All-vanadium redox flow battery performance prediction method based on parameter compensation

Abstract

The invention relates to the technical field of batteries, in particular to an all-vanadium redox flow battery performance prediction method based on parameter compensation. The method comprises the following steps: firstly, according to structure parameters of an all-vanadium redox flow battery in an energy storage power station, constructing a multi-physical field coupling simulation model comprising electrochemical reaction, ion transmission, fluid dynamics and thermal management; operating the model according to the operating parameters within a preset operating time period, and obtaining galvanic pile performance index data; carrying out sensitivity analysis on the structural parameters by adopting an orthogonal test method, and obtaining a regression coefficient through multiple linear regression; sorting the regression coefficients, selecting a target regression coefficient, establishing a parameter mapping relation according to a performance index fluctuation value, and performing compensation when the fluctuation value change exceeds a threshold value; and finally, determining a weight factor based on an analytic hierarchy process and an entropy weight method, and screening key structure parameters. According to the method, the limitations of fixed parameters and single environment in a traditional method are overcome, and accurate prediction of the performance of the all-vanadium redox flow battery in different operating environments is realized.

Description

Technical Field

[0001] The invention relates to the technical field of batteries, in particular to a method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation. Background Art

[0002] In the field of battery technology, accurate monitoring and estimation of the battery's state of charge (SOC) is crucial for battery performance evaluation, life prediction, and maintenance management. SOC is a key parameter in the battery management system (BMS). The SOC of a battery not only affects the battery's operating efficiency, but is also directly related to the battery's safety and service life. Especially in energy storage systems such as the Vanadium Redox Flow Battery (VRFB), accurate SOC monitoring is essential to ensure the performance and life of the battery. The vanadium redox flow battery is an electrochemical energy storage system based on a vanadium salt solution, and its working principle involves the redox reaction of vanadium ions between the positive and negative electrodes. During the operation of the battery, the valence concentration of vanadium ions at the positive and negative electrodes and the change in the battery's potential will directly affect the battery's SOC.

[0003] Liquid flow batteries are mainly composed of three parts: an external electrolyte storage tank and internal electrodes and ion-conducting membranes. The electrolyte is placed in a tank outside the battery stack and flows through the battery stack under the push of a circulation pump, where an electrochemical reaction occurs, thereby achieving the conversion of chemical energy into electrical energy.

[0004] At present, in the design process of liquid flow batteries in energy storage power stations, fixed influence values ​​of different stack parameters on performance indicators are usually adopted to design or adjust the stack structure. However, the operating environment of different energy storage power stations is different, and the combinations of stack parameters adopted are also different. Therefore, it is impossible to accurately obtain the weight distribution and key parameters that affect the performance indicators of the stack in a single energy storage power station. Summary of the invention

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

[0006] Therefore, the problem to be solved by the present invention is how to design or adjust the stack structure by taking into account the fixed influence values ​​of different stack parameters on performance indicators in the design process of liquid flow batteries in energy storage power stations. However, the operating environments of different energy storage power stations are different, and the combinations of stack parameters adopted are also different. Therefore, it is impossible to accurately obtain the weight distribution and key parameters that affect the stack performance indicators in a single energy storage power station.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

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

[0009] Within the preset operation time, according to the operation parameters of the all-vanadium liquid flow battery, the all-vanadium liquid flow battery simulation model is operated to obtain the performance index data of the battery stack;

[0010] Conduct sensitivity analysis on the structural parameters of the battery stack to obtain the sensitive values ​​of each structural parameter relative to the performance index change;

[0011] According to the sensitive values ​​of each structural parameter relative to the performance index change, the weight factor of each structural parameter for a single performance index is determined;

[0012] The weight factors that are among the top several of the preset number of performance indicators are marked as key structural parameters of the energy storage power station.

[0013] As a preferred solution of the all-vanadium liquid flow battery performance prediction method based on parameter compensation described in the present invention, the sensitivity analysis process includes:

[0014] According to each hardware parameter adjustment value, the hardware parameters of the all-vanadium liquid flow battery simulation model are adjusted in turn, and the adjusted all-vanadium liquid flow battery simulation model is re-run to obtain multiple adjustment performance index data of each all-vanadium liquid flow battery;

[0015] According to the multiple adjustments of the performance indicator data and the hardware parameter adjustment values, a multivariate linear regression model of each performance indicator is fitted to obtain the regression coefficient of the hardware parameter in each regression model.

[0016] As a preferred solution of the all-vanadium liquid flow battery performance prediction method based on parameter compensation described in the present invention, it also includes: sorting the regression coefficients of each performance indicator from large to small, and selecting a preset number of regression coefficients to obtain the target regression coefficient of each performance indicator;

[0017] The target regression coefficient is compensated according to the fluctuation value of each performance indicator corresponding to each adjustment of the hardware parameter value.

[0018] As a preferred solution of the all-vanadium liquid flow battery performance prediction method based on parameter compensation described in the present invention, the compensation process of the target regression coefficient includes:

[0019] Generate a reference performance index curve of performance index data and an adjusted performance index curve for each adjustment;

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

[0021] Construct a mapping relationship table between a single hardware parameter adjustment and the fluctuation value of each performance indicator.

[0022] As a preferred solution of the method for predicting the performance of all-vanadium liquid flow battery based on parameter compensation described in the present invention, it also includes:

[0023] In the mapping relationship table, the target hardware parameters corresponding to the target regression coefficients of the single performance indicators are matched to obtain the target fluctuation values ​​corresponding to the single performance indicators when the target hardware parameters are adjusted;

[0024] Determine whether the change values ​​of the multiple target fluctuation values ​​of the single performance indicator are higher than a preset change threshold.

[0025] As a preferred solution of the method for predicting the performance of all-vanadium liquid flow battery based on parameter compensation described in the present invention, it also includes:

[0026] 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;

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

[0028] As a preferred solution of the all-vanadium liquid flow battery performance prediction method based on parameter compensation described in the present invention, the structural parameters of the all-vanadium liquid flow battery include:

[0029] The battery stack structural parameters include electrode parameters, diaphragm parameters, battery stack parameters and thermal management parameters;

[0030] Among them, electrode parameters include electrode material, electrode area and electrode porosity; diaphragm parameters include diaphragm material, diaphragm thickness, diaphragm pore size and porosity; battery stack parameters include battery stack size, battery stack stacking method and battery cell spacing.

[0031] As a preferred solution of the all-vanadium liquid flow battery performance prediction method based on parameter compensation described in the present invention, the battery operating parameters include:

[0032] The operating parameters of the electrolyte tank include electrolyte temperature, electrolyte pressure, electrolyte concentration, electrolyte volume, electrolyte circulation rate and electrolyte purity;

[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 solution of the all-vanadium liquid flow battery performance prediction method based on parameter compensation described in the present invention, 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 solution of the method for predicting the performance of all-vanadium liquid flow battery based on parameter compensation described in the present invention, it also includes:

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

[0038] The weight factors that are among the top several of the preset performance indicators are marked as key structural parameters of the energy storage power station.

[0039] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the all-vanadium liquid flow battery performance prediction method based on parameter compensation as described in the first aspect of the present invention are implemented.

[0040] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for predicting the performance of an all-vanadium liquid flow battery based on parameter compensation as described in the first aspect of the present invention are implemented.

[0041] The beneficial effects of the present invention are as follows: for different energy storage power station environments, the weight of the influence of the coupling relationship between different battery stack hardware parameters on the performance indicators of a single battery stack in each energy storage power station can be mined, and the key parameters of each energy storage power station can be mined.

[0042] After analyzing different battery stack hardware parameters through a multivariate linear regression model, considering the diversity of factors affecting performance indicators due to battery stack hardware parameters, the changes in performance indicators are taken as reference values ​​to compensate for the impact weights, thereby improving the accuracy of weight calculation under various operating environments.

[0043] By analyzing large amounts of data, mining models can be established to predict the impact of different parameter adjustments on battery performance, providing data support for future design and research and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is a flow chart of the performance prediction method of all-vanadium liquid flow battery based on parameter compensation;

[0046] Figure 2 Computer equipment diagram for the all-vanadium liquid flow battery performance prediction method based on parameter compensation. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0050] Example 1

[0051] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a method for predicting the performance of an all-vanadium liquid flow battery based on parameter compensation, comprising:

[0052] S100: Construct a simulation model of the all-vanadium liquid flow battery based on the structural parameters of the all-vanadium liquid flow battery in the energy storage power station;

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

[0054] In the embodiments of the present application, the construction of the all-vanadium liquid flow battery simulation model is based on the electrochemical-thermodynamic coupling principle, and a multi-physics field simulation method is used to couple and calculate multiple physical processes such as electrochemical reactions, mass transfer and heat transfer processes, 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 may 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, α 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 is described using the Nernst-Planck equation:

[0060]

[0061] Among them, 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 electric potential, and v is the fluid velocity.

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

[0063] i eff =i(1-ε)a

[0064] Among them, ε is the porosity and a is the specific surface area.

[0065] It should be noted that the simulation model is solved using the finite element method, which discretizes the continuous physical field into a finite number of grid units, and obtains the spatial distribution of each physical quantity through iterative calculation. The specific steps include: geometric model construction: building a three-dimensional model according to the actual size of the battery; meshing: discretizing the geometric model into grid units of appropriate size; boundary condition setting: setting boundary conditions such as inlet flow rate, potential, and temperature; solver configuration: selecting the appropriate solution algorithm and convergence criteria; post-processing analysis: visualizing the calculation results and extracting data.

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

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

[0068] In the embodiment of the present application, the preset operating time can be set to 8-24 hours according to the actual application scenario 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: 0.05-0.2A / cm 2 ; Electrolyte flow rate: 10-30mL / min; Battery temperature: 15-40℃; Electrolyte concentration: 1.0-2.0mol / L; State of charge: 0%~100%.

[0070] In an optional embodiment, the process of obtaining performance indicator data is as follows:

[0071] Steady-state operation data acquisition: 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 coulomb efficiency, energy efficiency and other indicators based on input and output energy; capacity decay analysis: evaluate capacity decay trend through cyclic charge and discharge tests; temperature distribution measurement: use a thermocouple array to monitor the temperature field distribution of the battery stack.

[0072] It should be noted that the collection of performance indicator data needs to consider measurement errors and data reliability. For example, the voltage measurement uses a four-wire method to eliminate the influence of lead resistance, and the temperature measurement uses a calibrated PT100 sensor to ensure accuracy. Specific measures include: sensor calibration: regular calibration of measuring instruments; data filtering: using sliding average and other methods to reduce random noise; outlier identification: using the 3σ criterion to eliminate data with obvious deviations; data compensation: considering the influence of factors such as ambient temperature and pressure; uncertainty assessment: calculating the confidence interval of the measurement results.

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

[0074] The target regression coefficient is compensated according to the fluctuation value of each performance indicator corresponding to each adjustment of the hardware parameter value.

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

[0076] Generate a reference performance index curve of performance index data and an adjusted performance index curve for each adjustment;

[0077] Calculate the reference area between the reference index curve and the coordinate axis and the adjustment area between the adjustment performance index curve and the coordinate axis, calculate the area difference between the reference area and the adjustment area, and obtain the fluctuation value;

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

[0079] S203: Also includes:

[0080] In the mapping relationship table, the target hardware parameters corresponding to the target regression coefficients of the single performance indicators are matched to obtain the target fluctuation values ​​corresponding to the single performance indicators when the target hardware parameters are adjusted;

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

[0082] S204: Also includes:

[0083] 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;

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

[0085] S205: 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: Performance indicators include:

[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 sensitive values ​​of changes in each structural parameter relative to the performance index;

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

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

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

[0094]

[0095] Among them, S i For parameter X i Sensitivity to performance indicator Y, is the partial derivative, is the normalization factor.

[0096] It should be noted that the reliability of 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 the parameters are independent of each other; interaction analysis: consider the coupling effect between parameters; repeatability verification: conduct multiple repeated tests to evaluate the stability of the results; data standardization: eliminate the influence of parameters of different dimensions; error propagation analysis: evaluate the impact 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 all-vanadium liquid flow battery simulation model are adjusted in turn, and the adjusted all-vanadium liquid flow battery simulation model is re-run to obtain multiple adjustment performance index data of each all-vanadium liquid flow battery;

[0099] According to the multiple adjustments of the performance indicator data and the hardware parameter adjustment values, a multivariate 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 all-vanadium liquid flow batteries include:

[0101] The battery stack structural parameters include electrode parameters, diaphragm parameters, battery stack parameters and thermal management parameters;

[0102] Among them, electrode parameters include electrode material, electrode area and electrode porosity; diaphragm parameters include diaphragm material, diaphragm thickness, diaphragm pore size and porosity; battery stack parameters include battery stack size, battery stack stacking method and battery cell spacing.

[0103] S400: determining a weight factor of each structural parameter for a single performance indicator according to a sensitive value of each structural parameter relative to a change in the performance indicator;

[0104] In the embodiment of the present application, the weight factor is determined by using the analytic hierarchy process (AHP) combined with the entropy weight method, taking into account expert experience and judgment and objective data information.

[0105] Specifically, the weight calculation process includes: constructing a judgment matrix: based on the comparison of importance between parameters; consistency test: verifying the rationality of the judgment matrix; eigenvalue calculation: solving the eigenvector corresponding to the maximum eigenvalue; entropy value calculation: calculating the entropy weight based on the data distribution characteristics; weight synthesis: combining 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] Among them, e j is the entropy value of the jth indicator, p ij is the standardized value of the jth indicator of the i-th sample, w j is the weight.

[0109] It should be noted that the determination of weight factors needs to consider the following aspects: indicator relevance: analyzing the degree of correlation between indicators; data reliability: evaluating the impact of data quality on weights; consistency of expert opinions: coordinating differences in judgment among different experts; weight stability: conducting sensitivity analysis to verify the stability of weights; and applicability for practical applications: adjusting weights based on specific application scenarios.

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

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

[0112] The weight factors that are among the top several of the preset performance indicators are marked as key structural parameters of the energy storage power station.

[0113] In the embodiments of the present application, the screening of key structural parameters adopts a multi-criteria decision-making method, which comprehensively considers the importance of performance indicators and the controllability of parameters.

[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 the parameter in multiple indicators; parameter clustering: grouping parameters with similar functions; feasibility assessment: analyzing the technical feasibility of parameter adjustment; priority sorting: determining the order of parameter optimization.

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

[0116]

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

[0118] It should be noted that the following points should be noted when determining key parameters: parameter independence: avoid selecting highly correlated parameters; control difficulty: consider the operational 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 that affect battery performance can be systematically identified and determined, providing a basis for subsequent optimization design. This method has the following advantages: comprehensiveness: considering the coupling effects of multiple physical processes; accuracy: using rigorous mathematical models and statistical methods; practicality: screening parameters in combination with actual application needs; reliability: ensuring the credibility of the results through multiple verifications; adaptability: flexible adjustment according to specific application scenarios.

[0120] This embodiment finally determines the key structural parameters by constructing an accurate simulation model, collecting complete operating data, performing systematic sensitivity analysis and reasonable weight calculation, and provides scientific guidance for the performance optimization of all-vanadium liquid flow batteries.

[0121] In summary, by building a simulation model that includes multiple physical fields such as electrochemical reactions, ion transport, fluid dynamics and thermal management, a comprehensive simulation of the all-vanadium liquid 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 conduct sensitivity analysis on the battery stack structural parameters and combining it with the multivariate linear regression model for fitting, not only the number of tests is reduced, but also the quantitative evaluation of the influence of each parameter is achieved. This method avoids the lengthy process of traditional parameter-by-parameter analysis, improves the analysis efficiency, and ensures the reliability of the analysis results.

[0123] By introducing a compensation mechanism based on performance index fluctuations, a mapping relationship between hardware parameter adjustment and performance index fluctuations is established, and dynamic compensation of regression coefficients is achieved. This compensation mechanism overcomes the limitation of the traditional fixed weight method that cannot adapt to different operating environments and improves the adaptability of the model under different working conditions.

[0124] The multi-criteria decision-making method is used to comprehensively evaluate the weight factors, and the TOPSIS method is combined for parameter screening to achieve scientific identification of key structural parameters. This method not only considers the degree of influence of parameters on performance, but also takes into account the feasibility and economy of parameter regulation, providing a more practical optimization solution for actual engineering applications.

[0125] Example 2

[0126] Reference Figure 1-Figure 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, all-vanadium redox flow batteries are constructed for different all-vanadium redox flow batteries.

[0128] Simulation model.

[0129] For example, if a power station needs 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 liquid 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 (eg, Simulink, PSIM, etc.).

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

[0136] The corresponding all-vanadium liquid flow battery simulation model is run to obtain the performance indicator data of the battery stack.

[0137] Among them, 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] Performance indicators include energy density, power density, cycle life, state of charge, charge and discharge rate, voltage efficiency, current efficiency, etc.

[0140] Conduct sensitivity analysis on the structural parameters of the battery stack to obtain the sensitive values ​​of each structural parameter relative to the change of performance indicators, such as the sensitive values ​​corresponding to the electrode material, electrode area, and electrode porosity.

[0141] The process of sensitivity analysis is as follows:

[0142] According to each hardware parameter adjustment value of the battery stack, the battery stack hardware parameters of multiple all-vanadium liquid flow battery simulation models are adjusted in turn, and the adjusted all-vanadium liquid flow battery simulation models are re-run to obtain multiple adjustment performance indicator data of each all-vanadium liquid flow battery.

[0143] According to the multiple adjustments of the performance indicator data and the hardware parameter adjustment values, the multiple linear regression model of each performance indicator is fitted to obtain the regression coefficient of the hardware parameter in each multiple linear regression model.

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

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

[0146] It should be noted that each time a hardware parameter is adjusted, a set of performance indicator data will be generated. For the same performance indicator, when the hardware parameter is adjusted multiple times, the performance indicator data will be generated multiple times.

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

[0148] The target regression coefficient is compensated according to the fluctuation value of each performance indicator corresponding to each adjustment of the hardware parameter value.

[0149] The compensation process is as follows:

[0150] A reference performance indicator curve of the performance indicator data is generated, and an adjusted performance indicator curve for each adjustment.

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

[0152] Obtain the fluctuation value of each performance indicator.

[0153] The process of fluctuating values ​​is as follows:

[0154] Calculate the reference area between the reference index curve and the coordinate axis and the adjusted area between the adjusted performance index curve and the coordinate axis, calculate the area difference between the reference area and the adjusted area, and use the area difference as the fluctuation value.

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

[0156] In the mapping relationship table, the target hardware parameters corresponding to the target regression coefficients of the single performance indicators are matched to obtain the target fluctuation values ​​corresponding to the single performance indicators when the target hardware parameters are adjusted.

[0157] Determine whether the change value of multiple target fluctuation values ​​of a single performance indicator is higher than the 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 value.

[0159] Hardware parameters.

[0160] It should be noted that when the change is not higher than the preset threshold, no update is required.

[0161] In the compensation mapping 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, for performance indicator X, the target regression coefficient of hardware parameter A is a, and the target regression coefficient of hardware parameter B is b. When hardware parameter A and hardware parameter B are adjusted, if the difference or ratio of the two fluctuation values ​​of performance indicator X is higher than the preset difference or ratio threshold, if the fluctuation value of hardware parameter A is adjusted, the target regression coefficient of hardware parameter A is compensated.

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

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

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

[0167] Example 3

[0168] This embodiment also provides a computer device, which is applicable to a method for predicting the performance of an all-vanadium liquid flow battery based on parameter compensation, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0169] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0170] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via 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 operation of 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 an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0171] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. 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 as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0173] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0174] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0175] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention 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 invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for predicting the performance of an all-vanadium liquid flow battery based on parameter compensation, characterized in that: Including, building a simulation model of all-vanadium liquid flow battery according to the structural parameters of all-vanadium liquid flow battery in energy storage power station; Within the preset operation time, according to the operation parameters of the all-vanadium liquid flow battery, the all-vanadium liquid flow battery simulation model is operated to obtain the performance index data of the battery stack; Conduct sensitivity analysis on the structural parameters of the battery stack to obtain the sensitive values ​​of each structural parameter relative to the performance index change; According to the sensitive values ​​of each structural parameter relative to the performance index change, the weight factor of each structural parameter for a single performance index is determined; The weight factors that are among the top several of the preset number of performance indicators are marked as key structural parameters of the energy storage power station.

2. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 1, characterized in that: The sensitivity analysis process includes: According to each hardware parameter adjustment value, the hardware parameters of the all-vanadium liquid flow battery simulation model are adjusted in turn, and the adjusted all-vanadium liquid flow battery simulation model is re-run to obtain multiple adjustment performance index data of each all-vanadium liquid flow battery; According to the multiple adjustments of the performance indicator data and the hardware parameter adjustment values, a multivariate linear regression model of each performance indicator is fitted to obtain the regression coefficient of the hardware parameter in each regression model.

3. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 2, characterized in that: Also includes: The regression coefficients of each performance indicator are sorted from large to small, and a preset number of regression coefficients are selected to obtain the target regression coefficient of each performance indicator; The target regression coefficient is compensated according to the fluctuation value of each performance indicator corresponding to each adjustment of the hardware parameter value.

4. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 3, characterized in that: The compensation process of the target regression coefficient includes: Generate a reference performance index curve of performance index data and an adjusted performance index curve for each adjustment; Calculate the reference area between the reference index curve and the coordinate axis and the adjustment area between the adjustment performance index curve and the coordinate axis, calculate the area difference between the reference area and the adjustment area, and obtain the fluctuation value; Construct a mapping relationship table between a single hardware parameter adjustment and the fluctuation value of each performance indicator.

5. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 4, characterized in that: Also includes: In the mapping relationship table, the target hardware parameters corresponding to the target regression coefficients of the single performance indicators are matched to obtain the target fluctuation values ​​corresponding to the single performance indicators when the target hardware parameters are adjusted; Determine whether the change values ​​of the multiple target fluctuation values ​​of the single performance indicator are higher than a preset change threshold.

6. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 5, characterized in that: Also includes: 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; In the compensation mapping relationship table, the change value is matched to obtain the compensation coefficient, and the target regression coefficient of the compensation hardware parameter is compensated according to the compensation coefficient.

7. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 6, characterized in that: The structural parameters of the all-vanadium liquid flow battery include: The battery stack structural parameters include electrode parameters, diaphragm parameters, battery stack parameters and thermal management parameters; Among them, electrode parameters include electrode material, electrode area and electrode porosity; diaphragm parameters include diaphragm material, diaphragm thickness, diaphragm pore size and porosity; battery stack parameters include battery stack size, battery stack stacking method and battery cell spacing.

8. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 7, characterized in that: The battery operating parameters include: 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.

9. The method for predicting performance of an all-vanadium liquid flow battery based on parameter compensation according to claim 8, characterized in that: The performance indicators include: energy density, power density, cycle life, state of charge, charge and discharge rate, voltage efficiency and current efficiency.

10. The method for predicting performance of an all-vanadium redox flow battery based on parameter compensation according to claim 9, characterized in that: Also includes: The regression coefficient of each performance indicator is updated according to the compensated target regression coefficient, and the updated regression coefficient of each performance indicator is determined as a sensitive value; The weight factors that are among the top several of the preset performance indicators are marked as key structural parameters of the energy storage power station.

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