Low-temperature adaptive control method and system for air energy heat pump variable frequency drive system

Abstract

The application discloses a low-temperature adaptive control method and system of an air energy heat pump variable frequency driving system, and belongs to the air energy heat pump field. The application obtains suction pressure, temperature and pulse reflection waveform in a suction pipeline of a variable frequency compressor, and the current compressor frequency and the expansion valve opening degree. The real-time gas rate of the refrigerant is calculated based on the transit time of the pulse reflection waveform, and the pressure and temperature data are taken as measurement inputs; the frequency and opening degree information are taken as control inputs to estimate the superheat and dryness of the suction port of the compressor through an extended Kalman filter state observer. When the dryness estimation value is greater than a preset safety threshold, the target superheat value is determined according to the outdoor temperature and the compressor frequency, and the deviation between the target superheat value and the superheat estimation value is calculated. The opening degree of the electronic expansion valve and the frequency adjustment information of the compressor are determined according to the superheat deviation, and the two are cooperatively adjusted, so that the energy efficiency performance and operation safety of the air energy heat pump are improved.

Description

Technical Field

[0001] This application belongs to the field of air source heat pumps, and particularly relates to a low-temperature adaptive control method and system for an air source heat pump variable frequency drive system. Background Technology

[0002] As a highly efficient and energy-saving heating technology, the stable operation of air source heat pump variable frequency drive systems in low-temperature environments is crucial. Achieving low-temperature adaptive control can improve the system's heating efficiency and operational reliability in cold climates, thus possessing broad market demand and application prospects.

[0003] In the existing technology, the low-temperature control of air source heat pumps usually adopts a control method based on suction pressure and suction temperature sensors. The suction superheat is calculated by measuring the values, and this is used as feedback to adjust the opening of the electronic expansion valve and the operating frequency of the compressor, thereby maintaining the stable operation of the system.

[0004] However, under extremely cold conditions and drastic changes in heating load, the refrigerant at the evaporator outlet is prone to being in a gas-liquid two-phase flow state. The superheat calculated solely by pressure and temperature sensors suffers from significant measurement lag and insufficient accuracy, failing to accurately reflect the actual state of the refrigerant. This prevents the control system from making timely and effective adjustments, not only reducing the unit's energy efficiency but also potentially triggering the risk of liquid slugging in the compressor. Therefore, under extreme operating conditions, the existing low-temperature adaptive control methods for air source heat pump inverter drive systems are insufficient in ensuring both energy efficiency and operational safety. Summary of the Invention

[0005] This application provides a low-temperature adaptive control method, system, device, and computer storage medium for an air source heat pump variable frequency drive system, which can improve the energy efficiency and operational safety of the air source heat pump.

[0006] In a first aspect, this application provides a low-temperature adaptive control method for an air source heat pump variable frequency drive system, the method comprising:

[0007] The system acquires suction pressure data, suction temperature data, and pulse reflection waveform data from the suction line of the variable frequency compressor, as well as compressor frequency information and expansion valve opening information for the current control cycle. The pulse reflection waveform data is obtained by collecting electromagnetic pulse signals reflected by the refrigerant through sensors installed in the suction line.

[0008] Based on the transit time of the reflected waveform in the reflected waveform data and the preset sensor probe length information, the relative permittivity of the refrigerant in the suction line is determined, and the real-time gas content of the refrigerant in the suction line is determined by using the preset correspondence between the permittivity and the gas content.

[0009] Using real-time gas content, suction pressure, and suction temperature data as measurement inputs, and compressor frequency and expansion valve opening information as control inputs, the superheat and dryness estimates of the variable frequency compressor's suction port are calculated using a pre-constructed extended Kalman filter state observer.

[0010] When the estimated dryness value is greater than the preset dryness threshold, the target superheat deviation is obtained by calculating the difference between the estimated superheat value and the target superheat value. The target superheat value is obtained by querying the preset database based on the real-time outdoor temperature and compressor frequency information. The database includes the correspondence between outdoor temperature, compressor frequency and superheat.

[0011] Based on the target superheat deviation, determine the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor, and adjust the electronic expansion valve and the compressor according to the opening adjustment information and the frequency adjustment information.

[0012] In one feasible implementation, the method further includes:

[0013] Extract the amplitude attenuation rate of the main reflection peak from the reflected waveform data;

[0014] The first correction factor is calculated using the ratio of the amplitude attenuation rate to the preset reference attenuation rate;

[0015] After determining the real-time gas content of the refrigerant in the suction line using a preset relationship between dielectric constant and gas content, the method further includes:

[0016] The real-time gas content is adjusted using the first correction factor to obtain the adjusted real-time gas content.

[0017] In one feasible implementation, the method further includes:

[0018] Extract the power spectral density of the main reflection peak from the reflection waveform data;

[0019] The second correction coefficient is calculated based on the ratio of the energy proportion of the target frequency band in the power spectral density to the preset energy proportion.

[0020] The real-time gas content is adjusted using the first correction factor to obtain the adjusted real-time gas content, including:

[0021] The real-time gas content is adjusted using the first and second correction coefficients to obtain the adjusted real-time gas content.

[0022] In one feasible implementation, the method further includes:

[0023] If the estimated dryness value is less than or equal to the preset dryness threshold, calculate the dryness difference between the estimated dryness value and the preset dryness threshold.

[0024] The opening adjustment step size is determined based on the multiple of the preset dryness threshold and the dryness difference, and the electronic expansion valve is adjusted according to the opening adjustment step size of the target quantity.

[0025] In one feasible implementation, the relative permittivity of the refrigerant in the suction line is determined based on the transit time of the reflected waveform in the reflected waveform data and preset sensor probe length information, including:

[0026] In the pulse reflection waveform data, the starting reflection feature point where the electromagnetic pulse enters the sensor probe and the terminal reflection feature point where the electromagnetic pulse reaches the end of the sensor probe are determined, and the time interval between the starting reflection feature point and the terminal reflection feature point is calculated to determine the transit time of the electromagnetic pulse in the sensor probe.

[0027] Based on the transit time and the preset sensor probe length information, the propagation rate of the electromagnetic pulse in the refrigerant is calculated.

[0028] The relative permittivity of the refrigerant in the suction line is calculated by using the proportional relationship between the propagation rate and the electromagnetic pulse reference propagation rate.

[0029] In one feasible implementation, the extended Kalman filter state observer includes a state transition model, an observation model, and a Kalman gain matrix;

[0030] Using real-time gas content, suction pressure, and suction temperature data as measurement inputs, and compressor frequency and expansion valve opening information as control inputs, the superheat and dryness estimates at the suction port of the variable frequency compressor are calculated using a pre-constructed extended Kalman filter state observer. These estimates include:

[0031] The compressor frequency information, expansion valve opening information, and the superheat and dryness estimates from the previous control cycle are input into the state transition model to obtain the state prediction values, including the predicted superheat and dryness for the current control cycle.

[0032] The state prediction values ​​are input into the observation model to calculate the measurement prediction values, including the predicted gas content, predicted inhalation pressure, and predicted inhalation temperature. The observation model includes the conversion relationship between the state prediction values ​​and the measurement prediction values.

[0033] The predicted value is compared item by item with the real-time measured value composed of real-time gas content, inhalation pressure data and inhalation temperature data, and the predicted residual vector is calculated.

[0034] The predicted residual vector is corrected using the Kalman gain matrix to update the state prediction value. The predicted superheat and predicted dryness in the updated state prediction value are used as the superheat and dryness estimates for the current control cycle.

[0035] In one feasible implementation, the opening adjustment information includes the opening adjustment direction and the opening adjustment rate, and the frequency adjustment information includes the frequency adjustment direction.

[0036] Based on the target superheat deviation, determine the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor, including:

[0037] Based on the sign and magnitude of the target superheat deviation, determine the direction and rate of adjustment of the electronic expansion valve opening.

[0038] Based on the opening adjustment direction, the compressor frequency adjustment direction is determined from the preset compensation rule library.

[0039] Secondly, this application provides a low-temperature adaptive control system for an air source heat pump variable frequency drive system, the system comprising:

[0040] The acquisition module is used to acquire suction pressure data, suction temperature data, and pulse reflection waveform data in the suction line of the variable frequency compressor, as well as compressor frequency information and expansion valve opening information for the current control cycle. The pulse reflection waveform data is obtained by collecting electromagnetic pulse signals reflected by the refrigerant through sensors installed in the suction line.

[0041] The determination module is used to determine the relative permittivity of the refrigerant in the suction line based on the transit time of the reflected waveform in the reflected waveform data and the preset sensor probe length information, and to determine the real-time gas content of the refrigerant in the suction line using the preset correspondence between the permittivity and the gas content.

[0042] The calculation module is used to take real-time gas content, suction pressure data and suction temperature data as measurement inputs, and compressor frequency information and expansion valve opening information as control inputs. Through the constructed extended Kalman filter state observer, it calculates the estimated superheat and dryness of the variable frequency compressor's suction port.

[0043] The calculation module is also used to obtain the target superheat deviation by calculating the difference between the estimated superheat value and the target superheat value when the estimated dryness value is greater than the preset dryness threshold. The target superheat value is obtained by querying the preset database based on the real-time outdoor temperature and compressor frequency information. The database includes the correspondence between outdoor temperature, compressor frequency and superheat.

[0044] The adjustment module is also used to determine the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor based on the target superheat deviation, and to adjust the electronic expansion valve and the compressor according to the opening adjustment information and the frequency adjustment information.

[0045] Thirdly, this application provides an electronic device, the device comprising: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the low-temperature adaptive control method of the air source heat pump frequency conversion drive system as described in any embodiment of the first aspect.

[0046] Fourthly, this application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the low-temperature adaptive control method of the air-source heat pump variable frequency drive system as described in any embodiment of the first aspect.

[0047] This application discloses a low-temperature adaptive control method, system, device, and computer storage medium for an air-source heat pump variable frequency drive system. By introducing pulse reflection waveform data and calculating the real-time gas content, which directly represents the refrigerant gas-liquid two-phase flow state, it overcomes the inherent delay and insufficient accuracy problems caused by the indirect calculation relying solely on temperature and pressure sensors in existing technologies. Furthermore, it fuses the real-time gas content with traditional pressure and temperature data through multi-source information fusion, and obtains high-precision, high-dynamic-response estimates of the two key internal states of the compressor suction port: dryness and superheat, through an extended Kalman filter state observer. Based on accurate and hysteresis-free state estimation, the control system can make timely and precise coordinated adjustments to the electronic expansion valve and compressor, solving the control failure problem caused by inaccurate state perception under extremely cold and variable load conditions in existing technologies, and improving the low-temperature operation efficiency and safety reliability of air-source heat pumps.

[0048] Furthermore, by deeply mining the pulse reflection waveform data, not only is its time dimension information utilized, but also features from the energy and frequency dimensions are introduced. The amplitude attenuation rate can effectively characterize the microscopic interface density of the two-phase flow, and the power spectral density can reflect its flow morphology. By fusing and correcting these two features with the baseline gas content calculated from the transit time, the accuracy of the real-time measured gas content can be greatly improved. This allows the extended Kalman filter state observer to output estimates of dryness and superheat that more closely approximate the actual physical state, ultimately improving the low-temperature energy efficiency performance of the air source heat pump while ensuring safety. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a schematic flowchart of a low-temperature adaptive control method for an air-source heat pump variable frequency drive system provided in one embodiment of this application;

[0051] Figure 2 This is a flowchart illustrating a method for calculating superheat and dryness estimates according to an embodiment of this application.

[0052] Figure 3 This is a schematic flowchart of a low-temperature adaptive control method for an air-source heat pump variable frequency drive system provided in one embodiment of this application;

[0053] Figure 4 This is a schematic diagram of the low-temperature adaptive control system of an air source heat pump variable frequency drive system provided in one embodiment of this application;

[0054] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0055] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0057] In existing technologies, low-temperature control of air source heat pumps typically employs control methods based on suction pressure and temperature sensors. The suction superheat is calculated from these measurements and used as feedback to adjust the opening of the electronic expansion valve and the compressor's operating frequency, thereby maintaining stable system operation. However, under extremely cold conditions and drastic changes in heating load, the refrigerant at the evaporator outlet is prone to a gas-liquid two-phase flow state. The superheat calculated solely by pressure and temperature sensors suffers from significant measurement lag and insufficient accuracy, failing to accurately reflect the actual state of the refrigerant. This results in the control system's inability to adjust effectively and promptly, reducing unit energy efficiency and potentially triggering compressor liquid slugging. Therefore, existing low-temperature adaptive control methods for air source heat pump inverter drive systems under extreme conditions are insufficient in ensuring both energy efficiency and operational safety.

[0058] To address the problems of existing technologies, embodiments of this application provide a low-temperature adaptive control method, system, device, and computer storage medium for an air source heat pump variable frequency drive system. The low-temperature adaptive control method for the air source heat pump variable frequency drive system provided in this application embodiment will be described first below.

[0059] Figure 1 A schematic flowchart of a low-temperature adaptive control method for an air-source heat pump variable frequency drive system according to an embodiment of this application is shown. Figure 1 As shown, the method includes steps S110 to S150.

[0060] S110: Acquire suction pressure data, suction temperature data, and pulse reflection waveform data from the suction line of the variable frequency compressor, as well as compressor frequency information and expansion valve opening information for the current control cycle. The pulse reflection waveform data is obtained by collecting electromagnetic pulse signals reflected by the refrigerant through a sensor installed in the suction line.

[0061] Suction pressure data refers to a physical quantity representing the refrigerant pressure before entering compression, measured in real time by a pressure sensor installed at the inlet of the variable frequency compressor's suction line. It can be a sequence of pressure values ​​that varies over time. Suction temperature data refers to a physical quantity representing the refrigerant temperature before entering compression, measured in real time by a temperature sensor installed adjacent to the pressure sensor. It can be a sequence of temperature values ​​that varies over time. Pulse reflection waveform data refers to a waveform record of a complete electromagnetic pulse signal from transmission to reception, generated by a dedicated time-domain reflectometry sensor. It can be a set of continuous voltage amplitude points within a nanosecond timescale.

[0062] At the start of a control cycle, suction pressure data, suction temperature data, pulse reflection waveform data, compressor frequency information, and expansion valve opening information are acquired concurrently. Suction pressure data is provided by a pressure sensor installed in the suction line, which converts the refrigerant pressure in the suction line into a continuous electrical signal, forming a time-varying pressure sequence. Suction temperature data is provided by a temperature sensor installed in the suction line, which converts the measured refrigerant temperature into an electrical signal, forming a time-varying temperature sequence. Pulse reflection waveform data is provided by a time-domain reflectometry (TDRS) sensor, which emits a high-frequency electromagnetic pulse that propagates along its built-in probe and simultaneously captures and records the reflected signals generated by the discontinuity of the refrigerant medium, forming a complete waveform sequence composed of multiple voltage or current amplitude points. Compressor frequency information and expansion valve opening information are obtained from a memory storing the control commands for the current cycle. Compressor frequency information refers to the operating frequency command used to drive the inverter within the current control cycle, specifically a value in one Hz unit. Expansion valve opening information refers to the position command used to drive the electronic expansion valve within the current control cycle, indicating the degree of opening of the throttling component, which is a value of one stepper motor step.

[0063] For example, in an extreme cold load scenario, an air source heat pump operates at an outdoor ambient temperature of -25°C, while the indoor heating demand suddenly increases. For a control cycle, the control cycle duration can be 1 second. The suction pressure data from the suction pressure sensor is read as 0.25 MPa, and the suction temperature data from the suction temperature sensor is read as -20°C. The time-domain reflectometry sensor is triggered, emitting an electromagnetic pulse with a width of 1 ns into the refrigerant in the pipeline, and the returned signal is rapidly acquired to obtain a pulse reflection waveform data consisting of 1024 sampling points. Simultaneously, the compressor frequency information for the current control cycle, determined to cope with the high load, is obtained from the instruction register as 110 Hz, and the expansion valve opening information is 350 steps.

[0064] S120: Based on the transit time of the reflected waveform in the reflected waveform data and the preset sensor probe length information, determine the relative permittivity of the refrigerant in the suction line, and use the preset correspondence between the permittivity and the gas content to determine the real-time gas content of the refrigerant in the suction line.

[0065] The transit time refers to the time it takes for an electromagnetic pulse signal to travel one round trip within the probe of a time-domain reflectometry sensor, and can be on the order of nanoseconds. The relative permittivity is a physical quantity representing the ability of a dielectric to store electrical energy in an electric field; its value is closely related to the phase state and density of the refrigerant. The correspondence between the permittivity and gas content is represented by a mapping table, which is a multidimensional dataset established in a laboratory environment by calibrating the corresponding relative permittivity values ​​under various known temperature, pressure, and gas content conditions. Real-time gas content represents the volume percentage of gaseous refrigerant at a specific cross-section within the suction line at the current moment.

[0066] First, a peak detection algorithm is used to locate the characteristic points of the initial and terminal reflections in the pulse reflection waveform data, and the time difference between them is calculated to obtain the transit time. Then, based on this transit time and the known physical length of the preset sensor probe, the equivalent relative permittivity of the refrigerant in the gas-liquid mixture state in the pipeline is calculated backward using the formula for electromagnetic wave propagation speed. Finally, the real-time gas content is determined based on the calculated equivalent relative permittivity. The specific process is as follows: First, the equivalent relative permittivity, current suction pressure data, and suction temperature data are used as a lookup index; using the current suction pressure and suction temperature, a subset matching the current operating condition is first located in the mapping table; then, within this subset, the gas content value corresponding to the calculated equivalent relative permittivity value is found through interpolation or nearest neighbor search, and this found gas content value is taken as the final real-time gas content. For example, assuming the calculated equivalent relative permittivity is 9.9, the obtained intake pressure is 0.25 MPa, and the intake temperature is -20°C, this data is used as query conditions to look up the data in a preset mapping table of permittivity and gas content. First, based on the two operating parameters of 0.25 MPa and -20°C, the relevant calibration data area is located in the table. Then, within this area, the entry closest to the relative permittivity value of 9.9 is found, and its corresponding gas content value is read. For example, if the result is 0.95, it means the gas volume percentage is 95%.

[0067] S130: Using real-time gas content, suction pressure, and suction temperature data as measurement inputs, and compressor frequency and expansion valve opening information as control inputs, the superheat and dryness estimates of the variable frequency compressor's suction port are calculated using the constructed extended Kalman filter state observer.

[0068] The Extended Kalman Filter (EKF) state observer is an observer that uses the EKF algorithm to estimate the state of a nonlinear system. Through linearization and recursive calculations, it achieves real-time tracking and prediction of the system's internal state. The superheat estimate and dryness estimate are the outputs of the state observer. The superheat estimate represents a precise estimate of whether the refrigerant gas temperature at the compressor suction port exceeds its saturation temperature. The dryness estimate represents a precise estimate of the gas mass percentage in the refrigerant-liquid mixture at the compressor suction port.

[0069] First, the compressor frequency information, expansion valve opening information, and the state estimation results from the previous control cycle—namely, the superheat estimate and dryness estimate—are substituted into a pre-defined state transition model describing the system's thermodynamic behavior. Time-update calculations are then performed to obtain a priori system state prediction value containing the predicted superheat and dryness for the current cycle. Next, this priori system state prediction value is input into an observation model, converting it into a measurement prediction value corresponding to the sensor measurement dimensions. Then, this measurement prediction value is compared with real-time measurements consisting of real-time gas content, suction pressure, and suction temperature data, and the prediction residual is calculated. Finally, an optimal weighting of the prediction residual is applied using a Kalman gain matrix pre-defined based on the system noise characteristics. The weighted result is then used to correct the priori system state prediction value obtained in the first step, ultimately outputting the corrected predicted superheat and dryness as the superheat and dryness estimates for the current cycle.

[0070] S140: When the estimated dryness value is greater than the preset dryness threshold, the target superheat deviation is obtained by calculating the difference between the estimated superheat value and the target superheat value. The target superheat value is obtained by querying the preset database based on the real-time outdoor temperature and compressor frequency information. The database includes the correspondence between outdoor temperature, compressor frequency and superheat.

[0071] The target superheat value represents the ideal superheat value that enables the heat pump system to achieve its highest operating energy efficiency under the current operating conditions.

[0072] First, a conditional judgment is performed, comparing the estimated dryness value with a preset dryness threshold. If the estimated dryness value is greater than the preset dryness threshold, the current operation is deemed safe. After confirming safety, the outdoor ambient temperature obtained through external sensors and the current compressor frequency information obtained from step S110 are used as two key real-time operating parameters.

[0073] Then, using these two real-time operating parameters as indexes, a query is performed in a pre-defined performance graph database that includes the correspondence between optimal superheat and operating conditions to determine the target superheat value. For example, if the collected outdoor ambient temperature is -25℃ and the obtained compressor frequency information is 110Hz, this is used as the query condition. Assuming the database specifies a target superheat of 6.0℃ at -25℃ and 100Hz, and a target superheat of 6.5℃ at -25℃ and 120Hz, then a two-dimensional interpolation algorithm is used to calculate the target superheat value of 6.25℃ under the current 110Hz operating condition. Finally, the output superheat estimate is subtracted from the target superheat value just obtained from the query to obtain the final target superheat deviation. If the superheat estimate is 4.8℃, then the target superheat deviation is -1.45℃.

[0074] S150: Based on the target superheat deviation, determine the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor, and adjust the electronic expansion valve and the compressor according to the opening adjustment information and the frequency adjustment information.

[0075] Opening adjustment information refers to control commands used to drive the electronic expansion valve, which may include the adjustment direction, such as increasing or decreasing the opening, and the adjustment rate or amount, such as how many steps per second or the total number of steps. Frequency adjustment information refers to control commands used to drive the variable frequency compressor, which may include the adjustment direction, such as increasing or decreasing the frequency, and the adjustment rate or a specific frequency compensation value.

[0076] First, based on the target superheat deviation calculated by S140, the opening adjustment information for the electronic expansion valve is determined through a query operation in a preset mapping table between superheat deviation and opening adjustment information. This mapping table divides the numerical range of the deviation into multiple intervals and directly specifies the adjustment direction and rate for each interval. Then, the determined opening adjustment information is used as input and queried in a table serving as a compensation rule base to determine the frequency adjustment information for the compressor. This compensation rule base defines the correspondence between the opening adjustment action and the corresponding frequency compensation value, ensuring that the compressor adjustment is a proactive compensation for the expansion valve adjustment action, thereby maintaining the smoothness of system operation. Finally, the final determined opening adjustment information and frequency adjustment information, such as an opening increase of 22 steps per second and a frequency compensation of 2.0 Hz, are sent to the drive circuits of the electronic expansion valve and the variable frequency compressor, respectively, completing the coordinated adjustment of the two core actuators.

[0077] This embodiment overcomes the inherent delay and insufficient accuracy problems of existing technologies that rely solely on indirect calculations using temperature and pressure sensors by introducing pulse reflection waveform data and calculating the real-time gas content, which directly represents the refrigerant gas-liquid two-phase flow state. Furthermore, it fuses the real-time gas content with traditional pressure and temperature data through multi-source information fusion, and obtains high-precision, high-dynamic-response estimates of the two key internal states of the compressor suction port: dryness and superheat, through an extended Kalman filter state observer. Based on accurate and hysteresis-free state estimation, the control system can make timely and precise coordinated adjustments to the electronic expansion valve and compressor, solving the control failure problem caused by inaccurate state perception under extremely cold and variable load conditions in existing technologies, and improving the low-temperature operation efficiency and safety reliability of air source heat pumps.

[0078] In one feasible implementation, the method further includes:

[0079] Extract the amplitude attenuation rate of the main reflection peak from the reflected waveform data.

[0080] In the pulse reflection waveform data, a peak addressing algorithm is used to determine the peak amplitude of the terminal reflection feature point, while the initial amplitude of the transmitted pulse is a known fixed value. For example, the peak addressing algorithm determines the peak amplitude of the terminal reflection feature point to be 0.92V, while the preset initial amplitude of the transmitted pulse is 1.0V. By calculating the difference between the initial amplitude and the peak amplitude and the ratio of the initial amplitude, the current amplitude attenuation rate is obtained as 0.08.

[0081] The first correction factor is calculated using the ratio of the amplitude attenuation rate to the preset reference attenuation rate.

[0082] The preset reference attenuation rate refers to a reference signal attenuation value measured under laboratory calibration conditions when the sensor probe is filled with pure gaseous refrigerant. Based on the amplitude attenuation rate and the preset reference attenuation rate, the first correction coefficient is calculated using the function formula shown in formula (1). For example, by substituting the current amplitude attenuation rate of 0.08, the preset reference attenuation rate of 0.05, and the preset sensitivity coefficient k value of 0.4 into the formula, the first correction coefficient is calculated to be approximately 0.988.

[0083] (1)

[0084] in, is the first correction factor; k is the preset factor; Am is the amplitude attenuation rate of the current period; Ab is the preset reference attenuation rate.

[0085] After determining the real-time gas content of the refrigerant in the suction line in step S120 using a preset relationship between dielectric constant and gas content, the method further includes:

[0086] S121: Adjust the real-time gas content using the first correction coefficient to obtain the adjusted real-time gas content.

[0087] The real-time gas content is multiplied by the calculated first correction coefficient. For example, multiplying the real-time gas content of 0.95 by the first correction coefficient of 0.988 yields an adjusted real-time gas content of approximately 0.939.

[0088] In one feasible implementation, the method further includes:

[0089] Extract the power spectral density of the main reflection peak from the reflection waveform data.

[0090] Power spectral density (PSD) is a measure of the power distribution of a signal in the frequency domain, and is a list recording different frequency points and their corresponding signal power intensities. For pulse-reflection waveform data, PSD can reflect the changes in the signal's frequency domain characteristics caused by different flow patterns of the refrigerant two-phase flow, such as bubbly flow or mist flow. PSD data, i.e., a time-domain signal sequence consisting of 1024 sampling points, is converted into a frequency-domain sequence containing 1024 complex points using a first Fast Fourier Transform (FFT) algorithm. Then, by calculating the square of the modulus of each complex point in this frequency-domain sequence, a power spectral density data consisting of 1024 real points is obtained, representing the distribution of signal power at different frequency points.

[0091] The second correction coefficient is calculated based on the ratio of the energy proportion of the target frequency band in the power spectral density to the preset energy proportion.

[0092] The target frequency band refers to a specific frequency range in the power spectral density that can effectively distinguish different two-phase flow patterns. First, the target frequency band power and the total signal power are calculated by numerically integrating the power spectral density data obtained in the previous step (i.e., discrete summation). For example, assuming the preset target frequency band is 50MHz to 100MHz, by summing all power values ​​between 50MHz and 100MHz in the power spectral density data, the target frequency band power is calculated to be 23.5 units (V²). Simultaneously, by summing all power values ​​at all frequency points in the power spectral density data, the total signal power is calculated to be 44.5 units. Then, the energy percentage is calculated by dividing the target frequency band power by the total signal power, resulting in a current energy percentage of approximately 0.528.

[0093] Then, based on the current energy ratio and the preset baseline energy ratio, the second correction coefficient is calculated using the function formula shown in formula (2). For example, the baseline value obtained by calculating the pure gas phase standard waveform using the same method can be 0.456. The calculated ratio R is approximately 1.158. Assuming the preset gain coefficient β is 0.1, the second correction coefficient is calculated. It is approximately 0.984.

[0094] (2)

[0095] in, β is the first correction coefficient; R is the ratio of the current energy percentage to the preset energy percentage; β is the preset gain coefficient.

[0096] Step S121: Adjust the real-time gas content using the first correction coefficient to obtain the adjusted real-time gas content, including:

[0097] The real-time gas content is adjusted using the first and second correction coefficients to obtain the adjusted real-time gas content.

[0098] The real-time gas content determined in S120 is multiplied by the first correction coefficient and the second correction coefficient in a continuous multiplication operation. For example, the real-time gas content of 0.95 determined in S120 is multiplied by the calculated first correction coefficient of 0.988 and the second correction coefficient of 0.984 to obtain a final adjusted real-time gas content of approximately 0.92.

[0099] This embodiment deeply mines pulse reflection waveform data, utilizing not only its time dimension information but also introducing features from the energy and frequency dimensions. The amplitude attenuation rate effectively characterizes the microscopic interface density of the two-phase flow, while the power spectral density reflects its flow morphology. By fusing these two features with the baseline gas content calculated from the transit time, the accuracy of the real-time measured gas content is significantly improved. This allows the extended Kalman filter state observer to output estimates of dryness and superheat that more closely approximate the actual physical state, ultimately enhancing the low-temperature energy efficiency of the air source heat pump while ensuring safety.

[0100] In one feasible implementation, the method further includes:

[0101] If the estimated dryness value is less than or equal to the preset dryness threshold, calculate the dryness difference between the estimated dryness value and the preset dryness threshold.

[0102] The estimated dryness value is compared with a preset dryness threshold. When the estimated dryness value is less than or equal to the dryness threshold, the safety control logic is triggered, and the dryness difference is calculated by subtracting the estimated dryness value from the preset dryness threshold, resulting in a dryness difference greater than or equal to zero. For example, if the dryness estimate output by S130 is 0.97, and the preset dryness threshold is 0.98, the calculated dryness difference is 0.01.

[0103] The opening adjustment step size is determined based on the multiple of the preset dryness threshold and the dryness difference, and the electronic expansion valve is adjusted according to the opening adjustment step size of the target quantity.

[0104] The target number of opening adjustment steps is a specific command value used to directly drive the electronic expansion valve, representing the number of stepper motor steps required to close the valve. The target number of opening adjustment steps is calculated using the function formula shown in formula (3), which is a multiple of the preset dryness threshold and the dryness difference. Assuming the dryness difference is 0.01, the preset dryness threshold is 0.98, the maximum single adjustment step is 50 steps, and the adjustment sensitivity coefficient k is 10, the calculated target number of opening adjustment steps is approximately 5 steps. This command is then sent to the drive circuit of the electronic expansion valve, causing it to immediately close by 5 steps to quickly increase the dryness and remove the system from the danger zone.

[0105] (3)

[0106] Where N is the target number of opening adjustment step size; Nmax is the maximum single adjustment step size; k is the preset adjustment coefficient; Δd is the dryness difference; and dth is the dryness threshold.

[0107] In one feasible implementation, step S120, based on the transit time of the reflected waveform in the reflected waveform data and preset sensor probe length information, determines the relative permittivity of the refrigerant in the suction line, including:

[0108] In the pulse reflection waveform data, the starting reflection feature point where the electromagnetic pulse enters the sensor probe and the terminal reflection feature point where the electromagnetic pulse reaches the end of the sensor probe are determined, and the time interval between the starting reflection feature point and the terminal reflection feature point is calculated to determine the transit time of the electromagnetic pulse propagating back and forth within the sensor probe.

[0109] The initial reflection feature point refers to the specific time point in the pulse reflection waveform data that marks the beginning of the electromagnetic pulse signal entering the sensor probe and interacting with the refrigerant medium. It is generally represented by a sharp drop or rise in the waveform. The terminal reflection feature point refers to the specific time point in the same waveform data that marks the propagation of the electromagnetic pulse signal to the probe tip and the generation of the main reflection peak due to the impedance change. It is generally represented by a significant peak point in the waveform.

[0110] Feature identification is performed on the acquired pulse reflection waveform data using a signal processing algorithm. For example, in a pulse reflection waveform consisting of 1024 sampling points, the signal processing algorithm can use edge detection technology to locate the starting reflection feature point at the 50th sampling point, and simultaneously use peak addressing technology to locate the main peak of the ending reflection feature point at the 260th sampling point. Then, by calculating the difference between these two time points on the time axis, the time taken for the electromagnetic pulse to travel one round trip within the probe is obtained. If the sampling time interval is 0.02 ns, the calculated transit time is 4.2 ns.

[0111] Based on the transit time and the preset sensor probe length information, the propagation rate of the electromagnetic pulse in the refrigerant is calculated.

[0112] Obtain the known physical length information of the sensor probe, for example, 0.2m. Since the transit time is the time for the electromagnetic pulse to travel one round trip, i.e., the distance traveled is twice the probe length, the actual propagation speed of the electromagnetic pulse in the refrigerant is calculated to be approximately 0.952×10^8m / s by dividing twice the probe length by the transit time.

[0113] The relative permittivity of the refrigerant in the suction line is calculated by using the proportional relationship between the propagation speed and the reference propagation speed of the electromagnetic pulse. The reference propagation speed of the electromagnetic pulse refers to the speed at which an electromagnetic pulse propagates in a vacuum or air, i.e., the speed of light. By dividing the preset reference propagation speed of the electromagnetic pulse, for example, 3.0 × 10^8 m / s, by the actual propagation speed of the electromagnetic pulse in the refrigerant calculated in the previous step, and squaring the result, the relative permittivity of the refrigerant in the current suction line can be calculated to be approximately 9.9.

[0114] Figure 2 A flowchart illustrating a method for calculating superheat and dryness estimates according to an embodiment of this application is shown. Figure 2 As shown, the method includes steps S210 to S240.

[0115] In one feasible implementation, the extended Kalman filter state observer includes a state transition model, an observation model, and a Kalman gain matrix.

[0116] The state transition model is a set of mathematical equations based on the principles of energy and mass conservation in a heat pump system, used to represent the evolution of the system's internal state over time. This model uses the superheat and dryness fraction at the compressor suction port as core state variables, and the compressor frequency and expansion valve opening information as external control inputs. Its function is to predict the state at the next moment by solving internal nonlinear differential equations based on the state at the previous moment and the current control input.

[0117] The observation model is a set of mathematical equations based on publicly available refrigerant thermophysical property data tables and the dielectric theory of mixed media. The model's function is to translate the input internal system states, namely superheat and dryness, into external physical quantities that completely correspond in concept and dimension to the sensor measurements: gas content, suction pressure, and suction temperature, through a positive mapping relationship.

[0118] The Kalman gain matrix is ​​a dynamically calculated weighted matrix whose value indicates the extent to which new measurements should be accepted to correct the model's predictions at the current moment. This matrix is ​​constructed based on the quantification of the uncertainty of the state transition model, i.e., the process noise covariance, and the quantification of the sensor measurement uncertainty, i.e., the measurement noise covariance. In each control cycle, this matrix is ​​updated through a series of standard matrix operations based on the model linearization results, optimally distributing the prediction residuals to the various state variables.

[0119] Step S130: Using real-time gas content, suction pressure data, and suction temperature data as measurement inputs, and compressor frequency information and expansion valve opening information as control inputs, the superheat estimate and dryness estimate of the variable frequency compressor's suction port are calculated using the constructed extended Kalman filter state observer, including:

[0120] S210: Input the compressor frequency information, expansion valve opening information, and the estimated superheat and dryness of the previous control cycle into the state transition model to obtain the state prediction values ​​including the predicted superheat and predicted dryness of the current control cycle.

[0121] The acquired compressor frequency and expansion valve opening information are used as control inputs for the current cycle. Simultaneously, the estimated superheat and dryness values ​​calculated in the previous control cycle are used as the initial state. These data are then input into the state transition model. The state transition model predicts the theoretical evolution of the system state through a single forward time step calculation, yielding a state prediction value that includes the predicted superheat and dryness for the current cycle.

[0122] S220: Input the state prediction values ​​into the observation model to calculate the measurement prediction values, including the predicted gas content, predicted inhalation pressure, and predicted inhalation temperature. The observation model includes the conversion relationship between the state prediction values ​​and the measurement prediction values.

[0123] The state prediction value output from step S210 is input into the observation model. The observation model performs a forward mapping calculation, converting the input internal state variables into external measurable physical quantities, and obtaining a measurement prediction value that is consistent with the dimensions of the actual measurement data in S110, including the predicted gas content, predicted inhalation pressure, and predicted inhalation temperature.

[0124] S230: The predicted value is compared item by item with the real-time measured value consisting of real-time gas content, inhalation pressure data and inhalation temperature data, and the predicted residual vector is calculated.

[0125] The measurement prediction value calculated in the previous step S220 is subjected to a vector subtraction operation with the measurement data vector consisting of real-time gas content, inhalation pressure data and inhalation temperature data. This is done to compare the differences between the model prediction and the actual measurement item by item, thereby obtaining a prediction residual vector that includes the prediction error in all measurement dimensions.

[0126] S240: The predicted residual vector is corrected using the Kalman gain matrix to update the state prediction value, and the predicted superheat and predicted dryness in the updated state prediction value are used as the superheat and dryness estimates for the current control cycle.

[0127] First, based on the linearized Jacobian matrices of the state transition model and the observation model, and the preset process and measurement noise covariances, the Kalman gain matrix under the current operating condition is calculated using the standard Kalman filter equation. Then, this calculated Kalman gain matrix is ​​multiplied by the prediction residual vector obtained in step S230 to obtain an optimal state correction. Finally, this state correction is added to the original state prediction value obtained in S210 to update the state prediction, and the predicted superheat and predicted dryness components in the updated vector are output as the final superheat and dryness estimates for the current cycle.

[0128] Figure 3 A schematic flowchart of a low-temperature adaptive control method for an air-source heat pump variable frequency drive system according to an embodiment of this application is shown. Figure 3 As shown, the method includes steps S310 to S320.

[0129] In one feasible implementation, the opening adjustment information includes the opening adjustment direction and the opening adjustment rate, and the frequency adjustment information includes the frequency adjustment direction.

[0130] In step S150, based on the target superheat deviation, the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor are determined, including:

[0131] S310: Determine the direction and rate of adjustment of the electronic expansion valve opening based on the sign and magnitude of the target superheat deviation.

[0132] The opening adjustment direction indicates the tendency of the electronic expansion valve to open or close, and is a binary state command. The opening adjustment rate refers to the speed at which the electronic expansion valve performs the opening or closing action, which can be a specific adjustment step value per second. Using the target superheat deviation calculated by S140 as input, the adjustment direction and rate of the electronic expansion valve are determined in a mapping table of superheat deviation and opening adjustment information, as shown in Table 1. This mapping table divides the entire numerical range of the target superheat deviation into multiple intervals, each interval directly corresponding to a specific adjustment direction and rate. The adjustment direction and rate of the electronic expansion valve can be retrieved from the mapping table of superheat deviation and opening adjustment information based on the sign and magnitude of the superheat deviation.

[0133] Table 1: Mapping Table of Superheat Deviation and Opening Adjustment Information

[0134]

[0135] For example, if the calculated target superheat deviation is -1.45℃, a lookup in this mapping table will show a value falling within the range of [-1.8, -0.7). By finding the entry corresponding to this range, the direction of the opening adjustment is determined to be increasing, and the opening adjustment rate is determined to be 22 steps per second.

[0136] S320: Determine the compressor frequency adjustment direction from the preset compensation rule library according to the opening adjustment direction.

[0137] The frequency adjustment direction indicates the tendency of the variable frequency compressor to increase or decrease its operating frequency; it is a binary state command. The compensation rule base is a set of rules that includes the correspondence between the electronic expansion valve's action and the compressor's optimal response strategy. This rule base defines the correspondence between the opening adjustment direction and opening adjustment rate, and the frequency adjustment direction and frequency compensation value. It also determines a specific frequency compensation value based on the severity (i.e., rate) of the opening adjustment. Specifically, the compensation rule base can be shown in Table 2.

[0138] The opening adjustment direction determined in step S310 is used as a query index to match the corresponding frequency adjustment direction in the compensation rule base. For example, if the opening adjustment direction determined in S310 is "increase" and the rate is 22 steps per second, a query is performed in the compensation rule base based on this condition. The second rule is matched, which is "increase" and the rate is in the range (15, 40). Therefore, the compressor's frequency adjustment direction is determined to be "increase," and the specific frequency compensation value is 2.0 Hz.

[0139] Table 2: Compensation Rule Base

[0140]

[0141] Based on the same concept, this application provides a low-temperature adaptive control system for an air source heat pump variable frequency drive system, which is described below in conjunction with... Figure 4 The low-temperature adaptive control system of the air source heat pump variable frequency drive system provided in the embodiments of this application will be described in detail.

[0142] Figure 4 This is a structural block diagram of a low-temperature adaptive control system for an air-source heat pump variable frequency drive system, as shown in an embodiment of this application.

[0143] like Figure 4 As shown, the low-temperature adaptive control system of this air source heat pump variable frequency drive system may include:

[0144] The acquisition module 410 is used to acquire suction pressure data, suction temperature data and pulse reflection waveform data in the suction line of the variable frequency compressor, as well as compressor frequency information and expansion valve opening information in the current control cycle. The pulse reflection waveform data is obtained by collecting the electromagnetic pulse signal reflected by the refrigerant through a sensor set in the suction line.

[0145] The determination module 420 is used to determine the relative permittivity of the refrigerant in the suction line based on the transit time of the reflected waveform in the reflected waveform data and the preset sensor probe length information, and to determine the real-time gas content of the refrigerant in the suction line using the preset correspondence between the permittivity and the gas content.

[0146] The calculation module 430 is used to take real-time gas content, suction pressure data and suction temperature data as measurement inputs, and compressor frequency information and expansion valve opening information as control inputs. Through the constructed extended Kalman filter state observer, it calculates the estimated superheat and dryness of the suction port of the variable frequency compressor.

[0147] The calculation module 430 is also used to obtain the target superheat deviation by calculating the difference between the estimated superheat value and the target superheat value when the estimated dryness value is greater than the preset dryness threshold. The target superheat value is obtained by querying the preset database based on the real-time outdoor temperature and compressor frequency information. The database includes the correspondence between outdoor temperature, compressor frequency and superheat.

[0148] The adjustment module 440 is also used to determine the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor based on the target superheat deviation, and to adjust the electronic expansion valve and the compressor according to the opening adjustment information and the frequency adjustment information.

[0149] In one embodiment, the determining module 420 is further configured to extract the amplitude attenuation rate of the main reflection peak from the reflected waveform data; calculate a first correction coefficient using the ratio of the amplitude attenuation rate to a preset reference attenuation rate; and adjust the real-time gas content using the first correction coefficient to obtain the adjusted real-time gas content.

[0150] In one embodiment, the determining module 420 is further configured to extract the power spectral density of the main reflection peak from the reflection waveform data; calculate a second correction coefficient based on the ratio of the energy proportion of the target frequency band in the power spectral density to the preset energy proportion; and adjust the real-time gas content using the first correction coefficient and the second correction coefficient to obtain the adjusted real-time gas content.

[0151] In one embodiment, the calculation module 430 is further configured to calculate the dryness difference between the dryness estimate and the preset dryness threshold when the dryness estimate is less than or equal to the preset dryness threshold; determine the target number of opening adjustment steps according to the multiple of the preset dryness threshold and the dryness difference; and adjust the electronic expansion valve according to the target number of opening adjustment steps.

[0152] In one embodiment, the determining module 420 is specifically used to determine, in the pulse reflection waveform data, the initial reflection feature point where the electromagnetic pulse enters the sensor probe and the terminal reflection feature point where the electromagnetic pulse reaches the end of the sensor probe, and to calculate the time interval between the initial reflection feature point and the terminal reflection feature point to determine the transit time of the electromagnetic pulse propagating back and forth within the sensor probe; based on the transit time and preset sensor probe length information, to calculate the propagation rate of the electromagnetic pulse in the refrigerant; and to calculate the relative permittivity of the refrigerant in the suction line by the proportional relationship between the propagation rate and the electromagnetic pulse reference propagation rate.

[0153] In one embodiment, the extended Kalman filter state observer includes a state transition model, an observation model, and a Kalman gain matrix. The calculation module 430 is specifically used to input compressor frequency information, expansion valve opening information, and the superheat and dryness estimates from the previous control cycle into the state transition model to obtain state prediction values ​​including the predicted superheat and predicted dryness for the current control cycle. The state prediction values ​​are then input into the observation model to calculate measurement prediction values ​​including the predicted gas content, predicted suction pressure, and predicted suction temperature. The observation model includes the transformation relationship between the state prediction values ​​and the measurement prediction values. The measurement prediction values ​​are compared item by item with real-time measurement values ​​composed of real-time gas content, suction pressure data, and suction temperature data to calculate the prediction residual vector. The prediction residual vector is corrected using the Kalman gain matrix to update the state prediction values, and the predicted superheat and predicted dryness from the updated state prediction values ​​are used as the superheat and dryness estimates for the current control cycle.

[0154] In one embodiment, the opening adjustment information includes the opening adjustment direction and the opening adjustment rate, and the frequency adjustment information includes the frequency adjustment direction; the adjustment module 440 is specifically used to determine the opening adjustment direction and the opening adjustment rate of the electronic expansion valve according to the positive and negative signs and the magnitude of the target superheat deviation; and to determine the frequency adjustment direction of the compressor in a preset compensation rule library according to the opening adjustment direction.

[0155] Figure 4 Each module in the system shown has an implementation Figures 1 to 3 The functions of each step in the process and their corresponding technical effects are described briefly and will not be elaborated here.

[0156] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in one embodiment of this application is shown.

[0157] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0158] Specifically, the processor 510 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0159] Memory 520 may include mass storage for data or instructions. For example, and not limitingly, memory 520 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 520 may include removable or non-removable (or fixed) media. Where appropriate, memory 520 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 520 is non-volatile solid-state memory.

[0160] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of this disclosure.

[0161] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any of the low-temperature adaptive control methods for the air source heat pump variable frequency drive system in the above embodiments.

[0162] In one example, the electronic device may also include a communication interface 530 and a bus 540. Wherein, such as Figure 5 As shown, the processor 510, memory 520, and communication interface 530 are connected through bus 540 and complete communication with each other.

[0163] The communication interface 530 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0164] Bus 540 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 540 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0165] This electronic device can execute the low-temperature adaptive control method of the air-source heat pump variable frequency drive system in the embodiments of this application, thereby achieving a combination of Figures 1 to 3 The low-temperature adaptive control method for an air-source heat pump variable frequency drive system is described.

[0166] Furthermore, in conjunction with the low-temperature adaptive control method of the air-source heat pump inverter drive system in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the low-temperature adaptive control methods of the air-source heat pump inverter drive system in the above embodiments.

[0167] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0168] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0169] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0170] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in 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, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0171] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A low-temperature adaptive control method for an air-source heat pump variable frequency drive system, characterized in that, The method includes: The suction pressure data, suction temperature data, and pulse reflection waveform data of the suction line of the variable frequency compressor are acquired, as well as the compressor frequency information and expansion valve opening information of the current control cycle. The pulse reflection waveform data is obtained by collecting the electromagnetic pulse signal reflected by the refrigerant through a sensor installed in the suction line. Based on the transit time of the reflected waveform in the reflected waveform data and the preset sensor probe length information, the relative permittivity of the refrigerant in the suction line is determined, and the real-time gas content of the refrigerant in the suction line is determined by using the preset correspondence between the permittivity and the gas content. Using the real-time gas content, suction pressure data, and suction temperature data as measurement inputs, and the compressor frequency information and expansion valve opening information as control inputs, the superheat estimate and dryness estimate of the suction port of the variable frequency compressor are calculated through the constructed extended Kalman filter state observer. If the estimated dryness value is greater than the preset dryness threshold, the target superheat deviation is obtained by calculating the difference between the estimated superheat value and the target superheat value. The target superheat value is obtained by querying a preset database based on the real-time outdoor temperature and the compressor frequency information. The database includes the correspondence between outdoor temperature, compressor frequency and superheat. Based on the target superheat deviation, the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor are determined, and the electronic expansion valve and the compressor are adjusted according to the opening adjustment information and the frequency adjustment information.

2. The method according to claim 1, characterized in that, The method further includes: Extract the amplitude attenuation rate of the main reflection peak from the reflected waveform data; The first correction coefficient is calculated using the ratio of the amplitude attenuation rate to the preset reference attenuation rate; After determining the real-time gas content of the refrigerant in the suction line using a preset relationship between dielectric constant and gas content, the method further includes: The real-time gas content is adjusted using the first correction coefficient to obtain the adjusted real-time gas content.

3. The method according to claim 2, characterized in that, The method further includes: Extract the power spectral density of the main reflection peak from the reflected waveform data; The second correction coefficient is calculated based on the ratio of the energy proportion of the target frequency band in the power spectral density to the preset energy proportion. The step of adjusting the real-time gas content using the first correction coefficient to obtain the adjusted real-time gas content includes: The real-time gas content is adjusted using the first correction coefficient and the second correction coefficient to obtain the adjusted real-time gas content.

4. The method according to claim 1, characterized in that, The method further includes: If the estimated dryness value is less than or equal to the preset dryness threshold, calculate the dryness difference between the estimated dryness value and the preset dryness threshold; The target number of opening adjustment steps is determined based on the multiple of the preset dryness threshold and the difference in dryness, and the electronic expansion valve is adjusted according to the target number of opening adjustment steps.

5. The method according to claim 1, characterized in that, The determination of the relative permittivity of the refrigerant in the suction line based on the transit time of the reflected waveform in the reflected waveform data and preset sensor probe length information includes: In the pulse reflection waveform data, the starting reflection feature point where the electromagnetic pulse enters the sensor probe and the terminal reflection feature point where the electromagnetic pulse reaches the end of the sensor probe are determined, and the time interval between the starting reflection feature point and the terminal reflection feature point is calculated to determine the transit time of the electromagnetic pulse propagating back and forth within the sensor probe. Based on the transit time and the preset sensor probe length information, the propagation rate of the electromagnetic pulse in the refrigerant is calculated. The relative permittivity of the refrigerant in the intake line is calculated by using the proportional relationship between the propagation rate and the electromagnetic pulse reference propagation rate.

6. The method according to claim 1, characterized in that, The extended Kalman filter state observer includes a state transition model, an observation model, and a Kalman gain matrix; The process involves using the real-time gas content, suction pressure data, and suction temperature data as measurement inputs, and the compressor frequency information and expansion valve opening information as control inputs. Through a pre-constructed extended Kalman filter state observer, the estimated superheat and dryness values ​​at the suction port of the variable frequency compressor are calculated, including: The compressor frequency information, the expansion valve opening information, and the superheat and dryness estimates from the previous control cycle are input into the state transition model to obtain state prediction values ​​including the predicted superheat and dryness for the current control cycle. The state prediction value is input into the observation model to calculate the measurement prediction value, which includes the predicted gas content, the predicted inhalation pressure, and the predicted inhalation temperature. The observation model includes the conversion relationship between the state prediction value and the measurement prediction value. The predicted measurement value is compared item by item with the real-time measurement value composed of the real-time gas content, the inhalation pressure data and the inhalation temperature data to calculate the prediction residual vector. The predicted residual vector is corrected using the Kalman gain matrix to update the state prediction value, and the predicted superheat and predicted dryness in the updated state prediction value are used as the superheat and dryness estimates for the current control cycle.

7. The method according to claim 1, characterized in that, The opening adjustment information includes the opening adjustment direction and the opening adjustment rate, and the frequency adjustment information includes the frequency adjustment direction; The step of determining the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor based on the target superheat deviation includes: Based on the sign and magnitude of the target superheat deviation, determine the opening adjustment direction and the opening adjustment rate of the electronic expansion valve; Based on the opening adjustment direction, the frequency adjustment direction of the compressor is determined in a preset compensation rule base.

8. A low-temperature adaptive control system for an air-source heat pump variable frequency drive system, characterized in that, The system includes: The acquisition module is used to acquire suction pressure data, suction temperature data, and pulse reflection waveform data in the suction line of the variable frequency compressor, as well as compressor frequency information and expansion valve opening information for the current control cycle. The pulse reflection waveform data is obtained by collecting electromagnetic pulse signals reflected by the refrigerant through a sensor installed in the suction line. The determination module is used to determine the relative permittivity of the refrigerant in the suction line based on the transit time of the reflected waveform in the reflected waveform data and the preset sensor probe length information, and to determine the real-time gas content of the refrigerant in the suction line using the preset correspondence between the permittivity and the gas content. The calculation module is used to take the real-time gas content, the suction pressure data and the suction temperature data as measurement inputs, and the compressor frequency information and the expansion valve opening information as control inputs. Through the constructed extended Kalman filter state observer, it calculates the estimated superheat and dryness of the suction port of the variable frequency compressor. The calculation module is also used to obtain a target superheat deviation by calculating the difference between the estimated superheat value and the target superheat value when the estimated dryness value is greater than the preset dryness threshold. The target superheat value is obtained by querying a preset database based on the real-time outdoor temperature and the compressor frequency information. The database includes the correspondence between outdoor temperature, compressor frequency and superheat. The adjustment module is also used to determine the opening adjustment information of the electronic expansion valve and the frequency adjustment information of the compressor based on the target superheat deviation, and to adjust the electronic expansion valve and the compressor according to the opening adjustment information and the frequency adjustment information.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the low-temperature adaptive control method for the air source heat pump variable frequency drive system as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the low-temperature adaptive control method for the air-source heat pump variable frequency drive system as described in any one of claims 1-7.

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