Universal control method for optimal operation of transcritical CO2 heat pump system and related device thereof

Abstract

The invention discloses a universal control method for optimal operation of a transcritical CO2 heat pump system and a related device thereof, and belongs to the field of new energy automobiles, and the method comprises the following steps: determining the level number of optimization parameters, obtaining corresponding values of different levels of each optimization parameter according to the level number in an equal gradient manner, and determining the optimal operation of the transcritical CO2 heat pump system according to the corresponding values. A working condition table of the transcritical CO2 heat pump system is obtained through orthogonal matrix calculation; according to the working condition table experiment of the transcritical CO2 heat pump system, the optimal exhaust pressure of the transcritical CO2 heat pump system under each condition is obtained; performing average value analysis and variance analysis on the optimal exhaust pressure and the working condition table of the transcritical CO2 heat pump system under each condition to obtain an optimization parameter sensitivity proportion; and the optimal exhaust pressure of the real-time working condition is obtained by optimizing parameter sensitivity proportion prediction, the rotating speed of a CO2 compressor and the opening degree of an electronic expansion valve are adjusted according to the optimal exhaust pressure of the real-time working condition, and control over the transcritical CO2 heat pump system is completed. According to the method, the transcritical CO2 heat pump system can achieve the optimal performance.

Description

Technical Field

[0001] The present invention belongs to the field of new energy vehicles, and specifically relates to a universal control method for optimal operation of a transcritical CO2 heat pump system and a related device thereof. Background Art

[0002] With the replacement of refrigerants, CO2, as a natural working fluid, is widely used in the transcritical CO2 heat pump cycle of new energy vehicles. At present, the CO2 heat pump cycle has two working states, transcritical and subcritical, which are suitable for different working conditions. Since CO2 has a very low critical temperature, CO2 heat pump air conditioners mostly operate in a transcritical state. The heat pump air conditioning technology with transcritical CO2 as the cycle shows a good heating effect in winter heating, and can effectively alleviate the mileage anxiety of electric vehicles in cold winter; however, the limitations of electric vehicle space and operating conditions, such as the size of the heat exchanger and the setting of wind speed, cause the CO2 heat pump cycle to enter a subcritical working state under certain extreme conditions. In addition, since the parameters of the electric vehicle heat pump system vary greatly with the operating conditions, the performance of the electric vehicle heat pump is greatly affected by the operating conditions, and the system often deviates from the 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 to the subcritical region.

[0003] In summary, the optimal pressure formulas for CO2 transcritical heat pump air conditioning systems are not universal and cannot achieve optimal performance because of the transcritical-subcritical evolution characteristics. There is an urgent need for a thermal management method that can enable the system to achieve optimal performance even when it enters a subcritical cycle, thereby achieving energy saving and obtaining a higher COP (Coefficient of Performance). Summary of the invention

[0004] The purpose of the present invention is to provide a universal control method and related devices for optimal operation of a transcritical CO2 heat pump system, so as to solve the problem that the prior art cannot make the transcritical CO2 heat pump system achieve optimal performance due to the transcritical-subcritical evolution characteristics.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, a general control method for optimal operation of a transcritical CO2 heat pump system comprises: Obtain the optimized parameters of the transcritical CO2 heat pump system; Determine the number of levels of the optimization parameter, obtain the corresponding values ​​of different levels of each optimization parameter according to the equal gradient of the number of levels, and then obtain the operating table of the transcritical CO2 heat pump system through orthogonal matrix calculation; obtain the optimal exhaust pressure of the transcritical CO2 heat pump system in each case according to the operating table of the transcritical CO2 heat pump system; perform mean value analysis and variance analysis on the optimal exhaust pressure and operating table of the transcritical CO2 heat pump system in each case to obtain the sensitivity ratio of the optimization parameter; The optimal exhaust pressure of the real-time working condition is predicted by optimizing the parameter sensitivity ratio, and the CO2 compressor speed and the electronic expansion valve opening are adjusted according to the optimal exhaust pressure of the real-time working condition to complete the control of the transcritical CO2 heat pump system.

[0006] In some embodiments, the optimization parameters include ambient temperature, indoor air inlet temperature, indoor air outlet temperature and indoor air volume.

[0007] In some embodiments, the step of performing average value analysis on the optimal exhaust pressure and operating table of the transcritical CO2 heat pump system in each case specifically includes: Calculate a first average value of the optimal exhaust pressures corresponding to all operating conditions in the operating condition table; A second average value of all optimal exhaust pressures corresponding to each level number of the optimization parameter is calculated.

[0008] In some embodiments, the step of performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system in each case to obtain the optimization parameter sensitivity ratio specifically includes: After performing variance analysis on the first average value and the second average value, a variance analysis result of an optimal exhaust pressure corresponding to each optimization parameter is obtained; The sensitivity ratio of the optimized parameters is obtained according to the variance analysis results.

[0009] In some implementations, the optimal exhaust pressure of the real-time operating condition is predicted by optimizing the parameter sensitivity ratio using the following formula:

[0010]

[0011]

[0012]

[0013] In the above formula, is the optimal exhaust pressure prediction value, is the weight coefficient of ambient temperature, is the weight coefficient of indoor air inlet temperature, is the weight coefficient of indoor air outlet temperature, , , and is a constant obtained by linear regression of experimental data, is the indoor air volume, For sensitivity analysis, the average indoor air volume in the experimental group is is the sensitivity ratio of indoor air volume, is the sensitivity ratio of ambient temperature, is the sensitivity ratio of indoor air inlet temperature, is the sensitivity ratio of indoor air outlet temperature.

[0014] In some embodiments, the following steps are also included: When the indoor heat exchanger of the transcritical CO2 heat pump system changes, the optimal exhaust pressure of the real-time working condition is obtained by optimizing the parameter sensitivity ratio and combining the correction factor prediction, and the correction factor is the heat exchange efficiency of the heat exchanger.

[0015] In some embodiments, the optimal exhaust pressure of the real-time working condition is obtained by optimizing the parameter sensitivity ratio and combining the correction factor prediction using the following formula:

[0016]

[0017]

[0018]

[0019] In the above formula, In the above formula, is the optimal exhaust pressure prediction value, is the weight coefficient of ambient temperature, is the weight coefficient of indoor air inlet temperature, is the weight coefficient of indoor air outlet temperature, , , and is a constant obtained by linear regression of experimental data, is the indoor air volume, is the average indoor air volume in the sensitivity analysis group, is the heat transfer efficiency of the indoor heat exchanger, is the average value of heat transfer efficiency of each group of indoor heat exchangers in the sensitivity analysis experiment. , , is a constant.

[0020] In a second aspect, a control system of a transcritical CO2 heat pump system comprises: Optimization parameter acquisition module, used to obtain the optimization parameters of the transcritical CO2 heat pump system; A sensitivity analysis module is used to determine the number of levels of the optimization parameter, obtain the corresponding values ​​of different levels of each optimization parameter according to the number of levels and other gradients, and then obtain the operating table of the transcritical CO2 heat pump system through orthogonal matrix calculation; obtain the optimal exhaust pressure of the transcritical CO2 heat pump system in each case according to the operating table of the transcritical CO2 heat pump system; and obtain the sensitivity ratio of the optimization parameter after performing mean value analysis and variance analysis on the optimal exhaust pressure and operating table of the transcritical CO2 heat pump system in each case; The optimal exhaust pressure control module is used to predict the optimal exhaust pressure of the real-time working condition by optimizing the parameter sensitivity ratio, and adjust the CO2 compressor speed and the electronic expansion valve opening according to the optimal exhaust pressure of the real-time working condition to complete the control of the transcritical CO2 heat pump system.

[0021] In some embodiments, the sensitivity analysis module further comprises: The heat exchanger correction module is used to optimize the parameter sensitivity ratio and predict the optimal exhaust pressure of the real-time working condition by combining the correction factor when the indoor heat exchanger of the transcritical CO2 heat pump system changes. The correction factor is the heat exchange efficiency of the heat exchanger.

[0022] In a third aspect, an electronic device comprises a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, the steps of the general control method for optimal operation of a transcritical CO2 heat pump system are implemented.

[0023] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a universal control method for optimal operation of a transcritical CO2 heat pump system. The optimal exhaust pressure of the real-time working condition is predicted by optimizing the parameter sensitivity ratio, and the CO2 compressor speed and the electronic expansion valve opening are adjusted according to the optimal exhaust pressure of the real-time working condition to complete the control of the transcritical CO2 heat pump system. By considering the evolution characteristics of transcritical-subcritical, the sensitivity analysis method is used to control the transcritical CO2 heat pump system, which can achieve universal control of the optimal performance of the CO2 heat pump system.

[0024] Furthermore, the present invention not only takes into account 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 changes in the four parameters, but also introduces a correction factor to adjust the system according to the changes to keep it at optimal performance, even when the indoor heat exchanger changes, thereby saving energy consumption of the electric vehicle air-conditioning heat pump system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the heating mode structure of a transcritical CO2 heat pump air conditioning system for a new energy vehicle provided in Example 1; Figure 2 A schematic diagram of the cooling mode structure of a transcritical CO2 heat pump air conditioning system for a new energy vehicle provided in Example 1; Figure 3 A logic block diagram of a control method for a transcritical CO2 heat pump system provided in Example 1; Among them, 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 DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings, and the described content is intended to explain the present invention rather than to limit it.

[0027] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or are inherent to these processes, methods, systems, products or apparatus.

[0028] Embodiment 1 In the prior art, the CO2 heat pump cycle has two working states, transcritical and subcritical, which are suitable for different working conditions. The heat pump air conditioning technology with transcritical CO2 as the cycle shows good heating effect in winter heating, and can effectively alleviate the mileage anxiety of electric vehicles in cold winter. Many efficient control strategies have been proposed in the industry for transcritical CO2 heat pump systems. The current popular control strategies are logic threshold control, proportional-integral-differential control (PID control), model predictive control and intelligent control algorithms. The above control strategies are based on the optimal pressure prediction method to adjust the system to operate at the optimal performance. However, the limitations of electric vehicle space and operating conditions, such as the size of the heat exchanger and the setting of wind speed, cause the CO2 heat pump cycle to enter the subcritical working state under certain extreme conditions. The performance of the heat exchanger changes the evolution characteristics of transcritical-subcritical, making the prediction formula not universal. In addition, since the parameters of the electric vehicle heat pump system vary greatly with the operating conditions, the performance of the electric vehicle heat pump is greatly affected by the operating conditions, and the system often deviates from the actual optimal performance when running. When system parameters such as indoor air inlet temperature, indoor air outlet temperature and indoor air volume change, its optimal performance may evolve from transcritical to subcritical. Therefore, the existing optimal pressure prediction method has limited accuracy in dealing with the conditions where both transcritical and subcritical have optimal pressures, and is not universal.

[0029] This embodiment is based on the universal new energy vehicle transcritical CO2 heat pump system of the 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.

[0030] The transcritical CO2 heat pump system includes: a CO2 compressor 1, a four-way reversing valve 2, an outdoor heat exchanger 3, an outdoor fan 4, a regenerator 5, an electronic expansion valve 6, an indoor heat exchanger 7, an indoor fan 8, a passenger compartment 9, and a gas-liquid separator 10; like Figure 1 As shown, in the heating mode, the outlet of the CO2 compressor 1 is connected to the port a of the four-way reversing valve 2, the port a of the four-way reversing valve 2 is connected to the port b of the four-way reversing valve 2, the port b of the four-way reversing valve 2 is connected to the inlet of the electronic expansion valve 6 via the first heat exchange channel of the indoor heat exchanger 7, the outlet of the electronic expansion valve 6 is connected to the port c of the four-way reversing valve via the first heat exchange channel of the regenerator 5 and the first heat exchange channel of the outdoor heat exchanger 3, the port c of the four-way reversing valve 2 is connected to the port d of the four-way reversing valve 2, the port d of the four-way reversing valve 2 is connected to the inlet of the CO2 compressor 1 via the gas-liquid separator 10 and the second heat exchange channel of the regenerator 5, forming a cycle; like Figure 2As shown, in the cooling mode, the outlet of the CO2 compressor 1 is connected to the port a of the four-way reversing valve 2, the port a of the four-way reversing valve 2 is connected to the port c of the four-way reversing valve 2, the port c of the four-way reversing valve 2 is connected to the inlet of the electronic expansion valve 6 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 the port b of the four-way reversing valve 2 through the first heat exchange channel of the indoor heat exchanger 7, the port b of the four-way reversing valve 2 is connected to the port d of the four-way reversing valve 2, the port d of the four-way reversing valve 2 is connected to the inlet of the CO2 compressor 1 through the gas-liquid separator 10 and the second heat exchange channel of the regenerator 5, forming a cycle; The indoor fan 8 is used for ventilating the passenger compartment 9 through the second heat exchange channel of the indoor heat exchanger 7 , and the outdoor fan 4 is used for ventilating the passenger compartment 9 through the second heat exchange channel of the outdoor heat exchanger 3 .

[0031] 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, and the outlet air temperature is controlled by the rotation speed of the CO2 compressor; wherein, as the speed of the CO2 compressor increases, the outlet air temperature increases; as the speed of the CO2 compressor decreases, the outlet air temperature decreases; the rotation speed of the outdoor fan 4 decreases as the vehicle speed increases, and increases as the vehicle speed decreases; the indoor air volume is controlled by the indoor fan 8; wherein, as the difference between the vehicle compartment temperature demand and the ambient temperature decreases, the air supply volume of the indoor fan 8 increases; the high pressure of the CO2 heat pump system is controlled by the electronic expansion valve; 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.

[0032] like Figure 3 As shown, the control method of the transcritical CO2 heat pump system specifically includes the following steps: S1, determine the target parameters and optimization parameters, one target parameter: optimal exhaust pressure , four optimization parameters: ambient temperature , Indoor heat exchanger inlet air temperature (Indoor air inlet temperature), indoor heat exchanger outlet air temperature (Indoor air outlet temperature) and air volume of 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; Optimum exhaust pressure The compressor exhaust pressure when the system performance parameter COP reaches the maximum value under the operating parameters. Among them, the compressor exhaust pressure value of the transcritical CO2 heat pump system The air volume of the indoor heat exchanger is controlled by the electromagnetic expansion valve. Controlled by the fan of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger Controlled by the speed of the compressor.

[0033] S2, the target quantity of the sensitivity analysis method is the optimal exhaust pressure of the air conditioning system , the control quantity is the ambient temperature of the system , Indoor heat exchanger inlet air temperature , outlet air temperature of indoor heat exchanger and air volume of indoor heat exchanger The sensitivity analysis method uses Taguchi The method (Taguchi design) requires at least two sets of data. By giving different control quantities the same adjustment range, a partial derivative is calculated using the control quantity and target quantity before and after the adjustment as a reference value for parameter sensitivity. , the specific calculation method is as follows: Experimental optimization variables, determine the number of levels of the optimization parameters , take equal gradient values ​​for each optimization parameter, use its orthogonal matrix for design calculation, and determine the orthogonal table as follows: , respectively obtain the optimal exhaust pressure of the transcritical CO2 heat pump air conditioner in each case in the matrix At this time, there are four optimization parameters, and four levels are selected, i=4, j=4, so the four optimization parameters have 4 values ​​respectively: , , , , , , , , , , , , , , , , take equal gradient values ​​for each optimization parameter, use its orthogonal matrix for design calculation, and determine the orthogonal table as follows: , as shown in Table 1 below, the optimal exhaust pressure of the transcritical CO2 heat pump air conditioner in each case in the matrix is ​​obtained respectively .

[0034] Table 1 Orthogonal table of optimization parameters at each level

[0035] According to the requirements of the orthogonal matrix, the experiment obtains the optimal exhaust pressure under various working conditions. The analysis of the experimental results uses mean value analysis and variance analysis to obtain the sensitivity weight ratio, including: S2.1, Mean value analysis (1) The total average value refers to the average value of the optimization target in the calculation. ,in is the average value of the experiment, is the number of experiments, For the The target parameter value of the second; (2) The average value of the target parameter corresponding to a certain optimization variable. This average value is the average value of the target parameter results corresponding to each optimization parameter when the optimization parameter changes. Its value is the average value of all optimization targets at the same level when the optimization parameter level remains unchanged. The optimization variable ambient temperature corresponds to the variable at level 1. , level 2 corresponds to the variable , level 3 corresponds to the variable , level 4 corresponds to the variable , then the objective parameter of the optimization variable ambient temperature at level 2 is The experimental data is , , and , then the expression of the average value is: .

[0036] S2.2, ANOVA: The variance analysis is the proportion of the target parameter at each level of the optimization parameter, so the expression of the variance analysis is: , To optimize the parameters, is the target parameter, For a certain level The average value of is the average value of the optimization target. The sensitivity weight of each optimization parameter to the target parameter , , the unit is %.

[0037] S2.3, the optimal exhaust pressure prediction formula for the transcritical CO2 heat pump system is as follows:

[0038]

[0039]

[0040]

[0041] is the weight coefficient of the corresponding optimization parameter, and its value is ; is the experience factor, ; The maximum high pressure allowed for system operation, in MPa; , , , is a constant, obtained by linear regression, and has no unit; is the average indoor air volume in the sensitivity analysis group, in units .

[0042] Defining the heat transfer efficiency of the indoor heat exchanger ,in is the inlet temperature of the refrigerant side of the indoor heat exchanger, is the outlet temperature of the refrigerant side of the indoor heat exchanger, is the inlet temperature of the indoor heat exchanger air side. is the average value of the heat transfer efficiency of each group of indoor heat exchangers in the sensitivity analysis experiment; In this embodiment, when the heat exchanger is changed, the weights are changed, and the prediction formula is corrected based on the heat exchanger system. When the heat exchanger is changed, different heat exchange performances exist under different indoor air volumes, and the exhaust pressure of the system under optimal performance will also change. A correction factor is added: the heat exchange efficiency of the heat exchanger , the formula is proposed:

[0043]

[0044]

[0045]

[0046] is the weight coefficient of the corresponding optimization parameter, and its value is ; is the experience factor, ; The maximum high pressure allowed for system operation, in MPa; is the average indoor air volume in the sensitivity analysis group, in units ; , , , is a constant, obtained by linear regression, and has no unit; is the heat transfer efficiency of the indoor heat exchanger, without unit; It is the average value of the heat transfer efficiency of each group of indoor heat exchangers in the sensitivity analysis experiment, without unit; , , are constants, obtained by linear regression, and have no units.

[0047] After changing the heat exchanger, if the heat exchange performance of the heat exchanger is better, that is, the heat exchange efficiency is increased, the influence of indoor air volume on the optimal exhaust pressure is reduced, and the index When the heat exchanger has better heat transfer performance, the optimal exhaust pressure of the system decreases, and the index decreases, and the predicted optimal exhaust pressure decreases; After changing the heat exchanger, if the heat exchange performance of the heat exchanger is worse, that is, the heat exchange efficiency is reduced, the influence of indoor air volume on the optimal exhaust pressure increases, and the index Increase; when the heat exchange performance of the heat exchanger is worse, the optimal exhaust pressure of the system increases, the index increases, the predicted optimal exhaust pressure increases.

[0048] S3, after obtaining the indoor air volume, indoor air inlet temperature and indoor air outlet temperature, the optimal exhaust pressure value under real-time working conditions can be predicted according to the optimal exhaust pressure prediction formula of the transcritical CO2 heat pump system. The CO2 heat pump system can be controlled to be at the optimal exhaust pressure at all times by adjusting the CO2 compressor speed and the opening of the electronic expansion valve.

[0049] The control method of the transcritical CO2 heat pump system provided in this embodiment is based on the transcritical optimal pressure prediction method, takes into account the evolution characteristics of transcritical-subcritical, and uses a sensitivity analysis method to develop a CO2 heat pump control method to achieve universal control of the optimal performance of the CO2 heat pump. Not only does it consider 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 exhaust pressure according to changes in the four parameters, but even when the indoor heat exchanger changes, it follows the changes to adjust the system to keep it at optimal performance, saving energy consumption of the electric vehicle air conditioning heat pump system.

[0050] Embodiment 2 This embodiment provides a control system for a transcritical CO2 heat pump system, including an optimization parameter acquisition module, a sensitivity analysis module and an optimal exhaust pressure control module. Optimization parameter acquisition module, used to obtain the optimization parameters of the transcritical CO2 heat pump system; A sensitivity analysis module is used to determine the number of levels of the optimization parameter, obtain the corresponding values ​​of different levels of each optimization parameter according to the number of levels and other gradients, and then obtain the operating table of the transcritical CO2 heat pump system through orthogonal matrix calculation; obtain the optimal exhaust pressure of the transcritical CO2 heat pump system in each case according to the operating table of the transcritical CO2 heat pump system; and obtain the sensitivity ratio of the optimization parameter after performing mean value analysis and variance analysis on the optimal exhaust pressure and operating table of the transcritical CO2 heat pump system in each case; The optimal exhaust pressure of the real-time working condition is predicted by optimizing the parameter sensitivity ratio using the following formula:

[0051]

[0052]

[0053]

[0054] The optimal exhaust pressure control module is used to predict the optimal exhaust pressure of the real-time working condition by optimizing the parameter sensitivity ratio, and adjust the CO2 compressor speed and the opening of the electronic expansion valve according to the optimal exhaust pressure of the real-time working condition to complete the control of the transcritical CO2 heat pump system; The sensitivity analysis module also includes: A heat exchanger correction module, which is used to optimize the parameter sensitivity ratio and predict the optimal exhaust pressure of the real-time working condition by combining the correction factor when the indoor heat exchanger of the transcritical CO2 heat pump system changes, and the correction factor is the heat exchange efficiency of the heat exchanger; The optimal exhaust pressure of the real-time working condition is obtained by optimizing the parameter sensitivity ratio and combining the correction factor prediction using the following formula:

[0055]

[0056]

[0057]

[0058] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0059] In this embodiment, a computer device is also provided, 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, which can perform model calculation and model update), 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 other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a general control method for optimal operation of a transcritical CO2 heat pump system.

[0060] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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.

[0061] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does 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; Determine the number of levels of the optimization parameter, obtain the corresponding values ​​of different levels of each optimization parameter according to the equal gradient of the number of levels, and then obtain the operating table of the transcritical CO2 heat pump system through orthogonal matrix calculation; obtain the optimal exhaust pressure of the transcritical CO2 heat pump system in each case according to the operating table of the transcritical CO2 heat pump system; perform mean value analysis and variance analysis on the optimal exhaust pressure and operating table of the transcritical CO2 heat pump system in each case to obtain the sensitivity ratio of the optimization parameter; The optimal exhaust pressure of the real-time working condition is predicted by optimizing the parameter sensitivity ratio, and the CO2 compressor speed and the electronic expansion valve opening are adjusted according to the optimal exhaust pressure of the real-time working condition to complete the control of the transcritical CO2 heat pump system.

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

3. A general control method for optimal operation of a transcritical CO2 heat pump system according to claim 1, characterized in that: The step of performing average value analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system in each case specifically includes: Calculate a first average value of the optimal exhaust pressures corresponding to all operating conditions in the operating condition table; A second average value of all optimal exhaust pressures corresponding to each level number of the optimization parameter is calculated.

4. A general control method for optimal operation of a transcritical CO2 heat pump system according to claim 3, characterized in that: After performing mean value analysis and variance analysis on the optimal exhaust pressure and operating condition table of the transcritical CO2 heat pump system in each case, the steps of obtaining the optimization parameter sensitivity ratio specifically include: After performing variance analysis on the first average value and the second average value, a variance analysis result of an optimal exhaust pressure corresponding to each optimization parameter is obtained; The sensitivity ratio of the optimized parameters is obtained according to the variance analysis results.

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 for real-time working conditions is predicted by optimizing the parameter sensitivity ratio using the following formula: In the above formula, is the optimal exhaust pressure prediction value, is the weight coefficient of ambient temperature, is the weight coefficient of indoor air inlet temperature, is the weight coefficient of indoor air outlet temperature, , , and is a constant obtained by linear regression of experimental data, is the indoor air volume, For sensitivity analysis, the average indoor air volume in the experimental group is is the sensitivity ratio of indoor air volume, is the sensitivity ratio of ambient temperature, is the sensitivity ratio of indoor air inlet temperature, is the sensitivity ratio of indoor air outlet temperature.

6. A universal control method for optimal operation of a transcritical CO2 heat pump system according to claim 1, characterized in that: The following steps are also included: When the indoor heat exchanger of the transcritical CO2 heat pump system changes, the optimal exhaust pressure of the real-time working condition is obtained by optimizing the parameter sensitivity ratio and combining the correction factor prediction, and 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 of the real-time working condition is obtained by optimizing the parameter sensitivity ratio and combining the correction factor prediction using the following formula: In the above formula, In the above formula, is the optimal exhaust pressure prediction value, is the weight coefficient of ambient temperature, is the weight coefficient of indoor air inlet temperature, is the weight coefficient of indoor air outlet temperature, , , and is a constant obtained by linear regression of experimental data, is the indoor air volume, is the average indoor air volume in the sensitivity analysis group, is the heat transfer efficiency of the indoor heat exchanger, is the average value of heat transfer efficiency of each group of indoor heat exchangers in the sensitivity analysis experiment. , , is a constant.

8. A control system for a transcritical CO2 heat pump system, characterized in that: include: Optimization parameter acquisition module, used to obtain the optimization parameters of the transcritical CO2 heat pump system; A sensitivity analysis module is used to determine the number of levels of the optimization parameter, obtain the corresponding values ​​of different levels of each optimization parameter according to the number of levels and other gradients, and then obtain the operating table of the transcritical CO2 heat pump system through orthogonal matrix calculation; obtain the optimal exhaust pressure of the transcritical CO2 heat pump system in each case according to the operating table of the transcritical CO2 heat pump system; and obtain the sensitivity ratio of the optimization parameter after performing mean value analysis and variance analysis on the optimal exhaust pressure and operating table of the transcritical CO2 heat pump system in each case; The optimal exhaust pressure control module is used to predict the optimal exhaust pressure of the real-time working condition by optimizing the parameter sensitivity ratio, and adjust the CO2 compressor speed and the electronic expansion valve opening according to the optimal exhaust pressure of the real-time working condition 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 optimize the parameter sensitivity ratio and predict the optimal exhaust pressure of the real-time working condition by combining the correction factor when the indoor heat exchanger of the transcritical CO2 heat pump system changes. The correction factor is the heat exchange efficiency of the heat exchanger.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, 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 are implemented.

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