A fully coupled electromagnetic-thermal-mechanical simulation design method and system for SiC controllers

Abstract

This application provides a fully coupled electromagnetic-thermal-mechanical simulation design method and system for SiC controllers, belonging to the field of power electronic device simulation design technology. The method involves: constructing a two-way real-time coupled model of electromagnetic, thermal, and mechanical stress fields; determining the irradiation dose based on the expected operating environment of the SiC controller, introducing an irradiation damage factor to correct the SiC material parameters, and inputting this into the two-way real-time coupled model; performing multi-condition simulations by inputting different temperature and stress conditions into the two-way real-time coupled model, outputting the performance parameters of the SiC material, and establishing a quantitative relationship model between the performance parameters and temperature and stress to reveal the performance degradation law of the SiC material; and, based on the two-way real-time coupled model and the performance degradation law of the SiC material, synergistically optimizing the parameters of the SiC controller with switching losses and temperature rise as optimization targets. This application improves simulation accuracy, optimizes SiC controller performance, and enhances reliability and service life.

Description

Technical Field

[0001] This application belongs to the field of power electronic device simulation design technology, specifically relating to a SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method and system. Background Technology

[0002] With the continuous development of power electronics technology, silicon carbide (SiC) devices, due to their high-frequency, high-temperature, and high-voltage characteristics, are widely used in new energy vehicles, rail transit, smart grids, and other fields. SiC controllers have become a key core component for improving the performance of various systems. Simulation analysis is an indispensable part of the SiC controller design process. However, traditional simulation methods often only consider the influence of a single physical field, such as simulating only the electromagnetic field or analyzing only the thermal field, neglecting the interaction between different physical fields. In reality, during the operation of a SiC controller, the electromagnetic field generates switching losses, leading to temperature increases and the formation of a thermal field. Temperature changes, in turn, alter the electrical and mechanical properties of the material, affecting the distribution of the electromagnetic field. Simultaneously, the temperature increase also generates thermal stress, forming a mechanical stress field. The presence of mechanical stress may, in turn, affect the electrical performance and heat dissipation characteristics of the device. Therefore, simulation of a single physical field cannot accurately reflect the actual operating state of the SiC controller.

[0003] Furthermore, under high temperature and high pressure environments, SiC materials are subject to radiation damage, leading to changes in their material parameters. Traditional simulation methods do not consider the impact of radiation damage on material parameters, resulting in low simulation accuracy and difficulty in meeting engineering design requirements. Simultaneously, the performance parameters of SiC modules (such as on-resistance, switching speed, and breakdown voltage) degrade with changes in temperature and stress. Understanding these degradation patterns is crucial for the optimized design and reliability improvement of SiC controllers, but current research in this area is still incomplete.

[0004] Therefore, there is an urgent need for a simulation design method for SiC controllers that can take into account the coupling of multiple physical fields and the effects of irradiation damage, and to reveal the degradation law of SiC controller performance parameters with temperature and stress, so as to improve simulation accuracy and provide strong support for the optimization design of SiC controllers. Summary of the Invention

[0005] In a first aspect, embodiments of this application provide a fully coupled electromagnetic-thermal-mechanical simulation design method for a SiC controller, comprising the following steps:

[0006] S1. Construct a two-way real-time coupled model of electromagnetic field, thermal field, and mechanical stress field. The two-way real-time coupled model is achieved by integrating the electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model for two-way real-time coupling.

[0007] The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model;

[0008] The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model;

[0009] The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer.

[0010] S2. Determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters based on the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model;

[0011] S3. Input different temperature and stress conditions into the two-way real-time coupled model to perform multi-condition simulation, output the performance parameters of SiC material, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC material to reveal the performance degradation law of SiC material.

[0012] S4. Based on the bidirectional real-time coupling model and the performance degradation law of SiC material, the parameters of SiC controller are optimized in a coordinated manner with switching loss and temperature rise as optimization objectives.

[0013] Furthermore, the specific steps for constructing the electromagnetic field sub-model in step S1 are as follows:

[0014] Set electromagnetic field boundary conditions, calculate the electromagnetic field distribution based on Maxwell's equations, and output the total loss. The heat source serves as the input to the thermal field sub-model, and the output electromagnetic force serves as the input. As input loads for the mechanical stress field sub-model;

[0015] The specific steps for constructing the thermal field sub-model in step S1 are as follows:

[0016] Total loss output by electromagnetic field sub-model As the input heat source, the temperature distribution is calculated based on the preset thermal field boundary conditions and the heat conduction equation. And output it to the mechanical stress field sub-model as a temperature load;

[0017] Temperature distribution Output to the mechanical stress field sub-model, and simultaneously from the temperature distribution Extracting local temperature values This information is fed back to the electromagnetic field sub-model for conductivity correction using the following formula:

[0018]

[0019] in, The electrical conductivity of SiC material, Reference temperature The reference conductivity below, This is the temperature coefficient.

[0020] Furthermore, the specific steps for constructing the mechanical stress field sub-model in step S1 are as follows:

[0021] Temperature distribution output by the thermal field sub-model Electromagnetic force output by the electromagnetic field sub-model As the input load, based on the preset mechanical field boundary conditions, and by solving the mechanical stress field using the fundamental equations of elasticity, the stress tensor is obtained. ;

[0022] Based on the input temperature distribution Local temperature values ​​extracted Based on local temperature values Calculate thermal strain using the thermal strain formula ;

[0023] thermal strain Substituting the initial strain condition into the solution process, the stress tensor... After correction, the stress distribution including thermal stress is finally obtained. ;

[0024] Stress distribution The thermal field sub-model is fed back to correct the thermal conductivity k using the following formula:

[0025]

[0026] in, Thermal conductivity under stress-free conditions β Stress coefficient, To obtain stress distribution The local stress value extracted from it;

[0027] At the same time, the local stress value Feedback is sent to the electromagnetic field sub-model to correct the electromagnetic field boundary conditions caused by structural deformation.

[0028] Furthermore, electromagnetic field boundary conditions include electric field boundary conditions and magnetic field boundary conditions;

[0029] Thermal boundary conditions include convective heat transfer boundary conditions and adiabatic boundary conditions;

[0030] Mechanical field boundary conditions include displacement boundary conditions and frictionless contact boundary conditions.

[0031] Furthermore, the specific steps of step S2 are as follows:

[0032] S21. Based on the expected operating environment of the SiC controller, the irradiation dose is set within the preset dose range;

[0033] S22. Determine the irradiation damage factor based on the set irradiation dose, wherein the irradiation damage factor includes correction coefficients for the electrical conductivity, thermal conductivity, and elastic modulus of the SiC material;

[0034] S23. Correct the SiC material parameters using the irradiation damage factor to obtain the corrected conductivity. Corrected thermal conductivity and the corrected elastic modulus ;

[0035]

[0036]

[0037]

[0038] in, Indicates the original conductivity. Indicates the original thermal conductivity. Represents the original elastic modulus; , , These are the correction coefficients used to correct the electrical conductivity, thermal conductivity, and elastic modulus of SiC materials, respectively.

[0039] S24. Input the corrected SiC material parameters into the two-way real-time coupled model to update the material properties of the electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model.

[0040] Furthermore, the specific steps of step S3 are as follows:

[0041] S31. Set multi-condition simulation conditions, set temperature range and stress range, and generate simulation scenarios with different temperature and stress combinations;

[0042] S32. Input the simulation scenario into the bidirectional real-time coupled model, run the simulation, and output the performance parameters of the SiC material, including on-resistance. Switching speed and breakdown voltage;

[0043] S33. Statistical analysis is performed on the SiC material performance parameter data output from the simulation. The least squares method is used for curve fitting to establish a quantitative relationship model between the performance parameters and temperature and stress. The quantitative relationship model includes on-resistance. With temperature ,stress Relationship:

[0044]

[0045] Where R0 is the reference on-resistance under reference temperature and reference stress. , Here are the fitting coefficients, and T is the temperature. For stress;

[0046] S34. The degradation law of SiC material properties with temperature and stress is revealed by quantitative relationship model.

[0047] Furthermore, the curve fitting in step S33 also includes establishing thermal conductivity. Contact thermal resistance Contact resistance With temperature ,stress Quantitative relationship model:

[0048]

[0049] in, Based on the thermal conductivity, , These are the fitting coefficients;

[0050]

[0051] in, As a reference contact thermal resistance, , These are the fitting coefficients;

[0052]

[0053] in, As the reference contact resistance, , The fitting coefficients are denoted as .

[0054] Furthermore, the specific steps of step S4 are as follows:

[0055] S41. With the optimization objectives of minimizing switching losses and minimizing temperature rise, the following objective function is constructed:

[0056]

[0057] in, To optimize variable parameters, Total loss, The total loss under the initial parameters. For SiC controller temperature, The maximum temperature allowed by the SiC controller. and These are the weighting coefficients. ;

[0058] S42. Select target variable parameters, including the duty cycle D of the SiC controller and the switching frequency. and contact pressure P;

[0059] S43. Use a multi-objective optimization algorithm to optimize the target variable parameters, output the optimized target variable parameters, and update the corresponding parameters in the bidirectional real-time coupled model to perform coordinated optimization of switching losses and temperature rise.

[0060] Furthermore, in step S43, the multi-objective optimization algorithm is selected as the genetic algorithm; the specific steps for optimizing the objective variable parameters are as follows:

[0061] S431. The duty cycle D and switching frequency in the target variable parameters... The contact pressure P is encoded as a real number, the population size and number of iterations are set, and an initial population that meets the constraints is randomly generated to complete the population initialization.

[0062] S432. Define the following fitness function for fitness function calculation:

[0063]

[0064] in, The objective function is...

[0065] S433. Use the roulette wheel selection method to select parent individuals based on the proportion of individual fitness to total fitness;

[0066] S434. Using arithmetic crossover, let the parent individual be... and Generate offspring individuals and ;

[0067] in, The crossover factor has a value range of [0,1]. As the first parent individual, These are the duty cycle, switching frequency, and contact pressure of the first parent individual, respectively. For the second parent generation individual These are the duty cycle, switching frequency, and contact pressure of the second parent individual, respectively. As the first generation individual, As a second-generation individual;

[0068] S435. Mutate the offspring individuals according to the set mutation probability. The parameters after mutation are:

[0069]

[0070]

[0071]

[0072] in, For random numbers that follow a normal distribution, For the mutated duty cycle, For the modified switching frequency, The modified contact pressure; , These are the maximum and minimum duty cycles, respectively. , These are the maximum and minimum values ​​of the switching frequency, respectively. , These are the maximum and minimum values ​​of the contact pressure, respectively.

[0073] S436. When the number of iterations reaches the maximum number of iterations or the change in the optimal fitness value between two adjacent generations is less than the minimum fitness threshold, the iteration terminates and the optimal combination of target variable parameters is output.

[0074] Secondly, embodiments of this application also provide a SiC controller electromagnetic-thermal-mechanical fully coupled simulation design system, including:

[0075] A bidirectional real-time coupled modeling module is used to construct a bidirectional real-time coupled model of electromagnetic field, thermal field, and mechanical stress field. The bidirectional real-time coupled model achieves bidirectional real-time coupling by integrating electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model.

[0076] The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model;

[0077] The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model;

[0078] The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer.

[0079] The material parameter correction module is used to determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters according to the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model.

[0080] The performance degradation analysis module is used to input different temperature and stress conditions into a two-way real-time coupled model for multi-condition simulation, output the performance parameters of SiC materials, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC materials to reveal the performance degradation law of SiC materials.

[0081] The collaborative optimization module is used to collaboratively optimize the parameters of the SiC controller based on a bidirectional real-time coupling model and the performance degradation law of SiC materials, with switching loss and temperature rise as optimization objectives.

[0082] As can be seen from the above technical solutions, this application has the following advantages:

[0083] The electromagnetic-thermal-mechanical fully coupled simulation design method and system for SiC controllers provided in this application constructs a multi-physics field bidirectional real-time coupled model, considers the influence of irradiation damage on material parameters, and reveals the degradation law of performance parameters with temperature and stress. It realizes the synergistic optimization of switching losses and temperature rise, improves the simulation accuracy of SiC controllers under complex working conditions, and provides a reliable basis for optimized design. Attached Figure Description

[0084] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0085] Figure 1 This is a flowchart illustrating the electromagnetic-thermal-mechanical fully coupled simulation design method for SiC controllers according to the present invention.

[0086] Figure 2 This is a schematic diagram illustrating the interaction of the electromagnetic field, thermal field, and mechanical stress field of the present invention.

[0087] Figure 3 This is a schematic diagram of the electromagnetic-thermal-mechanical fully coupled simulation design system for the SiC controller of the present invention. Detailed Implementation

[0088] The various embodiments of this disclosure will be described more fully in the following detailed description of the specific steps of the electromagnetic-thermal-mechanical fully coupled simulation design method for SiC controllers. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0089] This embodiment provides a fully coupled electromagnetic-thermal-mechanical simulation design method for SiC controllers. By using multi-physics coupling and irradiation damage correction, it reveals the performance degradation law of SiC materials, optimizes switching losses and temperature rise, and improves controller performance and reliability.

[0090] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] Please see Figure 1 The diagram shows a flowchart of a SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method in a specific embodiment. The method includes the following steps:

[0092] S1. Construct a two-way real-time coupled model of electromagnetic field, thermal field, and mechanical stress field. The two-way real-time coupled model is achieved by integrating the electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model for two-way real-time coupling.

[0093] The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model;

[0094] The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model;

[0095] The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer.

[0096] It should be noted that by constructing a two-way real-time coupling model, the dynamic interaction between electromagnetic field, thermal field and mechanical stress field is realized, which can truly reflect the multi-physics coupling effect of SiC controller during operation and overcome the limitations of traditional single-physics simulation.

[0097] S2. Determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters based on the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model;

[0098] It should be noted that by introducing an irradiation damage factor to correct the SiC material parameters, the influence of irradiation damage on material properties under high temperature and high pressure is considered, which improves the accuracy of the simulation model under complex working conditions and provides a more reliable basis for the optimized design of SiC controllers.

[0099] S3. Input different temperature and stress conditions into the two-way real-time coupled model to perform multi-condition simulation, output the performance parameters of SiC material, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC material to reveal the performance degradation law of SiC material.

[0100] It should be noted that through multi-condition simulation and data analysis, the degradation law of SiC material performance parameters with temperature and stress was revealed, providing a theoretical basis for optimization design and improving the performance and service life of SiC controllers;

[0101] S4. Based on the bidirectional real-time coupling model and the performance degradation law of SiC material, the parameters of SiC controller are optimized in a coordinated manner with switching loss and temperature rise as optimization objectives;

[0102] It should be noted that by optimizing switching losses and temperature rise as optimization targets, the parameters of the SiC controller are synergistically optimized, thereby achieving a comprehensive improvement in the performance of the SiC controller.

[0103] This embodiment constructs a multi-physics bidirectional real-time coupled model, introduces an irradiation damage factor to correct material parameters, reveals the degradation law of performance parameters with temperature and stress, and performs synergistic optimization with switching loss and temperature rise as optimization targets. This improves simulation accuracy, optimizes controller performance, enhances reliability and service life, and provides support for the design and optimization of SiC controllers.

[0104] Furthermore, as a refinement and extension of the specific implementation methods described above, and to fully illustrate the specific implementation process in this embodiment, another electromagnetic-thermal-mechanical fully coupled simulation design method for SiC controllers is provided. Taking a SiC controller for new energy vehicles as the application object, this controller has a rated voltage of 800V and a rated current of 600A, and is applied to the high-temperature, high-radiation engine compartment environment. The expected operating temperature range is -50℃ to 200℃, and the radiation dose can reach 10... 15 neutrons / cm 2 The method includes the following steps:

[0105] S1. Construct a two-way real-time coupled model of electromagnetic field, thermal field, and mechanical stress field, such as... Figure 2 As shown, the bidirectional real-time coupling model achieves bidirectional real-time coupling by integrating the electromagnetic field sub-model, the thermal field sub-model, and the mechanical stress field sub-model:

[0106] The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model;

[0107] The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model;

[0108] The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer.

[0109] The specific steps for constructing the electromagnetic field sub-model in step S1 are as follows:

[0110] Set electromagnetic field boundary conditions, calculate the electromagnetic field distribution based on Maxwell's equations, and output the total loss. The heat source serves as the input to the thermal field sub-model, and the output electromagnetic force serves as the input. As input loads for the mechanical stress field sub-model;

[0111]

[0112]

[0113]

[0114] in, For conduction loss, For switching losses, Duty cycle, For saturation pressure drop, For current, For on-resistance, For switching frequency, The voltage at the moment of switching. The current at the moment of switching. Overlapping time;

[0115]

[0116] in, For current density, It represents the magnetic flux density;

[0117] For example, based on the actual structural parameters of the controller, a three-dimensional electromagnetic field simulation model is established using ANSYS Maxwell, which includes key components such as SiC MOSFET chips, gate drive circuits, busbars, and heat sinks.

[0118] The electric field boundary conditions are set as follows: the potential between the chip gate and source is fixed (driving voltage 15V), and an 800V DC voltage is applied between the drain and source. The magnetic field boundary conditions adopt the flux continuity boundary, and the outer boundary of the model is set as the far-field boundary (5 times the feature size from the device surface).

[0119] Based on Maxwell's equations, the electromagnetic field distribution is solved, and the total loss is calculated as follows: Conduction loss: The duty cycle D is initially set to 0.6, and the saturation voltage drop is... =0.8V, rated current I=600A, reference on-resistance =50mΩ (at 25℃ and under zero stress);

[0120] Switching losses: Switching frequency Initially set to 20kHz, voltage at switching time Peak voltage 800V, current during switching Peak 600A, overlap time =100ns;

[0121] Electromagnetic force calculation: According to F=J×B, where the current density J is calculated using the cross-sectional area of ​​the copper busbar (10mm×2mm) and the current, J=3×10 7 A / m 2 The magnetic flux density B was extracted from the electromagnetic field simulation results, with a maximum value of 0.5 T. The calculated peak electromagnetic force was 1.5 × 10⁻⁶. 4 N / m 2 , as the input load of the mechanical stress field sub-model;

[0122] The specific steps for constructing the thermal field sub-model in step S1 are as follows:

[0123] Total loss output by electromagnetic field sub-model Assuming a heat source as input, and based on preset thermal field boundary conditions, the temperature distribution is calculated using the following heat conduction equation. It is then output to the mechanical stress field sub-model as a temperature load:

[0124]

[0125] in, For material density, For specific heat capacity, Thermal conductivity;

[0126] Temperature distribution Output to the mechanical stress field sub-model, and simultaneously from the temperature distribution Extracting local temperature values (e.g., maximum temperature value), and feed it back to the electromagnetic field sub-model so that conductivity can be corrected using the following formula:

[0127]

[0128] in, The electrical conductivity of SiC material, Reference temperature The reference conductivity below, Temperature coefficient;

[0129] For example, the heat conduction equation is used. The density of SiC material is ρ = 3210 kg / m³. 3Specific heat capacity c = 1100 J / (kg·K), reference thermal conductivity under no stress k0 = 490 W / (m·K) (25℃); Thermal field boundary conditions: The radiator surface (area A = 0.05 m²) adopts a convective heat transfer boundary, with a heat transfer coefficient h = 150 W / (m²). 2 •K) (forced air cooling conditions), ambient temperature =25℃; the chip package sides and bottom are set as thermally insulating boundaries (the thermal conductivity of the packaging material is much lower than that of SiC); parameter feedback correction: extract the chip junction temperature from the temperature distribution. The local temperature value is fed back to the electromagnetic field sub-model for conductivity correction, and the correction formula is as follows: The reference temperature =25℃, reference conductivity =2×10 4 S / m, temperature coefficient =0.004 / K; for example, the local temperature value At 120℃, the corrected conductivity σ = 2 × 10⁻⁶ 4 ×[1+0.004×(120-25)]=2.76×10 4 S / m, corresponding to on-resistance (Chip conductive cross-sectional area S=1×10) -4 m 2 ), calculated ;

[0130] The specific steps for constructing the mechanical stress field sub-model in step S1 are as follows:

[0131] Temperature distribution output by the thermal field sub-model Electromagnetic force output by the electromagnetic field sub-model As the input load, based on the preset mechanical field boundary conditions, the mechanical stress field is solved using the following fundamental equations of elasticity to obtain the stress tensor. :

[0132]

[0133] in, It is a displacement vector. For stress tensor;

[0134] Based on the input temperature distribution Local temperature values ​​extracted Based on local temperature values The thermal strain is calculated using the following thermal strain formula. :

[0135]

[0136] in, The coefficient of thermal expansion is For reference temperature;

[0137] thermal strain Substituting the initial strain condition into the solution process, the stress tensor... After correction, the stress distribution including thermal stress is finally obtained. ;

[0138] Stress distribution The thermal field sub-model is fed back to correct the thermal conductivity k using the following formula:

[0139]

[0140] in, Thermal conductivity under stress-free conditions β Stress coefficient, To obtain stress distribution The local stress values ​​(such as the maximum equivalent stress value) extracted from it.

[0141] At the same time, the local stress value Feedback is fed back to the electromagnetic field sub-model to correct the electromagnetic field boundary conditions caused by structural deformation;

[0142] Electromagnetic field boundary conditions include electric field boundary conditions (such as fixed electric potential) and magnetic field boundary conditions (such as magnetic flux continuity).

[0143] Thermal boundary conditions include convective heat transfer boundary conditions and adiabatic boundary conditions;

[0144] The formula for the convective heat transfer boundary condition is as follows:

[0145]

[0146] in, The heat transfer coefficient, The surface area of ​​the heat sink. Surface temperature, Ambient temperature;

[0147] Mechanical field boundary conditions include displacement boundary conditions (i.e., u=0) and frictionless contact boundary conditions;

[0148] For example, a mechanical stress model is established using ANSYS Mechanical. The package shell is fixed with bolts, and the displacement boundary condition u=0 is set. The contact interfaces between the chip and the heat sink, and between the copper busbar and the insulating base, are set as frictionless contact boundaries with a friction coefficient of 0.15. The governing equations and stress calculations are based on the fundamental equations of elasticity. Solve for the stress field, where the displacement vector u and the stress tensor σ satisfy σ=E·ε (the elastic modulus E is initially 400GPa, and Poisson's ratio μ=0.24).

[0149] Thermal strain calculation: The coefficient of thermal expansion of SiC material =4.5×10 -6 / K, local temperature value Thermal strain at 120℃ =4.5×10 -6 ×(120-25)=4.275×10 -4 Substitute this as the initial strain condition into the solution;

[0150] Stress feedback correction: from stress distribution The maximum equivalent stress at the center of the chip is extracted as the local stress value. =300MPa, fed back to the thermal field sub-model to correct thermal conductivity: The stress coefficient β = -2 × 10 -7 MPa -1 The corrected thermal conductivity k = 490 × [1 + (-2 × 10⁻⁶) / 2] -7 [)×300]=489.97W / (m·K); Meanwhile, the deformation of the chip structure due to stress... =5×10 -6 m, fed back to the electromagnetic field sub-model to correct the boundary conditions of the copper busbar spacing and avoid electric field distortion;

[0151] S2. Determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters based on the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model;

[0152] The specific steps of step S2 are as follows:

[0153] S21. Based on the expected operating environment of the SiC controller, the irradiation dose is set within the preset dose range;

[0154] For example, based on the controller's expected operating environment (gamma rays and neutron irradiation in the engine nacelle), within a preset dose range (10... 14 ~10 15 neutrons / cm 2 Within ), a typical irradiation dose of 10 was selected. 15 neutrons / cm 2 As a correction benchmark;

[0155] S22. Determine the irradiation damage factor based on the set irradiation dose, wherein the irradiation damage factor includes correction coefficients for the electrical conductivity, thermal conductivity, and elastic modulus of the SiC material; for example, when the irradiation dose is 10... 15 neutrons / cm 2 At that time, the electrical conductivity correction factor was 0.8, the thermal conductivity correction factor was 0.7, and the elastic modulus correction factor was 0.95;

[0156] For example, by consulting experimental data and literature on SiC material irradiation, the damage factor at this irradiation dose was determined: the conductivity correction factor. Thermal conductivity correction factor Elastic modulus correction factor ;

[0157] S23. Correct the SiC material parameters using the irradiation damage factor to obtain the corrected conductivity. Corrected thermal conductivity and the corrected elastic modulus ;

[0158]

[0159]

[0160]

[0161] in, Indicates the original conductivity. Indicates the original thermal conductivity. Represents the original elastic modulus; , , These are the correction coefficients used to correct the electrical conductivity, thermal conductivity, and elastic modulus of SiC materials, respectively.

[0162] S24. Input the corrected SiC material parameters into the two-way real-time coupled model to update the material properties of the electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model.

[0163] For example, the corrected conductivity:

[0164] Corrected thermal conductivity:

[0165] Corrected elastic modulus:

[0166] The corrected parameters are input into the coupled model to update the material properties of the electromagnetic field, thermal field, and mechanical stress field sub-models, ensuring that the simulation reflects the effects of irradiation damage.

[0167] S3. Input different temperature and stress conditions into the two-way real-time coupled model to perform multi-condition simulation, output the performance parameters of SiC material, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC material to reveal the performance degradation law of SiC material.

[0168] The specific steps of step S3 are as follows:

[0169] S31. Set multi-condition simulation conditions, set the temperature range (e.g., -50℃ to 200℃) and stress range (e.g., 0 to 500MPa), and generate simulation scenarios with different temperature and stress combinations;

[0170] For example, multiple operating condition settings:

[0171] Temperature range: -50℃, -20℃, 0℃, 25℃, 50℃, 100℃, 150℃, 200℃ (covering the entire operating temperature range)

[0172] Stress range: 0MPa, 100MPa, 200MPa, 300MPa, 400MPa, 500MPa (covering thermal stress and mechanical assembly stress)

[0173] Simulation scenario combination: Using full factorial experimental design, 8×6=48 sets of temperature-stress combination simulation scenarios were generated. Each scenario includes material parameters after irradiation damage correction.

[0174] S32. Input the simulation scenario into the bidirectional real-time coupled model, run the simulation, and output the performance parameters of the SiC material, including on-resistance. Switching speed and breakdown voltage;

[0175] For example, 48 scenarios were sequentially input into the ANSYS Multiphysics coupled simulation platform, with a simulation time step of 1 μs and a total simulation time of 1 ms. The key performance parameters of SiC material under each scenario were then output.

[0176] Electrical properties: On-resistance Switching speed (rise time) descent time Breakdown voltage

[0177] Thermal properties: thermal conductivity k, contact thermal resistance

[0178] Mechanical-related electrical properties: contact resistance

[0179] Typical operating conditions (100℃, 200MPa, 10) 15neutrons / cm 2 Initial simulation results: =72mΩ, =80ns, =65ns, =1200V, thermal conductivity k=301W / (m·K), =0.2K / W, =15mΩ;

[0180] S33. Statistical analysis is performed on the SiC material performance parameter data output from the simulation. The least squares method is used for curve fitting to establish a quantitative relationship model between the performance parameters and temperature and stress. The quantitative relationship model includes on-resistance. With temperature ,stress Relationship:

[0181]

[0182] Where R0 is the reference on-resistance under reference temperature and reference stress. , Here are the fitting coefficients, and T is the temperature. For stress;

[0183] The curve fitting in step S33 also includes establishing thermal conductivity. Contact thermal resistance Contact resistance With temperature ,stress Quantitative relationship model:

[0184]

[0185] in, Based on the thermal conductivity, , These are the fitting coefficients;

[0186]

[0187] in, As a reference contact thermal resistance, , These are the fitting coefficients;

[0188]

[0189] in, As the reference contact resistance, , These are the fitting coefficients;

[0190] For example, the least squares method was used to perform curve fitting on 48 sets of simulation data to establish the relationship between each performance parameter and temperature T and stress. Quantitative relationship model: On-resistance: ,in =50mΩ, fitting coefficient , Coefficient of determination

[0191] Thermal conductivity: ,in, =343W / (m·K) (irradiation-corrected baseline value), fitting coefficient =-5.1×10 -4 / K, =-1.5×10 -7 MPa -1 Coefficient of determination R 2 =0.978

[0192] Contact thermal resistance: ,in, =0.15K / W, fitting coefficient =2.3×10 -3 / K, =3.1×10 -7 MPa -1 Coefficient of determination R 2 =0.965

[0193] Contact resistance: ,in, =10mΩ, fitting coefficient =2.8×10 -3 / K, =2.5×10 -7 MPa -1 Coefficient of determination R 2 =0.972

[0194] Switching speed (rise time): ,in, =60ns, fitting coefficient =1.2×10 -3 / K, =8.5×10 -8 MPa -1 Coefficient of determination R 2 =0.958;

[0195] S34. The degradation law of SiC material properties with temperature and stress is revealed by quantitative relationship model;

[0196] For example, the degradation law of SiC material properties was obtained through quantitative relationship model analysis:

[0197] Effects of temperature on performance: Within the range of -50℃ to 200℃, for every 50℃ increase in temperature, the on-resistance increases by 16%, the thermal conductivity decreases by 2.5%, the contact thermal resistance and contact resistance increase by 11.5% and 14% respectively, and the switching speed slows down by 6%.

[0198] Effects of stress on performance: Within the range of 0~500MPa, for every 100MPa increase in stress, the on-resistance increases by 1.8%, the thermal conductivity decreases by 0.15%, the contact thermal resistance and contact resistance increase by 3.1% and 2.5% respectively, and the switching speed slows down by 0.85%.

[0199] The coupling effect of irradiation and thermal stress: Irradiation damage reduces the reference conductivity by 20%, resulting in a 25% increase in the reference on-resistance. This degradation effect is amplified under high temperature and high stress conditions. For example, under the conditions of 200℃ and 500MPa, the on-resistance after irradiation is 32% higher than that without irradiation.

[0200] S4. Based on the bidirectional real-time coupling model and the performance degradation law of SiC material, the parameters of SiC controller are optimized in a coordinated manner with switching loss and temperature rise as optimization objectives;

[0201] The specific steps of step S4 are as follows:

[0202] S41. With the optimization objectives of minimizing switching losses and minimizing temperature rise, the following objective function is constructed:

[0203]

[0204] in, To optimize variable parameters, Total loss, The total loss under the initial parameters. For SiC controller temperature, The maximum temperature allowed by the SiC controller. and These are the weighting coefficients. ;

[0205] For example, with the dual optimization objectives of minimizing switching losses and minimizing temperature rise, the objective function is constructed as follows:

[0206]

[0207] Among them: the optimization variable X=[D, [,P], i.e., duty cycle D (constrained to 0.4~0.8), switching frequency (Constraint 10kHz~30kHz), Contact pressure P (Constraint 100N~500N)

[0208] Initial total loss =180W + 120W = 300W This is the initial conduction loss. This represents the initial switching loss;

[0209] The highest temperature allowed by the SiC controller =120℃ weighting coefficient =0.6, =0.4 (Focusing on loss optimization while also considering temperature rise control).

[0210] S42. Select target variable parameters, including the duty cycle D of the SiC controller and the switching frequency. and contact pressure P;

[0211] S43. Use a multi-objective optimization algorithm to optimize the target variable parameters, output the optimized target variable parameters, and update the corresponding parameters in the bidirectional real-time coupled model to perform coordinated optimization of switching losses and temperature rise.

[0212] In step S43, the multi-objective optimization algorithm is selected as the genetic algorithm; the specific steps for optimizing the objective variable parameters are as follows:

[0213] S431. The duty cycle D and switching frequency in the target variable parameters... The contact pressure P is encoded as a real number, the population size (e.g., 50) and the number of iterations (e.g., 100) are set, and an initial population that meets the constraints is randomly generated to complete the population initialization.

[0214] S432. Define the following fitness function for fitness function calculation:

[0215]

[0216] in, The objective function is...

[0217] S433. Use the roulette wheel selection method to select parent individuals based on the proportion of individual fitness to total fitness;

[0218] S434. Using arithmetic crossover, let the parent individual be... and Generate offspring individuals and ;

[0219] in, The crossover factor has a value range of [0,1]. As the first parent individual, These are the duty cycle, switching frequency, and contact pressure of the first parent individual, respectively. For the second parent generation individual These are the duty cycle, switching frequency, and contact pressure of the second parent individual, respectively. As the first generation individual, As a second-generation individual;

[0220] S435. Mutate the offspring individuals according to a set mutation probability (e.g., 0.05), and the parameters after mutation are:

[0221]

[0222]

[0223]

[0224] in, For random numbers that follow a normal distribution (e.g., N(0,0.1)), For the mutated duty cycle, For the modified switching frequency, The modified contact pressure; , These are the maximum and minimum duty cycles, respectively. , These are the maximum and minimum values ​​of the switching frequency, respectively. , These are the maximum and minimum values ​​of the contact pressure, respectively.

[0225] S436. When the number of iterations reaches the maximum number of iterations (e.g., 100 times) or the change in the optimal fitness value between two adjacent generations is less than the minimum fitness threshold (e.g., 10). -6 When the iteration terminates, the optimal combination of objective variable parameters is output.

[0226] For example, population initialization: The optimization variables are encoded as real numbers, the population size is set to 50, the number of iterations is set to 100, and an initial population that satisfies the constraints is randomly generated. For example, an initial individual might be [D=0.6, =20kHz, P=300N];

[0227] Fitness function calculation: Define the fitness function A higher fitness value indicates better individual performance; for example, an initial individual fitness value of 0.72.

[0228] Selection operation: The roulette wheel selection method is used to calculate the proportion of each individual's fitness to the total fitness. The top 30% of high-fit individuals are selected as parents, while 5% of low-fit individuals are retained to maintain population diversity.

[0229] Crossover operation: Arithmetic crossover method is used, crossover factor Take a random value between 0.3 and 0.7; for example, the parent individual. =[0.65,22kHz,350N] and =[0.55,18kHz,250N], generate offspring =0.4× +0.6× =[0.59,19.6kHz,290N], =0.6× +0.4× =[0.61,20.4kHz,310N];

[0230] Mutation operation: The mutation probability is set to 0.05, and the mutated parameters satisfy the constraints; for example, for... Variation of switching frequency =19.6kHz+N(0,0.1)×(30kHz-10kHz)=21.3kHz (N(0,0.1) is a random number following a normal distribution).

[0231] Iteration Termination: When the iteration reaches 85 times, the change in the optimal fitness value between two adjacent generations is 8.2 × 10⁻⁶. -7 Less than the threshold 10 -6 The iteration terminates, and the optimal parameter combination is output: D=0.68. =24kHz, P=420N;

[0232] The optimal parameters were substituted into the bidirectional real-time coupled model for verification simulation, and the results are as follows:

[0233] Total losses: Q=255W, a 15% decrease from the initial value of 300W, including 162W of conduction losses and 93W of switching losses.

[0234] Maximum temperature rise: =112℃, which meets the design requirement of below 120℃.

[0235] Key performance parameter: On-resistance =58mΩ, switching rise time =72ns, contact thermal resistance =0.17K / W, both of which are significant improvements compared to before optimization.

[0236] 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 the present invention.

[0237] like Figure 3 As shown, the following are embodiments of the SiC controller electromagnetic-thermal-mechanical fully coupled simulation design system provided in this disclosure. This system and the SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method of the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the SiC controller electromagnetic-thermal-mechanical fully coupled simulation design system, please refer to the embodiments of the above SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method.

[0238] The system includes:

[0239] A bidirectional real-time coupled modeling module is used to construct a bidirectional real-time coupled model of electromagnetic field, thermal field, and mechanical stress field. The bidirectional real-time coupled model achieves bidirectional real-time coupling by integrating electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model.

[0240] The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model;

[0241] The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model;

[0242] The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer.

[0243] The material parameter correction module is used to determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters according to the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model.

[0244] The performance degradation analysis module is used to input different temperature and stress conditions into a two-way real-time coupled model for multi-condition simulation, output the performance parameters of SiC materials, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC materials to reveal the performance degradation law of SiC materials.

[0245] The collaborative optimization module is used to collaboratively optimize the parameters of the SiC controller based on a bidirectional real-time coupling model and the performance degradation law of SiC materials, with switching loss and temperature rise as optimization objectives.

[0246] This embodiment improves the efficiency and automation of simulation design through the interactive collaboration of a bidirectional real-time coupled modeling module, a material parameter correction module, a performance degradation analysis module, and a collaborative optimization module.

[0247] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. 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 the invention. Therefore, the invention 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 fully coupled electromagnetic-thermal-mechanical simulation design method for SiC controllers, characterized in that, Includes the following steps: S1. Construct a two-way real-time coupled model of electromagnetic field, thermal field, and mechanical stress field. The two-way real-time coupled model is achieved by integrating the electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model for two-way real-time coupling. The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model; The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model; The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer. S2. Determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters based on the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model; S3. Input different temperature and stress conditions into the two-way real-time coupled model to perform multi-condition simulation, output the performance parameters of SiC material, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC material to reveal the performance degradation law of SiC material. S4. Based on the bidirectional real-time coupling model and the performance degradation law of SiC material, the parameters of SiC controller are optimized in a coordinated manner with switching loss and temperature rise as optimization objectives; The specific steps for constructing the electromagnetic field sub-model in step S1 are as follows: Set electromagnetic field boundary conditions, calculate the electromagnetic field distribution based on Maxwell's equations, and output the total loss. The heat source serves as the input to the thermal field sub-model, and the output electromagnetic force serves as the input to the model. As input loads for the mechanical stress field sub-model; The specific steps for constructing the thermal field sub-model in step S1 are as follows: Total loss output by electromagnetic field sub-model As the input heat source, the temperature distribution is calculated based on the preset thermal field boundary conditions and the heat conduction equation. And output it to the mechanical stress field sub-model as a temperature load; Temperature distribution Output to the mechanical stress field sub-model, and simultaneously from the temperature distribution Extracting local temperature values This information is fed back to the electromagnetic field sub-model for conductivity correction using the following formula: in, The electrical conductivity of SiC material, Reference temperature The reference conductivity below, Temperature coefficient; The specific steps for constructing the mechanical stress field sub-model in step S1 are as follows: Temperature distribution output by the thermal field sub-model Electromagnetic force output by the electromagnetic field sub-model As the input load, based on the preset mechanical field boundary conditions, and by solving the mechanical stress field using the fundamental equations of elasticity, the stress tensor is obtained. ; Based on the input temperature distribution Local temperature values ​​extracted Based on local temperature values Calculate thermal strain using the thermal strain formula thermal strain Substituting the initial strain condition into the solution process, the stress tensor... After correction, the stress distribution including thermal stress is finally obtained. ; Stress distribution The thermal field sub-model is fed back to correct the thermal conductivity k using the following formula: in, Thermal conductivity under stress-free conditions β Stress coefficient, To obtain stress distribution The local stress value extracted from it; At the same time, the local stress value Feedback is sent to the electromagnetic field sub-model to correct the electromagnetic field boundary conditions caused by structural deformation.

2. The SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method according to claim 1, characterized in that, Electromagnetic field boundary conditions include electric field boundary conditions and magnetic field boundary conditions; Thermal boundary conditions include convective heat transfer boundary conditions and adiabatic boundary conditions; Mechanical field boundary conditions include displacement boundary conditions and frictionless contact boundary conditions.

3. The SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method according to claim 1, characterized in that, The specific steps of step S2 are as follows: S21. Based on the expected operating environment of the SiC controller, the irradiation dose is set within the preset dose range; S22. Determine the irradiation damage factor based on the set irradiation dose, wherein the irradiation damage factor includes correction coefficients for the electrical conductivity, thermal conductivity, and elastic modulus of the SiC material; S23. Correct the SiC material parameters using the irradiation damage factor to obtain the corrected conductivity. Corrected thermal conductivity and the corrected elastic modulus ; in, Indicates the original conductivity. Indicates the original thermal conductivity. Represents the original elastic modulus; , , These are the correction coefficients used to correct the electrical conductivity, thermal conductivity, and elastic modulus of SiC materials, respectively. S24. Input the corrected SiC material parameters into the two-way real-time coupled model to update the material properties of the electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model.

4. The SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method according to claim 1, characterized in that, The specific steps of step S3 are as follows: S31. Set multi-condition simulation conditions, set temperature range and stress range, and generate simulation scenarios with different temperature and stress combinations; S32. Input the simulation scenario into the bidirectional real-time coupled model, run the simulation, and output the performance parameters of the SiC material, including on-resistance. Switching speed and breakdown voltage; S33. Statistical analysis is performed on the SiC material performance parameter data output from the simulation. The least squares method is used for curve fitting to establish a quantitative relationship model between the performance parameters and temperature and stress. This quantitative relationship model includes on-resistance. With temperature ,stress Relationship: Where R0 is the reference on-resistance under reference temperature and reference stress. , Here are the fitting coefficients, and T is the temperature. For stress; S34. The degradation law of SiC material properties with temperature and stress is revealed by quantitative relationship model.

5. The SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method according to claim 4, characterized in that, The curve fitting in step S33 also includes establishing thermal conductivity. Contact thermal resistance Contact resistance With temperature ,stress Quantitative relationship model: in, Based on the thermal conductivity, , These are the fitting coefficients; in, As a reference contact thermal resistance, , These are the fitting coefficients; in, As the reference contact resistance, , The fitting coefficients are denoted as .

6. The SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method according to claim 1, characterized in that, The specific steps of step S4 are as follows: S41. With the optimization objectives of minimizing switching losses and minimizing temperature rise, the following objective function is constructed: in, To optimize variable parameters, Total loss, The total loss under the initial parameters. For SiC controller temperature, The maximum temperature allowed by the SiC controller. and These are the weighting coefficients. ; S42. Select target variable parameters, including the duty cycle D of the SiC controller and the switching frequency. and contact pressure P; S43. Use a multi-objective optimization algorithm to optimize the target variable parameters, output the optimized target variable parameters, and update the corresponding parameters in the bidirectional real-time coupled model to perform coordinated optimization of switching losses and temperature rise.

7. The SiC controller electromagnetic-thermal-mechanical fully coupled simulation design method according to claim 6, characterized in that, In step S43, the multi-objective optimization algorithm is selected as the genetic algorithm; the specific steps for optimizing the objective variable parameters are as follows: S431. The duty cycle D and switching frequency in the target variable parameters... The contact pressure P is encoded as a real number, the population size and number of iterations are set, and an initial population that meets the constraints is randomly generated to complete the population initialization. S432. Define the following fitness function for fitness function calculation: in, The objective function is... S433. Use the roulette wheel selection method to select parent individuals based on the proportion of individual fitness to total fitness; S434. Using arithmetic crossover, let the parent individual be... and Generate offspring individuals and ; in, The crossover factor has a value range of [0,1]. As the first parent individual, These are the duty cycle, switching frequency, and contact pressure of the first parent individual, respectively. For the second parent generation individual These are the duty cycle, switching frequency, and contact pressure of the second parent individual, respectively. As the first generation individual, As a second-generation individual; S435. Mutate the offspring individuals according to the set mutation probability. The parameters after mutation are: in, For random numbers that follow a normal distribution, For the mutated duty cycle, For the modified switching frequency, The modified contact pressure; , These are the maximum and minimum duty cycles, respectively. , These are the maximum and minimum values ​​of the switching frequency, respectively. , These are the maximum and minimum values ​​of the contact pressure, respectively. S436. When the number of iterations reaches the maximum number of iterations or the change in the optimal fitness value between two adjacent generations is less than the minimum fitness threshold, the iteration terminates and the optimal combination of target variable parameters is output.

8. A fully coupled electromagnetic-thermal-mechanical simulation design system for a SiC controller, characterized in that, include: A bidirectional real-time coupled modeling module is used to construct a bidirectional real-time coupled model of electromagnetic field, thermal field, and mechanical stress field. The bidirectional real-time coupled model achieves bidirectional real-time coupling by integrating electromagnetic field sub-model, thermal field sub-model, and mechanical stress field sub-model. The output switching loss of the electromagnetic field sub-model is used as the input heat source of the thermal field sub-model; The output temperature distribution of the thermal field sub-model is used as the input temperature load of the mechanical stress field sub-model; The output stress distribution of the mechanical stress field sub-model is fed back to the thermal field sub-model and the electromagnetic field sub-model, and dynamic data interaction between the three fields is achieved through variable transfer. The material parameter correction module is used to determine the irradiation dose based on the expected working environment of the SiC controller, introduce an irradiation damage factor to correct the SiC material parameters according to the irradiation dose, and input the corrected SiC material parameters into the bidirectional real-time coupled model. The performance degradation analysis module is used to input different temperature and stress conditions into a two-way real-time coupled model for multi-condition simulation, output the performance parameters of SiC materials, and establish a quantitative relationship model between performance parameters and temperature and stress based on the simulated performance parameters of SiC materials to reveal the performance degradation law of SiC materials. The collaborative optimization module is used to collaboratively optimize the parameters of the SiC controller based on a bidirectional real-time coupling model and the performance degradation law of SiC materials, with switching loss and temperature rise as optimization objectives. The specific process of constructing the electromagnetic field sub-model in the bidirectional real-time coupled modeling module is as follows: Set electromagnetic field boundary conditions, calculate the electromagnetic field distribution based on Maxwell's equations, and output the total loss. The heat source serves as the input to the thermal field sub-model, and the output electromagnetic force serves as the input to the model. As input loads for the mechanical stress field sub-model; The specific process of constructing the thermal field sub-model in the bidirectional real-time coupled modeling module is as follows: Total loss output by electromagnetic field sub-model As the input heat source, the temperature distribution is calculated based on the preset thermal field boundary conditions and the heat conduction equation. And output it to the mechanical stress field sub-model as a temperature load; Temperature distribution Output to the mechanical stress field sub-model, and simultaneously from the temperature distribution Extracting local temperature values This information is fed back to the electromagnetic field sub-model for conductivity correction using the following formula: in, The electrical conductivity of SiC material, Reference temperature The reference conductivity below, Temperature coefficient; The specific process of constructing the mechanical stress field sub-model in the bidirectional real-time coupled modeling module is as follows: Temperature distribution output by the thermal field sub-model Electromagnetic force output by the electromagnetic field sub-model As the input load, based on the preset mechanical field boundary conditions, and by solving the mechanical stress field using the fundamental equations of elasticity, the stress tensor is obtained. ; Based on the input temperature distribution Local temperature values ​​extracted Based on local temperature values Calculate thermal strain using the thermal strain formula thermal strain Substituting the initial strain condition into the solution process, the stress tensor... After correction, the stress distribution including thermal stress is finally obtained. ; Stress distribution The thermal field sub-model is fed back to correct the thermal conductivity k using the following formula: in, Thermal conductivity under stress-free conditions β Stress coefficient, To obtain stress distribution The local stress value extracted from it; At the same time, the local stress value Feedback is sent to the electromagnetic field sub-model to correct the electromagnetic field boundary conditions caused by structural deformation.

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