Rotating speed prediction and control method of charging gun liquid cooling system

Abstract

The invention belongs to the technical field of liquid cooling system control, and discloses a rotating speed prediction and control method for a charging gun liquid cooling system, and the method comprises the steps: obtaining the performance parameters of key parts, including a radiator, a water pump, a fan, a gun line and a liquid storage tank, in the liquid cooling system; inputting the performance parameters into the one-dimensional simulation model to obtain the highest temperature Tcoolant of the cooling liquid under different working conditions; on the basis of the Tcoolants under different working conditions, the relationship between the different working conditions and the Tcoolants is fitted, so that a rotating speed prediction model is obtained; the environment temperature and the gun line resistor heating power under the target working condition are input into the rotating speed prediction model, under the constraint met by Tcoolant, multiple sets of rotating speed combinations meeting the constraint are obtained through prediction, and the rotating speed combination enabling the total energy consumption of the liquid cooling system to be the lowest is selected from the multiple sets of rotating speed combinations to serve as the optimal rotating speed combination of the target working condition; and during charging, the fan rotating speed and the water pump rotating speed are regulated and controlled in real time based on the optimal rotating speed combination. The system has the advantages of advanced adjustment, quick response, energy conservation, high efficiency and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of liquid cooling system control, and more specifically, relates to a speed prediction and control method for a charging gun liquid cooling system. Background Art

[0002] The new energy vehicle industry is developing by leaps and bounds. However, the lagging efficiency of charging infrastructure has become a prominent issue, restricting the further development of the industry. Liquid-cooled supercharging technology has become the key to its development.

[0003] Current liquid cooling systems primarily employ a hierarchical control strategy based on temperature thresholds, dynamically adjusting the speed of the water pump and fan by monitoring the coolant temperature or the temperature of key nodes. While this passive, temperature-based control scheme offers the advantage of ease of implementation, it has the following key limitations:

[0004] First, the liquid cooling system's response is delayed and carries the risk of temperature overshoot. Second, the fixed threshold control mode results in insufficient energy efficiency optimization. Finally, the failure to coordinate optimization of the water pump and fan operating parameters restricts the improvement of the overall system performance. Summary of the Invention

[0005] In response to the above-mentioned defects or improvement needs of the prior art, the present invention provides a method for predicting and controlling the speed of a charging gun liquid cooling system, the purpose of which is to achieve rapid response to the speed regulation of the charging gun liquid cooling system, reduce system energy consumption, and improve the overall performance of the system.

[0006] To achieve the above objectives, the present invention provides a method for predicting and controlling the speed of a charging gun liquid cooling system, comprising:

[0007] S1. Obtain the performance parameters of key components in the liquid cooling system, including the radiator, water pump, fan, gun line, and liquid storage tank; input the performance parameters into the one-dimensional simulation model of the liquid cooling system to obtain the maximum coolant temperature T under different working conditions. coolant ; Wherein, the different working conditions include different ambient temperatures T amb , Gun line resistance heating power P source 、Fan speed N pump And water pump speed N fan ;

[0008] S2, based on the maximum coolant temperature T under the different working conditions coolant , fitting the relationship between different working conditions and the maximum temperature of the coolant: T coolant =f(T amb ,P source ,N pump ,N fan ), and then the speed prediction model is obtained: (N pump ,Nfan )=f(T amb ,P source ,T coolant );

[0009] S3, the ambient temperature T under the target working condition amb And the gun line resistance heating power P source Input to the speed prediction model, with T coolant ≤T max As a constraint, multiple speed combinations that meet the constraints are predicted, and the speed combination that minimizes the total energy consumption of the liquid cooling system is selected from the multiple speed combinations as the optimal speed combination for the target working condition; wherein, T max is the preset maximum coolant temperature;

[0010] S4. During charging, the fan speed and the water pump speed are regulated in real time based on the optimal speed combination.

[0011] Furthermore, an approximate response surface second-order model is used to fit the relationship between different working conditions and the maximum temperature of the coolant.

[0012] Furthermore, the total energy consumption of the liquid cooling system is the fan energy consumption P pump and water pump energy consumption P fan The sum of the fan energy consumption P pump Its speed N pump There is a cubic fitting relationship between them:

[0013] P pump =a1N pump 3 +b1N pump 2 +c1N pump +d1

[0014] Where a1, b1, c1, and d1 are the corresponding fitting coefficients;

[0015] The water pump energy consumption P fan Its speed N fan There is a cubic fitting relationship between them:

[0016] P fan =a2N fan 3 +b2N fan 2 +c2N fan +d2

[0017] Where a2, b2, c2, and d2 are the corresponding fitting coefficients.

[0018] Furthermore, in S3, based on the charging power and the heating power P of the gun line resistance at different times during the charging process, source Proportional to the heating power P of the gun wire resistance at different times during the charging process source .

[0019] Furthermore, in S3, the optimal speed combination for each target operating condition during charging is obtained at the start time of charging;

[0020] Alternatively, the optimal speed combination for the current target operating condition is obtained during charging.

[0021] The present invention also provides a speed prediction and control device for a charging gun liquid cooling system, comprising a computer-readable storage medium and a processor;

[0022] The computer-readable storage medium is used to store executable instructions;

[0023] The processor is used to read the executable instructions stored in the computer-readable storage medium to execute any of the above-mentioned methods for predicting and controlling the speed of the charging gun liquid cooling system.

[0024] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the speed prediction and control method of the charging gun liquid cooling system as described in any one of the above items.

[0025] The present invention also provides a computer program product, including a computer program. When the computer program is run on a computer, the computer executes any of the above-mentioned methods for predicting and controlling the speed of the charging gun liquid cooling system.

[0026] The present invention also provides a charging terminal, comprising a liquid cooling system and a control device arranged in the liquid cooling system; wherein the control device is equipped with a speed prediction and control method for the charging gun liquid cooling system as described in any one of the above items, or the control device is the speed prediction and control device for the charging gun liquid cooling system as described above.

[0027] The present invention also provides a charging pile, comprising a host and a plurality of charging terminals connected to the host; wherein the charging terminals are the charging terminals described above.

[0028] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0029] (1) The present invention provides an "active prediction + real-time control" strategy for the speed of a liquid cooling system. Specifically, by obtaining the performance parameters of key components in the liquid cooling system, the data on the maximum temperature of the coolant under different working conditions are collected based on a one-dimensional simulation model, and the data is used as a data set to construct the relationship f satisfied between different working conditions and the maximum temperature of the coolant. The speed in the relationship is reversed to obtain a speed prediction model, and active and accurate prediction of the fan speed and water pump speed is achieved based on the speed prediction model, charging power and ambient temperature. The present invention controls the fan and water pump speeds in real time based on active speed prediction. Compared with the hierarchical control strategy based on temperature thresholds, it has the characteristics of advanced regulation and rapid response. At the same time, under the constraint of the maximum temperature of the coolant, the fan speed and water pump speed are collaboratively optimized based on the principle of minimum energy consumption. The determined optimal speed combination can achieve speed regulation with minimized energy consumption under the premise of ensuring heat dissipation, and there is no risk of temperature overshoot. Therefore, the method of the present invention has the advantages of rapid response, energy saving and high efficiency, and improves the overall performance of the liquid cooling system.

[0030] (2) As a preference, an approximate response surface second-order model is used to fit the relationship between different working conditions and the maximum temperature of the coolant. The calculation is simple and the model accuracy can be guaranteed.

[0031] (3) As a preferred method, based on the battery charging power, the gun line resistance heating power P of the thermal load at different times during the battery charging process can be known in advance. source During the fast charging process, the ambient temperature can be considered constant. Therefore, based on the heat load of the battery charging process at different times known in advance, the heating power P of the gun wire resistance is calculated. source , the optimal speed combination for each target working condition during the charging process can be obtained at the beginning of charging, realizing advanced adjustment, fast response, energy-saving and efficient speed adjustment and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of a method for predicting and controlling the speed of a charging gun liquid cooling system in an embodiment of the present invention;

[0033] Figure 2 Schematic diagram of a one-dimensional model of a liquid cooling system in an embodiment of the present invention;

[0034] Figure 3 This is a flow chart of a speed prediction model according to an embodiment of the present invention;

[0035] Figure 4 This is the result diagram of threshold control based on thermal load;

[0036] Figure 5 This is a diagram of the real-time control result of speed prediction in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0038] Example 1

[0039] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting and controlling the speed of a charging gun liquid cooling system, which mainly includes:

[0040] S1. Obtain the performance parameters of key components in the liquid cooling system, including the radiator, water pump, fan, gun line, and liquid storage tank; input the performance parameters of each key component into the one-dimensional simulation model of the liquid cooling system to obtain the maximum coolant temperature data T under different working conditions. coolant ; Among them, different working conditions include different ambient temperatures T amb , Gun line resistance heating power P source 、Fan speed N pump And water pump speed N fan .

[0041] In the embodiment of the present invention, the key components of the liquid cooling system include a radiator, a water pump, a fan, a gun line and a liquid storage tank. The performance parameters of each component are obtained through experimental calibration: heat exchange characteristics, flow resistance characteristics, work characteristics, etc. For the charging gun line, its performance parameters include: flow resistance characteristics, volume characteristics and heat characteristics. The pressure loss is measured by arranging pressure sensors at the inlet and outlet of the charging gun line and its connecting pipelines, and the flow is measured by a turbine flowmeter in the pipeline, and the pressure loss at different flow rates (the flow resistance characteristics of the charging gun line) is recorded. As a key component in the liquid cooling system, the flow resistance characteristics of the charging gun line directly affect the flow efficiency of the coolant and the overall performance of the system. In order to accurately evaluate the flow resistance characteristics of the gun line, ΔP=SQ 2 Fit the fluid resistance coefficient, where S is the resistance coefficient of the gun line, ΔP is the pressure difference between the inlet and outlet of the charging gun line, and Q is the flow rate of the coolant flowing through the charging gun line. Assuming that the flow path of the gun line is uniform and local resistance can be ignored, the fluid resistance coefficient S is proportional to the gun line length, and the resistance coefficient of the gun line of any length can also be extrapolated. The volume characteristics of the charging gun line are measured by measuring the internal liquid volume capacity using a graduated cylinder, and the heat generation characteristics are based on formula I 2R can be calculated, where I is the current flowing through the charging gun cable and R is the gun cable resistance. For water pumps, performance parameters include work characteristics and efficiency parameters at different flow rates. For radiators, performance parameters include flow resistance and heat transfer characteristics on both sides. For liquid tanks, performance parameters include tank capacity. These parameters are tested according to existing testing methods.

[0042] In the embodiment of the present invention, a one-dimensional simulation model of the liquid cooling system is constructed based on experimental calibration. The constructed one-dimensional simulation model is as follows: Figure 2 The water pump drives the coolant through the charging cable, absorbing and removing the large amount of heat generated by the cable during charging. The hot coolant then flows through the radiator, transferring the heat to the external environment. The cooled coolant then flows back to the water pump and reservoir.

[0043] Based on the above liquid cooling cycle process, a one-dimensional simulation model of the liquid cooling system was established in the embodiment of the present invention. The radiator consists of three parts: the air cooling circuit, the heat exchanger, and the liquid side. The charging cable is replaced by the volume chamber, the heat flow source, and the resistance element.

[0044] The constructed one-dimensional simulation model can be used to analyze the temperature rise of the charging gun cable under different ambient temperatures and different heating powers. When the external ambient temperature and charging power change, the speed of the water pump and fan will also change accordingly, thereby achieving energy saving. The one-dimensional model can be used to perform coupling analysis of the water pump and fan at different speeds. In addition, to address the high iteration cost and difficulty of coupling design in existing liquid cooling system designs, the constructed one-dimensional simulation model can quickly verify the effectiveness of the design solution, realize the coupled optimization design of liquid cooling system components, and effectively reduce the cost of trial and error.

[0045] S2. The coolant maximum temperature data under different working conditions are used as a data set, and the relationship f between different working conditions and the coolant maximum temperature is fitted based on the data set:

[0046] T coolant =f(T amb ,P source ,N pump ,N fan )

[0047] Reverse the speed to get the speed prediction model:

[0048] (N pump ,N fan )=f(T amb ,P source ,T coolant )

[0049] In the embodiment of the present invention, f is an approximate second-order response surface model, that is, based on the data set, an approximate second-order response surface model is constructed to fit the relationship between different operating conditions and the maximum temperature of the coolant. The approximate second-order response surface model is used to fit the relationship between different operating conditions and the maximum temperature of the coolant. The calculation is simple and the model accuracy can be guaranteed. In other embodiments, a neural network model or other low-order model can also be used for fitting. During the model training process, the data set is divided into a training set and a validation set to train and validate the model to obtain a model with better prediction effect.

[0050] S3, at the current ambient temperature T amb And the gun line resistance heating power P source Next, T coolant ≤T max As a constraint, the speed prediction model is used to predict a series of speed combinations that meet the constraints, where T max is the maximum coolant temperature that the liquid cooling system can withstand; and based on the principle of minimum energy consumption, the speed combination that minimizes the total energy consumption of the liquid cooling system is selected from a series of speed combinations that meet the constraints as the optimal speed combination for the current working condition.

[0051] Considering that the heat dissipation effect in the cooling system is greatly affected by the cooling outlet temperature, a constraint is imposed on the coolant outlet temperature when predicting the speed, that is, T coolant ≤T max ,Under the constraints, the speed combinations obtained will not have the risk of temperature overshoot.

[0052] The total energy consumption of the liquid cooling system is the sum of the fan and the water pump, that is, P total =P pump +P fan The power consumption and rotational speed of fans and water pumps are positively correlated. In the embodiment of the present invention, the power consumption and rotational speed of the water pump are expressed using a cubic fitting relationship:

[0053] P pump =a1N pump 3 +b1N pump 2 +c1N pump +d1

[0054] Among them, a1, b1, c1, and d1 are the corresponding fitting coefficients and are known quantities.

[0055] The power consumption and speed of the fan are related by a cubic fitting equation:

[0056] P fan =a2N fan 3 +b2N fan 2+c2N fan +d2

[0057] Among them, a2, b2, c2, and d2 are the corresponding fitting coefficients and are known quantities.

[0058] Based on the relationship between the power consumption and speed of the water pump and the relationship between the power consumption and speed of the fan, the total energy consumption of the liquid cooling system under different speed combinations can be calculated.

[0059] In an embodiment of the present invention, based on a one-dimensional simulation model, the maximum temperature of the coolant under different working conditions (ambient temperature, heating power, pump speed, fan speed) can be simulated and predicted, thereby obtaining a training set and a validation set of the prediction model. Figure 3 This is a flow chart of the speed prediction model. Using the training and validation sets obtained through simulation, an approximate second-order response surface model of input and output parameters is constructed. Based on this model, the speed is reversely predicted, along with the inputs of ambient temperature, heat generation power, and maximum coolant temperature. This results in a combination of fan and pump speeds. Within a series of speeds that meet the temperature requirements, the optimal speed combination is selected based on the principle of minimum energy consumption, which is then used to regulate the system. The increase in power consumption and the decrease in temperature are nonlinear, and blindly pursuing lower temperatures will lead to exponential growth in power consumption. Therefore, when implementing a speed regulation strategy, energy savings should be achieved while ensuring that the maximum temperature does not exceed the system's tolerance. By applying constraints that the ambient temperature, heat generation power, and maximum coolant temperature do not exceed certain values, a series of different speed combinations can be obtained. For example, when the ambient temperature is 50°C, the heating power is 2569W (in the embodiment of the present invention, the corresponding charging power is 800A), and the coolant temperature is not higher than 88°C, three combinations of fan and water pump speeds can be obtained: [4000 20000], [3500 24000], and [4500 16000]. The three coolant temperatures are 86.5°C, 86.9°C, and 87.2°C, respectively, and the power consumption is 333.5W, 554.5W, and 188.5W, respectively. All three speed combinations meet the coolant maximum temperature constraint, and the temperature control effect of the third group with the lowest energy consumption is indeed the best. The power of fans and water pumps varies greatly, and the improvement in heat dissipation effect is also different. While ensuring the heat dissipation effect, the speed of low-power devices should be increased first to improve the heat dissipation effect.

[0060] S4. During charging, the fan speed and water pump speed are controlled in real time based on the optimal speed combination. The heating power P of the gun line resistance is calculated according to the change of current during charging. source .

[0061] In one implementation, an optimal rotational speed combination under the current target operating condition is obtained during charging.

[0062] In another implementation, during the battery charging period, due to the difference between the battery charging power and the gun line resistance heating power P source Positive correlation, based on the battery charging power, the gun line resistance heating power P of the thermal load at different times during the battery charging process can be known in advance source , and the fast charging time is short, the ambient temperature during the charging process can be considered constant, therefore, based on the gun line resistance heating power P of the heat load at different times of the battery charging process known in advance source , it can obtain the optimal speed combination at different times during the charging process at the beginning of charging, and has the characteristics of advanced adjustment, fast response, energy saving and high efficiency.

[0063] In the embodiment of the present invention, a speed prediction technology is introduced, and a second-order approximate model of the system response surface is established through training set data to predict the speed of the water pump and the fan. Figure 4 and Figure 5 To simulate the charging process of an electric vehicle with a battery power of 100kWh on an 800kw supercharging platform, the charging power increases linearly at the beginning and decreases linearly at the end, while maintaining high power operation in the middle. Based on this background, two different charging strategies are compared. Figure 4 The charging strategy shown is a threshold control strategy based on thermal load. Figure 5 The control strategy 2 shown is a control strategy based on a speed prediction model in an embodiment of the present invention. In strategy 1, when the heat load changes, the speed of the fan and water pump can be adjusted in five gears. Strategy 2 predicts the speed of the fan and water pump and performs real-time control based on load changes. It can be seen that the two control strategies show significant differences: Strategy 1 adopts a fixed threshold trigger mode. When the system load exceeds a certain value, the water pump and fan will be started synchronously at a constant speed, and the overall control is relatively vague; Strategy 2 is based on the energy consumption principle and dynamically adjusts the heat dissipation equipment according to the heat load - initially, the speed of the low-power fan is prioritized to achieve gradient heat dissipation. When the fan is fully loaded and still cannot balance the heat load, the high-power water pump is started to work in coordination. Finally, the control strategy based on speed prediction significantly reduces energy consumption while achieving temperature control effect, saving 22.8% of electricity.

[0064] The speed prediction and control method for the charging gun liquid cooling system proposed in this embodiment realizes efficient evaluation and rapid iteration of liquid cooling design schemes through parametric modeling and dynamic coupling optimization, significantly reducing the R&D trial and error costs; at the same time, compared with the traditional threshold control method, the control strategy based on speed prediction has the advantages of advanced adjustment, rapid response, energy saving and high efficiency. Compared with the traditional threshold control strategy, it can reduce the energy consumption of the liquid cooling system by about 23% within a simplified charging cycle, significantly improving energy utilization efficiency and reducing the operating costs of charging pile manufacturers.

[0065] Example 2

[0066] An embodiment of the present invention provides a speed prediction and control device for a charging gun liquid cooling system, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the speed prediction and control method for the charging gun liquid cooling system in the above-mentioned embodiment 1 are implemented.

[0067] The relevant technical solutions are the same as above and will not be repeated here.

[0068] Example 3

[0069] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for predicting and controlling the speed of the charging gun liquid cooling system in the above-mentioned embodiment 1 are implemented.

[0070] Specifically, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0071] The relevant technical solutions are the same as above and will not be repeated here.

[0072] Example 4

[0073] An embodiment of the present invention provides a computer program product, including a computer program. When the computer program is run on a computer, the computer executes the steps of the method for predicting and controlling the speed of the charging gun liquid cooling system in the above-mentioned embodiment 1.

[0074] The relevant technical solutions are the same as above and will not be repeated here.

[0075] Example 5

[0076] An embodiment of the present invention provides a charging terminal, comprising a liquid cooling system and a control device arranged in the liquid cooling system; wherein the control device is equipped with the speed prediction and control method of the charging gun liquid cooling system in Example 1, or the control device is the speed prediction and control device of the charging gun liquid cooling system in Example 2.

[0077] The relevant technical solutions are the same as above and will not be repeated here.

[0078] Example 6

[0079] An embodiment of the present invention provides a charging pile, including a host and multiple charging terminals connected to the host; wherein each charging terminal is the charging terminal in Example 5.

[0080] The relevant technical solutions are the same as above and will not be repeated here.

[0081] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting and controlling the rotational speed of a charging gun liquid cooling system, characterized in that: include: S1. Obtain the performance parameters of key components in the liquid cooling system, including the radiator, water pump, fan, gun line, and liquid storage tank; input the performance parameters into the one-dimensional simulation model of the liquid cooling system to obtain the maximum coolant temperature T under different working conditions. coolant ; Wherein, the different working conditions include different ambient temperatures T amb , Gun line resistance heating power P source 、Fan speed N pump And water pump speed N fan ; S2, based on the maximum coolant temperature T under the different working conditions coolant , fitting the relationship between different working conditions and the maximum temperature of the coolant: T coolant =f(T amb ,P source ,N pump ,N fan ), and then the speed prediction model is obtained: (N pump ,N fan )=f(T amb ,P source ,T coolant ); S3, the ambient temperature T under the target working condition amb And the gun line resistance heating power P source Input to the speed prediction model, with T coolant ≤T max As a constraint, multiple speed combinations that meet the constraints are predicted, and the speed combination that minimizes the total energy consumption of the liquid cooling system is selected from the multiple speed combinations as the optimal speed combination for the target working condition; wherein, T max is the preset maximum coolant temperature; S4. During charging, the fan speed and the water pump speed are regulated in real time based on the optimal speed combination.

2. The method for predicting and controlling the speed of the charging gun liquid cooling system according to claim 1, characterized in that: An approximate response surface second-order model was used to fit the relationship between different working conditions and the maximum temperature of the coolant.

3. The method for predicting and controlling the speed of a charging gun liquid cooling system according to claim 1 or 2, wherein: The total energy consumption of the liquid cooling system is the fan energy consumption P pump and water pump energy consumption P fan The sum of the fan energy consumption P pump Its speed N pump There is a cubic fitting relationship between them: P pump =a1N pump 3 +b1N pump 2 +c1N pump +d1 Where a1, b1, c1, and d1 are the corresponding fitting coefficients; The water pump energy consumption P fan Its speed N fan There is a cubic fitting relationship between them: <h2 style=";text-align:left;direction:ltr">P<h2 style=";text-align:left;direction:ltr"> fan <h2 style=";text-align:left;direction:ltr"> =a2N<h2 style=";text-align:left;direction:ltr"> fan <h2 style=";text-align:left;direction:ltr"> 3 <h2 style=";text-align:left;direction:ltr"> +b2N<h2 style=";text-align:left;direction:ltr"> fan <h2 style=";text-align:left;direction:ltr"> 2 <h2 style=";text-align:left;direction:ltr"> +c2N<h2 style=";text-align:left;direction:ltr"> fan <h2 style=";text-align:left;direction:ltr"> +d2 Where a2, b2, c2, and d2 are the corresponding fitting coefficients.

4. The method for predicting and controlling the speed of a charging gun liquid cooling system according to claim 1 or 2, wherein: In S3, based on the charging power and the heating power P of the gun line resistance at different times during the charging process, source Proportional to the heating power P of the gun wire resistance at different times during the charging process source .

5. The method for predicting and controlling the speed of the charging gun liquid cooling system according to claim 4, characterized in that: In S3, at the start of charging, the optimal speed combination for each target operating condition during charging is obtained; Alternatively, the optimal speed combination for the current target operating condition is obtained during charging.

6. A speed prediction and control device for a charging gun liquid cooling system, characterized in that: comprising a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the speed prediction and control method of the charging gun liquid cooling system according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by the processor, the speed prediction and control method of the charging gun liquid cooling system according to any one of claims 1 to 5 is implemented.

8. A computer program product, characterized in that It includes a computer program, which, when running on a computer, enables the computer to execute the speed prediction and control method of the charging gun liquid cooling system according to any one of claims 1 to 5.

9. A charging terminal, characterized in that: It includes a liquid cooling system and a control device arranged in the liquid cooling system; wherein, the control device is equipped with the speed prediction and control method of the charging gun liquid cooling system according to any one of claims 1 to 5, or the control device is the speed prediction and control device of the charging gun liquid cooling system according to claim 6.

10. A charging pile, characterized in that: It comprises a host and a plurality of charging terminals connected to the host; wherein the charging terminal is the charging terminal according to claim 9.

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