Control method and device of energy storage air conditioning system, air conditioner and storage medium

Abstract

The invention relates to the technical field of energy storage and heat dissipation, in particular to a control method and device of an energy storage air conditioning system, an air conditioner and a storage medium. The energy storage air conditioning system comprises a compressor, and the control method of the energy storage air conditioning system comprises the steps that current operation data and predicted operation data are obtained; inputting the current operation data into a current heat dissipation model to obtain current heat dissipation data at the current moment, and inputting the predicted operation data into a predicted heat dissipation model to obtain predicted heat dissipation data at the next moment; performing calculation processing on the current heat dissipation data and the predicted heat dissipation data to obtain a current comprehensive parameter at the current moment and a predicted comprehensive parameter at the next moment; and predicting based on the current compressor rotating speed, the current comprehensive parameters and the predicted comprehensive parameters to obtain a predicted compressor rotating speed of the compressor at the next moment, and controlling the compressor to work based on the predicted compressor rotating speed at the next moment. Therefore, the operation efficiency and the temperature control precision of the energy storage air conditioning system are improved.

Description

Technical Field

[0001] This application relates to the field of energy storage and heat dissipation technology, specifically to a control method, device, air conditioner, and storage medium for an energy storage air conditioning system. Background Technology

[0002] Most current energy storage air conditioning systems employ dual-position control. Specifically, in cooling mode, when the coolant temperature reaches the upper limit of the set temperature, the energy storage air conditioning unit starts to cool the system. When the coolant temperature drops to the lower limit of the set temperature, the unit shuts down, but the water pump continues to circulate. The coolant operates between the upper and lower temperature limits. In heating mode, when the coolant temperature reaches the lower limit of the set temperature, the energy storage air conditioning unit starts to heat the system. When the cooled coolant temperature reaches the upper limit of the set temperature, the unit stops, but the water pump continues to circulate.

[0003] However, the above control method only starts and stops the air conditioning unit based on the upper and lower limits of the coolant temperature, while the water pump continues to run. This method does not take into account dynamic thermal processes such as outdoor heat transfer and system thermal inertia, which may lead to problems such as excessive energy supply at low loads, delayed cooling supply at peak loads, and insufficient cooling supply. Summary of the Invention

[0004] This application provides a control method, device, air conditioner, and storage medium for an energy storage air conditioning system, which can adapt to dynamic heat dissipation processes, improve the accuracy of system energy supply, and reduce overall operating energy consumption.

[0005] In a first aspect, embodiments of this application provide a control method for an energy storage air conditioning system, the energy storage air conditioning system including a compressor, the control method comprising: acquiring current operating data of the energy storage air conditioning system at the current moment and predicted operating data for the next moment; inputting the current operating data into a current heat dissipation model to obtain current heat dissipation data at the current moment, and inputting the predicted operating data into a predicted heat dissipation model to obtain predicted heat dissipation data for the next moment; performing calculation processing on the current heat dissipation data to obtain current comprehensive parameters at the current moment, and performing calculation processing on the predicted heat dissipation data to obtain predicted comprehensive parameters for the next moment; performing prediction based on the current compressor speed, the current comprehensive parameters, and the predicted comprehensive parameters to obtain the predicted compressor speed of the compressor at the next moment, and controlling the compressor to operate based on the predicted compressor speed at the next moment.

[0006] In some embodiments, the current heat dissipation model includes a current heat dissipation quantity model and a current heat dissipation rate model, the current heat dissipation data includes current heat dissipation quantity and current heat dissipation rate, and the step of inputting the current operating data into the current heat dissipation model to obtain the current heat dissipation data at the current moment includes: inputting the current operating data into the current heat dissipation quantity model to obtain the current heat dissipation quantity; and inputting the current heat dissipation quantity and the current operating data into the current heat dissipation rate model to obtain the current heat dissipation rate.

[0007] In some embodiments, the current heat dissipation model is constructed by: acquiring cooling capacity data of the energy storage air conditioning system, outdoor temperature data, and cabin temperature data of the battery compartment of the energy storage air conditioning system; performing calculations on the cooling capacity data, outdoor temperature data, and cabin temperature data to obtain the target heat transfer coefficient of the battery compartment; and constructing the current heat dissipation model based on the cooling capacity data, the target heat transfer coefficient, the outdoor temperature data, and the cabin temperature data.

[0008] In some embodiments, the predictive heat dissipation model includes a predictive heat dissipation amount model and a predictive heat dissipation rate model, the predictive heat dissipation data includes a predicted heat dissipation amount and a predicted heat dissipation rate, and the step of inputting the predictive running data into the predictive heat dissipation model to obtain the predicted heat dissipation data for the next moment includes: inputting the predictive running data into the predictive heat dissipation model to obtain the predicted heat dissipation amount; and inputting the predicted heat dissipation amount and the predictive running data into the predictive heat dissipation rate model to obtain the predicted heat dissipation rate.

[0009] In some embodiments, the current heat dissipation data includes the current heat dissipation amount, the current heat dissipation rate, the internal and external temperature difference data of the battery compartment of the energy storage air conditioning system, and the highest cell temperature of the battery compartment. The calculation and processing of the current heat dissipation data to obtain the current comprehensive parameters at the current moment includes: standardizing the current heat dissipation amount, the current heat dissipation rate, the internal and external temperature difference data, and the highest cell temperature to obtain the current standard heat dissipation amount, the current standard heat dissipation rate, the standard internal and external temperature difference data, and the standard highest cell temperature; and performing a weighted calculation on the current standard heat dissipation amount, the current standard heat dissipation rate, the standard internal and external temperature difference data, and the standard highest cell temperature to obtain the current comprehensive parameters.

[0010] In some embodiments, the energy storage air conditioning system includes a water pump, and the method further includes: acquiring the current maximum battery temperature and the current cell temperature difference of the energy storage air conditioning system; determining a predicted water pump increment based on the current maximum battery temperature and the current cell temperature difference; and controlling the water pump to adjust based on the predicted water pump increment.

[0011] In some embodiments, determining the predicted water pump increment based on the current maximum battery temperature and the current cell temperature difference includes: matching the current maximum battery temperature and the current cell temperature difference in a preset coolant supply and return temperature difference table to obtain a target coolant supply and return temperature difference; and calculating the current water pump speed, the current coolant supply and return temperature difference, and the target coolant supply and return temperature difference to obtain the predicted water pump increment.

[0012] Secondly, embodiments of this application provide a control device for an energy storage air conditioning system, the energy storage air conditioning system including a compressor, and the control device for the energy storage air conditioning system including:

[0013] The data acquisition module is used to acquire the current operating data of the energy storage air conditioning system at the current moment and the predicted operating data for the next moment; The model input module is used to input the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and to input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment. The parameter calculation module is used to calculate and process the current heat dissipation data to obtain the current comprehensive parameters at the current moment, and to calculate and process the predicted heat dissipation data to obtain the predicted comprehensive parameters at the next moment. The speed prediction module is used to predict the compressor speed at the next moment based on the current compressor speed, the current comprehensive parameters, and the predicted comprehensive parameters, and to control the compressor to operate based on the predicted compressor speed at the next moment.

[0014] Thirdly, embodiments of this application provide an air conditioner, the air conditioner including: one or more processors, a memory, and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the control method of the energy storage air conditioning system described in any of the above embodiments.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to execute the steps in the control method of the energy storage air conditioning system described in any of the above embodiments.

[0016] The beneficial effects of the embodiments of this application are as follows: The embodiments of this application provide a control method, device, air conditioner, and storage medium for an energy storage air conditioning system. By introducing current operating data and predicted operating data, and combining a heat dissipation model and comprehensive parameters to predictively control the compressor speed, the limitations of traditional two-position regulation control in failing to fully consider the dynamic characteristics of the thermal process are addressed to a certain extent. Therefore, the embodiments of this application can match the actual heat dissipation needs of the battery, reduce excessive energy waste under low load, and respond in advance during peak heat dissipation periods, improving the timeliness and accuracy of cooling and heating, thereby enhancing the operating efficiency and temperature control accuracy of the energy storage air conditioning system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the control method of an energy storage air conditioning system provided in an embodiment of the present invention.

[0019] Figure 2 This is a flowchart illustrating the specific steps for obtaining current heat dissipation data according to an embodiment of the present invention.

[0020] Figure 3 This is a flowchart illustrating the specific steps for obtaining predicted heat dissipation data according to an embodiment of the present invention.

[0021] Figure 4 This is a flowchart illustrating the specific steps for obtaining the current comprehensive parameters according to an embodiment of the present invention.

[0022] Figure 5 This is a flowchart illustrating supplementary steps of the control method for an energy storage air conditioning system provided in an embodiment of the present invention.

[0023] Figure 6 This is a flowchart illustrating the specific steps for obtaining the predicted pump increment according to an embodiment of the present invention.

[0024] Figure 7 This is a structural block diagram of the control device for an energy storage air conditioning system provided in an embodiment of the present invention.

[0025] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more features.

[0028] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0030] This application provides a control method for an energy storage air conditioning system, which includes a compressor. For example... Figure 1 As shown, the control method for the energy storage air conditioning system includes steps S1 to S4: Step S1: Obtain the current operating data and the predicted operating data for the next moment of the energy storage air conditioning system.

[0031] An energy storage air conditioning system refers to a system that integrates energy storage units with air conditioning cooling / heating functions to manage the temperature of energy storage batteries or related equipment. This system ensures that the energy storage units operate within a suitable temperature range by regulating the coolant temperature, thereby optimizing their performance and lifespan. Typically, an energy storage air conditioning system includes a battery, a battery compartment, a compressor, and a water pump, with the battery housed within the battery compartment.

[0032] This system can record the cumulative charging and discharging times of the batteries in the energy storage air conditioning system. The cumulative charging time is calculated from the start of the current charging cycle until the current moment. The cumulative discharging time is calculated from the start of the current discharging cycle until the current moment. The cumulative charging time is divided into multiple consecutive moments, with equal time intervals between each pair of moments, such as 5 minutes, 1 hour, or 3 hours. The same applies to the cumulative discharging time.

[0033] Current operating data refers to the real-time operating status data collected from the energy storage air conditioning system or its operating environment at the current moment of battery charging or discharging. For example, current operating data may include the current time and the current battery level. The current time represents the time corresponding to the current moment within the cumulative charging or discharging time. The current battery level represents the battery level corresponding to the current moment within the cumulative charging or discharging time. Both the current time and the current battery level can be obtained through real-time data collection.

[0034] Predictive operational data refers to operational status data that estimates the operating status of an energy storage air conditioning system at the next moment. For example, predicted operational data may include the time of the next moment and the battery level at the next moment. The time of the next moment represents the time corresponding to the next moment within the cumulative charging or discharging time. The battery level at the next moment represents the battery level corresponding to the next moment within the cumulative charging or discharging time. The time of the next moment can be obtained by adding a time interval to the time of the current moment. The battery level at the next moment can be estimated using historical battery charging or discharging data. For example, when the battery is discharging, the battery level at the current moment is subtracted from the battery level at the previous moment to obtain the battery level at the next moment. Similarly, when the battery is charging, the battery level at the current moment is added to the battery level at the previous moment to obtain the battery level at the next moment.

[0035] Step S2: Input the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment.

[0036] The current heat dissipation model is a mathematical or algorithmic model used to calculate or evaluate the current heat dissipation data of the energy storage air conditioning system at the current moment based on the current operating data.

[0037] A predictive heat dissipation model is a mathematical or algorithmic model used to calculate or evaluate the predicted heat dissipation data of an energy storage air conditioning system at the next moment based on predicted operating data.

[0038] Current heat dissipation data refers to the specific values ​​obtained after processing the current running data through the current heat dissipation model, reflecting the system's heat dissipation status at the current moment, such as the current heat dissipation amount and the current heat dissipation rate.

[0039] Predicted heat dissipation data refers to specific values ​​that reflect the system's heat dissipation status at the next moment, obtained by processing predicted operating data through a predictive heat dissipation model. Examples include the heat dissipation amount and heat dissipation rate at the next moment.

[0040] Step S3: Calculate and process the current heat dissipation data to obtain the current comprehensive parameters at the current moment, and calculate and process the predicted heat dissipation data to obtain the predicted comprehensive parameters at the next moment.

[0041] Among them, the current comprehensive parameter refers to the value or formula obtained after calculating and processing the current heat dissipation data, which can comprehensively represent the heat dissipation requirements of the system at the current moment.

[0042] Predictive comprehensive parameters refer to the numerical values ​​or formulas obtained after multi-dimensional calculation and processing of predicted heat dissipation data, which can comprehensively characterize the system's heat dissipation requirements at the next moment.

[0043] Step S4: Based on the current compressor speed, current comprehensive parameters and predicted comprehensive parameters, predict the compressor speed at the next moment, and control the compressor to work based on the predicted compressor speed at the next moment.

[0044] The current compressor speed represents the speed of the compressor in the energy storage air conditioning system at the current moment.

[0045] Predicted compressor speed characterizes the compressor speed in the energy storage air conditioning system at the next moment, obtained through prediction.

[0046] Specifically, firstly, the current operating data and predicted operating data for the next moment of the energy storage air conditioning system are acquired. Then, the current operating data is input into the current heat dissipation model to obtain the current heat dissipation data, and the predicted operating data is input into the predicted heat dissipation model to obtain the predicted heat dissipation data for the next moment. Further, the current heat dissipation data is processed to obtain the current comprehensive parameters, and the predicted heat dissipation data is processed to obtain the predicted comprehensive parameters for the next moment. Based on this, a prediction is made using the current compressor speed, the current comprehensive parameters, and the predicted comprehensive parameters to obtain the predicted compressor speed for the next moment, and the compressor is controlled to operate based on this predicted compressor speed in the next moment. Finally, the predicted compressor speed is sent to the compressor controller to adjust the compressor's operating speed.

[0047] In this embodiment, by introducing current operating data and predicted operating data, and combining them with a heat dissipation model and comprehensive parameters to predictively control the compressor speed, the limitations of traditional two-position regulation control in failing to fully consider the dynamic characteristics of the thermal process are addressed to some extent. Therefore, this embodiment can match the actual heat dissipation needs of the battery, reducing excessive energy waste under low loads, while responding in advance during peak heat dissipation periods, improving the timeliness and accuracy of cooling and heating, thereby enhancing the operating efficiency and temperature control accuracy of the energy storage air conditioning system.

[0048] It should be noted that the compressor speed is controlled independently during the battery charging and discharging processes of the energy storage air conditioning system. The following explanation focuses on the compressor speed control method during battery charging; the method for controlling the compressor speed during battery discharging is similar and will not be repeated hereafter.

[0049] In some embodiments, the current heat dissipation model includes a current heat dissipation quantity model and a current heat dissipation rate model, and the current heat dissipation data includes the current heat dissipation quantity and the current heat dissipation rate. For example... Figure 2 As shown, step S2 above, which inputs the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, includes steps S21 and S23: Step S21: Input the current running data into the current heat dissipation model to obtain the current heat dissipation.

[0050] The current heat dissipation model is used to calculate the current heat dissipation. This model can be constructed based on historical operating data, environmental parameters (such as outdoor temperature and battery compartment temperature), and system load (such as cooling capacity). The current heat dissipation model can be expressed as a mathematical formula.

[0051] The current heat dissipation represents the amount of heat dissipated by the battery in the energy storage air conditioning system at the current moment.

[0052] In some embodiments, the current heat dissipation model is constructed as follows: acquiring cooling capacity data of the energy storage air conditioning system, outdoor temperature data, and cabin temperature data of the battery compartment of the energy storage air conditioning system; performing calculations on the cooling capacity data, outdoor temperature data, and cabin temperature data to obtain the target heat transfer coefficient of the battery compartment; and constructing the current heat dissipation model based on the cooling capacity data, target heat transfer coefficient, outdoor temperature data, and cabin temperature data.

[0053] Among them, the cooling capacity data characterizes the cooling output of the energy storage air conditioning system used to assist in battery heat dissipation. Specifically, the calculation process for the cooling capacity data can be expressed as follows: ;in, This indicates the cooling capacity data. Indicates the specific heat capacity of the coolant. Indicates the coolant mass flow rate. This represents the absolute value of the coolant supply and return temperature difference. Specifically, the coolant supply and return temperature difference characterizes the deviation between the supply and return temperatures of the coolant in the energy storage air conditioning system. The current coolant supply and return temperature difference at the current moment can be expressed as... This indicates that the coolant supply and return temperature difference can be obtained and calculated using a temperature sensor.

[0054] Outdoor temperature data characterizes the ambient temperature outside the battery compartment. Outdoor temperature data can be collected using external temperature sensors. Specifically, outdoor temperature data can also be obtained by setting up multiple outdoor temperature test points outside the battery compartment, collecting the temperatures at these points, and then calculating an algebraic average of the temperatures from these multiple outdoor temperature test points.

[0055] Internal temperature data characterizes the temperature inside the battery compartment. This data can be collected using temperature sensors installed inside the battery compartment. Specifically, internal temperature data can also be obtained by setting up multiple internal temperature test points within the battery compartment, collecting the temperatures at each point, and then calculating an algebraic average of these temperatures.

[0056] The target heat transfer coefficient characterizes the heat transfer performance of the battery compartment. It comprehensively reflects the battery compartment's structure, insulation material performance, and heat exchange efficiency with the external environment.

[0057] Specifically, the calculation and processing of cooling capacity data, outdoor temperature data, and cabin temperature data to obtain the target heat transfer coefficient of the battery compartment may include: calculating the absolute value of the cabin temperature data and the outdoor temperature data, and then dividing the cooling capacity data by the absolute value to obtain the target heat transfer coefficient.

[0058] Specifically, the method for constructing a current heat dissipation model based on cooling capacity data, target heat transfer coefficient, outdoor temperature data, and cabin temperature data may include: at any time during multiple moments of accumulated charging or discharging time, calculating the heat dissipation data at that moment based on the cooling capacity data, target heat transfer coefficient, outdoor temperature data, and cabin temperature data at that moment; calculating the heat dissipation distance between the heat dissipation data at that moment and the expected heat dissipation data at that moment; sorting the calculated heat dissipation distances from multiple moments in ascending order to obtain a sequence of heat dissipation data; and performing a weighted expectation calculation on the heat dissipation data in the sequence of heat dissipation data to obtain the current heat dissipation.

[0059] The expected heat dissipation data at a given moment represents the ideal heat dissipation at that moment. The expected heat dissipation data at multiple moments within the cumulative charging or discharging duration may differ. The specific value of the expected heat dissipation data can be set according to actual needs.

[0060] Specifically, based on the cooling capacity data, target heat transfer coefficient, outdoor temperature data, and cabin temperature data at that moment, the method for calculating the heat dissipation data at that moment can be expressed as follows (taking the battery charging process as an example; the discharging process is similar): ;in, This represents the heat dissipation data at that moment during the charging process (i.e., the heat dissipation data for a single charge). This represents the cooling capacity data at that moment, and K represents the target heat transfer coefficient at that moment. This represents the outdoor temperature data at that moment. This indicates the cabin temperature data at that moment.

[0061] During the battery charging process, each moment in the cumulative charging time corresponds to its heat dissipation data (that is, it has multiple heat dissipation data). This indicates the amount of heat dissipated during a single charge cycle of the battery.

[0062] Specifically, the method to obtain the current heat dissipation is to calculate the weighted expectation of the heat dissipation data in the sequence of heat dissipation data, or the current heat dissipation model can be expressed as (taking the battery charging process as an example, the discharging process is similar):

[0063] Where SOC represents the battery level at the current moment, and τ represents the time at the current moment. This represents the current heat dissipation at the current moment during the charging process, and E represents the expected calculated amount. This represents the weight assigned to the i-th heat dissipation data point in the sequence of heat dissipation data. This represents the i-th heat dissipation data point in the sequence of heat dissipation data during the charging process.

[0064] In this sequence of heat dissipation data, the heat dissipation data is sorted in ascending order according to the distance between heat dissipation data. Therefore, in this sequence, the weight of the first heat dissipation data (the distance between the heat dissipation data at this moment and the expected heat dissipation data at this moment is the smallest) is 1 / 1, which is 1; the weight of the second heat dissipation data is 1 / 2; ...; and finally, the weight of the i-th heat dissipation data (the distance between the heat dissipation data at this moment and the expected heat dissipation data at this moment is the largest) is 1 / i.

[0065] Step S23: Input the current heat dissipation and current operating data into the current heat dissipation rate model to obtain the current heat dissipation rate.

[0066] The current heat dissipation rate model is used to calculate the current heat dissipation rate. This model is related to the current heat dissipation amount and can be expressed as a mathematical formula.

[0067] The current heat dissipation rate characterizes the rate at which the battery in the energy storage air conditioning system dissipates heat at the current moment.

[0068] Specifically, the method for constructing the current heat dissipation rate model may include: differentiating the current heat dissipation model with respect to the current time to obtain the time rate of the current heat dissipation model; dividing the time rate of the current heat dissipation model by the average of the durations of multiple moments in the cumulative charging time or the average of the durations of multiple moments in the cumulative discharging time to obtain the standard time rate of the current heat dissipation model; differentiating the current heat dissipation model with respect to the battery charge at the current time to obtain the charge rate of the current heat dissipation model; dividing the charge rate of the current heat dissipation model by the average of the battery charge at multiple moments in the cumulative charging time or the average of the battery charge at multiple moments in the cumulative discharging time to obtain the standard charge rate of the current heat dissipation model; and finally, weighting and summing the standard time rate and the standard charge rate of the current heat dissipation model to obtain the current heat dissipation rate model.

[0069] Specifically, the current heat dissipation rate model can be expressed as follows (taking the battery charging process as an example; the discharging process is similar):

[0070] Where SOC represents the battery level at the current moment, and τ represents the time at the current moment. This indicates the current heat dissipation rate during the charging process. This represents the standard time rate of the current heat dissipation model during the charging process. This represents the standard charge rate of the current heat dissipation model during the charging process. This represents the weight assigned to the standard time rate of the current heat dissipation model. This represents the weight assigned to the standard charge rate of the current heat dissipation model.

[0071] The weights assigned to the standard time rate and the standard power rate of the current heat dissipation model are both related to the specific operating conditions of the energy storage air conditioning system and can be set according to actual needs.

[0072] In this embodiment, by acquiring cooling capacity data from the energy storage air conditioning system, outdoor temperature data, and the internal temperature data of the battery compartment, and calculating the target heat transfer coefficient of the battery compartment based on this data, a more accurate and adaptive current heat dissipation model can be constructed. This model considers the actual heat load, environmental conditions, and heat transfer characteristics of the battery compartment, thus accurately predicting the heat dissipation at the current moment. This heat dissipation prediction provides a reliable foundation for subsequent calculations, thereby making the prediction and control of compressor speed more precise. Ultimately, this helps to achieve refined thermal management of the battery compartment by the energy storage air conditioning system and optimize system operating efficiency.

[0073] In some embodiments, the predicted heat dissipation model includes a predicted heat dissipation amount model and a predicted heat dissipation rate model, and the predicted heat dissipation data includes predicted heat dissipation amount and predicted heat dissipation rate. For example... Figure 3 As shown, step S2 above, which involves inputting the predicted operating data into the predicted heat dissipation model to obtain the predicted heat dissipation data for the next time step, includes steps S22 and S24: Step S22: Input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation.

[0074] The predictive heat dissipation model is a model that calculates the predicted heat dissipation based on preheating operation data. This predictive heat dissipation model can be expressed as a mathematical formula.

[0075] Specifically, the predicted heat dissipation model is constructed as follows: at any point during multiple moments of accumulated charging or discharging time, the actual recorded heat dissipation at that moment is obtained; the absolute value of the difference between the current heat dissipation at that moment and the actual recorded heat dissipation at that moment is taken as the predicted heat dissipation deviation at that moment; the predicted heat dissipation deviations at multiple moments are weighted and summed to obtain the current comprehensive heat dissipation deviation at the current moment; the predicted running data is input into the current heat dissipation model to obtain the predicted heat dissipation value at the next moment; the current comprehensive heat dissipation deviation at the current moment is added to the predicted heat dissipation value at the next moment to construct the predicted heat dissipation model.

[0076] Specifically, the method of weighting and summing the predicted heat dissipation deviations at multiple times to obtain the current comprehensive heat dissipation deviation can be expressed as follows (taking the battery charging process as an example; the discharging process is similar):

[0077] in, This represents the current overall heat dissipation deviation at the current moment during the charging process. λ represents a constant, and λ∈(0,1), whose specific value can be set according to actual needs. This represents the deviation of the predicted heat dissipation during the charging process from the past k-1 time (equivalent to the current time). This represents the deviation of the predicted heat dissipation during the charging process from the past k-2 time points (relative to the current time). This indicates the deviation of the predicted heat dissipation at the current moment during the charging process.

[0078] Specifically, the model for predicting heat dissipation can be expressed as follows (taking the battery charging process as an example; the discharging process is similar):

[0079] Where SOC(τ+1) represents the battery charge at the next moment, and τ+1 represents the time of the next moment. This indicates the predicted heat dissipation at the next moment during the charging process. This indicates the deviation of the current total heat dissipation at the current moment during the charging process. This represents the predicted heat dissipation value at the next moment during the charging process.

[0080] Step S24: Input the predicted heat dissipation and predicted operating data into the predicted heat dissipation rate model to obtain the predicted heat dissipation rate.

[0081] The predicted heat dissipation rate model is used to calculate the predicted heat dissipation rate. This predicted heat dissipation rate model can be expressed as a mathematical formula.

[0082] The predicted heat dissipation rate characterizes the predicted heat dissipation rate of the battery in the energy storage air conditioning system at the next moment.

[0083] Specifically, the predicted heat dissipation rate model can be constructed as follows: at any time during multiple moments of accumulated charging or discharging time, obtain the actual recorded heat dissipation rate at that moment; use the absolute value of the difference between the current heat dissipation rate at that moment and the actual recorded heat dissipation rate at that moment as the predicted heat dissipation rate deviation at that moment; weight and sum the predicted heat dissipation rate deviations at multiple moments to obtain the current comprehensive heat dissipation rate deviation at the current moment; input the predicted running data into the current heat dissipation rate model to obtain the predicted heat dissipation rate value at the next moment; and add the current comprehensive heat dissipation rate deviation at the current moment to the predicted heat dissipation rate value at the next moment to construct the predicted heat dissipation rate model.

[0084] Specifically, the method of weighting and summing the predicted heat dissipation rate deviations at multiple times to obtain the current comprehensive heat dissipation rate deviation can be expressed as follows (taking the battery charging process as an example; the discharging process is similar):

[0085] in, This represents the current overall heat dissipation rate deviation at the current moment during the charging process. α represents a constant, and Its specific value can be set according to actual needs. This represents the deviation of the predicted heat dissipation rate during the charging process from the past k-1 time points (equivalent to the current time). This represents the deviation of the predicted heat dissipation rate during the charging process from the past k-2 time points (relative to the current time). This indicates the deviation of the predicted heat dissipation rate at the current moment during the charging process.

[0086] Specifically, the model for predicting the heat dissipation rate can be expressed as follows (taking the battery charging process as an example; the discharging process is similar):

[0087] in, This indicates the predicted heat dissipation rate at the next moment during the charging process. This indicates the deviation of the current overall heat dissipation rate at the current moment during the charging process. This represents the predicted heat dissipation rate at the next moment during the charging process.

[0088] In this embodiment, by refining the predicted heat dissipation model into a predicted heat dissipation amount model and a predicted heat dissipation rate model, and outputting the predicted heat dissipation amount and predicted heat dissipation rate respectively, the heat dissipation characteristics of the energy storage air conditioning system at the next moment can be captured more comprehensively and precisely. The predicted heat dissipation amount provides information on the overall heat load, while the predicted heat dissipation rate reflects the dynamic trend of heat dissipation. This predicted data provides a more accurate basis for subsequent comprehensive parameter calculations, making the compressor speed prediction based on these parameters more accurate, and enabling the control strategy to respond more timely and effectively to changes in the system's heat dissipation demand. This further optimizes the compressor's operating efficiency and improves the overall energy efficiency of the energy storage air conditioning system and the operational stability of the battery.

[0089] In some embodiments, the current heat dissipation data includes the current heat dissipation amount, the current heat dissipation rate, the temperature difference between the inside and outside of the battery compartment of the energy storage air conditioning system, and the highest cell temperature in the battery compartment. For example... Figure 4 As shown, the calculation and processing of the current heat dissipation data in step S3 above to obtain the current comprehensive parameters at the current moment includes steps S31 and S32: Step S31: Standardize the current heat dissipation, current heat dissipation rate, internal and external temperature difference data, and maximum cell temperature respectively to obtain the current standard heat dissipation, current standard heat dissipation rate, standard internal and external temperature difference data, and standard maximum cell temperature.

[0090] The current standard heat dissipation represents the parameter obtained after standardizing the current heat dissipation.

[0091] The current standard heat dissipation rate is a parameter obtained after standardizing the current heat dissipation rate.

[0092] The temperature difference between the outside and inside cabins represents the deviation between outdoor temperature data and cabin temperature data. Specifically, the temperature difference between the outside and inside cabins can be obtained by calculating the difference between the current outdoor temperature data and the current cabin temperature data.

[0093] Standard internal and external temperature difference data represent parameters obtained after standardizing the internal and external temperature difference data.

[0094] The highest cell temperature represents the highest cell temperature inside the battery compartment at the current moment, and can be obtained through a temperature sensor.

[0095] Standard maximum cell temperature is a parameter obtained after standardizing the maximum cell temperature.

[0096] Specifically, step S31, which involves standardizing the current heat dissipation, current heat dissipation rate, internal and external temperature difference data, and maximum cell temperature to obtain the current standard heat dissipation, current standard heat dissipation rate, standard internal and external temperature difference data, and standard maximum cell temperature, may include steps S311 to S314: Step S311: Standardize the current heat dissipation to obtain the current standard heat dissipation.

[0097] Specifically, a method for standardizing the current heat dissipation to obtain the current standard heat dissipation may include: dividing the current heat dissipation by the maximum heat dissipation of the battery at multiple times to obtain the current standard heat dissipation.

[0098] Among them, the maximum heat dissipation of the battery at multiple moments represents the maximum heat dissipation value calculated by inputting the operating data of that moment into the current heat dissipation model among multiple moments of the cumulative charging time or cumulative discharging time.

[0099] Step S312: Standardize the current heat dissipation rate to obtain the current standard heat dissipation rate.

[0100] Specifically, a method for standardizing the current heat dissipation rate to obtain the current standard heat dissipation rate may include: dividing the current heat dissipation rate by the maximum heat dissipation rate of the battery at multiple times to obtain the current standard heat dissipation rate.

[0101] Among them, the maximum heat dissipation rate of the battery at multiple moments represents the heat dissipation rate with the highest value calculated by inputting the operating data of that moment into the current heat dissipation rate model among multiple moments of the cumulative charging time or cumulative discharging time.

[0102] Step S313: Standardize the internal and external temperature difference data to obtain standard internal and external temperature difference data.

[0103] Specifically, a method for standardizing the internal and external temperature difference data to obtain standard internal and external temperature difference data may include: dividing the internal and external temperature difference data by the difference between the maximum outdoor temperature and the minimum cabin temperature at multiple times to obtain standard internal and external temperature difference data.

[0104] Step S314: Standardize the highest cell temperature to obtain the standard highest cell temperature.

[0105] Specifically, a method for standardizing the maximum cell temperature to obtain a standard maximum cell temperature may include: dividing the difference between the current maximum cell temperature and the lower limit temperature of the cell operation by the difference between the upper limit temperature of the cell operation and the lower limit temperature of the cell operation, to obtain the standard maximum cell temperature.

[0106] The lower limit temperature of the battery cell represents the lower temperature limit for battery operation in the energy storage air conditioning system. The upper limit temperature of the battery cell represents the upper temperature limit for battery operation in the energy storage air conditioning system. Both the lower and upper limit temperatures of the battery cell can be obtained directly from the manufacturer or the system.

[0107] Step S32: Perform a weighted calculation on the current standard heat dissipation, current standard heat dissipation rate, standard internal and external temperature difference data, and standard maximum cell temperature to obtain the current comprehensive parameters.

[0108] Specifically, step S32, which involves weighting the current standard heat dissipation, current standard heat dissipation rate, standard internal and external temperature difference data, and standard maximum cell temperature to obtain the current comprehensive parameters, may include: adding the products of the current standard heat dissipation and the first weighted parameter, the standard internal and external temperature difference data and the first weighted parameter, the current standard heat dissipation rate and the second weighted parameter, and the standard maximum cell temperature and the third weighted parameter together to obtain the current comprehensive parameters.

[0109] The first, second, and third weight parameters can be set according to actual needs. Specifically, the sum of the first, second, and third weight parameters must equal 1. Furthermore, the first weight parameter is greater than the second weight parameter, and the second weight parameter is greater than the third weight parameter.

[0110] Specifically, the steps for calculating and processing the predicted heat dissipation data to obtain the predicted comprehensive parameters for the next moment are similar to the steps for calculating and processing the current heat dissipation data to obtain the current comprehensive parameters for the current moment, and will not be repeated here.

[0111] In some embodiments, step S4, the method of predicting the compressor speed at the next moment based on the current compressor speed, the current comprehensive parameters, and the predicted comprehensive parameters, may specifically include: performing polynomial incremental calculation based on the current comprehensive parameters and the predicted comprehensive parameters to obtain the compressor speed adjustment amount at the next moment; calculating the sum of the current compressor speed and the compressor speed adjustment amount at the next moment to obtain the predicted compressor speed at the next moment.

[0112] Specifically, the method of obtaining the compressor speed adjustment at the next moment by performing polynomial incremental calculations based on the current comprehensive parameters and the predicted comprehensive parameters can be expressed as follows (taking the battery charging process as an example, the discharging process is similar):

[0113] in, This indicates the predicted compressor speed at the next moment during the charging process. This represents the current comprehensive parameters at the current moment during the charging process. This represents the predicted comprehensive parameters for the next moment during the charging process. Here, q is the highest degree of the polynomial model, rounded to the nearest integer. and All are coefficients, for the formula The value (any q value) must satisfy the following condition. .

[0114] at the same time, That is, when the predicted comprehensive parameter is greater than or equal to the current comprehensive parameter, the value is 1; when the predicted comprehensive parameter is less than the current comprehensive parameter, the value is -1.

[0115] In some embodiments, after calculating the sum of the current compressor speed and the compressor speed adjustment amount at the next moment to obtain the predicted compressor speed at the next moment, the method may further include: using parameters such as the upper limit of the single adjustment range of the compressor, the lower limit of the single adjustment range, the upper limit of the compressor speed, and the lower limit of the compressor speed to perform windowing truncation processing on the predicted compressor speed at the next moment, so as to update the predicted compressor speed at the next moment.

[0116] The upper limit of a single adjustment range represents the upper limit of the compressor speed adjustment amount in a single operation. The lower limit of a single adjustment range represents the lower limit of the compressor speed adjustment amount in a single operation. The upper limit of compressor speed represents the upper limit of the compressor speed. The lower limit of compressor speed represents the lower limit of the compressor speed. The aforementioned upper and lower limits can be set according to actual needs.

[0117] Specifically, when the calculated compressor speed adjustment for the next moment is greater than the upper limit of the single adjustment range, the upper limit of the single adjustment range is used as the compressor speed adjustment for the next moment. When the calculated compressor speed adjustment for the next moment is less than the lower limit of the single adjustment range, the lower limit of the single adjustment range is used as the compressor speed adjustment for the next moment. When the calculated predicted compressor speed for the next moment is greater than the upper limit of the compressor speed, the upper limit of the compressor speed is used as the predicted compressor speed for the next moment. When the calculated predicted compressor speed for the next moment is less than the lower limit of the compressor speed, the lower limit of the compressor speed is used as the predicted compressor speed for the next moment.

[0118] In this embodiment, the current heat dissipation data is refined into multiple dimensions, including current heat dissipation amount, current heat dissipation rate, temperature difference between the inside and outside of the battery compartment, and the highest cell temperature in the battery compartment. These data are standardized to eliminate the influence of different physical dimensions and magnitudes, making the data comparable. Based on this, weighted calculations can more comprehensively and accurately assess the energy storage air conditioning system, especially the overall heat dissipation status and thermal management requirements of the battery compartment. This multi-dimensional, standardized, and weighted fusion of parameters provides a more refined and reliable input for subsequent compressor speed prediction, enabling the compressor to respond more accurately to actual heat load changes in the system, optimize operating efficiency, reduce battery overheating or overcooling, extend battery life, and improve the overall energy efficiency and stability of the energy storage air conditioning system.

[0119] In some embodiments, the energy storage air conditioning system further includes a water pump. For example... Figure 5 As shown, the method also includes: Step S6: Obtain the current highest battery temperature and current cell temperature difference of the energy storage air conditioning system.

[0120] Among them, the current highest battery temperature represents the highest temperature value inside the battery compartment of the energy storage air conditioning system at the current moment, which can be obtained by temperature sensors.

[0121] The current cell temperature difference represents the difference between the highest and lowest temperatures inside the battery compartment of the energy storage air conditioning system at the current moment.

[0122] Step S7: Determine the predicted water pump increment based on the current highest battery temperature and the current cell temperature difference, and control the water pump to adjust based on the predicted water pump increment.

[0123] The predicted water pump increment represents the incremental value by which the energy storage air conditioning system needs to adjust the water pump at the next moment, based on the current highest battery temperature and the current cell temperature difference. It should be noted that this predicted water pump increment can be positive or negative.

[0124] In this embodiment, a water pump regulation mechanism is further introduced based on the overall heat dissipation control of the compressor in the energy storage air conditioning system. By acquiring the current maximum battery temperature and current cell temperature difference of the battery pack in real time, the local hot spots and temperature distribution uniformity inside the battery pack can be directly reflected. Based on these battery thermal state parameters, the predicted water pump increment can be determined more accurately, and the operation of the water pump can be precisely adjusted accordingly. This dynamically adjusts the circulation rate and flow rate of the coolant, thereby enhancing the efficiency of heat removal from the battery pack. Especially when the battery is locally overheated or the temperature difference is too large, intervention can be carried out more promptly and accurately to reduce heat accumulation. Therefore, this solution not only reduces the phenomenon of local overheating of the battery pack but also reduces the temperature difference between cells, thereby improving the temperature uniformity of the battery pack and extending the battery's lifespan.

[0125] In some embodiments, such as Figure 6 As shown, step S7 above, which determines the predicted water pump increment based on the current highest battery temperature and the current cell temperature difference, includes: Step S71: Match the current highest battery temperature and the current cell temperature difference in the preset coolant supply and return temperature difference table to obtain the target coolant supply and return temperature difference.

[0126] The target coolant supply-return temperature difference characterizes the deviation of the coolant supply and return temperatures in the energy storage air conditioning system at the next moment.

[0127] The preset coolant supply and return temperature difference table represents the correspondence between the current highest battery temperature, the current cell temperature difference, and the target coolant supply and return temperature difference. Specifically, the preset coolant supply and return temperature difference table can be preset or dynamically adjusted according to actual needs.

[0128] For example, the specific settings of the preset coolant supply and return temperature difference table can be found in Table 1 below. Table 1 is the target coolant supply and return temperature difference table under different current maximum battery temperatures and different current cell temperature differences.

[0129] Table 1. Target Coolant Supply and Return Temperature Difference Values

[0130] As shown in the table above, T1 represents the current highest battery temperature, and △T2 represents the current cell temperature difference. This indicates the target coolant supply and return temperature difference. W1 to W5 all represent the target coolant supply and return temperature difference. The values ​​of U1 to U4 represent the critical values ​​of the current highest battery temperature T1, and V1 to V4 represent the critical values ​​of the current cell temperature difference ΔT2. The specific values ​​of W1 to W5, U1 to U4, and V1 to V4 can be determined according to the system conditions.

[0131] Step S72: Calculate and process the current water pump speed, the current coolant supply and return temperature difference, and the target coolant supply and return temperature difference to obtain the predicted water pump increment.

[0132] The current coolant supply and return temperature difference, which represents the deviation of the coolant supply and return temperatures in the energy storage air conditioning system at the current moment, can be obtained by collecting and calculating through temperature sensors.

[0133] The target coolant supply-return temperature difference represents the deviation of the coolant supply and return temperatures in the energy storage air conditioning system at the next moment (relative to the current moment).

[0134] Specifically, the method for calculating and processing the current water pump speed, the current coolant supply and return temperature difference, and the target coolant supply and return temperature difference to obtain the predicted water pump increment may include: calculating and processing the current water pump speed, the current coolant supply and return temperature difference, and the target coolant supply and return temperature difference to obtain the predicted water pump speed; and subtracting the current water pump speed from the predicted water pump speed to obtain the predicted water pump increment.

[0135] Specifically, the method for calculating and processing the current water pump speed, the current coolant supply and return temperature difference, and the target coolant supply and return temperature difference to obtain the predicted water pump speed can be expressed as follows: ; in, To predict the pump speed; This refers to the current pump speed. The correction factor can be set according to actual needs; The current coolant supply and return temperature difference; The target coolant supply and return temperature difference is denoted by s, where s is the polynomial exponent coefficient.

[0136] In this embodiment, when determining the predicted water pump increment, the energy storage air conditioning system first performs precise matching based on the current highest battery temperature and the current cell temperature difference, using a preset coolant supply and return temperature difference table to obtain a target coolant supply and return temperature difference closely related to the actual thermal state of the battery. Subsequently, it further calculates the target coolant supply and return temperature difference by combining the current water pump speed and the current coolant supply and return temperature difference, resulting in a more accurate predicted water pump increment. This ensures that the water pump's operating state closely matches the actual cooling needs of the battery, reducing energy waste caused by overcooling and lowering the risk of battery overheating due to insufficient cooling. Therefore, this solution improves the accuracy and efficiency of the energy storage air conditioning system's battery thermal management, helps extend battery life, and optimizes the overall energy consumption performance of the system.

[0137] In summary, the control method for the energy storage air conditioning system provided in this embodiment, by introducing current operating data and predicted operating data, and combining a heat dissipation model and comprehensive parameters to predictively control the compressor speed, and simultaneously predictively controlling the water pump speed based on battery temperature, to a certain extent solves the limitation of traditional two-position regulation control that fails to fully consider the dynamic characteristics of the thermal process. Therefore, this embodiment can match the actual heat dissipation needs of the battery, reduce excessive energy waste under low load, and respond in advance during peak heat dissipation periods, improving the timeliness and accuracy of cooling and heating, thereby enhancing the operating efficiency and temperature control accuracy of the energy storage air conditioning system.

[0138] This application also provides a control device for an energy storage air conditioning system, which includes a compressor. For example... Figure 7 As shown, the control device 700 of the energy storage air conditioning system includes: The data acquisition module 701 is used to acquire the current operating data of the energy storage air conditioning system at the current moment and the predicted operating data for the next moment; The model input module 702 is used to input the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and to input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment. The parameter calculation module 703 is used to calculate and process the current heat dissipation data to obtain the current comprehensive parameters at the current moment, and to calculate and process the predicted heat dissipation data to obtain the predicted comprehensive parameters at the next moment. The speed prediction module 704 is used to predict the compressor speed at the next moment based on the current compressor speed, the current comprehensive parameters and the predicted comprehensive parameters, and to control the compressor to work based on the predicted compressor speed at the next moment.

[0139] It should be noted that the specific structure and function of the control device 700 of the energy storage air conditioning system correspond to the control method of the energy storage air conditioning system in the above embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0140] This application also provides an air conditioner, which includes one or more processors, a memory, and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the control method of the energy storage air conditioning system in any of the above embodiments.

[0141] This application also provides an electronic device that integrates the control device of any of the energy storage air conditioning systems provided in this application. For example... Figure 8 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 801 with one or more processing cores, a memory 802 with one or more computer-readable storage media, a power supply 803, and an input unit 804. Those skilled in the art will understand that... Figure 8 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 801 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802, and by calling data stored in the memory 802, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. Optionally, the processor 801 may include one or more processing cores; preferably, the processor 801 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 801.

[0142] The memory 802 can be used to store software programs and modules. The processor 801 executes various functional applications and data processing by running the software programs and modules stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 802 may also include a memory controller to provide the processor 801 with access to the memory 802.

[0143] The electronic device also includes a power supply 803 that supplies power to the various components. Preferably, the power supply 803 can be logically connected to the processor 801 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 803 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0144] The electronic device may also include an input unit 804, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0145] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 801 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 802 according to the following instructions, and the processor 801 runs the application programs stored in the memory 802 to realize various functions, such as: Obtain the current operating data and the predicted operating data for the next moment of the energy storage air conditioning system; Input the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment; The current heat dissipation data is processed to obtain the current comprehensive parameters at the current moment, and the predicted heat dissipation data is processed to obtain the predicted comprehensive parameters at the next moment. Based on the current compressor speed, current comprehensive parameters, and predicted comprehensive parameters, a prediction is made to obtain the predicted compressor speed at the next moment, and the compressor is controlled to operate based on the predicted compressor speed at the next moment.

[0146] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0147] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in the control method of any of the energy storage air conditioning systems provided in embodiments of this application. For example, the computer program loaded by the processor can execute the following steps: Obtain the current operating data and the predicted operating data for the next moment of the energy storage air conditioning system; Input the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment; The current heat dissipation data is processed to obtain the current comprehensive parameters at the current moment, and the predicted heat dissipation data is processed to obtain the predicted comprehensive parameters at the next moment. Based on the current compressor speed, current comprehensive parameters, and predicted comprehensive parameters, a prediction is made to obtain the predicted compressor speed at the next moment, and the compressor is controlled to operate based on the predicted compressor speed at the next moment.

[0148] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0149] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0150] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0151] The control method, device, air conditioner, and storage medium of an energy storage air conditioning system provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A control method for an energy storage air conditioning system, characterized in that, The energy storage air conditioning system includes a compressor, and the control method of the energy storage air conditioning system includes: Obtain the current operating data and the predicted operating data for the next moment of the energy storage air conditioning system; The current operating data is input into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and the predicted operating data is input into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment. The current heat dissipation data is processed to obtain the current comprehensive parameters at the current moment, and the predicted heat dissipation data is processed to obtain the predicted comprehensive parameters at the next moment. Based on the current compressor speed, the current comprehensive parameters, and the predicted comprehensive parameters, a prediction is made to obtain the predicted compressor speed at the next moment, and the compressor is controlled to operate based on the predicted compressor speed at the next moment.

2. The method according to claim 1, characterized in that, The current heat dissipation model includes a current heat dissipation quantity model and a current heat dissipation rate model. The current heat dissipation data includes current heat dissipation quantity and current heat dissipation rate. The step of inputting the current operating data into the current heat dissipation model to obtain the current heat dissipation data at the current moment includes: The current operating data is input into the current heat dissipation model to obtain the current heat dissipation. The current heat dissipation and the current operating data are input into the current heat dissipation rate model to obtain the current heat dissipation rate.

3. The method according to claim 2, characterized in that, The current heat dissipation model is constructed in the following way: Acquire the cooling capacity data, outdoor temperature data, and internal temperature data of the battery compartment of the energy storage air conditioning system. The cooling capacity data, the outdoor temperature data, and the cabin temperature data are calculated and processed to obtain the target heat transfer coefficient of the battery compartment. Based on the cooling capacity data, the target heat transfer coefficient, the outdoor temperature data, and the cabin temperature data, the current heat dissipation model is constructed.

4. The method according to claim 1, characterized in that, The predicted heat dissipation model includes a predicted heat dissipation amount model and a predicted heat dissipation rate model. The predicted heat dissipation data includes predicted heat dissipation amount and predicted heat dissipation rate. The step of inputting the predicted operating data into the predicted heat dissipation model to obtain the predicted heat dissipation data for the next time step includes: The predicted operating data is input into the predicted heat dissipation model to obtain the predicted heat dissipation. The predicted heat dissipation and the predicted operating data are input into the predicted heat dissipation rate model to obtain the predicted heat dissipation rate.

5. The method according to claim 1, characterized in that, The current heat dissipation data includes the current heat dissipation amount, the current heat dissipation rate, the temperature difference between the inside and outside of the battery compartment of the energy storage air conditioning system, and the highest cell temperature of the battery compartment. The calculation and processing of the current heat dissipation data to obtain the current comprehensive parameters at the current moment includes: The current heat dissipation, the current heat dissipation rate, the internal and external temperature difference data, and the highest cell temperature are standardized to obtain the current standard heat dissipation, the current standard heat dissipation rate, the standard internal and external temperature difference data, and the standard highest cell temperature. The current standard heat dissipation, the current standard heat dissipation rate, the standard internal and external temperature difference data, and the standard maximum cell temperature are weighted and calculated to obtain the current comprehensive parameters.

6. The method according to claim 1, characterized in that, The energy storage air conditioning system includes a water pump, and the method further includes: Obtain the current highest battery temperature and current cell temperature difference of the energy storage air conditioning system; The predicted water pump increment is determined based on the current highest battery temperature and the current cell temperature difference, and the water pump is controlled to adjust based on the predicted water pump increment.

7. The control method for the energy storage air conditioning system according to claim 6, characterized in that, Determining the predicted water pump increment based on the current highest battery temperature and the current cell temperature difference includes: The target coolant supply and return temperature difference is obtained by matching the current highest battery temperature and the current cell temperature difference with a preset coolant supply and return temperature difference table. The current water pump speed, the current coolant supply and return temperature difference, and the target coolant supply and return temperature difference are calculated to obtain the predicted water pump increment.

8. A control device for an energy storage air conditioning system, characterized in that, The energy storage air conditioning system includes a compressor, and the control device for the energy storage air conditioning system includes: The data acquisition module is used to acquire the current operating data of the energy storage air conditioning system at the current moment and the predicted operating data for the next moment; The model input module is used to input the current running data into the current heat dissipation model to obtain the current heat dissipation data at the current moment, and to input the predicted running data into the predicted heat dissipation model to obtain the predicted heat dissipation data at the next moment. The parameter calculation module is used to calculate and process the current heat dissipation data to obtain the current comprehensive parameters at the current moment, and to calculate and process the predicted heat dissipation data to obtain the predicted comprehensive parameters at the next moment. The speed prediction module is used to predict the compressor speed at the next moment based on the current compressor speed, the current comprehensive parameters, and the predicted comprehensive parameters, and to control the compressor to operate based on the predicted compressor speed at the next moment.

9. An air conditioner, characterized in that, The air conditioner includes: one or more processors, a memory, and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the processor to implement the control method of the energy storage air conditioning system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps in the control method of the energy storage air conditioning system according to any one of claims 1 to 7.

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