A general control method and related devices for optimal operation of a transcritical CO2 heat pump system

Abstract

This invention discloses a general control method and related device for the optimal operation of a transcritical CO2 heat pump system, belonging to the field of new energy vehicles. The method includes the following steps: determining the number of levels of the optimization parameter; obtaining the corresponding values ​​of different levels of each optimization parameter according to the gradient of the number of levels; calculating the operating condition table of the transcritical CO2 heat pump system through orthogonal matrix calculation; experimentally obtaining the optimal exhaust pressure of the transcritical CO2 heat pump system under each condition based on the operating condition table; performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each condition to obtain the sensitivity ratio of the optimization parameter; predicting the optimal exhaust pressure of the real-time operating condition based on the sensitivity ratio of the optimization parameter; and adjusting the CO2 compressor speed and electronic expansion valve opening according to the optimal exhaust pressure of the real-time operating condition to complete the control of the transcritical CO2 heat pump system. This invention enables the transcritical CO2 heat pump system to achieve optimal performance.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicles, specifically relating to a general control method and related devices for the optimal operation of a transcritical CO2 heat pump system. Background Technology

[0002] With the evolution of refrigerants, CO2, as a natural working fluid, has been widely used in transcritical CO2 heat pump cycles for new energy vehicles. Currently, CO2 heat pump cycles operate in both transcritical and subcritical states, each suitable for different operating conditions. Due to CO2's very low critical temperature, CO2 heat pump air conditioners mostly operate in a transcritical state. Heat pump air conditioning technology using transcritical CO2 as the cycle exhibits good heating performance in winter and effectively alleviates range anxiety for electric vehicles in cold winters. However, limitations imposed by the space and operating conditions of electric vehicles, such as the size of the heat exchanger and the fan speed settings, cause the CO2 heat pump cycle to enter a subcritical operating state under certain extreme conditions. Furthermore, because the parameters of the electric vehicle heat pump system vary significantly with operating conditions, the performance of the electric vehicle heat pump is greatly affected by these conditions, and the system often deviates from its optimal performance during operation. When system parameters such as indoor inlet air temperature, indoor outlet air temperature, and indoor air volume change, its optimal performance may evolve from transcritical to subcritical.

[0003] In summary, the current optimal pressure formulas for CO2 transcritical heat pump air conditioning systems lack universality and cannot achieve optimal system performance due to the transcritical-to-subcritical evolutionary characteristics. There is an urgent need for a thermal management method that allows the system to achieve optimal performance even when entering a subcritical cycle, thereby achieving energy savings and a higher COP (Coefficient of Performance). Summary of the Invention

[0004] The purpose of this invention is to provide a general control method and related device for the optimal operation of a transcritical CO2 heat pump system, so as to solve the problem that the existing technology cannot achieve the optimal performance of the transcritical CO2 heat pump system due to the evolution characteristics of transcritical-subcritical.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, a general control method for optimal operation of a transcritical CO2 heat pump system includes:

[0007] Obtain the optimized parameters of the transcritical CO2 heat pump system;

[0008] After determining the number of levels of the optimization parameter, and obtaining the corresponding values ​​of different levels of each optimization parameter according to the gradient of the number of levels, the operating condition table of the transcritical CO2 heat pump system is obtained by calculating through an orthogonal matrix; the optimal exhaust pressure of the transcritical CO2 heat pump system under each condition is obtained according to the operating condition table of the transcritical CO2 heat pump system; after performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each condition, the sensitivity ratio of the optimization parameter is obtained;

[0009] The optimal exhaust pressure under real-time operating conditions is obtained by optimizing the parameter sensitivity ratio prediction. Based on the optimal exhaust pressure under real-time operating conditions, the CO2 compressor speed and the opening of the electronic expansion valve are adjusted to complete the control of the transcritical CO2 heat pump system.

[0010] In some implementations, the optimized parameters include ambient temperature, indoor intake air temperature, indoor outlet air temperature, and indoor air volume.

[0011] In some embodiments, the step of averaging the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each of the aforementioned conditions specifically includes:

[0012] Calculate the first average value of the optimal exhaust pressure corresponding to all operating conditions in the operating condition table;

[0013] Calculate the second average of all optimal exhaust pressures corresponding to each level of the optimization parameters.

[0014] In some implementations, the step of performing mean and variance analysis on the optimal exhaust pressure and operating condition tables of the transcritical CO2 heat pump system under each of the aforementioned conditions to obtain the proportion of sensitivity to optimized parameters specifically includes:

[0015] The variance analysis results for the optimal exhaust pressure corresponding to each optimization parameter are obtained by performing variance analysis based on the first average value and the second average value.

[0016] The sensitivity ratio of the optimized parameters is obtained based on the results of the analysis of variance.

[0017] In some implementations, the optimal exhaust pressure for real-time operating conditions is obtained by optimizing the parameter sensitivity ratio prediction using the following formula:

[0018]

[0019]

[0020]

[0021]

[0022] In the above formula, This is the optimal predicted exhaust pressure value. The weighting coefficient for ambient temperature. This is the weighting coefficient for indoor air intake temperature. The weighting coefficient for indoor air outlet temperature. , , and The constant is obtained through linear regression of experimental data. This refers to the indoor airflow volume. The average indoor air volume in the experimental group was used for sensitivity analysis. The percentage of sensitivity to indoor airflow volume. The percentage of sensitivity to ambient temperature. The percentage of sensitivity to indoor air intake temperature. The percentage of sensitivity to indoor air outlet temperature.

[0023] In some implementations, the following steps are also included:

[0024] When the indoor heat exchanger of the transcritical CO2 heat pump system is changed, the optimal exhaust pressure under real-time operating conditions is obtained by optimizing the parameter sensitivity ratio and combining it with the correction factor, where the correction factor is the heat exchange efficiency of the heat exchanger.

[0025] In some implementations, the optimal exhaust pressure for real-time operating conditions is obtained by optimizing the parameter sensitivity ratio and combining it with a correction factor using the following formula:

[0026]

[0027]

[0028]

[0029]

[0030] In the above formula, in the above formula, This is the optimal predicted exhaust pressure value. The weighting coefficient for ambient temperature. This is the weighting coefficient for indoor air intake temperature. The weighting coefficient for indoor air outlet temperature. , , and The constant is obtained through linear regression of experimental data. This refers to the indoor airflow volume. This represents the average indoor air volume within the sensitivity analysis group. The heat exchange efficiency of the indoor heat exchanger. This represents the average heat exchange efficiency of the indoor heat exchangers in each group of the sensitivity analysis experiment. , , It is a constant.

[0031] Secondly, a control system for a transcritical CO2 heat pump system includes:

[0032] The optimization parameter acquisition module is used to acquire the optimization parameters of the transcritical CO2 heat pump system.

[0033] The sensitivity analysis module is used to determine the number of levels of the optimization parameters. After obtaining the corresponding values ​​of different levels of each optimization parameter according to the gradient of the number of levels, the operating condition table of the transcritical CO2 heat pump system is obtained through orthogonal matrix calculation. The optimal exhaust pressure of the transcritical CO2 heat pump system under each condition is obtained according to the operating condition table of the transcritical CO2 heat pump system. After performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each condition, the sensitivity ratio of the optimization parameters is obtained.

[0034] The optimal exhaust pressure control module is used to predict the optimal exhaust pressure under real-time operating conditions by optimizing the parameter sensitivity ratio. Based on the optimal exhaust pressure under real-time operating conditions, the CO2 compressor speed and the opening of the electronic expansion valve are adjusted to complete the control of the transcritical CO2 heat pump system.

[0035] In some implementations, the sensitivity analysis module further includes:

[0036] The heat exchanger correction module is used to predict the optimal exhaust pressure under real-time operating conditions by optimizing the parameter sensitivity ratio and combining it with a correction factor when the indoor heat exchanger of the transcritical CO2 heat pump system is changed. The correction factor is the heat exchange efficiency of the heat exchanger.

[0037] Thirdly, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the general control method for optimal operation of a transcritical CO2 heat pump system.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] This invention provides a general control method for the optimal operation of a transcritical CO2 heat pump system. The optimal exhaust pressure under real-time operating conditions is predicted by optimizing the parameter sensitivity ratio. Based on this optimal exhaust pressure, the CO2 compressor speed and the opening of the electronic expansion valve are adjusted to complete the control of the transcritical CO2 heat pump system. By considering the evolution characteristics of the transcritical-subcritical phase and employing sensitivity analysis for the control of the transcritical CO2 heat pump system, a general control method for achieving optimal performance of the CO2 heat pump system can be realized.

[0040] Furthermore, this invention not only considers the impact of changes in ambient temperature, indoor air inlet temperature, indoor air outlet temperature, and indoor air volume on system performance, and adjusts the optimal exhaust pressure according to the changes in the four parameters, but also introduces a correction factor to adjust the system to its optimal performance even when the indoor heat exchanger changes, thereby saving energy consumption of the electric vehicle air conditioning heat pump system. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the heating mode structure of a transcritical CO2 heat pump air conditioning system for new energy vehicles provided in Embodiment 1;

[0042] Figure 2 This is a schematic diagram of the cooling mode structure of a transcritical CO2 heat pump air conditioning system for new energy vehicles, provided in Example 1.

[0043] Figure 3 A logic block diagram of the control method for the transcritical CO2 heat pump system provided in Example 1;

[0044] The components include: 1. CO2 compressor; 2. Four-way reversing valve; 3. Outdoor heat exchanger; 4. Outdoor fan; 5. Regenerator; 6. Electronic expansion valve; 7. Indoor heat exchanger; 8. Indoor fan; 9. Passenger compartment; 10. Gas-liquid separator. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings. The content described herein is for explanation rather than limitation of the present invention.

[0046] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification and claims of this invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, systems, products, or devices.

[0047] Example 1

[0048] In existing technologies, CO2 heat pump cycles operate in both transcritical and subcritical states, each suitable for different working conditions. Heat pump air conditioning technology using transcritical CO2 as the cycle exhibits excellent heating performance in winter and effectively alleviates range anxiety for electric vehicles in cold winters. The industry has proposed many efficient control strategies for transcritical CO2 heat pump systems, with popular strategies including logic threshold control, proportional-integral-derivative (PID) control, model predictive control, and intelligent control algorithms. These control strategies are based on optimal pressure prediction methods to adjust the system to operate at its optimal performance. However, limitations imposed by electric vehicle space and operating conditions, such as the size of the heat exchanger and the setting of the airflow, cause the CO2 heat pump cycle to enter a subcritical state under certain extreme conditions. The performance of the heat exchanger alters the evolution characteristics from transcritical to subcritical, thus rendering the prediction formulas non-universal. Furthermore, because the parameters of the electric vehicle heat pump system vary significantly with operating conditions, the performance of the electric vehicle heat pump is greatly affected by these conditions, often deviating from its actual optimal performance during operation. When system parameters such as indoor inlet air temperature, indoor outlet air temperature, and indoor air volume change, its optimal performance may evolve from the transcritical region to the subcritical region. Therefore, existing optimal pressure prediction methods have limited accuracy in dealing with operating conditions where optimal pressure exists in both transcritical and subcritical regions, and lack universality.

[0049] This embodiment is based on a generalized transcritical CO2 heat pump system for new energy vehicles, which utilizes transcritical-subcritical evolution characteristics. Figure 1 and Figure 2 As shown, a general control method for optimal operation of a transcritical CO2 heat pump system is proposed.

[0050] The transcritical CO2 heat pump system includes: CO2 compressor 1, four-way reversing valve 2, outdoor heat exchanger 3, outdoor fan 4, regenerator 5, electronic expansion valve 6, indoor heat exchanger 7, indoor fan 8, passenger compartment 9, and gas-liquid separator 10.

[0051] like Figure 1 As shown, in heating mode, the outlet of CO2 compressor 1 is connected to port a of four-way reversing valve 2, port a of four-way reversing valve 2 is connected to port b of four-way reversing valve 2, port b of four-way reversing valve 2 is connected to the inlet of electronic expansion valve 6 through the first heat exchange channel of indoor heat exchanger 7, the outlet of electronic expansion valve 6 is connected to port c of four-way reversing valve through the first heat exchange channel of regenerator 5 and the first heat exchange channel of outdoor heat exchanger 3, port c of four-way reversing valve 2 is connected to port d of four-way reversing valve 2, and port d of four-way reversing valve 2 is connected to the inlet of CO2 compressor 1 through gas-liquid separator 10 and the second heat exchange channel of regenerator 5, forming a cycle;

[0052] like Figure 2 As shown, in the cooling mode, the outlet of the CO2 compressor 1 is connected to port a of the four-way reversing valve 2, port a of the four-way reversing valve 2 is connected to port c of the four-way reversing valve 2, port c of the four-way reversing valve 2 is connected to the inlet of the electronic expansion valve 6 after passing through the first heat exchange channel of the outdoor heat exchanger 3 and the first heat exchange channel of the regenerator 5, the outlet of the electronic expansion valve 6 is connected to port b of the four-way reversing valve 2 after passing through the first heat exchange channel of the indoor heat exchanger 7, port b of the four-way reversing valve 2 is connected to port d of the four-way reversing valve 2, and port d of the four-way reversing valve 2 is connected to the inlet of the CO2 compressor 1 after passing through the second heat exchange channel of the gas-liquid separator 10 and the regenerator 5, forming a cycle;

[0053] The indoor fan 8 is used to ventilate the passenger compartment 9 through the second heat exchange channel of the indoor heat exchanger 7, and the outdoor fan 4 is used to ventilate the environment through the second heat exchange channel of the outdoor heat exchanger 3.

[0054] The controlled components involved in the control method include: a CO2 compressor 1, an electronic expansion valve 6, an outdoor fan 4, and an indoor fan 8. The outlet air temperature is controlled by the rotational speed of the CO2 compressor; wherein, as the CO2 compressor speed increases, the outlet air temperature increases; as the CO2 compressor speed decreases, the outlet air temperature decreases. The rotational speed of the outdoor fan 4 decreases as the vehicle speed increases and increases as the vehicle speed decreases. The indoor fan 8 controls the indoor air volume; wherein, as the difference between the vehicle's temperature requirement and the ambient temperature decreases, the air volume supplied by the indoor fan 8 increases. The electronic expansion valve controls the high pressure of the CO2 heat pump system; wherein, as the optimal exhaust pressure of the system increases, the opening of the electronic expansion valve decreases; as the optimal exhaust pressure of the system decreases, the opening of the electronic expansion valve increases.

[0055] like Figure 3 As shown, the control method of the transcritical CO2 heat pump system specifically includes the following steps:

[0056] S1, Determine the target parameter and optimization parameter. One target parameter: optimal exhaust pressure. Four optimization parameters: ambient temperature Inlet air temperature of indoor heat exchanger (Indoor air inlet temperature), indoor heat exchanger outlet air temperature (Indoor air outlet temperature) and air volume of the indoor heat exchanger (Indoor air volume); one of the target parameters is the target quantity of the sensitivity analysis method, and the four optimization parameters are the control quantities of the sensitivity analysis method;

[0057] Optimal exhaust pressure Under operating conditions, the compressor discharge pressure at which the system's performance parameter COP reaches its maximum value is given. Specifically, this refers to the compressor discharge pressure value of the transcritical CO2 heat pump system. The air volume of the indoor heat exchanger is controlled by an electromagnetic expansion valve. The fan control of the indoor heat exchanger and the outlet air temperature of the indoor heat exchanger are controlled by the indoor heat exchanger. It is controlled by the compressor speed.

[0058] S2, The objective quantity of the sensitivity analysis method is the optimal exhaust pressure of the air conditioning system. The control variable is the ambient temperature during system operation. Inlet air temperature of indoor heat exchanger The outlet air temperature of the indoor heat exchanger and the air volume of the indoor heat exchanger Sensitivity analysis method adopted Taguchi The method (Taguchi design) requires at least two sets of data. By applying the same adjustment range to different control variables, a partial derivative is calculated using the control variables before and after the adjustment and the target variable as a reference value for parameter sensitivity. The specific calculation method is as follows:

[0059] Experiments have Given several optimization variables, determine the number of levels for the optimization parameters. For each optimization parameter, equal gradient values ​​are taken, and its orthogonal matrix is ​​used for design calculations to determine the orthogonal array as follows: The optimal exhaust pressure of the transcritical CO2 heat pump air conditioner is obtained for each case in the matrix. Now there are four optimization parameters. Choosing four levels, i.e., i=4, j=4, gives each of the four optimization parameters four possible values: , , , , , , , , , , , , , , , For each optimization parameter, equal gradient values ​​are taken, and its orthogonal matrix is ​​used for design calculations to determine the orthogonal array as follows: As shown in Table 1 below, the optimal exhaust pressure of the transcritical CO2 heat pump air conditioner is obtained for each case in the matrix. .

[0060] Table 1. Orthogonal table of optimization parameters for each level.

[0061]

[0062] Based on the orthogonal matrix requirements, the optimal exhaust pressure under various operating conditions was obtained experimentally. The experimental results were analyzed using mean value analysis and variance analysis to determine the sensitivity weighting, specifically including:

[0063] S2.1, Average Value Analysis

[0064] (1) The total average value refers to the average value of the optimization objective in the calculation. ,in This is the average value of the experiment. For the number of experiments, For the first The target parameter value for this time;

[0065] (2) The average value of the target parameter corresponding to a certain optimization variable. This average value is the average value of the result of a certain target parameter when each optimization parameter changes. Its value is the average value of all optimization objectives at a certain level, assuming that the level number of an optimization parameter remains unchanged. The optimization variable, ambient temperature, corresponds to the variable at level number 1. Level number 2 corresponds to the variable Level number 3 corresponds to the variable The level number 4 corresponds to the variable The objective parameter for optimizing the ambient temperature variable at level 2 is... The experimental data are , , and Then the expression for this average value is: .

[0066] S2.2, Analysis of Variance:

[0067] Analysis of variance (ANOVA) measures the proportion of the target parameter at each level of the optimization parameter. Therefore, the expression for ANOVA is: , To optimize parameters, For the target parameter, For a certain level number The average value, The goal is to optimize the average value of the objective parameter. The sensitivity weights of each optimization parameter to the objective parameter are also considered. , The unit is %.

[0068] S2.3, the formula for predicting the optimal exhaust pressure of a transcritical CO2 heat pump system is as follows:

[0069]

[0070]

[0071]

[0072]

[0073] These are the weighting coefficients for the corresponding optimization parameters, and their values ​​are... ;

[0074] As an empirical factor, take ;

[0075] The system is allowed to operate at maximum pressure, in MPa.

[0076] , , , It is a constant, obtained through linear regression, and has no unit.

[0077] This represents the average indoor air volume within the sensitivity analysis group, in units of... .

[0078] Define the heat exchange efficiency of an indoor heat exchanger ,in This refers to the refrigerant inlet temperature of the indoor heat exchanger. This refers to the refrigerant side outlet temperature of the indoor heat exchanger. This refers to the air-side inlet temperature of the indoor heat exchanger. This represents the average heat exchange efficiency of the indoor heat exchangers in each group of the sensitivity analysis experiment.

[0079] In this embodiment, the weights change when the heat exchanger is changed, and the prediction formula is corrected based on the heat exchanger system. When the heat exchanger changes, different heat exchange performances exist under different indoor air volumes, and the exhaust pressure at which the system operates at optimal performance also changes. Therefore, a correction factor is added: the heat exchanger's heat exchange efficiency. The formula is proposed:

[0080]

[0081]

[0082]

[0083]

[0084] These are the weighting coefficients for the corresponding optimization parameters, and their values ​​are... ;

[0085] As an empirical factor, take ;

[0086] The system is allowed to operate at maximum pressure, in MPa.

[0087] This represents the average indoor air volume within the sensitivity analysis group, in units of... ;

[0088] , , , It is a constant, obtained through linear regression, and has no unit.

[0089] This is the heat exchange efficiency of the indoor heat exchanger, without units.

[0090] This represents the average heat exchange efficiency of the indoor heat exchangers in each group of the sensitivity analysis experiment, without units.

[0091] , , These are constants, obtained through linear regression, and are unitless.

[0092] After changing the heat exchanger, if the heat exchanger's heat exchange performance is improved (i.e., heat exchange efficiency increases), the impact of indoor air volume on the optimal exhaust pressure decreases, and the index... Decrease; when the heat exchanger has better heat exchange performance, the optimal exhaust pressure of the system decreases, exponentially. The predicted optimal exhaust pressure decreases as the pressure decreases.

[0093] After changing the heat exchanger, if the heat exchanger's heat exchange performance is worse (i.e., the heat exchange efficiency decreases), the influence of indoor air volume on the optimal exhaust pressure increases, and the index... Increase; when the heat exchanger's heat exchange performance is worse, the system's optimal exhaust pressure increases, exponentially. The increase indicates an increase in the predicted optimal exhaust pressure.

[0094] S3, after obtaining the indoor air volume, indoor air inlet temperature, and indoor air outlet temperature, can predict the optimal exhaust pressure value under real-time operating conditions based on the optimal exhaust pressure prediction formula of the transcritical CO2 heat pump system. By adjusting the CO2 compressor speed and the opening of the electronic expansion valve, the CO2 heat pump system can be controlled to always be at the optimal exhaust pressure.

[0095] The control method for a transcritical CO2 heat pump system provided in this embodiment is based on the transcritical optimal pressure prediction method. Considering the evolution characteristics of transcritical-subcritical systems, a CO2 heat pump control method was developed using sensitivity analysis to achieve universal control of optimal CO2 heat pump performance. This method not only considers the impact of changes in ambient temperature, indoor inlet air temperature, indoor outlet air temperature, and indoor air volume on system performance, adjusting the exhaust pressure according to changes in these four parameters, but also adjusts the system to its optimal performance even when changes occur in the indoor heat exchanger, thus saving energy consumption in electric vehicle air conditioning heat pump systems.

[0096] Example 2

[0097] This embodiment provides a control system for a transcritical CO2 heat pump system, including an optimized parameter acquisition module, a sensitivity analysis module, and an optimal exhaust pressure control module.

[0098] The optimization parameter acquisition module is used to acquire the optimization parameters of the transcritical CO2 heat pump system.

[0099] The sensitivity analysis module is used to determine the number of levels of the optimization parameters. After obtaining the corresponding values ​​of different levels of each optimization parameter according to the gradient of the number of levels, the operating condition table of the transcritical CO2 heat pump system is obtained through orthogonal matrix calculation. The optimal exhaust pressure of the transcritical CO2 heat pump system under each condition is obtained according to the operating condition table of the transcritical CO2 heat pump system. After performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each condition, the sensitivity ratio of the optimization parameters is obtained.

[0100] The optimal exhaust pressure for real-time operating conditions is obtained by predicting the parameter sensitivity ratio using the following formula:

[0101]

[0102]

[0103]

[0104]

[0105] The optimal exhaust pressure control module is used to predict the optimal exhaust pressure under real-time operating conditions by optimizing the parameter sensitivity ratio, and adjust the CO2 compressor speed and electronic expansion valve opening according to the optimal exhaust pressure under real-time operating conditions to complete the control of the transcritical CO2 heat pump system.

[0106] The sensitivity analysis module also includes:

[0107] The heat exchanger correction module is used to predict the optimal exhaust pressure under real-time operating conditions by optimizing the parameter sensitivity ratio and combining it with a correction factor when the indoor heat exchanger of the transcritical CO2 heat pump system is changed. The correction factor is the heat exchange efficiency of the heat exchanger.

[0108] The optimal exhaust pressure for real-time operating conditions is obtained by optimizing the parameter sensitivity ratio and combining it with a correction factor prediction using the following formula:

[0109]

[0110]

[0111]

[0112] The module division in this embodiment of the invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the invention can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0113] This embodiment also provides a computer device, which includes a processor and a memory. The memory is used to store a computer program (in this embodiment, the computer program includes a computing component and an iterative component, capable of model calculation and model updating). The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to realize the corresponding method flow or corresponding function. The processor described in this embodiment can be used to operate a general control method for the optimal operation of a transcritical CO2 heat pump system.

[0114] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A general control method for optimal operation of a transcritical CO2 heat pump system, characterized in that, include: Obtain the optimized parameters of the transcritical CO2 heat pump system; After determining the number of levels of the optimization parameter, and obtaining the corresponding values ​​of different levels of each optimization parameter according to the gradient of the number of levels, the operating condition table of the transcritical CO2 heat pump system is obtained by calculating through an orthogonal matrix; the optimal exhaust pressure of the transcritical CO2 heat pump system under each condition is obtained according to the operating condition table of the transcritical CO2 heat pump system; after performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each condition, the sensitivity ratio of the optimization parameter is obtained; The optimal exhaust pressure under real-time operating conditions is obtained by optimizing the parameter sensitivity ratio prediction. Based on the optimal exhaust pressure under real-time operating conditions, the CO2 compressor speed and the opening of the electronic expansion valve are adjusted to complete the control of the transcritical CO2 heat pump system.

2. The general control method for optimal operation of a transcritical CO2 heat pump system according to claim 1, characterized in that, The optimized parameters include ambient temperature, indoor air intake temperature, indoor air outlet temperature, and indoor air volume.

3. The general control method for optimal operation of a transcritical CO2 heat pump system according to claim 1, characterized in that, The step of averaging the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each of the above conditions specifically includes: Calculate the first average value of the optimal exhaust pressure corresponding to all operating conditions in the operating condition table; Calculate the second average of all optimal exhaust pressures corresponding to each level of the optimization parameters.

4. The general control method for optimal operation of a transcritical CO2 heat pump system according to claim 3, characterized in that, The step of performing average and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each of the above conditions to obtain the sensitivity ratio of the optimized parameters specifically includes: The variance analysis results for the optimal exhaust pressure corresponding to each optimization parameter are obtained by performing variance analysis based on the first average value and the second average value. The sensitivity ratio of the optimized parameters is obtained based on the results of the analysis of variance.

5. A general control method for optimal operation of a transcritical CO2 heat pump system according to claim 4, characterized in that, The optimal exhaust pressure under real-time operating conditions is obtained by optimizing the parameter sensitivity ratio prediction using the following formula: In the above formula, This is the optimal predicted exhaust pressure value. The weighting coefficient for ambient temperature. This is the weighting coefficient for indoor air intake temperature. The weighting coefficient for indoor air outlet temperature. , , and The constant is obtained through linear regression of experimental data. This refers to the indoor airflow volume. The average indoor air volume in the experimental group was used for sensitivity analysis. The percentage of sensitivity to indoor airflow volume. The percentage of sensitivity to ambient temperature. The percentage of sensitivity to indoor air intake temperature. The percentage of sensitivity to indoor air outlet temperature.

6. The general control method for optimal operation of a transcritical CO2 heat pump system according to claim 1, characterized in that, It also includes the following steps: When the indoor heat exchanger of the transcritical CO2 heat pump system is changed, the optimal exhaust pressure under real-time operating conditions is obtained by optimizing the parameter sensitivity ratio and combining it with the correction factor, where the correction factor is the heat exchange efficiency of the heat exchanger.

7. A general control method for optimal operation of a transcritical CO2 heat pump system according to claim 6, characterized in that, The optimal exhaust pressure for real-time operating conditions, obtained by optimizing the parameter sensitivity ratio and combining it with a correction factor prediction, is calculated using the following formula: In the above formula, This is the optimal predicted exhaust pressure value. The weighting coefficient for ambient temperature. This is the weighting coefficient for indoor air intake temperature. The weighting coefficient for indoor air outlet temperature. , , and The constant is obtained through linear regression of experimental data. This refers to the indoor airflow volume. This represents the average indoor air volume within the sensitivity analysis group. The heat exchange efficiency of the indoor heat exchanger. This represents the average heat exchange efficiency of the indoor heat exchangers in each group of the sensitivity analysis experiment. , , It is a constant.

8. A control system for a transcritical CO2 heat pump system, characterized in that, include: The optimization parameter acquisition module is used to acquire the optimization parameters of the transcritical CO2 heat pump system. The sensitivity analysis module is used to determine the number of levels of the optimization parameters. After obtaining the corresponding values ​​of different levels of each optimization parameter according to the gradient of the number of levels, the operating condition table of the transcritical CO2 heat pump system is obtained through orthogonal matrix calculation. The optimal exhaust pressure of the transcritical CO2 heat pump system under each condition is obtained according to the operating condition table of the transcritical CO2 heat pump system. After performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system under each condition, the sensitivity ratio of the optimization parameters is obtained. The optimal exhaust pressure control module is used to predict the optimal exhaust pressure under real-time operating conditions by optimizing the parameter sensitivity ratio. Based on the optimal exhaust pressure under real-time operating conditions, the CO2 compressor speed and the opening of the electronic expansion valve are adjusted to complete the control of the transcritical CO2 heat pump system.

9. The control system of a transcritical CO2 heat pump system according to claim 8, characterized in that, The sensitivity analysis module also includes: The heat exchanger correction module is used to predict the optimal exhaust pressure under real-time operating conditions by optimizing the parameter sensitivity ratio and combining it with a correction factor when the indoor heat exchanger of the transcritical CO2 heat pump system is changed. The correction factor is the heat exchange efficiency of the heat exchanger.

10. An electronic device, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of a general control method for optimal operation of a transcritical CO2 heat pump system as described in any one of claims 1 to 7.

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