A multi-physics field optimization design method and system for large-capacity high-frequency transformers

Abstract

This invention proposes a multi-physics field optimization design method and system for large-capacity high-frequency transformers, belonging to the technical field of high-frequency transformers. The method includes: establishing optimization targets for high-frequency transformer cost, power density, and efficiency based on the application requirements of the high-frequency transformer; introducing calculation models for core loss and winding loss based on these optimization targets to obtain the loss of the high-frequency transformer; introducing a core size constraint based on the maximum feasible size of the high-frequency transformer core and thermal and electromagnetic constraints; optimizing the high-frequency transformer design based on the core size constraint using the Taguchi method; and performing a Pareto front relationship analysis on the optimized high-frequency transformer using a selective non-dominated genetic algorithm to obtain the optimal design solution for the high-frequency transformer. This method can effectively address the comprehensive optimization needs of multiple physical fields.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-frequency transformers, and in particular relates to a multi-physical field optimization design method and system for a large-capacity high-frequency transformer. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of power electronics technology, high-frequency transformers (HFTs) are increasingly being used in power transmission, renewable energy generation (such as wind and solar power), and electric vehicle charging. To meet the demands of modern power systems for higher frequencies, lighter weight, and higher efficiency, HFT designs are evolving from traditional 50 / 60Hz AC systems to higher frequencies, smaller sizes, and lower losses. Achieving high power density and efficiency while controlling costs has become a key challenge in HFT design.

[0004] At high frequencies, the performance of high-frequency transformers is influenced by the interplay of multiple physical fields, including electric, magnetic, thermal, and mechanical fields. These factors are closely intertwined, placing high demands on the heat dissipation and structural stability of high-frequency transformers. Especially in high-capacity, high-frequency applications, electromagnetic stress, temperature rise, and vibration effects significantly impact the long-term operational reliability of the equipment. Therefore, comprehensive optimization design encompassing multiple physical fields is necessary to ensure efficient and stable operation of high-frequency transformers under complex operating conditions.

[0005] The choice of core material plays a critical role in the efficiency of high-frequency transformers. At high frequencies, core losses, including hysteresis and eddy current losses, increase significantly, making traditional silicon steel insufficient. Consequently, nanocrystalline and amorphous alloys, due to their low losses and high saturation flux density, have become the mainstream choice. However, as frequency increases, the temperature rise caused by core losses cannot be ignored. Effectively reducing core losses and managing temperature become key design challenges.

[0006] Winding design also needs to account for high-frequency effects. In high-frequency, high-power applications, skin effect and proximity effect significantly increase AC losses in the winding. To reduce these losses, Litz wire windings are often used. These windings consist of multiple strands of insulated conductor twisted together, effectively reducing resistance at high frequencies. Multiphysics simulation and optimization enable precise design of winding and core materials, electrical parameters, and other aspects, thereby improving the power density and efficiency of high-frequency transformers while controlling overall costs.

[0007] Conventional high-frequency transformer designs currently experience significant increases in electromagnetic losses at high frequencies, making it difficult to achieve high efficiency. Furthermore, temperature rise in the core and windings is difficult to effectively address, impacting the transformer's long-term stability. Due to the intertwined influences of the electromagnetic, magnetic, thermal, and mechanical fields, conventional design methods are unable to effectively address the comprehensive optimization requirements of these multiple fields. Summary of the Invention

[0008] To overcome the deficiencies of the above-mentioned prior art, the present invention provides a multi-physics field optimization design method and system for large-capacity high-frequency transformers. Through multi-physics field coupling analysis, the influence of the four physical fields of electricity, magnetism, heat, and force are comprehensively considered to optimize the design of the high-frequency transformer and ensure its efficient and stable operation under high-frequency and high-capacity working conditions.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] A first aspect of the present invention provides a multi-physics field optimization design method for a large-capacity high-frequency transformer;

[0011] A multi-physics field optimization design method for a large-capacity high-frequency transformer, comprising:

[0012] According to the application requirements of high-frequency transformers, the optimization targets of high-frequency transformer cost, power density and efficiency are established respectively;

[0013] Based on the optimization goals of high-frequency transformer cost, power density and efficiency, a calculation model for core loss and winding loss is introduced to obtain the loss of high-frequency transformer;

[0014] Based on the maximum feasible size of the high-frequency transformer core, electrical constraints, thermal and electromagnetic constraints, the core size constraint is introduced;

[0015] Based on the constraint of core size, the Taguchi method is used to optimize the design of the high-frequency transformer. Based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the optimized high-frequency transformer is performed to obtain the optimal design scheme of the high-frequency transformer.

[0016] As a further technical solution, the optimization goal based on power density is:

[0017]

[0018] Where, P d is the power density; P0 is the rated power of the high-frequency transformer, V c is the volume of the core, V w is the volume of the Litz wire winding;

[0019] Cost C t for:

[0020] C t =MLT×(N p +N s )C w +V c C c

[0021] Where MLT is the average turn length of the winding, N p 、N s is the number of turns of the primary and secondary windings, C w 、C c are the cost coefficients of Litz wire and nanocrystalline core respectively;

[0022] The efficiency η is:

[0023]

[0024] Among them, P t is the total loss of the high-frequency transformer.

[0025] As a further technical solution, the core loss is modeled using the modified generalized Steinmetz equation, where:

[0026] The improved generalized Steinmetz equation is as follows:

[0027]

[0028] Where, is the core loss; T is the period; B is the magnetic flux density; α, β, k are the core parameters used for loss calculation, which depend on the type of core used.

[0029] Since the core loss depends on the magnetic flux density and the operating frequency, the optimal flux density required to achieve the minimum power loss in the high-frequency transformer is further obtained as follows:

[0030]

[0031] Where B op The optimal magnetic flux density required to minimize power loss in high-frequency transformers; H c is the coercive force of the core material; k a , K c , k w is the coefficient of the core material used; ΔT a is the temperature change; ρ cu is the resistivity of copper; k u is the window utilization coefficient; k c is a constant related to the eddy current loss of the core material; f s a f sa to the power of a, where a is the Steinmetz coefficient, which is determined by the core material; K v is the voltage waveform type applied to the HFT; f is the operating frequency of the high-frequency transformer; k f is the core stacking factor; ∑VA is the total rated power; A p The product of the core window area and the core cross-sectional area of ​​the frequency transformer; f s is the switching frequency of the high-frequency transformer; K f is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; K t is the correction factor related to heat conduction.

[0032] As a further technical solution, the winding loss modeling process is specifically as follows:

[0033] Calculate the AC resistance of the winding as follows:

[0034]

[0035] in is the diameter of the Litz wire; l is the number of winding layers; n s is the packing factor; ξ is the number of strands in each litz wire conductor; d cu is the diameter of the copper conductor, δ s is the skin effect; N is the number of winding turns.

[0036] Maintain the ratio of the AC to DC resistance of the winding at a specific value, as shown in the following equation:

[0037]

[0038] Where r o is the radius of the circular conductor, frequency f s Skin effect δ s Expressed as:

[0039]

[0040] The power losses in the primary and secondary windings are expressed as:

[0041]

[0042] in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively;

[0043]

[0044] Where, P winding is the winding loss.

[0045] As a further technical solution, the core size constraint is:

[0046]

[0047] Where h c is the core height; d c is the width of the core yoke; t c is the core thickness; l c N is the core width. s,p is the number of turns of the secondary winding and the number of turns of the primary winding; B sat is the saturation magnetic flux density; h max 、l max , t max d max are the maximum height, maximum width, maximum thickness and maximum yoke width allowed for the core; T is the operating temperature of the high-frequency transformer; T max N is the maximum allowable temperature rise of the high-frequency transformer; min and N max Refers to the minimum number of winding turns and the maximum number of winding turns respectively.

[0048] As a further technical solution, based on the constraints of core size, the process of optimizing the design of high-frequency transformer using the Taguchi method is as follows:

[0049] The core width, height, thickness and yoke width are used as the design variables of the core size, and the level number is set to describe the range of variation;

[0050] In combination with the characteristics of the four-variable mixed level, an orthogonal table is selected to determine the experimental points as the design scheme; the experimental points generate representative parameter combinations in the design space through the balanced distribution characteristics of the orthogonal table;

[0051] The target performance is simulated and evaluated based on the parameter combination, and the influence of each design variable is evaluated by using range analysis and variance analysis methods.

[0052] As a further technical solution, based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the high-frequency transformer after optimization design is performed, and the process of obtaining the optimal design solution of the high-frequency transformer is as follows:

[0053] Performing fitness evaluation on the high-frequency transformer after optimized design, wherein the fitness is evaluated with cost, power density, and efficiency as objective functions;

[0054] Set the stopping criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, it stops running.

[0055] The width, height, thickness and yoke width of the core are used as design variables, and the design variables that meet the multi-objective optimization criteria are selected;

[0056] By selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables.

[0057] As a further technical solution, it also includes conducting multi-physical field bidirectional coupling simulation analysis on the scheme, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

[0058] A second aspect of the present invention provides a multi-physics field optimization design system for large-capacity high-frequency transformers.

[0059] A multi-physics field optimization design system for large-capacity high-frequency transformers, including:

[0060] The optimization target building module is configured to: build optimization targets for high-frequency transformer cost, power density, and efficiency respectively according to application requirements of the high-frequency transformer;

[0061] The loss acquisition module is configured to: based on the optimization objectives of high-frequency transformer cost, power density, and efficiency, introduce a calculation model for core loss and winding loss to obtain the loss of the high-frequency transformer;

[0062] The constraint establishment module is configured to: introduce a core size constraint based on a maximum feasible size of the high-frequency transformer core and thermal and electromagnetic constraints;

[0063] The optimal design scheme acquisition module is configured as follows: based on the constraint of core size, the Taguchi method is used to optimize the design of the high-frequency transformer; based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the optimized high-frequency transformer is performed to obtain the optimal design scheme of the high-frequency transformer.

[0064] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the multi-physics field optimization design method for a large-capacity high-frequency transformer as described in the first aspect of the present invention.

[0065] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the multi-physical field optimization design method for a large-capacity high-frequency transformer as described in the first aspect of the present invention are implemented.

[0066] One or more of the above technical solutions have the following beneficial effects:

[0067] (1) This invention introduces a multi-objective optimization algorithm to achieve a multi-faceted balance in high-frequency transformer design. By comprehensively considering multiple factors such as efficiency, power density, and cost, it ensures that production costs are effectively controlled while meeting high performance. This optimization method can minimize material usage and production costs without sacrificing equipment performance, and is particularly suitable for the market demand in large-scale production.

[0068] (2) This invention optimizes the core size and material, adjusts its magnetic properties and geometric dimensions, and reduces eddy current and hysteresis losses caused by high-frequency effects, thereby effectively achieving a high-power density design. This optimization method significantly improves the electromagnetic efficiency of high-frequency transformers and provides a more reliable solution for high-frequency, high-power applications.

[0069] (3) This invention adopts a multi-physics field coupling analysis method to solve the common electromagnetic loss and thermal management problems in high-frequency transformers through comprehensive simulation and optimization of electric, magnetic, thermal, and mechanical fields. Based on electromagnetic coupling optimization, by accurately simulating temperature rise and stress distribution, the loss and temperature rise are significantly reduced, thereby improving the working efficiency and long-term reliability of high-frequency transformers. This method can achieve more accurate thermal management and electromagnetic optimization, providing a scientific basis for the design of high-efficiency high-frequency transformers.

[0070] (4) The present invention optimizes the geometric structure of the high-frequency transformer through mechanical field analysis, improving its vibration and impact resistance. Under the combined effects of electromagnetic force and mechanical stress, the structure of the high-frequency transformer may experience vibration, deformation, and other problems, affecting its operating performance. Through precise mechanical simulation and optimized design, the present invention effectively avoids deformation and damage caused by mechanical stress, ensuring the stability and reliability of the high-frequency transformer under long-term and high-load conditions.

[0071] (5) The multi-physics field coupling optimization design adopted by the present invention is not limited to electromagnetic performance, but also covers multiple aspects such as thermal management and mechanical structure stability. Through the joint optimization of multiple physical fields, it is possible to ensure high efficiency and high power density while taking into account the thermal stability, structural strength and electromagnetic compatibility of the high-frequency transformer, thus avoiding the performance imbalance caused by a single optimization. It provides a more comprehensive solution for the design of high-frequency transformers, which is particularly suitable for high-power and high-frequency applications and can provide efficient and stable performance in actual use.

[0072] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0074] Figure 1 This is a flow chart of the method of the first embodiment.

[0075] Figure 2 Schematic diagram of the design variables of the core size in the first embodiment.

[0076] Figure 3 This is a schematic diagram of the optimization results of the non-dominated sorting genetic algorithm in the first embodiment.

[0077] Figure 4 This is a schematic diagram of the analysis results between the design variables and the objective function of the first embodiment.

[0078] Figure 5 This is a system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0079] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0080] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0081] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0082] Example 1

[0083] This embodiment discloses a multi-physics field optimization design method for a large-capacity high-frequency transformer;

[0084] like Figure 1 As shown in FIG, a multi-physics field optimization design method for a large-capacity high-frequency transformer includes:

[0085] Step S1, constructing optimization targets for high-frequency transformer cost, power density, and efficiency based on application requirements of the high-frequency transformer;

[0086] The optimization goal based on power density is:

[0087]

[0088] Where, P d is the power density; P0 is the rated power of the high-frequency transformer, V cis the volume of the core, V w is the volume of the Litz wire winding;

[0089] Cost C t for:

[0090] C t =MLT×(N p +N s )C w +V c C c

[0091] Where MLT is the average turn length of the winding, N p 、N s is the number of turns of the primary and secondary windings, C w 、C c are the cost coefficients of Litz wire and nanocrystalline core respectively;

[0092] The efficiency η is:

[0093]

[0094] Among them, P t is the total loss of the high-frequency transformer.

[0095] Step S2: Based on the optimization goals of high-frequency transformer cost, power density, and efficiency, a calculation model for core loss and winding loss is introduced to obtain the loss of the high-frequency transformer; further, the optimization target efficiency of the high-frequency transformer is obtained, and a suitable design scheme is selected based on the optimization goal.

[0096] The significance of core loss depends mainly on the characteristics of the selected core material. To accurately estimate the core loss in high-frequency transformers, a modified generalized Steinmetz equation is used, as follows:

[0097]

[0098] Where, is the core loss; T is the period; B is the magnetic flux density; α, β, k are the core parameters used for loss calculation, which depend on the type of core used.

[0099] Since the core power loss depends on the magnetic flux density and operating frequency, the optimal magnetic flux density required to achieve minimum power loss in the high-frequency transformer is further obtained as follows:

[0100]

[0101]

[0102] Where B opThe optimal magnetic flux density required to minimize power loss in high-frequency transformers; H c is the coercive force of the core material; k a , K c , k w is the coefficient of the core material used; ΔT a is the temperature change; ρ cu is the resistivity of copper; k u is the window utilization coefficient; k c is a constant related to the eddy current loss of the core material; f s a to the power of a, where a is the Steinmetz coefficient, which is determined by the core material; K v is the voltage waveform type applied to the HFT; f is the operating frequency of the high-frequency transformer; k f is the core stacking factor; ∑VA is the total rated power; A p The product of the core window area and the core cross-sectional area of ​​the frequency transformer; f s is the switching frequency of the high-frequency transformer; K f is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; K t is the correction factor related to heat conduction.

[0103] Furthermore, in the process of modeling winding losses, the power loss in the high-frequency transformer winding depends on the winding resistance and the effective value of the current flowing through the winding. Considering the impact of high frequency on the winding resistance, the winding AC resistance value can be calculated using the following formula:

[0104]

[0105] in is the diameter of the Litz wire; l is the number of winding layers; n s is the packing factor; ξ is the number of strands in each litz wire conductor; d cu is the diameter of the copper conductor, δ s is the skin effect; N is the number of winding turns, N=N s =N p .

[0106] To minimize proximity and winding losses, the ratio of the AC / DC resistance of the winding should be around 2. This ratio can be estimated using the following formula,

[0107]

[0108] Where r o is the radius of the circular conductor, frequency f s Skin effect δ s Expressed as:

[0109]

[0110] The power losses in the primary and secondary windings can be estimated using the following equations:

[0111]

[0112] in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively.

[0113]

[0114] Where, P winding is the winding loss.

[0115] The optimized target efficiency of the high-frequency transformer can be calculated by the core loss and winding loss, and the appropriate solution can be selected based on the optimization target.

[0116] Step S3, introducing core size constraints based on the maximum feasible size of the high-frequency transformer core, electrical constraints, thermal and electromagnetic constraints;

[0117] In step S3, electrical constraints (such as voltage, current, frequency, etc.) will impose specific requirements on the core size. For example, high-voltage applications require a larger insulation distance, which will affect the size and structural design of the core. Size optimization needs to take these electrical constraints into account to ensure that the transformer can operate safely and reliably under various operating conditions. For example, in high-frequency applications, the skin effect and proximity effect will cause the current to concentrate on the surface of the conductor, requiring an increase in the cross-sectional area of ​​the conductor or the use of a special structure, which will also affect the size design of the core. Thermal constraints (temperature rise limits) will affect the choice of core size. A larger core can provide better heat dissipation performance, but will increase the volume and cost of the transformer. Electromagnetic constraints (such as electromagnetic interference, magnetic induction intensity, etc.) will affect the core size and structural design. For example, in order to reduce electromagnetic interference, special core materials may need to be used, which will also affect the size and cost of the transformer. Size optimization needs to consider electromagnetic constraints in a coordinated manner to ensure that the transformer can operate normally in various electromagnetic environments.

[0118] Electrical constraints are mainly used to ensure that the designed equipment can meet electrical performance requirements, avoid system failure due to unsatisfied electrical conditions, and ensure that high-voltage transformers can operate safely and reliably under various working conditions.

[0119] Common electrical constraints include:

[0120] Maximum current I max: Limits the maximum current flowing through a transformer or inductor to prevent overload.

[0121] Maximum magnetic flux density L f,max : Ensure that the flux density does not exceed the allowable range to avoid core saturation.

[0122] Minimum voltage V min and the maximum voltage V max : Limit the operating voltage range to ensure that the device can operate within a safe range.

[0123] Efficiency constraint η min : Ensure that the conversion efficiency of the equipment is higher than the minimum requirement.

[0124] Thermal constraints primarily consider the heat generated by the equipment during operation and its heat dissipation capacity. By limiting temperature rise, they ensure that the high-frequency transformer does not overheat under rated load, thereby preventing faults such as insulation aging and winding short circuits. Furthermore, good heat dissipation performance can extend the service life of the high-frequency transformer and improve its reliability and stability. Common thermal constraints include:

[0125] Maximum temperature rise T max : Limits the temperature rise of the equipment, usually determined by material properties or operating environment conditions.

[0126] Heat conduction and heat dissipation capabilities: Through thermal field analysis, ensure that the designed heat dissipation path and cooling method can effectively reduce the device temperature.

[0127] Electromagnetic constraints are designed to ensure that the electromagnetic performance of the equipment meets design requirements, avoid excessive electromagnetic stress or field strength, and effectively reduce electromagnetic interference. By setting electromagnetic interference and electromagnetic compatibility constraints, we ensure that the transformer can operate normally in various electromagnetic environments and does not interfere with other equipment. This includes the following aspects:

[0128] Magnetic induction intensity B max :Limit the magnetic induction intensity of the core to prevent the core from entering saturation state,

[0129] Electromagnetic compatibility constraints: Ensure that equipment operation does not generate excessive electromagnetic interference and meets standard electromagnetic compatibility requirements.

[0130] Electromagnetic field boundary condition check: Through finite element analysis, the electromagnetic field distribution is accurately calculated to verify the rationality of the electromagnetic field at the boundary.

[0131] Furthermore, electrical performance can be ensured by constraining the core size. By limiting the core size, it is ensured that the transformer can provide the required electrical performance such as inductance and flux density under specific voltage, current and frequency; optimize volume and cost: while meeting the electrical performance, the transformer's volume and cost can be minimized through size optimization.

[0132] Combine Figure 2 , based on the above constraints, the multi-objective optimization problem of high-frequency transformer is expressed as:

[0133] (l c ,h c ,t c ,d c ,N p ,N s )=min(P t ,C t ,-P d )

[0134] Where h c is the core height; d c is the width of the core yoke; t c is the core thickness; l c N is the core width. p 、N s is the number of turns of the primary and secondary windings. In the multi-objective optimization process, multiple objectives may conflict with each other. Optimizing one objective may lead to performance degradation of other objectives. Therefore, it is necessary to find a balance between the objectives. The Pareto optimal solution is used to describe this balance relationship.

[0135] The search space for optimal values ​​of the high-frequency transformer core parameters and windings is limited based on the maximum feasible core size and the limitations of the power electronic converter used. The optimization is performed based on the core size to achieve the minimum number of high-frequency transformer turns N. p 、N s , which will ensure a reasonable value for the leakage inductance without any serious impact on the efficiency of the high-frequency transformer. The maximum allowable temperature rise and the saturation flux density of the core are used to set thermal and electromagnetic constraints during the optimization process. In addition, in order to obtain the design value of the core size according to the selected core data sheet, the core size (h c 、l c , t c d c ) are constrained as follows:

[0136]

[0137] Where N s,p is the number of turns of the secondary winding and the number of turns of the primary winding; B sat is the saturation magnetic flux density; h max 、l max , t max d max are the maximum height, maximum width, maximum thickness and maximum yoke width allowed for the core; T is the operating temperature of the high-frequency transformer; T maxThe maximum allowable temperature rise of the high-frequency transformer. ; N min and N max Refers to the minimum number of winding turns and the maximum number of winding turns respectively.

[0138] Step S4: Based on the constraint of the core size, the high-frequency transformer is optimized by using the Taguchi method, and the Pareto front relationship analysis of the optimized high-frequency transformer is performed based on the selective non-dominated genetic algorithm to obtain the optimal design scheme of the high-frequency transformer.

[0139] In the multi-objective optimization design of high-frequency transformers, to reduce computational resources and time consumption while covering different combinations in the design space, this example uses the Taguchi method to design a four-variable mixed-level orthogonal experiment for core size. The design variables include core width, height, thickness, and yoke width, with a reasonable number of levels set to describe their range of variation. For example, core width and height are set to three levels (small, medium, and large), and thickness and yoke width are set to two levels (thin and thick). Taking into account the characteristics of the four-variable mixed-level, an L18 (33×22) orthogonal table is selected, and 18 test points are identified as the design solution. These test points, through the balanced distribution characteristics of the orthogonal table, generate representative parameter combinations within the design space, covering the main trends in the variable levels while significantly reducing the theoretically required 3×3×2×2=36 full-factorial experiments. The core size combination corresponding to each design point directly affects the transformer's flux density and losses. Therefore, these combinations are used to simulate and evaluate target performance (such as losses and efficiency). Finally, methods such as range analysis and variance analysis are used to evaluate the influence of each design variable to ensure the accuracy of the optimization results and the test efficiency, providing a scientific basis for core optimization.

[0140] During the performance evaluation phase, the study will quantitatively analyze the objective function of each design point. Specifically, efficiency improvements will help reduce energy consumption, and temperature control below 75°C will ensure the stability of the high-frequency transformer. Cost optimization targets manufacturing and material costs to ensure optimal performance within the budget. Power density improvements can reach 1×10 7 W / m 3 .

[0141] Furthermore, based on the selective non-dominated genetic algorithm, a Pareto front relationship analysis is performed on the optimized high-frequency transformer to obtain the optimal design scheme of the high-frequency transformer. In the above process, the fitness of the optimized high-frequency transformer is first evaluated, and the fitness is evaluated based on cost, power density, and efficiency as the objective function;

[0142] Set the stopping criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, it stops running.

[0143] The width, height, thickness and yoke width of the core are used as design variables, and design variables with better performance in multiple objectives are selected. Specifically, in the process of Pareto frontier relationship analysis of high-frequency transformers based on selective non-dominated genetic algorithm, "better performing" design variables refer to variables that meet the specific quantitative standards of multi-objective optimization. These standards include: in terms of cost targets, design variables must make the manufacturing cost of high-frequency transformers lower than the set budget threshold (for example, less than 200 yuan); in terms of power density targets, design variables must make the power density reach or exceed the set lower limit (for example, not less than 50W / cm 3 ); for efficiency objectives, the design variables must ensure that their efficiency is no less than a set minimum value (e.g., no less than 95%). Furthermore, the selected design variables must satisfy the conditions for a Pareto optimal solution, meaning they must exhibit a "non-dominant" characteristic relative to other design variables in terms of optimization objectives: outperforming other design variables in at least one objective and not inferior to any other design variables in terms of other objectives. Therefore, "better performance" specifically refers to design variables that meet the constraints of the aforementioned multi-objective optimization and are located in the Pareto frontier set.

[0144] Furthermore, by selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables.

[0145] During the selection process, a selective dominance mechanism is introduced. For two chromosomes X1 and X2, if X1 is not inferior to X2 in all objectives and is superior to X2 in at least one objective, then X1 is said to dominate X2. The chromosome with the stronger dominance is selected for the next generation.

[0146] Genetic operations include crossover and mutation. Crossover selects design variables that have an advantage in dominance relationships and performs crossover, thereby transferring advantageous genetic information. Crossover combines the genes of two parent chromosomes to generate new daughter chromosomes, thereby increasing population diversity. One or more crossover points are randomly selected. The parts of the two parent chromosomes after the crossover point are exchanged to generate two daughter chromosomes. For example, the core thickness t of chromosome X1 and chromosome X2 is t. c and core height h c exchange.

[0147] Mutation prioritizes design variables that underperform but show potential, thereby improving the diversity and exploration capabilities of the population. Mutation increases population diversity and prevents the algorithm from falling into local optima by randomly changing certain genes within a chromosome. For example, changing a design variable within a chromosome can generate a new chromosome.

[0148] By performing Pareto front relationship analysis on the optimized high-frequency transformer based on selective non-dominated genetic algorithm, the optimal design scheme of the high-frequency transformer is obtained.

[0149] Furthermore, it also includes multi-physical field bidirectional coupling simulation analysis of the optimal design scheme of the high-frequency transformer, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

[0150] The heat sources of high-frequency transformers include heat loss in the windings and eddy currents:

[0151]

[0152] Among them A, v, J e ,σ,ω are magnetic vector potential, magnetic reluctance, eddy current density, material conductivity and frequency, respectively, and the unit is rad / s.

[0153]

[0154] Among them, ρ v 、C p , k and T a are bulk density, specific heat capacity, thermal conductivity and ambient temperature, respectively.

[0155]

[0156] where Q p , u are the point heat source and velocity vector respectively.

[0157]

[0158] Among them, ρ f 、u f , F, and p are the fluid density, velocity, volume force, and pressure, respectively.

[0159] The steady-state multiphysics model allows for a comprehensive analysis of the temperature, electromagnetic, and stress fields within a high-frequency transformer. The temperature distribution directly affects the magnetic permeability of the core material and the resistance of the windings, necessitating multiple rounds of iterative calculations between the electromagnetic and thermal fields to ensure accurate results.

[0160] Furthermore, in this embodiment, the design optimization of a 1 MVA, 10 kHz high-frequency transformer is studied as an example.

[0161] Multiphysics simulation and optimization were performed using finite element analysis software. Detailed technical specifications of the high-frequency transformer are shown in Table 1(a). The selected core exhibits a high saturation flux density (1.56T), reasonable core losses (W / m³), and is relatively low cost compared to nanocrystalline cores used in high-frequency power converter magnetic applications. Cost optimization factors are shown in Table 1(b).

[0162] Table 1(a) HFT system design parameters (design values)

[0163] parameter Numerical Capacity S 1MVA <![CDATA[Operating frequency f s > 10kHz Core type U shape <![CDATA[Input / output voltage U i / U o > 5000 / 750V <![CDATA[Input / Output Current I p / I s > 211 / 235A

[0164] Table 1(b) Cost coefficients of HFT components

[0165] parameter Numerical <![CDATA[Cost coefficient C of nanocrystalline materials core > 500(￥ / kg) <![CDATA[Cost coefficient C of Litz wire Litz > 50(￥ / m)

[0166] In the multi-objective optimization design of high-frequency transformers, calculating every level of each variable consumes significant computer resources and time, necessitating appropriate sample screening. In this example, the Taguchi method was employed, with the core width l, height h, thickness t, and yoke width d as design variables. Through a four-variable, mixed-level orthogonal experimental design, 18 design points were ultimately identified, covering different combinations of the design space. The selection of each design point was based on a reasonable distribution of the design variables to ensure representative experimental results. Specifically, changes in the core's dimensions directly affect its magnetic flux density and losses.

[0167] During the performance evaluation phase, the study will quantitatively analyze the objective function of each design point. Specifically, efficiency improvements will help reduce energy consumption, and temperature control below 75°C will ensure the stability of the high-frequency transformer. Cost optimization targets manufacturing and material costs to ensure optimal performance within the budget. Power density improvements can reach 1×10 7 W / m 3 .

[0168] Statistical analysis methods are used to analyze the test data in depth and evaluate the performance of each design point on the objective function to identify the optimal design point. Ultimately, the multi-objective design optimization of the high-frequency transformer is achieved by comprehensively considering the mutual influence between each objective.

[0169] The design results of the proposed optimization algorithm are shown in Table 2. The simulation results give 18 POS with different core wire sizes, as well as their efficiency values, power density, total cost and number of turns. According to the defined objective function, the three-dimensional graph of POS is shown in Figure 3 shown.

[0170] Table 2 POS results of 1MVA, 10kHz high-frequency transformer

[0171]

[0172] The POS data sheet (Table 2) demonstrates the impact of the design variable combinations selected using the Taguchi method on the optimization objectives. Table 2 shows the significant impact of design variables on cost, indicating that core size optimization is crucial for cost control. High power density designs, however, do not significantly increase costs, demonstrating that a reasonable design can achieve a balance between power density and cost. The efficiency η ranges from 92.84% to 99.45%, and some high power density designs still maintain high efficiency, indicating that optimization can maintain high efficiency while increasing power density. The average temperature Tavg is controlled between 51.88°C and 79.87°C, and the low-temperature design demonstrates good thermal management, indicating that the heat load can be effectively controlled through optimized variable selection.

[0173] The multi-objective optimization results generated by the non-dominated genetic algorithm show the Pareto front relationship of efficiency, power density and cost. Comprehensive analysis found the following patterns: First, there is a trade-off between power density and cost. The increase in power density is generally accompanied by an increase in cost, but the design solutions on the Pareto front prove that low costs can still be maintained at some high power densities. Second, efficiency and power density can be optimized synergistically. The results of the selective non-dominated genetic algorithm show that increasing power density will not significantly reduce efficiency, meeting the project's requirements for high efficiency and high power density. Finally, the heat load can be effectively controlled. The POS data shows that under the optimized design, the temperature can be controlled within a reasonable range (not exceeding 73°C), meeting the high heat load requirements. By reasonably selecting design variables such as core width and thickness, good thermal management can be achieved while improving power density.

[0174] Analysis of POS data and optimization results from a selective non-dominated genetic algorithm demonstrates that multi-objective optimization of high-frequency transformers can be achieved by optimizing combinations of design variables. Pareto-front design solutions provide guidance for achieving a balance between performance and cost while meeting the requirements of high efficiency, high heat load, and high power density. During the optimization process, solutions on the Pareto front are prioritized to improve power density and efficiency while controlling cost and temperature, providing a viable approach for the practical design of high-performance high-frequency transformers.

[0175] Figure 4 The left graph shows the Pearson correlation coefficient (r) between the design variables (P1: core width, P2: core height, P3: yoke width, P4: yoke thickness) and the optimization objectives (P5: cost, P6: efficiency, P7: power density). This clearly illustrates the impact of different design variables on each target parameter.

[0176] Through Figure 4The following potential patterns can be summarized from the analysis: Cost is mainly affected by the core width (P1) and height (P2), and cost management can be effectively achieved by rationally controlling these two variables. There is a synergistic relationship between efficiency and power density, that is, improving efficiency will also increase power density, which means that efficiency can be optimized to achieve higher power density, thereby meeting the high efficiency and high power density goals of the project. The width and thickness of the yoke play a key role in thermal load performance. Sensitivity analysis shows that increasing the yoke thickness helps to improve power density, while the yoke width has little effect on power density. Therefore, under the premise of meeting the thermal load and mechanical strength, it is possible to give priority to adjusting the yoke thickness to achieve the best balance between power density and cost.

[0177] In the multi-objective optimization design of high-frequency transformers, optimizing core width (P1) and core height (P2) significantly impacts cost, efficiency, and power density. Prioritizing core width to control cost, while appropriately increasing core height can improve efficiency and power density. Furthermore, increasing yoke thickness (P4) positively impacts power density, further meeting the project's high efficiency and power density requirements. Based on the above analysis, Scheme 8 in Table 2 was ultimately selected as the optimal design for a 1MVA high-frequency transformer.

[0178] Example 2

[0179] This embodiment discloses a multi-physics field optimization design system for large-capacity high-frequency transformers;

[0180] like Figure 5 As shown, a multi-physics field optimization design system for large-capacity high-frequency transformers includes:

[0181] The optimization target building module is configured to: build optimization targets based on power density, efficiency, and cost respectively, and set priorities according to application requirements;

[0182] The core optimization module is configured to: introduce a calculation model for core loss and winding power loss, model the core loss based on the generalized Steinmetz equation, and optimize the winding loss using the skin effect and proximity effect;

[0183] The design variables and constraints module is configured to: determine design variables and constraints, perform sensitivity analysis on the design variables through multi-physics simulation, and determine the design variables that have the greatest impact on power density, efficiency, and cost for optimization;

[0184] The optimal solution acquisition module is configured to: establish a steady-state electric-magnetic-thermal-mechanical multi-physics field bidirectional coupling model, simulate the interaction between different physical fields through finite element analysis methods, analyze the temperature field, electromagnetic field and stress field of the iron core and winding, and optimize the electromagnetic performance and heat dissipation performance of the high-frequency transformer.

[0185] Example 3

[0186] The purpose of this embodiment is to provide a computer-readable storage medium.

[0187] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a multi-physics field optimization design method for a large-capacity high-frequency transformer as described in Example 1.

[0188] Example 4

[0189] The purpose of this embodiment is to provide an electronic device.

[0190] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps of the multi-physics field optimization design method for a large-capacity high-frequency transformer as described in Example 1 are implemented.

[0191] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0192] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0193] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A multi-physics field optimization design method for a large-capacity high-frequency transformer, characterized in that: include: According to the application requirements of high-frequency transformers, the optimization targets of high-frequency transformer cost, power density and efficiency are established respectively; The optimization goal based on power density is: Where, is the power density; is the rated power of the high-frequency transformer, is the volume of the core, is the volume of the Litz wire winding; cost for: Where MLT is the average turn length of the winding, 、 are the number of turns of primary and secondary windings, 、 are the cost coefficients of Litz wire and nanocrystalline core respectively; The efficiency η is: in, is the total loss of the high-frequency transformer; Based on the optimization goals of high-frequency transformer cost, power density and efficiency, a calculation model for core loss and winding loss is introduced to obtain the loss of high-frequency transformer; Based on the maximum feasible size of the high-frequency transformer core, electrical constraints, thermal and electromagnetic constraints, the core size constraint is introduced; The core size constraints are: Where, is the core height; is the width of the core yoke; is the core thickness; is the core width; is the number of turns of the secondary winding and the number of turns of the primary winding; is the saturation magnetic flux density; 、 、 、 They are the maximum height, maximum width, maximum thickness and maximum yoke width allowed for the core respectively; is the operating temperature of the high-frequency transformer; is the maximum allowable temperature rise of the high-frequency transformer; and Refers to the minimum number of winding turns and the maximum number of winding turns respectively; Based on the constraints of core size, the Taguchi method is used to optimize the design of the high-frequency transformer. The process is as follows: The core width, height, thickness and yoke width are used as the design variables of the core size, and the level number is set to describe the range of variation; In combination with the characteristics of the four-variable mixed level, an orthogonal table is selected to determine the experimental points as the design scheme; the experimental points generate representative parameter combinations in the design space through the balanced distribution characteristics of the orthogonal table; Simulating and evaluating the target performance based on the parameter combination, and using range analysis and variance analysis methods to evaluate the impact of each design variable; Based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the high-frequency transformer after optimization design is performed to obtain the optimal design scheme of the high-frequency transformer. The process is as follows: Performing fitness evaluation on the high-frequency transformer after optimized design, wherein the fitness is evaluated with cost, power density, and efficiency as objective functions; Set the stopping criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, it stops running. The width, height, thickness and yoke width of the core are used as design variables, and the design variables that meet the multi-objective optimization criteria are selected; By selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables.

2. A multi-physics field optimization design method for a large-capacity high-frequency transformer according to claim 1, characterized in that: The core losses are modeled using a modified generalized Steinmetz equation, where: The improved generalized Steinmetz equation is as follows: Where, is the core loss; T is the period; B is the magnetic flux density; , , The core parameters used for loss calculation depend on the type of core used; Since the core loss depends on the magnetic flux density and the operating frequency, the optimal flux density required to achieve the minimum power loss in the high-frequency transformer is further obtained as follows: Where, Optimal magnetic flux density required to minimize power losses in high-frequency transformers; is the coercivity of the core material; , , is the coefficient of the core material used; is temperature change; is the resistivity of copper; is the window utilization coefficient; is a constant related to the eddy current loss of the core material; for of Power, is the Steinmetz coefficient, which is determined by the core material; is dependent on the type of voltage waveform applied to the HFT; is the operating frequency of the high-frequency transformer; is the core stacking factor; is the total rated power; It is the product of the core window area and the core cross-sectional area of ​​the frequency transformer; is the switching frequency of the high-frequency transformer; is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; is the correction factor related to heat conduction.

3. The multi-physics field optimization design method for a large-capacity high-frequency transformer according to claim 1, characterized in that: The modeling process of the winding loss is specifically as follows: Calculate the winding AC resistance as follows: in is the litz wire strand diameter; l is the number of winding layers; is the packing factor; ξ is the number of strands in each litz wire conductor; is the diameter of the copper conductor, It is the skin effect; N is the number of winding turns; Maintain the ratio of the AC to DC resistance of the winding at a specific value, as shown in the following equation: Where, is the radius of the circular conductor, the frequency Skin effect Expressed as: The power losses in the primary and secondary windings are expressed as: in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively; Where, is the winding loss.

4. A multi-physics field optimization design method for a large-capacity high-frequency transformer according to claim 1, characterized in that: It also includes multi-physics field bidirectional coupling simulation analysis, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

5. A multi-physics field optimization design system for large-capacity high-frequency transformers, characterized in that: include: The optimization target building module is configured to: build optimization targets for high-frequency transformer cost, power density, and efficiency respectively according to application requirements of the high-frequency transformer; The optimization goal based on power density is: Where, is the power density; is the rated power of the high-frequency transformer, is the volume of the core, is the volume of the Litz wire winding; cost for: Where MLT is the average turn length of the winding, 、 are the number of turns of primary and secondary windings, 、 are the cost coefficients of Litz wire and nanocrystalline core respectively; The efficiency η is: in, is the total loss of the high-frequency transformer; The loss acquisition module is configured to: based on the optimization objectives of high-frequency transformer cost, power density, and efficiency, introduce a calculation model for core loss and winding loss to obtain the loss of the high-frequency transformer; The constraint establishment module is configured to: introduce a core size constraint based on a maximum feasible size of the high-frequency transformer core, electrical constraints, thermal constraints, and electromagnetic constraints; The core size constraints are: Where, is the core height; is the width of the core yoke; is the core thickness; is the core width; is the number of turns of the secondary winding and the number of turns of the primary winding; is the saturation magnetic flux density; 、 、 、 They are the maximum height, maximum width, maximum thickness and maximum yoke width allowed for the core respectively; is the operating temperature of the high-frequency transformer; is the maximum allowable temperature rise of the high-frequency transformer; and Refers to the minimum number of winding turns and the maximum number of winding turns respectively; The optimal design solution acquisition module is configured to use the Taguchi method to optimize the design of the high-frequency transformer based on the core size constraint. The process is as follows: The core width, height, thickness and yoke width are used as the design variables of the core size, and the level number is set to describe the range of variation; In combination with the characteristics of the four-variable mixed level, an orthogonal table is selected to determine the experimental points as the design scheme; the experimental points generate representative parameter combinations in the design space through the balanced distribution characteristics of the orthogonal table; Simulating and evaluating the target performance based on the parameter combination, and using range analysis and variance analysis methods to evaluate the impact of each design variable; Based on the selective non-dominated genetic algorithm, the Pareto front relationship analysis of the high-frequency transformer after optimization design is performed to obtain the optimal design scheme of the high-frequency transformer. The process is as follows: Performing fitness evaluation on the high-frequency transformer after optimized design, wherein the fitness is evaluated with cost, power density, and efficiency as objective functions; Set the stopping criteria of the selective dominance genetic algorithm. When the number of iterations of the selective dominance genetic algorithm reaches the set requirement, it stops running. The width, height, thickness and yoke width of the core are used as design variables, and the design variables that meet the multi-objective optimization criteria are selected; By selecting genetic operations to transfer advantageous gene information, the design variables are continuously adjusted, gradually approaching the Pareto optimal frontier, and obtaining the optimal design variables.

6. A large-capacity high-frequency transformer multi-physics field optimization design system according to claim 5, characterized in that: The core losses are modeled using a modified generalized Steinmetz equation, where: The improved generalized Steinmetz equation is as follows: Where, is the core loss; T is the period; B is the magnetic flux density; , , The core parameters used for loss calculation depend on the type of core used; Since the core loss depends on the magnetic flux density and the operating frequency, the optimal flux density required to achieve the minimum power loss in the high-frequency transformer is further obtained as follows: Where, Optimal magnetic flux density required to minimize power losses in high-frequency transformers; is the coercivity of the core material; , , is the coefficient of the core material used; is temperature change; is the resistivity of copper; is the window utilization coefficient; is a constant related to the eddy current loss of the core material; for of Power, is the Steinmetz coefficient, which is determined by the core material; is dependent on the type of voltage waveform applied to the HFT; is the operating frequency of the high-frequency transformer; is the core stacking factor; is the total rated power; It is the product of the core window area and the core cross-sectional area of ​​the frequency transformer; is the switching frequency of the high-frequency transformer; is the stacking factor, which relates the effective cross-sectional area to the physical area of ​​the core; is the correction factor related to heat conduction.

7. A multi-physics field optimization design system for a large-capacity high-frequency transformer according to claim 5, characterized in that: The modeling process of the winding loss is specifically as follows: Calculate the winding AC resistance as follows: in is the litz wire strand diameter; l is the number of winding layers; is the packing factor; ξ is the number of strands in each litz wire conductor; is the diameter of the copper conductor, It is the skin effect; N is the number of winding turns; Maintain the ratio of the AC to DC resistance of the winding at a specific value, as shown in the following equation: Where, is the radius of the circular conductor, the frequency Skin effect Expressed as: The power losses in the primary and secondary windings are expressed as: in and are the primary and secondary rms currents, respectively, and are the primary and secondary winding AC resistances respectively; Where, is the winding loss.

8. A multi-physics field optimization design system for a large-capacity high-frequency transformer according to claim 5, characterized in that: It also includes multi-physics field bidirectional coupling simulation analysis, comparing the simulation results of unidirectional coupling and bidirectional coupling, and verifying the coupling relationship of the optimal design scheme in the electromagnetic field, temperature field and stress field.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the multi-physics field optimization design method for a large-capacity high-frequency transformer are implemented as described in any one of claims 1 to 4.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the multi-physical field optimization design method for a large-capacity high-frequency transformer are implemented as described in any one of claims 1-4.

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