Optimal control method and optimal control system for refrigeration device of liquid flow energy storage system

Abstract

The application provides an optimization control method and an optimization control system for a refrigerating device of a liquid flow energy storage system. The optimization control method comprises: obtaining historical operation data of the liquid flow energy storage system under multiple complete charge-discharge cycles; constructing a regression model based on the historical operation data, the regression model comprising an output parameter and multiple input parameters, the multiple input parameters comprising a starting temperature of electrolyte in a charging stage, a set frequency of the refrigerating device in a constant-power charging stage, a set frequency of the refrigerating device in a constant-voltage charging stage, and a set temperature of electrolyte in a discharging stage, and the output parameter comprising an overall energy conversion efficiency of the liquid flow energy storage system; in the historical operation data, obtaining a data combination of the multiple input parameters corresponding to the highest overall energy conversion efficiency; obtaining an optimized combination of the input parameters based on the data combination and the regression model; and determining a control strategy of the refrigerating device according to the optimized combination.

Description

Technical Field

[0001] This application mainly relates to the field of liquid flow energy storage system technology, and in particular to an optimized control method and optimized control system for a refrigeration device of a liquid flow energy storage system. Background Technology

[0002] During charging and discharging, flow hydride energy storage systems generate a significant amount of heat through electrochemical reactions. Currently, cooling devices are commonly used to control the electrolyte temperature, maintaining it within a reasonable range to ensure efficient electrochemical reactions. Existing temperature control schemes for flow hydride energy storage systems often employ fixed-frequency direct-cooling units, which operate at full frequency throughout the entire process. The control methods for these cooling devices typically involve threshold control, time relay control, or a combination of both: threshold control activates and stops the cooling device only when the electrolyte temperature reaches a set threshold, while time relay control activates the cooling device only during preset fixed time periods.

[0003] The aforementioned control methods cannot adapt to the different heat generation rates and cooling demands at different stages of vanadium redox flow storage charging and discharging. The electrolyte temperature cannot be stabilized within the optimal temperature range for the electrochemical reaction, resulting in reduced electrochemical reaction efficiency and a long-term deviation of the performance coefficient of the refrigeration device from its optimal range. According to actual engineering statistics, the energy consumption of the fixed-frequency refrigeration device accounts for 9% to 12% of the total net loss of the vanadium redox flow storage system. This makes it difficult for the actual overall energy conversion efficiency of the vanadium redox flow storage power station to reach the standard requirement of 65%. The system suffers from high losses and poor energy efficiency, hindering the large-scale promotion and application of flow storage technology. Summary of the Invention

[0004] This application addresses the aforementioned technical problems by providing an optimized control method and control system for a cooling device in a flow liquid energy storage system. To solve these problems, the optimized control method for a cooling device in a flow liquid energy storage system includes: obtaining historical operating data of the flow liquid energy storage system under multiple complete charge-discharge cycles; constructing a regression model based on the historical operating data, the regression model including output parameters and multiple input parameters, the multiple input parameters including the initial temperature of the electrolyte during the charging phase, the set frequency of the cooling device during the constant power charging phase, the set frequency of the cooling device during the constant voltage charging phase, and the set temperature of the electrolyte during the discharging phase, the output parameter including the overall energy conversion efficiency of the flow liquid energy storage system; obtaining a data combination of the multiple input parameters corresponding to the highest overall energy conversion efficiency from the historical operating data; obtaining an optimized combination of input parameters based on the data combination and the regression model, wherein the optimized combination is obtained by adjusting the data in the data combination, and the overall energy conversion efficiency corresponding to the optimized combination is greater than the highest one; and determining a control strategy for the cooling device based on the optimized combination.

[0005] In one embodiment of this application, the regression model includes multiple terms, which include a linear term for each input parameter, a quadratic term for each input parameter, an interaction term between any two input parameters, and coefficients corresponding to the linear term, the quadratic term, and the interaction term, respectively; the step of constructing the regression model based on the historical operating data includes: determining the coefficients based on the historical operating data.

[0006] In one embodiment of this application, the regression model is expressed by the following formula: Y=K0+K1x1+K2x2+K3x3+K4x4+K 11 x1 2 +K 22 x2 2 +K 33 x3 2 +K 44 x4 2 +K 12 x1x2+K 13 x1x3+K 14 x1x4+K 23 x2x3+K 24 x2x4+K 34 x3x4+ξ, where K0 is a constant term, x1 is the initial temperature of the electrolyte during the charging stage, x2 is the set frequency of the cooling device during the constant power charging stage, x3 is the set frequency of the cooling device during the constant voltage charging stage, x4 is the set temperature of the electrolyte during the discharging stage, and K1, K2, K3, and K4 are all coefficients of the first-order term, K 11， K 22， K 33， K 44 All are coefficients of the quadratic term, K 12、 K 13、 K 14、 K 23、 K 24、 K 34 All are interaction term coefficients between input parameters, ξ is the error compensation term, and Y is the overall energy conversion efficiency.

[0007] In one embodiment of this application, obtaining an optimized combination of input parameters based on the data combination and the regression model includes: determining the adjustment priority and adjustment direction of the input parameters according to the sign and absolute value of the coefficients; and, based on the data combination, adjusting the data corresponding to the multiple input parameters with a preset step size according to the adjustment priority and the adjustment direction to obtain multiple adjustment combinations; substituting the multiple adjustment combinations into the regression model to calculate the overall energy conversion efficiency, wherein the optimized combination is the adjustment combination that maximizes the overall energy conversion efficiency among the multiple adjustment combinations.

[0008] In one embodiment of this application, determining the adjustment priority and direction of the input parameter based on the sign and absolute value of the coefficient includes: if the absolute value of the coefficient is larger, the adjustment priority of the term corresponding to the coefficient is higher; if the coefficient of the first-order term is positive, the adjustment direction is to increase the input parameter of the first-order term; if the coefficient of the first-order term is negative, the adjustment direction is to decrease the input parameter of the first-order term; if the coefficient of the second-order term is positive, the adjustment direction is to move the input parameter of the second-order term away from a first preset threshold; if the coefficient of the second-order term is negative, the adjustment direction is to move the input parameter of the second-order term closer to a second preset threshold; if the coefficient of the interaction term is positive, the adjustment direction includes the direction in which the input parameters in the interaction term increase collaboratively; if the coefficient of the interaction term is negative, the adjustment direction includes the direction in which the input parameters in the interaction term decrease collaboratively.

[0009] In one embodiment of this application, determining the control strategy of the cooling device according to the optimized combination includes: in the optimized combination, if the initial temperature of the electrolyte in the charging stage is lower than the set temperature of the electrolyte in the discharging stage, then in the early stage of the charging stage, the cooling device is used to lower the electrolyte temperature to the initial temperature of the electrolyte in the charging stage; if the initial temperature of the electrolyte in the charging stage is higher than the set temperature of the electrolyte in the discharging stage, then the cooling device is shut down for a preset time at the end of the discharging stage.

[0010] In one embodiment of this application, the historical operating data includes first historical operating data, which is obtained according to the following steps: setting a first exploration strategy, including: setting a first frequency of a cooling device in multiple charging stages, wherein the first frequency of the cooling device includes: 0Hz, at least one frequency in the theoretical high-efficiency frequency range of the cooling device, and at least one combination of a first constant power charging stage frequency and a first constant voltage charging stage frequency; setting a first set temperature of the electrolyte in multiple discharging stages in multiple discharging stages; obtaining a first complete combination of the first frequency of the cooling device in multiple charging stages and the first set temperature in multiple discharging stages; and applying the first complete combination to a charge-discharge cycle to obtain first historical operating data under several complete charge-discharge cycles.

[0011] In one embodiment of this application, the historical operating data further includes second historical operating data, which is obtained according to the following steps: setting a second exploration strategy, including: selecting, from the first historical operating data, a first exploration strategy corresponding to the highest overall energy conversion efficiency of the liquid flow energy storage system in a complete charge-discharge cycle as a basis; adjusting the first set temperature of the electrolyte in the discharge stage by a first preset step size to obtain a second set temperature of the electrolyte in multiple discharge stages; adjusting the first frequency of the cooling device in the charging stage by a second preset step size to obtain a second frequency of the cooling device in multiple charging stages; obtaining a second complete combination of the second frequency of the cooling device in multiple charging stages and the second set temperature in multiple discharge stages; and applying the second complete combination to a charge-discharge cycle to obtain second historical operating data under several complete charge-discharge cycles.

[0012] In one embodiment of this application, the first exploration strategy and / or the second exploration strategy further include safety constraints, the safety constraints including: when the electrolyte temperature is higher than a first safety threshold, the cooling device operates at a frequency not lower than a first preset frequency; and when the electrolyte temperature is higher than a second safety threshold, the cooling device operates at full frequency.

[0013] To address the aforementioned technical problems, this application also provides an optimized control system for a refrigeration device in a flow energy storage system, comprising: a memory for storing instructions executed by a processor; and a processor for executing the instructions to implement the optimized control method for the variable frequency refrigeration device in the flow energy storage system as described above.

[0014] Compared with existing technologies, this application has the following advantages: The optimized control method for the cooling device of the flow refrigeration system provided in this application obtains historical operating data, uses parameters related to the cooling device during the charging and discharging stages as input parameters, and constructs a regression model with the overall energy conversion efficiency as the output parameter. This accurately matches the differences in heat generation characteristics during the charging and discharging stages of the flow refrigeration system, and can meet the cooling needs of the electrolyte at different stages of charging and discharging. Furthermore, this application obtains an optimized combination based on the data combination with the highest overall energy conversion efficiency from historical operating data, and determines the control strategy for the variable frequency cooling device based on this optimized combination, thereby improving the overall energy conversion efficiency of the flow refrigeration system and saving energy consumption. Attached Figure Description

[0015] The accompanying drawings are included to provide a further understanding of this application; they are incorporated into and constitute a part of this application. The drawings illustrate embodiments of this application and, together with this specification, serve to explain the principles of this application. In the drawings:

[0016] Figure 1 This is a flowchart of an optimized control method for a cooling device in a liquid flow energy storage system according to an embodiment of this application;

[0017] Figure 2 This is a schematic diagram of the operating frequency of a fixed-frequency direct-cooling machine during the charging and discharging phase.

[0018] Figure 3 This is a schematic diagram of the operating frequency of a variable frequency direct cooler according to an embodiment of this application during the charging and discharging phase;

[0019] Figure 4 This is a system block diagram of the optimized control system of the refrigeration device of a liquid flow energy storage system according to an embodiment of this application. Detailed Implementation

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this application. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0021] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0022] Furthermore, it should be noted that the use of terms such as "first" and "second" to define parameters or data is merely for the purpose of distinguishing the corresponding parameters or data. Unless otherwise stated, these terms have no special meaning and therefore should not be construed as limiting the scope of protection of this application. In addition, although the terminology used in this application is selected from commonly known and used terms, some terms mentioned in this application's specification may have been chosen by the applicant according to his or her judgment, and their detailed meanings are explained in the relevant sections of this description. Moreover, this application should be understood not only through the actual terms used, but also through the meaning implied by each term.

[0023] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more steps may be removed from these processes.

[0024] The cooling requirements of the electrolyte often differ at different stages of charging and discharging. For example, in the constant power charging and constant voltage charging stages, the cooling requirements differ due to the different rates of temperature decrease in the electrolyte. Existing control methods cannot adapt to the different heat generation rates and cooling requirements at different stages of charging and discharging in flow liquid energy storage systems. The solution provided in this application is applicable to flow liquid energy storage systems, especially to all-vanadium redox flow liquid energy storage systems.

[0025] The optimized control method for the cooling device of the flow hydride energy storage system provided in this application utilizes historical operating data. Based on parameters related to the cooling device during the charging and discharging phases as input parameters, and using the overall energy conversion efficiency as the output parameter, a regression model is constructed. This model accurately matches the differences in heat generation characteristics during the charging phases of the flow hydride energy storage system, thus meeting the cooling requirements of the electrolyte at different stages of charging and discharging. Furthermore, this application obtains an optimized combination based on the data combination with the highest overall energy conversion efficiency from historical operating data and determines the control strategy for the variable frequency cooling device according to this optimized combination. This improves the overall energy conversion efficiency of the flow hydride energy storage system and saves energy consumption.

[0026] Figure 1 A flowchart illustrating an optimized control method for a refrigeration device in a liquid flow energy storage system according to an embodiment of this application is shown. Figure 1 As shown, this application provides an optimized control method 10 for a refrigeration device in a flow energy storage system, wherein the refrigeration device is a variable frequency refrigeration device, comprising:

[0027] Step S11: Obtain historical operating data of the flow battery system under multiple complete charge-discharge cycles. A complete charge-discharge cycle consists of one complete charge and one complete discharge. In some embodiments, a complete charge refers to the entire process of the flow battery system charging from the calibrated rated capacity starting point to the charging termination point. During a complete charge, when the accumulated charge reaches the system's factory-calibrated rated capacity, the charging termination point is determined to have been reached; this charging termination point is the discharge starting point of the complete discharge process. Similarly, during a complete discharge, when the accumulated discharge reaches the system's factory-calibrated rated capacity, the discharge termination point is determined to have been reached; this discharge termination point is the charging starting point of the complete charge process. In most commercially available vanadium redox flow battery systems, based on industry-standard experience, the lower limit threshold of the operational velocity (OCV) is calibrated to 1.25V, and the upper limit threshold is calibrated to 1.50V. When configuring the electrolyte based on this, the charging starting point corresponds to an OCV value of 1.25V, and the discharging starting point corresponds to an OCV value of 1.5V.

[0028] In some embodiments, the refrigeration device is a variable frequency refrigeration device, such as a variable frequency direct cooler. The following description uses a variable frequency direct cooler as an example. The higher the frequency of the variable frequency direct cooler, the greater its cooling capacity. The maximum cooling capacity of the variable frequency direct cooler is 1.2 times the maximum heat generated during the discharge phase of the liquid flow energy storage system, or the maximum cooling capacity of the variable frequency direct cooler can be selected according to actual conditions to ensure that the direct cooler can operate in the high-efficiency region of the Coefficient of Performance (COP) for a long time, and to ensure that the liquid flow energy storage system can still operate during the discharge phase.

[0029] In step S11, the historical operating data includes: the initial temperature of the electrolyte during the charging phase, the set frequency of the variable frequency direct cooler during the constant power charging phase, the set frequency of the variable frequency direct cooler during the constant voltage charging phase, and the set temperature of the electrolyte during the discharging phase. During the discharging phase, the operating frequency of the variable frequency direct cooler is dynamically adjusted to maintain the temperature of the electrolyte passing through the cooler near the set temperature. In some embodiments, a temperature sensor can be installed on the pipeline after the electrolyte enters the fuel cell stack to collect data on the electrolyte temperature after entering the stack, which can then be used as a reference for adjusting the operating frequency of the variable frequency direct cooler during the charging and discharging phases. The variable frequency direct cooler is located on the pipeline before the electrolyte enters the fuel cell stack.

[0030] In some embodiments, historical operating data includes first historical operating data, which is obtained according to the following steps: setting a first exploration strategy, including: setting a first frequency of the cooling device in multiple charging stages, the first frequency of the cooling device including: 0Hz, at least one frequency in the theoretical high-efficiency frequency range of the cooling device, and at least one combination of the frequency of the first constant power charging stage and the frequency of the first constant voltage charging stage; setting a first set temperature of the electrolyte in multiple discharging stages; obtaining a first complete combination of the first frequency of the cooling device in multiple charging stages and the first set temperature of the first discharging stages; and applying the first complete combination to a charge-discharge cycle to obtain first historical operating data under several complete charge-discharge cycles. By setting the first exploration strategy, first historical operating data under charge-discharge cycles corresponding to multiple first complete combinations are obtained, thereby providing a reliable data foundation for subsequently obtaining optimized combinations.

[0031] Taking a variable frequency direct cooler as an example, 0Hz corresponds to the cooler's non-starting operating state. The theoretical high-efficiency frequency range of the variable frequency direct cooler is 40Hz-45Hz. Therefore, at least one frequency within the 40Hz-45Hz range can be explored. It should be noted that the cases of 0Hz and at least one frequency within the theoretical high-efficiency frequency range refer to the entire charging stage using only one fixed frequency, whether it is the constant power charging stage or the constant voltage charging stage. In some embodiments, the values ​​of the first constant power charging stage frequency and the first constant voltage charging stage frequency can also be selected within the theoretical high-efficiency frequency range. However, it should be understood that operating within the theoretical high-efficiency frequency range of the variable frequency direct cooler does not necessarily mean that the overall energy conversion efficiency of the fluid energy storage system is at its highest. Therefore, the values ​​of the first constant power charging stage frequency and the first constant voltage charging stage frequency can also be selected within or near the theoretical high-efficiency frequency range. For example, a combination of the first constant power charging stage frequency of 38Hz and the first constant voltage charging stage frequency of 46Hz can be obtained within the 35Hz-50Hz range. Theoretically, an electrolyte temperature of 34-35℃ is more conducive to chemical reactions during the discharge phase. Therefore, the first set temperature can be selected from 34-35℃. For more thorough exploration, the first set temperature can also be set below 34℃, or even just 36℃. It should be understood that the more first frequencies and the more first set temperatures are available—for example, using three frequencies within the theoretically efficient frequency range and setting ten temperatures during the discharge phase—the more thoroughly the operating states of the variable frequency direct cooler at various frequencies during the charge and discharge phases will be explored, and the higher the overall energy conversion efficiency is likely to be achieved.

[0032] This application provides an example of obtaining first historical operating data: The charging phase is set with six first frequencies: 0Hz; three frequencies within the theoretically efficient frequency range of the cooling device: a stable frequency of 40Hz, a stable frequency of 42Hz, and a stable frequency of 45Hz; and two frequency combinations: a constant power charging phase frequency of 35Hz and a constant voltage charging phase frequency of 42Hz, and a constant power phase of 0Hz and a constant voltage phase of 45Hz. For the cases of 0Hz, 40Hz, 42Hz, and 45Hz stable frequencies, only one of these frequencies is used as a fixed frequency throughout a complete charging phase, whether it is the constant power charging phase or the constant voltage charging phase. The discharging phase attempts five first set temperatures: 34℃, 34.5℃, 35℃, 36℃, and 37℃. In this example, there are a total of 30 possible combinations of the first frequencies and first set temperatures. Applying these 30 combinations to the charging and discharging phases yields first historical operating data for 30 complete charge-discharge cycles.

[0033] In some embodiments, the historical operating data further includes second historical operating data, which is obtained according to the following steps: setting a second exploration strategy includes: selecting, from the first historical operating data, a first exploration strategy corresponding to the highest overall energy conversion efficiency of the liquid flow energy storage system in a complete charge-discharge cycle as a basis; adjusting the first set temperature of the electrolyte in the discharge stage by a first preset step size to obtain second set temperatures of the electrolyte in multiple discharge stages; adjusting the first frequency of the cooling device in the charging stage by a second preset step size to obtain second frequencies of the cooling device in multiple charging stages; obtaining a second complete combination of the second frequencies of the cooling device in multiple charging stages and the second set temperatures in multiple discharge stages; and applying the second complete combination to a charge-discharge cycle to obtain second historical operating data under several complete charge-discharge cycles. By setting a second exploration strategy, based on the first exploration strategy corresponding to the highest overall energy conversion efficiency, obtaining second historical operating data corresponding to multiple second complete combinations further provides a reliable data foundation for subsequently obtaining optimized combinations.

[0034] This application does not impose specific restrictions on the specific values ​​of the first preset step size, the second preset step size, or the specific exploration methods.

[0035] This application provides a specific example of obtaining second historical operating data: the first frequency corresponding to the cycle with the highest overall energy conversion efficiency among the aforementioned 30 charge-discharge cycles is denoted as the reference frequency f. base The first set temperature is denoted as the reference temperature T. base The exploration is conducted using a constrained ε-Greedy strategy. The exploration rate ε is set to 0.5, and the second frequency f... new The safety threshold range is [f min =f base -1,f max =f base +1], The safety threshold range of the second set temperature is [T] min =T base -0.3, T max =T base +0.3], where f min For the second frequency f new minimum value ， f max For the second frequency f new The maximum value, T min Minimum value for the second set temperature ， T ma The maximum value for the second set temperature is determined. The exploration rules are as follows: When exploring the charging phase of each charge-discharge cycle, a random probability p1 is generated. If p1 < 1 - ε, the reference frequency f is used. base As the second frequency f newIf p1 ≥ 1 - ε, randomly select a frequency within the safety threshold range as the second frequency f. new When exploring the discharge phase of each charge-discharge cycle, a random probability p2 is generated. If p2 < 1 - ε, a reference temperature T is used. base As the second set temperature T new If p2 ≥ 1 - ε, randomly select a temperature within the safety threshold range as the second set temperature T. new Following the aforementioned exploration rules, the process was run continuously for 10 cycles to obtain the second historical operational data corresponding to 10 charge-discharge cycles. Through the first and second exploration strategies, a total of 40 charge-discharge cycles of historical operational data were obtained.

[0036] In some embodiments, the first exploration strategy and / or the second exploration strategy further include safety constraints, which include: when the electrolyte temperature is higher than a first safety threshold, the cooling device operates at a frequency not lower than a first preset frequency; and when the electrolyte temperature is higher than a second safety threshold, the cooling device operates at full frequency. The specific values ​​of the first safety threshold, the first preset frequency, and the second safety threshold are determined based on the actual operation of the fluid energy storage system. In some embodiments, the first safety threshold is 38°C, the first preset frequency is 48Hz, the second safety threshold is 39°C, and the full frequency of the variable frequency direct cooler is 50Hz. When the actual electrolyte temperature is higher than 38°C, the first frequency and / or the second frequency is not lower than 48Hz; when the actual electrolyte temperature is higher than 39°C, the first frequency and / or the second frequency is 50Hz.

[0037] In some embodiments, after obtaining the first historical operating data and / or the second historical operating data, the overall energy conversion efficiency of the fluid energy storage system corresponding to each charge-discharge cycle is calculated.

[0038] In some embodiments, the overall energy conversion efficiency is calculated using the following formula (1):

[0039] (1)

[0040] Where η is the rated energy efficiency of the battery, i.e., the overall energy conversion efficiency; E d The discharge energy of a flow storage system is expressed in kilowatt-hours (kWh); W d E represents the energy consumed by all auxiliary equipment during the discharge process, expressed in kWh. c The charging energy for a flow energy storage system, expressed in kWh; W c E represents the energy consumed by all auxiliary equipment during the charging process, measured in kWh. d W d E c and W cAll of these can be recorded by corresponding measuring instruments, such as electricity meters; the energy consumed by auxiliary equipment includes pump consumption, direct cooling machine power consumption, etc.

[0041] In some embodiments, before calculating the overall energy conversion efficiency, the method further includes: calculating the unit reserve energy consumed by the fluid energy storage system for each degree Celsius decrease in electrolyte temperature without charging or discharging; and calculating the reserve charge consumed when the electrolyte temperature decreases from the current temperature to 34 degrees Celsius, i.e., reserve charge = unit reserve energy. (Current temperature -34℃). Then, reserve the power and the W from the previous cycle. c Add to obtain the corrected W c Using the corrected W c Calculate the overall energy conversion efficiency of the previous cycle.

[0042] In some embodiments, in order to explore the highest value of overall energy conversion efficiency under more operating conditions, historical operating data may also include data such as charge / discharge state, OCV value, electrolyte flow rate, charging current, discharging current, charging voltage, and discharging voltage.

[0043] Step S12: Construct a regression model based on historical operating data. The regression model includes output parameters and multiple input parameters. The multiple input parameters include the initial temperature of the electrolyte during the charging stage, the set frequency of the cooling device during the constant power charging stage, the set frequency of the cooling device during the constant voltage charging stage, and the set temperature of the electrolyte during the discharging stage. The output parameters include the overall energy conversion efficiency of the liquid flow energy storage system.

[0044] In some embodiments, the regression model includes multiple terms, including a linear term for each input parameter, a quadratic term for each input parameter, an interaction term between any two input parameters, and coefficients corresponding to the linear, quadratic, and interaction terms, respectively. Constructing the regression model based on historical operating data includes determining the coefficients based on the historical operating data. This application, by setting the regression model to include the linear and quadratic terms of each input parameter, the interaction term between any two parameters, and the corresponding coefficients, can simultaneously fit the linear and nonlinear effects of input parameters on the overall energy conversion efficiency, as well as the trend of the interaction effects between multiple parameters. Furthermore, by determining the regression coefficients based on historical operating data, an efficiency prediction model adapted to the target fluid energy storage system is obtained, providing an accurate basis for subsequent optimization of the refrigeration unit combination and improvement of the overall system energy conversion efficiency.

[0045] In some embodiments, the regression model is expressed by the following formula (2):

[0046] Y = K0 + K1x1 + K2x2 + K3x3 + K4x4 + K 11 x1 2 +K 22x2 2 +K 33 x3 2 +K 44 x4 2 +K 12 x1x2+K 13 x1x3+K 14 x1x4+K 23 x2x3+K 24 x2x4+K 34 x3x4+ξ (2)

[0047] Where K0 is a constant term, x1 is the initial temperature of the electrolyte during the charging stage, x2 is the set frequency of the cooling device during the constant power charging stage, x3 is the set frequency of the cooling device during the constant voltage charging stage, and x4 is the set temperature of the electrolyte during the discharging stage. K1, K2, K3, and K4 are all coefficients of the first-order terms. 11， K 22， K 33， K 44 All are coefficients of the quadratic term, K 12、 K 13、 K 14、 K 23、 K 24、 K 34 All are interaction term coefficients between input parameters, ξ is the error compensation term, and Y is the overall energy conversion efficiency.

[0048] The regression model used in this application is a multinomial regression model. This application primarily considers linear and quadratic terms, mainly because cubic terms have a relatively small impact on overall energy conversion efficiency. In some embodiments, the regression model may also include higher-order terms such as cubic terms. In some embodiments, only coefficients with a significance level of P < 0.05 are retained.

[0049] In some embodiments, the output parameters and data corresponding to multiple input parameters are collected from the historical running data obtained in step S11, and the data are standardized. In some embodiments, the Z-score, or zero-mean standardization method, is used to standardize the data. This method can convert the original data into standard normal distribution data with a mean of 0 and a standard deviation of 1, while preserving the distribution characteristics of the original data. In the Z-score method, the following formula (3) is used to standardize each data corresponding to the input parameter or output parameter:

[0050] (3)

[0051] Where, x' i For standardized data, Let μ be one of the original data points, μ be the mean of the original data points, and σ be the standard deviation of the original data points.

[0052] In some embodiments, the standardization steps are as follows: 1) For each parameter in the input and output parameters, calculate its sample mean μ and standard deviation σ; 2) Correct missing values ​​and outliers. Using the 3σ criterion, outliers exceeding μ+3σ are replaced with μ+3σ, and outliers below μ-3σ are replaced with μ-3σ, ensuring the validity of the data participating in the standardization process; 3) Perform standardization transformation to obtain the standardized parameter dataset. Standardization eliminates problems caused by different units or large differences in numerical ranges between data points.

[0053] In some embodiments, the standardized data is used in formula (2) to fit the coefficients in formula (2). In some embodiments, the historical operating data of each parameter obtained from the above 40 charge-discharge cycles are substituted into the equation, and K0 to K are obtained by minimizing the sum of squared errors between the actual overall energy conversion efficiency and the theoretical overall energy conversion efficiency using the least squares method. 34 These 15 coefficients. The theoretical overall energy conversion efficiency is obtained by solving the coefficients in formula (2) using the least squares method, and then substituting the historical operating data in any set of charge and discharge cycles into the polynomial equation after the coefficients are determined, and calculating the overall energy conversion efficiency, which is the theoretical overall energy conversion efficiency.

[0054] Step S13: Obtain the data combination of multiple input parameters corresponding to the highest overall energy conversion efficiency from historical operating data. For example, the data combination is: charging stage: the initial temperature of the electrolyte is 36℃; the set frequency of the cooling device is 35Hz in the constant power charging stage and 42Hz in the constant voltage charging stage; the set temperature of the electrolyte is 35℃ in the discharging stage.

[0055] Step S14: Obtain an optimal combination of input parameters based on the data combination and regression model. The optimal combination is obtained by adjusting the data in the data combination, and the overall energy conversion efficiency corresponding to the optimal combination is greater than that of the highest one.

[0056] In some embodiments, obtaining an optimized combination of input parameters based on data combination and a regression model includes: determining the adjustment priority and direction of the input parameters according to the sign and absolute value of the coefficients; adjusting the data corresponding to multiple input parameters with a preset step size based on the data combination, according to the adjustment priority and adjustment direction, and obtaining multiple adjustment combinations; substituting the multiple adjustment combinations into the regression model to calculate the overall energy conversion efficiency, and optimizing the combination to maximize the overall energy conversion efficiency among the multiple adjustment combinations. This application does not impose specific limitations on the value of the preset step size. In some embodiments, determining the adjustment priority and direction of the input parameters based on the sign and absolute value of the coefficients includes: if the absolute value of the coefficient is larger, the adjustment priority of the term corresponding to the coefficient is higher; if the coefficient of the first-order term is positive, the adjustment direction is to increase the input parameter of the first-order term; if the coefficient of the first-order term is negative, the adjustment direction is to decrease the input parameter of the first-order term; if the coefficient of the quadratic term is positive, the adjustment direction is to move the input parameter of the quadratic term away from a first preset threshold; if the coefficient of the quadratic term is negative, the adjustment direction is to move the input parameter of the quadratic term closer to a second preset threshold; if the coefficient of the interaction term is positive, the adjustment direction includes the direction in which the input parameters in the interaction term increase together; if the coefficient of the interaction term is negative, the adjustment direction includes the direction in which the input parameters in the interaction term decrease together. Wherein, the first preset threshold and the second preset threshold are both vertices of the parabola formed by the quadratic term parameters and the overall energy conversion efficiency Y.

[0057] For example, the data combination is: charging stage: the initial temperature of the electrolyte is 36℃; the set frequency of the cooling device in the constant power charging stage is 35 Hz, and the set frequency of the cooling device in the constant voltage charging stage is 42 Hz; the set temperature of the electrolyte in the discharging stage is 35℃. For example, in the first term, if K1 in formula (2) is greater than 0, then when the values ​​corresponding to other input parameters remain unchanged, the initial temperature of the electrolyte in the charging stage is gradually increased from 36℃ in a preset step size of 0.1℃, and the overall energy conversion efficiency Y will also gradually increase. Each time a preset step size is adjusted, an adjustment combination is obtained. For example, an adjustment combination can be: charging stage: the initial temperature of the electrolyte is 36.1℃; the set frequency of the cooling device in the constant power charging stage is 35 Hz, and the set frequency of the cooling device in the constant voltage charging stage is 42 Hz; discharging stage: the set temperature of the electrolyte is 35℃; if K1 is less than 0, then when the values ​​corresponding to other input parameters remain unchanged, the initial temperature of the electrolyte in the charging stage is gradually decreased from 36℃ in a preset step size of 0.2℃. If the coefficient K of the quadratic term in formula (2) is... 22When the value is negative, the curve formed by the set frequency of the cooling device and the overall energy conversion efficiency Y during the constant power charging stage is a downward-opening parabola. After exceeding the second preset threshold, i.e., the vertex of the parabola, the efficiency begins to decrease. Therefore, the set frequency of the cooling device during the constant power charging stage can be gradually adjusted from 35Hz towards the second preset threshold in a preset step of 0.05Hz. If the interaction coefficient K in formula (2) is negative... 13 If positive, the initial temperature of the electrolyte during the charging phase and the set frequency of the cooling device during the constant-voltage charging phase will be increased in tandem to improve the overall energy conversion efficiency Y. Where the absolute values ​​are sorted as follows: K1 > K 22 >K 13 The adjustment sequence can be as follows: adjusting only the initial temperature of the electrolyte during the charging phase to obtain multiple combinations; adjusting only the set frequency of the cooling device during the constant power charging phase; and adjusting both the initial temperature of the electrolyte during the charging phase and the set frequency of the cooling device during the constant voltage charging phase. In some embodiments, based on the adjustment sequence, other input parameters are adjusted to obtain more adjustment combinations based on the already obtained adjustment combinations. For example, multiple adjustment combinations are obtained by adjusting only the initial temperature of the electrolyte during the charging phase. Based on the data showing the highest overall energy conversion efficiency among these adjustment combinations, a new adjustment combination is obtained by adjusting only the set frequency of the cooling device during the constant power charging phase.

[0058] In some embodiments, after adjusting all input parameters, based on the data with the highest overall energy conversion efficiency among these adjustment combinations, small-scale joint fine-tuning is performed on the data corresponding to multiple input parameters or all input parameters to find possible optimized combinations with higher overall energy conversion efficiency.

[0059] Step S15: Determine the control strategy of the refrigeration unit based on the optimized combination.

[0060] In some embodiments, the optimized combination can be directly used as the control strategy for the variable frequency direct cooler.

[0061] In some embodiments, determining the control strategy of the refrigeration device according to the optimized combination includes: in the optimized combination, if the initial temperature of the electrolyte in the charging stage is lower than the set temperature of the electrolyte in the discharging stage, then in the initial stage of the charging stage, the refrigeration device is operated to lower the electrolyte temperature to the initial temperature of the electrolyte in the charging stage; if the initial temperature of the electrolyte in the charging stage is higher than the set temperature of the electrolyte in the discharging stage, then the refrigeration device is shut down for a preset time at the end of the discharging stage. The initial stage of the charging stage refers to the period from the start of charging until the electrolyte temperature drops to the initial temperature of the electrolyte in the charging stage in the optimized combination. During this stage, the direct-cooling unit is started directly from the start of charging to perform full-frequency cooling. The end of the discharging stage refers to the period from reaching the set temperature of the electrolyte in the discharging stage in the optimized combination to the end of discharging. If the initial temperature of the electrolyte in the charging stage deviates significantly from the set temperature of the electrolyte in the discharging stage, the duration of the end of the discharging stage will be longer; if the deviation is small, the duration of the end of the discharging stage will be shorter. The exact length of the final discharge phase needs to be calculated. The goal is to allow the electrolyte temperature at the end of the discharge phase to naturally rise to the starting temperature of the electrolyte in the next charging phase of the optimized combination. In this embodiment, this application proposes corresponding measures to address the situation where the starting temperature of the electrolyte in the charging phase differs from the set temperature of the electrolyte in the discharging phase. This is to avoid the inability to start a new charge-discharge cycle due to the difference between the electrolyte temperature after the previous charge-discharge cycle and the starting temperature of the electrolyte in the charging phase of the new charge-discharge cycle.

[0062] For example, an optimized combination could be: During the charging phase, the initial electrolyte temperature is 35°C; during the constant power charging phase, the cooling device's set frequency is 35 Hz; during the constant voltage charging phase, the cooling device's set frequency is 40 Hz; and during the discharging phase, the electrolyte's set temperature is 36°C. That is, in this optimized combination, the initial electrolyte temperature of 35°C during the charging phase is lower than the set electrolyte temperature of 36°C during the discharging phase. Therefore, based on this optimized combination, the control strategy further includes: cooling the electrolyte to 35°C during the initial charging phase of the current charge / discharge cycle, or simultaneously cooling the electrolyte during the charging phase to bring the electrolyte temperature down to 35°C. This simultaneous charging and cooling mode allows for the collection of real-time operational data on electrolyte temperature reduction during the charging process. For example, an optimized combination for the idling mode could be: During the charging phase, the initial electrolyte temperature is 36°C; during the constant power charging phase, the cooling device's set frequency is 35 Hz; during the constant voltage charging phase, the cooling device's set frequency is 40 Hz; and during the discharging phase, the electrolyte's set temperature is 35°C. In the current charge-discharge cycle, the cooling device is shut down for a pre-set time at the end of the discharge phase until the electrolyte temperature reaches 36°C, after which the next charge-discharge cycle begins. In some embodiments, a regression model is reconstructed based on data collected in the simultaneous charging and cooling mode and the idling pre-cooling mode, allowing the regression model to learn the impact of "cooling cost" and "temperature deviation cost," thereby optimizing the control strategy. The cooling cost refers to the energy consumption cost in the idling pre-cooling mode. In the idling state, the liquid circuit system remains operational, the circuit system is not connected to the PCS, and no charging or discharging operations are performed. The electrolyte circulates between the storage tank and the stack. The combined energy consumption of the circulation pump and the cooling unit, driven by the circulation pump and the operation of the cooling unit, affects the overall system efficiency for every 1°C decrease in electrolyte temperature. The temperature deviation cost is the efficiency loss cost caused by the initial charging temperature deviating from the electrolyte's initial temperature. When using the simultaneous charging and cooling mode, if the electrolyte temperature does not reach the initial temperature of the electrolyte during the charging phase at the start of charging, the deviation in electrolyte temperature will still have a negative impact on the final charging and discharging efficiency of the system, even if the temperature is cooled down while charging.

[0063] In some embodiments, a control strategy is input into the PLC, with constraints set: when the electrolyte temperature is ≥38℃, the forced variable frequency direct cooler operates at full frequency; when the temperature is ≥40℃, the flow storage system automatically shuts down. Then, the operation of the flow storage system under this control strategy is tested. During the testing phase, the overall energy conversion efficiency and other input parameters for each charge-discharge cycle are recorded and compared with previous data to analyze the efficiency improvement rate and verify the accuracy of the multinomial regression model. In some embodiments, at regular intervals, such as quarterly, new historical operating data is obtained using a new exploration strategy, and new optimized combinations are derived. These new optimized combinations are then deployed to the flow storage system.

[0064] Figure 2A schematic diagram of the operating frequency of a fixed-frequency direct-cooler during the charging and discharging phases is shown. The horizontal axis represents the time corresponding to the charging and discharging phases, and the vertical axis represents the electrolyte temperature and the operating frequency of the fixed-frequency direct-cooler. Table 1 shows... Figure 2 The results show the overall energy conversion efficiency of the fixed-frequency direct-cooling machine at its operating frequency.

[0065] Table 1: Overall Energy Conversion Efficiency of Fixed-Frequency Direct Cooler

[0066]

[0067] like Figure 2 As shown in Table 1, the frequency of the fixed-frequency direct cooler is a constant 50Hz throughout the charging and discharging stages. At the end of charging, the electrolyte temperature is 33.1℃, and at the end of discharging, the electrolyte temperature is 39.1℃. The overall energy conversion efficiency η is 0.709810699. The overall energy conversion efficiency η is calculated according to formula (2). The sum of the pump consumption, direct cooler power consumption, and SOC power consumption during the charging stage in Table 1 is the W in formula (2). d The sum of pump power consumption, direct cooler power consumption, and SOC power consumption during the charging and discharging phases is W in formula (2). c The charging capacity is E in formula (2). c The discharge capacity is E in formula (2). d .

[0068] Figure 3 A schematic diagram of the operating frequency of a variable frequency direct cooler according to an embodiment of this application during the charging and discharging phases is shown. The horizontal axis represents the time corresponding to the charging and discharging phases, and the vertical axis represents the electrolyte temperature and the operating frequency of the variable frequency direct cooler. Table 2 shows... Figure 3 The results show the overall energy conversion efficiency of the variable frequency direct cooler at its operating frequency.

[0069] Table 2:

[0070]

[0071] like Figure 3 As shown in Table 2, the operating frequencies of the variable frequency direct cooler are 30.6Hz, 32.1Hz, 45.5Hz, and 40.8Hz during the constant power charging stage, the constant voltage charging stage to the end of charging, the electrolyte temperature reaching the discharge set temperature to before constant voltage discharge, and the constant voltage discharge stage to the end of discharge. The set temperature of the electrolyte during the discharge stage is 34.9℃. At the end of charging, the electrolyte temperature is 31.4℃, and at the end of discharging, the electrolyte temperature is 35.6℃. The overall energy conversion efficiency η is 0.718255611. As in Table 1, the overall energy conversion efficiency η is also calculated according to formula (2).

[0072] As shown in Tables 1 and 2, using the control strategy of this application, the overall energy conversion efficiency of the flow fuel cell energy storage system is improved by 0.8%-0.9%. For flow fuel cell energy storage systems with large charge / discharge volumes and high overall power consumption, even a 0.1% improvement in energy conversion efficiency is significantly challenging. The control strategy of this application improves the overall efficiency by 0.8%~0.9%, which is a very significant reduction in power consumption for large-scale energy storage systems that are charged and discharged daily, given their high base power consumption.

[0073] This application's optimized control method is the first to introduce multinomial regression analysis into the control of direct-cooling machines in flow liquefaction energy storage. For the electrochemical reaction where charging is endothermic and discharging is exothermic, a novel "frequency-temperature" exploration method (i.e., first and second exploration strategies) and a "frequency-temperature" fitting method (i.e., a regression model) are designed to find the optimal control frequency for the charging phase and the optimal control temperature for the discharging phase. Safety constraints are also considered, achieving a unification of control strategy optimization and safety control. Through this optimized control method, the overall energy conversion efficiency of the flow liquefaction energy storage system is improved by 0.8%-0.9%. Given that energy efficiency is a key factor restricting the large-scale promotion of flow liquefaction energy storage technology, this optimized control method provides important technical support for the technological upgrading and industrial development of the flow liquefaction energy storage field. Furthermore, this method has strong applicability and can be applied to other similar engineering control fields.

[0074] This application also includes an optimized control system for a cooling device in a flow energy storage system, comprising a memory and a processor. The memory stores instructions executable by the processor; the processor executes these instructions to implement the optimized control method for the cooling device in the flow energy storage system described above.

[0075] Figure 4 This is a system block diagram of the optimized control system of the refrigeration device of a liquid flow energy storage system according to an embodiment of this application. (Reference) Figure 4As shown, the optimized control system 400 may include an internal communication bus 401, a processor 402, a read-only memory (ROM) 403, a random access memory (RAM) 404, and a communication port 405. When applied to a personal computer, the optimized control system 400 may also include a hard disk 406. The internal communication bus 401 enables data communication between the components of the optimized control system 400. The processor 402 can make judgments and issue prompts. In some embodiments, the processor 402 may consist of one or more processors. The communication port 405 enables data communication between the optimized control system 400 and external systems. In some embodiments, the optimized control system 400 can send and receive information and data from a network through the communication port 405. The optimized control system 400 may also include different forms of program storage units and data storage units, such as the hard disk 406, the read-only memory (ROM) 403, and the random access memory (RAM) 404, capable of storing various data files used for computer processing and / or communication, as well as possible program instructions executed by the processor 402. The processor executes these instructions to implement the main part of the method. The results processed by the processor are transmitted to the user device through the communication port and displayed on the user interface.

[0076] The above-mentioned optimization control method can be implemented as a computer program, stored in the hard disk 406, and loaded into the processor 402 for execution to implement the optimization control method of this application.

[0077] This application also includes a computer-readable medium storing computer program code that, when executed by a processor, implements the optimization control method described above.

[0078] When the optimized control method is implemented as a computer program, it can also be stored as an article of manufacture in a computer-readable storage medium. For example, computer-readable storage media can include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical discs (e.g., compact discs (CDs), digital multifunction discs (DVDs)), smart cards, and flash memory devices (e.g., electrically erasable programmable read-only memory (EPROM), cards, sticks, key drives). Furthermore, the various storage media described herein can represent one or more devices and / or other machine-readable media for storing information. The term "machine-readable medium" can include, but is not limited to, wireless channels and various other media (and / or storage media) capable of storing, containing, and / or carrying code and / or instructions and / or data.

[0079] It should be understood that the embodiments described above are merely illustrative. The embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For hardware implementation, the processor may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and / or other electronic units designed to perform the functions described herein, or combinations thereof.

[0080] Some aspects of this application can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The aforementioned hardware or software may be referred to as a "data block," "module," "engine," "unit," "component," or "system." The processor may be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DAPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, or combinations thereof. Furthermore, aspects of this application may manifest as computer products residing in one or more computer-readable media, including computer-readable program code. For example, computer-readable media may include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), optical discs (e.g., compressed CDs, digital multifunction DVDs, etc.), smart cards, and flash memory devices (e.g., cards, sticks, key drives, etc.).

[0081] A computer-readable medium may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and so on, or suitable combinations thereof. A computer-readable medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer-readable medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, radio frequency signals, or similar media, or any combination of the above media.

[0082] The basic concepts have been described above. Obviously, for those skilled in the art, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0083] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0084] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification are approximate values, which may be changed according to the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

Claims

1. An optimized control method for a refrigeration device in a fluid energy storage system, wherein the refrigeration device is a variable frequency refrigeration device, characterized in that, include: Obtain historical operating data of the fluid energy storage system under multiple complete charge-discharge cycles; A regression model is constructed based on the historical operating data. The regression model includes output parameters and multiple input parameters. The multiple input parameters include the initial temperature of the electrolyte during the charging stage, the set frequency of the cooling device during the constant power charging stage, the set frequency of the cooling device during the constant voltage charging stage, and the set temperature of the electrolyte during the discharging stage. The output parameters include the overall energy conversion efficiency of the liquid flow energy storage system. From the historical operating data, obtain the data combination of the multiple input parameters corresponding to the highest overall energy conversion efficiency; An optimized combination of input parameters is obtained based on the data combination and the regression model, wherein the optimized combination is obtained by adjusting the data in the data combination, and the overall energy conversion efficiency corresponding to the optimized combination is greater than the highest one; and The control strategy for the refrigeration device is determined based on the optimized combination.

2. The optimization control method as described in claim 1, characterized in that, The regression model includes multiple terms, which include a first-order term for each input parameter, a second-order term for each input parameter, an interaction term between any two input parameters, and coefficients corresponding to the first-order term, the second-order term, and the interaction term, respectively. The construction of the regression model based on the historical operating data includes: determining the coefficients based on the historical operating data.

3. The optimization control method as described in claim 1, characterized in that, The regression model is expressed by the following formula: Y=K0+K1x1+K2x2+K3x3+K4x4+K 11 x1 2 +K 22 x2 2 +K 33 x3 2 +K 44 x4 2 +K 12 x1x2+K 13 x1x3+K 14 x1x4+K 23 x2x3+K 24 x2x4+K 34 x3x4+ξ, Where K0 is a constant term, x1 is the initial temperature of the electrolyte during the charging stage, x2 is the set frequency of the cooling device during the constant power charging stage, x3 is the set frequency of the cooling device during the constant voltage charging stage, and x4 is the set temperature of the electrolyte during the discharging stage. K1, K2, K3, and K4 are all coefficients of the first-order terms. 11， K 22， K 33， K 44 All are coefficients of the quadratic term, K 12、 K 13、 K 14、 K 23、 K 24、 K 34 All are interaction term coefficients between input parameters, ξ is the error compensation term, and Y is the overall energy conversion efficiency.

4. The optimized control method according to claim 2, characterized in that, The optimized combination of input parameters obtained based on the data combination and the regression model includes: Based on the sign and absolute value of the coefficients, the adjustment priority and direction of the input parameters are determined; and Based on the data combination, according to the adjustment priority and the adjustment direction, the data corresponding to the multiple input parameters are adjusted with a preset step size to obtain multiple adjustment combinations; the multiple adjustment combinations are substituted into the regression model to calculate the overall energy conversion efficiency, and the optimized combination is the adjustment combination that maximizes the overall energy conversion efficiency among the multiple adjustment combinations.

5. The optimized control method according to claim 4, characterized in that, The step of determining the adjustment priority and direction of the input parameters based on the sign and absolute value of the coefficients includes: The larger the absolute value of the coefficient, the higher the adjustment priority of the item corresponding to the coefficient; If the coefficient of the first-order term is positive, the adjustment direction is to increase the input parameter of the first-order term; if the coefficient of the first-order term is negative, the adjustment direction is to decrease the input parameter of the first-order term. If the coefficient of the second-order term is positive, the adjustment direction is to move the input parameter of the second-order term away from a first preset threshold; if the coefficient of the second-order term is negative, the adjustment direction is to move the input parameter of the second-order term closer to a second preset threshold. If the coefficient of the interaction term is positive, the adjustment direction includes the direction in which the input parameters in the interaction term increase collaboratively; if the coefficient of the interaction term is negative, the adjustment direction includes the direction in which the input parameters in the interaction term decrease collaboratively.

6. The optimized control method according to claim 1, characterized in that, The step of determining the control strategy for the refrigeration device based on the optimized combination includes: In the optimized combination, if the initial temperature of the electrolyte in the charging stage is lower than the set temperature of the electrolyte in the discharging stage, the electrolyte temperature is lowered to the initial temperature of the electrolyte in the charging stage by running the cooling device at the beginning of the charging stage; if the initial temperature of the electrolyte in the charging stage is higher than the set temperature of the electrolyte in the discharging stage, the cooling device is shut down for a preset time at the end of the discharging stage.

7. The optimized control method according to claim 1, characterized in that, The historical operation data includes first historical operation data, which is obtained according to the following steps: The first exploration strategy includes: setting a first frequency for a cooling device in each of the multiple charging stages, wherein the first frequency for the cooling device includes: 0Hz, at least one frequency in the theoretical high-efficiency frequency range of the cooling device, and at least one combination of the frequency of the first constant power charging stage and the frequency of the first constant voltage charging stage; and setting a first set temperature for the electrolyte in each of the multiple discharge stages. Obtain a first complete combination of a first frequency of the cooling device during multiple charging stages and a first set temperature during multiple discharging stages; and The first combination is applied to the charge-discharge cycle to obtain the first historical operating data under several complete charge-discharge cycles.

8. The optimized control method according to claim 7, characterized in that, The historical operating data also includes second historical operating data, which is obtained according to the following steps: setting a second exploration strategy, including: selecting the first exploration strategy corresponding to the highest overall energy conversion efficiency of the liquid flow energy storage system in the complete charge-discharge cycle from the first historical operating data as the basis, adjusting the first set temperature of the electrolyte in the discharge stage with a first preset step size to obtain the second set temperature of the electrolyte in multiple discharge stages, and adjusting the first frequency of the cooling device in the charging stage with a second preset step size to obtain the second frequency of the cooling device in multiple charging stages; Obtain a second complete combination of the second frequency of the cooling device for multiple charging stages and the second set temperature for multiple discharging stages; and The second combination is applied to the charge-discharge cycle to obtain the second historical operating data under several complete charge-discharge cycles.

9. The optimized control method according to claim 8, characterized in that, The first exploration strategy and / or the second exploration strategy further include safety constraints, the safety constraints including: when the electrolyte temperature is higher than a first safety threshold, the cooling device operates at a frequency not lower than a first preset frequency; and When the electrolyte temperature is higher than the second safety threshold, the refrigeration device operates at full frequency.

10. An optimized control system for a refrigeration device in a fluid energy storage system, comprising: Memory is used to store instructions executed by the processor; as well as A processor for executing the instructions to implement the optimized control method for the cooling device of the fluid energy storage system as described in any one of claims 1-9.

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