Method for efficiently improving refrigerating efficiency of vehicle-mounted refrigerator

Abstract

The invention relates to the technical field of refrigeration vehicle-mounted refrigerators, in particular to a method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigerator. Refrigeration data of a vehicle-mounted refrigerator are obtained and comprise the pressure value of a pressure sensor, the temperature value of a temperature sensor, the opening degree of an electronic expansion valve, the rotating speed of a fan, the working frequency of a compressor, the external temperature and the set temperature of a compartment; and inputting the refrigeration data into the refrigeration efficiency model, and outputting refrigeration adjustment parameters. Through innovative design and optimized application of the micro-channel radiator and the evaporator core body, the refrigeration performance of the vehicle-mounted refrigerator is remarkably improved, and the ever-increasing vehicle cold chain transportation and refrigeration requirements are met.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigerated vehicle-mounted refrigerators, and in particular to a method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigerator. Background Art

[0002] In the field of on-board refrigeration, traditional refrigeration systems mainly rely on conventional radiators and evaporators to achieve heat exchange and transfer. However, this traditional architecture has significant defects. Taking conventional radiators as an example, they usually adopt a channel design with a larger diameter, which leads to a relatively low flow rate of the coolant in the channel, a more obvious laminar flow phenomenon, and heat exchange mainly relies on heat conduction. The efficiency of convective heat transfer has not been fully utilized, resulting in low heat dissipation efficiency. In addition, the cooling fins of traditional radiators have a simple structure, limited surface area, and insufficient contact with the air, which further restricts the heat dissipation effect and makes it difficult to efficiently carry out the condensation link in the refrigeration cycle. It not only affects the refrigeration efficiency, but may also cause problems such as compressor overheating protection due to heat accumulation, reducing the overall reliability and stability of the on-board refrigeration machine.

[0003] As for the evaporator, the traditional evaporator core mostly adopts a bare tube design. The evaporation process of the refrigerant in the tube is not uniform and the heat exchange area is small, which cannot fully absorb the heat in the car. At the same time, the fin structure matched with it is easy to frost in long-term use. After frost, the heat exchange efficiency between the fin and the air is greatly reduced, and frequent defrosting is required, which not only increases energy consumption, but also causes refrigeration interruption during defrosting, and cannot continuously maintain a low temperature environment.

[0004] As an emerging heat dissipation technology, microchannel radiator has the advantages of small channel size and large surface area to volume ratio. It can significantly enhance the heat exchange efficiency between coolant and heat dissipation fins and effectively improve the heat dissipation effect. The optimization of the evaporator core is also critical. The evaporation effect of the refrigerant can be enhanced by improving the evaporator tube structure. Combining with the new fin structure design can improve its anti-frost performance and heat exchange efficiency with the air. However, in the field of vehicle-mounted refrigeration machines, the coordinated application of microchannel radiators and evaporator cores to achieve an improvement in overall refrigeration efficiency still requires further research and development. Summary of the invention

[0005] The present invention aims to provide a method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigerator, by synergistically applying a microchannel radiator and an evaporator core to improve the refrigeration efficiency of the vehicle-mounted refrigerator.

[0006] To achieve the above object, the present invention adopts the following technical scheme: a method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine, comprising a compressor, a condenser, a microchannel radiator and an evaporator core, wherein the evaporator core is provided with an inlet pipe and an outlet pipe of a refrigerant, a flow controller is provided in the inlet pipe, and a pressure sensor and a temperature sensor are provided in the outlet pipe;

[0007] Acquire refrigeration data of the vehicle-mounted refrigeration machine, wherein the refrigeration data includes a pressure value of a pressure sensor, a temperature value of a temperature sensor, an opening of an electronic expansion valve, a fan speed, a compressor operating frequency, an outside temperature, and a set vehicle compartment temperature;

[0008] The refrigeration data is input into the refrigeration efficiency model and the refrigeration adjustment parameters are output.

[0009] The principles and advantages of this solution are: in actual application, compared with traditional radiators, microchannel radiators have higher heat dissipation efficiency and smaller volume, which helps to quickly dissipate the heat generated by the condenser. The liquid refrigerant evaporates in the evaporator core, absorbing the heat in the car, thereby reducing the temperature of the car. The flow controller, pressure sensor, and temperature sensor can accurately control the refrigerant flow entering the evaporator core to avoid a decrease in refrigeration efficiency caused by too much or too little refrigerant. Real-time monitoring of the pressure and temperature in the outlet pipe provides key data for the refrigeration efficiency model, helping the system to adjust the refrigeration strategy in a timely manner. The refrigeration data is input into the refrigeration efficiency model, which is based on advanced algorithms and can analyze the operating status of the refrigeration system in real time, and output refrigeration adjustment parameters based on current environmental conditions and refrigeration needs.

[0010] The solution can be customized according to different vehicle models and cooling requirements. When the outside temperature is high, the system can automatically increase the operating frequency of the compressor and the fan speed to improve the cooling efficiency; when the cabin temperature is close to the set temperature, the system can automatically reduce the operating frequency of the compressor and the fan speed to save energy. In addition, the solution can also be integrated with other vehicle systems (such as navigation systems, battery management systems, etc.) to achieve a higher level of intelligent control and optimization. For example, when the vehicle is about to enter a tunnel or a shaded area, the system can predict the change in outside temperature in advance and adjust the operating status of the refrigeration system in advance to further improve the cooling efficiency and passenger comfort. In summary, this solution achieves efficient cooling and intelligent adjustment of the on-board refrigeration machine by integrating advanced hardware components and optimizing software algorithms, improves passenger comfort and the service life of the equipment, and meets the requirements of environmental protection and energy saving.

[0011] Preferably, as an improvement, in the output refrigeration adjustment parameters, when the pressure value is greater than a preset value, the compressor frequency is increased, the fan speed is increased, and the opening of the electronic expansion valve is increased; when the pressure value is lower than the preset value, the compressor frequency is reduced, the fan speed is reduced, and the opening of the electronic expansion valve is reduced.

[0012] Preferably, as an improvement, in the output refrigeration adjustment parameters, when the temperature value is greater than a preset value, the compressor frequency is increased, the fan speed is increased, and the opening of the electronic expansion valve is increased; when the temperature value is lower than the preset value, the compressor frequency is reduced, the fan speed is reduced, and the opening of the electronic expansion valve is reduced.

[0013] Through the dynamic adjustment strategy based on the pressure value, it can be ensured that the refrigeration system always operates in the best state. When the pressure is too high, by increasing the compressor frequency, increasing the fan speed and increasing the opening of the electronic expansion valve, the system pressure can be quickly reduced to avoid energy waste and reduced refrigeration efficiency; when the pressure is too low, by reducing the compressor frequency, reducing the fan speed and reducing the opening of the electronic expansion valve, the system pressure can be stabilized, the heat absorption effect of the refrigerant can be improved, and thus the refrigeration efficiency can be improved. Long-term high-pressure or low-pressure operation will cause damage to the refrigeration equipment. Through the dynamic adjustment strategy based on the pressure value, it can be ensured that the refrigeration system always operates within the appropriate pressure range, reducing the wear and failure rate of the equipment, thereby extending the service life of the equipment. For another example, the pressure sensor detects an abnormal increase in the refrigerant pressure. This abnormal data is immediately sent to the intelligent control system, and the system determines after analysis that there may be a risk of blockage or leakage.

[0014] Preferably, as an improvement, the refrigeration data is uploaded to a server, and the server verifies the refrigeration adjustment parameters, wherein the verification is to compare the prediction result after outputting the refrigeration adjustment parameters with the refrigeration data corresponding to the refrigeration adjustment parameters.

[0015] Preferably, as an improvement, the verification calculation on the server side forms a corresponding relationship between the prediction result and the refrigeration data, and uses the prediction result and the refrigeration data for machine learning of the refrigeration efficiency model. Using the machine learning algorithm, the future refrigeration demand is predicted based on the uploaded data, and the system parameters are adjusted in advance. The flow path and flow distribution of the refrigerant are optimized, thereby improving the heat exchange efficiency of the evaporator core.

[0016] By uploading the refrigeration data to the server, the server can verify the refrigeration adjustment parameters. This verification process is achieved by comparing the prediction results after outputting the refrigeration adjustment parameters with the refrigeration data corresponding to the refrigeration adjustment parameters. This comparison can timely discover possible deviations or deficiencies in the refrigeration efficiency model, so as to correct and optimize it and improve the accuracy of the model. The server forms a corresponding relationship between the prediction results and the refrigeration data, and uses these data for machine learning of the refrigeration efficiency model. Using machine learning algorithms, the system can learn and predict future refrigeration needs based on a large amount of uploaded historical data. This predictive capability enables the system to adjust parameters in advance to cope with upcoming high or low temperature environments, thereby ensuring that the temperature in the vehicle compartment is always kept within a comfortable range. Through the analysis of refrigeration data by machine learning algorithms, the system can more accurately control the flow path and flow distribution of the refrigerant. This optimization can not only improve the heat exchange efficiency of the evaporator core, but also reduce the waste of refrigerant and energy consumption.

[0017] Preferably, as an improvement, the operating time T, speed fluctuation ΔN, pressure change rate ΔP / Δt, and temperature rise value ΔT of the compressor are obtained; and the wear prediction value W of the compressor is calculated.

[0018] Preferably, as an improvement, it also includes a user interface, through which the desired cabin set temperature can be set, and the operating status and refrigeration efficiency of the on-board refrigeration machine can be checked in real time.

[0019] Preferably, as an improvement, the service end verifies the wear prediction value W, and the verification is to compare the prediction result after outputting the refrigeration adjustment parameter with the refrigeration data corresponding to the refrigeration adjustment parameter.

[0020] Preferably, as an improvement, the inlet pipe and the outlet pipe of the evaporator core are streamlined to reduce the flow resistance of the refrigerant in the pipeline and improve the flow efficiency.

[0021] Preferably, as an improvement, the microchannel heat sink further comprises heat dissipation fins, and the heat dissipation fins are located between adjacent microchannels. DETAILED DESCRIPTION

[0022] The following is further described in detail through specific implementation methods:

[0023] The specific implementation process is as follows:

[0024] A microchannel radiator is used to replace the traditional radiator and installed after the condenser. The microchannel radiator has higher heat dissipation efficiency and smaller volume, and can quickly dissipate the heat generated by the condenser to ensure the stable operation of the refrigeration system. At the same time, heat dissipation fins are arranged between adjacent tiny channels of the microchannel radiator to further enhance the heat dissipation effect. An evaporator core is designed and installed, which is provided with an inlet pipe and an outlet pipe for the refrigerant. A flow controller is arranged in the inlet pipe to accurately control the refrigerant flow entering the evaporator core to avoid a decrease in refrigeration efficiency caused by too much or too little refrigerant. A pressure sensor and a temperature sensor are arranged in the outlet pipe to monitor the pressure and temperature in the outlet pipe in real time, providing key data for the refrigeration efficiency model. In addition, the inlet pipe and outlet pipe of the evaporator core are designed to be streamlined to reduce the flow resistance of the refrigerant in the pipeline and improve the flow efficiency.

[0025] The refrigeration data of the vehicle-mounted refrigeration machine is obtained in real time through pressure sensors, temperature sensors, electronic expansion valve opening sensors, fan speed sensors, compressor operating frequency sensors, and external temperature and cabin set temperature input devices. The real-time refrigeration data is input into the refrigeration efficiency model, and the model outputs refrigeration adjustment parameters based on the data analysis results, including compressor operating frequency, fan speed, electronic expansion valve opening, etc. These parameters are used to adjust the operating status of the refrigeration system to improve refrigeration efficiency.

[0026] The server verifies the refrigeration adjustment parameters and promptly discovers and corrects the deviation or deficiency of the refrigeration efficiency model by comparing the prediction results after outputting the refrigeration adjustment parameters with the refrigeration data corresponding to the refrigeration adjustment parameters. The server forms a corresponding relationship between the prediction results and the refrigeration data, and uses these data for machine learning of the refrigeration efficiency model. Using machine learning algorithms, the system can learn and predict future refrigeration needs based on a large amount of uploaded historical data, and adjust system parameters in advance to optimize the refrigeration effect.

[0027] A high-performance electronic expansion valve is used as the refrigerant flow controller. The electronic expansion valve has the characteristics of fast response and precise regulation, and can adjust the refrigerant flow in real time according to the refrigeration demand. According to the sensor signal, the intelligent controller can automatically adjust the operating frequency of the compressor, the opening of the electronic expansion valve, and the speed of the fan to achieve a more efficient refrigeration effect. When the external ambient temperature changes or the internal load of the refrigerated truck (such as the number of door openings, cargo loading) increases, the high-performance electronic expansion valve accurately calculates the required refrigerant flow through an internal algorithm based on the real-time monitored data. Then, the electronic expansion valve automatically adjusts its opening to quickly increase or decrease the refrigerant flow, thereby ensuring that the internal temperature of the refrigerated truck always remains within the set range.

[0028] Furthermore, when transporting perishable foods (such as seafood), refrigerated trucks need to maintain a constant low temperature environment. High-performance electronic expansion valves automatically adjust the refrigerant flow rate based on the temperature and pressure data monitored in real time by high-precision sensors to ensure that the refrigeration system operates in an optimal state. For example, when the external ambient temperature rises, the sensor quickly senses the change and transmits the data to the electronic expansion valve, which then increases the refrigerant flow rate to quickly lower the temperature inside the refrigerated truck. This immediate response reduces unnecessary energy consumption while ensuring the freshness of the food and reducing losses during transportation.

[0029] When constructing the refrigeration efficiency model, it is assumed that the refrigeration efficiency Y can be expressed as a linear combination of the input variables (pressure value P, temperature value T, electronic expansion valve opening E, fan speed F, compressor operating frequency C, outside temperature O, and cabin set temperature S). This linear relationship can be expressed by a linear regression model.

[0030] The prediction formula for refrigeration efficiency Y is:

[0031] Y=β 0 +β 1 P+β 2 T+β 3 E+β 4 F+β 5 C+β 6 O+β 7 S+; among which:

[0032] Among them, β 0 ,β 1 ,…,β 7 is the regression coefficient, which indicates the influence of each input variable on the refrigeration efficiency, β 0 is the intercept term, which represents the baseline value of cooling efficiency when all input variables are 0; is the error term.

[0033] Y is the cooling efficiency (or cooling capacity) and is the target variable.

[0034] The objective function is the residual sum of squares, that is:

[0035]

[0036] Where n is the number of data points, yi is the observed value of the cooling efficiency at the i-th data point, and x i1 ,x i2 ,…,x i7 are the values ​​of the input variables for the ith data point.

[0037] By taking the partial derivative of the objective function and setting it to 0, we can get a set of linear equations, namely the normal equations. By solving the normal equations, we can get the estimated values ​​of the regression coefficients.

[0038]

[0039] Solving this system of equations, we can get the estimated values ​​of the regression coefficients For example: The following 5 sets of data are collected, each set of data contains pressure value P, temperature value T and refrigeration efficiency Y.

[0040] Serial number Pressure value P Temperature value T Refrigeration efficiency Y 1 20 -5 100 2 22 -8 110 3 24 -10 120 4 26 -12 130 5 28 -15 140

[0041] Y=β 0 +β 1 P+β 2 T;

[0042] The objective function is the residual sum of squares:

[0043]

[0044] Among them, y i is the observed value of the cooling efficiency at the i-th data point, p i and t i are the pressure and temperature values ​​of the ith data point, respectively. Taking the partial derivative of the objective function and setting it to 0, we get the normal equation:

[0045]

[0046] After simplification, we get:

[0047]

[0048] β 0 =80;β 1 =2.0;β 2 =-1.5.

[0049] Therefore, the linear regression model is:

[0050] Y = 80 + 2.0P - 1.5T;

[0051] Of course, more data points could be used to validate and fully evaluate the performance of the model.

[0052] You can also use big data and artificial intelligence technology to predict the wear trend of the compressor. By building a prediction system based on a machine learning model. This system will extract features related to compressor wear from the preprocessed data and predict the wear status of the compressor through the trained model. The following is a simplified example of how to build a prediction formula and issue an early warning signal.

[0053] The following compressor wear-related features were extracted from the preprocessed refrigeration data:

[0054] Running time (T): The cumulative number of hours the compressor has been running.

[0055] Speed ​​fluctuation (ΔN): The fluctuation range of compressor speed within a certain period of time.

[0056] Pressure change rate (ΔP / Δt): The rate of change of the compressor outlet pressure within a certain period of time.

[0057] Temperature rise (ΔT): The increase in compressor operating temperature relative to the normal operating temperature.

[0058] Wear prediction value (W) = a*T+b*ΔN+c*(ΔP / Δt)+d*ΔT+e;

[0059] Among them, a, b, c, d and e are coefficients obtained during the model training process, reflecting the degree of influence of each feature on the compressor wear trend. During real-time operation, the collected compressor operation data (i.e., operation time, speed fluctuation, pressure change rate, temperature rise) is input into the above prediction formula to calculate the wear prediction value (W). A wear threshold (W_threshold) is set. When the wear prediction value exceeds this threshold, the system will issue a warning signal.

[0060] For example: Assume that the coefficients obtained from model training are as follows: a = 0.01, b = 0.5, c = 2.0, d = 0.3, e = -0.1 (this is a constant term used to adjust the benchmark of the predicted value);

[0061] At the same time, we set the wear threshold W_threshold = 0.8. During the real-time operation, the compressor operation data collected at a certain moment is:

[0062] Running time T = 1000 hours;

[0063] Speed ​​fluctuation ΔN = 5 rpm;

[0064] Pressure change rate ΔP / Δt=0.1MPa / h;

[0065] Temperature rise ΔT = 5°C;

[0066] Substituting these data into the prediction formula, the wear prediction value is calculated:

[0067] W=0.01*1000+0.5*5+2.0*0.1+0.3*5-0.1=10+2.5+0.2+1.5-0.1=13.1+1.5-0.1=14.5-0.1=14.4

[0068] Since W=14.4>W_threshold=0.8, the system will issue a warning signal, indicating that the compressor wear may accelerate or has reached a critical state, and inspection and maintenance are required as soon as possible. By timely indicating the degree of compressor wear, effective inspection and maintenance can be carried out to improve the refrigeration effect of the compressor.

[0069] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical schemes and / or characteristics in the scheme is not described in detail here. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the technical scheme of the present invention. In the present invention, unless otherwise clearly specified and limited, the terms "install", "connect", "connect", "fix" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal connection of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. The scope of protection claimed in this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine, characterized in that: It includes a compressor, a condenser, a microchannel radiator and an evaporator core, wherein the evaporator core is provided with an inlet pipe and an outlet pipe of the refrigerant, a flow controller is provided in the inlet pipe, and a pressure sensor and a temperature sensor are provided in the outlet pipe; Acquire refrigeration data of the vehicle-mounted refrigeration machine, wherein the refrigeration data includes a pressure value of a pressure sensor, a temperature value of a temperature sensor, an opening of an electronic expansion valve, a fan speed, a compressor operating frequency, an outside temperature, and a set vehicle compartment temperature; The refrigeration data is input into the refrigeration efficiency model and the refrigeration adjustment parameters are output.

2. A method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 1, characterized in that: In the output refrigeration adjustment parameters, when the pressure value is greater than the preset value, the compressor frequency is increased, the fan speed is increased, and the opening of the electronic expansion valve is increased; when the pressure value is lower than the preset value, the compressor frequency is reduced, the fan speed is reduced, and the opening of the electronic expansion valve is reduced.

3. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 1, characterized in that: In the output refrigeration adjustment parameters, when the temperature value is greater than the preset value, the compressor frequency is increased, the fan speed is increased, and the opening of the electronic expansion valve is increased; when the temperature value is lower than the preset value, the compressor frequency is reduced, the fan speed is reduced, and the opening of the electronic expansion valve is reduced.

4. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 1, characterized in that: The refrigeration data is uploaded to the server, and the server verifies the refrigeration adjustment parameters. The verification is to compare the prediction result after outputting the refrigeration adjustment parameters with the refrigeration data corresponding to the refrigeration adjustment parameters.

5. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 4, characterized in that: The verification on the server side forms a correspondence between the prediction results and the refrigeration data, and uses the prediction results and refrigeration data for machine learning of the refrigeration efficiency model.

6. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 1, characterized in that: The running time T, speed fluctuation ΔN, pressure change rate ΔP / Δt, and temperature rise value ΔT of the compressor are obtained; and the wear prediction value W of the compressor is calculated.

7. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 1, characterized in that: It also includes a user interface, through which a desired cabin set temperature can be set, and the operating status and cooling efficiency of the onboard refrigeration machine can be viewed in real time.

8. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 7, characterized in that: The server verifies the wear prediction value W, and the verification is to compare the prediction result after outputting the refrigeration adjustment parameter with the refrigeration data corresponding to the refrigeration adjustment parameter.

9. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 8, characterized in that: The inlet pipe and the outlet pipe of the evaporator core are streamlined.

10. The method for efficiently improving the refrigeration efficiency of a vehicle-mounted refrigeration machine according to claim 9, characterized in that: The microchannel radiator further comprises heat dissipation fins, and the heat dissipation fins are located between adjacent microchannels.

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