Liquid Cooling Plate Structure Parameter Optimization Method, Device, Computer Equipment and Storage Medium

Abstract

The present application relates to a method, device, computer equipment and storage medium for optimizing the structural parameters of a liquid cooling plate. The method includes: establishing a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate; conducting a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain first test data; determining the influence degree of each structural optimization parameter on each working effect index according to the first test data; and optimizing each structural optimization parameter according to the priority of each working effect index to determine the final value of each structural optimization parameter. By conducting experimental analysis on the geometric structure of the liquid cooling plate, exploring the influencing factors of the cooling effect of the liquid cooling plate, obtaining the ranking of the influence degrees of these factors on the cooling effect of the liquid cooling plate, and then optimizing the structural parameters according to the ranking of the influence degrees, it is ensured that the obtained result is the optimal result, reducing labor costs and improving work efficiency.

Description

Technical Field

[0001] This application relates to the technical field of liquid cooling plate structure optimization, and particularly to a method, device, computer device, storage medium, and computer program product for determining the structure parameters of a liquid cooling plate. Background Art

[0002] Battery thermal runaway has always been a major safety issue for new energy vehicles. There are three main reasons for battery thermal runaway: one is thermal abuse, where local overheating of the battery triggers thermal runaway; the second is mechanical abuse, where internal short circuits and electrolyte leakage occur due to collisions and squeezes of the battery; the third is electrical abuse, where the battery is overcharged or overdischarged, or an external short circuit occurs. Currently, the cooling methods for new energy vehicles are mainly divided into three types: air cooling, liquid cooling, and direct cooling. Among them, the liquid cooling method is widely used in the new energy vehicle field due to its advantages such as high efficiency, moderate cost, good temperature uniformity, and the ability to achieve both heating and cooling. The working principle of the liquid cooling system is as follows: when the battery is working, complex chemical reactions occur inside, generating heat. The heat is transferred through the contact between the battery or module and the liquid cooling plate, and finally taken away by the coolant flowing in the flow channels of the liquid cooling plate, achieving the purpose of dissipating heat from the battery system. Therefore, the structural design and optimization of the liquid cooling plate have become the key to the design of the liquid cooling system.

[0003] In the related art, by automatically optimizing and solving the inlet diameters and flow channel heights of each flow channel of the liquid cooling plate, with the average flow difference of each flow channel cross-section as the optimization target, the scheme with the smallest average flow difference at the interface of each branch pipe of the liquid cooling plate is selected to determine the structural dimensions of the liquid cooling plate. However, in the above scheme, the optimized structural parameters are only the diameter of the flow channel and the height of the flow channel, and other parameters that affect the cooling effect of the liquid cooling plate have not been explored and optimized. Summary of the Invention

[0004] Based on this, it is necessary to provide an accurate and efficient method, device, computer device, computer-readable storage medium, and computer program product for optimizing the structural parameters of a liquid cooling plate in view of the above technical problems.

[0005] In the first aspect, this application provides a method for optimizing the structural parameters of a liquid cooling plate. The method includes:

[0006] Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0007] Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indicators of the liquid cooling plate to obtain first test data;

[0008] Determine the influence degree of each structural optimization parameter on each working effect indicator according to the first test data;

[0009] Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0010] In one embodiment, the structural optimization parameters include at least one of flow velocity, number of flow channels, flow channel depth, or flow channel length.

[0011] In one embodiment, the working effect indexes include at least one of the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, or the maximum temperature difference between battery cells.

[0012] In one embodiment, determining the influence degree of the structural optimization parameter on each working effect index according to the first test data includes:

[0013] For any working effect index, determine the influence rate of each structural optimization parameter on any working effect index according to the first test data;

[0014] Based on the sorting result of the influence rate, obtain the influence degree of each structural optimization parameter on any working efficiency index.

[0015] In one embodiment, the structural optimization parameters include flow velocity, number of flow channels, flow channel depth, and flow channel length; optimizing each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determining the final value of each structural optimization parameter includes:

[0016] According to the first test data, as well as the priority of each working effect index and the influence degree of the structural optimization parameter on each working effect index, determine the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length;

[0017] Perform a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length to obtain the second test data;

[0018] According to the second test data, as well as the priority of each working effect index and the influence degree of the structural optimization parameter on each working effect index, determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel;

[0019] Input the final value of the flow velocity, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into a preset model to obtain the final value of the flow channel length.

[0020] In one embodiment, the method further includes:

[0021] Obtain the preset threshold of the liquid pressure drop of the liquid cooling plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid cooling plate;

[0022] If the verification fails, return to the step of performing a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and continue to execute.

[0023] In a second aspect, the present application also provides a device for optimizing the structural parameters of a liquid cooling plate. The device includes:

[0024] A model establishment module, configured to establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0025] A simulation test module, configured to perform a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indicators of the liquid cooling plate, and obtain first test data;

[0026] An index analysis module, configured to determine the influence degree of each structural optimization parameter on each working effect indicator according to the first test data;

[0027] A parameter optimization module, configured to optimize each structural optimization parameter according to the priority of each working effect indicator and the influence degree of each structural optimization parameter on each working effect indicator, and determine the final value of each structural optimization parameter.

[0028] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0029] Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0030] Perform a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indicators of the liquid cooling plate, and obtain first test data;

[0031] Determine the influence degree of each structural optimization parameter on each working effect indicator according to the first test data;

[0032] Optimize each structural optimization parameter according to the priority of each working effect indicator and the influence degree of each structural optimization parameter on each working effect indicator, and determine the final value of each structural optimization parameter.

[0033] In a fourth aspect, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0034] Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0035] Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain the first test data;

[0036] Determine the influence degree of each structural optimization parameter on each working effect index according to the first test data;

[0037] Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0038] In a fifth aspect, the present application also provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0039] Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0040] Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain the first test data;

[0041] Determine the influence degree of each structural optimization parameter on each working effect index according to the first test data;

[0042] Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0043] For the above liquid cooling plate structure parameter optimization method, device, computer device, storage medium and computer program product, a geometric model of the liquid cooling plate is established according to the structural optimization parameters of the liquid cooling plate; a first simulation test is conducted on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain the first test data; the influence degree of each structural optimization parameter on each working effect index is determined according to the first test data; each structural optimization parameter is optimized according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and the final value of each structural optimization parameter is determined. By conducting experimental analysis on the geometric structure of the liquid cooling plate, exploring the influencing factors of the cooling effect of the liquid cooling plate, obtaining the ranking of the influence degrees of these factors on the cooling effect of the liquid cooling plate, and then optimizing the structural parameters of the liquid cooling plate through the ranking of the influence degrees of each factor, it is ensured that the obtained result is the optimal result, while reducing the labor cost and improving the work efficiency. Brief Description of the Drawings

[0044] Figure 1 It is an application environment diagram of the method for optimizing the structural parameters of the liquid cooling plate in an embodiment;

[0045] Figure 2 It is a schematic flowchart of the method for optimizing the structural parameters of the liquid cooling plate in an embodiment;

[0046] Figure 3 It is a schematic range chart of the first test data in an embodiment;

[0047] Figure 4 It is a schematic flowchart of the method for optimizing the structural parameters of the liquid cooling plate in another embodiment;

[0048] Figure 5 It is a schematic flowchart of the method for optimizing the structural parameters of the liquid cooling plate in yet another embodiment;

[0049] Figure 6 It is a schematic diagram of the test results of the second test data in an embodiment;

[0050] Figure 7 It is a structural block diagram of the device for optimizing the structural parameters of the liquid cooling plate in an embodiment;

[0051] Figure 8 It is an internal structure diagram of a computer device in an embodiment. Detailed Description of the Embodiments

[0052] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be 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 used to explain the present application and are not used to limit the present application.

[0053] The method for optimizing the structural parameters of the liquid cooling plate provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 wherein, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0054] In one embodiment, as shown in Figure 2As shown, an optimization method for the structural parameters of a liquid cooling plate is provided. Taking the application of this method to the server 104 in Figure 1 as an example, the method includes the following steps:

[0055] Step 202: Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0056] Step 204: Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain first test data;

[0057] Step 206: Determine the influence degree of each structural optimization parameter on each working effect index according to the first test data;

[0058] Step 208: Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0059] Among them, the structural optimization parameters refer to one or several of the structural parameters of the liquid cooling plate, which are the optimization objectives in the embodiments of the present application. For example, in one embodiment, according to heat transfer theory, the main heat transfer methods of heat in the battery system are: heat conduction and heat convection. The factors affecting the cooling performance of the liquid cooling plate include: flow velocity V, number of flow channels N, flow channel depth D, and flow channel length L. It can be understood that after the above structural parameters are determined, the structural shape of the liquid cooling plate can be determined. Therefore, in one embodiment, the above parameters can be optimized. The geometric model of the liquid cooling plate can be obtained by inputting the initial values of the structural optimization parameters of the liquid cooling plate into modeling software and modeling the liquid cooling plate. The embodiments of the present application do not specifically limit the type of software.

[0060] It should be noted that the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate are for DOE experimental design. For example, in one embodiment, the structural optimization parameters include flow velocity V, number of flow channels N, flow channel depth D, and flow channel length L, and the working effect indexes include the maximum temperature rise of the battery cells, the liquid pressure drop of the liquid cooling plate, and the maximum temperature difference between the battery cells. Then, a four-factor three-level experiment is conducted.

[0061] Specifically, the combined model corresponding to each group of experiments is processed and then imported into Fluent for simulation analysis to obtain a large amount of experimental data, which is the first test data. By comparing the influence of the experimental data on the maximum temperature rise ΔT of the battery cells, the system pressure drop Δp, and the maximum temperature difference Δm between the battery cells, the influence degree of the working effect indexes is obtained. In one embodiment, the four-factor three-level experimental data obtained according to the DOE design is shown in Table 1 below:

[0062] Table 1 First test data

[0063]

[0064] Analyze according to the data in the table. For example, draw a range chart. The rightmost side of the range chart shows the ranking of the influence degrees of the influencing factors on the corresponding indicators (ΔT, Δm, Δp). See Figure 3 , the factors affecting ΔT from strong to weak are: flow rate, number of flow channels, flow channel depth, flow channel length. The factors affecting Δp from strong to weak are: flow channel depth, flow rate, number of flow channels, flow channel length. The factors affecting Δm from strong to weak are: flow channel length, flow rate, number of flow channels, flow channel depth.

[0065] The priority of the working effect indicators refers to the priority degree of the influence of the working effect indicators on the working effect of the liquid cooling plate. For example, in an embodiment, since the indicator "maximum temperature rise of the battery cell ΔT" directly determines whether thermal diffusion occurs in the battery system, the indicator "system pressure drop Δp" directly determines whether the head of the selected water pump can meet the cooling requirements of the liquid cooling plate, and the indicator "maximum temperature difference between battery cells Δm" mainly affects the battery life, and this study mainly explores the cooling performance of the cold plate. Therefore, the priority of the indicators from high to low should be: maximum temperature rise of the battery cell ΔT, system pressure drop Δp, maximum temperature difference between battery cells Δm.

[0066] Determine the optimization order of the structural optimization parameters through the priority of each working effect indicator and the influence degree of the corresponding structural optimization parameters, and optimize the structural parameters ranked in the front of the optimization order first. For example, first explore to make the cold plate performance optimal, that is: the two indicators of the maximum temperature rise of the battery cell ΔT and the system pressure drop Δp are optimal, then the structural parameter flow rate corresponding to the maximum temperature rise of the battery cell ΔT is optimized first. Through the priority and influence degree, gradually optimize all the structural parameters until all the structural parameters are optimized.

[0067] In the method provided in the above embodiment, according to the structural optimization parameters of the liquid cooling plate, establish a geometric model of the liquid cooling plate; according to the number of structural optimization parameters and the number of working effect indicators of the liquid cooling plate, conduct the first simulation test on the geometric model of the liquid cooling plate to obtain the first test data; according to the first test data, determine the influence degree of each structural optimization parameter on each working effect indicator; according to the priority of each working effect indicator and the influence degree of each structural optimization parameter on each working effect indicator, optimize each structural optimization parameter to determine the final value of each structural optimization parameter. Through the experimental analysis of the geometric structure of the liquid cooling plate, explore the influencing factors of the cooling effect of the liquid cooling plate, obtain the ranking of the influence degrees of these factors on the cooling effect of the liquid cooling plate, and then optimize the structural parameters of the liquid cooling plate through the ranking of the influence degrees of each factor to ensure that the obtained result is the optimal result, while reducing the labor cost and improving the work efficiency.

[0068] In one of the embodiments, refer to Figure 4 , to determine the influence degree of the structure optimization parameters on each working effect index according to the first test data, including:

[0069] Step 402, for any working effect index, determine the influence rate of each structure optimization parameter on any working effect index according to the first test data;

[0070] Step 404, based on the sorting result of the influence rates, obtain the influence degree of each structure optimization parameter on any working efficiency index.

[0071] Among them, the influence rate is used to indicate the influence degree of the size of each structure optimization parameter on the working effect index. Briefly understood, for the influence rate of any structure optimization parameter on any working effect index, that is, taking the structure optimization parameter as the independent variable and the working effect index as the dependent variable, it is the slope of the straight line after data plotting by a large amount of test data. It should be noted that when plotting the actual test data, it cannot be guaranteed to be linear, so data fitting can be performed to determine the final slope as the influence rate.

[0072] After determining the influence rate of each structure optimization parameter on any working effect index, sort the structure optimization parameters according to the influence rate to determine the rank order of the influence degree of all structure optimization parameters on any working effect index, which is used to determine the optimization order of the structure optimization parameters subsequently.

[0073] In the method provided in the above embodiment, the influence rate is directly determined through data analysis, and all structure optimization parameters are sorted, which is fast and effective and improves work efficiency.

[0074] In one of the embodiments, refer to Figure 5 , the structure optimization parameters include flow rate, number of flow channels, flow channel depth, and flow channel length; according to the priority of each working effect index and the influence degree of each structure optimization parameter on each working effect index, optimize each structure optimization parameter to determine the final value of each structure optimization parameter, including:

[0075] Step 502, according to the first test data, the priority of each working effect index, and the influence degree of the structure optimization parameter on each working effect index, determine the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length;

[0076] Step 504, perform a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length to obtain the second test data;

[0077] Step 506: Determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel according to the second test data, the priority of each working effect index, and the influence degree of the structure optimization parameters on each working effect index.

[0078] Step 508: Input the final value of the flow velocity, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into the preset model to obtain the final value of the flow channel length.

[0079] In one embodiment, the structure optimization parameters include the flow velocity V, the number of flow channels N, the flow channel depth D, and the flow channel length L. The working effect indexes include the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, and the maximum temperature difference between battery cells. According to the data in Table 1, the factors affecting ΔT from strong to weak are: flow velocity, number of flow channels, flow channel depth, and flow channel length. The factors affecting Δp from strong to weak are: flow channel depth, flow velocity, number of flow channels, and flow channel length. The factors affecting Δm from strong to weak are: flow channel length, flow velocity, number of flow channels, and flow channel depth. After that, according to Figure 3 the range chart, for the determination of the flow velocity V, look at the three charts in the first column:

[0080] When ΔT is optimal, the value of V is 10;

[0081] When ΔP is optimal, the value of V is 6;

[0082] Since the priority of ΔT is higher, it is determined that V = 10;

[0083] For the determination of the number of flow channels N, look at the three charts in the second column:

[0084] When ΔT is optimal, the value of N is 4;

[0085] When ΔP is optimal, the value of N is 3, and ΔP is within the allowable range of process design;

[0086] Since the priority of ΔT is higher, it is determined that N = 4.

[0087] For the determination of the flow channel depth D, look at the three charts in the third column:

[0088] When ΔT is optimal, the value of D is 3;

[0089] When ΔP is optimal, the value of D is 10;

[0090] According to the above, since the priority of ΔT is higher, D should be taken as 3. However, when D = 3, the value of ΔP is too large, resulting in too much impact on the liquid cooling plate, which does not meet the process requirements. And when D takes 3 and 6, the values of ΔT are very close. Assume there is a value x between 6 and 10 for D. When D = x, ΔT(D = x) ≈ ΔT(D = 3). To determine x, take D = 6 - 10 for further exploration.

[0091] For the determination of the flow channel depth L, look at the three figures in the fourth column:

[0092] When ΔT is optimal, the value of L is 515;

[0093] When ΔP is optimal, the value of L is 615;

[0094] According to the above, since the priority of ΔT is higher, take L = 515.

[0095] Since the flow channel depth L has the least influence on ΔT and ΔP, but the greatest influence on Δm, further exploration will be continued later

[0096] In summary, the optimal combination A is: V = 10, N = 4, D = 6 - 10, L = 515.

[0097] Since in combination A, the value of D is undetermined and the value of N has room for further exploration, so in this round, to find the optimal values of N and D, the idea is the same as above.

[0098] Table 2 Second - trial data of five factors and four levels

[0099] Test No. Factor D Factor N1 Factor N2 Factor N3 Factor N4 1 6 3 3 3 3 2 8 4 4 4 4 3 9 6 6 6 6 4 10 8 8 8 8

[0100] See the test results Figure 6 For the test results shown, when ΔT is optimal, D = 10, N1 = 3, N2 = 3, N3 = 4, N4 = 8, V = 10, L = 515. Since the above first and second tests are to make ΔT and ΔP optimal, so in this round, make the index Δm optimal. The factor that most affects Δm is the flow channel length L. Therefore, by setting variable parameters in the model and automatically solving, the optimal value of L is obtained. Specifically, using the "parameter set module", set the variance Pi of the flow rate in each flow channel region, and the sum of the variances of the flow rates in the four flow channel regions P106 = P102 + P103 + P104 + P105, monitor Pi and the pressure drop Δp at the inlet and outlet, determine a reasonable threshold for Δp according to the pump head. In this exploration, the reasonable threshold for Δp is within 30 KPa. Optimize the above process with response surface to make the value of the sum of variances P the smallest, and obtain the final optimal solution combination C.

[0101] In the method provided by the above embodiment, using the Ansys Workbench platform, integrate the optimization process into a module, and obtain the best answer through automatic calculation and optimization, reducing the development cost, improving the development efficiency, and ensuring the accuracy of the results.

[0102] In one of the embodiments, the method further includes:

[0103] Obtain a preset threshold for the liquid pressure drop of the liquid - cooled plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid - cooled plate;

[0104] If the verification fails, return the steps of performing a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and continue to execute.

[0105] By monitoring the flow rate of the coolant in the cross-section of each flow channel, the sum of the variances of the flow channels in each region is set as an index for evaluating the cooling uniformity temperature of the cold plate. By automatically monitoring and optimizing this value, the purpose of optimizing the performance of the liquid cooling plate is achieved. Using variance for evaluation can not only observe the differences between each variable and the overall average, but also perform automatic optimization without having to find the maximum and minimum values in the sample every time.

[0106] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0107] Based on the same inventive concept, the embodiments of the present application also provide a liquid cooling plate structure parameter optimization device for implementing the liquid cooling plate structure parameter optimization method involved above. The solution provided by this device for solving problems is similar to the solution recorded in the above method. Therefore, the specific limitations in one or more embodiments of the liquid cooling plate structure parameter optimization device provided below can refer to the limitations on the liquid cooling plate structure parameter optimization method in the above text, and will not be repeated here.

[0108] In one embodiment, as Figure 7 shown, a liquid cooling plate structure parameter optimization device is provided, including: a model establishment module 701, a simulation test module 702, an index analysis module 703, and a parameter optimization module 704, where:

[0109] The model establishment module 701 is used to establish a geometric model of the liquid cooling plate according to the structure optimization parameters of the liquid cooling plate;

[0110] The simulation test module 702 is used to perform a first simulation test on the geometric model of the liquid cooling plate according to the number of structure optimization parameters and the number of working effect indexes of the liquid cooling plate, and obtain first test data;

[0111] The index analysis module 703 is configured to determine the influence degree of each structural optimization parameter on each working effect index according to the first test data;

[0112] The parameter optimization module 704 is configured to optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0113] In one embodiment, the structural optimization parameters include at least one of flow velocity, number of flow channels, flow channel depth, or flow channel length.

[0114] In one embodiment, the working effect indexes include at least one of the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, or the maximum temperature difference between battery cells.

[0115] In one embodiment, the index analysis module 703 is further configured to:

[0116] For any working effect index, determine the influence rate of each structural optimization parameter on any working effect index according to the first test data;

[0117] Based on the sorting result of the influence rates, obtain the influence degree of each structural optimization parameter on any working efficiency index.

[0118] In one embodiment, the parameter optimization module 704 is further configured to:

[0119] According to the first test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index, determine the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length;

[0120] Perform a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and obtain the second test data;

[0121] According to the second test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index, determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel;

[0122] Input the final value of the flow velocity, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into a preset model to obtain the final value of the flow channel length.

[0123] In one embodiment, the parameter optimization module 704 is further configured to:

[0124] Obtain the preset threshold of the liquid pressure drop of the liquid cooling plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid cooling plate;

[0125] If the verification fails, return to the step of performing the second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and continue to execute.

[0126] Each module in the above liquid cooling plate structure parameter optimization device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0127] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store test data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it realizes a method for optimizing the structure parameters of a liquid cooling plate.

[0128] Those skilled in the art can understand that Figure 8 the structure shown in

[0129] is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0130] According to the structure optimization parameters of the liquid cooling plate, establish a geometric model of the liquid cooling plate;

[0131] According to the number of structure optimization parameters and the number of working effect indexes of the liquid cooling plate, perform the first simulation test on the geometric model of the liquid cooling plate to obtain the first test data;

[0132] Determine the influence degree of each structural optimization parameter on each working effect index according to the first test data;

[0133] Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0134] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Determine that the structural optimization parameters include at least one of flow velocity, number of flow channels, flow channel depth, or flow channel length.

[0135] In one embodiment, when the processor executes the computer program, the following steps are further implemented: Determine that the working effect indexes include at least one of the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, or the maximum temperature difference between battery cells.

[0136] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0137] For any working effect index, determine the influence rate of each structural optimization parameter on any working effect index according to the first test data;

[0138] Based on the sorting result of the influence rate, obtain the influence degree of each structural optimization parameter on any working efficiency index.

[0139] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0140] According to the first test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index, determine the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length;

[0141] Conduct a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and obtain the second test data;

[0142] According to the second test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index, determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel;

[0143] Input the final value of the flow velocity, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into the preset model to obtain the final value of the flow channel length.

[0144] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0145] Obtain the preset threshold of the liquid pressure drop of the liquid cooling plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid cooling plate;

[0146] If the verification fails, return to the step of performing a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and continue to execute.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0148] Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0149] Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain first test data;

[0150] According to the first test data, determine the influence degree of each structural optimization parameter on each working effect index;

[0151] Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0152] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Determine that the structural optimization parameters include at least one of flow velocity, number of flow channels, flow channel depth, or flow channel length.

[0153] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: Determine that the working effect indexes include at least one of the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, or the maximum temperature difference between battery cells.

[0154] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0155] For any working effect index, determine the influence rate of each structural optimization parameter on any working effect index according to the first test data;

[0156] Based on the sorting result of the influence rates, obtain the influence degree of each structural optimization parameter on any working efficiency index.

[0157] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0158] Determine the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length according to the first test data, the priority of each working effect index, and the influence degree of the structural optimization parameters on each working effect index;

[0159] Conduct a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length to obtain the second test data;

[0160] Determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel according to the second test data, the priority of each working effect index, and the influence degree of the structural optimization parameters on each working effect index;

[0161] Input the final value of the flow rate, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into the preset model to obtain the final value of the flow channel length.

[0162] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0163] Obtain the preset threshold of the liquid pressure drop of the liquid cooling plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid cooling plate;

[0164] If the verification fails, return to the step of conducting a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length and continue to execute.

[0165] In one embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0166] Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate;

[0167] Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain the first test data;

[0168] Determine the influence degree of each structural optimization parameter on each working effect index according to the first test data;

[0169] Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter.

[0170] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: determining that the structural optimization parameters include at least one of flow rate, number of flow channels, flow channel depth, or flow channel length.

[0171] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented: determining that the working effect indicators include at least one of the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, or the maximum temperature difference between battery cells.

[0172] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0173] For any working effect indicator, determine the influence rate of each structural optimization parameter on any working effect indicator according to the first test data;

[0174] Based on the sorting result of the influence rates, obtain the influence degree of each structural optimization parameter on any working efficiency indicator.

[0175] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0176] According to the first test data, the priority of each working effect indicator, and the influence degree of the structural optimization parameter on each working effect indicator, determine the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length;

[0177] Conduct a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length to obtain the second test data;

[0178] According to the second test data, the priority of each working effect indicator, and the influence degree of the structural optimization parameter on each working effect indicator, determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel;

[0179] Input the final value of the flow rate, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into a preset model to obtain the final value of the flow channel length.

[0180] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0181] Obtain the preset threshold of the liquid pressure drop of the liquid cooling plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid cooling plate;

[0182] If the verification fails, return to the step of conducting a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow rate, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length and continue to execute.

[0183] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0184] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0185] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for optimizing the structural parameters of a liquid cooling plate, characterized in that, The method includes: Establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate; Conduct a first simulation test on the geometric model of the liquid cooling plate according to the number of the structural optimization parameters and the number of working effect indexes of the liquid cooling plate to obtain first test data; Determine the influence degree of each structural optimization parameter on each working effect index according to the first test data; Optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter; The structural optimization parameters include flow velocity, number of flow channels, flow channel depth, and flow channel length; the step of optimizing each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index to determine the final value of each structural optimization parameter includes: Determine the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length according to the first test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index; Conduct a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length to obtain second test data; Determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel according to the second test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index; Input the final value of the flow velocity, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into a preset model to obtain the final value of the flow channel length.

2. The method according to claim 1, wherein The structural optimization parameters include at least one of flow velocity, number of flow channels, flow channel depth, or flow channel length.

3. The method according to claim 2, characterized in that, The working effect indexes include at least one of the maximum temperature rise of the battery cell, the liquid pressure drop of the liquid cooling plate, or the maximum temperature difference between battery cells.

4. The method according to claim 3, wherein The step of determining the influence degree of the structural optimization parameter on each working effect index according to the first test data includes: For any working effect index, determine the influence rate of each structural optimization parameter on the any working effect index according to the first test data; Based on the sorting result of the influence rate, obtain the influence degree of each structural optimization parameter on the any working efficiency index.

5. The method according to claim 1, characterized in that, The method further includes: Obtain a preset threshold of the liquid pressure drop of the liquid cooling plate, and verify the final value of the flow channel length according to the preset threshold of the liquid pressure drop of the liquid cooling plate; If the verification fails, return to the step of conducting a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length and continue to execute.

6. An apparatus for optimizing the structural parameters of a liquid cooling plate, characterized in that, The device includes: A model establishment module, configured to establish a geometric model of the liquid cooling plate according to the structural optimization parameters of the liquid cooling plate; the structural optimization parameters include flow velocity, number of flow channels, flow channel depth, and flow channel length; A simulation test module, configured to perform a first simulation test on the geometric model of the liquid cooling plate according to the number of the structural optimization parameters and the number of working effect indexes of the liquid cooling plate, and obtain first test data; An index analysis module, configured to determine the influence degree of each structural optimization parameter on each working effect index according to the first test data; A parameter optimization module, configured to optimize each structural optimization parameter according to the priority of each working effect index and the influence degree of each structural optimization parameter on each working effect index, and determine the final value of each structural optimization parameter; The parameter optimization module is further configured to determine the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length according to the first test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index; perform a second simulation test on the geometric model of the liquid cooling plate according to the final value of the flow velocity, the final value of the number of flow channels, the initial range of the flow channel depth, and the initial value of the flow channel length, and obtain second test data; determine the final value of the flow channel depth and the number of sub-flow channels in each flow channel according to the second test data, the priority of each working effect index, and the influence degree of the structural optimization parameter on each working effect index; input the final value of the flow velocity, the final value of the number of flow channels, the final value of the flow channel depth, the initial value of the flow channel length, and the number of sub-flow channels in each flow channel into a preset model to obtain the final value of the flow channel length.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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