A method and system for predicting the vibration fatigue life of an aluminum substrate of a motor controller for a vehicle

Abstract

The application relates to a kind of vibration fatigue life prediction method and system of motor controller aluminum substrate for vehicle, including the following steps: receiving simulation parameter set from user end in real time, simulation parameter set is sent to preset simulation coupling model, simulation parameter set includes vibration field parameter, temperature field parameter and electromagnetic field parameter;Parameter characteristic information of simulation parameter is identified;And based on parameter characteristic information, the simulation parameter is coupled calculation, and damage parameter is output to preset crack evolution model;Crack evolution model outputs crack prediction information based on preset damage calculation rule and damage parameter, including crack initiation location and time information and crack trend information;Based on simulation parameter and parameter characteristic information of simulation parameter, test terminal is run, and test data is input into crack evolution model;Crack evolution model is calibrated based on test data and updates the crack prediction information output. The application has the effect of improving the accuracy of aluminum substrate vibration fatigue life test working condition.

Description

Technical Field

[0001] This application relates to the technical field of new energy vehicle power, and in particular to a method and system for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller. Background Technology

[0002] New energy intelligent connected vehicles are evolving from simple electrification to a three-pronged approach encompassing green energy, intelligent control, and vehicle-network integration. Technological innovation is placing increasing demands on the materials used in precision components. For example, the aluminum substrate of the motor controller is subjected to long-term high-frequency vibrations, such as road noise, motor harmonic vibrations, and temperature cycling stress, which can easily lead to problems like solder joint breakage and copper layer peeling. Therefore, accurate prediction of the vibration fatigue life of the aluminum substrate is necessary to provide a safer guarantee for the power system of new energy vehicles.

[0003] Currently, most methods for predicting the fatigue life of aluminum substrates use single-physics simulation, considering only vibration or only thermal analysis, ignoring the coupling effect of multiple physics fields, and combining feedback through single physical tests, resulting in low accuracy of the test conditions reflected. Therefore, improvements are needed. Summary of the Invention

[0004] To improve the accuracy of vibration fatigue life testing conditions for aluminum substrates, and thus improve the accuracy of aluminum substrate life prediction, this application provides a method and system for predicting the vibration fatigue life of aluminum substrates for automotive motor controllers.

[0005] The above-mentioned objective of this application is achieved through the following technical solution:

[0006] A method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller includes the following steps:

[0007] The simulation parameter set is received from the user terminal in real time and sent to the preset simulation coupling model. The simulation parameter set includes three types of simulation parameters: vibration field parameters, temperature field parameters, and electromagnetic field parameters.

[0008] When the simulation coupling model receives the simulation dataset, it identifies the parameter feature information of the simulation parameters; and performs coupling calculation on the simulation parameters based on the parameter feature information, and outputs the damage parameters to the preset crack evolution model;

[0009] The crack evolution model outputs crack prediction information based on preset damage calculation rules and damage parameters. The crack prediction information includes crack initiation location and time information as well as crack trend information.

[0010] The test terminal is run based on simulation parameters and parameter feature information of simulation parameters to obtain test data and input it into the crack evolution model;

[0011] The crack evolution model is calibrated and updated based on test data to update the output crack prediction information.

[0012] By adopting the above technical solution and combining multiple physical field coupling simulations of vibration field parameters, temperature field parameters, and electromagnetic field parameters, the simulation effect is closer to the actual test environment. Furthermore, the damage parameters of multi-physical field coupling are calculated through the crack evolution model to predict the crack initiation location, time, and trend, realizing a cross-scale mechanism from microscopic calculation to macroscopic crack formation. Testers can more intuitively observe the crack prediction effect. After outputting the crack prediction information, the same simulation parameters are debugged to conduct physical tests on the test terminal, feeding back the real test data of the aluminum substrate to the crack evolution model, including crack initiation location, time, and crack trend, so as to realize closed-loop debugging of crack evolution prediction, improve the accuracy of crack prediction information, and thus improve the accuracy of the vibration fatigue life test conditions of the aluminum substrate, making the life prediction of the aluminum substrate more accurate.

[0013] Optionally, the step of identifying parameter feature information of simulation parameters when the simulation coupling model receives the simulation dataset includes:

[0014] Obtain the type information and corresponding parameter values ​​of each simulation parameter in the simulation dataset;

[0015] Identify the intervention sequence and timing information of simulation parameters;

[0016] The simulation parameters' type information, parameter values, intervention sequence information, and intervention timing information are packaged together to obtain parameter characteristic information.

[0017] By adopting the above technical solution, in order to simulate various test conditions, it is necessary to identify the timing and order of intervention of simulation parameters and obtain parameter characteristic information for the crack evolution model to identify the calculation order. For example, when the vehicle is not started or the vehicle is not moving, the temperature of the environment where the aluminum substrate is located will be different. Therefore, it is necessary to consider the pre-intervention of the ambient temperature parameter in the temperature field parameters to simulate the vibration test environment of the aluminum substrate under different temperature fields, thereby improving the accuracy of multi-physics coupling.

[0018] Optionally, the step of coupling the simulation parameters based on the parameter feature information and outputting the damage parameters to the preset crack evolution model includes:

[0019] Calculate electromagnetic field parameters and temperature field parameters, and output temperature field data for the aluminum substrate;

[0020] Calculate vibration field parameters and output vibration load data;

[0021] The temperature field data is mapped to a preset network structure as thermal load data, and superimposed with vibration load data to output thermal and mechanical load data as damage parameters and send them to the crack evolution model.

[0022] By adopting the above technical solution, this calculation method is a unidirectional fluid-structure-thermal coupling solution method, which is used to output the spatiotemporal distribution of stress tensor. That is, only the thermal field calculation results are used as boundary conditions to input the solid mechanics module. In other words, the vibration field parameters are input for calculation without reverse data feedback, which shortens the calculation time, reduces the calculation cost, and avoids the iterative convergence problem of bidirectional coupling. It has significant advantages in the vibration fatigue life prediction of aluminum substrate of automotive motor controller. Its core value lies in accurately capturing the coupling effect of key physical fields, while taking into account both calculation efficiency and engineering practicality.

[0023] Optionally, the preset crack evolution model includes a pre-constructed grain-scale model of the aluminum substrate. The step of the crack evolution model outputting crack prediction information based on preset damage calculation rules and damage parameters includes:

[0024] The crack evolution model inputs thermal and mechanical load data into the aluminum substrate grain-scale model;

[0025] Using preset grain-scale stress and strain calculation rules, the slip direction and stress of grains are calculated based on thermal and mechanical load data. The fatigue damage accumulation point after grain slip is determined and output as the crack initiation location, and the time information is recorded.

[0026] Based on a preset energy release criterion, the traction force and traction force vector at the crack tip at the crack initiation location are calculated, and the crack trend information is output.

[0027] By employing the above technical solution, a polycrystalline aggregate of aluminum substrate, i.e., a grain-scale model of aluminum substrate, is constructed using crystal plasticity mechanics and damage mechanics. Each grain has a specific orientation and slip system. By solving for the plastic deformation driven by dislocation slip, the crack initiation and propagation path is predicted. After inputting the damage parameters into the grain-scale model of aluminum substrate, the slip system of grains and dislocation motion are decomposed and calculated using stress and strain calculation rules. The fatigue damage accumulation point is further calculated to determine the unknown preferential occurrence of crack initiation, such as the Al / Cu interface, grain boundary junctions, and around hard inclusions. Finally, the traction force vector at the crack tip is calculated using the energy release criterion, thereby determining the crack trend, including transgranular propagation and intergranular propagation.

[0028] Optionally, the step of running the test terminal based on simulation parameters and parameter feature information of the simulation parameters to obtain test data and input it into the crack evolution model includes:

[0029] Identify parameter characteristic information to determine whether temperature field parameters and electromagnetic field parameters have been pre-stacked;

[0030] When it is necessary to pre-overlay temperature field parameters and electromagnetic field parameters, a first parameter debugging instruction is sent to the test terminal. After receiving the parameter debugging instruction, the test terminal sends a control instruction to the corresponding vehicle-mounted operating terminal and a monitoring instruction to the vehicle-mounted monitoring terminal to control the temperature field parameters and electromagnetic field parameters of the aluminum substrate.

[0031] Send a second parameter debugging command to the test terminal to input the vibration field parameters;

[0032] The test data of the aluminum substrate during the test is acquired and sent to the crack evolution model. The time sequence is synchronized and compared with the crack initiation location and time information and crack trend information predicted by the aluminum substrate grain scale model.

[0033] By adopting the above technical solution, during the physical testing phase, the physical test field that matches the simulation parameters is debugged by controlling the operation and monitoring ends of the test terminal with commands, thereby improving the reliability of the test data obtained from the physical test.

[0034] Optionally, the test data includes measured images of the aluminum substrate acquired by the test terminal, and the step of calibrating and updating the output crack prediction information based on the test data in the crack evolution model includes:

[0035] The crack evolution model is based on time-series information and aligns the measured image of the aluminum substrate with the grain image of the aluminum substrate in the grain-scale model.

[0036] Based on image recognition, the deviation image between the measured image of the aluminum substrate and the image of the aluminum substrate grains is obtained, and the deviation feature information and the corresponding timing information are recorded.

[0037] Using deviation feature information, deviation image, and corresponding time series information as test data, the calculation parameters in the damage calculation rule are adjusted.

[0038] By adopting the above technical solution, the deviation image between the measured image of the aluminum substrate and the grain image of the aluminum substrate is identified by comparing the image features of macroscopic cracks. That is, the image of the deviation in crack initiation location, time and trend between the measured image of the aluminum substrate and the grain image of the aluminum substrate is identified. By feeding the deviation feature information of the identified image back to the calculation parameters of the damage calculation rules in the crack evolution model, the closed-loop feedback adjustment of the crack prediction information output is realized, thereby improving the accuracy of the crack prediction information output.

[0039] Optionally, the step of acquiring the deviation image between the measured image of the aluminum substrate and the grain image of the aluminum substrate, and recording the deviation feature information and the corresponding timing information, includes:

[0040] Acquire the measured image of the aluminum substrate when cracks appear and record the corresponding time information. Based on the crack initiation location and time information predicted by the crack evolution model, acquire the corresponding aluminum substrate grain image to form the first deviation image between the measured image of the aluminum substrate and the aluminum substrate grain image.

[0041] Align the measured image of the aluminum substrate and the grain image of the aluminum substrate based on the pre-constructed coordinate system, and identify the crack location coordinates in the measured image of the aluminum substrate and the crack location coordinates in the grain image of the aluminum substrate from the coordinate system as the first deviation feature information.

[0042] By adopting the above technical solution, the acquisition of deviation images and deviation feature information is divided into two steps. The first step is to acquire the aluminum substrate grain image and the measured image of the aluminum substrate at the time of crack initiation, and record the corresponding time information, that is, the time of crack initiation. By constructing a coordinate system for the measured image and the aluminum substrate grain image, the position of the crack initiation point is accurately mapped by the coordinates to form the first deviation data, which is convenient for the crack evolution model to calculate the offset vector of the crack initiation position and the time difference information.

[0043] Optionally, after acquiring the first deviation feature information, the step of acquiring the deviation image between the measured image of the aluminum substrate and the grain image of the aluminum substrate, and recording the deviation feature information and the corresponding timing information, further includes:

[0044] Based on a pre-constructed coordinate system, the crack trend of the measured image of the aluminum substrate and the grain image of the aluminum substrate are compared. The second deviation image with the deviation of crack trend characteristics is selected and the corresponding time series information is recorded.

[0045] Identify the inflection point coordinates of the crack trend, calculate the crack trend change parameters based on the inflection point coordinates, and use the inflection point coordinates and crack trend change parameters as the second deviation feature information.

[0046] After acquiring the first deviation feature information by adopting the above technical solution, the crack trend deviation is identified and recorded. Similarly, the inflection point coordinates of the crack trend are identified and recorded through a pre-constructed coordinate system, and the crack trend features are mapped as the second deviation feature information. The second deviation image and the corresponding time series information are sent to the crack evolution model so that the crack evolution model can identify and calculate the crack trend deviation vector.

[0047] The above-mentioned objective three of this application is achieved through the following technical solution:

[0048] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller.

[0049] The fourth objective of this application is achieved through the following technical solution:

[0050] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] By combining vibration field parameters, temperature field parameters, and electromagnetic field parameters in a multi-physical field coupling simulation, the simulation effect is closer to the actual test environment. Furthermore, the damage parameters of the multi-physical field coupling are calculated through the crack evolution model to predict the crack initiation location, time, and trend. This realizes a cross-scale mechanism from microscopic calculation to macroscopic crack formation. By debugging the same simulation parameters and conducting physical tests on the test terminal, the real test data of the aluminum substrate is fed back to the crack evolution model to achieve closed-loop debugging of crack evolution prediction, improve the accuracy of crack prediction information, and thus improve the accuracy of the vibration fatigue life test conditions of the aluminum substrate, making the life prediction of the aluminum substrate more accurate.

[0053] During the physical testing phase, by controlling the operation and monitoring ends of the test terminal with commands, a physical test field consistent with the simulation parameters is debugged to improve the reliability of the test data obtained from the physical test.

[0054] By feeding back the deviation feature information of the identified and extracted image to the calculation parameters of the damage calculation rule in the crack evolution model, closed-loop feedback adjustment of the crack prediction information output is achieved, thereby improving the accuracy of the crack prediction information output.

[0055] By constructing a coordinate system for the measured images of the aluminum substrate and the grain images of the aluminum substrate, the position of the crack initiation point is accurately mapped using coordinates to form the first deviation data, which facilitates the crack evolution model to calculate the offset vector of the crack initiation position and the time difference information. Attached Figure Description

[0056] Figure 1 This is a flowchart of an embodiment of the vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller according to this application.

[0057] Figure 2 This is a diagram of the architecture of a unidirectional fluid-structure-thermal coupling solver in an embodiment of a vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller according to this application.

[0058] Figure 3 This is a schematic diagram illustrating the calculation principle of the grain size model of the aluminum substrate in an embodiment of the vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller according to this application.

[0059] Figure 4 This is a schematic diagram of a computer device according to this application. Detailed Implementation

[0060] The following is in conjunction with the appendix Figure 1-4 This application will be described in further detail.

[0061] In the example, reference is made to Figure 1 This application discloses a method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller, which specifically includes the following steps:

[0062] S10: Receive the simulation parameter set from the user terminal in real time and send the simulation parameter set to the preset simulation coupling model. The simulation parameter set includes three types of simulation parameters: vibration field parameters, temperature field parameters, and electromagnetic field parameters.

[0063] In this embodiment, the user terminal is a PC or mobile terminal used by test simulation personnel. The vibration field parameters include the measured road spectrum vibration load, which is usually a random vibration PSD of 0-2000Hz. The temperature field parameters mainly include the superimposed IGBT switching loss temperature rise model and the ambient temperature. The electromagnetic field parameters include the fused bus current ripple excitation force.

[0064] The simulation coupling model is a model of the joint simulation process and parameter mapping relationship for multi-physics field coupled calculation of vibration-thermal-current loads, i.e., a unidirectional fluid-structure-thermal coupling solver. Only the fluid / thermal field calculation results are input as boundary conditions into the solid mechanics module, such as temperature distribution, with no reverse data feedback.

[0065] Specifically, in the set of simulation parameters output by the user, each simulation parameter includes a single value or a range of values.

[0066] Reference Figure 2 The unidirectional fluid-structure-thermal coupling solver consists of a fluid solver, a data transfer interface, and a solid domain solver. In the fluid domain solver, the coolant velocity, pressure, and temperature distribution are first calculated using the Navier-Stokes fluid control equations, the turbulence model, and the coolant field. Then, the surface temperature field of the aluminum substrate is solved using the heat transfer equation ∇·(k∇T)=0 and sent to the data transfer interface. The data transfer interface includes a mesh mapping module and a thermal load conversion model. The surface temperature field of the aluminum substrate is output to the solid domain solver through mesh mapping and thermal load conversion. The solid domain solver combines thermoelastic calculations such as the heat conduction equation and thermal expansion deformation with the structural dynamics equations and incorporates vibration loads, i.e., vibration field parameters, to output the stress tensor.

[0067] S20: When the simulation coupling model receives the simulation dataset, it identifies the parameter feature information of the simulation parameters; and performs coupling calculation on the simulation parameters based on the parameter feature information, and outputs the damage parameters to the preset crack evolution model;

[0068] In this embodiment, the feature information includes the parameter value or parameter range of the simulation parameter, as well as the intervention order and intervention timing of the parameter. The type information, parameter value, intervention order information and intervention timing information of the simulation parameter are packaged into data to obtain the parameter feature information.

[0069] The coupled calculation sequence is as follows: fluid field / thermal field calculation, output temperature field data to solid mechanics solver, solid mechanics solver performs deformation / stress calculation, and finally vibration fatigue analysis of aluminum substrate is performed based on strain / stress.

[0070] Specifically, step S20 includes the following steps:

[0071] S21: Calculate electromagnetic field parameters and temperature field parameters and output the temperature field data T(x,y,z,t) of the aluminum substrate;

[0072] S22: Calculate vibration field parameters and output vibration load data;

[0073] S23: Map the temperature field data to a preset network structure as thermal load data, and superimpose it with the vibration load data to output thermal and mechanical load data as damage parameters and send them to the crack evolution model.

[0074] The calculation of fluid flow and heat transfer within the IGBT cooling channel outputs the temperature field data of the aluminum substrate. The thermal and mechanical load data σij(x,y,z,t) includes the spatial and temporal distributions of six independent stress components, as shown in equation (1):

[0075] (1).

[0076] S30: The crack evolution model outputs crack prediction information based on preset damage calculation rules and damage parameters. The crack prediction information includes crack initiation location and time information as well as crack trend information.

[0077] In this embodiment, refer to Figure 3 The preset crack evolution model includes a pre-constructed grain-scale model of the aluminum substrate. The basic framework of the crack evolution model includes crystal plastic finite element method, which regards the aluminum substrate as a polycrystalline aggregate, i.e., the grain-scale model of the aluminum substrate. Each grain has a specific orientation and slip system. By solving the cumulative plastic deformation driven by dislocation slip, the crack initiation and propagation path is predicted.

[0078] The core architecture of the grain-scale model of aluminum substrate includes the establishment of a single-crystal constitutive model, modeling of polycrystalline aggregates, definition of slip system, hardening law and damage evolution, finite element discretization and solution, boundary condition setting, and solvers for cumulative plastic strain distribution and crack propagation path.

[0079] Among them, the single-crystal constitutive model includes deformation gradient decomposition and calculation of slip system shear strain rate; the polycrystalline aggregate modeling adopts representative volume elements based on EBSD data, including EBSD scanning, grain identification, orientation allocation and finite element mesh generation; the hardening law adopts a nonlinear hardening model, in which the interaction hardening matrix equation is Equation (2):

[0080] (2);

[0081] h0: initial hardening rate; g∞: saturated slip resistance; q: latent hardening coefficient (1.4 for FCC aluminum).

[0082] The damage evolution equation is defined as dD / dN = C·(Δτ)^m, where Δτ is the shear stress amplitude of the slip system and C / m is a material constant.

[0083] Finite element discretization and solution employs weak-form equilibrium equations (3):

[0084] (3);

[0085] P: First PK stress tensor; tt: Surface force vector.

[0086] Crack initiation sites are usually at the Al / Cu interface, where residual stress is generated due to the difference in thermal expansion coefficients, and at grain boundary junctions and around hard inclusions where stress is concentrated due to dislocation pile-up.

[0087] Specifically, step S30 includes the following steps:

[0088] S31: The crack evolution model inputs thermal and mechanical load data into the aluminum substrate grain-scale model;

[0089] S32: Using preset grain-scale stress and strain calculation rules, the grain slip direction and stress are calculated based on thermal and mechanical load data. The fatigue damage accumulation point after grain slip is determined and output as the crack initiation location, and the time information is recorded.

[0090] S33: Based on the preset energy release criterion, calculate the traction force and traction force vector at the crack tip at the crack initiation location, and output the crack trend information.

[0091] The stress and strain calculation rules include two steps: decomposition of the slip system and calculation of slip ratio based on dislocation motion. Further, the critical threshold of the cumulative plastic shear strain APSS is used to determine whether damage has occurred, and the fatigue damage accumulation point is thus identified as the crack initiation location. Crack trajectory information is obtained by using the energy release criterion to calculate the crack tip J integral, as shown in equation (4):

[0092] (4);

[0093] W: Strain energy density; T: Traction force vector acting on the integral path Г.

[0094] S40: Run the test terminal based on the simulation parameters and the parameter characteristic information of the simulation parameters to obtain test data and input it into the crack evolution model;

[0095] In this embodiment, the testing terminal includes an operating terminal for controlling the operation of the motor controller, switch, temperature controller, and vibration device, used to simulate parameters. It also includes a monitoring terminal for monitoring temperature field data, electromagnetic field data, and vibration field data to acquire corresponding monitoring data, and an image acquisition terminal for capturing images of the aluminum substrate to obtain real-time changes on the aluminum substrate surface.

[0096] Specifically, step S40 includes the following steps:

[0097] S41: Identify parameter characteristic information and determine whether temperature field parameters and electromagnetic field parameters are pre-superimposed;

[0098] S42: When it is necessary to pre-superimpose temperature field parameters and electromagnetic field parameters, a first parameter debugging instruction is sent to the test terminal. After receiving the parameter debugging instruction, the test terminal sends a control instruction to the corresponding vehicle-mounted operation terminal and a monitoring instruction to the vehicle-mounted monitoring terminal to control the temperature field parameters and electromagnetic field parameters of the aluminum substrate.

[0099] S43: Send the second parameter debugging command to the test terminal to input the vibration field parameters;

[0100] S44: Acquire test data of the aluminum substrate during the test and send it to the crack evolution model. Synchronize the timing and compare the data with the crack initiation location and time information and crack trend information predicted by the aluminum substrate grain scale model.

[0101] The first parameter debugging command is used to debug the temperature field data at the operating end, while the second parameter debugging command is used to control the vibration device, motor controller, switch operation and shutdown, and parameter changes. The on-board monitoring terminal is used to monitor the measured data values ​​that are the same as the simulation data and to control the measured data within the same range as the simulation data.

[0102] S50: The crack evolution model is calibrated and updated based on the test data to update the output crack prediction information.

[0103] In this embodiment, the test data includes measured images of the aluminum substrate obtained by the test terminal;

[0104] Specifically, step S50 includes the following steps:

[0105] S51: The crack evolution model is based on time information and aligns the measured image of the aluminum substrate with the grain image of the aluminum substrate in the grain-scale model;

[0106] S52: Based on image recognition, obtain the deviation image between the measured image of the aluminum substrate and the image of the aluminum substrate grains, and record the deviation feature information and the corresponding timing information;

[0107] S53: Using the deviation feature information, deviation image, and corresponding time series information as test data, debug the calculation parameters in the damage calculation rule.

[0108] The measured image of the aluminum substrate and the grain image of the aluminum substrate are first aligned based on time information. Then, image feature recognition is performed through an AI big data model to obtain the deviation image between the measured image of the aluminum substrate and the grain image of the aluminum substrate at the same time, as well as the time deviation and position deviation when cracks appear in the measured image of the aluminum substrate and the grain image of the aluminum substrate.

[0109] Deviation feature information includes the location of the deviation in the image, local image information, and time information. Calculation parameters include variable parameters used in stress calculation, slip calculation, and crack traction force calculation.

[0110] Furthermore, step S52 also includes the following steps:

[0111] S521: Obtain the measured image of the aluminum substrate when the crack appears, and record the corresponding time information. Based on the crack initiation location and time information predicted by the crack evolution model, obtain the corresponding aluminum substrate grain image to form the first deviation image between the measured image of the aluminum substrate and the aluminum substrate grain image.

[0112] S522: Align the measured image of the aluminum substrate and the grain image of the aluminum substrate based on the pre-constructed coordinate system, and identify the crack location coordinates in the measured image of the aluminum substrate and the crack location coordinates in the grain image of the aluminum substrate from the coordinate system as the first deviation feature information.

[0113] S523: Based on a pre-constructed coordinate system, compare the crack trend of the measured image of the aluminum substrate with the image of the aluminum substrate grains, select the second deviation image where the crack trend characteristics deviate, and record the corresponding time series information.

[0114] S524: Identify the inflection point coordinates of the crack trend, calculate the crack trend change parameters based on the inflection point coordinates, and use the inflection point coordinates and crack trend change parameters as the second deviation feature information.

[0115] In this embodiment, the first deviation image is the measured image of the aluminum substrate and the grain image of the aluminum substrate corresponding to the occurrence of a crack. The dimensions of the measured image and the grain image of the aluminum substrate are adjusted to be consistent through a constructed coordinate system, and the crack location coordinates of each are identified. At this time, the corresponding timing information and crack location coordinates can be either the same or different. If the timing information and crack location coordinates are the same or within a preset error range, it is determined that there is no first deviation image; if the measured image and the grain image of the aluminum substrate have timing information or the crack location coordinates are outside the deviation range, it is determined that there is a first deviation image.

[0116] Furthermore, based on a pre-constructed coordinate system, the measured image of the aluminum substrate after cracking and the image of the aluminum substrate grains are obtained. The crack trend is compared based on multiple consecutive images to obtain the coordinates of the inflection points in the crack trend. Based on the coordinates of the inflection points, the crack change parameters between the two points are calculated, including slope, length, vector, etc.

[0117] In one embodiment, an automotive IGBT aluminum substrate was designed as the test object for the test terminal. The simulation was compared with that of a 500-crystal aluminum substrate. The load was subjected to random vibration GRMS = 15g (20-2000Hz); the PSD spectrum was used, and a thermal load ΔT = 100℃ was applied; the cycle count was 50,000. The crack initiation stages of solder joints A, B, and C were compared. The results of the crack initiation comparison are shown in Table 1 below.

[0118]

[0119] The crack propagation rate (μm / thousand cycles) and the feature comparison results are shown in Table 2 below:

[0120]

[0121] After testing, the CPFEM model showed an error of less than 8% in predicting the crack initiation location and a matching degree of more than 85% in the propagation path.

[0122] It should be understood that the sequence number of each step in the above embodiments does not imply 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 embodiments of this application.

[0123] In one embodiment, a vibration fatigue life prediction system for an aluminum substrate of an automotive motor controller is provided. This system corresponds to the vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller described in the previous embodiment. The vibration fatigue life prediction system for an aluminum substrate of an automotive motor controller includes:

[0124] The parameter receiving module is used to receive the simulation parameter set from the user terminal in real time and send the simulation parameter set to the preset simulation coupling model. The simulation parameter set includes three types of simulation parameters: vibration field parameters, temperature field parameters, and electromagnetic field parameters.

[0125] The parameter coupling module is used to identify the parameter feature information of the simulation parameters when the simulation coupling model receives the simulation dataset; and to perform coupling calculation on the simulation parameters based on the parameter feature information, and output the damage parameters to the preset crack evolution model.

[0126] An evolution module is used by the crack evolution model to output crack prediction information based on preset damage calculation rules and damage parameters. The crack prediction information includes crack initiation location and time information as well as crack trend information.

[0127] The test module is used to run the test terminal based on the simulation parameters and the parameter feature information of the simulation parameters, and to obtain test data to input into the crack evolution model;

[0128] The feedback module is used to calibrate and update the output crack prediction information based on the test data of the crack evolution model.

[0129] Specific limitations regarding the vibration fatigue life prediction system for an aluminum substrate of an automotive motor controller can be found in the above-described limitations regarding the vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller, and will not be repeated here. Each module in the aforementioned vibration fatigue life prediction system for an aluminum substrate of an automotive motor controller can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0130] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller.

[0131] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller.

[0132] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0133] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to 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.

[0135] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller, characterized in that: The simulation parameter set is received from the user terminal in real time and sent to the preset simulation coupling model. The simulation parameter set includes three types of simulation parameters: vibration field parameters, temperature field parameters, and electromagnetic field parameters. When the simulation coupling model receives the simulation dataset, it identifies the parameter feature information of the simulation parameters; Based on the parameter feature information, the simulation parameters are coupled and calculated, and the damage parameters are output to the preset crack evolution model; The crack evolution model outputs crack prediction information based on preset damage calculation rules and damage parameters. The crack prediction information includes crack initiation location and time information as well as crack trend information. The test terminal is run based on simulation parameters and parameter feature information of simulation parameters to obtain test data and input it into the crack evolution model; The crack evolution model is calibrated and updated based on test data to update the output crack prediction information; The step of identifying parameter feature information of simulation parameters when the simulation coupling model receives the simulation dataset includes: Obtain the type information and corresponding parameter values ​​of each simulation parameter in the simulation dataset; Identify the intervention sequence and timing information of simulation parameters; The simulation parameters' type information, parameter values, intervention sequence information, and intervention timing information are packaged together to obtain parameter characteristic information. The step of coupling the simulation parameters based on the parameter feature information and outputting the damage parameters to the preset crack evolution model includes: Calculate electromagnetic field parameters and temperature field parameters, and output temperature field data for the aluminum substrate; Calculate vibration field parameters and output vibration load data; The temperature field data is mapped to a preset network structure as thermal load data, and superimposed with vibration load data to output thermal and mechanical load data as damage parameters and send them to the crack evolution model. The preset crack evolution model includes a pre-constructed grain-scale model of the aluminum substrate. The steps of outputting crack prediction information based on preset damage calculation rules and damage parameters include: The crack evolution model inputs thermal and mechanical load data into the aluminum substrate grain-scale model; Using preset grain-scale stress and strain calculation rules, the slip direction and stress of grains are calculated based on thermal and mechanical load data. The fatigue damage accumulation point after grain slip is determined and output as the crack initiation location, and the time information is recorded. Based on the preset energy release criterion, the traction force and traction force vector at the crack tip at the crack initiation location are calculated, and the crack trend information is output. The pre-built crack evolution model includes a pre-constructed grain-scale model of the aluminum substrate. The basic framework of the crack evolution model includes crystal plastic finite element method, which treats the aluminum substrate as a polycrystalline aggregate, i.e., the aluminum substrate grain-scale model. Each grain has a specific orientation and slip system. By solving the cumulative plastic deformation driven by dislocation slip, the crack initiation and propagation path is predicted. The core architecture of the aluminum substrate grain-scale model includes the establishment of a single crystal constitutive model, modeling of polycrystalline aggregate, definition of slip system, hardening law and damage evolution, finite element discretization and solution, boundary condition setting, and solvers for cumulative plastic strain distribution and crack propagation path.

2. The method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller according to claim 1, characterized in that, The step of running the test terminal based on simulation parameters and parameter feature information of the simulation parameters to obtain test data and input it into the crack evolution model includes: Identify parameter characteristic information to determine whether temperature field parameters and electromagnetic field parameters have been pre-stacked; When it is necessary to pre-overlay temperature field parameters and electromagnetic field parameters, a first parameter debugging instruction is sent to the test terminal. After receiving the parameter debugging instruction, the test terminal sends a control instruction to the corresponding vehicle-mounted operating terminal and a monitoring instruction to the vehicle-mounted monitoring terminal to control the temperature field parameters and electromagnetic field parameters of the aluminum substrate. Send a second parameter debugging command to the test terminal to input the vibration field parameters; The test data of the aluminum substrate during the test is acquired and sent to the crack evolution model. The time sequence is synchronized and compared with the crack initiation location and time information and crack trend information predicted by the aluminum substrate grain scale model.

3. The method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller according to claim 1, characterized in that, The test data includes measured images of the aluminum substrate acquired by the test terminal. The steps of calibrating and updating the output crack prediction information based on the test data for the crack evolution model include: The crack evolution model is based on time-series information and aligns the measured image of the aluminum substrate with the grain image of the aluminum substrate in the grain-scale model. Based on image recognition, the deviation image between the measured image of the aluminum substrate and the image of the aluminum substrate grains is obtained, and the deviation feature information and the corresponding timing information are recorded. Using deviation feature information, deviation image, and corresponding time series information as test data, the calculation parameters in the damage calculation rule are adjusted.

4. The method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller according to claim 3, characterized in that, The step of acquiring the deviation image between the measured image of the aluminum substrate and the image of the aluminum substrate grains, and recording the deviation feature information and the corresponding timing information includes: Acquire the measured image of the aluminum substrate when cracks appear and record the corresponding time information. Based on the crack initiation location and time information predicted by the crack evolution model, acquire the corresponding aluminum substrate grain image to form the first deviation image between the measured image of the aluminum substrate and the aluminum substrate grain image. Align the measured image of the aluminum substrate and the grain image of the aluminum substrate based on the pre-constructed coordinate system, and identify the crack location coordinates in the measured image of the aluminum substrate and the crack location coordinates in the grain image of the aluminum substrate from the coordinate system as the first deviation feature information.

5. The method for predicting the vibration fatigue life of an aluminum substrate for an automotive motor controller according to claim 3, characterized in that, After acquiring the first deviation feature information, the step of acquiring the deviation image between the measured image of the aluminum substrate and the grain image of the aluminum substrate, and recording the deviation feature information and the corresponding timing information, further includes: Based on a pre-constructed coordinate system, the crack trend of the measured image of the aluminum substrate and the grain image of the aluminum substrate are compared. The second deviation image with the deviation of crack trend characteristics is selected and the corresponding time series information is recorded. Identify the inflection point coordinates of the crack trend, calculate the crack trend change parameters based on the inflection point coordinates, and use the inflection point coordinates and crack trend change parameters as the second deviation feature information.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the vibration fatigue life prediction method for an aluminum substrate of an automotive motor controller as described in any one of claims 1 to 5.

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