Control system and control method for energy storage thermal management electric control cabinet

Abstract

The invention discloses a control system and a control method for an energy storage thermal management electric control cabinet, relates to the technical field of energy storage systems, and solves the technical problems of low control accuracy and low control efficiency of energy storage thermal management caused by neglecting the condition of equipment wear in the prior art. The target temperature is calculated according to the current environment temperature and the unit frequency; generating a theoretical adjustment opening degree according to an exhaust temperature value in the equipment parameters and a target temperature; calculating a wear coefficient according to the historical working duration and the historical working environment of the electronic expansion valve; the actual adjustment opening degree is calculated according to the wear coefficient and the theoretical adjustment opening degree, the opening degree of the electronic expansion valve is adjusted through the actual adjustment opening degree, the opening degree of the electronic expansion valve is controlled in a self-adaptive mode, automatic adjustment of energy storage heat management is achieved, the control precision of energy storage heat management is improved on the basis of energy saving, and the efficiency of a control system is improved.

Description

Technical Field

[0001] The present application belongs to the technical field of energy storage systems, and specifically relates to a control system and a control method for an energy storage thermal management electric control cabinet. Background Art

[0002] Energy storage thermal management refers to the technology of temperature control and thermal management of energy storage systems. Energy storage systems generate a lot of heat during charging and discharging. If this heat is not effectively managed, it will cause the energy storage system to overheat, which will not only affect its working efficiency but also shorten its service life. In addition, excessively high temperatures will also increase the rate of chemical reactions in the energy storage system, thereby exacerbating the aging of the battery. With the rapid development of renewable energy and the advancement of energy transformation, the importance of energy storage systems has become increasingly prominent.

[0003] As a key component of the energy storage system, the energy storage thermal management electric control cabinet is responsible for monitoring and managing the temperature of the energy storage battery to ensure that the battery operates in the best condition, thereby improving the overall performance and safety of the energy storage system. However, the control system of the existing energy storage thermal management electric control cabinet often ignores the wear of the equipment, resulting in low control accuracy and low control efficiency of the energy storage thermal management; for example, if the equipment is severely worn, if the equipment wear is not considered for adjustment, the effect after adjustment will not achieve the desired result, which will make the control accuracy of the energy storage thermal management low; therefore, the control system of the energy storage thermal management electric control cabinet still needs further improvement. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a control system and a control method for an energy storage thermal management electric control cabinet, which are used to solve the technical problem that the prior art often ignores the wear of equipment, resulting in low control accuracy and low control efficiency of energy storage thermal management.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a control system for an energy storage thermal management electric control cabinet, comprising: a data acquisition module, a data analysis module, a control module, an early warning module and a database;

[0006] The data acquisition module is used to acquire environmental data and equipment parameters through data acquisition equipment;

[0007] The data analysis module: obtains the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, calculates the target temperature according to the current ambient temperature and the unit frequency; generates a theoretical adjustment opening according to the exhaust temperature value in the equipment parameters and the target temperature; calculates the wear coefficient according to the historical working time and historical working environment of the electronic expansion valve; calculates the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening; generates an alarm signal according to the wear coefficient;

[0008] The control module adjusts the opening of the electronic expansion valve by actually adjusting the opening;

[0009] The early warning module: makes prompts according to the alarm signal and contacts the management personnel.

[0010] Through the above steps, the present application realizes adaptive adjustment of the opening of the electronic expansion valve, improves the accuracy of the adjustment, and thus achieves the goal of automated adjustment of energy storage thermal management. While effectively saving energy, it improves the control accuracy of energy storage thermal management and enhances the overall efficiency of the control system.

[0011] Further, the target temperature is calculated according to the current ambient temperature and the unit frequency, including:

[0012] Obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters;

[0013] The current ambient temperature and unit frequency are input into a target temperature prediction model to obtain a target temperature; the target temperature prediction model is constructed by an artificial intelligence model.

[0014] Furthermore, the target temperature prediction model is constructed by an artificial intelligence model, including:

[0015] Obtaining historical unit frequencies from several historical ambient temperatures and equipment parameters and their corresponding historical target temperatures at optimal energy efficiency ratios;

[0016] Divide the historical unit frequencies in several historical ambient temperatures and equipment parameters and their corresponding historical target temperatures into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set;

[0017] Choose artificial intelligence as the base model;

[0018] Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model;

[0019] By verifying the pre-trained model on the test set, we finally obtained a target temperature prediction model that inputs the current ambient temperature and unit frequency and outputs the target temperature.

[0020] Furthermore, generating a theoretical adjustment opening according to the exhaust temperature value and the target temperature in the equipment parameters includes:

[0021] Obtain the exhaust temperature value PW and target temperature MW in the equipment parameters;

[0022] Calculate the exhaust superheat ΔW by the formula ΔW=MW-PW;

[0023] Adjust the opening according to the exhaust superheat calculation theory.

[0024] Furthermore, the adjusting the opening according to the exhaust superheat calculation theory includes:

[0025] Obtain exhaust superheat ΔW;

[0026] Determine whether the absolute value of the exhaust superheat is less than the heat threshold;

[0027] Yes, do nothing;

[0028] No, calculate the opening step KB according to the current ambient temperature and unit frequency; calculate the theoretical adjustment opening LTK by the formula LTK=KB×ΔW.

[0029] Furthermore, the calculation of the opening step KB according to the current ambient temperature and the unit frequency includes:

[0030] Get the current ambient temperature and unit frequency corresponding to the historical ambient temperature and unit frequency under several historical target temperatures LMW i and historical opening LK i ;

[0031] Select the maximum historical target temperature max{LMW i} and the historical target temperature minimum value min{LMW i} and its corresponding historical opening max{LK i} and min{LK i};

[0032] By formula Calculate the opening step length KB; where max{} is the symbol for taking the maximum value, and min{} is the symbol for taking the minimum value.

[0033] Through the above steps, the present application adjusts the current opening by adaptively selecting the opening step size for different ambient temperatures and unit frequencies, so that the current opening in each state can adjust the exhaust temperature value to the target temperature by theoretically adjusting the opening, thereby improving the accuracy of energy storage thermal management regulation.

[0034] Further, the calculation of the wear coefficient according to the historical working time and historical working environment of the electronic expansion valve includes:

[0035] Get the historical working time GT of the electronic expansion valve j And its corresponding historical unit time flow DTL j and historical work environments;

[0036] Calculate the environmental impact coefficient HYX based on the historical working environment j ;

[0037] By formula Calculate the wear coefficient MX; where α 1 and α 2 is the weight coefficient, α 1 and α 2 ∈(0,1); g 1 is the proportionality coefficient, g 1 ∈(0,π / 2); DT is the unit time, DL is the unit flow.

[0038] Furthermore, the environmental impact coefficient HYX is calculated based on the historical working environment. j ,include:

[0039] Obtain the working temperature GWj and working humidity GSj in several historical working environments and their corresponding optimum working temperature GZW and optimum working humidity GZS;

[0040] By formula Calculate the environmental impact coefficient HYXj; where α 3 and α 4 is the weight coefficient, α 3 and α 4 ∈(0,1); g 2 is the proportionality coefficient, g 2 ∈(0,π / 2); DW is the unit temperature, DS is the unit humidity.

[0041] Further, the actual adjustment opening is calculated according to the wear coefficient and the theoretical adjustment opening, including:

[0042] Obtain the wear coefficient MX, theoretical adjustment opening LTK and current opening DK;

[0043] Determine whether the wear coefficient is less than a wear threshold value of one;

[0044] Yes, let the actual adjustment opening STK = LTK;

[0045] No, by formula Calculate the actual adjustment opening STK; determine whether the sum of DK and STK exceeds the opening range; if yes, set the actual adjustment opening to the difference between the opening range boundary value and DK; if no, do nothing; where β is the exponential coefficient, β∈(0,1).

[0046] Further, generating an alarm signal according to the wear coefficient includes:

[0047] Obtain the wear coefficient;

[0048] Determine whether the wear coefficient is greater than the wear threshold 2;

[0049] Yes, generate an equipment scrapping alarm signal;

[0050] No, determine whether the wear coefficient is greater than D times the wear threshold value 2; if yes, generate an equipment maintenance warning signal; if no, do nothing; wherein D is the proportional coefficient, D∈(0,1).

[0051] Another aspect of the present invention provides a control method for an energy storage thermal management electric control cabinet, comprising:

[0052] S0: Get ambient temperature and device parameters;

[0053] S1: Obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, and calculate the target temperature based on the current ambient temperature and the unit frequency;

[0054] S2: Generate theoretical adjustment opening according to the exhaust temperature value and target temperature in the equipment parameters;

[0055] S3: Calculate the wear coefficient based on the historical working hours and historical working environment of the electronic expansion valve;

[0056] S4: Calculate the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening, and adjust the opening of the electronic expansion valve according to the actual adjustment opening;

[0057] S5: Generate an alarm signal according to the wear coefficient, make a prompt according to the alarm signal, and contact the management personnel.

[0058] Compared with the prior art, the beneficial effects of this application are:

[0059] 1. The present application calculates the target temperature according to the current ambient temperature and the unit frequency; generates a theoretical adjustment opening according to the exhaust temperature value in the equipment parameters and the target temperature; calculates the wear coefficient according to the historical working time and the historical working environment of the electronic expansion valve; calculates the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening, adjusts the opening of the electronic expansion valve according to the actual adjustment opening, adaptively controls the opening of the electronic expansion valve, realizes automatic adjustment of energy storage thermal management, improves the control accuracy of energy storage thermal management on the basis of energy saving, and improves the efficiency of the control system.

[0060] 2. This application obtains the target temperature through a target temperature prediction model. The target temperature is achieved under the optimal energy efficiency ratio, which means it is the most energy-saving state. By comparing the current exhaust temperature value and the target temperature value, the current opening is adjusted based on the theoretical adjustment opening so that the exhaust temperature value reaches the target temperature, thereby improving the adjustment accuracy and thus improving the control accuracy and control efficiency of energy storage thermal management.

[0061] 3. The present application calculates the wear coefficient of the equipment through the historical working time and historical working environment of the electronic expansion valve. The larger the wear coefficient, the less likely the expansion valve will meet the expected requirements when adjusting the opening. The actual adjustment opening is obtained by adaptively calculating the theoretical adjustment opening through the wear coefficient, taking the impact of wear into account, which can improve the efficiency after adjusting the opening. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the embodiments of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0063] Figure 1 A schematic diagram of the control system principle of an electric control cabinet for energy storage thermal management in this application;

[0064] Figure 2 This is a flow chart of a control method for an energy storage thermal management electric control cabinet in the present application. DETAILED DESCRIPTION

[0065] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0066] See also Figure 1 , the first aspect of the present application provides a control system for an energy storage thermal management electric control cabinet, including: a data acquisition module, a data analysis module, a control module, an early warning module and a database;

[0067] Data acquisition module: obtain environmental data and equipment parameters through data acquisition equipment; data acquisition equipment includes various sensors, etc.;

[0068] Data analysis module: obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, calculate the target temperature based on the current ambient temperature and the unit frequency, the target temperature is the temperature value to which the exhaust temperature value in the current state is to be adjusted; generate a theoretical adjustment opening based on the exhaust temperature value in the equipment parameters and the target temperature, the theoretical adjustment opening refers to the opening amplitude value that needs to be adjusted without considering the wear state of the equipment; calculate the wear coefficient based on the historical working time and historical working environment of the electronic expansion valve, the wear coefficient refers to the degree of wear of the equipment caused by long-term use; calculate the actual adjustment opening based on the wear coefficient and the theoretical adjustment opening, the actual adjustment opening refers to the opening amplitude value required to adjust the exhaust temperature value in the current state to the target temperature after considering the wear coefficient; generate an alarm signal based on the wear coefficient;

[0069] Control module: adjust the opening of the electronic expansion valve by actually adjusting the opening;

[0070] Early warning module: Prompts are given based on alarm signals and management personnel are contacted. Alarm signals include equipment scrapping alarm signals and equipment maintenance alarm signals.

[0071] In this embodiment, the target temperature is calculated according to the current ambient temperature and the unit frequency, including:

[0072] Obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters;

[0073] The current ambient temperature and unit frequency are input into the target temperature prediction model to obtain the target temperature; the target temperature prediction model is constructed through an artificial intelligence model.

[0074] The target temperature prediction model in this embodiment is constructed by an artificial intelligence model, including:

[0075] Obtaining historical unit frequencies from several historical ambient temperatures and equipment parameters and their corresponding historical target temperatures at optimal energy efficiency ratios;

[0076] The historical unit frequencies in several historical ambient temperatures and equipment parameters and their corresponding historical target temperatures are divided into training data, verification data and test data; the training data, verification data and test data are preprocessed to obtain the training set, verification set and test set; the ratio between the training set, the test set and the verification set is 7:2:1;

[0077] Select artificial intelligence as the basic model; artificial intelligence models include BP neural network model, etc.;

[0078] Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model;

[0079] By verifying the pre-trained model on the test set, we finally obtained a target temperature prediction model that inputs the current ambient temperature and unit frequency and outputs the target temperature.

[0080] In this embodiment, the theoretical adjustment opening is generated according to the exhaust temperature value and the target temperature in the equipment parameters, including:

[0081] Obtain the exhaust temperature value PW and target temperature MW in the equipment parameters;

[0082] The exhaust superheat ΔW is calculated by the formula ΔW=MW-PW; wherein ΔW can be a positive number or a negative number; once the current ambient temperature and unit frequency are determined, the corresponding target temperature is also determined. When MW is determined, the greater the difference between the current exhaust temperature value and the target temperature value, the greater the absolute value of the exhaust superheat will be. It is necessary to adjust the current opening so that the current exhaust temperature value is close to the target temperature, so as to achieve the effect of energy storage thermal management;

[0083] Adjust the opening according to the exhaust superheat calculation theory.

[0084] In this embodiment, adjusting the opening according to the exhaust superheat calculation theory includes:

[0085] Obtain exhaust superheat ΔW;

[0086] Determine whether the absolute value of the exhaust gas superheat is less than a heat threshold; the heat threshold is set based on experience, and in this embodiment, the heat threshold is set to 1°C;

[0087] Yes, no operation is performed; that is, the current exhaust temperature value is not much different from the target temperature, indicating that the current thermal management system is in good function and does not need to be adjusted;

[0088] No, the opening step KB is calculated based on the current ambient temperature and unit frequency. The opening step refers to the amplitude value of the opening that needs to be adjusted at a unit temperature. The theoretical adjustment opening LTK is calculated using the formula LTK=KB×ΔW. The opening step in the current state is fixed. As the exhaust superheat increases, the theoretical adjustment opening will also increase accordingly.

[0089] This embodiment obtains the target temperature through the target temperature prediction model in the above steps, and by comparing the current exhaust temperature value with the target temperature value, the current opening is adjusted based on the theoretical adjustment opening so that the exhaust temperature value reaches the target temperature, thereby improving the adjustment accuracy, thereby improving the control accuracy and control efficiency of energy storage thermal management.

[0090] In this embodiment, the opening step KB is calculated according to the current ambient temperature and the unit frequency, including:

[0091] Get the current ambient temperature and unit frequency corresponding to the historical ambient temperature and unit frequency under several historical target temperatures LMW i and historical opening LK i ;

[0092] Select the historical target temperature maximum value max{LMW i} and the historical target temperature minimum value min{LMW i} and its corresponding historical opening max{LK i} and min{LK i};

[0093] By formula Calculate the opening step KB; where max{} is the maximum value symbol, and min{} is the minimum value symbol; where the difference between the historical target temperature maximum value and the historical target temperature minimum value is small, the corresponding historical opening difference is larger, and the opening step per unit temperature will increase, that is, the equipment under the same state needs to adjust more openings to meet the target temperature requirements.

[0094] This embodiment adjusts the current opening by adaptively selecting the opening step size according to different ambient temperatures and unit frequencies, so that the current opening in each state can adjust the exhaust temperature value to the target temperature through theoretically adjusting the opening, thereby improving the accuracy of energy storage thermal management regulation.

[0095] In this embodiment, the wear coefficient is calculated according to the historical working time and historical working environment of the electronic expansion valve, including:

[0096] Get the historical working time GT of the electronic expansion valve j And its corresponding historical unit time flow DTL j and historical work environments;

[0097] Calculate the environmental impact coefficient HYX based on the historical working environment j ;

[0098] By formula Calculate the wear coefficient MX; where α 1 and α 2 is the weight coefficient, α 1 and α 2 ∈(0, 1), the specific value is set according to experience. When the historical working time has a greater impact on the wear of the electronic expansion valve than the historical unit time flow, α can be 1 >α 2 In this embodiment, α 1 and α 2 Both are set to 0.5, which means that the wear effects of the two on the electronic expansion valve are the same; g 1is the proportionality coefficient, g 1 ∈(0,π / 2), the specific value is set according to experience, g 1 The setting is to make the wear coefficient MX∈(0,1); DT is the unit time, DL is the unit flow rate, the specific value is set according to experience, and in this embodiment, DT is set to 1 day; the longer the working time, the more serious the damage to the equipment; the more flow rate per unit time, it means that a large amount of refrigerant passes through the expansion valve, which will have a greater impact on it, and thus make its wear more serious, and the working environment will also have a serious impact on the wear of the equipment during work; with the increase of flow rate per unit time and the increase of working time, and the larger the environmental impact coefficient, the easier it is for the equipment to wear, so the wear coefficient of the equipment will increase accordingly.

[0099] In this embodiment, the environmental impact coefficient HYX is calculated based on the historical working environment. j ,include:

[0100] Obtain the working temperature GWj and working humidity GSj in several historical working environments and their corresponding optimum working temperature GZW and optimum working humidity GZS;

[0101] By formula Calculate the environmental impact coefficient HYXj; where α 3 and α 4 is the weight coefficient, α 3 and α 4 ∈(0, 1), the specific value is set according to experience. When the influence of working temperature on the environmental influence coefficient is higher than that of working humidity, α 3 >α 4 In this embodiment, α 3 and α 4 Both are set to 0.5, which means that the influence of the two on the environmental impact coefficient is the same; g 2 is the proportionality coefficient, g 2 ∈(0,π / 2), the specific value is set according to experience, g 2 The setting is to make the environmental impact coefficient HYXj∈(0,1); DW is the unit temperature, DS is the unit humidity, the specific values ​​are set according to experience, in this embodiment, DW is set to 1°C, DS is set to 1%; when the equipment is working, the more the working temperature deviates from the optimum temperature, the more the working humidity deviates from the optimum working humidity, the more serious the impact on the equipment will be, and the process of equipment wear will be aggravated. Therefore, the environmental impact coefficient increases accordingly.

[0102] In this embodiment, the actual adjustment opening is calculated based on the wear coefficient and the theoretical adjustment opening, including:

[0103] Obtain the wear coefficient MX, theoretical adjustment opening LTK and current opening DK;

[0104] Determine whether the wear coefficient is less than the wear threshold 1; the wear threshold 1 is set based on experience;

[0105] Yes, let the actual adjustment opening STK = LTK;

[0106] No, by formula Calculate the actual adjustment opening STK; determine whether the sum of DK and STK exceeds the opening range; if yes, set the actual adjustment opening to the difference between the opening range boundary value and the previous value of DK; if no, do nothing; wherein β is an exponential coefficient, β∈(0,1), and the specific value is set based on experience, and β is set to reduce the rate of change of the actual adjustment opening to avoid the wear of the electronic expansion valve in a short period of time causing the actual adjustment opening to change rapidly; with the increase of the wear coefficient, while the theoretical adjustment opening remains unchanged, the actual adjustment opening of the equipment needs to be set to a higher opening value to achieve better results in order to reduce the impact of wear, so the actual adjustment opening will increase accordingly.

[0107] In this embodiment, generating an alarm signal according to the wear coefficient includes:

[0108] Obtain the wear coefficient;

[0109] Determine whether the wear coefficient is greater than the wear threshold 2; the wear threshold 2 is set according to experience, and the wear threshold 2 is greater than the wear threshold 1;

[0110] Yes, generate an equipment scrapping alarm signal;

[0111] No, determine whether the wear coefficient is greater than D times the wear threshold value 2; Yes, generate an equipment maintenance warning signal; No, do nothing; wherein D is the proportional coefficient, D∈(0,1), the specific value is set according to experience, and in this embodiment, D is set to 0.7.

[0112] This embodiment monitors the wear of the equipment in real time. Once an alarm signal appears in the equipment, an alarm is issued in time and the management personnel are contacted, which can reduce resource waste and improve the efficiency of the control system of energy storage thermal management.

[0113] See also Figure 2 Another aspect of the present application provides a control method for an energy storage thermal management electric control cabinet, including:

[0114] S0: Get ambient temperature and device parameters;

[0115] S1: Obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, and calculate the target temperature based on the current ambient temperature and the unit frequency;

[0116] S2: Generate theoretical adjustment opening according to the exhaust temperature value and target temperature in the equipment parameters;

[0117] S3: Calculate the wear coefficient based on the historical working hours and historical working environment of the electronic expansion valve;

[0118] S4: Calculate the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening, and adjust the opening of the electronic expansion valve according to the actual adjustment opening;

[0119] S5: Generate an alarm signal according to the wear coefficient, make a prompt according to the alarm signal, and contact the management personnel.

[0120] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0121] The working principle of the present application is as follows: by obtaining the ambient temperature and equipment parameters; obtaining the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, and calculating the target temperature according to the current ambient temperature and the unit frequency; generating a theoretical adjustment opening according to the exhaust temperature value in the equipment parameters and the target temperature; calculating the wear coefficient according to the historical working time and historical working environment of the electronic expansion valve; calculating the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening, and adjusting the opening of the electronic expansion valve according to the actual adjustment opening; generating an alarm signal according to the wear coefficient, making a prompt according to the alarm signal, and contacting the management personnel to adaptively control the opening of the electronic expansion valve, so as to realize automatic adjustment of energy storage thermal management, improve the control accuracy of energy storage thermal management on the basis of energy saving, improve the efficiency of the control system, and avoid the situation that the prior art often ignores equipment wear, resulting in low control accuracy and low control efficiency of energy storage thermal management.

[0122] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A control system for an energy storage thermal management electric control cabinet, characterized in that: include: Data acquisition module, data analysis module, control module, early warning module and database; The data acquisition module is used to acquire environmental data and equipment parameters through data acquisition equipment; The data analysis module: obtains the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, calculates the target temperature according to the current ambient temperature and the unit frequency; generates a theoretical adjustment opening according to the exhaust temperature value in the equipment parameters and the target temperature; calculates the wear coefficient according to the historical working time and historical working environment of the electronic expansion valve; Calculate the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening; generating an alarm signal based on the wear factor; The control module adjusts the opening of the electronic expansion valve by actually adjusting the opening; The early warning module: makes prompts according to the alarm signal and contacts the management personnel.

2. A control system for an energy storage thermal management electric control cabinet according to claim 1, characterized in that: The calculating the target temperature according to the current ambient temperature and the unit frequency includes: Obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters; The current ambient temperature and unit frequency are input into a target temperature prediction model to obtain a target temperature; the target temperature prediction model is constructed by an artificial intelligence model.

3. A control system for an energy storage thermal management electric control cabinet according to claim 2, characterized in that: The target temperature prediction model is constructed by an artificial intelligence model, including: Obtaining historical unit frequencies from several historical ambient temperatures and equipment parameters and their corresponding historical target temperatures at optimal energy efficiency ratios; Divide the historical unit frequencies in several historical ambient temperatures and equipment parameters and their corresponding historical target temperatures into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; Choose artificial intelligence as the base model; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtained a target temperature prediction model that inputs the current ambient temperature and unit frequency and outputs the target temperature.

4. A control system for an energy storage thermal management electric control cabinet according to claim 1, characterized in that: The method of generating a theoretical adjustment opening according to the exhaust temperature value and the target temperature in the equipment parameters includes: Obtain the exhaust temperature value PW and target temperature MW in the equipment parameters; Calculate the exhaust superheat ΔW by the formula ΔW=MW-PW; Adjust the opening according to the exhaust superheat calculation theory.

5. A control system for an energy storage thermal management electric control cabinet according to claim 4, characterized in that: The adjusting the opening according to the exhaust superheat calculation theory includes: Obtain exhaust superheat ΔW; Determine whether the absolute value of the exhaust superheat is less than the heat threshold; Yes, do nothing; No, calculate the opening step KB according to the current ambient temperature and unit frequency; calculate the theoretical adjustment opening LTK by the formula LTK=KB×ΔW.

6. A control system for an energy storage thermal management electric control cabinet according to claim 5, characterized in that: The calculation of the opening step KB according to the current ambient temperature and the unit frequency includes: Get the current ambient temperature and unit frequency corresponding to the historical ambient temperature and unit frequency under several historical target temperatures LMW i and historical opening LK i ; Select the maximum historical target temperature max{LMW i } and the historical target temperature minimum value min{LMW i } and its corresponding historical opening max{LK i } and min{LK i }; By formula Calculate the opening step length KB; where max{} is the symbol for taking the maximum value, and min{} is the symbol for taking the minimum value.

7. A control system for an energy storage thermal management electric control cabinet according to claim 1, characterized in that: The calculation of the wear coefficient according to the historical working hours and historical working environment of the electronic expansion valve includes: Get the historical working time GT of the electronic expansion valve j And its corresponding historical unit time flow DTL j and historical work environments; Calculate the environmental impact coefficient HYX based on the historical working environment j ; By formula Calculate the wear coefficient MX; where α1 and α2 are weight coefficients, α1 and α2∈(0,1); g1 is the proportional coefficient, g1∈(0,π / 2); DT is the unit time, and DL is the unit flow.

8. A control system for an energy storage thermal management electric control cabinet according to claim 7, characterized in that: The environmental impact coefficient HYX is calculated based on the historical working environment j ,include: Obtain the working temperature GWj and working humidity GSj in several historical working environments and their corresponding optimum working temperature GZW and optimum working humidity GZS; By formula Calculate the environmental impact coefficient HYXj; where α3 and α4 are weight coefficients, α3 and α4∈(0, 1); g2 is the proportionality coefficient, g2∈(0, π / 2); DW is the unit temperature, and DS is the unit humidity.

9. The control system for an energy storage thermal management electric control cabinet according to claim 1, characterized in that: The actual adjustment opening is calculated according to the wear coefficient and the theoretical adjustment opening, including: Obtain the wear coefficient MX, theoretical adjustment opening LTK and current opening DK; Determine whether the wear coefficient is less than a wear threshold value 1; Yes, let the actual adjustment opening STK = LTK; No, by formula Calculate the actual adjustment opening STK; determine whether the sum of DK and STK exceeds the opening range; if yes, set the actual adjustment opening to the difference between the opening range boundary value and DK; if no, do nothing; where β is the exponential coefficient, β∈(0,1).

10. A control method for an energy storage thermal management electric control cabinet, applied to a control system for an energy storage thermal management electric control cabinet according to any one of claims 1 to 9, characterized in that: include: S0: Get ambient temperature and device parameters; S1: Obtain the current ambient temperature in the environmental data and the unit frequency in the equipment parameters, and calculate the target temperature based on the current ambient temperature and the unit frequency; S2: Generate theoretical adjustment opening according to the exhaust temperature value and target temperature in the equipment parameters; S3: Calculate the wear coefficient based on the historical working hours and historical working environment of the electronic expansion valve; S4: Calculate the actual adjustment opening according to the wear coefficient and the theoretical adjustment opening, and adjust the opening of the electronic expansion valve according to the actual adjustment opening; S5: Generate an alarm signal according to the wear coefficient, make a prompt according to the alarm signal, and contact the management personnel.

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