Cabin model construction method and device, computer equipment and storage medium

Abstract

The invention relates to the field of vehicle model development, and discloses a cabin model construction method and device, computer equipment and a storage medium, and the method comprises the steps: receiving a configuration instruction, and configuring a rack device equivalent to a cabin according to the configuration instruction; receiving a test instruction, testing on the rack device according to the test instruction, and obtaining a test result; and optimizing the initial cabin heat flow field model according to a test result to obtain a target cabin heat flow field model. According to the target cabin heat flow field model provided by the invention, cabin internal resistance and cabin heat data can be calculated, so that the balance between the cabin internal resistance and the part heat damage problem is allowed to be adjusted, the automobile wind resistance is further reduced, and the high-speed cruising ability of the electric automobile is improved.

Description

Technical Field

[0001] The present invention relates to the field of vehicle model development, and in particular to a method, device, computer equipment and storage medium for building a cabin model. Background Art

[0002] With the development of battery technology, the market penetration of electric vehicles is getting higher and higher. However, compared with gasoline vehicles, electric vehicles have poor high-speed endurance. Studies have shown that when electric vehicles are driving at high speeds, about 60%-70% of the power is used to combat wind resistance. Therefore, reducing the wind resistance of the car can solve the problem of poor high-speed endurance of electric vehicles to a certain extent.

[0003] On the one hand, the internal resistance of the car cabin accounts for about 15% of the total wind resistance, and reducing the internal resistance of the cabin becomes an important part of reducing wind resistance. On the other hand, when driving at high speed, the motor and battery pack will generate a lot of heat, which is prone to the risk of heat damage.

[0004] Therefore, it is necessary to build a new cabin model that can simultaneously calculate the cabin internal resistance and cabin heat data with high precision, so as to better develop new vehicle models, reduce cabin internal resistance, and prevent heat damage to components. Summary of the invention

[0005] Based on this, it is necessary to provide a cabin model construction method, device, computer equipment and storage medium to address the above technical problems, so as to calculate the cabin internal resistance and cabin heat data with high precision.

[0006] A method for constructing a cabin model, comprising:

[0007] receiving a configuration instruction, and configuring a platform device equivalent to the cabin according to the configuration instruction;

[0008] receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result;

[0009] The initial cabin thermal flow field model is optimized according to the test results to obtain a target cabin thermal flow field model.

[0010] Optionally, the receiving a configuration instruction and configuring a platform device equivalent to the cabin according to the configuration instruction includes:

[0011] Acquiring structural data of the cabin;

[0012] dividing the cabin into a plurality of measurable areas according to the structural data;

[0013] The stage device is configured according to the plurality of measurable areas.

[0014] Optionally, the test results include intake grille wake flow field data;

[0015] The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes:

[0016] Perform a wake test on the air intake grille to obtain wake flow field data of the air intake grille.

[0017] Optionally, the test results include engine compartment wake field data;

[0018] The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes:

[0019] Perform a wake test on the engine compartment to obtain wake field data of the engine compartment.

[0020] Optionally, the test results include engine compartment rear area test data;

[0021] The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes:

[0022] The rear area of ​​the engine compartment is tested to obtain test data of the rear area of ​​the engine compartment.

[0023] Optionally, the test results include exhaust pipe cabin test data;

[0024] The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes:

[0025] The exhaust pipe cabin is tested to obtain the exhaust pipe cabin test data.

[0026] Optionally, the initial cabin thermal flow field model includes steady-state flow field data and transient flow field data;

[0027] Before optimizing the initial cabin thermal flow field model according to the test results to obtain the target cabin thermal flow field model, the method further includes:

[0028] Build a 3D vehicle model including cabin structural features;

[0029] configuring a turbulence model and boundary conditions of the engine room in the three-dimensional vehicle model;

[0030] The steady-state flow field data and the transient flow field data are calculated according to the turbulence model and the boundary conditions.

[0031] Optionally, the optimizing the initial cabin thermal flow field model according to the test result to obtain a target cabin thermal flow field model further includes:

[0032] Dividing the initial cabin thermal flow field model into a number of local areas;

[0033] Acquire a local optimal model parameter combination of the local area according to the test result;

[0034] All the local optimal model parameter combinations are coupled to generate the target cabin thermal flow field model.

[0035] Optionally, obtaining the local optimal model parameter combination of the local area according to the test result includes:

[0036] Adjusting the model parameter combination of the local area, and obtaining the local model prediction result corresponding to the model parameter combination;

[0037] extracting a local test result associated with the local area from the test results;

[0038] Determining the local uncertainty of the model parameter combination according to the local model prediction result and the local test result;

[0039] If the local uncertainty meets the specified prediction requirements, the model parameter combination is determined as the local preferred model parameter combination.

[0040] Optionally, after optimizing the initial cabin thermal flow field model according to the test results to obtain a target cabin thermal flow field model, the method further includes:

[0041] Coupling the target cabin thermal flow field model with the vehicle wind tunnel model to obtain the vehicle thermal flow field model;

[0042] The vehicle's operating parameters at a specified speed are predicted using the vehicle's thermal flow field model, wherein the operating parameters include internal resistance, flow velocity, and / or surface temperature of cabin components.

[0043] A cabin model building device, comprising:

[0044] A configuration module, used to receive a configuration instruction and configure a platform device equivalent to the cabin according to the configuration instruction;

[0045] A test module, used to receive a test instruction, perform a test on the test bench device according to the test instruction, and obtain a test result;

[0046] The optimization module is used to optimize the initial cabin thermal flow field model according to the test results to obtain a target cabin thermal flow field model.

[0047] A computer device comprises a memory, a processor and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements any one of the above-mentioned cabin model building methods when executing the computer-readable instructions.

[0048] One or more readable storage media storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors execute any one of the above-mentioned cabin model building methods.

[0049] The above-mentioned cabin model construction method, device, computer equipment and storage medium receive configuration instructions, configure a bench device equivalent to the cabin according to the configuration instructions, so as to obtain test results with the help of the bench device and reduce vehicle research and development costs. Receive test instructions, perform tests on the bench device according to the test instructions, and obtain test results, so as to optimize the initial cabin thermal flow field model according to the test results to ensure the accuracy of the target cabin thermal flow field model. The initial cabin thermal flow field model is optimized according to the test results to obtain the target cabin thermal flow field model, and the cabin internal resistance and cabin heat data can be calculated with high precision through the target cabin thermal flow field model, which is convenient for the balance between the cabin internal resistance and the thermal damage of components. The target cabin thermal flow field model provided by the present invention can calculate the cabin internal resistance and cabin heat data, thereby allowing the balance between the cabin internal resistance and the thermal damage of components to be adjusted, further reducing the wind resistance of the car and improving the high-speed endurance of the electric car. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0051] Figure 1 is a schematic diagram of a process of a method for constructing a cabin model in one embodiment of the present invention;

[0052] Figure 2 is an example schematic diagram of a wake test performed on an engine compartment of a test bench in one embodiment of the present invention;

[0053] Figure 3 is an example diagram of testing the rear area of ​​the engine compartment of a test bench device in one embodiment of the present invention;

[0054] Figure 4 is a schematic diagram of an example of testing an exhaust pipe cabin of a bench device in one embodiment of the present invention;

[0055] Figure 5is a structural schematic diagram of a cabin model building device in one embodiment of the present invention;

[0056] Figure 6 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In one embodiment, if Figure 1 As shown, a method for constructing a cabin model is provided, comprising the following steps S10-S30.

[0059] S10. Receive a configuration instruction, and configure a platform device equivalent to the cabin according to the configuration instruction.

[0060] Understandably, the engine compartment refers to the installation area of ​​the engine on the car. Usually, the engine compartment is located at the front of the car and is protected by a closed metal shell. The configuration instruction can be an instruction issued by a tester. The configuration instruction can be executed by the tester or by a test robot. Through the configuration instructions, a bench device equivalent to the engine compartment can be constructed to facilitate the acquisition of engine compartment test data. The bench device can be divided into multiple measurable areas, such as the measurable area in the engine compartment, the measurable area in the exhaust pipe compartment, etc. Real car parts can be placed in the measurable area in the engine compartment to obtain test data that is close to the actual driving environment.

[0061] S20, receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result.

[0062] Understandably, the test instruction may be an instruction issued by a tester. The configuration instruction may be executed by a tester or by a test robot. Different tests may be performed on the test bench to obtain test results. The test performed on the test bench may be an air intake grille wake field test, an engine compartment temperature field test, and some single specialty product tests. After the test, an initial test result may be obtained, that is, the original recorded data obtained after the test. The initial test result may be further processed to form a test result.

[0063] In some examples, the preliminary test results can be cleaned to obtain purified data, which can then be visualized to facilitate testers to analyze the regularity of the visualized data and obtain analysis results. These analysis results can be used as test results for the next step of optimizing the initial cabin thermal flow field model.

[0064] S30. Optimizing the initial cabin thermal flow field model according to the test results to obtain a target cabin thermal flow field model.

[0065] Understandably, the initial cabin thermal flow field model is a computational model based on fluid dynamics (CFD, Computational FluidDynamics). After obtaining the test results, the initial cabin thermal flow field model can be calibrated according to the test results to obtain the target cabin thermal flow field model. In some examples, the individual items and individual areas of the initial cabin thermal flow field model can be calibrated first, and finally the calibration of the entire cabin can be completed.

[0066] After calibration, the target cabin thermal flow field model can simulate and calculate simulation data that is close to the operating data in the real scenario.

[0067] In steps S10 to S30, a configuration instruction is received, and a test bench device equivalent to the cabin is configured according to the configuration instruction, so as to obtain test results with the help of the test bench device and reduce vehicle R&D costs. A test instruction is received, and a test is performed on the test bench device according to the test instruction to obtain test results, so as to optimize the initial cabin thermal flow field model according to the test results and ensure the accuracy of the target cabin thermal flow field model. The initial cabin thermal flow field model is optimized according to the test results to obtain the target cabin thermal flow field model, and the cabin internal resistance and cabin heat data can be calculated with high precision through the target cabin thermal flow field model, so as to facilitate the balance between the cabin internal resistance and the thermal damage problems of components. The target cabin thermal flow field model provided in this embodiment can calculate the cabin internal resistance and cabin heat data, thereby allowing the balance between the cabin internal resistance and the thermal damage problems of components to be adjusted, further reducing the wind resistance of the vehicle and improving the high-speed endurance of the electric vehicle.

[0068] Optionally, step S10, i.e., receiving a configuration instruction and configuring a platform device equivalent to the cabin according to the configuration instruction, includes:

[0069] S101, acquiring structural data of the cabin;

[0070] S102, dividing the cabin into a plurality of measurable areas according to the structural data;

[0071] S103: configuring the platform device according to the multiple measurable areas.

[0072] Understandably, the structural data of the cabin may include the position distribution of the components in the cabin. Since the components in the cabin are closely arranged, it is not conducive to the arrangement of measuring instruments (such as thermocouples, etc.). Therefore, the cabin can be divided into multiple measurable areas according to the structural data, such as a real grille (several parts are cut according to actual needs), a real front cooling module, a real fan, a simplified engine model (temperature-controlled heating), a simplified exhaust pipe (the exhaust pipe cabin at the rear end of the box, temperature-controlled heating), etc. The bench device can be regarded as an integration of these measurable areas.

[0073] This embodiment builds a test bench device, which allows corresponding measuring instruments to be arranged in each measurable area, which is conducive to obtaining test results.

[0074] Optionally, the test result includes the air intake grille wake flow field data; the measurable area is the air intake grille;

[0075] Step S20, i.e., receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result, includes:

[0076] S201, performing a wake test on the air intake grille to obtain wake flow field data of the air intake grille.

[0077] Understandably, the test bench device includes an air intake grille. The air intake grille can be placed in a wind tunnel for wake testing to obtain the air intake grille wake field data. The air intake grille wake field data includes the airflow distribution after the airflow passes through the air intake grille. The separation of the micro-structure of the grille produces a certain degree of pressure loss on the wake. Since the structure of the air intake grille is relatively complex, its wake has a strong three-dimensional feature. In order to obtain the spatial distribution law of the flow velocity and total pressure, it is necessary to measure at a large number of spatial positions. Here, the space automatic scanning technology can be used to quickly scan and measure the flow velocity and total pressure in the measured space, thereby improving the test efficiency and reducing the test uncertainty.

[0078] This embodiment can evaluate the wake characteristics of the air intake grille by measuring the wake field data of the air intake grille.

[0079] Optionally, the test result includes wake field data of the engine compartment; the measurable area is the engine compartment;

[0080] Step S20, i.e., receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result, includes:

[0081] S202: Perform a wake test on the engine compartment to obtain wake field data of the engine compartment.

[0082] Understandably, if Figure 2 As shown, Figure 2The following is a simplified diagram of an example of a wake test on a test bench engine compartment. Figure 2 The PIV test area at the front of the engine compartment is tested. PIV testing refers to the process of measuring the velocity in the fluid field using particle image velocimetry (PIV) technology. This technology uses high-speed photography technology to calculate the fluid velocity information at different positions and time points by recording the movement trajectory of small particles added to the fluid at different time intervals. This part of the test mainly studies whether the radiator pressure loss is consistent with the single product test results and whether the temperature has an effect on the radiator.

[0083] This embodiment can evaluate the temperature characteristics of the radiator by measuring the wake flow field data of the engine compartment.

[0084] Optionally, the test result includes engine compartment rear area test data; the measurable area is the engine compartment rear area;

[0085] Step S20, i.e., receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result, includes:

[0086] S203: Test the rear area of ​​the engine compartment to obtain test data of the rear area of ​​the engine compartment.

[0087] Understandably, if Figure 3 As shown, Figure 3 The following is a simplified diagram of an example of testing the rear area of ​​the engine compartment on a test bench. Figure 3 The PIV test area at the rear end of the engine compartment is tested. This part of the test mainly studies the influence of the radiator temperature gradient on the cabin temperature field. If the influence is greater than the preset threshold, the stratified radiator temperature needs to be set in the model.

[0088] This embodiment obtains test data of the rear area of ​​the engine compartment and reasonably arranges temperature sensors.

[0089] Optionally, the test result includes exhaust pipe cabin test data; the measurable area is the exhaust pipe cabin;

[0090] Step S20, i.e., receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result, includes:

[0091] S204: Test the exhaust pipe cabin to obtain exhaust pipe cabin test data.

[0092] Understandably, if Figure 4 As shown, Figure 4The following is a simplified diagram of an example of testing an exhaust pipe cabin on a bench. Figure 4 The PIV test area at the rear end of the engine compartment is tested. This part of the test mainly studies the influence of radiation and convection heat transfer, whether the heat transfer is accurately simulated when the radiation heat transfer and convection heat transfer are balanced, and whether the heat generation rate of typical components is correctly set.

[0093] This embodiment obtains exhaust pipe cabin test data to adjust the heating rate of components and improve the simulation accuracy of the target cabin thermal flow field model.

[0094] Optionally, the test results include air intake grille wake field data, engine compartment wake field data, engine compartment rear area test data and exhaust pipe compartment test data; the multiple measurable areas include the air intake grille, the engine compartment, the engine compartment rear area and the exhaust pipe compartment;

[0095] Step S20, i.e., receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result, includes:

[0096] S201, performing a wake test on the air intake grille to obtain wake flow field data of the air intake grille.

[0097] S202: Perform a wake test on the engine compartment to obtain wake field data of the engine compartment.

[0098] S203: Test the rear area of ​​the engine compartment to obtain test data of the rear area of ​​the engine compartment.

[0099] S204: Test the exhaust pipe cabin to obtain exhaust pipe cabin test data.

[0100] It is understandable that in some examples, it is necessary to test multiple measurable areas to obtain corresponding test data. The test steps can refer to the specific implementation of the above embodiment, and will not be repeated here.

[0101] Optionally, the initial cabin thermal flow field model includes steady-state flow field data and transient flow field data;

[0102] Before step S30, that is, before optimizing the initial cabin thermal flow field model according to the test results to obtain the target cabin thermal flow field model, the method further includes:

[0103] S31, constructing a three-dimensional vehicle model including cabin structural features;

[0104] S32, configuring the turbulence model and boundary conditions of the cabin in the three-dimensional vehicle model;

[0105] S33. Calculate the steady-state flow field data and the transient flow field data according to the turbulence model and the boundary conditions.

[0106] Understandably, a 3D mesh of the whole vehicle model can be established, the cabin structural features are retained, and it is placed in a rectangular air domain, and multiple layers of encryption are performed on the whole vehicle to the entire rectangular computational domain to form a three-dimensional whole vehicle model. Among them, the cabin structural features include the tiny geometric features of the cabin part.

[0107] The turbulence model and boundary conditions of the cabin can be configured in the three-dimensional vehicle model, and then fluid mechanics calculations can be performed to obtain steady-state flow field data and transient flow field data. Boundary conditions can be set according to actual needs. Boundary conditions include but are not limited to inlet boundary parameters, outlet boundary parameters, ground parameters and other surface parameters. In one example, the inlet boundary parameters include velocity inlet, inlet air density, inlet boundary length, etc.; outlet boundary parameters include pressure outlet, outlet air density, outlet boundary length, etc.; ground parameters include moving ground, moving speed, etc.; other surface parameters include fixed no-slip wall.

[0108] The steady-state flow field data is the average flow field result of the current time step, which is only related to the number of iterations and has nothing to do with the physical time. The transient flow field data is the result obtained by performing transient calculations after obtaining the steady-state flow field data.

[0109] This embodiment can construct an initial cabin thermal flow field model to be optimized.

[0110] Optionally, step S30, i.e., optimizing the initial cabin thermal flow field model according to the test results to obtain a target cabin thermal flow field model, further includes:

[0111] S301, dividing the initial cabin thermal flow field model into a plurality of local areas; each of the local areas corresponds to the measurable area one by one;

[0112] S302, obtaining a local optimal model parameter combination for the local area according to the test result;

[0113] S303: Coupling all the local optimal model parameter combinations to generate the target cabin thermal flow field model.

[0114] Understandably, the initial cabin thermal flow field model can be divided into several local areas, such as the fan wake area, the grille wake area, etc. Each local area corresponds to the measurable area of ​​the test bench device one by one. The parameters of the local area can be optimized according to the test results of each measurable area to form a local optimal model parameter combination. For example, for the fan wake area, turbulence models, transient methods, fan simulation methods (such as MRF, moving wall or moving grid), etc. can be preferentially selected; the boundary layer, PI, PV, viscosity, turbulence intensity ratio, formula coefficient and other parameters of the CFD model are changed one by one to obtain model test results consistent with the test results. The multiple parameters corresponding to the model test results are determined as the local optimal model parameter combination. Finally, all local optimal model parameter combinations are coupled to generate the target cabin thermal flow field model.

[0115] This embodiment first performs local optimization and then performs parameter combination coupling, so as to improve the generation efficiency of the target cabin thermal flow field model.

[0116] Optionally, step S302, i.e., obtaining a local optimal model parameter combination of the local area, includes:

[0117] S3021, adjusting the model parameter combination of the local area, and obtaining the local model prediction result corresponding to the model parameter combination;

[0118] S3022, extracting a local test result associated with the local area from the test results;

[0119] S3023, determining the local uncertainty of the model parameter combination according to the local model prediction result and the local test result;

[0120] S3024. If the local uncertainty meets the specified prediction requirements, determine the model parameter combination as the local preferred model parameter combination.

[0121] Understandably, the model parameter combination of the local area can be adjusted, such as the boundary layer, PI, PV, viscosity, turbulence intensity ratio, formula coefficients and other parameters in the model parameter combination, the corresponding local model prediction results are calculated, the local model prediction results are compared with the local test results, and the local uncertainty of the model parameter combination is calculated. The specified prediction requirements can be set according to actual needs. In some examples, the specified prediction requirements can refer to selecting the minimum local uncertainty. The model parameter combination that meets the specified prediction requirements is determined as the local preferred model parameter combination.

[0122] This embodiment can achieve calibration of a local area to ensure that the working conditions of the model simulation are consistent with the test conditions.

[0123] Optionally, after step S30, that is, after optimizing the initial cabin thermal flow field model according to the test results to obtain the target cabin thermal flow field model, the method further includes:

[0124] S40, coupling the target cabin thermal flow field model with the vehicle wind tunnel model to obtain a vehicle thermal flow field model;

[0125] S50. Predicting operating parameters of the vehicle at a specified speed using the thermal flow field model of the entire vehicle, wherein the operating parameters include internal resistance, flow velocity, and / or surface temperature of cabin components.

[0126] Understandably, the vehicle wind tunnel model can be a pre-built aerodynamic vehicle model. The vehicle wind tunnel model can be modified according to the target cabin thermal flow field model, adding radiator temperature, modifying turbulence model, parameters, etc., and after conversion, a vehicle thermal flow field model is formed. The vehicle thermal flow field model is a transient model, which is related to the real physical time and has more precise prediction accuracy.

[0127] The vehicle's operating parameters at a specified speed, such as internal resistance, air volume, flow rate, and / or cabin component surface temperature, can be predicted using the vehicle's thermal flow field model.

[0128] This embodiment provides an application scenario of the target cabin thermal flow field model, which is used to predict the operating parameters of the vehicle at a specified speed, thereby guiding further research and development of the vehicle.

[0129] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0130] In one embodiment, a cabin model construction device is provided, and the cabin model construction device corresponds to the cabin model construction method in the above embodiment. Figure 5 As shown, the cabin model building device includes a configuration module 10, a test module 20 and an optimization module 30. The detailed description of each functional module is as follows:

[0131] A configuration module 10, configured to receive a configuration instruction and configure a platform device equivalent to the cabin according to the configuration instruction;

[0132] A test module 20, used to receive a test instruction, perform a test on the test bench device according to the test instruction, and obtain a test result;

[0133] The optimization module 30 is used to optimize the initial cabin thermal flow field model according to the test results to obtain a target cabin thermal flow field model.

[0134] Optionally, the configuration module 10 includes:

[0135] An acquisition receiving data unit is used to acquire the structural data of the cabin;

[0136] a region division unit, configured to divide the cabin into a plurality of measurable regions according to the structural data;

[0137] A configuration stage unit is used to configure the stage device according to the plurality of measurable areas.

[0138] Optionally, the test result includes the air intake grille wake flow field data; the measurable area is the air intake grille;

[0139] The test module 20 includes:

[0140] The air intake grille test unit is used to perform a wake test on the air intake grille and obtain wake flow field data of the air intake grille.

[0141] Optionally, the test result includes wake field data of the engine compartment; the measurable area is the engine compartment;

[0142] The test module 20 includes:

[0143] The engine compartment test unit is used to perform a wake test on the engine compartment and obtain wake field data of the engine compartment.

[0144] Optionally, the test result includes engine compartment rear area test data; the measurable area is the engine compartment rear area;

[0145] The test module 20 includes:

[0146] The engine compartment rear area test unit is used to test the engine compartment rear area and obtain the engine compartment rear area test data.

[0147] Optionally, the test result includes exhaust pipe cabin test data; the measurable area is the exhaust pipe cabin;

[0148] The test module 20 includes:

[0149] The exhaust pipe cabin test unit is used to test the exhaust pipe cabin and obtain the exhaust pipe cabin test data.

[0150] Optionally, the test results include air intake grille wake field data, engine compartment wake field data, engine compartment rear area test data and exhaust pipe compartment test data; the multiple measurable areas include the air intake grille, the engine compartment, the engine compartment rear area and the exhaust pipe compartment;

[0151] The test module 20 includes:

[0152] An air intake grille test unit, used to perform a wake test on the air intake grille to obtain wake flow field data of the air intake grille;

[0153] An engine compartment test unit, used to perform a wake test on the engine compartment and obtain wake field data of the engine compartment;

[0154] An engine compartment rear area test unit, used to test the engine compartment rear area and obtain test data of the engine compartment rear area;

[0155] The exhaust pipe cabin test unit is used to test the exhaust pipe cabin and obtain the exhaust pipe cabin test data.

[0156] Optionally, the initial cabin thermal flow field model includes steady-state flow field data and transient flow field data;

[0157] The cabin model building device further includes an initial model building module, and the initial model building module includes:

[0158] Building a vehicle model unit, used to build a three-dimensional vehicle model including cabin structural features;

[0159] A model setting unit, used to configure the turbulence model and boundary conditions of the cabin in the three-dimensional vehicle model;

[0160] A data calculation unit is used to calculate the steady-state flow field data and the transient flow field data according to the turbulence model and the boundary conditions.

[0161] Optionally, the optimization module 30 further includes:

[0162] A model division unit, used for dividing the initial cabin thermal flow field model into a plurality of local areas; each of the local areas corresponds to the measurable area one by one;

[0163] A local optimization unit, used for obtaining a local optimal model parameter combination of the local area according to the test result;

[0164] A target model generation unit is used to couple all the local optimal model parameter combinations to generate the target cabin thermal flow field model.

[0165] Optionally, the local optimization unit includes:

[0166] A local model prediction unit, used to adjust the model parameter combination of the local area and obtain a local model prediction result corresponding to the model parameter combination;

[0167] A local test unit, configured to extract a local test result associated with the local area from the test result;

[0168] A calculation uncertainty unit, used for determining the local uncertainty of the model parameter combination according to the local model prediction result and the local test result;

[0169] A local parameter combination unit is generated, which is used to determine the model parameter combination as the local preferred model parameter combination if the local uncertainty meets the specified prediction requirement.

[0170] Optionally, the cabin model building device further includes:

[0171] A whole vehicle coupling module, used for coupling the target cabin thermal flow field model with the whole vehicle wind tunnel model to obtain a whole vehicle thermal flow field model;

[0172] The parameter prediction module is used to predict the operating parameters of the vehicle at a specified speed through the thermal flow field model of the whole vehicle, and the operating parameters include internal resistance, flow rate and / or surface temperature of cabin components.

[0173] For the specific definition of the cabin model building device, please refer to the definition of the cabin model building method above, which will not be repeated here. Each module in the above-mentioned cabin model building device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be 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 the above modules.

[0174] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface and a database connected via 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 readable storage medium and an internal memory. The readable storage medium stores an operating system, computer-readable instructions and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The database of the computer device is used to store data involved in the cabin model building method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instructions are executed by the processor, a cabin model building method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0175] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor executes the computer-readable instructions to implement the following steps:

[0176] receiving a configuration instruction, and configuring a platform device equivalent to the cabin according to the configuration instruction;

[0177] receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result;

[0178] The initial cabin thermal flow field model is optimized according to the test results to obtain a target cabin thermal flow field model.

[0179] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage media. The readable storage media store computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the following steps are implemented:

[0180] receiving a configuration instruction, and configuring a platform device equivalent to the cabin according to the configuration instruction;

[0181] receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result;

[0182] The initial cabin thermal flow field model is optimized according to the test results to obtain a target cabin thermal flow field model.

[0183] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0184] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0185] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for constructing a cabin model, characterized in that: include: receiving a configuration instruction, and configuring a platform device equivalent to the cabin according to the configuration instruction; receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result; The initial cabin thermal flow field model is optimized according to the test results to obtain a target cabin thermal flow field model.

2. The method for constructing a cabin model according to claim 1, characterized in that: The receiving of the configuration instruction and configuring the platform device equivalent to the cabin according to the configuration instruction includes: Acquiring structural data of the cabin; dividing the cabin into a plurality of measurable areas according to the structural data; The stage device is configured according to the plurality of measurable areas.

3. The method for constructing a cabin model according to claim 2, characterized in that: The test results include the air intake grille wake flow field data; the measurable area is the air intake grille; The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes: Perform a wake test on the air intake grille to obtain wake flow field data of the air intake grille.

4. The method for constructing a cabin model according to claim 2, wherein: The test results include wake field data of the engine compartment; the measurable area is the engine compartment; The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes: Perform a wake test on the engine compartment to obtain wake field data of the engine compartment.

5. The method for constructing a cabin model according to claim 2, wherein: The test results include engine compartment rear area test data; the measurable area is the engine compartment rear area; The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes: The rear area of ​​the engine compartment is tested to obtain test data of the rear area of ​​the engine compartment.

6. The method for constructing a cabin model according to claim 2, wherein: The test results include exhaust pipe cabin test data; the measurable area is the exhaust pipe cabin; The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes: The exhaust pipe cabin is tested to obtain the exhaust pipe cabin test data.

7. The method for constructing a cabin model according to claim 2, wherein: The test results include air intake grille wake field data, engine compartment wake field data, engine compartment rear area test data and exhaust pipe compartment test data; the multiple measurable areas include the air intake grille, engine compartment, engine compartment rear area and exhaust pipe compartment; The receiving a test instruction, performing a test on the test bench device according to the test instruction, and obtaining a test result includes: Performing a wake test on the air intake grille to obtain wake field data of the air intake grille; Performing a wake test on the engine compartment to obtain wake field data of the engine compartment; Testing the rear area of ​​the engine compartment to obtain test data of the rear area of ​​the engine compartment; The exhaust pipe cabin is tested to obtain the exhaust pipe cabin test data.

8. The method for constructing a cabin model according to claim 1, wherein: The initial cabin thermal flow field model includes steady-state flow field data and transient flow field data; Before optimizing the initial cabin thermal flow field model according to the test results to obtain the target cabin thermal flow field model, the method further includes: Build a 3D vehicle model including cabin structural features; configuring a turbulence model and boundary conditions of the engine room in the three-dimensional vehicle model; The steady-state flow field data and the transient flow field data are calculated according to the turbulence model and the boundary conditions.

9. The method for constructing a cabin model according to claim 2, wherein: The optimizing the initial cabin thermal flow field model according to the test results to obtain the target cabin thermal flow field model also includes: Dividing the initial cabin thermal flow field model into a plurality of local regions; each of the local regions corresponds to the measurable region one by one; Acquire a local optimal model parameter combination of the local area according to the test result; All the local optimal model parameter combinations are coupled to generate the target cabin thermal flow field model.

10. The method for constructing a cabin model according to claim 9, characterized in that: The obtaining of the local optimal model parameter combination of the local area according to the test result includes: Adjusting the model parameter combination of the local area, and obtaining the local model prediction result corresponding to the model parameter combination; extracting a local test result associated with the local area from the test results; Determining the local uncertainty of the model parameter combination according to the local model prediction result and the local test result; If the local uncertainty meets the specified prediction requirements, the model parameter combination is determined as the local preferred model parameter combination.

11. The method for constructing a cabin model according to claim 1, wherein: After optimizing the initial cabin thermal flow field model according to the test results to obtain the target cabin thermal flow field model, the method further includes: Coupling the target cabin thermal flow field model with the vehicle wind tunnel model to obtain the vehicle thermal flow field model; The vehicle's operating parameters at a specified speed are predicted using the vehicle's thermal flow field model, wherein the operating parameters include internal resistance, flow velocity, and / or surface temperature of cabin components.

12. A cabin model building device, characterized in that: include: A configuration module, used to receive a configuration instruction and configure a platform device equivalent to the cabin according to the configuration instruction; A test module, used to receive a test instruction, perform a test on the test bench device according to the test instruction, and obtain a test result; The optimization module is used to optimize the initial cabin thermal flow field model according to the test results to obtain a target cabin thermal flow field model.

13. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the cabin model building method according to any one of claims 1 to 11 is implemented.

14. One or more readable storage media storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors execute the cabin model building method according to any one of claims 1 to 11.

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