A method for predicting performance of a vanadium redox flow battery based on parameter compensation

Abstract

The present application relates to the technical field of battery, especially a kind of performance prediction method of all-vanadium redox flow battery based on parameter compensation.The method first constructs the multi-physical field coupling simulation model including electrochemical reaction, ion transport, fluid dynamics and thermal management according to the structural parameters of all-vanadium redox flow battery in energy storage power station;In the preset running length, according to the operating parameters, the model is run, and the stack performance index data is obtained;The orthogonal test method is used for sensitivity analysis of structural parameters, and the regression coefficient is obtained by multiple linear regression;The regression coefficients are sorted and the target regression coefficients are selected, the parameter mapping relationship is established according to the performance index fluctuation value, and the compensation is carried out when the fluctuation value changes more than the threshold value;Finally, the weight factor is determined based on the analytic hierarchy process and entropy weight method, and the key structural parameters are screened out.The present application overcomes the limitations of fixed parameters and single environment in traditional methods, and realizes the accurate prediction of the performance of all-vanadium redox flow battery under different operating environments.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of batteries, and in particular to a performance prediction method for a vanadium redox flow battery based on parameter compensation. BACKGROUND

[0002] In the field of battery technology, accurate monitoring and estimation of the state of charge (SOC) of a battery is crucial for performance evaluation, life prediction, and maintenance management of the battery. SOC is a key parameter in the battery management system (BMS). The SOC of a battery not only affects the working efficiency of the battery, but also directly relates to the safety and service life of the battery. In particular, in a vanadium redox flow battery (VRFB) energy storage system, accurate SOC monitoring is crucial to ensure the performance and life of the battery. The vanadium redox flow battery is an electrochemical energy storage system based on vanadium salt solution, and its working principle involves the oxidation-reduction reaction of vanadium ions between the positive and negative electrodes. During the operation of the battery, the concentration of vanadium ion valence state at the positive and negative electrodes and the change of the potential of the battery directly affect the SOC of the battery.

[0003] A flow battery mainly consists of three parts: an external electrolyte storage tank and internal electrodes and ion-conducting membranes. The electrolyte is placed in the storage tank outside the stack and flows through the stack under the push of the circulating pump, and undergoes electrochemical reaction, thereby realizing the conversion of chemical energy and electrical energy.

[0004] Currently, in the design process of flow batteries in energy storage power stations, different fixed influence values of battery parameters on performance indicators are usually adopted for design or adjustment of the battery structure. However, the operating environments of different energy storage power stations are different, and the adopted combinations of battery parameters are also different, thus leading to the inability to accurately obtain the weight distribution and key parameters of the influence of battery performance indicators under a single energy storage power station. SUMMARY

[0005] In view of the problems existing in the prior art, the present application is proposed.

[0006] Therefore, the problem to be solved by the present application is how to, in the design process of flow batteries in energy storage power stations, usually adopt different fixed influence values of battery parameters on performance indicators for design or adjustment of the battery structure, but the operating environments of different energy storage power stations are different, and the adopted combinations of battery parameters are also different, thus leading to the inability to accurately obtain the weight distribution and key parameters of the influence of battery performance indicators under a single energy storage power station.

[0007] To solve the above technical problems, the present application provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present application provides a method for predicting performance of a vanadium redox flow battery based on parameter compensation, which comprises: constructing a simulation model of the vanadium redox flow battery according to structural parameters of the vanadium redox flow battery in an energy storage power station;

[0009] Within a preset running duration, running the simulation model of the vanadium redox flow battery according to running parameters of the vanadium redox flow battery to obtain performance index data of the battery stack;

[0010] Performing sensitivity analysis on the structural parameters of the battery stack to obtain a sensitivity value of each structural parameter relative to a change in the performance index;

[0011] Determining a weight factor of each structural parameter for a single performance index according to the sensitivity value of each structural parameter relative to the change in the performance index;

[0012] Marking the weight factors that are in the top several in a preset number of performance indexes as key structural parameters of the energy storage power station.

[0013] As a preferred scheme of the method for predicting performance of the vanadium redox flow battery based on parameter compensation, the process of the sensitivity analysis comprises:

[0014] Adjusting the hardware parameters of the simulation model of the vanadium redox flow battery according to each hardware parameter adjustment value, re-running the adjusted simulation model of the vanadium redox flow battery to obtain multiple adjustment performance index data of each vanadium redox flow battery;

[0015] Fitting a multiple linear regression model for each performance index according to the multiple adjustment performance index data and the hardware parameter adjustment value to obtain a regression coefficient of the hardware parameter in each regression model.

[0016] As a preferred scheme of the method for predicting performance of the vanadium redox flow battery based on parameter compensation, the method further comprises: sorting the regression coefficients of each performance index from large to small, and selecting a preset number of regression coefficients to obtain target regression coefficients of each performance index;

[0017] Compensating the target regression coefficients according to fluctuation values of each performance index corresponding to each adjustment hardware parameter value.

[0018] As a preferred scheme of the method for predicting performance of the vanadium redox flow battery based on parameter compensation, the compensation process of the target regression coefficients comprises:

[0019] Generating a reference performance index curve of the performance index data and an adjustment performance index curve of each adjustment;

[0020] Calculate the reference index curve, the reference area of the coordinate axis, and the adjustment performance index curve and the adjustment area of the coordinate axis, calculate the area difference between the reference area and the adjustment area, and obtain the fluctuation value;

[0021] A mapping relationship table between single hardware parameter adjustment and fluctuation value of each performance index is constructed.

[0022] As a preferred scheme of the all-vanadium redox flow battery performance prediction method based on parameter compensation, the method further comprises:

[0023] In the mapping relationship table, the target hardware parameter corresponding to the target regression coefficient of a single performance index is matched to obtain the target fluctuation value corresponding to the target hardware parameter adjustment of the single performance index;

[0024] It is judged whether the change value of the multiple target fluctuation values of the single performance index is higher than a preset change threshold.

[0025] As a preferred scheme of the all-vanadium redox flow battery performance prediction method based on parameter compensation, the method further comprises:

[0026] If the target fluctuation value is higher than the preset change threshold, the target hardware parameter corresponding to the highest target fluctuation value is determined as the compensation hardware parameter.

[0027] In the compensation mapping relationship table, the change value is matched to obtain a compensation coefficient, and the target regression coefficient of the compensation hardware parameter is compensated according to the compensation coefficient.

[0028] As a preferred scheme of the all-vanadium redox flow battery performance prediction method based on parameter compensation, the structure parameters of the all-vanadium redox flow battery comprise:

[0029] The battery stack structure parameters comprise electrode parameters, separator parameters, battery stack parameters, and thermal management parameters.

[0030] The electrode parameters comprise electrode materials, electrode areas, and electrode porosities, the separator parameters comprise separator materials, separator thicknesses, separator pore diameters, and porosities, and the battery stack parameters comprise battery stack sizes, battery stack stacking modes, and cell spacings.

[0031] As a preferred scheme of the all-vanadium redox flow battery performance prediction method based on parameter compensation, the battery operation parameters comprise:

[0032] The electrolyte tank operation parameters comprise electrolyte temperatures, electrolyte pressures, electrolyte concentrations, electrolyte volumes, electrolyte circulation rates, and electrolyte purities.

[0033] The operating parameters of the battery stack include battery stack voltage, battery stack current, battery stack power, battery stack efficiency, battery stack internal resistance and battery stack temperature.

[0034] As a preferred scheme of the performance prediction method for the vanadium redox flow battery based on parameter compensation, the performance indicators include:

[0035] Energy density, power density, cycle life, state of charge, charge and discharge rate, voltage efficiency and current efficiency.

[0036] As a preferred scheme of the performance prediction method for the vanadium redox flow battery based on parameter compensation, the performance indicators include:

[0037] According to the compensated target regression coefficient, the regression coefficient of each performance indicator is updated, and the updated regression coefficient of each performance indicator is determined as a sensitive value.

[0038] Among the preset number of performance indicators, the weight factor of the top several is marked as the key structural parameter of the energy storage power station.

[0039] In the second aspect, the embodiments of the present application provide a computer device, including a memory and a processor, the memory stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the performance prediction method for the vanadium redox flow battery based on parameter compensation according to the first aspect of the present application.

[0040] In the third aspect, the embodiments of the present application provide a computer readable storage medium, which stores a computer program, wherein: the computer program instructions are executed by the processor to realize the steps of the performance prediction method for the vanadium redox flow battery based on parameter compensation according to the first aspect of the present application.

[0041] The present application has the beneficial effects that: for different energy storage power station environments, the influence weight of the coupling relationship between different stack hardware parameters and the performance indicators of a single stack can be mined, and the key parameters of each energy storage power station can be mined.

[0042] After analyzing different stack hardware parameters through a multiple linear regression model, considering the diversity of the influence factors of the stack hardware parameters on the performance indicators, the change of the performance indicators is taken as a reference value to compensate the influence weight, thereby improving the weight calculation accuracy under a variety of operating environments.

[0043] Through analysis of a large amount of data, the mining model can be used to predict the influence of different parameter adjustments on the battery performance, and provide data support for future design and research and development. BRIEF DESCRIPTION OF DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0045] Fig. 1 Flow chart of the parameter compensation-based performance prediction method of the all-vanadium redox flow battery;

[0046] Fig. 2 Computer device diagram of the parameter compensation-based performance prediction method of the all-vanadium redox flow battery. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present application more apparent and understandable, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other ways different from those described herein without departing from the scope of the present application, and those skilled in the art can make similar extensions without departing from the concept of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The term "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor does it mean an embodiment that is independent of or mutually exclusive with other embodiments.

[0050] Embodiment 1

[0051] Reference Figs. 1-2 For the first embodiment of the present application, the embodiment provides a parameter compensation-based performance prediction method of an all-vanadium redox flow battery, comprising,

[0052] S100: Constructing an all-vanadium redox flow battery simulation model according to the structural parameters of the all-vanadium redox flow battery in the energy storage power station;

[0053] According to the structural parameters of the all-vanadium redox flow battery in the energy storage power station, an all-vanadium redox flow battery simulation model is constructed.

[0054] In the embodiments of the present application, the construction of the all-vanadium redox flow battery simulation model is based on the electrochemical-thermodynamic coupling principle, and a multi-physical field simulation method is adopted to couple and calculate multiple physical processes such as electrochemical reaction, mass and heat transfer process, and fluid dynamics.

[0055] Specifically, the simulation model includes the following sub-models: electrochemical reaction model: describes the redox reaction kinetics on the electrode surface; ion transport model: describes the migration and diffusion process of ions in the electrolyte; fluid dynamics model: describes the flow characteristics of the electrolyte in the battery system; thermal management model: describes the heat transfer process and temperature distribution of the system; battery performance model: calculates the output characteristics of the battery.

[0056] In an optional embodiment, the electrochemical reaction model can use the Butler-Volmer equation to describe the electrode kinetics process:

[0057]

[0058] where i is the current density, i0 is the exchange current density, a is the transfer coefficient, η is the overpotential, F is the Faraday constant, R is the gas constant, and T is the temperature.

[0059] In an optional embodiment, the ion transport model uses the Nernst-Planck equation to describe:

[0060]

[0061] where J i is the ion flux, D i is the diffusion coefficient, c i is the concentration, z i is the charge number, φ is the potential, and v is the fluid velocity.

[0062] It should be noted that the construction process of the model needs to consider the influence of factors such as electrode material, separator characteristics, and electrolyte properties. For example, for a graphite felt electrode, the porous structure characteristics need to be considered, and the modified Butler-Volmer equation can be used:

[0063] i eff = i (1-ε)a

[0064] where ε is the porosity and a is the specific surface area.

[0065] It should be noted that the solution of the simulation model uses the finite element method to discretize the continuous physical field into a finite number of grid elements, and the spatial distribution of each physical quantity is obtained through iterative calculation. The specific steps include: geometric model construction: a three-dimensional model is established according to the actual size of the battery; meshing: the geometric model is discretized into grid elements of appropriate size; boundary condition setting: set the inlet flow rate, potential, temperature, etc. boundary conditions; solver configuration: select appropriate solving algorithm and convergence criterion; post-processing analysis: visualize and extract data from the calculation results.

[0066] S200: running the simulation model of the all-vanadium redox flow battery according to the operating parameters of the all-vanadium redox flow battery within a preset running time, to obtain performance index data of the stack;

[0067] Within a preset running time, the simulation model of the all-vanadium redox flow battery is run according to the operating parameters of the all-vanadium redox flow battery, to obtain performance index data of the stack.

[0068] In the embodiments of the present application, the preset running time can be set to 8-24 hours according to the actual application scenario, so as to cover the performance of the battery under different working conditions. The collection frequency of the operating parameters can be set to 1-5 minutes / time, to ensure that the time resolution of the data meets the analysis requirements.

[0069] Specifically, the operating parameters include: current density: 0.05-0.2 A / cm 2 ; electrolyte flow rate: 10-30 mL / min; battery temperature: 15-40℃; electrolyte concentration: 1.0-2.0 mol / L; state of charge: 0%~100%.

[0070] In an optional embodiment, the performance index data acquisition process is as follows:

[0071] Steady-state operation data collection: record voltage, current, power and other parameters under stable working conditions; dynamic response test: record the transient response characteristics of the battery under load mutation conditions; efficiency calculation: calculate the coulombic efficiency, energy efficiency and other indicators according to the input and output energy; capacity attenuation analysis: evaluate the capacity attenuation trend through cyclic charge-discharge test; temperature distribution measurement: use thermocouple array to monitor the temperature field distribution of the battery stack.

[0072] It should be noted that the collection of performance index data needs to consider measurement error and data reliability. For example, four-wire method is used for voltage measurement to eliminate the influence of lead resistance, and calibrated PT100 sensor is used for temperature measurement to ensure accuracy. Specific measures include: sensor calibration: calibrate the measuring instrument regularly; data filtering: use sliding average method to reduce random noise; outlier identification: use 3σ criterion to eliminate obviously deviated data; data compensation: consider the influence of environmental temperature, pressure and other factors; uncertainty evaluation: calculate the confidence interval of the measurement result.

[0073] S201: further comprising: sorting the regression coefficients of each performance index from large to small, and selecting a preset number of regression coefficients to obtain the target regression coefficient of each performance index;

[0074] According to the fluctuation value of each performance index corresponding to each adjustment of the hardware parameter value, the target regression coefficient is compensated.

[0075] S202: the compensation process of the target regression coefficient includes:

[0076] generating a reference performance index curve of the performance index data and an adjustment performance index curve of each adjustment;

[0077] calculating a reference area of the reference index curve and the coordinate axis and an adjustment area of the adjustment performance index curve and the coordinate axis, calculating an area difference between the reference area and the adjustment area, and obtaining a fluctuation value;

[0078] constructing a mapping relationship table between a single hardware parameter adjustment and the fluctuation value of each performance index.

[0079] S203: further comprising:

[0080] In the mapping relationship table, the target hardware parameter corresponding to the target regression coefficient of a single performance index is matched to obtain a target fluctuation value corresponding to the target hardware parameter adjustment of the single performance index;

[0081] determining whether the change value of the multiple target fluctuation values of the single performance index is higher than a preset change threshold.

[0082] S204: further comprising:

[0083] If the change value is higher than the preset change threshold, the target hardware parameter corresponding to the highest target fluctuation value is determined as a compensation hardware parameter;

[0084] In the compensation mapping relationship table, the change value is matched to obtain a compensation coefficient, and the target regression coefficient of the compensation hardware parameter is compensated according to the compensation coefficient.

[0085] S205: the battery operating parameters include:

[0086] The operating parameters of the electrolyte tank include electrolyte temperature, electrolyte pressure, electrolyte concentration, electrolyte volume, electrolyte circulation rate and electrolyte purity;

[0087] The operating parameters of the battery stack include battery stack voltage, battery stack current, battery stack power, battery stack efficiency, battery stack internal resistance and battery stack temperature.

[0088] S206: the performance index includes:

[0089] Energy density, power density, cycle life, state of charge, charge and discharge rate, voltage efficiency and current efficiency.

[0090] S300: performing sensitivity analysis on the structural parameters of the battery stack to obtain a sensitivity value of each structural parameter relative to the performance index change;

[0091] In the embodiments of the present application, the sensitivity analysis adopts an orthogonal test method, and by designing a reasonable parameter combination, the influence degree of each parameter is obtained while minimizing the number of tests.

[0092] Specifically, the sensitivity analysis includes the following steps: parameter screening: selecting key structural parameters for analysis; experimental design: developing an orthogonal test table and determining parameter levels; data collection: recording performance indicators under different parameter combinations; range analysis: calculating the range of parameter changes on indicators; significance test: evaluating the statistical significance of parameter effects.

[0093] In an optional embodiment, the sensitivity of the structural parameters can be calculated by the following equation:

[0094]

[0095] where S i is the sensitivity of the parameter X i to the performance indicator Y, is the partial derivative, and is the normalization factor.

[0096] It should be noted that the reliability of the sensitivity analysis results is affected by multiple factors, and the following measures need to be taken to ensure the quality of the analysis: parameter independence test: ensure that each parameter is independent of each other; interaction analysis: consider the coupling effect between parameters; repeatability verification: perform multiple repeated tests to evaluate the stability of the results; data standardization: eliminate the influence of parameters with different dimensions; error propagation analysis: evaluate the influence of parameter uncertainty on the results.

[0097] S301: The process of sensitivity analysis includes:

[0098] According to each hardware parameter adjustment value, the hardware parameters of the full vanadium flow battery simulation model are adjusted in turn, and the adjusted full vanadium flow battery simulation model is re-run to obtain multiple adjustment performance indicator data of each full vanadium flow battery;

[0099] According to the multiple adjustment performance indicator data and the hardware parameter adjustment value, the multiple linear regression model of each performance indicator is fitted to obtain the regression coefficient of the hardware parameter in each regression model.

[0100] S302: The structural parameters of the full vanadium flow battery include:

[0101] The cell stack structural parameters include electrode parameters, separator parameters, cell stack parameters, and thermal management parameters;

[0102] Among them, the electrode parameters include electrode material, electrode area and electrode porosity, the separator parameters include separator material, separator thickness, separator pore size and porosity, the cell stack parameters include cell stack size, cell stack stacking method and cell unit spacing.

[0103] S400: Determine the weight factor of each structure parameter for a single performance index according to the sensitive value of the relative performance index change of each structure parameter;

[0104] In the embodiment of the application, the weight factor is determined by combining the analytic hierarchy process (AHP) with the entropy weight method, and the expert experience judgment and the data objective information are comprehensively considered.

[0105] Specifically, the weight calculation process includes: constructing a judgment matrix based on the importance comparison between parameters; consistency check: verifying the rationality of the judgment matrix; eigenvalue calculation: solving the characteristic vector corresponding to the maximum eigenvalue; entropy value calculation: calculating the entropy weight based on the data distribution characteristics; and weight synthesis: combining the subjective and objective weights to obtain the final weight.

[0106] In an optional embodiment, the entropy weight method calculation formula is as follows:

[0107]

[0108] wherein e j is the entropy value of the jth index, p ij is the standardized value of the jth index of the ith sample, w j is the weight.

[0109] It should be noted that the determination of the weight factor needs to consider the following aspects: index correlation: analyzing the correlation between indexes; data reliability: evaluating the influence of data quality on weight; expert opinion consistency: coordinating the differences in the judgments of different experts; weight stability: performing sensitivity analysis to verify the stability of the weight; and practical application applicability: adjusting the weight in combination with specific application scenarios.

[0110] S500: Mark the weight factors that are among the top several in the preset number of performance indexes as the key structure parameters of the energy storage power station.

[0111] S501: Further comprising: updating the regression coefficient of each performance index according to the compensated target regression coefficient, and determining the updated regression coefficient of each performance index as the sensitive value;

[0112] Mark the weight factors that are among the top several in the preset number of performance indexes as the key structure parameters of the energy storage power station.

[0113] In the embodiment of the application, the key structure parameter is selected by using a multi-criteria decision method, and the importance of the performance index and the controllability of the parameter are comprehensively considered.

[0114] Specifically, the screening process includes the following steps: determining the screening threshold: setting the critical value of the weight factor; cross-validation: testing the importance of parameters in multiple indicators; parameter clustering: grouping parameters with similar functions; feasibility assessment: analyzing the technical feasibility of parameter adjustment; priority ranking: determining the order of parameter optimization.

[0115] In an optional embodiment, the TOPSIS method can be used for comprehensive evaluation:

[0116]

[0117] where C i is the comprehensive evaluation index, and D i are the distances from the positive ideal solution and the negative ideal solution, respectively.

[0118] It should be noted that the determination of key parameters needs to pay attention to the following points: parameter independence: avoid selecting highly correlated parameters; control difficulty: consider the operation complexity of parameter adjustment; economy: evaluate the cost-effectiveness of parameter optimization; stability: analyze the impact of parameters on system stability; universality: consider the applicability of parameters under different working conditions.

[0119] Through the above steps, the key structural parameters affecting the performance of the battery can be systematically identified and determined, providing a basis for subsequent optimization design. This method has the following advantages: comprehensiveness: considering the coupling effect of multiple physical processes; accuracy: using strict mathematical models and statistical methods; practicality: combining actual application requirements for parameter screening; reliability: ensuring the credibility of the results through multiple verifications; adaptability: flexible adjustment according to specific application scenarios.

[0120] This embodiment determines the key structural parameters by constructing an accurate simulation model, collecting complete operation data, performing systematic sensitivity analysis, and reasonably calculating the weights, providing scientific guidance for the performance optimization of the all-vanadium redox flow battery.

[0121] In summary, by constructing a simulation model that includes the coupling of multiple physical fields such as electrochemical reactions, ion transport, fluid dynamics, and thermal management, a comprehensive simulation of the all-vanadium redox flow battery system is achieved. Compared with traditional single physical field modeling, this multi-physical field coupling modeling method can more accurately reflect the interaction of various physical processes inside the battery, thereby improving the accuracy of performance prediction.

[0122] By using the orthogonal test method to perform sensitivity analysis on the battery stack structural parameters, and combining with the multiple linear regression model for fitting, not only the number of tests is reduced, but also the quantitative evaluation of the influence degree of each parameter is realized. This method avoids the lengthy process of traditional individual parameter analysis, improves the analysis efficiency, and at the same time ensures the reliability of the analysis results.

[0123] By introducing a compensation mechanism based on performance indicator fluctuations, a mapping relationship between hardware parameter adjustments and performance indicator fluctuations is established, enabling dynamic compensation of regression coefficients. This compensation mechanism overcomes the limitations of traditional fixed-weight methods, which are unable to adapt to different operating environments, and improves the model's adaptability under different operating conditions.

[0124] A multi-criteria decision-making approach was used to comprehensively evaluate weight factors, combined with the TOPSIS method for parameter screening, to scientifically identify key structural parameters. This approach not only considers the degree of parameter impact on performance but also takes into account the feasibility and cost-effectiveness of parameter control, providing a more practical optimization solution for actual engineering applications.

[0125] Example 2

[0126] Reference Figs. 1-2 , which is the second embodiment of the present invention.

[0127] According to the structural parameters of the all-vanadium redox flow battery in the energy storage power station, the all-vanadium redox flow battery is constructed for different all-vanadium redox flow batteries.

[0128] Simulation model.

[0129] For example, if a power station requires more flexible energy storage capacity, it may use multiple liquid storage tanks of different capacities and models.

[0130] flow batteries to adapt to different load patterns and energy management strategies.

[0131] It should be noted that the structural parameters of the all-vanadium redox flow battery include the battery stack structural parameters and the electrolyte tank structural parameters.

[0132] Among them, the battery stack structural parameters include electrode parameters (such as electrode material, electrode area, electrode porosity), diaphragm parameters (such as diaphragm material, diaphragm thickness, diaphragm pore size and porosity), battery stack parameters (such as battery stack size, battery stack stacking method, battery cell spacing), and thermal management parameters (such as coolant circulation system or air cooling system).

[0133] The electrolyte structural parameters include electrolyte tank size, electrolyte tank material, electrolyte tank thickness, etc.

[0134] It should be noted that the construction process of the simulation model can be set up by those skilled in the art through battery simulation software (such as Simulink, PSIM, etc.).

[0135] In the same simulation environment, within the preset operating time, according to the operating parameters of each all-vanadium redox flow battery, the

[0136] The performance index data of the battery stack is obtained by running a corresponding full vanadium flow battery simulation model.

[0137] The operating parameters of the electrolyte tank include electrolyte temperature, electrolyte pressure, electrolyte concentration, electrolyte volume, electrolyte circulation rate, and electrolyte purity.

[0138] The operating parameters of the battery stack include battery stack voltage, battery stack current, battery stack power, battery stack efficiency, battery stack internal resistance, and battery stack temperature.

[0139] The performance indicators include energy density, power density, cycle life, state of charge, charge and discharge rate, voltage efficiency, and current efficiency.

[0140] The sensitivity analysis is performed on the structural parameters of the battery stack to obtain the sensitivity values of the performance indicators with respect to the structural parameters. For example, the sensitivity values of the electrode material, electrode area, and electrode porosity.

[0141] The sensitivity analysis process is as follows:

[0142] According to the adjustment values of the hardware parameters of the battery stack, the hardware parameters of the battery stack are adjusted in the multiple full vanadium flow battery simulation models, and the adjusted full vanadium flow battery simulation models are re-run to obtain the performance index data of each full vanadium flow battery after multiple adjustments.

[0143] According to the multiple adjustment performance index data and the hardware parameter adjustment values, the multiple linear regression models of each performance indicator are fitted to obtain the regression coefficients of the hardware parameters in each multiple linear regression model.

[0144] It should be noted that the dependent variable of the multiple linear regression model is the performance indicator, and the independent variable is the hardware parameter.

[0145] It should be noted that ridge regression or LASSO regression methods can be used for processing. Then, the statistical software (such as R, Python's scikit-learn library, SPSS, etc.) is used to input the data of the independent variables and dependent variables to perform regression analysis and obtain the regression coefficients of the hardware parameters in each multiple linear regression model. It should be noted that the regression coefficients here are all greater than 0, i.e., the absolute values of the coefficients.

[0146] It should be noted that each adjustment of the hardware parameters corresponds to a set of performance indicator data. For the same performance indicator, multiple adjustments of the hardware parameters will result in multiple performance indicator data.

[0147] The regression coefficients of each performance indicator are sorted from large to small, and the preset number of regression coefficients are selected from left to right in the sorted regression coefficients to obtain the target regression coefficients of each performance indicator.

[0148] According to the fluctuation value of each performance index corresponding to each adjustment of the hardware parameter value, the target regression coefficient is compensated.

[0149] The compensation process is as follows:

[0150] The reference performance index curve of the performance index data is generated, and the adjustment performance index curve of each adjustment is generated.

[0151] The X-axis is time, and the Y-axis is the index value.

[0152] The fluctuation value of each performance index is calculated.

[0153] The process of the fluctuation value is as follows:

[0154] The reference area of the reference index curve and the coordinate axis and the adjustment area of the adjustment performance index curve and the coordinate axis are calculated, the area difference between the reference area and the adjustment area is calculated, and the area difference value is taken as the fluctuation value.

[0155] A mapping relationship table between a single hardware parameter adjustment and the fluctuation value of each performance index is constructed. The fields of the mapping relationship table include the hardware parameter type, the hardware parameter adjustment value, the performance index type, and the performance index fluctuation value.

[0156] In the mapping relationship table, the target hardware parameter corresponding to the target regression coefficient of a single performance index is matched to obtain the target fluctuation value corresponding to the target hardware parameter adjustment of a single performance index.

[0157] It is judged whether the change value of the multiple target fluctuation values of a single performance index is higher than a preset change threshold.

[0158] If it is higher than the preset change threshold, the target hardware parameter corresponding to the highest target fluctuation value is determined as the compensation

[0159] hardware parameter.

[0160] It should be noted that when it is not higher than the preset change threshold, it is not necessary to update.

[0161] In the compensation mapping relationship table, the change value is matched to obtain a compensation coefficient, and the target regression coefficient of the compensation hardware parameter is compensated according to the compensation coefficient. The compensation coefficient is greater than 1.

[0162] It should be noted that the fields of the compensation mapping relationship are the fluctuation value change value and the compensation coefficient.

[0163] It should be noted that, for example, the target regression coefficient of the A hardware parameter is a, and the target regression coefficient of the B hardware parameter is b, when the fluctuation value of the A hardware parameter adjustment and the fluctuation value of the B hardware parameter adjustment are higher than the preset difference or ratio threshold, if the fluctuation value of the A hardware parameter adjustment is high, the target regression coefficient of the A hardware parameter is compensated.

[0164] The regression coefficient of each performance index is updated according to the compensated target regression coefficient, and the updated regression coefficient of each performance index is determined as a sensitive value.

[0165] According to the sensitive value of the change of each structure parameter relative to the performance index, the weight factor of each structure parameter for a single performance index is determined.

[0166] The weight factor table of each key parameter is sent to the client, and the weight factors in the top several (for example, the top 2) of the preset number (for example, 3) of performance indicators are marked as key structure parameters of the energy storage power station.

[0167] Embodiment 3

[0168] The embodiment also provides a computer device suitable for a parameter compensation-based all-vanadium redox flow battery performance prediction method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the forced oscillation detection and positioning method for a power distribution network proposed in the above embodiment.

[0169] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the forced oscillation detection and positioning method for a power distribution network proposed in the above embodiment.

[0170] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device. In addition, an external keyboard, touchpad or mouse can also be used.

[0171] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions of the present application can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0172] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instructions execution systems, apparatuses, or devices. For the purpose of this specification, the "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatuses, or devices.

[0173] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting, or otherwise processing, if necessary, in other suitable ways, to be electronically obtained, and then stored in the computer memory.

[0174] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technology, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0175] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, which should be covered by the claims of the present application.

Claims

1. A method for predicting the performance of a vanadium redox flow battery based on parametric compensation, characterized by: The method comprises the following steps: According to the structural parameters of the all-vanadium redox flow battery in the energy storage power station, a simulation model of the all-vanadium redox flow battery is constructed; Within a preset running time, the simulation model of the all-vanadium redox flow battery is run according to the running parameters of the all-vanadium redox flow battery, and performance index data of the cell stack is obtained; Sensitivity analysis is performed on the structural parameters of the cell stack, and a sensitive value of each structural parameter relative to the change in the performance index is obtained; The sensitivity analysis process comprises: According to each hardware parameter adjustment value, the hardware parameters of the all-vanadium redox flow battery simulation model are adjusted in sequence, the adjusted all-vanadium redox flow battery simulation model is re-run, and multiple adjustment performance index data of each all-vanadium redox flow battery are obtained; According to the multiple adjustment performance index data and the hardware parameter adjustment value, a multiple linear regression model of each performance index is fitted, and a regression coefficient of the hardware parameter in each regression model is obtained; According to the sensitive value of each structural parameter relative to the change in the performance index, a weight factor of each structural parameter for a single performance index is determined; According to the compensated target regression coefficient, the regression coefficient of each performance index is updated, and the updated regression coefficient of each performance index is determined as the sensitive value; 2. The parameter compensation based performance prediction method for a vanadium redox flow battery as claimed in claim 1, wherein: The weight factors in the top several in the preset number of performance indexes are marked as the key structural parameters of the energy storage power station. Further comprising: The regression coefficients of each performance index are sorted from large to small, and a preset number of regression coefficients are selected to obtain the target regression coefficient of each performance index; 3. The method for predicting performance of an all-vanadium redox flow battery based on parameter compensation according to claim 2, wherein: According to the fluctuation value of each performance index corresponding to each adjustment hardware parameter value, the target regression coefficient is compensated. The compensation process of the target regression coefficient comprises: A reference performance index curve of the performance index data and an adjustment performance index curve of each adjustment are generated; The reference area of the reference index curve and the coordinate axis and the adjustment area of the adjustment performance index curve and the coordinate axis are calculated, the area difference between the reference area and the adjustment area is calculated, and the fluctuation value is obtained; 4. The parameter compensation based performance prediction method for a vanadium redox flow battery of claim 3, wherein: A mapping relationship table between a single hardware parameter adjustment and the fluctuation value of each performance index is constructed. Further comprising: In the mapping relationship table, the target hardware parameter corresponding to the target regression coefficient of a single performance index is matched to obtain the target fluctuation value of the single performance index corresponding to the target hardware parameter adjustment; 5. The parameter compensation based performance prediction method for a vanadium redox flow battery as claimed in claim 4, wherein: It is judged whether the change value of the multiple target fluctuation values of the single performance index is higher than a preset change threshold. Further comprising: If it is higher than the preset change threshold, the target hardware parameter corresponding to the highest target fluctuation value is determined as the compensation hardware parameter; 6. The parameter compensation based performance prediction method for a vanadium redox flow battery as claimed in claim 5, wherein: In the compensation mapping relationship table, the change value is matched to obtain a compensation coefficient, and the target regression coefficient of the compensation hardware parameter is compensated according to the compensation coefficient. The structural parameters of the all-vanadium redox flow battery comprise: The cell stack structural parameters comprise electrode parameters, separator parameters, cell stack parameters and thermal management parameters; 7. The parameter compensation based performance prediction method for a vanadium redox flow battery as claimed in claim 6, wherein: The electrode parameters comprise electrode material, electrode area and electrode porosity, the separator parameters comprise separator material, separator thickness, separator pore size and porosity, and the cell stack parameters comprise cell stack size, cell stack stacking mode and cell unit spacing. The cell running parameters comprise: The operating parameters of the electrolyte tank include electrolyte temperature, electrolyte pressure, electrolyte concentration, electrolyte volume, electrolyte circulation rate and electrolyte purity. The operating parameters of the battery stack include battery stack voltage, battery stack current, battery stack power, battery stack efficiency, battery stack internal resistance and battery stack temperature.

8. The parameter compensation based performance prediction method of a vanadium redox flow battery as claimed in claim 7, wherein: The performance indicators include energy density, power density, cycle life, state of charge, charge and discharge rate, voltage efficiency and current efficiency.

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