Multi-physical field model coupling simulation method of motor and related equipment
By iteratively calculating and controlling the mitigation factor of the multiphysics model of the motor, the problem of data transmission error caused by manual intervention was solved, and high-precision multiphysics coupling simulation was achieved.
Patent Information
- Application Number
- CN202511644907.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-11
- Publication Date
- 2026-01-13
AI Technical Summary
In existing technologies, manual intervention in parameter adjustment during multi-physics model coupling simulation of motors leads to large data transmission errors, affecting coupling accuracy.
By performing iterative calculations on the multiphysics model, load data is transferred according to the preset solution order and load transfer order, and the load transfer speed is controlled by a mitigation factor determined by the load change rate until the iteration termination condition is met.
It enables load data transfer without human intervention, improves coupling accuracy and stability, and enhances the simulation accuracy of multiphysics models.
Smart Images
Figure CN121328147A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of motor technology, and in particular to a multiphysics model coupling simulation method and related equipment for motors. Background Technology
[0002] As the core component for power output in new energy vehicles, the electric motor's performance directly impacts the vehicle's power, energy efficiency, and safety. During motor operation, there is a significant coupling effect between the electromagnetic field, temperature field, and structural stress field: Joule heating generated by the electromagnetic field alters the temperature distribution of the material, and temperature changes affect electromagnetic parameters and mechanical properties, leading to changes in structural stress. Conversely, structural deformation can affect the electromagnetic gap and magnetic field distribution. Therefore, multiphysics model coupling simulation is a key technology for optimizing performance and mitigating the risks of overheating and structural failure in motor design, and it is widely used in scenarios such as motor topology optimization, heat dissipation scheme design, and structural reliability verification.
[0003] In related technologies, multiphysics model coupling simulation mainly adopts a weak coupling method, which uses multiple independent CAE software to calculate each physical field model separately. Indirect coupling is achieved by manually exporting / importing data (e.g., manually inputting the heat source file of the electromagnetic field calculation into the temperature field software, and then manually importing the temperature field result into the structural stress field software). The iterative process requires manual intervention to adjust parameters, resulting in large data transmission errors between the fluid field and the temperature field and the electrical field, which affects the coupling accuracy. Summary of the Invention
[0004] This application provides a multiphysics model coupling simulation method and related equipment for electric motors, which solves the problem in the prior art that manual intervention in parameter adjustment leads to large data transmission errors and affects coupling accuracy.
[0005] According to a first aspect of the embodiments of this application, a multiphysics model coupled simulation method for an electric motor is provided, comprising: The following iterative calculations are performed on the multiphysics model: load data is transferred between different physics models according to the preset solution order and the preset load transfer order; After each iteration is completed, the load change rate between the two target physics models that transfer load data in this iteration is determined. The target physics models are two physics models with adjacent load transfer order in the multiphysics model. Using a calculation strategy corresponding to the load change rate, the next mitigation factor for the next iteration is determined, and the next mitigation factor is used to control the load transmission speed. The next load data for the next iteration operation is determined based on the next mitigation factor. The iterative calculation is performed again based on the next load data until the termination condition of the iterative calculation is met.
[0006] Optionally, determining the load change rate of the load data transferred between the two target physics models in this iteration includes: Obtain the previous load data and the current load data transferred between the two target physics models in this iteration. The previous load data is the load data transferred between the two target physics models in the most recent iteration. The load change rate is determined based on the current load data and the previous load data.
[0007] Optionally, determining the next mitigation factor for the next iteration using a calculation strategy corresponding to the load change rate includes: Obtain the current mitigation factor in this iteration; When the load change rate is less than a preset change rate threshold, the calculation strategy includes: Based on the current mitigation factor, the preset rate of change threshold, and the load rate of change, determine the mitigation factor growth information; Based on the mitigation factor growth information and the current mitigation factor, the next mitigation factor is determined; When the load change rate is greater than a preset change rate threshold, the calculation strategy includes: Based on the current mitigation factor and the preset rate of change threshold, the mitigation factor reduction information is determined; Based on the mitigation factor reduction information and the current mitigation factor, the next mitigation factor is determined.
[0008] Optionally, the two target physics models include a first physics model and a second physics model, wherein the first physics model transmits the load data to the second physics model; The step of determining the next load data for the next iteration based on the next mitigation factor includes: Obtain the current load data passed by the first physics model during this iteration calculation; Determine the external load data to be applied to the first physics model in the next iteration calculation; Based on the current load data, external load data, and the next mitigation factor, determine the load adjustment data; The next load data is determined based on the current load data and the load adjustment data.
[0009] Optionally, the two target physical field models include a first physical field model and a second physical field model, wherein the first physical field model transfers load data to the second physical field model; if the first physical field model is a temperature field model, the transfer of load data between different physical field models includes: Obtain the temperature distribution information calculated by the temperature field model; The distribution calculation parameters of the second physical field model are updated based on the temperature distribution information. The distribution calculation parameters are used to participate in the calculation of the model distribution information of the second physical field model. The distribution calculation parameters are passed to the second physical field model.
[0010] Optionally, updating the distribution calculation parameters of the second physical field model based on the temperature distribution information includes: The temperature change is determined based on the temperature distribution information and the preset reference temperature. Obtain the initial distribution calculation parameters at the reference temperature; The updated distribution calculation parameters are determined based on the initial distribution calculation parameters and the temperature change.
[0011] Optionally, the termination conditions for the iterative operation include: the number of iterations reaches a preset number, or the load change rate is less than a preset value.
[0012] According to a second aspect of the embodiments of this application, a multiphysics model coupling simulation device for an electric motor is provided, comprising: The iteration unit is used to perform the following iterative calculations on the multiphysics model: transfer load data between different physics models according to a preset solution order and a preset load transfer order; The first determining unit is used to determine the load change rate between the two target physical field models that transfer load data in the current iteration operation after each iteration operation is completed. The target physical field models are two physical field models with adjacent load transfer order in the multi-physics model. The second determining unit is used to determine the next mitigation factor for the next iteration operation using a calculation strategy corresponding to the load change rate. The next mitigation factor is used to control the load transmission speed. The third determining unit is used to determine the next load data for the next iteration operation based on the next mitigation factor; The judgment unit is used to perform the iterative calculation again based on the next load data until the iterative calculation termination condition is met.
[0013] According to a third aspect of the embodiments of this application, an electronic device is provided, including a memory and a processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the multiphysics model coupling simulation method for motors as described in the first aspect by running the program in the memory.
[0014] According to a fourth aspect of the embodiments of this application, a storage medium is provided, on which a computer program is stored, and when the computer program is run by a processor, it implements the multiphysics model coupling simulation method for motors as described in the first aspect.
[0015] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including computer program instructions, which, when executed by a processor, cause the processor to perform the multiphysics model coupling simulation method for an electric motor as described in the first aspect.
[0016] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application performs the following iterative calculation on a multiphysics model: Load data is transferred between different physics models according to a preset solution order and a preset load transfer order; after each iteration, the load change rate of the load data transferred between the two target physics models in this iteration is determined, where the target physics models are two physics models with adjacent load transfer orders in the multiphysics model; a calculation strategy corresponding to the load change rate is used to determine the next mitigation factor for the next iteration, which is used to control the load transfer speed; the next load data for the next iteration is determined based on the next mitigation factor; the iterative calculation is performed again based on the next load data until the iteration termination condition is met. Thus, without human intervention, the load data between different physics models can be transferred through a preset solution order and a preset load transfer order, realizing the iteration of load data between each physics model. Furthermore, during the load transfer process, the determined next mitigation factor controls the transfer speed of the load data, thereby improving the stability of the load data transfer and increasing the coupling accuracy. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application 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 embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0018] Figure 1 A flowchart of a multiphysics model coupling simulation method for an electric motor is provided as an embodiment of this application.
[0019] Figure 2 A flowchart for acquiring dynamic data of various physical fields is provided for one embodiment of this application.
[0020] Figure 3 A flowchart of the load data mapping and iterative transfer process is provided for one embodiment of this application.
[0021] Figure 4 This application provides a flowchart for updating the distribution calculation parameters of each physical field according to one embodiment.
[0022] Figure 5 A flowchart for updating the mitigation factor is provided for one embodiment of this application.
[0023] Figure 6 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0024] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0025] Exemplary Implementation Environment The multiphysics model coupling simulation method for motors according to embodiments of this application can be executed by electronic devices such as terminal devices or servers. Terminal devices can be user equipment (UE), mobile devices, user terminals, terminals, cellular phones, cordless phones, personal digital assistants (PDAs), handheld devices, computing devices, in-vehicle devices, wearable devices, etc. Servers can be independent physical servers, server clusters composed of multiple physical servers, or cloud servers capable of cloud computing. This method can be implemented by a processor calling computer-readable program instructions stored in memory. This application uses the execution of the multiphysics model coupling simulation method for motors by a server as an example for explanation, but does not limit it.
[0026] Exemplary methods Please see Figure 1 In one exemplary embodiment, a multiphysics model coupled simulation method for an electric motor is provided, comprising: Step 101: Perform the following iterative calculations on the multiphysics model: transfer load data between different physics models according to the preset solution order and the preset load transfer order.
[0027] In some embodiments, the multiphysics model of the new energy vehicle motor includes an electromagnetic field model, a temperature field model, and a structural stress field model.
[0028] See Figure 2 Dynamic data related to coupling effects can be extracted from the aforementioned electromagnetic field model, temperature field model, and structural stress field model through standardized interfaces. This is accomplished by calling the standardized input / output interfaces of each physical field model, without requiring code modification or parameter redefinition of the original model.
[0029] The dynamic data information in the electromagnetic field model includes the instantaneous heat source distribution generated by the Joule effect. The dynamic data information of the electromagnetic field model also includes electric field strength. With magnetic induction intensity The distribution of is mathematically described based on Maxwell's equations: .
[0030] Dynamic data information in the temperature field model includes real-time calculated temperature distribution. The dynamic data information of the temperature field model also includes heat flux density. The distribution of these variables is mathematically described based on Fourier's law: , where k is thermal conductivity, T is temperature, and ∇ is the gradient operator.
[0031] Stress field update terms caused by changes in the coefficient of thermal expansion in the structural stress field model ,in For elastic modulus, The coefficient of thermal expansion is The reference temperature is used. The dynamic data information of the structural stress field model also includes the displacement field. The distribution of is mathematically described based on the equilibrium equations of structural mechanics: ,in For stress tensor, For physical strength, For density, This represents the displacement field.
[0032] By extracting the aforementioned dynamic data, the load data transmitted each time can be determined based on the dynamic data. The extracted dynamic data represents the subsequent interactions between fields, such as the influence of the electromagnetic field's heat source on the temperature field, and the influence of the temperature field on the electromagnetic field and the structural stress field. As the core input, it is a prerequisite for realizing coupled simulation. Focusing on data directly related to the coupling effect provides a clear variable basis for subsequent setting of coupling relationships and iterative solutions, ensuring the relevance and effectiveness of coupled simulation.
[0033] Furthermore, the coupling relationship, solution order, and convergence control criteria between the electromagnetic field model, temperature field model, and structural stress field model can be pre-configured.
[0034] The coupling relationship includes the positive influence of Joule heating on material properties and the negative influence of temperature changes on electromagnetic field distribution and mechanical stress. Based on this coupling relationship, the load transfer order between the various physical field models can be determined, namely, the output load of the electromagnetic field model is transferred to the temperature field model, and the output load of the temperature field model is transferred to the electromagnetic field model and the structural stress field model.
[0035] The solution order prioritizes the electromagnetic field model based on coupling strength, followed by iteratively updating the temperature field model, and finally calculating the structural stress field model. The convergence control criterion is the relative error threshold of interface load transfer. As the core indicator, ,in, This refers to the load data transferred from one physics model to another in the current iteration step. This refers to the load data transferred between the same physical field models in the previous iteration step. and It refers to the value passed from the extracted dynamic data during the iteration process. A small positive number is preset for the user to determine whether the difference in load transfer between two iterations is small enough to meet the convergence requirement. Modulo operation on vectors is used to quantify the overall differences in payload data.
[0036] The relative error of load transfer between two adjacent iterations is calculated using the above formula to determine whether the multiphysics model coupling iteration has reached a stable state. When the relative error is less than... When the load transfer has stabilized and the calculation results of each physics model no longer change significantly, the iteration can be terminated; otherwise, the iteration needs to continue. Meanwhile, the error threshold in the convergence control criterion... It can be dynamically adjusted according to project requirements, for example, setting a smaller value during the motor start-up phase. To ensure transient accuracy, the tolerance is relaxed during steady-state operation. To improve computational efficiency.
[0037] Understandably, load data conversion is required during load transfer. See [link / reference] Figure 3 The load data conversion includes mapping the Joule heat source distribution Q output by the electromagnetic field model to the grid nodes of the temperature field model, and mapping the temperature distribution T calculated by the temperature field model to the electromagnetic field model and the structural stress field model as input for material property updates.
[0038] The mapping operation employs a linear interpolation method based on element local coordinates, including the following sub-steps: For the target mesh node, firstly, determine the element containing the node in the source mesh; secondly, calculate the local coordinate values of the node in the source element; finally, based on the physical quantity values and local coordinate values of each node in the source element, use the linear interpolation formula: The interpolation result of the target node is calculated.
[0039] in, For the source unit Temperature values at each node These are the weighting coefficients corresponding to the local coordinates. This represents the number of source unit nodes.
[0040] After setting the coupling relationship, solution order, and convergence control criteria among the electromagnetic field, temperature field, and structural stress field models, an iterative loop mechanism needs to be initiated based on these rules to provide an execution framework for the conversion and transfer of load data. Specifically, the process control module of the coupling interface platform will automatically trigger the first iteration according to the determined solution order: first, the electromagnetic field model is called to perform calculations and obtain the initial load data for this iteration; then, according to the preset coupling relationship, this load data is passed as input to the temperature field model, driving the temperature field model to start calculations; after the temperature field model completes its calculations, its output load data is passed to the structural stress field model, completing the sequential calculations of the three field models in the first iteration.
[0041] Meanwhile, after each iteration, the system automatically judges the load transfer error according to the convergence control criterion: by calculating the load transferred in the current iteration step. Load transferred from the previous iteration step The relative error between them, and the preset threshold. Compare them. If the relative error is less than... If the relative error is greater than or equal to the convergence requirement, it indicates that the current iteration has met the convergence requirement and the iteration loop can be stopped; if the relative error is greater than or equal to the convergence requirement, it indicates that the current iteration has met the convergence requirement and the iteration loop can be stopped. If the problem persists, the next iteration must be initiated, repeating the process of transferring and calculating the load data between the three field models until the convergence condition is met.
[0042] Understandably, the convergence control criterion can also be set to reach a preset number of iterations. After each iteration, the number of iterations is incremented by one. Once the number of iterations reaches the preset number, the convergence requirement is considered met, and the iteration loop is stopped.
[0043] In an optional embodiment, the two target physical field models include a first physical field model and a second physical field model, wherein the first physical field model transfers load data to the second physical field model; when the first physical field model is a temperature field model, the transfer of load data between different physical field models includes: Obtain the temperature distribution information calculated by the temperature field model; The distribution calculation parameters of the second physical field model are updated based on the temperature distribution information. The distribution calculation parameters are used to participate in the calculation of the model distribution information of the second physical field model. The distribution calculation parameters are passed to the second physical field model.
[0044] In some embodiments, when the temperature field model transfers load data to other physical field models, see [reference needed]. Figure 4 The temperature field model calculates temperature distribution information by acquiring relevant dynamic data. And then utilize this temperature distribution The distributed calculation parameters of the second physical field model are updated. These distributed calculation parameters include material property parameters in the electromagnetic field model and thermal stress calculation parameters in the structural stress field model.
[0045] Among them, the material property parameters in the electromagnetic field model include resistivity. Coefficient of thermal expansion Thermal conductivity Specific heat capacity The coefficient of thermal expansion in the structural stress field model .
[0046] The updated resistivity, coefficient of thermal expansion, thermal conductivity, specific heat capacity, and other parameters are passed to the corresponding physical field model for the next round of iterative calculations.
[0047] In an optional embodiment, updating the distribution calculation parameters of the second physical field model based on the temperature distribution information includes: The temperature change is determined based on the temperature distribution information and the preset reference temperature. Obtain the initial distribution calculation parameters at the reference temperature; The updated distribution calculation parameters are determined based on the initial distribution calculation parameters and the temperature change.
[0048] In some embodiments, the temperature distribution information is the same as the temperature distribution calculated by the temperature field model in this instance. Correspondingly, the temperature change is .
[0049] Furthermore, the material property update is achieved through a preset nonlinear functional relationship, such as the function of resistivity as a function of temperature. .in, Resistivity at the reference temperature Temperature coefficient. Thermal conductivity. With temperature The relationship is a polynomial function fitted to the experimental data: .in Based on the thermal conductivity, , These are the preset fitting coefficients. Specific heat capacity. With temperature The relationship is achieved through piecewise linear functions. ,in, Based on specific heat capacity, This represents the increase in specific heat capacity due to temperature changes. This represents the change in temperature.
[0050] The function of thermal expansion coefficient with temperature .in, The reference thermal expansion coefficient, This is the temperature sensitivity coefficient.
[0051] The above polynomial function achieves nonlinear correction of thermal conductivity with temperature variation, accurately reflecting the dynamic characteristics of material thermal conductivity at different temperatures. The polynomial function is obtained by fitting experimentally measured thermal conductivity and temperature data, covering the temperature range that may occur during motor operation. The introduction of a second-order term effectively improves the fitting accuracy in high-temperature ranges, avoiding error accumulation caused by linear approximation and achieving a dynamic relationship description. Based on the temperature range differences, piecewise coefficients are set to accurately reflect the change in the material's heat storage capacity at different temperatures, ensuring that the material's specific heat capacity input in the temperature field model closely matches actual thermal characteristics. This assists in more accurately simulating heat transfer and storage processes in scenarios such as motors. Combined with the polynomial correction of thermal conductivity, it improves the accuracy of multi-physics model coupled simulation in simulating material thermal behavior. The updated material properties are directly applied to the physics model in subsequent iterations without the need to regenerate the mesh or rebuild the material database.
[0052] Step 102: After each iteration is completed, determine the load change rate between the two target physics models in this iteration, where the target physics models are two physics models with adjacent load transfer order in the multiphysics model.
[0053] In some embodiments, the load change rate can reflect the relative error of load transfer between two adjacent iterations, and the load change rate can be used to determine whether the multiphysics model coupling iteration has reached a stable state.
[0054] Among them, the two target physical field models mentioned above can be an electromagnetic field model and a temperature field model (the load is transferred from the electromagnetic field model to the temperature field model), or a temperature field model and a structural stress field model (the load is transferred from the temperature field model to the structural stress field model), or a temperature field model and an electromagnetic field model (the load is transferred from the temperature field model to the electromagnetic field model).
[0055] In an optional embodiment, determining the load change rate of the load data transferred between the two target physics models in this iteration includes: Obtain the previous load data and the current load data transferred between the two target physics models in this iteration. The previous load data is the load data transferred between the two target physics models in the most recent iteration. The load change rate is determined based on the current load data and the previous load data.
[0056] In some embodiments, the load change rate is calculated using the following formula: .
[0057] in, This refers to the load data transferred from one physics model to another in this iteration. This refers to the load data transferred between the same physical fields in the previous iteration.
[0058] Step 103: Using a calculation strategy corresponding to the load change rate, determine the next mitigation factor for the next iteration operation. The next mitigation factor is used to control the load transmission speed.
[0059] In some embodiments, the load change rate reflects the stability of the iteration results. The corresponding calculation strategy is configured through the load change rate to facilitate the rapid convergence of the iteration results.
[0060] When the load change rate is large, increase the mitigation factor. To accelerate convergence, a linearly increasing strategy can be adopted; when the rate of change of the load is small, the mitigation factor can be reduced. To suppress oscillations, a conservative adjustment strategy can be adopted. This includes a mitigation factor. The faster the growth rate, the more significant the acceleration convergence effect.
[0061] In an optional embodiment, the next mitigation factor for the next iteration is determined using a calculation strategy corresponding to the load change rate, including: Obtain the current mitigation factor in this iteration; When the load change rate is less than a preset change rate threshold, the calculation strategy includes: Based on the current mitigation factor, the preset rate of change threshold, and the load rate of change, determine the mitigation factor growth information; Based on the mitigation factor growth information and the current mitigation factor, the next mitigation factor is determined; When the load change rate is greater than a preset change rate threshold, the calculation strategy includes: Based on the current mitigation factor and the preset rate of change threshold, the mitigation factor reduction information is determined; Based on the mitigation factor reduction information and the current mitigation factor, the next mitigation factor is determined.
[0062] In some embodiments, mitigation factor The value is dynamically adjusted based on the convergence state of the current iteration step, and is based on the load change rate. Compared with the preset rate of change threshold For comparison, see Figure 5 There are two types of adjustment strategies: If the rate of change of load is less than the threshold, i.e. This indicates a good convergence trend and that the iteration has stabilized, allowing for further increases in speed. To accelerate convergence, a linear incremental strategy is employed. , in, This refers to the current mitigation factor in this iteration. The adjusted mitigation factor for the next period of time. The convergence acceleration coefficient is mentioned. To normalize the "convergence excess degree" The smaller, The faster the growth rate, the more significant the acceleration convergence effect.
[0063] If the rate of change of load exceeds the threshold, i.e. This indicates large convergence fluctuations, suggesting insufficient iterative stability. It is necessary to maintain or slightly reduce α to suppress oscillations, adopting a conservative adjustment strategy. , Among them, through limit The lower limit should be set to avoid being overly conservative and slowing down convergence.
[0064] like Much larger ,ensure ≥0.5, balancing stability and computational efficiency.
[0065] Understandably, in the early stages of iteration, the mitigation factor can be set within a certain range, for example... This range ensures iterative stability while also considering a certain level of convergence efficiency. In the later stages of iteration: after each iteration, first calculate... Determine the convergence state, then update according to the above strategy. Substituting the basic formula into the next iteration drives the entire process of dynamic control, achieving stable startup, adaptive acceleration, and stability maintenance.
[0066] Step 104: Determine the next load data for the next iteration operation based on the next mitigation factor.
[0067] In some embodiments, after the next mitigation factor is calculated, the next load data can be calculated using the calculated next mitigation factor in the next iteration, thereby achieving control over the speed of the load data during each transmission.
[0068] The two-target physics model includes a first physics model and a second physics model, with the first physics model transmitting the load data to the second physics model.
[0069] In an optional embodiment, determining the next load data for the next iteration operation based on the next mitigation factor includes: Obtain the current load data passed by the first physics model during this iteration calculation; Determine the external load data to be applied to the first physics model in the next iteration calculation; Based on the current load data, external load data, and the next mitigation factor, determine the load adjustment data; The next load data is determined based on the current load data and the load adjustment data.
[0070] In some embodiments, the external load data can be calculated from the dynamic data obtained in the above embodiments, and the load adjustment data can be obtained by calculating the difference between the current load data and the external load data and multiplying the difference with the next mitigation factor, thereby determining the sum of the current load data and the load adjustment data as the next load data.
[0071] Specifically, the next load data can be calculated using the following formula: ,in, For the first The load data passed in the iteration step For the first The load data passed in the iteration step This is the latest data on the external loads that should be applied.
[0072] Step 105: Perform the iterative calculation again based on the next load data until the iterative calculation termination condition is met.
[0073] In some embodiments, determining whether further iterative calculations are needed can be made by ensuring that the iteration parameters in the iterative calculation meet the termination condition. The iteration parameters can be the number of iterations or the load change rate. Correspondingly, the termination condition for the iterative calculation is: the number of iterations reaches a preset number, or the load change rate is less than a preset value.
[0074] In one optional embodiment, after all physical field models have completed iterative convergence, the calculation results of each field, including electromagnetic field distribution, temperature field distribution, and structural stress field distribution, are displayed through a unified data visualization interface. The results display supports synchronous updates and correlation analysis of multiple field variables to verify the safety and efficiency of the motor under electromagnetic-thermal-mechanical coupling effects.
[0075] This application presents a multiphysics model coupling simulation method for electric motors, applied to the multiphysics coupling simulation of new energy vehicle motors. It involves the coordinated analysis of electromagnetic fields, temperature fields, and structural stress fields, a key technology for verifying performance and safety in motor design. By directly calling the original model data through a standardized interface, there is no need to modify the code or redefine the interfaces of the CAE models for electromagnetic fields, temperature fields, and structural stress fields, significantly lowering the implementation threshold of multiphysics coupling simulation and reducing operational errors caused by manual intervention. Through dynamic adjustment of mitigation factors and optimization of data transmission and update mechanisms, the convergence performance of nonlinear coupling is effectively improved, the convergence speed is accelerated, and the convergence stability is enhanced, while ensuring convergence accuracy. This achieves fast, stable, and high-precision convergence even in multiphysics scenarios with strong nonlinear coupling.
[0076] By combining cross-program weak coupling and full coupling techniques, this approach overcomes the functional limitations caused by the need for a unified software framework in traditional strong coupling methods. It also avoids the complex operations of manually modifying CAE software interfaces required in existing weak coupling methods, achieving efficient real-time solutions for multi-physics nonlinear coupling. By introducing a mitigation factor to control iteration speed, linear interpolation techniques based on element local coordinates to handle mesh subdivision differences, and standardized interfaces to call original model data, computational resource consumption is significantly reduced while convergence speed is improved, meeting the dual requirements of high precision and real-time performance in the design of new energy vehicle motors.
[0077] Exemplary device Accordingly, this application also provides a multiphysics model coupling simulation device for an electric motor, including: The iteration unit is used to perform the following iterative calculations on the multiphysics model: transfer load data between different physics models according to a preset solution order and a preset load transfer order; The first determining unit is used to determine the load change rate between the two target physical field models that transfer load data in the current iteration operation after each iteration operation is completed. The target physical field models are two physical field models with adjacent load transfer order in the multi-physics model. The second determining unit is used to determine the next mitigation factor for the next iteration operation using a calculation strategy corresponding to the load change rate. The next mitigation factor is used to control the load transmission speed. The third determining unit is used to determine the next load data for the next iteration operation based on the next mitigation factor; The judgment unit is used to perform the iterative calculation again based on the next load data until the iterative calculation termination condition is met.
[0078] The multiphysics model coupling simulation device for motors provided in this embodiment belongs to the same concept as the multiphysics model coupling simulation method for motors provided in the above embodiments of this application. It can execute the method provided in any of the above embodiments of this application and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the specific processing content of the multiphysics model coupling simulation method for motors provided in the above embodiments of this application, and will not be repeated here.
[0079] The functions implemented by each unit in the multiphysics model coupling simulation device for motors described above can be implemented by the same or different processors, and this application embodiment does not limit this.
[0080] It should be understood that each unit in the above device can be implemented by a processor calling software. For example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of each unit in the device. The processor can be a general-purpose processor, such as a CPU or microprocessor, and the memory can be internal or external to the device. Alternatively, the units in the device can be implemented as hardware circuits. By designing the hardware circuits, some or all of the unit functions can be implemented. The hardware circuits can be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are implemented by designing the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a PLD, such as an FPGA, which can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files to implement the functions of some or all of the above units. All units in the above device can be implemented entirely by a processor calling software, entirely by hardware circuits, or partially by a processor calling software with the remaining parts implemented by hardware circuits.
[0081] In this application embodiment, a processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a CPU, microprocessor, GPU, or DSP. In another implementation, the processor can implement certain functions through the logical relationships of hardware circuits. These logical relationships are fixed or reconfigurable. For example, the processor may be a hardware circuit implemented as an ASIC or PLD, such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and configuring the hardware circuit can be understood as the processor loading instructions to implement the functions of some or all of the above units. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as an NPU, TPU, or DPU.
[0082] As can be seen, each unit in the above device can be one or more processors (or processing circuits) configured to implement the above methods, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.
[0083] Furthermore, the units in the above devices can be integrated in whole or in part, or they can be implemented independently. In one implementation, these units are integrated together and implemented in the form of a System-on-Chip (SoC). The SoC may include at least one processor for implementing any of the above methods or implementing the functions of the units in the device. The at least one processor may be of different types, such as CPU and FPGA, CPU and artificial intelligence processor, CPU and GPU, etc.
[0084] Exemplary electronic devices Another embodiment of this application also provides an electronic device, see [link to relevant documentation] Figure 6 As shown, the device includes: Memory 600 and processor 610; The memory 600 is connected to the processor 610 and is used to store programs; The processor 610 is used to implement the multiphysics model coupling simulation method for motors disclosed in any of the above embodiments by running the program stored in the memory 600.
[0085] Specifically, the multiphysics model coupling simulation device for the above-mentioned motor may also include: a bus, a communication interface 620, an input device 630, and an output device 640.
[0086] The processor 610, memory 600, communication interface 620, input device 630, and output device 640 are interconnected via a bus. Among them: A bus can include a pathway for transmitting information between various components of a computer system.
[0087] The processor 610 can be a general-purpose processor, such as a general-purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention. It can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.
[0088] The processor 610 may include a main processor, as well as a baseband chip, modem, etc.
[0089] The memory 600 stores a program that executes the technical solution of this invention, and may also store an operating system and other key business functions. Specifically, the program may include program code, which includes computer operation instructions. More specifically, the memory 600 may include read-only memory (ROM), other types of static storage devices capable of storing static information and instructions, random access memory (RAM), other types of dynamic storage devices capable of storing information and instructions, disk storage, flash memory, etc.
[0090] Input device 630 may include a device for receiving user input data and information, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor.
[0091] Output device 640 may include devices that allow information to be output to a user, such as a display screen, printer, speaker, etc.
[0092] The communication interface 620 may include a device that uses any transceiver to communicate with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Network (WLAN), etc.
[0093] The processor 610 executes the program stored in the memory 600 and calls other devices, which can be used to implement each step of any of the multiphysics model coupling simulation methods for motors provided in the above embodiments of this application.
[0094] Exemplary computer program products and storage media In addition to the methods and devices described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the multiphysics model coupling simulation method for motors according to various embodiments of this application as described in any of the above embodiments of this specification.
[0095] The computer program product can be written in any combination of one or more programming languages to perform the operations of the embodiments of this application. The programming languages include object-oriented programming languages such as Java and C++, as well as conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.
[0096] Furthermore, embodiments of this application may also be storage media storing a computer program, which is executed by a processor using the steps of the multiphysics model coupling simulation method for a motor according to various embodiments of this application described in any of the above embodiments of this specification. Specifically, the following steps can be implemented: The following iterative calculations are performed on the multiphysics model: load data is transferred between different physics models according to the preset solution order and the preset load transfer order; After each iteration is completed, the load change rate between the two target physics models that transfer load data in this iteration is determined. The target physics models are two physics models with adjacent load transfer order in the multiphysics model. Using a calculation strategy corresponding to the load change rate, the next mitigation factor for the next iteration is determined, and the next mitigation factor is used to control the load transmission speed. The next load data for the next iteration operation is determined based on the next mitigation factor. The iterative calculation is performed again based on the next load data until the termination condition of the iterative calculation is met.
[0097] For the foregoing method embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.
[0098] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.
[0099] The steps in the methods of the various embodiments of this application can be adjusted, merged, or deleted in order according to actual needs, and the technical features described in each embodiment can be replaced or combined.
[0100] The modules and sub-modules in the apparatus and terminal in the various embodiments of this application can be merged, divided, and deleted according to actual needs.
[0101] It should be understood that the disclosed terminals, devices, and methods can be implemented in other ways, given the several embodiments provided in this application. For example, the terminal embodiments described above are merely illustrative. For instance, the division of modules or sub-modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple sub-modules or modules may be combined or integrated into another module, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.
[0102] The modules or submodules described as separate components may or may not be physically separate. The components that constitute a module or submodule may or may not be physical modules or submodules; that is, they may be located in one place or distributed across multiple network modules or submodules. Some or all of the modules or submodules can be selected to achieve the purpose of this embodiment's solution, depending on actual needs.
[0103] Furthermore, the functional modules or sub-modules in the various embodiments of this application can be integrated into one processing module, or each module or sub-module can exist physically separately, or two or more modules or sub-modules can be integrated into one module. The integrated modules or sub-modules described above can be implemented in hardware or in the form of software functional modules or sub-modules.
[0104] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0105] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software unit executed by a processor, or a combination of both. The software unit can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.
[0106] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0107] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A multiphysics model coupling simulation method for an electric motor, characterized in that, include: The following iterative calculations are performed on the multiphysics model: load data is transferred between different physics models according to the preset solution order and the preset load transfer order; After each iteration is completed, the load change rate between the two target physics models that transfer load data in this iteration is determined. The target physics models are two physics models with adjacent load transfer order in the multiphysics model. Using a calculation strategy corresponding to the load change rate, the next mitigation factor for the next iteration is determined, and the next mitigation factor is used to control the load transmission speed. The next load data for the next iteration operation is determined based on the next mitigation factor. The iterative calculation is performed again based on the next load data until the termination condition of the iterative calculation is met.
2. The method according to claim 1, characterized in that, The determination of the load change rate between the two target physics models in this iteration includes: Obtain the previous load data and the current load data transferred between the two target physics models in this iteration. The previous load data is the load data transferred between the two target physics models in the most recent iteration. The load change rate is determined based on the current load data and the previous load data.
3. The method according to claim 1, characterized in that, The step of determining the next mitigation factor for the next iteration using a calculation strategy corresponding to the load change rate includes: Obtain the current mitigation factor in this iteration; When the load change rate is less than a preset change rate threshold, the calculation strategy includes: Based on the current mitigation factor, the preset rate of change threshold, and the load rate of change, determine the mitigation factor growth information; Based on the mitigation factor growth information and the current mitigation factor, the next mitigation factor is determined; When the load change rate is greater than a preset change rate threshold, the calculation strategy includes: Based on the current mitigation factor and the preset rate of change threshold, the mitigation factor reduction information is determined; Based on the mitigation factor reduction information and the current mitigation factor, the next mitigation factor is determined.
4. The method according to claim 1, characterized in that, The two target physics models include a first physics model and a second physics model, wherein the first physics model transmits the load data to the second physics model; The step of determining the next load data for the next iteration based on the next mitigation factor includes: Obtain the current load data passed by the first physics model during this iteration calculation; Determine the external load data to be applied to the first physics model in the next iteration calculation; Based on the current load data, external load data, and the next mitigation factor, determine the load adjustment data; The next load data is determined based on the current load data and the load adjustment data.
5. The method according to claim 1, characterized in that, The two target physics models include a first physics model and a second physics model, wherein the first physics model transmits the load data to the second physics model; When the first physical field model is a temperature field model, the transfer of load data between different physical field models includes: Obtain the temperature distribution information calculated by the temperature field model; The distribution calculation parameters of the second physical field model are updated based on the temperature distribution information. The distribution calculation parameters are used to participate in the calculation of the model distribution information of the second physical field model. The distribution calculation parameters are passed to the second physical field model.
6. The method according to claim 5, characterized in that, The updating of the distribution calculation parameters of the second physical field model based on the temperature distribution information includes: The temperature change is determined based on the temperature distribution information and the preset reference temperature. Obtain the initial distribution calculation parameters at the reference temperature; The updated distribution calculation parameters are determined based on the initial distribution calculation parameters and the temperature change.
7. The method according to claim 1, characterized in that, The termination conditions for iterative operations include: the number of iterations reaches a preset number, or the load change rate is less than a preset value.
8. A multiphysics model coupling simulation device for an electric motor, characterized in that, include: The iteration unit is used to perform the following iterative calculations on the multiphysics model: transfer load data between different physics models according to a preset solution order and a preset load transfer order; The first determining unit is used to determine the load change rate between the two target physical field models that transfer load data in the current iteration operation after each iteration operation is completed. The target physical field models are two physical field models with adjacent load transfer order in the multi-physics model. The second determining unit is used to determine the next mitigation factor for the next iteration operation using a calculation strategy corresponding to the load change rate. The next mitigation factor is used to control the load transmission speed. The third determining unit is used to determine the next load data for the next iteration operation based on the next mitigation factor; The judgment unit is used to perform the iterative calculation again based on the next load data until the iterative calculation termination condition is met.
9. An electronic device, characterized in that, Including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the multiphysics model coupling simulation method for motors as described in any one of claims 1 to 7 by running the program in the memory.
10. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the multiphysics model coupling simulation method for motors as described in any one of claims 1 to 7.