All-vanadium redox flow battery system operation optimization method and system

Abstract

The invention discloses an all-vanadium redox flow battery system operation optimization method and system, and relates to the technical field of battery energy storage, and the method comprises the following steps: obtaining a plurality of operation parameters and a plurality of evaluation indexes of an all-vanadium redox flow battery, and dividing different evaluation indexes based on different application scenes; obtaining a plurality of evaluation index values corresponding to the plurality of operation parameters at different values through prediction models in different application scenes; iterating the values of the plurality of operation parameters in the optimization process of the optimal solution of each objective function to obtain an optimized operation parameter solution set in the corresponding application scene; and scoring multiple groups of operation parameters in the optimized operation parameter solution set in different application scenes, and taking the multiple groups of operation parameters with scores greater than a set threshold as optimized operation parameters. According to the method, the local optimal solution problem existing in an existing method can be effectively avoided, the overall calculation load is effectively reduced, and the accuracy of an optimization result is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery energy storage, and in particular to an operation optimization method and system for an all-vanadium liquid flow battery system. Background Art

[0002] With the growing demand for renewable energy, energy storage technology has become increasingly important. In this context, Vanadium Redox Flow Battery (VRFB), as an innovative energy storage solution, has attracted widespread attention due to its significant potential in high-proportion intermittent renewable energy power generation systems. Technological advances continue to promote the optimization of VRFB performance, involving multiple aspects such as battery design, fluid management, charging and discharging strategies, and system integration. At present, indicators for evaluating VRFB performance include battery efficiency, discharge capacity, energy density, and power density. However, most existing technologies use 1~2 indicators to evaluate battery performance. In practical applications, it is not comprehensive enough to rely on only 1~2 indicators to evaluate battery performance. Therefore, the development of a comprehensive and applicable evaluation system is crucial to promote the development and application of VRFB technology.

[0003] The multi-objective operating condition optimization technology of VRFB is a complex task, which involves multiple key aspects, including the selection of optimization parameters, the method of obtaining sample data, the formulation of optimization objectives, the applicability of optimization algorithms, and the screening of optimization solution sets. In the optimization research of VRFB system, multi-physics field coupling modeling simulation, system-level simulation, and experimental analysis have been widely used to obtain sample data. Optimization algorithms such as parametric scanning method, genetic algorithm, particle swarm optimization, and NSGA-II have also been applied to battery performance prediction or operating condition optimization. However, for the optimization parameters of VRFB, such as flow channel size, operating conditions (electrolyte flow rate and charge and discharge current density), and electrode thickness, the current optimization objectives are usually concentrated on a single objective. At the same time, the operating parameters of VRFB, such as electrolyte flow rate, charge and discharge current density, and electrode compression rate, have a complex nonlinear relationship in their coupling effect on battery performance. Traditional optimization methods, such as particle swarm optimization and parametric sweep, rely on simple mathematical models and heuristic rules, and are prone to local optimal problems during the optimization process. Therefore, their prediction accuracy and search capabilities are limited, making it difficult to handle such highly complex nonlinear optimization problems, and their computational costs and implementation complexity are high. In addition, traditional methods usually do not have the ability to handle high-dimensional data, and are also relatively lacking in dynamic changes or real-time adjustments.

[0004] In summary, the existing battery system operation optimization methods only rely on 1~2 indicators to evaluate battery performance and cannot adapt to diverse application scenarios. At the same time, since the coupling effect of VRFB operating parameters on battery performance is a complex nonlinear relationship, the existing optimization methods are prone to local optimal problems in the process of optimizing the optimization parameters, and the calculation cost is high, which makes the optimization results of the operating parameters inaccurate, thereby reducing the performance and efficiency of VRFB. Summary of the invention

[0005] The present invention provides an all-vanadium liquid flow battery system operation optimization method and system, develops a comprehensive performance evaluation system applicable to different application scenarios and needs, and solves the problem that existing optimization methods are difficult to handle some highly complex nonlinear optimization problems, which easily causes local optimal problems in the model, resulting in high computational cost and implementation complexity.

[0006] In a first aspect, the present invention provides a method for optimizing the operation of an all-vanadium liquid flow battery system, comprising the following steps: Obtain multiple operating parameters of the all-vanadium liquid flow battery system and multiple evaluation indicators for evaluating the performance of the all-vanadium liquid flow battery, and divide different evaluation indicators based on different application scenarios; Construct prediction models for different application scenarios through multiple operating parameters and multiple evaluation indicators for different application scenarios; obtain multiple evaluation indicator values ​​corresponding to different values ​​of multiple operating parameters through the prediction models for different application scenarios; The corresponding objective function is established through multiple evaluation index values ​​under different application scenarios. In the process of finding the optimal solution of each objective function, the values ​​of multiple operating parameters are iterated to obtain the optimized operating parameter solution set under the corresponding application scenario. Scores are given to multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios, and multiple groups of operating parameters with scores greater than a set threshold are taken as optimized operating parameters.

[0007] Preferably, the multiple operating parameters include electrode compression rate, electrolyte flow rate and current density, the multiple evaluation indicators include energy efficiency, system efficiency, power density, energy density and discharge capacity, and the application scenarios include economic application scenarios, sustainable application scenarios, load application scenarios and environmental protection application scenarios.

[0008] Preferably, the different evaluation indicators are divided based on different application scenarios, including: For economic application scenarios, the indicators include energy efficiency, system efficiency, power density and energy density; For continuous application scenarios, the indicators include energy efficiency, system efficiency, power density, energy density and discharge capacity; For load application scenarios, the indicators include power density and discharge capacity; For environmentally friendly application scenarios, the indicators include energy efficiency, system efficiency, energy density and discharge capacity.

[0009] Preferably, the construction of prediction models in different application scenarios by using multiple operating parameters and multiple evaluation indicators of different application scenarios comprises the following steps: Obtain battery charge and discharge curves under multiple operating parameters, and obtain multiple evaluation index values ​​under different application scenarios through the battery charge and discharge curves to obtain a data set; Multiple neural network models are established with multiple operating parameters as input and multiple evaluation indicators under different application scenarios as output; Multiple neural network models are trained through data sets to obtain prediction models for different application scenarios.

[0010] Preferably, the electrode compression rate is in the range of 0% to 80%, the electrolyte flow rate is in the range of 10 mL / min to 310 mL / min, and the current density is in the range of 100 A / m 2 ~3600A / m 2 .

[0011] Preferably, in the process of searching for the optimal solution of each objective function, iterating the values ​​of multiple operating parameters to obtain an optimized operating parameter solution set in the corresponding application scenario includes the following steps: Set the population size, iteration conditions, objective function and boundary values ​​of multiple operating parameters of the genetic algorithm; Randomly generate a plurality of individuals as an initial population, wherein the individual is a set of operating parameters; Use the trained prediction models under different application scenarios to obtain multiple objective functions corresponding to the initial population and screen out the initial optimal solution of the objective function; Perform crossover and mutation operations on the initial population to obtain a new population, obtain multiple objective functions corresponding to the new population and screen out new optimal solutions of the objective functions; The above crossover, mutation and comparison iterations are performed on the new population, and when the iteration conditions are met, the optimal solution of the objective function and the corresponding optimized operating parameter solution set are output.

[0012] Preferably, scoring the multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios comprises the following steps: The evaluation index values ​​corresponding to different application scenarios are constructed into a normalized decision matrix; The normalized decision matrix is ​​forward-processed and standardized to extract the ideal optimal solution and the ideal worst solution; Obtain the distance between each evaluation index and the ideal optimal solution and the ideal worst solution, and obtain the positive ideal solution and the negative ideal solution; The positive ideal solution and the negative ideal solution are fitted to obtain the scores of multiple groups of operating parameters.

[0013] In a second aspect, the present invention provides an all-vanadium liquid flow battery system operation optimization system, comprising: An acquisition module is used to acquire multiple operating parameters of the all-vanadium liquid flow battery system and multiple evaluation indicators for evaluating the performance of the all-vanadium liquid flow battery, and to divide different evaluation indicators based on different application scenarios; A prediction module is used to construct prediction models under different application scenarios through multiple operating parameters and multiple evaluation indicators of different application scenarios; and obtain multiple evaluation indicator values ​​corresponding to different values ​​of multiple operating parameters through the prediction models under different application scenarios; The optimization module is used to establish the corresponding objective function through multiple evaluation index values ​​under different application scenarios, and iterate the values ​​of multiple operating parameters in the process of finding the optimal solution of each objective function to obtain the optimized operating parameter solution set under the corresponding application scenario; The scoring module is used to score multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios, and take the multiple groups of operating parameters with scores greater than a set threshold as optimized operating parameters.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention first divides different evaluation indicators based on different application scenarios, and clarifies the performance evaluation indicators of different application scenarios. Then, for each application scenario, a prediction model is constructed through multiple operating parameters and multiple evaluation indicators of different application scenarios, and the complex nonlinear relationship between input and output is quickly captured. Finally, multiple evaluation indicator values ​​under the current application scenario are used as the objective function, and the values ​​of multiple operating parameters are iterated in the optimization process of the optimal solution of the objective function. The present invention combines the prediction model, the optimization process, and the optimal solution screening and scoring process, and can conduct a comprehensive search for the values ​​of all operating parameters, which can effectively avoid the local optimal solution problem existing in the existing method, effectively reduce the overall calculation load, improve the accuracy of the optimization result, and improve the performance and efficiency of VRFB. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0016] Picture 1 A flow chart of an all-vanadium liquid flow battery system operation optimization method of the present invention; Picture 2 It is a schematic diagram of the optimization results of the economic performance of the present invention; Picture 3 It is a schematic diagram of the optimization result of sustainability of the present invention; Picture 4 It is a schematic diagram of the optimization result of the loading amount of the present invention; Picture 5 It is a schematic diagram of the optimization results of the environmental protection of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] A method for optimizing the operation of an all-vanadium liquid flow battery system, specifically a method for evaluating and selecting the optimal operating conditions of an all-vanadium liquid flow battery system based on a neural network combined with a genetic algorithm, which handles the complex problem of finding the optimal parameter configuration for multi-objective optimization in different application scenarios. Picture 1 , specifically including the following steps: Step 1: Obtain multiple operating parameters and multiple evaluation indicators of the all-vanadium liquid flow battery.

[0019] Multiple operating parameters of the all-vanadium liquid flow battery system include electrode compressibility CR , electrolyte flow rate Q in and current density I app . Multiple evaluation indicators including energy efficiency E.E. , System Efficiency SE , power density P power , energy density E power and discharge capacity C capacity These indicators not only reflect the key performance of the battery in terms of energy conversion and the overall operating efficiency of the system, but are also closely related to the battery's ability to efficiently store and release energy.

[0020] Step 2: Configure the operating parameters.

[0021] Determine the parameter configuration of the input variables. In order to find the optimal parameter configuration suitable for different scenarios and avoid local optimality, the present invention expands the range of parameter configuration. Among them, the electrode compression rate CR The value range is 0%~80%, the step length is 10%, and the electrolyte flow rate Q in The value range is 10mL / min~310mL / min, the step size is 30mL / min, and the current density I app The value range is 100A / m 2 ~3600A / m 2 , step size is 300A / m 2 .

[0022] Step 3: Divide different evaluation indicators according to different application scenarios.

[0023] In order to comprehensively evaluate the performance of the battery, the present invention emphasizes the optimization criteria of integrating efficiency, utilization and safety. These criteria are systematically divided into the following four categories to adapt to different application scenarios of all-vanadium liquid flow batteries, including load capacity, environmental protection, sustainability and economy. The evaluation indicators of load capacity include P power and C capacity The environmental protection evaluation indicators include E.E. , SE , E power and C capacity , continuous evaluation indicators include E.E. , SE , P power , E power and C capacity The economic evaluation indicators include E.E. , SE , P power and E power .

[0024] Economics is a key criterion for evaluating all-vanadium flow batteries, which includes comprehensive cost-effectiveness from initial investment to operation and maintenance. E.E. and SE Reduce operating costs by improving energy conversion and reducing transmission losses, P power and E powerImproving power density reduces startup investment and operating expenses, and improves market competitiveness. E.E. , SE , P power and E power Divided into application scenarios of battery economy. Optimizing these indicators can reduce costs, increase market share and return on investment, thereby enhancing the company's market competitiveness and development prospects. The key to sustainability is to improve energy density, extend cycle life, and ensure environmentally friendly material recycling and processing. Systems with strong continuous operation capabilities can provide frequency regulation and load balancing in a timely manner to stabilize load fluctuations during operation. Improve the reliability of power supply, reduce maintenance costs, and improve overall economic benefits. Therefore, key indicators include E.E. , SE , P power , E power and C capacity Load capacity is a key indicator for evaluating energy output capacity and load bearing capacity in practical applications. P power and C capacity . High power density can meet applications with higher instantaneous energy demands, while large capacity represents the battery's service life and the ability to sustain energy supply to ensure continuous power supply, which is suitable for long discharge cycles. The optimization of power density and capacity is critical to battery energy storage systems and renewable energy applications. In terms of environmental protection, the consumption of natural resources and environmental pollution can be reduced by optimizing VRFB performance. Key indicators include E.E. , SE and E power These three indicators are of great significance for promoting sustainable development and ensuring ecological health.

[0025] Step 4: Obtain multiple evaluation index values ​​corresponding to multiple operating parameters in different application scenarios.

[0026] The present invention uses the VRFB numerical model established by COMSOL to perform fixed-step parametric scanning for calculation, obtains the battery charge and discharge curves under different operating parameters, and calculates the corresponding evaluation index value through a formula.

[0027] The collected data was screened for outliers. The screening steps were as follows: (1) At low compression rates, the internal resistance of the battery will also be high. When the current density is too high, a large polarization will occur, which will affect the normal calculation and solution of the model. Therefore, these extreme conditions were deleted. (2) Data with performance indicators equal to 0 and less than 0.01 were deleted to prevent large errors in neural network fitting.

[0028] In order to avoid the adverse effects of data span on subsequent neural network training and make neural network training more effective, the data after removing outliers is normalized and the index range is processed to [0, -1]. The processing formula is as follows: ; In the formula, is the normalized evaluation index, x is the evaluation index before normalization, X The dataset is used for evaluation indicators.

[0029] Step 5: Take multiple operating parameters as input and multiple evaluation index values ​​corresponding to different scenarios as output, and train multiple prediction models for different application scenarios.

[0030] Load volume divided by application scenario ( P power and C capacity )、Environmental protection( E.E. , SE , E power and C capacity ), persistence( E.E. , SE , P power , E power and C capacity ) and economical ( E.E. , SE , P power and E power ) 4 optimization objectives as output, Q in , I app and CRThe input is . Four neural network models were trained using the neural network toolbox in MATLAB. By adjusting the size of the network layers and the proportion of the training set, the percentage of training data was controlled between 60% and 80%. The Bayesian regularization technique was used to train the network model, and four independent neural network prediction models were established to adapt to the nonlinear relationship between input and output and improve the generalization ability of the training process.

[0031] Through multiple prediction models, multiple evaluation index values ​​corresponding to different scenarios when multiple operating parameters have different values ​​are obtained respectively.

[0032] Step 6: Multi-objective optimization through genetic algorithm.

[0033] The corresponding objective function is established through multiple evaluation index values ​​in different application scenarios, and the values ​​of multiple operating parameters are iterated through the optimization process of finding the optimal value of each objective function to obtain the optimized operating parameter solution set in the corresponding application scenario. Specifically including: (1) The population size, iteration conditions (maximum number of iterations, target fitness, or no significant improvement over multiple generations), and boundary values ​​of multiple operating parameters are set through genetic algorithms.

[0034] (2) Randomly generate multiple individuals as the initial population, where the individual is a set of operating parameters and the objective function is used as the fitness value.

[0035] (3) Use the trained prediction models under different application scenarios to obtain multiple objective functions corresponding to the initial population and screen out the initial optimal solution of the objective function.

[0036] (4) Perform crossover and mutation operations on the initial population to obtain a new population, obtain multiple objective functions corresponding to the new population, and screen out the new optimal solution of the objective function.

[0037] (5) Perform the above crossover, mutation, and comparison iterations on the new population. When the iteration conditions are met, output the optimal solution of the objective function and the corresponding set of optimized operating parameter solutions.

[0038] The genetic algorithm sets the population size as the total number of samples, the maximum number of iterations as 300, the stagnation iteration limit as 200, and the evaluation index under different application scenarios as the objective function, with a tolerance of 1e -10, and a drawing function for real-time display of the Pareto front is specified. The lower and upper bounds of the optimized operating parameters (compression rate, electrolyte flow rate, and current density) are the minimum boundary [0 100 10] and the maximum boundary [0.8 3600 310] of the operating parameters, respectively. The gamultiobj function is used to find the optimal solution of the operating parameters and return the solution and the corresponding objective function value, and the solution set of the Pareto frontier is obtained as the data set for optimizing the operating parameters in this application scenario.

[0039] The global search capability of genetic algorithms is used to explore potential optimal parameter configurations, prevent the results from falling into local optimality, and obtain the optimized operating parameter solution set.

[0040] Step 7: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) multi-objective decision-making is used for comprehensive scoring and ranking.

[0041] First, the evaluation index values ​​corresponding to each application scenario are constructed into a normalized decision matrix. Since all evaluation indicators are maximization indicators, forward normalization is performed directly: (1); in, z ij is the value after normalization, x ij is the eigenvalue in the matrix, i For the line, j For the columns, n is the total number of rows. After the process of normalization and standardization, the present invention can extract the ideal optimal solution z + and the ideal worst solution z - A vector of: (2).

[0042] Use the positive ideal solution ( d + ) and the negative ideal solution ( d - ) Calculate the distance between each evaluation index and the ideal optimal solution and the ideal worst solution: (3); (4); in, m is the total number of columns, z j + is the optimal solution for the column, z j -is the worst solution for the series.

[0043] The score for each set of operating parameters is calculated according to the following formula: (5).

[0044] The multiple scores are sorted, and the multiple groups of operating parameters with scores greater than a set threshold are used as optimized operating parameters.

[0045] Based on the sorting results, the optimal operating conditions of four evaluation systems (economy, sustainability, load capacity and environmental performance) in different application scenarios are selected.

[0046] The final optimization results are obtained, and the optimized parameter configurations under different application scenarios are obtained through sorting analysis, such as Picture 2-Picture 4 As shown in the figure, the optimization result diagrams under four application scenarios are obtained.

[0047] The present invention uses the evaluation indicators contained in four different scenarios as four groups of output variables. CR , Q in and I app As input variables, the optimal parameter configuration is found by using neural network combined with genetic algorithm optimization technology. The optimal solution set generated by the genetic algorithm is comprehensively evaluated and sorted by applying the TOPSIS algorithm to accurately screen out the operating condition configuration that best suits each specific scenario. This process not only enhances the performance of VRFB, but also ensures its adaptability and practicality in different environments.

[0048] Compared with traditional optimization methods such as parametric scanning, the present invention shows significant superiority when facing multi-objective optimization challenges. In general, this method combines the dual advantages of neural networks and genetic algorithms, specifically targeting complex and multi-dimensional optimization problems. Through comprehensive search and adaptive mechanisms, this method can effectively locate the Pareto frontier solution set of the optimal parameter configuration, thereby improving the efficiency of optimization and the quality of the solution. At the same time, traditional methods are often limited by falling into local optimality and high computational complexity.

[0049] The present invention evaluates and selects the optimal operating parameter configuration of the all-vanadium liquid flow battery by combining a neural network with a genetic algorithm and using the TOPSIS decision-making method, providing an accurate and efficient solution to the complex problem of optimizing and finding the optimal operating parameter configuration for multi-objective evaluation indicators in different application scenarios.

[0050] Based on the same concept, the present invention also provides an all-vanadium liquid flow battery system operation optimization system, including an acquisition module, a prediction module, an optimization module and a scoring module.

[0051] The acquisition module is used to obtain multiple operating parameters of the all-vanadium liquid flow battery system and multiple evaluation indicators for evaluating the performance of the all-vanadium liquid flow battery, and divide different evaluation indicators based on different application scenarios.

[0052] The prediction module is used to construct prediction models under different application scenarios through multiple operating parameters and multiple evaluation indicators of different application scenarios; and obtain multiple evaluation indicator values ​​corresponding to multiple operating parameters at different values ​​through the prediction models under different application scenarios.

[0053] The optimization module is used to establish the corresponding objective function through multiple evaluation index values ​​under different application scenarios. In the process of finding the optimal solution of each objective function, the values ​​of multiple operating parameters are iterated to obtain the optimized operating parameter solution set under the corresponding application scenario.

[0054] The scoring module is used to score multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios, and take multiple groups of operating parameters with scores greater than a set threshold as optimized operating parameters.

[0055] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0056] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the operation of an all-vanadium liquid flow battery system, characterized in that: The following steps are involved: Obtain multiple operating parameters of the all-vanadium liquid flow battery system and multiple evaluation indicators for evaluating the performance of the all-vanadium liquid flow battery, and divide different evaluation indicators based on different application scenarios; Construct prediction models for different application scenarios through multiple operating parameters and multiple evaluation indicators for different application scenarios; obtain multiple evaluation indicator values ​​corresponding to different values ​​of multiple operating parameters through the prediction models for different application scenarios; The corresponding objective function is established through multiple evaluation index values ​​under different application scenarios. In the process of finding the optimal solution of each objective function, the values ​​of multiple operating parameters are iterated to obtain the optimized operating parameter solution set under the corresponding application scenario. Scores are given to multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios, and multiple groups of operating parameters with scores greater than a set threshold are taken as optimized operating parameters.

2. The method for optimizing the operation of an all-vanadium liquid flow battery system according to claim 1, characterized in that: The multiple operating parameters include electrode compression rate, electrolyte flow rate and current density, the multiple evaluation indicators include energy efficiency, system efficiency, power density, energy density and discharge capacity, and the application scenarios include economic application scenarios, sustainable application scenarios, load application scenarios and environmental protection application scenarios.

3. The method for optimizing the operation of an all-vanadium liquid flow battery system according to claim 2, characterized in that: Different evaluation indicators are divided based on different application scenarios, including: For economic application scenarios, the indicators include energy efficiency, system efficiency, power density and energy density; For continuous application scenarios, the indicators include energy efficiency, system efficiency, power density, energy density and discharge capacity; For load application scenarios, the indicators include power density and discharge capacity; For environmentally friendly application scenarios, the indicators include energy efficiency, system efficiency, energy density and discharge capacity.

4. The method for optimizing the operation of an all-vanadium liquid flow battery system according to claim 1, characterized in that: The method of constructing prediction models for different application scenarios by using multiple operating parameters and multiple evaluation indicators for different application scenarios includes the following steps: Obtain battery charge and discharge curves under multiple operating parameters, and obtain multiple evaluation index values ​​under different application scenarios through the battery charge and discharge curves to obtain a data set; Multiple neural network models are established with multiple operating parameters as input and multiple evaluation indicators under different application scenarios as output; Multiple neural network models are trained through data sets to obtain prediction models for different application scenarios.

5. The method for optimizing the operation of an all-vanadium liquid flow battery system according to claim 2, characterized in that: The electrode compression rate ranges from 0% to 80%, the electrolyte flow rate ranges from 10 mL / min to 310 mL / min, and the current density ranges from 100 A / m 2 ~3600A / m 2 .

6. The method for optimizing the operation of an all-vanadium liquid flow battery system according to claim 1, characterized in that: The method of iterating the values ​​of multiple operating parameters in the process of finding the optimal solution of each objective function to obtain an optimized operating parameter solution set in the corresponding application scenario includes the following steps: Set the population size, iteration conditions, objective function and boundary values ​​of multiple operating parameters of the genetic algorithm; Randomly generate a plurality of individuals as an initial population, wherein the individual is a set of operating parameters; Use the trained prediction models under different application scenarios to obtain multiple objective functions corresponding to the initial population, and screen out the initial optimal solution of the objective function; Perform crossover and mutation operations on the initial population to obtain a new population, obtain multiple objective functions corresponding to the new population and screen out new optimal solutions of the objective functions; The above crossover, mutation and comparison iterations are performed on the new population, and when the iteration conditions are met, the optimal solution of the objective function and the corresponding optimized operating parameter solution set are output.

7. The method for optimizing the operation of an all-vanadium liquid flow battery system according to claim 1, characterized in that: Scoring the multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios comprises the following steps: The evaluation index values ​​corresponding to different application scenarios are constructed into a normalized decision matrix; The normalized decision matrix is ​​forward-processed and standardized to extract the ideal optimal solution and the ideal worst solution; Obtain the distance between each evaluation index and the ideal optimal solution and the ideal worst solution, and obtain the positive ideal solution and the negative ideal solution; The positive ideal solution and the negative ideal solution are fitted to obtain the scores of multiple groups of operating parameters.

8. An all-vanadium liquid flow battery system operation optimization system, characterized in that: include: An acquisition module is used to acquire multiple operating parameters of the all-vanadium liquid flow battery system and multiple evaluation indicators for evaluating the performance of the all-vanadium liquid flow battery, and to divide different evaluation indicators based on different application scenarios; A prediction module is used to construct prediction models under different application scenarios through multiple operating parameters and multiple evaluation indicators of different application scenarios; and obtain multiple evaluation indicator values ​​corresponding to different values ​​of multiple operating parameters through the prediction models under different application scenarios; The optimization module is used to establish the corresponding objective function through multiple evaluation index values ​​under different application scenarios, and iterate the values ​​of multiple operating parameters in the process of finding the optimal solution of each objective function to obtain the optimized operating parameter solution set under the corresponding application scenario; The scoring module is used to score multiple groups of operating parameters in the optimized operating parameter solution set under different application scenarios, and take the multiple groups of operating parameters with scores greater than a set threshold as optimized operating parameters.

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