations of active substances will significantly increase the viscosity of the electrolyte, increase pump power loss, and thus reduce system efficiency; excessively high concentrations of active substances will also reduce the ionic conductivity of the electrolyte, increase the battery's ohmic internal resistance, and cause the battery's ohmic polarization to be too high, affecting battery efficiency and electrolyte utilization.

[0004] On the other hand, the supporting electrolyte salt ion concentration in the flow battery electrolyte will also significantly affect the battery's energy efficiency and system efficiency. If the supporting electrolyte salt ion concentration is too low, the electrolyte viscosity is low, the pump power loss is low, the system efficiency is high, but the ion conductivity is low, the battery ohmic polarization is large, and the energy efficiency is low; a higher salt concentration can increase the electrolyte ion conductivity, thereby improving energy efficiency, but a high concentration will lead to an increase in electrolyte viscosity, resulting in greater pump power loss and reduced system efficiency.

[0005] Therefore, considering the energy density, energy efficiency, system efficiency and electrolyte utilization of flow batteries, it is necessary to optimize the electrolyte formula. In practice, people often rely on experiments to design and find the optimal electrolyte formula. However, there are many choices of electrolyte active substances and supporting electrolytes, and there are many ways to combine formulas. Experiments are extremely time-consuming and material-intensive, and the optimal formula obtained from the experiment is only applicable to specific batteries and experimental conditions. When the battery structure or temperature and other conditions change, it is necessary to re-optimize the experiment to determine the electrolyte concentration that is more suitable for the needs or has the best performance.

[0006] However, this method often requires high time and material costs, and the low experimental reaction efficiency will affect the progress of the entire R&D and production. Therefore, the existing technology has the problem that the performance evaluation of liquid flow battery electrolyte is not accurate and efficient enough, and in the R&D and production of liquid flow batteries, it is impossible to determine the optimal electrolyte concentration at low cost and high efficiency. Summary of the invention

[0007] The embodiments of the present application provide a method and device for determining the concentration of a liquid flow battery electrolyte, thereby at least to a certain extent solving the problem of inaccurate and inefficient performance evaluation of the liquid flow battery electrolyte.

[0008] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0009] According to one aspect of the present application, a method for determining the concentration of a flow battery electrolyte is provided, comprising:

[0010] The relationship between electrolyte viscosity and active material concentration is fitted to determine the hydrodynamic radius of the active material; based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the electrolyte viscosity, and the hydrodynamic radius, the ionic conductivity of the active material in the electrolyte is determined; based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of active materials in the positive and negative electrolytes of the flow battery over time during the charge and discharge process is established, and the active material concentration corresponding to the current moment is obtained according to the change model; the current battery voltage is determined based on the active material concentration; based on the battery voltage, the energy efficiency parameters of the battery are determined, so as to determine the target concentration of the active material through the energy efficiency parameters.

[0011] In the present application, based on the aforementioned scheme, the energy efficiency parameters of the battery are determined based on the battery voltage, including: based on the permeability of the porous electrode, the cross-sectional area of ​​the electrode, and the height of the electrode, determining the pressure difference of the electrolyte flowing through the porous electrode as one of the energy efficiency parameters.

[0012] In the present application, based on the aforementioned scheme, after determining the energy efficiency parameters of the battery based on the battery voltage, it also includes: determining the electrolyte utilization rate based on the discharge current, active material concentration and electrolyte volume corresponding to the current moment.

[0013] In the present application, based on the aforementioned scheme, the relationship between the electrolyte viscosity and the active substance concentration is fitted to determine the hydrodynamic radius of the active substance, including: fitting the Higgins equation for the relationship between the electrolyte viscosity and the active substance concentration to determine the hydrodynamic radius of the active substance.

[0014] In the present application, based on the aforementioned scheme, the ionic conductivity of the active substance in the electrolyte is determined based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, as well as the hydrodynamic radius, including: determining the self-diffusion coefficient of the active substance in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, as well as the absolute temperature and the hydrodynamic radius of the active substance; determining the ionic conductivity of the electrolyte through the Nernst-Einstein equation based on the self-diffusion coefficient, and the number of cations and anions in the active substance and the number of charges they carry.

[0015] In the present application, based on the aforementioned scheme, based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of active substances in the positive and negative electrolytes of the liquid flow battery during the charging and discharging process over time is established, and the active substance concentration corresponding to the current moment is obtained according to the change model, including: based on the theory of conservation of matter, the concentration of active substances in the positive and negative electrolytes of the liquid flow battery during the charging and discharging process, the volume of the electrolyte, and the volume flow rate generate a change equation as the change model; based on the change equation, the concentration of active substances in the electrodes and the liquid storage tank is determined.

[0016] In the present application, based on the aforementioned scheme, the determining of the current battery voltage based on the active substance concentration includes: determining the current overpotential of the battery based on battery parameters; determining the open circuit voltage of the battery based on the active substance concentration and the equilibrium potential of the battery under standard conditions; and determining the battery voltage based on the overpotential and the open circuit voltage.

[0017] According to one aspect of the present application, a device for determining the concentration of an electrolyte in a flow battery is provided, comprising:

[0018] A fitting unit is used to fit the relationship between electrolyte viscosity and active material concentration to determine the hydrodynamic radius of the active material;

[0019] A conductivity unit for determining the ionic conductivity of the active material in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius;

[0020] A model unit, for establishing a model of the change of the concentration of active substances in the positive and negative electrolytes of the flow battery over time during the charge and discharge process based on the theory of conservation of matter and the ion conductivity, and obtaining the active substance concentration corresponding to the current moment according to the change model;

[0021] a voltage unit, configured to determine a current battery voltage based on the active material concentration;

[0022] A parameter unit is used to determine an energy efficiency parameter of the battery based on the battery voltage, so as to determine a target concentration of the active material through the energy efficiency parameter.

[0023] According to one aspect of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for determining the concentration of an electrolyte in a flow battery as described in the above embodiment is implemented.

[0024] According to one aspect of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method for determining the concentration of a liquid flow battery electrolyte as described in the above-mentioned embodiments.

[0025] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method for determining the concentration of a flow battery electrolyte provided in the above-mentioned various optional implementations.

[0026] In the technical solution of the present application, the relationship between electrolyte viscosity and active material concentration is fitted to determine the hydrodynamic radius of the active material; based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the electrolyte viscosity, and the hydrodynamic radius, the ionic conductivity of the electrolyte is determined; based on the theory of conservation of matter and the ionic conductivity, a model of the change of the concentration of the active material in the positive and negative electrolytes of the flow battery during the charging and discharging process over time is established, and the active material concentration corresponding to the current moment is obtained according to the change model; the current battery voltage is determined based on the active material concentration; based on the battery voltage, the energy efficiency parameters of the battery are determined to determine the target concentration of the active material through the energy efficiency parameters. The technical solution of the present application evaluates the ionic conductivity based on the relationship between viscosity and self-diffusion coefficient, improves the accuracy of battery performance prediction, monitors the battery state in real time by establishing a dynamic model of the change of active material concentration during charging and discharging, calculates the battery voltage and energy efficiency parameters in real time according to the model, optimizes the electrolyte formula through performance evaluation, determines the optimal electrolyte active material concentration, improves the efficiency of electrolyte formula design and performance evaluation, provides a reliable data basis for subsequent battery production, and reduces R&D and production costs.

[0027] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 A flow chart of a method for determining the electrolyte concentration of a flow battery in one embodiment of the present application is schematically shown.

[0030] Figure 2 The figure schematically shows an example of the effect of changes in VOSO4 concentration on the viscosity, ion diffusion coefficient and ion conductivity of the electrolyte in one embodiment of the present application.

[0031] Figure 3 A comparison diagram of the experimental effect and the model effect in an embodiment of the present application is schematically shown.

[0032] Figure 4 Schematically illustrates an example diagram of the effect of changes in VOSO4 concentration on the energy density, energy efficiency, system efficiency, and electrolyte utilization of a flow battery in an experiment and model of an embodiment of the present application.

[0033] Figure 5 An example diagram schematically shows a multi-objective optimization diagram for finding the optimal electrolyte active substance concentration in an embodiment of the present application.

[0034] Figure 6 An exemplary diagram schematically shows the effect of the concentration of the H2SO4 supporting electrolyte on the viscosity and ionic conductivity of the electrolyte in one embodiment of the present application.

[0035] Figure 7 An example diagram schematically shows the effect of concentration on electrolyte viscosity and ion conductivity in an embodiment of the present application.

[0036] Figure 8 A schematic diagram of a device for determining the electrolyte concentration of a flow battery in an embodiment of the present application is schematically shown.

[0037] Fig. 9 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0038] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0039] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0040] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0041] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0042] The implementation details of the technical solution of this application are described in detail below:

[0043] Figure 1 A flow chart of a method for determining the electrolyte concentration of a flow battery according to an embodiment of the present application is shown. Figure 1 As shown, the method for determining the electrolyte concentration of a flow battery includes at least steps S110 to S150, which are described in detail as follows:

[0044] In step S110 , the relationship between the viscosity of the electrolyte and the concentration of the active material is fitted to determine the hydrodynamic radius of the active material.

[0045] In one embodiment of the present application, the relationship between electrolyte viscosity and active substance concentration is fitted to determine the hydrodynamic radius of the active substance, including: fitting the Higgins equation to the relationship between electrolyte viscosity and active substance concentration to determine the hydrodynamic radius of the active substance.

[0046] In this embodiment, it is assumed that the solute in the electrolyte can be simplified as an electrically neutral, rigid, inelastic, non-interactive sphere. When the electrolyte is in a dilute electrolyte, as the concentration c (number of moles per unit volume) increases, the real-time viscosity μ increases linearly. After entering a semi-dilute electrolyte, the viscosity growth accelerates. This phenomenon can be described by the Higgins equation:

[0047]

[0048] where μ0 is the viscosity of the solvent itself and [η] is called the intrinsic viscosity, which is related to the hydrodynamic radius r of the spherical solute active substance. H Related.

[0049]

[0050] Among them, N A is Avogadro's constant, k H is the Higgins coefficient, which quantifies the interaction between particles.

[0051] For example, when only the hydrodynamic interaction and steric effect are considered, k H = 0.992. Therefore, by fitting equation (1.1), the hydrodynamic radius of vanadyl sulfate VOSO4 can be calculated.

[0052] In this way, the hydrodynamic radius of the active material can be obtained by fitting the equation under the condition of constant concentration and real-time viscosity, thereby improving the accuracy of active material detection and evaluation. The above process accurately characterizes the physical and chemical parameters such as viscosity and hydrodynamic radius of the electrolyte at different active material concentrations through experiments and theoretical calculations. These parameters are the basis for understanding the properties of the electrolyte, optimizing the formulation, and predicting battery performance.

[0053] In step S120 , the ionic conductivity of the active material in the electrolyte is determined based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius.

[0054] In one embodiment of the present application, based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius, the ionic conductivity of the active substance in the electrolyte is determined, including:

[0055] Determining the self-diffusion coefficient of the active substance in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, as well as the absolute temperature and the hydrodynamic radius of the active substance;

[0056] Based on the self-diffusion coefficient, the number of cations and anions in the active material and the number of charges they carry, the ionic conductivity of the active material in the electrolyte is determined by the Nernst-Einstein equation.

[0057] In this embodiment, the self-diffusion coefficient of ions in the electrolyte is inversely proportional to the viscosity of the electrolyte, because the migration rate of ions slows down under greater viscous resistance. Using the Stokes-Einstein equation, VO 2+ and SO4 2 The self-diffusion coefficient of the ion is:

[0058]

[0059] Among them, D + and D - They are VO 2+ and SO4 2- The self-diffusion coefficient of the ion, k is the Boltzmann constant, T is the absolute temperature, r + and r - They are VO 2+ and SO4 2 The hydrodynamic radius of the ion.

[0060] Based on the measured viscosity and the inferred hydrodynamic radius, the VO2+ concentration can be predicted. 2+ and SO4 2 The self-diffusion coefficient of the ion.

[0061] The ionic conductivity of the electrolyte can be estimated by the self-diffusion coefficient of the charged species (ion mobility) and the Nernst-Einstein equation, namely:

[0062]

[0063] where e is the elementary charge, ν + and ν - are the number of cations and anions in each active substance, and z+ and z- are the charges carried by the cations and anions, respectively.

[0064] The above process successfully predicted the ionic conductivity of active substances in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active substances in the electrolyte and the viscosity of the electrolyte, as well as the known hydrodynamic radius. Ionic conductivity is one of the key parameters for evaluating the electrochemical performance of batteries, and its accurate prediction helps improve the scientificity and effectiveness of battery design.

[0065] In step S130, based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of active substances in the positive and negative electrolytes of the flow battery over time during the charge and discharge process is established, and the active substance concentration corresponding to the current moment is obtained according to the change model.

[0066] In one embodiment of the present application, based on the theory of conservation of matter and the ionic conductivity, a model of the change of the concentration of active substances in the positive and negative electrolytes of the flow battery during the charge and discharge process over time is established, and the concentration of active substances corresponding to the current moment is obtained according to the change model, including:

[0067] Based on the theory of conservation of matter, the concentration of active substances in the positive and negative electrolytes of the flow battery during the charge and discharge process, the volume of the electrolyte, and the volume flow rate generate a change equation as the change model;

[0068] Based on the transformation equation, the concentration of the active material in the electrode and the reservoir is determined.

[0069] To maintain generality, we assume that during charging, the redox active substance A gains an electron e at the negative electrode. - , get negative ion A - , the above process is expressed as:

[0070]

[0071] At the same time, substance C loses an electron e at the positive electrode. - , to obtain the positive ion C + , the above process is expressed as:

[0072]

[0073] Among them, k n and k p are the reaction rate constants for the negative and positive electrode reactions, respectively. The active molecules A and C and their valences in reduced and oxidized states can be adjusted for different redox reactions.

[0074] Assuming that the positive and negative electrodes are symmetrical, the volume of electrolyte stored in the tank is V r The volume flow rate Q on both sides is the same; the viscosity μ and ionic conductivity σ of the electrolyte on both sides are also the same, that is:

[0075]

[0076] Among them, the suffix neg represents the parameters of the negative electrode, and the suffix pos represents the parameters of the positive electrode.

[0077] Assuming that the active substance does not diffuse across the membrane, and no active material degradation or other side reactions occur during the reaction, the following equation can be written based on the law of conservation of matter:

[0078]

[0079] Where Q is the volume flow rate, F is the Faraday constant, and cA and c A res are the concentrations of substance A in the electrode and the reservoir, respectively, and c A- and c A- res are the concentrations of substance A- in the electrode and the reservoir, respectively. e is the volume of electrolyte in the electrode, V r is the volume of electrolyte, and I is the current. The above equations (1.8) have a theoretical solution, namely:

[0080]

[0081] in:

[0082] c A,0 、c A-,0 is the A and A in the electrode and the reservoir - The initial concentration of

[0083] t is the charge / discharge time;

[0084] w e is the thickness of the electrode;

[0085] i is the current density, defined as i = I / A, where A is the cross-sectional area of ​​the electrode and current collector;

[0086] δ is the ratio of the volume of the reservoir to the volume of the electrode, i.e., δ = Vr / Ve. For efficient operation of the flow battery, the volume of the reservoir is generally much larger than the volume of the electrode.

[0087] Due to the symmetry of positive and negative poles, substances C and C + The concentration of substances A and A - The concentration is the same.

[0088] The above process combines the theory of conservation of matter and the predicted ion conductivity to establish a model for the change of active material concentration over time in the positive and negative electrolytes of the flow battery during the charge and discharge process. This model can dynamically reflect the change of active material concentration inside the battery, providing a powerful tool for real-time monitoring and optimization of battery performance.

[0089] In step S140 , the current battery voltage is determined based on the active material concentration.

[0090] In one embodiment of the present application, determining the current battery voltage based on the active material concentration includes:

[0091] Based on the battery parameters, determine the current overpotential of the battery;

[0092] Determining the open circuit voltage of the battery based on the concentration of the active material and the equilibrium potential of the battery under standard conditions;

[0093] A battery voltage is determined based on the overpotential and the open circuit voltage.

[0094] In this embodiment, the battery overpotential is related to the substance concentration, and the battery voltage during the charging and discharging process can be calculated. OCV It can be calculated by the Nernst equation:

[0095]

[0096] R is the gas constant, T is the thermodynamic temperature, and E 0 pos and E 0 neg is the equilibrium potential under standard conditions, n is the electrochemical equivalent, and F is the Faraday constant. A- is the concentration of substance A- in the electrode, c A is the concentration of substance A in the electrode, is the substance C in the electrode + The concentration, c C is the concentration of substance C in the electrode.

[0097] During the charge and discharge process, the battery voltage is determined by the open circuit voltage and overpotential, and the charge voltage and discharge voltage are:

[0098]

[0099] Among them, η R , η A and η C They are ohmic overpotential, activation overpotential and concentration overpotential (also called ohmic, activation and concentration polarization).

[0100] In the above process, in a flow battery, the ohmic overpotential is related to the ohmic resistance of the battery, including the internal resistance of the electrolyte, porous electrodes, and membrane (the resistance of the current collector is much lower than other components, so it is ignored in this model). The ohmic overpotential can be calculated by the following formula:

[0101]

[0102] Among them, w m and σ m are the film thickness and conductivity, w e is the thickness of the electrode; σ e is the apparent conductivity of the porous electrode, and σ is the conductivity of the electrolyte. It is worth noting that the conductivity of both the membrane and the electrolyte is related to the concentration of the electrolyte, because the concentration affects not only the ion transport in the electrolyte, but also the ion transport across the membrane.

[0103] Activation overpotential ηA is caused by charge transfer at the electrode electrolyte interface and is related to the reaction rate constant as follows:

[0104]

[0105] Where R is the gas constant, T is the absolute temperature, F is the Faraday constant, and k n and k p are the reaction rate constants for the negative and positive electrode reactions, respectively.

[0106] Concentration overpotential η C is caused by mass transfer from the electrolyte to the electrode surface and can be written as follows:

[0107]

[0108] Where S is the surface area of ​​the electrode, k m is the mass transfer coefficient, which depends on the self-diffusion coefficient of the transferred ions, the viscosity of the electrolyte, and the flow rate U, and is expressed as:

[0109]

[0110] Where D is the self-diffusion coefficient, d f is the diameter of the carbon fiber in the porous electrode, ρ is the density of the electrolyte, U is the flow rate, and μ is the viscosity.

[0111] The above process can calculate the current battery voltage in real time based on the concentration of active substances obtained through dynamic simulation. Furthermore, key performance parameters such as battery energy density, energy efficiency, and system efficiency can be further determined based on the battery voltage. This real-time evaluation capability helps to detect battery performance anomalies in a timely manner, prevent failures, and optimize battery operation strategies.

[0112] In step S150 , an energy efficiency parameter of the battery is determined based on the battery voltage, so as to determine a target concentration of the active material through the energy efficiency parameter.

[0113] In one embodiment of the present application, the charge and discharge curve of the flow battery can be obtained through the above calculation, and the performance parameters of the battery can be calculated through the charge and discharge curve.

[0114] The energy efficiency parameters in this embodiment include energy density, energy efficiency and system efficiency.

[0115] For a flow battery that stores a certain volume of electrolyte, the energy density (ED) can be obtained by integrating the discharge curve:

[0116]

[0117] Where I is the current, E cell is the battery voltage during discharge, V r is the volume of electrolyte, and t is the discharge time, from the start of discharge (disch,start) to the end of discharge (disch,end). The energy density of the electrolyte is an important consideration when evaluating battery cost. Improving the energy density of the electrolyte can significantly reduce the system volume and floor space, and reduce manufacturing costs.

[0118] In addition, the energy efficiency (EE) of the battery is also an important parameter that needs to be evaluated, which is defined as:

[0119]

[0120] Energy efficiency evaluates how efficiently a battery utilizes input energy during charging and discharging.

[0121] In the system-level evaluation, the system efficiency (SE) takes into account the pump power loss and is defined as:

[0122]

[0123] Among them, φ pump represents the pump efficiency, Q is the volume flow rate, and ΔP is the pressure difference of the electrolyte flowing through the porous electrode.

[0124] In one embodiment of the present application, the energy efficiency parameters of the battery are determined based on the battery voltage, including: based on the permeability of the porous electrode, the cross-sectional area of ​​the electrode, and the height of the electrode, determining the pressure difference of the electrolyte flowing through the porous electrode as one of the energy efficiency parameters.

[0125] Specifically, the pressure difference of the electrolyte flowing through the porous electrode can be calculated by the permeability of the electrode:

[0126]

[0127] Where K is the permeability of the porous electrode, Q is the volume flow rate, A is the electrode cross-sectional area, and H is the height of the electrode.

[0128] In one embodiment of the present application, after determining the energy efficiency parameter of the battery based on the battery voltage, the method further includes:

[0129] The electrolyte utilization rate is determined based on the discharge current, active material concentration and electrolyte volume corresponding to the current moment.

[0130] Specifically, another parameter that greatly affects the cost of flow batteries is the electrolyte utilization (EU), which represents the ratio between the discharge capacity and the theoretical capacity, and is specifically defined as:

[0131]

[0132] Among them, V r represents the volume of electrolyte and I is the current.

[0133] The above parameters together constitute a multi-dimensional and multi-performance evaluation system for evaluating the performance of flow batteries, which helps us optimize the concentration of the electrolyte and determine the target concentration of the electrolyte active substance with the best effect based on the evaluation parameters corresponding to a large number of electrolytes.

[0134] In addition, the above process can further determine key performance parameters such as the battery's energy density, energy efficiency, and system efficiency based on the battery voltage. This real-time evaluation capability helps to detect battery performance anomalies in a timely manner, prevent failures, and optimize the battery's operating strategy. By comparing the performance parameters of batteries under different conditions, the electrolyte formula can be optimized, the charging and discharging strategy can be adjusted, etc., in order to improve the overall performance and economic benefits of the battery on the basis of production and research and development with the lowest cost and highest efficiency.

[0135] It should be noted that this optimization strategy based on experimental data and theoretical models is scientific, practical and universal. The above process is generally applicable to electrolytes of various active substances and will help promote the further development of liquid flow battery technology.

[0136] Embodiment 1:

[0137] To calculate a certain model, the active area is 20mm×20mm, the electrolyte volume is 10mL×2, and the electrolyte flow rate is 46mL min -1 Taking the optimal VOSO4 concentration of a single-stage all-vanadium liquid flow battery as an example, the specific embodiments of the present application are introduced in detail.

[0138] The specific steps are as follows:

[0139] (1) Measure the viscosity of electrolytes with different vanadium ion concentrations and predict the ion diffusion coefficient and conductivity of electrolytes of each concentration. By measuring the viscosity of the electrolyte, we can understand the magnitude of the internal friction resistance of the electrolyte, which is an important factor affecting the efficiency of ion transmission. Low-viscosity electrolytes usually provide more efficient ion transmission channels. Conductivity is an important indicator to measure the conductivity of the electrolyte and is closely related to ion concentration, ion mobility, etc. Accurately predicting conductivity helps to evaluate the conductive properties of the electrolyte and its impact on battery performance.

[0140] (2) Using the zero-dimensional mass transfer coupled electrochemical reaction dynamic model, the charge and discharge curves of electrolytes of different concentrations are calculated. Through model simulation, the voltage changes of electrolytes with different vanadium ion concentrations during the charge and discharge process, i.e., the charge and discharge curves, can be predicted. This curve reflects the energy conversion efficiency and stability of the battery.

[0141] (3) Using the charge and discharge curves, calculate the battery performance parameters under different electrolyte concentrations. By comparing the performance parameters under different electrolyte concentrations, the specific impact of electrolyte concentration on battery performance can be quantified, providing data support for optimizing the electrolyte formula.

[0142] (4) Compare various performance parameters and select the optimal electrolyte concentration. By comprehensively comparing the battery performance parameters under different electrolyte concentrations, it can be determined which concentrations show better performance in which aspects. Finally, the optimal electrolyte concentration that meets performance requirements and is cost-effective is selected. Optimizing the electrolyte concentration can significantly improve key performance indicators such as battery energy density, cycle stability and safety, thereby promoting the further development of battery technology.

[0143] Embodiment 2:

[0144] Taking VOSO4 aqueous electrolyte without sulfuric acid as an example, the change of electrolyte viscosity with VOSO4 concentration was measured, the hydrodynamic radius of VOSO4 in the aqueous electrolyte was calculated by fitting the Higgins formula, and the changes of ion diffusion coefficient and ion conductivity of VOSO4 aqueous electrolyte at different concentrations were predicted based on viscosity and hydrodynamic radius.

[0145] like Figure 2 As shown, Figure 2 (a) The viscosity of the electrolyte increases linearly with concentration first, and then increases nonlinearly and rapidly. By fitting the Higgins formula, the hydrodynamic radius of VOSO4 can be obtained. Figure 2 (b) Predicting the variation of ion diffusion coefficient with concentration through viscosity; Figure 2 (c) in Figure 1 predicts the variation of ionic conductivity with concentration through viscosity.

[0146] like Figure 2 As shown in the figure, the viscosity of VOSO4 (vanadium oxysulfate, also known as vanadium yl sulfate) aqueous electrolyte is experimentally measured as a function of VOSO4 concentration. As the concentration increases, the viscosity first increases linearly, and then increases significantly and rapidly. The Higgins formula is used to fit the data, where the coefficient of the first-order term is related to the hydrodynamic radius of the solute VOSO4. The hydrodynamic radius of VOSO4 can be calculated by the formula to be 0.42nm. Based on the measured electrolyte viscosity and hydrodynamic radius, the ion diffusion coefficient and ion conductivity at different concentrations can be predicted, where ν + =v - =1, z + =z -=2. The calculated VO 2+ and SO4 2- The ion diffusion coefficient and ionic conductivity of VOSO4 water electrolyte vary with VOSO4 concentration. Figure 2 As shown in the figure, the diffusion coefficient decreases with the increase of concentration, and the ionic conductivity first increases and then decreases with the increase of concentration.

[0147] Embodiment 3:

[0148] like Figure 3 As shown, Figure 3 The following is an example of the performance parameters of the flow battery calculated according to this model, namely, the energy density, energy efficiency, system efficiency and electrolyte utilization rate as a function of concentration. The vanadium ion concentration is 1 mol / L, the sulfuric acid concentration is 3 mol / L, and the current density is 200 mA cm -2 , volume flow rate is 46mL min -1 As an example, the charge and discharge curves of the flow battery are calculated and compared with the experiment to verify the accuracy of the model. As shown in the figure, the average relative error of the model prediction is within 0.2% compared with the experimental data.

[0149] In the all-vanadium liquid flow battery, the electrochemical reactions at the positive and negative electrodes are as follows:

[0150]

[0151] Among them, V 3+ and V 2+ Represents trivalent vanadium ion and divalent vanadium ion, VO 2+ It is the vanadium oxide ion H + is a hydrogen ion. n and k p are the reaction rate constants of the negative and positive electrode reactions respectively. The electrochemical reaction rate constants of the positive and negative electrodes include: k n =6.8×10 -7 ms -1 and k p =1.7×10 -7 ms -1 The choice of other parameters, such as electrode thickness, membrane thickness, etc., is determined by the geometric parameters of the specific battery.

[0152] Compared with the experimental data, the average relative error of the model prediction is within 0.2%. It can be seen that the model in this application can completely simulate the actual experimental data and has a high accuracy.

[0153] Embodiment 4:

[0154] like Figure 4 As shown, it shows the influence of electrolyte concentration on various aspects of battery status, including: Figure 4(a) Energy density, Figure 4 (b) Energy efficiency, Figure 4 (c) System efficiency and Figure 4 (d) Variation of electrolyte utilization with VOSO4 concentration.

[0155] The steps of Example 3 were repeated to calculate the charge and discharge curves of electrolytes with different vanadium ion concentrations and the battery performance parameters, including energy density, energy efficiency, system efficiency and electrolyte utilization.

[0156] Embodiment 5:

[0157] like Figure 5 As shown, Figure 5 An example diagram of the multi-objective optimization provided in the embodiment of the present application to find the optimal electrolyte active substance concentration. The electrolyte vanadium ion concentration is optimized using multi-objective optimization to find the optimal concentration. When the multi-objective optimization of the vanadium ion concentration of the all-vanadium liquid flow battery electrolyte is considered, the optimal concentration of the electrolyte is about 1.25 mol / L.

[0158] Embodiment 6:

[0159] like Figure 6 As shown, Figure 6 It shows the effect of sulfuric acid concentration on electrolyte viscosity and electrolyte ion conductivity. Taking H2SO4 in the electrolyte of all-vanadium liquid flow battery as an example, the concentration of supporting electrolyte in the electrolyte is optimized. The higher the concentration of supporting electrolyte in the electrolyte, the higher the electrolyte viscosity and the higher the ion conductivity. The method for optimizing the ion concentration of supporting electrolyte salt is similar to that of active material. The specific steps are as follows:

[0160] (1) Measure the viscosity of electrolytes with different sulfuric acid concentrations and predict how the ion diffusion coefficient and conductivity of electrolytes of different concentrations change with sulfuric acid concentration.

[0161] (2) The charge and discharge curves of electrolytes of various concentrations were calculated using a zero-dimensional mass transfer coupled electrochemical reaction dynamic model.

[0162] (3) Using the charge and discharge curves, calculate the battery performance parameters under various electrolyte concentrations.

[0163] (4) Compare various performance parameters and select the optimal sulfuric acid concentration.

[0164] The above process achieves dynamic simulation and real-time monitoring of the battery state by establishing a model of the change of active material concentration over time in the positive and negative electrolytes of the flow battery during the charge and discharge process. By obtaining the active material concentration in real time and calculating the battery voltage, key performance parameters such as the battery's energy density, energy efficiency, and system efficiency are further evaluated. This provides a scientific basis for optimizing battery performance.

[0165] Embodiment 7:

[0166] Taking the change of viscosity and ionic conductivity of organic active substance Dex-Vi in 1 mol / L sodium chloride NaCl aqueous electrolyte with the concentration of active substance as an example, the concentration of Dex-Vi is optimized. The specific steps are as follows:

[0167] (1) Figure 7 As shown in the figure, the viscosity of VOSO4 water electrolyte is experimentally measured as a function of VOSO4 concentration. As the concentration increases, the viscosity first increases linearly and then increases significantly and rapidly. The Higgins formula is used to fit the data, in which the coefficient of the first-order term is related to the hydrodynamic radius of the solute Dex-Vi. The hydrodynamic radius of Dex-Vi can be calculated by the formula to be 0.72nm. Based on the viscosity and hydrodynamic radius, the changes in ion diffusion coefficient and ionic conductivity at different electrolyte concentrations can be predicted. As the concentration increases, the ionic conductivity first increases and then decreases.

[0168] (2) The charge and discharge curves of electrolytes with different Dex-Vi concentrations were calculated using a zero-dimensional mass transfer coupled electrochemical reaction dynamic model.

[0169] (3) Using the charge and discharge curves, calculate the battery performance parameters at each concentration.

[0170] (4) Compare various performance parameters and select the optimal Dex-Vi concentration.

[0171] The above process experimentally measured the change of viscosity of VOSO4 aqueous electrolyte with VOSO4 concentration, and found its nonlinear growth trend. The data was successfully fitted using the Higgins formula, which not only revealed the complex relationship between viscosity and concentration, but also calculated the hydrodynamic radius of the solute Dex-Vi through the fitting parameters, providing important parameters for understanding the behavior of the solute in the electrolyte. Based on the information of viscosity and hydrodynamic radius, combined with the theoretical model, the change trend of the ion diffusion coefficient and ion conductivity of the electrolyte at different concentrations was successfully predicted. This predictive ability is crucial for understanding the electrochemical processes inside the battery, optimizing battery design, and improving battery performance. Based on the charge and discharge curves, the battery performance parameters at various concentrations, such as energy density, energy efficiency, power output, etc., were calculated. The comprehensive evaluation of these parameters provides a scientific basis for selecting the optimal Dex-Vi concentration, which helps to maximize battery performance.

[0172] In the technical solution of the present application, the relationship between the viscosity of the electrolyte and the concentration of the active substance is fitted to determine the hydrodynamic radius of the active substance; based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius, the ionic conductivity of the active substance in the electrolyte is determined; based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of the active substance in the positive and negative electrolytes of the flow battery over time during the charge and discharge process is established, and the active substance concentration corresponding to the current moment is obtained according to the change model; the current battery voltage is determined based on the active substance concentration; based on the battery voltage, the energy efficiency parameters of the battery are determined, so as to determine the target concentration of the active substance through the energy efficiency parameters. The technical solution of the present application evaluates ionic conductivity based on the relationship between viscosity and self-diffusion coefficient, improves the accuracy of battery performance prediction, monitors the battery status in real time by establishing a dynamic model of changes in active substance concentration during charging and discharging, calculates the battery voltage in real time according to the model and determines performance parameters such as energy density, energy efficiency, system efficiency and electrolyte utilization rate, optimizes the electrolyte formulation through performance evaluation, determines the optimal electrolyte active substance concentration, improves the efficiency of electrolyte formulation design and performance evaluation, provides a reliable data basis for subsequent battery production, and reduces R&D and production costs.

[0173] The following describes an embodiment of the device of the present application, which can be used to execute the method for determining the concentration of the electrolyte of a flow battery in the above-mentioned embodiment of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for determining the concentration of the electrolyte of a flow battery in the above-mentioned embodiment of the present application.

[0174] Figure 8 A block diagram of a device for determining electrolyte concentration of a flow battery according to an embodiment of the present application is shown.

[0175] Reference Figure 8 As shown, according to an embodiment of the present application, a device for determining the concentration of a flow battery electrolyte includes:

[0176] A fitting unit 310, for fitting the relationship between electrolyte viscosity and active material concentration to determine the hydrodynamic radius of the active material;

[0177] A conductive unit 320, for determining the ionic conductivity of the active material in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius;

[0178] The model unit 330 is used to establish a model of the change of the concentration of active substances in the positive and negative electrolytes of the flow battery over time during the charge and discharge process based on the theory of conservation of matter and the ion conductivity, and obtain the active substance concentration corresponding to the current moment according to the change model;

[0179] A voltage unit 340, configured to determine a current battery voltage based on the active material concentration;

[0180] The parameter unit 350 is used to determine the energy efficiency parameter of the battery based on the battery voltage, so as to determine the target concentration of the active material through the energy efficiency parameter.

[0181] In the present application, based on the aforementioned scheme, the energy efficiency parameters of the battery are determined based on the battery voltage, including: based on the permeability of the porous electrode, the cross-sectional area of ​​the electrode, and the height of the electrode, determining the pressure difference of the electrolyte flowing through the porous electrode as one of the energy efficiency parameters.

[0182] In the present application, based on the aforementioned scheme, after determining the energy efficiency parameters of the battery based on the battery voltage, it also includes: determining the electrolyte utilization rate based on the discharge current, active material concentration and electrolyte volume corresponding to the current moment.

[0183] In the present application, based on the aforementioned scheme, the relationship between the electrolyte viscosity and the active substance concentration is fitted to determine the hydrodynamic radius of the active substance, including: fitting the Higgins equation for the relationship between the electrolyte viscosity and the active substance concentration to determine the hydrodynamic radius of the active substance.

[0184] In the present application, based on the aforementioned scheme, the ionic conductivity of the active substance in the electrolyte is determined based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, as well as the hydrodynamic radius, including: determining the self-diffusion coefficient of the active substance in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, as well as the absolute temperature and the hydrodynamic radius of the active substance; determining the ionic conductivity of the active substance in the electrolyte through the Nernst-Einstein equation based on the self-diffusion coefficient, and the number of cations and anions in the active substance and the number of charges they carry.

[0185] In the present application, based on the aforementioned scheme, based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of active substances in the positive and negative electrolytes of the liquid flow battery during the charging and discharging process over time is established, and the active substance concentration corresponding to the current moment is obtained according to the change model, including: based on the theory of conservation of matter, the concentration of active substances in the positive and negative electrolytes of the liquid flow battery during the charging and discharging process, the volume of the electrolyte, and the volume flow rate generate a change equation as the change model; based on the change equation, the concentration of active substances in the electrodes and the liquid storage tank is determined.

[0186] In the present application, based on the aforementioned scheme, the determining of the current battery voltage based on the active substance concentration includes: determining the current overpotential of the battery based on battery parameters; determining the open circuit voltage of the battery based on the active substance concentration and the equilibrium potential of the battery under standard conditions; and determining the battery voltage based on the overpotential and the open circuit voltage.

[0187] In the technical solution of the present application, the relationship between the viscosity of the electrolyte and the concentration of the active substance is fitted to determine the hydrodynamic radius of the active substance; based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius, the ionic conductivity of the active substance in the electrolyte is determined; based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of the active substance in the positive and negative electrolytes of the flow battery over time during the charge and discharge process is established, and the active substance concentration corresponding to the current moment is obtained according to the change model; the current battery voltage is determined based on the active substance concentration; based on the battery voltage, the energy efficiency parameters of the battery are determined, so as to determine the target concentration of the active substance through the energy efficiency parameters. The technical solution of the present application evaluates ionic conductivity based on the relationship between viscosity and self-diffusion coefficient, improves the accuracy of battery performance prediction, monitors the battery status in real time by establishing a dynamic model of changes in active substance concentration during charging and discharging, calculates battery voltage, energy density, efficiency and other performance parameters in real time based on the model, optimizes the electrolyte formulation through performance evaluation, determines the optimal electrolyte active substance concentration, improves the efficiency of electrolyte formulation design and performance evaluation, provides a reliable data basis for subsequent battery production, and reduces R&D and production costs.

[0188] Fig. 9 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0189] It should be noted that the computer system 400 of the electronic device shown in the figure is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0190] The computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, the ROM 402 and the RAM 403 are connected to each other through the bus 404. The input / output (I / O) interface 405 is also connected to the bus 404.

[0191] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.

[0192] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.

[0193] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0194] The flowchart and block diagram in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0195] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0196] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in the above-mentioned various optional implementations.

[0197] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0198] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0199] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0200] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0201] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for determining the concentration of a flow battery electrolyte, characterized in that: include: Fit the relationship between electrolyte viscosity and active material concentration to determine the hydrodynamic radius of the active material; Determining the ionic conductivity of the active material in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius; Based on the theory of conservation of matter and the ionic conductivity, a model for the change of the concentration of active substances in the positive and negative electrolytes of the flow battery over time during the charge and discharge process is established, and the concentration of active substances corresponding to the current moment is obtained according to the change model; determining a current battery voltage based on the active material concentration; Based on the battery voltage, an energy efficiency parameter of the battery is determined, so as to determine a target concentration of the active material through the energy efficiency parameter.

2. The method according to claim 1, characterized in that: Fit the relationship between electrolyte viscosity and active material concentration to determine the hydrodynamic radius of the active material, including: The relationship between electrolyte viscosity and active material concentration was fitted with the Higgins equation to determine the hydrodynamic radius of the active material.

3. The method according to claim 1, characterized in that Based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius, the ionic conductivity of the active material in the electrolyte is determined, including: Determining the self-diffusion coefficient of the active substance in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active substance in the electrolyte and the viscosity of the electrolyte, as well as the absolute temperature and the hydrodynamic radius of the active substance; Based on the self-diffusion coefficient, the number of cations and anions in the active material and the number of charges they carry, the ionic conductivity of the electrolyte is determined by the Nernst-Einstein equation.

4. The method according to claim 1, characterized in that Based on the theory of conservation of matter and the ionic conductivity, a model of the change of the concentration of active substances in the positive and negative electrolytes of the flow battery over time during the charge and discharge process is established, and the concentration of active substances corresponding to the current moment is obtained according to the change model, including: Based on the theory of conservation of matter, the concentration of active substances in the positive and negative electrolytes of the flow battery during the charge and discharge process, the volume of the electrolyte, and the volume flow rate generate a change equation as the change model; Based on the transformation equation, the concentration of the active material in the electrode and the reservoir is determined.

5. The method according to claim 1, characterized in that Determining a current battery voltage based on the active material concentration includes: Based on the battery parameters, determine the current overpotential of the battery; Determining the open circuit voltage of the battery based on the concentration of the active material and the equilibrium potential of the battery under standard conditions; A battery voltage is determined based on the overpotential and the open circuit voltage.

6. A device for determining the concentration of electrolyte in a flow battery, characterized in that: include: A fitting unit is used to fit the relationship between electrolyte viscosity and active material concentration to determine the hydrodynamic radius of the active material; A conductivity unit for determining the ionic conductivity of the active material in the electrolyte based on the inverse relationship between the self-diffusion coefficient of the active material in the electrolyte and the viscosity of the electrolyte, and the hydrodynamic radius; A model unit, for establishing a model of the change of the concentration of active substances in the positive and negative electrolytes of the flow battery over time during the charge and discharge process based on the theory of conservation of matter and the ion conductivity, and obtaining the active substance concentration corresponding to the current moment according to the change model; a voltage unit, configured to determine a current battery voltage based on the active material concentration; A parameter unit is used to determine an energy efficiency parameter of the battery based on the battery voltage, so as to determine a target concentration of the active material through the energy efficiency parameter.

7. A computer readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for determining the concentration of an electrolyte in a flow battery according to any one of claims 1 to 5 is implemented.

8. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method for determining the concentration of a liquid flow battery electrolyte as described in any one of claims 1 to 5.

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