Gearbox thermal model construction method and device of electric drive axle, equipment and medium

Abstract

The invention provides a gearbox thermal model construction method and device for an electric drive axle, equipment and a medium, and the method comprises the steps: obtaining a parameterized response surface of a first type of thermal resistance through finite element simulation; converting the parameterized response surface into a low-dimensional first model; calculating a second type of thermal resistance theoretically to obtain a low-dimensional second model; according to the heat transfer path of the gearbox, the first model and the second model, an initial thermal model of the gearbox is built in low-dimensional simulation software; and calibrating other thermal resistances in the initial thermal model of the gearbox according to experimental data to obtain a thermal model of the gearbox. For a first type of thermal resistance which can be simulated, a response surface of a gearbox is quickly established through finite element simulation, the characteristic order of a three-dimensional model is reduced into a low-dimensional model, a low-dimensional model of a second type of thermal resistance is obtained by adopting a theoretical calculation mode, a third type of thermal resistance is obtained by adopting an experimental calibration mode, and different types of thermal resistance are established through differentiation, so that the performance of the gearbox is improved. And accurate modeling of the gearbox thermal model is realized.

Description

Technical Field

[0001] This invention relates to the field of vehicle simulation technology, and in particular to a method, apparatus, equipment, and medium for constructing a thermal model of an electric drive axle gearbox. Background Technology

[0002] Currently, the powertrain system architecture of new energy vehicles mainly adopts a compact three-in-one electric drive axle, where the electronic control unit, motor, and transmission are highly integrated through a shared housing and cooling system. Compared to independent electronic control units, motors, and transmissions, the highly integrated electric drive axle requires careful examination of the temperature field coupling between its components and whether this coupling affects the thermal design of each component. Therefore, simulation prediction of the temperature of key components in the electric drive axle is of great significance for fully utilizing its performance, protecting component safety, and extending component lifespan.

[0003] In electric drive axles, the transmission, being a mechanical component, rarely has sensors installed for real-time monitoring and protection of its temperature. Therefore, during the design phase, it is crucial to accurately predict the transmission's extreme temperatures and assess the impact of temperature loads on its lifespan through simulation calculations and other methods, ensuring the safe operation of the transmission.

[0004] The basic idea of ​​electric drive axle thermal simulation is to model the heat transfer interface between each pair of components based on the individual thermal simulation models or results of the electronic control unit, motor, and gearbox, thereby establishing a complete thermal model of the electric drive axle. While the construction of individual thermal models of the electronic control unit and motor is supported by mature methods and commercial software, gearboxes, due to their significant differences in structure, shape, and number of gear pairs, and the complex heat convection and conduction relationships within their thermal field, are typically simulated using the traditional finite element method. However, for highly integrated electric drive axles, the temperature performance of the gearbox differs significantly from that of a single gearbox, thus highlighting the limitations of traditional finite element simulation methods. These limitations are mainly reflected in the following aspects:

[0005] Firstly, describing the physical field is quite difficult. The heat transfer behavior of a gearbox can be simply described as "gear frictional heating" - "convective heat transfer between gears and oil" - "convective heat transfer between oil and housing" - "heat conduction of housing" - "convective heat transfer between housing and air," which also needs to take into account the heat conduction of the rotor and stator, as well as the heat convection of cooling water. The complex physical field requires the use of CFD simulation, which leads to a very large computational load for finite element simulation, and the influence of certain factors is easily overlooked during the setting of simulation boundaries.

[0006] Secondly, it is difficult to establish the heat transfer interface with the motor and electronic control system. The gearbox in the electric drive axle needs to consider the influence of the motor and electronic control system on its temperature. Traditional finite element methods require the creation of models of the motor and electronic control system for simulation, but this method will significantly increase the number of meshes, and overly complex models are difficult to obtain accurate simulation results. At the same time, due to the high integration of the electric drive axle, the heat transfer interface between components is difficult to describe with a finite number of surfaces. Therefore, methods that make it difficult to establish the heat transfer interface load the temperature field distribution of the motor and electronic control system as input into the finite element model of the gearbox, which will lead to inconsistencies between the simulation results and the actual temperature performance of the gearbox.

[0007] Third, the finite element simulation calculation speed is too slow. Because the electric drive axle has a wide range of speeds and torques, and the heat generation varies significantly at different operating points, and the gearbox model is typically quite complex with a mesh size of 5 to 10 million, even simulations of a single operating condition require a long computation time. Therefore, it is difficult to obtain accurate simulation results across the entire speed and torque range within a limited time.

[0008] Fourth, the cost of deformation development is high. Finite element simulation is based on three-dimensional models for calculation. When the three-dimensional model of the gearbox is deformed, all the steps of finite element simulation, such as modeling, solving, and post-processing, need to be repeated. Moreover, the previous simulation results are difficult to use as a reference, which leads to an increase in the simulation cycle and cost, thus hindering the deformation development of the gearbox. Summary of the Invention

[0009] In view of the above-mentioned defects in the prior art, the present invention provides a method, apparatus, equipment and medium for constructing a thermal model of an electric drive axle gearbox, so as to solve the technical problems of difficulty in establishing a thermal model of an electric drive axle gearbox, slow simulation speed and high deformation development cost in the prior art.

[0010] To achieve the above and other related objectives, this invention provides a method for constructing a thermal model of an electric drive axle gearbox, comprising: obtaining a parameterized response surface of a first type of thermal resistance through finite element simulation; converting the parameterized response surface into a low-dimensional first model; obtaining a low-dimensional second model by theoretically calculating a second type of thermal resistance; constructing an initial thermal model of the gearbox in low-dimensional simulation software based on the heat transfer path of the gearbox, the first model, and the second model; and calibrating other thermal resistances in the initial thermal model of the gearbox based on experimental data to obtain the gearbox thermal model.

[0011] In one embodiment of the present invention, the first type of thermal resistance includes the thermal conductivity of the gearbox housing and the convective heat transfer resistance of the cooling water channel; the parameterized response surface of the first type of thermal resistance is obtained through finite element simulation, including: performing finite element thermal steady-state simulation on the parameterized gearbox housing model, and combining experimental design methods to obtain a first response surface of the input parameters and the thermal conductivity of the gearbox housing; performing CFD simulation on the parameterized cooling water channel model, and combining experimental design methods to obtain a second response surface of the input parameters and the convective heat transfer resistance of the cooling water channel.

[0012] In one embodiment of the present invention, a finite element thermal steady-state simulation is performed on a parameterized gearbox housing model. Combined with an experimental design method, a first response surface is obtained between the input parameters and the thermal resistance of the gearbox housing. This includes: defining heat transfer boundaries after meshing the gearbox housing model; parameterizing first variables related to the heat transfer boundaries and setting the value range for each first variable; generating multiple sets of boundary condition combinations using Latin hypercube sampling based on the value ranges of all first variables to obtain the first input parameters; performing the finite element thermal steady-state simulation to calculate the thermal resistance of the gearbox housing based on the first input parameters; and fitting the first input parameters and their corresponding thermal resistance calculation results to obtain the first response surface.

[0013] In one embodiment of the present invention, after fitting the first input parameter and its corresponding thermal resistance calculation result to obtain the first response surface, the method further includes: determining whether the accuracy of the first response surface meets the preset accuracy; if not, optimizing the first response surface by supplementing the boundary condition combination until the accuracy of the first response surface meets the preset accuracy.

[0014] In one embodiment of the present invention, a CFD simulation is performed on a parameterized cooling channel model. Combined with an experimental design method, a second response surface is obtained, relating the input parameters to the convective heat transfer resistance of the cooling channel. This includes: defining flow boundaries after meshing the cooling channel model; parameterizing second variables related to the flow boundaries and setting the value range for each second variable; generating multiple sets of boundary condition combinations using Latin hypercube sampling based on the value ranges of all second variables to obtain the second input parameters; performing the CFD simulation based on the second input parameters to calculate the convective heat transfer coefficient of the cooling channel; obtaining the convective heat transfer resistance of the cooling channel based on the convective heat transfer coefficient and the heat transfer area of ​​the cooling channel; and fitting the second input parameters and their corresponding thermal resistance calculation results to obtain the second response surface.

[0015] In one embodiment of the present invention, the second type of thermal resistance includes the convective heat transfer thermal resistance between the gearbox housing and the external environment, and the other thermal resistance includes the convective heat transfer thermal resistance between the lubricating oil and the housing and gears.

[0016] In one embodiment of the present invention, an initial thermal model of the gearbox is built in low-dimensional simulation software based on the heat transfer path of the gearbox, the first model, and the second model, including: building a modular initial thermal model of the gearbox in low-dimensional simulation software by taking the first model and the second model as sub-modules according to the heat transfer path of the gearbox.

[0017] To achieve the above and other related objectives, the present invention also provides a device for constructing a thermal model of an electric drive axle transmission, comprising: a pre-simulation unit for obtaining a parameterized response surface of a first type of thermal resistance through finite element simulation; a model reduction unit for converting the parameterized response surface into a low-dimensional first model; a model definition unit for obtaining a low-dimensional second model by theoretically calculating a second type of thermal resistance; a modeling unit for building an initial thermal model of the transmission in low-dimensional simulation software based on the heat transfer path of the transmission, the first model, and the second model; and a model optimization unit for calibrating other thermal resistances in the initial thermal model of the transmission based on experimental data to obtain the transmission thermal model.

[0018] To achieve the above and other related objectives, the present invention also provides an electronic device, including a processor, a memory, and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the method provided in any of the above embodiments.

[0019] To achieve the above and other related objectives, the present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause a computer to perform the method provided in any of the above embodiments.

[0020] The beneficial effects of this invention are as follows: This invention proposes a method, device, equipment, and medium for constructing a thermal model of an electric drive axle gearbox. This method classifies thermal resistance. For the first type of thermal resistance that can be simulated, the response surface of the gearbox is quickly established through finite element simulation, and the features of the three-dimensional model are reduced to a low-dimensional model. The low-dimensional model of the second type of thermal resistance is obtained by theoretical calculation, and the third type of thermal resistance is obtained by experimental calibration. By constructing different types of thermal resistance in the gearbox thermal model in a differentiated manner, the accurate modeling of the gearbox thermal model can be achieved. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a first method for constructing a thermal model of a gearbox according to an embodiment of the present invention;

[0023] Figure 2 A detailed flowchart of step S100 provided in an embodiment of the present invention;

[0024] Figure 3 A detailed flowchart of step S110 provided in an embodiment of the present invention;

[0025] Figure 4 A detailed flowchart of step S120 provided in an embodiment of the present invention;

[0026] Figure 5 A flowchart illustrating a second method for constructing a gearbox thermal model according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a gearbox thermal model construction device provided in an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0029] Figure 8 A comparison chart of measured values ​​and simulation results of transmission lubricating oil temperature provided in an embodiment of the present invention;

[0030] Figure 9 A comparison diagram of the oil temperatures of two transmissions after the transmission design was changed, provided as an embodiment of the present invention;

[0031] Explanation of reference numerals in the attached figures: 101, pre-simulation unit; 102, model reduction unit; 103, model definition unit; 104, modeling unit; 105, model optimization unit; 201, processor; 202, memory. Detailed Implementation

[0032] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. It should be noted that, unless otherwise specified, the following embodiments and features can be combined with each other. In addition to the specific methods, equipment, and materials used in the embodiments, based on the knowledge of the prior art and the description of the present invention by those skilled in the art, any prior art methods, equipment, and materials similar to or equivalent to the methods, equipment, and materials in the embodiments of the present invention can be used to implement the present invention.

[0033] It should be understood that the terminology used in the embodiments of this invention is for describing specific implementations and not for limiting the scope of protection of this invention. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art.

[0034] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In some embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0035] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions, and operations that may be implemented in the methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0036] Please see Figure 1 , Figure 1 A method for constructing a thermal model of a gearbox for an electric drive axle, as provided in an embodiment of the present invention, includes steps S100 to S500.

[0037] Step S100: Obtain the parameterized response surface of the first type of thermal resistance through finite element simulation. The first type of thermal resistance is the type that can be accurately calculated using finite element simulation. For this type of thermal resistance, its parameterized response surface can be obtained through finite element simulation. A parametric response surface (PRS) is a mathematical modeling method used to describe the explicit relationship between input parameters (independent variables) and system output (response variable). It constructs an explicit function (such as a polynomial or radial basis function) by fitting experimental data or simulation results, thereby quickly predicting the system's behavior under different parameter combinations without repeatedly running costly simulations or experiments.

[0038] For the transmission of an electric drive axle, the heat transfer path is relatively complex. The thermal resistance during the heat transfer process mainly falls into four categories: the conductive thermal resistance of the transmission housing, the convective thermal resistance of the cooling water channels, the convective thermal resistance between the housing and the external environment, and the convective thermal resistance between the lubricating oil and the housing and gears. Among these, the conductive thermal resistance of the transmission housing and the convective thermal resistance of the cooling water channels can be accurately calculated using the finite element method. Therefore, the first category of thermal resistance includes these two types.

[0039] Please see Figure 2 Taking the two types of thermal resistance mentioned above as examples, in a specific embodiment of the present invention, step S100 includes steps S110 and S120, which respectively simulate the thermal resistance of the transmission housing and the convective heat transfer resistance of the cooling water channel.

[0040] Step S110: Perform finite element thermal steady-state simulation on the parameterized gearbox housing model. Combined with the design of experiments (DOE) method, obtain the first response surface of the input parameters and the thermal resistance of the gearbox housing. Finite element thermal steady-state simulation is a numerical simulation technique based on the finite element method (FEM) used to calculate the temperature distribution and heat flow field of an object when it reaches thermal equilibrium (steady state) under the combined effects of heat sources, boundary conditions, and material properties. Its core is solving the steady-state heat conduction equation, neglecting time-varying terms, and is suitable for engineering problems under long-term operation or constant temperature conditions (such as electric drive axle housings, electronic device heat dissipation, etc.). The design of experiments (DOE) method is a systematic statistical technique used to plan, execute, and analyze experiments to efficiently identify the influence of input variables (factors) on the output response (result) and optimize process or product performance. Its core objective is to obtain the maximum amount of information with the fewest number of experiments. In this step, the first response surface is accurately calculated through finite element thermal steady-state simulation combined with the design of experiments method.

[0041] Please see Figure 3In a specific embodiment of the present invention, step S110 includes: S111, defining heat transfer boundaries after meshing the gearbox housing model; S112, parameterizing the first variables related to the heat transfer boundaries and setting the value range of each first variable; S113, generating multiple sets of boundary condition combinations through Latin Hypercube Sampling (LHS) based on the value ranges of all first variables to obtain first input parameters; S114, performing finite element thermal steady-state simulation to calculate the thermal resistance of the gearbox housing based on the first input parameters; S115, fitting the first input parameters and their corresponding thermal resistance calculation results to obtain a first response surface.

[0042] In this embodiment, a parameterized approach is used to systematically manage all key thermodynamic variables. By defining the value range of each variable, it is ensured that all variables are within a reasonable range. Then, based on the accuracy requirements of the response surface methodology, Latin hypercube sampling (LHS) is used as the sampling method for Design of Experiments (DOE). A certain number of combinations of thermodynamic variables are randomly generated and sequentially input into the finite element software for thermal resistance calculation. LHS is an efficient multidimensional sampling technique widely used in Design of Experiments (DOE), uncertainty analysis, simulation optimization, and machine learning. Its core idea is to ensure that the value range of each input variable is uniformly covered through stratified sampling, thereby obtaining high-precision statistical properties with fewer sample points.

[0043] Please see Figure 3 In a specific embodiment of the present invention, after step S115, the method further includes: S116, determining whether the accuracy of the first response surface meets the preset accuracy; if not, optimizing the first response surface by supplementing the boundary condition combination until the accuracy of the first response surface meets the preset accuracy. In step S113, the multiple boundary condition combinations generated by Latin hypercube sampling are only partial boundary condition combinations, which are a small number of sample points selected according to a certain method. It can be understood that because the number of sample points is small, the accuracy of the obtained first response surface may not be sufficient. Therefore, in this embodiment, the accuracy is improved by supplementing the boundary condition combination.

[0044] Step S120: Perform CFD simulation on the parameterized cooling channel model. Combined with experimental design methods, obtain the second response surface of the input parameters and the convective heat transfer thermal resistance of the cooling channel. CFD (Computational Fluid Dynamics) is a simulation technique that uses numerical methods to solve physical phenomena such as fluid flow, heat transfer, and chemical reactions. It is widely used in aerospace, automotive, energy, and electronic heat dissipation fields. Its core is to simulate real fluid behavior using computers by discretizing the governing equations (such as the Navier-Stokes equations).

[0045] Please see Figure 4 In a specific embodiment of the present invention, step S120 includes: S121, defining the flow boundary after meshing the cooling channel model; S122, parameterizing the second variables related to the flow boundary and setting the value range of each second variable; S123, generating multiple sets of boundary condition combinations through Latin hypercube sampling based on the value ranges of all second variables to obtain the second input parameters; S124, performing CFD simulation based on the second input parameters to calculate the convective heat transfer coefficient of the cooling channel; S125, obtaining the convective heat transfer thermal resistance of the cooling channel based on the convective heat transfer coefficient and the heat transfer area of ​​the cooling channel; S126, fitting the second input parameters and their corresponding thermal resistance calculation results to obtain the second response surface.

[0046] In this embodiment, the calculation of the second response surface of the convective heat transfer thermal resistance of the cooling water channel is similar to the calculation process of the first response surface of the conductive thermal resistance of the gearbox housing. The difference lies in the simulation methods used. In step S122, the second variable can be, for example, the temperature and flow rate of the cooling water. When performing CFD simulation in step S124, for example, the standard k-ε turbulence model can be used. The standard k-ε model is one of the most commonly used turbulence models in the RANS (Reynolds-averaged Navier-Stokes) method. By solving the transport equations of turbulent kinetic energy (k) and turbulent dissipation rate (ε), it simulates turbulent flow at high Reynolds numbers. Its core advantages are high computational efficiency and strong robustness.

[0047] Understandably, after step S126, the accuracy of the second response surface can be further assessed. Similar to step S116, if the accuracy is insufficient, the second response surface can be optimized by supplementing the combination of boundary conditions.

[0048] Step S200: Transform the parameterized response surface into a low-dimensional first model. This transformation step can be achieved using Model Order Reduction (MOR) tools. Model Order Reduction (MOR) is a technique that uses mathematical methods to simplify high-dimensional, high-complexity system models (such as finite element models, CFD models, and control systems) into low-dimensional approximate models, aiming to significantly reduce computational costs while preserving the key dynamic characteristics of the original model.

[0049] Step S300: Obtain a low-dimensional second model by theoretically calculating the second type of thermal resistance. The second type of thermal resistance includes the convective heat transfer resistance between the gearbox housing and the external environment. For this type of thermal resistance that can be calculated theoretically, it can be extracted separately and manually defined based on theoretical calculations and empirical data. When defining it, it is done in a low-dimensional way so as to combine it with the first model mentioned above.

[0050] Step S400: Based on the heat transfer path of the gearbox, the first model, and the second model, build an initial thermal model of the gearbox in low-dimensional simulation software. This step mainly establishes the initial thermal model of the gearbox. It is called "initial" because some thermal resistances in this model cannot be accurately calculated and need to be calibrated later.

[0051] In a specific embodiment of the present invention, step S400 includes: constructing a modular initial thermal model of the gearbox in low-dimensional simulation software, using the first model and the second model as sub-modules according to the heat transfer path of the gearbox. In this embodiment, the low-dimensional simulation software adopts a modular design, so that the first model and the second model can be used as sub-modules. The low-dimensional simulation software can be, for example, Simulink or software with similar functions. In step S200 above, the response surface is converted into a one-dimensional ROM model that can be recognized in Simulink using a model reduction tool. In step S300 above, the constructed second model should also be a one-dimensional model that can be recognized by Simulink.

[0052] Step S500: Based on experimental data, calibrate other thermal resistances in the initial thermal model of the gearbox to obtain the gearbox thermal model. Other thermal resistances include the convective heat transfer resistance between the lubricating oil and the housing and gears. In this embodiment, by calibrating other thermal resistances that cannot be accurately calculated using experimental data, a highly accurate gearbox thermal model can be obtained. The final gearbox thermal model can simulate the temperature change trends of key nodes in the gearbox under different operating conditions. Furthermore, since it is a one-dimensional model calculation, its computation speed is thousands or even hundreds of thousands of times faster than typical finite element simulations, significantly improving simulation efficiency.

[0053] It should be noted that the steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this patent.

[0054] Please see Figure 5 The preliminary finite element simulation corresponds to step S100, the model reduction corresponds to step S200, and the Simulink modeling and calibration correspond to steps S300 to S500.

[0055] In the preliminary finite element simulation, the above part corresponds to step S110. Specifically, importing the gearbox model and mesh, and defining the gearbox heat transfer path correspond to step S111. Parametric thermodynamic variables, variable DOE design, and finite element steady-state thermal simulation correspond to steps S112, S113, and S114, respectively. It's important to note that establishing the gearbox model response surface corresponds to step S115. Figure 5 The model is divided into the dashed box for model order reduction. Although it does not correspond one-to-one with the division in the above steps, the overall idea remains unchanged. Optimizing the DOE design based on the response surface accuracy corresponds to the content of step S116. Figure 5 The lower part of the pre-finite element simulation corresponds to step S120. Its sub-box content corresponds to steps S121 to S126, and it also contains the division of the waterway model response surface into the model reduction dashed box.

[0056] In the model reduction part, in addition to steps S115, S116, S126 and the optimization of the second response surface, it also includes the content of step S200 mentioned above, that is, transforming the parameterized response surface into a low-dimensional first model. Since two response surfaces were obtained before, two low-dimensional first models will be obtained here, namely the gearbox reduction model and the waterway reduction model.

[0057] In the Simulink modeling and calibration section, this invention specifically uses Simulink software for modeling. Step S300 corresponds to the heat exchange interface of the motor, resulting in a low-dimensional second model, which corresponds to the theoretically calculable thermal resistance. Then, based on the low-dimensional first and second models, a thermal model is built in Simulink software, corresponding to step S400. The thermal test data of the electric drive system corresponds to the experimental data in step S500. The Simulink model calibration, and the modification and optimization of the thermal model based on the calibration results, correspond to the calibration process in step S500. The final model output is the gearbox thermal model.

[0058] Figure 5 The illustration shows another construction process for the thermal model of the gearbox. Although the contents and divisions of the boxes in the illustration do not correspond one-to-one with the steps mentioned above, the overall inventive concept remains the same.

[0059] Please see Figure 6 , Figure 6 An embodiment of the present invention provides a thermal model construction device for an electric drive axle transmission, comprising a pre-simulation unit 101, a model reduction unit 102, a model definition unit 103, a modeling unit 104, and a model optimization unit 105. The pre-simulation unit 101 is used to obtain a parameterized response surface of a first type of thermal resistance through finite element simulation; the model reduction unit 102 is used to convert the parameterized response surface into a low-dimensional first model; the model definition unit 103 is used to obtain a low-dimensional second model by theoretically calculating a second type of thermal resistance; the modeling unit 104 is used to build an initial thermal model of the transmission in low-dimensional simulation software based on the heat transfer path of the transmission, the first model, and the second model; and the model optimization unit 105 is used to calibrate other thermal resistances in the initial thermal model of the transmission based on experimental data to obtain the transmission thermal model.

[0060] It should be noted that the model building apparatus in this embodiment is a device corresponding to the model building method described above, and the functional modules in the model building apparatus may correspond to the corresponding steps in the model building method. The model building apparatus in this embodiment can be implemented in conjunction with the model building method; that is, where there is no conflict, the relevant technical details mentioned in the model building method of the above embodiments can also be applied to the model building apparatus in this embodiment.

[0061] Please see Figure 7 , Figure 7 An electronic device provided in one embodiment of the present invention includes a processor 201, a memory 202, and a communication bus; the communication bus is used to connect the processor 201 and the memory 202; the processor 201 is used to execute a computer program stored in the memory 202 to implement the above-described model construction method.

[0062] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause a computer to execute the above-described model building method.

[0063] This invention develops a gearbox thermal model based on the concept of model order reduction, building upon finite element simulation. This model rapidly establishes the gearbox's response surface through finite element simulation, reducing the features of the three-dimensional model to a one-dimensional model and importing it into Simulink. Finally, the entire physics field is constructed in Simulink. After construction, only a small amount of calibration and verification work is required to quickly and accurately calculate the temperature of key nodes in the gearbox under different operating conditions. Besides accurately predicting the temperature of components such as lubricating oil and housings within the gearbox, this model can also establish connections with different motor and electronic control thermal resistance networks through custom heat transfer interfaces, further improving simulation accuracy. Furthermore, for the development of modified gearboxes, in most cases, only a re-establishment of the reduced-order model through finite element simulation and replacement is needed to quickly obtain accurate temperature data for the modified scheme. This is of great significance for the development of modified gearboxes. Compared with existing technologies, this invention has many advantages.

[0064] Firstly, the thermal resistance of the gearbox in the three-in-one electric drive axle is classified: Finite element simulation is performed on the first type of thermal resistance suitable for calculation, yielding accurate thermal resistance data; for the second type of thermal resistance that can be theoretically analyzed, it is extracted and defined separately; for other thermal resistances that are difficult to analyze using the above methods, they are corrected using experimental data calibration. This method not only significantly reduces the number of thermal resistances requiring experimental calibration and lowers experimental costs, but also fully ensures the overall accuracy of the model.

[0065] Secondly, the model reduction method can significantly improve simulation speed. By using model reduction tools to simplify the three-dimensional finite element simulation model into a one-dimensional model, the calculation speed is greatly improved compared to traditional finite element simulation. Furthermore, the method of generating the reduced model through the response surface can ensure the accuracy of the reduced model calculation within the range of response surface values.

[0066] Third, the one-dimensional modeling method can conveniently model the heat transfer interface between the gearbox and the motor, thus more accurately describing the heat generation / heat dissipation behavior of the gearbox during actual operation, and thus more accurately predicting the instantaneous temperature of the gearbox under actual working conditions.

[0067] Fourth, it can significantly reduce the difficulty and cycle of modified transmission development. When developing modified transmissions of the same type, it is only necessary to generate a reduced-order model of the new transmission and reuse other calibrated thermal resistances to complete the construction of the temperature thermal model of the new transmission. This can significantly reduce the cost of sample production and experimental testing, while shortening the development cycle. Using this method, there is no need to make samples or conduct thermal testing experiments, and the impact of modified design on transmission temperature can be evaluated with considerable accuracy.

[0068] Figure 8 The comparison between the actual measured value and the simulation calculation result of the transmission lubricating oil temperature (T Oil) under complex operating conditions is described. It can be seen that the accuracy of the transmission thermal model is maintained at a high level in most cases, with an overall average deviation within 3℃ and a maximum deviation of about 7-8℃. This accuracy fully meets the requirements of the transmission's thermal design and durability design.

[0069] Figure 9 This is a comparison of the oil temperatures of two transmissions after the transmission design was changed. It can be observed that by changing only the transmission, the reduced-order model can produce curves with the same trend under the same operating conditions, and at the same time, it can also show the influence of different transmission designs on the transmission temperature field.

[0070] Based on the above analysis and verification, it can be concluded that the thermal model of the three-in-one electric drive axle gearbox established using the finite element method and model reduction method not only has the advantages of fast calculation speed, high calculation accuracy, and low dependence on experimental calibration, but also can quickly establish the heat transfer interface with the motor, environment, and other external environments, thereby improving the model accuracy. At the same time, the model reduction method can quickly and accurately verify the impact of gearbox deformation schemes on gearbox temperature, significantly reducing the overall gearbox development cycle, sample cost, and experimental expenses.

[0071] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for constructing a thermal model of a transmission for an electric drive axle, characterized in that, include: The parameterized response surface of the first type of thermal resistance was obtained through finite element simulation. The parameterized response surface is then converted into a low-dimensional first model. By theoretically calculating the second type of thermal resistance, a low-dimensional second model is obtained; Based on the heat transfer path of the gearbox, the first model, and the second model, an initial thermal model of the gearbox is built in low-dimensional simulation software. Based on experimental data, other thermal resistances in the initial thermal model of the gearbox were calibrated to obtain the gearbox thermal model.

2. The method for constructing a thermal model of the gearbox for an electric drive axle according to claim 1, characterized in that, The first type of thermal resistance includes the thermal conductivity of the gearbox housing and the convective heat transfer resistance of the cooling water channels. Through finite element simulation, the parameterized response surface of the first type of thermal resistance is obtained, including: Finite element thermal steady-state simulation was performed on the parameterized gearbox housing model. Combined with experimental design methods, the first response surface of the input parameters and the thermal resistance of the gearbox housing was obtained. CFD simulation was performed on the parameterized cooling channel model, and combined with experimental design methods, the second response surface of the input parameters and the convective heat transfer thermal resistance of the cooling channel was obtained.

3. The method for constructing a thermal model of the gearbox for an electric drive axle according to claim 2, characterized in that, Finite element thermal steady-state simulation was performed on a parameterized gearbox housing model. Combined with experimental design methods, the first response surface between the input parameters and the thermal resistance of the gearbox housing was obtained, including: After meshing the gearbox housing model, heat transfer boundaries are defined. The first variable related to the heat transfer boundary is parameterized, and the value range of each first variable is set; Based on the value range of all first variables, multiple sets of boundary condition combinations are generated through Latin hypercube sampling to obtain the first input parameters; Based on the first input parameters, the thermal resistance of the gearbox housing is calculated using the finite element thermal steady-state simulation. The first response surface is obtained by fitting the first input parameters and their corresponding thermal resistance calculation results.

4. The method for constructing a thermal model of the gearbox for an electric drive axle according to claim 3, characterized in that, After the step of fitting the first input parameters and their corresponding thermal resistance calculation results to obtain the first response surface, the method further includes: Determine whether the accuracy of the first response surface meets the preset accuracy. If not, optimize the first response surface by supplementing the boundary condition combination until the accuracy of the first response surface meets the preset accuracy.

5. The method for constructing a thermal model of the gearbox for an electric drive axle according to claim 2, characterized in that, CFD simulations were performed on a parameterized cooling channel model. Combined with experimental design methods, the second response surface of the input parameters and the convective heat transfer thermal resistance of the cooling channel was obtained, including: After meshing the cooling water channel model, the flow boundary is defined. The second variable related to the flow boundary is parameterized, and the value range of each second variable is set; Based on the value range of all second variables, multiple sets of boundary condition combinations are generated through Latin hypercube sampling to obtain the second input parameters; Based on the second input parameter, the CFD simulation is performed to calculate the convective heat transfer coefficient of the cooling water channel; The convective heat transfer coefficient and the heat transfer area of ​​the cooling water channel are used to obtain the convective heat transfer thermal resistance of the cooling water channel. The second input parameter and its corresponding thermal resistance calculation results are fitted to obtain the second response surface.

6. The method for constructing a thermal model of the gearbox for an electric drive axle according to claim 1, characterized in that, The second type of thermal resistance includes the convective heat transfer resistance between the gearbox housing and the external environment, and the other thermal resistances include the convective heat transfer resistance between the lubricating oil and the housing and gears.

7. The method for constructing a thermal model of the gearbox for an electric drive axle according to claim 1, characterized in that, Based on the heat transfer path of the gearbox, the first model, and the second model, an initial thermal model of the gearbox is built in low-dimensional simulation software, including: Based on the heat transfer path of the gearbox, the first model and the second model are used as sub-modules to build a modular initial thermal model of the gearbox in low-dimensional simulation software.

8. A device for constructing a thermal model of a gearbox for an electric drive axle, characterized in that, include: The pre-simulation unit is used to obtain the parameterized response surface of the first type of thermal resistance through finite element simulation; A model reduction unit is used to convert the parameterized response surface into a low-dimensional first model. The model definition unit is used to obtain a low-dimensional second model by theoretically calculating the second type of thermal resistance; The modeling unit is used to build an initial thermal model of the gearbox in low-dimensional simulation software based on the heat transfer path of the gearbox, the first model, and the second model. The model optimization unit is used to calibrate other thermal resistances in the initial thermal model of the gearbox based on experimental data, thereby obtaining the gearbox thermal model.

9. An electronic device, characterized in that, The system includes a processor, a memory, and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It contains a computer program that enables the computer to perform the method as described in any one of claims 1 to 7.

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