Embedded refrigerator intelligent heat exchange control method

Abstract

This invention belongs to the field of freezer energy-saving technology, specifically disclosing an intelligent heat exchange control method for embedded refrigerators, including the following steps: S1, collecting regional temperature information; real-time collection of the inlet and outlet temperatures of the left, right, and rear sides of the condenser; S2, constructing an embedded micro-environment dynamic thermal model: where , , , ; S3, making predictions and adjustments based on the model; based on the embedded micro-environment dynamic thermal model, predicting the predicted condensing temperature at time Δt under the current heat dissipation conditions in real time; if the predicted condensing temperature of the i-th side is close to the preset safety threshold, then proactively increasing the condenser fan speed of the i-th side in advance; through regional sensing and independent control, effectively solving the problems of uneven heat dissipation and local overheating caused by embedded installation, improving system reliability and lifespan, saving energy and reducing costs, improving user experience, and ensuring cooling effect.

Description

Technical Field

[0001] This invention belongs to the field of energy-saving technology for freezers, and specifically relates to an intelligent heat exchange control method for embedded refrigerators. Background Technology

[0002] Built-in refrigerators have become a standard feature in modern kitchen designs due to their aesthetic appeal. However, once embedded in cabinets, their heat dissipation environment deteriorates dramatically. The narrow and asymmetrical spaces on the left, right, and rear sides often render traditional simple on / off control based on a single temperature sensor or compressor inverter control ineffective, leading to uneven heat dissipation and localized overheating. The narrow and asymmetrical installation space results in uneven airflow resistance, causing localized overheating of the condenser, increased system condensing pressure, decreased coefficient of performance (COP), and prolonged high-load operation of the compressor, shortening its lifespan. Furthermore, the noise generated by the compressor and high-speed fan interferes with the user's cooking or resting time, impacting the user experience. Summary of the Invention

[0003] To address the problems in existing technologies, this invention proposes an embedded intelligent heat exchange control method for refrigerators, achieving a combination of balanced heat dissipation, minimized energy consumption costs, and optimized user experience. The invention provides the following technical solution: An intelligent heat exchange control method for an embedded refrigerator includes the following steps: S1. Collect temperature information for the area; Real-time acquisition of inlet and outlet temperatures on the left, right, and rear sides of the condenser; S2. Construct an embedded microenvironment dynamic thermal model: , , , ; in, For the total heat capacity of the system, This is the condensation temperature. For time, To dissipate heat from the refrigerant, This represents the real-time heat dissipation of the system to the external environment. This is the refrigerant mass flow rate. Specific enthalpy of the refrigerant; For an effective convective heat transfer coefficient, The effective heat exchange area of ​​the condenser. The average ambient temperature of the cabinet's microenvironment; , , These are the undetermined parameters for the model, obtained from experimental data; Let t be the rotational speed of the i-th fan. Let be the heat dissipation efficiency coefficient of the i-th side. This is the maximum rated speed of the fan. This is a simplification of natural convection and radiation effects; S3. Make predictions and adjustments based on the model; Based on the embedded microenvironment dynamic thermal model, the predicted condensing temperature at time Δt under the current heat dissipation conditions is predicted in real time. If the predicted condensing temperature on the i-th side is close to the preset safety threshold, the condensing fan speed on the i-th side is proactively increased in advance.

[0004] Preferably, in step S2, , For the desired temperature difference, Let i be the outlet temperature on the i-th side. Let be the inlet temperature of the i-th side.

[0005] Preferably, in step S3, multiple sets of records are made while the refrigerator is running stably. Data; at discrete points in time, , ,get , will multiple groups Data and calculations The data pairs are substituted into the above formula for curve fitting. Optimize.

[0006] Preferably, in step S3, the currently measured Current fan speed And the calculated Substituting into the model, the differential equation is solved numerically to obtain the predicted condensation temperature after the future scheduling period T. .

[0007] Preferably, the condensation temperature will be predicted. Converted to the corresponding predicted condensation pressure and the system's preset safe pressure threshold In comparison, if The controller will immediately feed forward and calculate the required fresh air fan speed before the compressor load actually increases. And execute; if If the controller maintains or reduces the fan speed, then the fan speed will be reduced.

[0008] Preferably, the refrigerator is equipped with a pre-installed temperature sensor assembly, which is distributed at least on the left, right and rear sides of the condenser, and the control of each condenser is independent of each other.

[0009] Preferably, the refrigerator is equipped with a human body sensor to determine the presence of the user; when the user is nearby, the maximum operating frequency of the compressor and the maximum speed of the fan are reduced.

[0010] Preferably, the real-time electricity price signal within a future scheduling period T is received via a communication module. Generate an energy-saving preference coefficient based on electricity price signals. , Substitute the controller output into Then output it.

[0011] Preferably, the user sets the lower and upper limits of the temperature fluctuation range of the refrigerator compartment, as well as the lower and upper limits of the allowable temperature fluctuation range of the freezer compartment, as constraints.

[0012] Preferably, the user sets a learning cycle, and after each learning cycle, records data on changes in power consumption, noise levels, and temperature. This data is then calibrated and evaluated by the user as a basis for the next learning cycle. Basis for data verification.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. By using regional sensing and independent control, the uneven heat dissipation and local overheating caused by embedded installation are effectively solved, thereby improving system reliability and lifespan; 2. By taking real-time electricity prices as the core optimization target, we proactively schedule cooling tasks, significantly reducing the user's lifetime electricity costs; 3. The refrigerator is equipped with a human body sensor. When a user approaches, the maximum operating frequency of the compressor and the maximum speed of the fan are reduced to prioritize the quiet operation and achieve an intelligent experience of "quiet when people are around and efficient when they leave", thereby improving user satisfaction. Attached Figure Description

[0014] Figure 1 This is a schematic diagram illustrating the principle of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. The directional terms mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only for reference to the directions in the accompanying drawings. Therefore, the directional terms used are for illustration and not for limiting the invention.

[0016] Example 1 like Figure 1As shown, an embedded refrigerator intelligent heat exchange control method includes pre-installing temperature sensor components in the refrigerator. These temperature sensor components are distributed on the left, right, and rear sides of the condenser, and the control of each condenser is independent. The method also includes the following steps: S1. Collect temperature information for the area; Real-time acquisition of inlet and outlet temperatures on the left, right, and rear sides of the condenser; The temperatures at the left inlet, left outlet, right inlet, right outlet, rear inlet, and rear outlet are as follows: , , , , , .

[0017] S2. Construct an embedded microenvironment dynamic thermal model: ; in, For the total heat capacity of the system, This is the condensation temperature. For time, To dissipate heat from the refrigerant, This represents the real-time heat dissipation of the system to the external environment. ; in, This is the refrigerant mass flow rate. Specific enthalpy of refrigerant, a fixed parameter; ; in, For effective convective heat transfer coefficient; Where is the effective heat exchange area of ​​the condenser, and is an inherent parameter of the equipment; The average ambient temperature of the cabinet's microenvironment; ; in, , , These are the undetermined parameters for the model, obtained from experimental data; Let t be the rotational speed of the i-th fan. Let be the heat dissipation efficiency coefficient of the i-th side. This is the maximum rated speed of the fan. This is a simplification of natural convection and radiation effects; Specifically, The smaller the value, the worse the heat dissipation conditions on that side.

[0018] in, The desired temperature difference is the rated reference temperature difference of the system under good heat dissipation conditions, which is experimentally determined and pre-stored. Let i be the outlet temperature on the i-th side. Let the inlet temperature i ∈ {left} be the inlet temperature of the i-th side. right back}

[0019] S3. Make predictions and adjustments based on the model; Based on the embedded microenvironment dynamic thermal model, the predicted condensing temperature at time Δt under the current heat dissipation conditions is predicted in real time. If the predicted condensing temperature on the i-th side is close to the preset safety threshold, the condensing fan speed on the i-th side is proactively increased in advance.

[0020] This invention effectively solves the problems of uneven heat dissipation and local overheating caused by embedded installation by using regional sensing and independent control, thereby improving system reliability and lifespan.

[0021] Specifically, in step S3, multiple sets of data are recorded while the refrigerator is running stably. Data; at discrete points in time, , ,get , will multiple groups Data and calculations The data pairs are substituted into the above formula for curve fitting. Optimize.

[0022] Specifically, in step S3, the currently measured... Current fan speed And the calculated Substituting into the model, the differential equation is solved numerically to obtain the predicted condensation temperature after the future scheduling period T. .

[0023] Specifically, the condensation temperature will be predicted. Converted to the corresponding predicted condensation pressure and the system's preset safe pressure threshold In comparison, if The controller will immediately feed forward and calculate the required fresh air fan speed before the compressor load actually increases. And execute; if If the controller maintains or reduces the fan speed, then the fan speed will be reduced.

[0024] Specifically, let's take a dual-frequency (inverter compressor + inverter fan) embedded refrigerator as an example. Its central controller uses a high-performance MCU with an ARM Cortex-M7 core and runs a real-time operating system (RTOS) to ensure real-time performance for multitasking.

[0025] The controller periodically (e.g., every 10 seconds) collects data from each temperature sensor and calculates... , , .Discover After maintaining a speed of 0.6 (minimum) for more than 5 minutes, the right-side fan speed is increased individually using a PID algorithm until it reaches its maximum speed. Improved to 0.9 or higher.

[0026] Example 2 Building upon Example 1, this method uses real-time electricity pricing as the core optimization objective to proactively schedule cooling tasks, significantly reducing the user's lifetime electricity costs. Specifically, it receives real-time electricity price signals for future scheduling periods T via a communication module. Generate an energy-saving preference coefficient based on electricity price signals. , Substitute the controller output into Then output it.

[0027] For example, the controller obtains the daily electricity price curve via the network at midnight and finds that the peak electricity price is at 8 pm (0.8 yuan / kWh). Therefore, it starts the "pre-cooling" program before 7 pm to lower the freezer temperature to -23°C. After 8 pm, the compressor power is greatly reduced, and the temperature is mainly maintained by cold storage until the electricity price drops at 10 pm.

[0028] Example 3 Building upon Example 1 or Example 2, to provide a better user experience, a human presence sensor (i.e., a human presence sensor) is pre-installed in the refrigerator. This sensor determines the presence of a user. When a user approaches, the system lowers the maximum operating frequency of the compressor and the maximum speed of the fan to prioritize quiet operation, achieving a smart experience of "quiet when people are present, efficient when people are gone," thus improving user satisfaction. For example, during dinner time, if the PIR sensor continuously detects someone, the system limits the compressor's maximum frequency to 45Hz and the fan speed to 80%. At 3 AM, if the sensor determines no one is present, the system removes the restrictions, and the compressor runs at its highest frequency of 70Hz, working in conjunction with the fan at full speed to quickly complete the cold storage task. At this time, the electricity price is 0.3 yuan / kWh.

[0029] Specifically, within the system's preset range, users can further narrow down the range by setting the lower and upper limits of the temperature fluctuation range in the refrigerator compartment, as well as the lower and upper limits of the allowable temperature fluctuation range in the freezer compartment, as constraints.

[0030] Specifically, users set the learning cycle through the client application interface. The preferred learning cycles are 24 hours, one week, and one month. After each learning cycle, power consumption changes, noise changes, and temperature changes are recorded and presented in chart form. The controller provides optimized data during stable refrigerator operation. The data is used as a reference, and user-defined evaluations are used to inform the next cycle. The basis for final confirmation of the data.

Claims

1. An embedded intelligent heat exchange control method for a refrigerator, characterized in that, Includes the following steps: S1. Collect temperature information for the area; Real-time acquisition of inlet and outlet temperatures on the left, right, and rear sides of the condenser; S2. Construct an embedded microenvironment dynamic thermal model: ; in, , , , in, For the total heat capacity of the system, This is the condensation temperature. For time, To dissipate heat from the refrigerant, This represents the real-time heat dissipation of the system to the external environment. This is the refrigerant mass flow rate. Specific enthalpy of the refrigerant; For an effective convective heat transfer coefficient, The effective heat exchange area of ​​the condenser. The average ambient temperature of the cabinet's microenvironment; , , These are the undetermined parameters for the model, obtained from experimental data; Let t be the rotational speed of the i-th fan. Let be the heat dissipation efficiency coefficient of the i-th side. This is the maximum rated speed of the fan. A simplification of natural convection and radiation effects; S3. Make predictions and adjustments based on the model; Based on the embedded microenvironment dynamic thermal model, the predicted condensing temperature at time Δt under the current heat dissipation conditions is predicted in real time. If the predicted condensing temperature on the i-th side is close to the preset safety threshold, the speed of the condensing fan on the i-th side is actively increased. 2.The embedded refrigerator intelligent heat exchange control method of claim 1, wherein, In step S2, , For the desired temperature difference, Let i be the outlet temperature on the i-th side. Let be the inlet temperature of the i-th side.

3. The embedded refrigerator intelligent heat exchange control method according to claim 1, characterized in that, In step S3, multiple sets of records are made while the refrigerator is running stably. Data; at discrete points in time, , ,get , will multiple groups Data and calculations The data pairs are substituted into the above formula for curve fitting. Optimize.

4. The embedded refrigerator intelligent heat exchange control method according to claim 1 or 3, characterized in that, In step S3, the currently measured Current fan speed And the calculated Substituting into the model, the differential equation is solved numerically to obtain the predicted condensation temperature after the future scheduling period T. . 5.The embedded refrigerator intelligent heat exchange control method of claim 4, characterized in that, Predicting condensation temperature Converted to the corresponding predicted condensation pressure and the system's preset safe pressure threshold In comparison, if The controller will immediately feed forward and calculate the required fresh air fan speed before the compressor load actually increases. And execute; if If the controller maintains or reduces the fan speed, then the fan speed will be reduced. 6.The embedded refrigerator intelligent heat exchange control method of claim 1, wherein, The refrigerator is equipped with a pre-installed temperature sensor assembly, which is distributed at least on the left, right and rear sides of the condenser, and the control of each condenser is independent of each other. 7.The embedded refrigerator intelligent heat exchange control method of claim 1 or 6, characterized in that, The refrigerator is equipped with a human body sensor to determine the presence of the user; when the user is nearby, the maximum operating frequency of the compressor and the maximum speed of the fan are reduced. 8.The embedded refrigerator intelligent heat exchange control method of claim 7, wherein, Receive real-time electricity price signals within the future scheduling period T via the communication module. Generate an energy-saving preference coefficient based on electricity price signals. , Substitute the controller output into Then output it. 9.The embedded refrigerator intelligent heat exchange control method of claim 1, wherein, Users set the lower and upper limits of the temperature fluctuation range in the refrigerator compartment, as well as the lower and upper limits of the allowable temperature fluctuation range in the freezer compartment, as constraints. 10.The embedded refrigerator intelligent heat exchange control method of claim 3 or 9, characterized in that, Users set learning cycles, and within each learning cycle, they record data on changes in power consumption, noise levels, and temperature. The data is then calibrated and evaluated by the user to inform the next learning cycle. Basis for data verification.

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