A superheat control method and system for suppressing superheat oscillations

Abstract

This invention discloses a superheat control method and system for suppressing superheat oscillations. The method includes: a process identification module identifying the dynamic characteristics of the superheat control system through a transfer function; a predictive control module constructing a state-space model based on the dynamic characteristics, and performing state prediction based on state estimates and disturbance estimates, outputting a state prediction vector and calculating a first objective function; a parameter identification and self-tuning module self-tuning the PID control parameters based on the dynamic characteristics and a second objective function; a PID control module outputting an electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset superheat target value, and the tuned PID control parameters; and an electronic expansion valve driver driving the electronic expansion valve to adjust the refrigerant flow rate according to the electronic expansion valve drive signal. The technical solution provided by this invention significantly suppresses superheat oscillations caused by the refrigeration system operating at low superheat, improving system stability.

Description

Technical Field

[0001] This invention relates to the field of refrigeration system control technology, and in particular to a superheat control method and system for suppressing superheat oscillations. Background Technology

[0002] When a refrigeration system operates at low superheat, the random alternation of refrigerant flow patterns at the evaporator outlet can cause drastic changes in the heat transfer coefficient, leading to superheat oscillations. These oscillations can cause superheat fluctuations and data errors, hindering the rapid response and tracking control of superheat under dynamic operating conditions of the refrigeration system, and in severe cases, even causing liquid slugging in the compressor.

[0003] Existing technologies typically adjust the opening of electronic expansion valves using a proportional-integral-derivative (PID) method based on the deviation between the measured superheat and the target superheat, or the rate of change of that deviation. However, these existing technologies suffer from oscillation characteristics that are difficult to adapt to dynamic changes, especially under variable frequency operation, where the oscillation amplitude intensifies with increasing frequency, leading to poor system stability. Summary of the Invention

[0004] This invention provides a superheat control method and system for suppressing superheat oscillations, which can effectively improve system stability by suppressing superheat oscillations caused by the random alternation of refrigerant flow at the evaporator outlet during low superheat operation in variable frequency refrigeration systems.

[0005] According to one aspect of the present invention, a superheat control method for suppressing superheat oscillations is provided, executed by a superheat control system for suppressing superheat oscillations, the superheat control system comprising: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module; the superheat control method comprises:

[0006] The evaporator introduces process noise, the measurement module introduces observation noise, and the measurement module measures the evaporator outlet parameters in real time; the evaporator outlet parameters include evaporator outlet superheat, refrigerant mass flow rate out of the evaporator, and flow regime identifier;

[0007] The Kalman filter processes the evaporator outlet parameters and outputs state estimates and disturbance estimates.

[0008] The process identification module identifies the dynamic characteristics of the superheat control system through a transfer function;

[0009] The predictive control module constructs a state-space model based on the dynamic characteristics, performs state prediction based on the state estimate and disturbance estimate, outputs the state prediction vector, and calculates the first objective function.

[0010] The parameter identification and self-tuning module performs self-tuning of the PID control parameters based on the dynamic characteristics and the second objective function.

[0011] The PID control module outputs an electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset target value of superheat, as well as the tuned PID control parameters.

[0012] The electronic expansion valve driver drives the electronic expansion valve to operate according to the electronic expansion valve drive signal, so as to regulate the refrigerant flow.

[0013] Optionally, the transfer function of the process identification module satisfies the following relationship:

[0014]

[0015] Where G(s) is the transfer function of the superheat control system, K is the gain of the superheat control system, τ is the pure time delay, T1 and T2 are two time constants of the second-order element, and s is a complex variable.

[0016] Optionally, the first objective function satisfies the following relationship:

[0017]

[0018] Among them, J Kk Let the first objective function be... A target value for superheat is preset, ΔS is the flow regime switching frequency, and λ is the weighting coefficient.

[0019] Optionally, the predictive control module outputs a state prediction vector according to the following formula:

[0020] x k|k-1 =F k-1 x k-1|k-1 +B k-1 u k-1 ;

[0021] x k|k-1 Let F be the state prediction vector at time k. k-1 Let B be the state transition matrix. k-1 To control the input matrix, u k-1 Let x be the control input vector at time k-1. k-1|k-1 This is the state estimation vector at time k-1.

[0022] Optionally, the second objective function satisfies the following relationship:

[0023]

[0024] Among them, J ITAE Let |e(t)| be the objective function for the absolute error of the integral time, which is also the second objective function; |e(t)| is the absolute value of the error; and t is the time variable.

[0025] Optionally, the state transition matrix and the control input matrix are established using the following model:

[0026]

[0027] In the formula: M is the mass of refrigerant flowing out of the evaporator. T is the refrigerant mass flow rate exiting the evaporator. evap,ou h represents the refrigerant temperature at the evaporator outlet. at Let A be the convective heat transfer coefficient between the evaporator wall and the outside environment. evap c is the area of ​​the evaporator wall. evap,out T is the specific heat of the refrigerant at the evaporator outlet. at The ambient temperature, h represents the change in refrigerant mass flow rate within the evaporator. evap,in T is the specific enthalpy of the refrigerant at the evaporator inlet. sat C is the refrigerant saturation temperature corresponding to the refrigerant pressure at the evaporator outlet. d A is the flow coefficient of the electronic expansion valve. eev Let ρ be the flow area of ​​the electronic expansion valve, ρ be the density of the refrigerant at the inlet of the electronic expansion valve, ΔP be the pressure difference across the electronic expansion valve, and h be the flow area of ​​the electronic expansion valve. eev β is the valve needle opening of the electronic expansion valve, β is the valve needle cone angle of the electronic expansion valve, and d is the valve needle opening. eev This refers to the valve needle orifice diameter of the electronic expansion valve.

[0028] Optionally, the parameter identification and self-tuning module performs self-tuning of the PID control parameters based on the dynamic characteristics and the second objective function, including:

[0029] The parameter identification and self-tuning module calculates the objective function value under the initial PID control parameters based on the second objective function, and uses the simplex method to optimize the PID control parameters so that the second objective function is minimized.

[0030] Optionally, the observation noise covariance is dynamically adjusted based on the flow regime identifier.

[0031] Optionally, the observed noise covariance, dynamically adjusted according to the flow regime identifier, should satisfy the following relationship:

[0032]

[0033] Wherein, FlowState represents the observation noise covariance. k R is the flow regime identifier at time k.stable For the observation noise covariance of the steady-state vapor flow, R is taken as the value. oscillate1 R represents the observed covariance value when the flow pattern is identified as the first oscillating state, mist flow. oscillate2 R represents the observed covariance when the flow pattern is identified as the second oscillating state, annular flow. oscillate2 ≥R oscillate1 >R stable .

[0034] According to another aspect of the present invention, a superheat control system for suppressing superheat oscillations is provided, for executing the superheat control method for suppressing superheat oscillations according to any embodiment of the present invention. The superheat control system includes: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module connected in sequence.

[0035] The evaporator introduces process noise, the measurement module introduces observation noise, and the measurement module is used to measure the evaporator outlet parameters in real time; the evaporator outlet parameters include evaporator outlet superheat, refrigerant mass flow rate out of the evaporator, and flow regime identifier;

[0036] The Kalman filter is used to process the evaporator outlet parameters and output state estimates and disturbance estimates.

[0037] The process identification module is used to identify the dynamic characteristics of the superheat control system through the transfer function;

[0038] The predictive control module is used to construct a state-space model based on the dynamic characteristics, and to predict the state based on the state estimate and disturbance estimate, outputting a state prediction vector and calculating a first objective function;

[0039] The parameter identification and self-tuning module is used to self-tune the PID control parameters based on the dynamic characteristics and the second objective function.

[0040] The PID control module is used to output an electronic expansion valve drive signal based on the deviation between the superheat predicted value in the state prediction vector and the preset superheat target value, as well as the tuned PID control parameters.

[0041] The electronic expansion valve driver is used to drive the electronic expansion valve to operate according to the electronic expansion valve drive signal, so as to regulate the refrigerant flow.

[0042] This invention provides a superheat control method and system for suppressing superheat oscillations. The method includes: introducing process noise into the evaporator and observation noise into a measurement module; the measurement module measures the evaporator outlet parameters in real time; a Kalman filter processes the evaporator outlet parameters and outputs state estimates and disturbance estimates; a process identification module identifies the dynamic characteristics of the superheat control system through a transfer function; a predictive control module constructs a state-space model based on the dynamic characteristics and performs state prediction based on the state estimates and disturbance estimates, outputting a state prediction vector and calculating a first objective function; a parameter identification and self-tuning module self-tunes the PID control parameters based on the dynamic characteristics and the second objective function; a PID control module outputs an electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset target superheat value, as well as the tuned PID control parameters; and an electronic expansion valve driver drives the electronic expansion valve to adjust the refrigerant flow rate according to the electronic expansion valve drive signal. The technical solution provided by this invention effectively reduces control deviation by using flow regime identification and noise adaptation, and predicting superheat using an improved Kalman filter. It identifies the dynamic characteristics of the superheat control system by setting up a process identification module and using a transfer function; it constructs a state-space model based on the dynamic characteristics and performs state prediction based on state estimates and disturbance estimates, outputting a state prediction vector and calculating a first objective function; and it sets up a parameter identification and self-tuning module to self-tune the PID control parameters based on the dynamic characteristics and a second objective function. This makes it suitable for rapid response and tracking control of superheat under dynamic operating conditions such as variable frequency compressors and variable loads, significantly suppressing superheat oscillations caused by the refrigeration system operating at low superheat and improving system stability.

[0043] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of a superheat control system for suppressing superheat oscillations provided in an embodiment of the present invention;

[0046] Figure 2 A flowchart of a superheat control method for suppressing superheat oscillations provided in an embodiment of the present invention;

[0047] Figure 3 This is a schematic diagram of the structure of a refrigeration system employing a superheat control method for suppressing superheat oscillations provided in an embodiment of the present invention;

[0048] Figure 4 A curve comparison of the optimal overheat estimate of a conventional Kalman filter and the optimal overheat estimate of an improved Kalman filter provided in an embodiment of the present invention;

[0049] Figure 5 An adaptive observation noise covariance curve for improved Kalman filter control provided in this embodiment of the invention;

[0050] Figure 6 This is a superheat oscillation curve under conventional control provided in an embodiment of the present invention;

[0051] Figure 7 The diagram shows the overheating oscillation curve controlled by the improved Kalman filter provided in this embodiment of the invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0053] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0054] Figure 1 This is a schematic diagram of a superheat control system for suppressing superheat oscillations provided in an embodiment of the present invention. Figure 2This is a flowchart illustrating a superheat control method for suppressing superheat oscillations, provided in an embodiment of the present invention. This embodiment is applicable to variable frequency refrigeration systems operating at low superheat levels, where superheat oscillations occur due to the random alternation of refrigerant flow patterns at the evaporator outlet. This method can be executed by a superheat control system for suppressing superheat oscillations. See also... Figure 1 The system includes: a PID control module 11, an electronic expansion valve driver 12, an electronic expansion valve 13, an evaporator 14, a measurement module 15, a Kalman filter 16, a process identification module 17, a parameter identification and self-tuning module 18, and a predictive control module 19. See also... Figure 2 The method includes:

[0055] S110, Evaporator introduces process noise, Measurement module introduces observation noise, Measurement module measures evaporator outlet parameters in real time.

[0056] The evaporator outlet parameters include the evaporator outlet superheat, the refrigerant mass flow rate out of the evaporator, and the flow regime indicator.

[0057] Specifically, Figure 3 This is a schematic diagram of a refrigeration system employing a superheat control method for suppressing superheat oscillations provided in an embodiment of the present invention. (See attached diagram.) Figure 3 The refrigeration system includes, but is not limited to, the following components: an evaporator, an electronic expansion valve, a flow pattern identifier, a refrigerant mass flow meter, and sensors. Temperature and pressure sensors are installed at the inlet of the electronic expansion valve, the inlet of the evaporator, and the outlet of the evaporator to collect the temperature and pressure at the inlet and outlet of the electronic expansion valve, and at the inlet and outlet of the evaporator, respectively. Simultaneously, a flow pattern identifier is installed at the evaporator outlet, and a refrigerant mass flow meter is installed at the inlet of the electronic expansion valve. The flow pattern identifier can identify the refrigerant flow pattern at the evaporator outlet through image processing or thermodynamic features. The refrigerant mass flow meter is used to detect the refrigerant mass flow rate. Therefore, the evaporator outlet superheat can be calculated by subtracting the saturation temperature from the evaporator outlet temperature. The evaporator outlet temperature can be acquired by the evaporator outlet temperature sensor. The saturation temperature can be obtained by looking up the corresponding saturation temperature value based on the pressure acquired by the evaporator outlet pressure sensor. The refrigerant mass flow rate can be detected by the mass flow meter, and the flow pattern identifier can be identified by the flow pattern identifier based on image processing or thermodynamic features at the evaporator outlet. Refrigerant flow patterns include vapor flow, mist flow, and annular flow.

[0058] After processing the evaporator outlet parameters, the S120 and Kalman filter output state estimates and disturbance estimates.

[0059] See also Figure 1After the measurement module measures the evaporator outlet parameters in real time, it transmits the evaporator outlet parameters to the Kalman filter. The Kalman filter performs noise reduction and filtering on the evaporator outlet parameters and outputs the state estimate and disturbance estimate.

[0060] S130, the process identification module identifies the dynamic characteristics of the superheat control system through the transfer function.

[0061] Specifically, the inverse M-sequence is a sequence with good pseudo-random characteristics. It is used to excite the electronic expansion valve to estimate parameters of the evaporator outlet superheat control model. The process mainly includes designing the inverse M-sequence, establishing the superheat control model, conducting experimental excitation, data acquisition and processing, and parameter estimation. Based on specific application requirements and system characteristics, the generation rules of the inverse M-sequence are determined, including the sequence length, value range, and variation law. For example, it is necessary to consider whether the frequency characteristics of the sequence can effectively excite the electronic expansion valve to produce appropriate opening changes. Based on the physical principles of the refrigeration system and the working characteristics of the evaporator, a mathematical model is constructed to describe the relationship between the evaporator outlet superheat and factors such as the electronic expansion valve opening, refrigerant mass flow rate, and refrigerant flow state. The designed inverse M-sequence is used as the input signal and input into the superheat control system, causing the electronic expansion valve to change its opening according to the law of the inverse M-sequence, thereby stimulating the evaporator outlet superheat and causing corresponding dynamic changes in superheat. Sensors are used to collect relevant data such as evaporator outlet superheat, electronic expansion valve opening, and refrigerant mass flow rate under inverse M-sequence excitation. The collected data may contain noise and other interference, requiring preprocessing methods such as denoising, filtering, and smoothing to improve data quality and provide a reliable data foundation for subsequent parameter estimation. Using specific parameter estimation methods, such as least squares and maximum likelihood estimation, the estimated values ​​of each parameter in the superheat control model are calculated based on the collected and processed data. By continuously adjusting the parameters, the model output is made as close as possible to the actual collected data, thereby determining the model parameters that best reflect the true characteristics of the system.

[0062] A second-order mathematical model with pure time delay is used to approximate the superheat control process; optionally, the transfer function of the process identification module satisfies the following relationship:

[0063]

[0064] Where G(s) is the transfer function of the superheat control system, representing the input-output relationship of the system in the complex frequency domain s; K is the gain of the superheat control system, representing the amplification factor of the system to the input signal; τ is the pure time delay, which reflects the time delay in the system, that is, the input signal only affects the system after time τ; T1 and T2 are two time constants of the second-order element, which determine the dynamic response characteristics of the system. The larger the time constant, the slower the system response speed; s is a complex variable, which is used in the Laplace transform to convert the time-domain signal to the complex frequency domain for analysis.

[0065] S140. The predictive control module constructs a state-space model based on dynamic characteristics, and performs state prediction based on state estimates and disturbance estimates, outputting a state prediction vector and calculating the first objective function.

[0066] Specifically, a state-space model is constructed: Under the given conditions of the controlled object, since the characteristic parameters of the evaporator have the greatest impact on the superheat, the characteristic parameters of the evaporator are established using the first law of thermodynamics and experimental data, and a dynamic model is applied to predict the future trend of the refrigerant state at the evaporator outlet.

[0067] State equation: x k =F k x k-1 +B k u k +w k .

[0068] Observation equation: z k =H k x k +v k .

[0069] in:

[0070] x k Let the state vector be defined as:

[0071] T sh,k Let be the evaporator outlet superheat at the k-th sampling time. Let FlowState be the refrigerant mass flow rate exiting the evaporator at the k-th sampling time. k This is the flow regime identifier at the k-th sampling time. Discrete values: 1 = vapor flow, 2 = mist flow, 3 = annular flow.

[0072] u k To control the input vector, it is defined as: u k =[EVA k ].

[0073] EVA kThe pulse opening of the electronic expansion valve (EEV) at the k-th sampling time is given.

[0074] F k The state transition matrix is ​​defined as follows:

[0075] f ij (i = 1, 2, 3; j = 1, 2, 3) are coefficients that need to be determined based on the physical characteristics of the actual system, experimental data, or theoretical analysis. For example, f 12 This represents the influence coefficient of the mass flow rate at the previous moment on the superheat at the current moment.

[0076] B k The control input matrix is ​​defined as follows:

[0077] b 11 b 21 b 31 These represent the influence coefficients of the electronic expansion valve opening on superheat, mass flow rate, and flow regime indication, respectively, and need to be determined based on the actual situation.

[0078] w k Let Q be the process noise vector, assuming it follows a Gaussian distribution with zero mean and a covariance matrix of Q. k .

[0079] H k The observation matrix, since the sensors in this system can directly measure superheat, mass flow rate, and flow regime indicators, can be defined as follows:

[0080]

[0081] v k To observe the noise vector, it is assumed that it follows a Gaussian distribution with zero mean and a covariance matrix of R. k .

[0082] The predictive control module uses the state estimate as the initial condition for the current state, while also considering the impact of the disturbance estimate on the system. Using the pre-constructed state-space model, it predicts the state changes of the system over a future period through mathematical calculations and derivations, thus obtaining the state prediction value.

[0083] To improve the filter's adaptability to dynamic system conditions and enhance algorithm stability, objective function optimization is employed: a flow regime switching penalty term is introduced, and the Kalman gain K is adjusted accordingly. k To minimize the objective function; optionally, the first objective function satisfies the following relationship:

[0084]

[0085] Among them, JKk Let the first objective function be... A target value for superheat is preset, ΔS is the flow regime switching frequency, and λ is the weighting coefficient.

[0086] The algorithm consists of two steps: prediction and update.

[0087] Prediction steps:

[0088] Predicted state: x k|k-1 =F k-1 x k-1|k-1 +B k-1 u k-1

[0089] Predicting covariance:

[0090] Update steps:

[0091] Calculate the Kalman gain:

[0092] Update status: x k|k =x k|k-1 +K k (z k -H k x k|k-1 )

[0093] Update covariance: P k|k =(IK k H k )P k|k-1

[0094] S150, the parameter identification and self-tuning module, performs self-tuning of the PID control parameters based on dynamic characteristics and the second objective function.

[0095] Specifically, the dynamic characteristics of the system directly determine the initial range and adjustment direction of the PID control parameters. The second objective function is a quantitative indicator that measures the control effect, transforming the control objective into an optimizable value through a mathematical expression. For example, the integral of time-multiplied absolute error (ITAE) can be used as the second objective function. Figure 1 The switch K is connected to point a. The parameter identification and self-tuning module generates a set of candidate PID parameter combinations based on the current system dynamic characteristics. For each set of parameters, the value of the second objective function is calculated through simulation or actual operation. The parameters are iteratively adjusted using optimization algorithms, such as the simplex method, until the objective function value is minimized, thus finding the optimal PID control parameters. These optimal PID control parameters are then assigned to the PID control module. Figure 1 The switch K is connected to point b for conventional PID control.

[0096] The S160 PID control module outputs the electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset target value of superheat, as well as the tuned PID control parameters.

[0097] Specifically, an incremental PID controller is used, based on the system state x estimated by the Kalman filter. k |k-1=F k- 1x k -1|k-1+B k-1 u k-1 The control input is calculated using a proportional (P) terminator, which outputs the control input proportionally to the error between the current estimated state and the desired state, reacting quickly to the error. The integral (I) terminator integrates the error to eliminate steady-state error, ensuring the system eventually stabilizes in the desired state. The derivative (D) terminator adjusts the control input based on the rate of change of the error, improving the system's dynamic performance. By providing accurate state estimation through a Kalman filter, the PID controller can control more precisely, reducing overshoot, shortening settling time, and improving the system's resistance to disturbances and model uncertainties. The deviation between the predicted state value and the preset target value for overheat is...

[0098] S170, the electronic expansion valve driver drives the electronic expansion valve to operate according to the electronic expansion valve drive signal, so as to regulate the refrigerant flow.

[0099] Specifically, Figure 4 This is a curve comparison chart of the optimal overheat estimate of a conventional Kalman filter and the optimal overheat estimate of an improved Kalman filter provided in an embodiment of the present invention. Figure 4 It can be seen that the overheating prediction structure of the improved Kalman filter is more accurate. Figure 5 This invention provides an embodiment of an adaptive observation noise covariance curve for an improved Kalman filter control, derived from... Figure 5 It can be seen that the adaptive observation noise covariance controlled by the improved Kalman filter has a relatively stable oscillation amplitude at the flow state switching point. Figure 6 This is a superheat oscillation curve under conventional control provided in an embodiment of the present invention. Figure 7 The overheating oscillation curve of the improved Kalman filter control provided in the embodiment of the present invention is shown in the figure. Figure 6 and Figure 7 A comparison shows that the control method for suppressing overheating oscillations provided in this embodiment of the invention reduces the oscillation amplitude by 70%.

[0100] This invention provides a superheat control method and system for suppressing superheat oscillations. The method includes: introducing process noise into the evaporator and observation noise into a measurement module; the measurement module measures the evaporator outlet parameters in real time; a Kalman filter processes the evaporator outlet parameters and outputs state estimates and disturbance estimates; a process identification module identifies the dynamic characteristics of the superheat control system through a transfer function; a predictive control module constructs a state-space model based on the dynamic characteristics and performs state prediction based on the state estimates and disturbance estimates, outputting a state prediction vector and calculating a first objective function; a parameter identification and self-tuning module self-tunes the PID control parameters based on the dynamic characteristics and the second objective function; a PID control module outputs an electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset target superheat value, as well as the tuned PID control parameters; and an electronic expansion valve driver drives the electronic expansion valve to adjust the refrigerant flow rate according to the electronic expansion valve drive signal. The technical solution provided by this invention effectively reduces control deviation by using flow regime identification and noise adaptation, and predicting superheat using an improved Kalman filter. It identifies the dynamic characteristics of the superheat control system by setting up a process identification module and using a transfer function; it constructs a state-space model based on the dynamic characteristics and performs state prediction based on state estimates and disturbance estimates, outputting a state prediction vector and calculating a first objective function; and it sets up a parameter identification and self-tuning module to self-tune the PID control parameters based on the dynamic characteristics and a second objective function. This makes it suitable for rapid response and tracking control of superheat under dynamic operating conditions such as variable frequency compressors and variable loads, significantly suppressing superheat oscillations caused by the refrigeration system operating at low superheat and improving system stability.

[0101] Optionally, the predictive control module outputs a state prediction vector according to the following formula:

[0102] x k |k-1=F k-1 x k-1 |k-1+B k-1 u k-1 In the formula, x k|k-1 Let F be the state prediction vector at time k. k-1 Let B be the state transition matrix. k-1 To control the input matrix, u k-1 Let x be the control input vector at time k-1. k-1|k-1 This is the state estimation vector at time k-1.

[0103] Optionally, the second objective function satisfies the following relationship:

[0104] Among them, J ITAELet |e(t)| be the objective function for the absolute error of the integral time, which is also the second objective function; |e(t)| is the absolute value of the error; and t is the time variable.

[0105] Optionally, the state transition matrix and control input matrix are established using the following model:

[0106]

[0107] In the formula: M is the mass of refrigerant flowing out of the evaporator. T is the refrigerant mass flow rate exiting the evaporator. evap,ou h represents the refrigerant temperature at the evaporator outlet. at Let A be the convective heat transfer coefficient between the evaporator wall and the outside environment. evap c is the area of ​​the evaporator wall. evap,out T is the specific heat of the refrigerant at the evaporator outlet. at The ambient temperature, h represents the change in refrigerant mass flow rate within the evaporator. evap,in T is the specific enthalpy of the refrigerant at the evaporator inlet. sat C is the refrigerant saturation temperature corresponding to the refrigerant pressure at the evaporator outlet. d A is the flow coefficient of the electronic expansion valve. eev Let ρ be the flow area of ​​the electronic expansion valve, ρ be the density of the refrigerant at the inlet of the electronic expansion valve, ΔP be the pressure difference across the electronic expansion valve, and h be the flow area of ​​the electronic expansion valve. eev β is the valve needle opening of the electronic expansion valve, β is the valve needle cone angle of the electronic expansion valve, and d is the valve needle opening. eev This refers to the valve needle orifice diameter of the electronic expansion valve.

[0108] Specifically, scatter plots are generated from experimental data to observe the distribution trend of the data, and an appropriate function form is selected to fit the data to obtain the flow regime identifier. The specific functional relationship was determined. A scatter plot was created using experimental data, and polynomial fitting was employed to determine the valve needle opening h of the electronic expansion valve. eev With pulse opening EVA k The specific functional relationship h between them eev =a0+a1EVA k +a2EVA k 2 +a3EVA k 3 +…+a n EVA k n .

[0109] Optionally, step S150 specifically includes:

[0110] The parameter identification and self-tuning module calculates the objective function value under the initial PID control parameters based on the second objective function, and uses the simplex method to optimize the PID control parameters to minimize the second objective function.

[0111] Optionally, the observation noise covariance is dynamically adjusted based on the flow regime identifier.

[0112] Optionally, the observation noise covariance, dynamically adjusted according to the flow regime, should satisfy the following relationship:

[0113]

[0114] Wherein, FlowState represents the observation noise covariance. k R is the flow regime identifier at time k. stable For the observation noise covariance of the steady-state vapor flow, R is taken as the value. oscillate1 R represents the observed covariance value when the flow pattern is identified as the first oscillating state, mist flow. oscillate2 R represents the observed covariance when the flow pattern is identified as the second oscillating state, annular flow. oscillate2 ≥R oscillate1 >R stable .

[0115] See also Figure 1 The superheat control system includes: a PID control module 11, an electronic expansion valve driver 12, an electronic expansion valve 13, an evaporator 14, a measurement module 15, a Kalman filter 16, a process identification module 17, a parameter identification self-tuning module 18, and a predictive control module 19, connected in sequence.

[0116] Evaporator 14 introduces process noise, and measurement module 15 introduces observation noise. Measurement module 15 is used to measure evaporator outlet parameters in real time. Evaporator outlet parameters include evaporator outlet superheat, refrigerant mass flow rate out of evaporator, and flow regime indicator.

[0117] The Kalman filter 16 is used to process the evaporator outlet parameters and output state estimates and disturbance estimates.

[0118] The process identification module 17 is used to identify the dynamic characteristics of the superheat control system through the transfer function.

[0119] The predictive control module 19 is used to construct a state-space model based on dynamic characteristics, and to predict the state based on the state estimate and disturbance estimate, outputting the state prediction vector and calculating the first objective function.

[0120] The parameter identification and self-tuning module 18 is used to self-tun the PID control parameters based on dynamic characteristics and the second objective function.

[0121] The PID control module 11 is used to output the electronic expansion valve drive signal based on the deviation between the superheat predicted value in the state prediction vector and the preset superheat target value, as well as the tuned PID control parameters.

[0122] The electronic expansion valve driver 12 is used to drive the electronic expansion valve 13 to operate according to the electronic expansion valve drive signal, so as to regulate the refrigerant flow.

[0123] The superheat control system for suppressing superheat oscillations provided in the embodiments of the present invention can execute the superheat control method for suppressing superheat oscillations provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0124] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A superheat control method of suppressing superheat oscillation, characterized by, The control is executed by a superheat control system that suppresses superheat oscillations. The superheat control system includes: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification and self-tuning module, and a predictive control module. The superheat control method includes: The evaporator introduces process noise, the measurement module introduces observation noise, and the measurement module measures the evaporator outlet parameters in real time; the evaporator outlet parameters include evaporator outlet superheat, refrigerant mass flow rate out of the evaporator, and flow regime identifier; The Kalman filter processes the evaporator outlet parameters and outputs state estimates and disturbance estimates. The process identification module identifies the dynamic characteristics of the superheat control system through a transfer function; The predictive control module constructs a state-space model based on the dynamic characteristics, and performs state prediction based on the state estimate and disturbance estimate, outputting a state prediction vector and calculating the first objective function; The parameter identification and self-tuning module performs self-tuning of the PID control parameters based on the dynamic characteristics and the second objective function. The PID control module outputs an electronic expansion valve drive signal based on the deviation between the predicted superheat value in the state prediction vector and the preset target value of superheat, as well as the tuned PID control parameters. The electronic expansion valve driver drives the electronic expansion valve to operate according to the electronic expansion valve drive signal, so as to regulate the refrigerant flow rate; The transfer function of the process identification module satisfies the following relationship: ； in, The transfer function of the superheat control system, For the gain of the superheat control system, For pure time delay, and These are the two time constants of the second-order element. It is a complex variable; The first objective function satisfies the following relationship: ； wherein, is a first objective function, is a preset target value of superheat degree, is a flow state switching frequency, is a weight coefficient.

2. The superheat control method according to claim 1, characterized by, The predictive control module outputs a state prediction vector according to the following formula: ； is a state prediction vector for time instant k, is a state transition matrix, is a control input matrix, is a control input vector for time instant k-1, is a state estimation vector for time instant k-1.

3. The superheat control method according to claim 1, characterized by, The second objective function satisfies the following relationship: ； wherein is the integral time absolute error objective function, i.e. the second objective function; is the absolute value of the error; is the time variable.

4. The superheat control method according to claim 2, characterized by, The state transition matrix and the control input matrix are established using the following model: ； In the formula: The quality of the refrigerant flowing out of the evaporator. The refrigerant mass flow rate exiting the evaporator. The refrigerant temperature at the evaporator outlet. The convective heat transfer coefficient between the evaporator wall and the outside environment. This represents the area of ​​the evaporator wall. The specific heat of the refrigerant at the evaporator outlet. The ambient temperature, This represents the change in refrigerant mass flow rate within the evaporator. The specific enthalpy of the refrigerant at the evaporator inlet. This represents the refrigerant saturation temperature corresponding to the refrigerant pressure at the evaporator outlet. The flow coefficient of the electronic expansion valve. The flow area of ​​the electronic expansion valve. The density of the refrigerant at the inlet of the electronic expansion valve. The pressure difference before and after the electronic expansion valve. This refers to the valve needle opening of the electronic expansion valve. The valve needle cone angle of the electronic expansion valve. This refers to the valve needle orifice diameter of the electronic expansion valve.

5. The superheat control method according to claim 1, characterized by, The parameter identification and self-tuning module performs self-tuning of the PID control parameters based on the dynamic characteristics and the second objective function, including: The parameter identification and self-tuning module calculates the objective function value under the initial PID control parameters based on the second objective function, and uses the simplex method to optimize the PID control parameters so that the second objective function is minimized.

6. The superheat control method of claim 1, wherein The observed noise covariance is dynamically adjusted based on the flow regime identifier.

7. The superheat control method according to claim 6, characterized in that, The observed noise covariance, dynamically adjusted according to the flow regime identifier, should satisfy the following relationship: ； Wherein, is the observation noise covariance. This is the flow state identifier at time k. The values ​​are assigned to the observation noise covariance for steady-state vapor flow. The observed covariance values ​​are taken when the flow pattern is identified as the first oscillating state, mist flow. The observed covariance values ​​are taken when the flow pattern is identified as the second type of oscillating annular flow. ≥ > .

8. A superheat control system that suppresses superheat oscillation, characterized by For performing the superheat control method for suppressing superheat oscillations according to any one of claims 1-7, the superheat control system comprises: a PID control module, an electronic expansion valve driver, an electronic expansion valve, an evaporator, a measurement module, a Kalman filter, a process identification module, a parameter identification self-tuning module, and a predictive control module connected in sequence. The evaporator introduces process noise, the measurement module introduces observation noise, and the measurement module is used to measure the evaporator outlet parameters in real time; the evaporator outlet parameters include evaporator outlet superheat, refrigerant mass flow rate out of the evaporator, and flow regime identifier; The Kalman filter is used to process the evaporator outlet parameters and output state estimates and disturbance estimates. The process identification module is used to identify the dynamic characteristics of the superheat control system through the transfer function; The predictive control module is used to construct a state-space model based on the dynamic characteristics, and to predict the state based on the state estimate and disturbance estimate, outputting a state prediction vector and calculating a first objective function; The parameter identification and self-tuning module is used to self-tune the PID control parameters based on the dynamic characteristics and the second objective function. The PID control module is used to output an electronic expansion valve drive signal based on the deviation between the superheat predicted value in the state prediction vector and the preset superheat target value, as well as the tuned PID control parameters. The electronic expansion valve driver is used to drive the electronic expansion valve to operate according to the electronic expansion valve drive signal, so as to regulate the refrigerant flow.

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