High-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization

Abstract

The invention provides a high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization. The method can be applied to the field of high-frequency transformers. The method comprises the steps of calculating multiple groups of losses based on fixed parameters of a to-be-optimized transformer and optimization variables for the nth iteration in the nth iteration; determining a target optimization variable parameter of which the insulation distance meets the insulation constraint, the leakage inductance value meets the leakage inductance constraint and the loss is minimum from the multiple groups of optimization variable parameters; based on the fixed parameters and the target optimization variable parameters, electromagnetic-thermal bidirectional coupling simulation is carried out on the to-be-optimized transformer, and a temperature rise result is obtained; under the condition that the temperature rise result does not meet a preset condition, performing multi-objective optimization on the optimization variable of the nth iteration to obtain an optimization variable of the (n + 1) th iteration so as to execute the (n + 1) th iteration; and under the condition that the temperature rise result meets a preset condition, determining the target optimization variable parameter as an optimization parameter of the to-be-optimized transformer.

Description

Technical Field

[0001] This disclosure relates to the field of high-frequency transformer technology, and in particular to a method for determining the optimization parameters of high-frequency transformers based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization. Background Technology

[0002] With the rapid development of power electronics technology, high-frequency transformers, as core components in power electronic devices such as DC-DC (Direct Current to Direct Current) converters, are widely used in new energy power generation, electric vehicles, aerospace, and other fields. Especially in high-capacity, high-frequency applications, the operating performance of the transformer directly affects the efficiency, reliability, and power density of the entire system.

[0003] However, high-frequency transformers generate a large amount of heat during operation, and excessive temperature rise can lead to aging of the transformer's insulation materials, shortened lifespan, and consequently affect the efficiency and reliability of the entire system. Therefore, how to effectively predict the temperature rise of large-capacity high-frequency transformers and optimize their performance is a technical problem that needs to be solved in related technologies. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a method for determining the optimization parameters of high-frequency transformers based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization.

[0005] According to the first aspect of this disclosure, a method for determining the optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization is provided, including repeatedly performing the following operations until preset conditions are met:

[0006] In the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, multiple sets of losses of the transformer to be optimized under non-sinusoidal excitation signals are calculated. The optimization variables for the nth iteration include multiple sets of optimization variable parameters. The optimization variable parameters and losses are in one-to-one correspondence. n is an integer greater than or equal to 1. The optimization variables for the first iteration are the initial optimization variables.

[0007] From the above multiple sets of optimization variable parameters, the target optimization variable parameters are determined to ensure that the insulation distance of the transformer to be optimized satisfies the insulation constraint, the leakage inductance value satisfies the leakage inductance constraint, and the loss is minimized.

[0008] Based on the fixed parameters and target optimization variable parameters of the transformer to be optimized, an electromagnetic-thermal bidirectional coupling simulation was performed on the transformer to be optimized to obtain the temperature rise results of the transformer to be optimized.

[0009] If the temperature rise result of the transformer to be optimized does not meet the above preset conditions, multi-objective optimization is performed on the optimization variables of the nth iteration to obtain the optimization variables for the (n+1)th iteration, so as to use the optimization variables of the (n+1)th iteration to execute the (n+1)th iteration.

[0010] If the temperature rise result of the above-mentioned transformer to be optimized meets the above-mentioned preset conditions, the target optimization variable parameter for the above-mentioned temperature rise result is determined as the optimization parameter of the above-mentioned transformer to be optimized.

[0011] The second aspect of this disclosure provides a device for determining the optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization, comprising: a calculation module, a first determination module, a simulation module, an optimization module, and a second determination module.

[0012] The calculation module is used to calculate multiple sets of losses of the transformer to be optimized under a non-sinusoidal excitation signal in the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration. The optimization variables for the nth iteration include multiple sets of optimization variable parameters, and the optimization variable parameters correspond one-to-one with the losses. n is an integer greater than or equal to 1, and the optimization variables for the first iteration are the initial optimization variables.

[0013] The first determining module is used to determine the target optimization variable parameters from the above multiple sets of optimization variable parameters, such that the insulation distance of the transformer to be optimized satisfies the insulation constraint, the leakage inductance value satisfies the leakage inductance constraint, and the loss is minimized.

[0014] The simulation module is used to perform electromagnetic-thermal bidirectional coupling simulation on the transformer to be optimized based on the fixed parameters and target optimization variable parameters, and to obtain the temperature rise result of the transformer to be optimized.

[0015] The optimization module is used to perform multi-objective optimization on the optimization variables of the nth iteration when the temperature rise result of the above-mentioned variable to be optimized does not meet the above-mentioned preset conditions, so as to obtain the optimization variables for the (n+1)th iteration, and to use the optimization variables of the (n+1)th iteration to execute the (n+1)th iteration.

[0016] The second determining module is used to determine the target optimization variable parameter for the temperature rise result as the optimization parameter of the transformer to be optimized when the temperature rise result of the transformer to be optimized meets the preset conditions.

[0017] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0018] A fourth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0019] The fifth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0020] According to the high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization provided in this disclosure, in the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, multiple sets of losses of the transformer to be optimized under non-sinusoidal excitation signals are first calculated. This allows for the determination of target optimization variable parameters from these multiple sets of optimization variable parameters, ensuring that the insulation distance meets insulation constraints, the leakage inductance value meets leakage inductance constraints, and the loss is minimized. This achieves the screening of multiple sets of optimization variable parameters based on insulation constraints, leakage inductance constraints, and losses. Based on this, electromagnetic-thermal bidirectional coupling simulation is performed on the transformer to be optimized based on the target optimization variable parameters. This allows for a more accurate simulation of the actual operating state of the transformer by simultaneously considering the feedback effect between the electromagnetic field and the thermal field, resulting in a more accurate temperature rise result for the transformer to be optimized. Therefore, if the temperature rise result of the transformer to be optimized does not meet the preset conditions, multi-objective optimization is performed on the optimization variables of the nth iteration to consider multiple objectives involved in the optimization design of the transformer to be optimized. This ensures that the target optimization variable parameters corresponding to the temperature rise result of the transformer to be optimized meet the optimization design requirements of the transformer to be optimized. Attached Figure Description

[0021] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0022] Figure 1 This illustration schematically depicts an application scenario of a high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization according to embodiments of the present disclosure.

[0023] Figure 2 A flowchart illustrating a method for determining optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization according to an embodiment of the present disclosure is shown.

[0024] Figure 3 A flowchart illustrating the process of obtaining the losses of the transformer to be optimized according to an embodiment of the present disclosure is shown schematically.

[0025] Figure 4A schematic diagram illustrating the Litz wire winding area equivalence process of the transformer to be optimized according to an embodiment of the present disclosure is shown.

[0026] Figure 5 A flowchart illustrating the process of obtaining the winding losses of a transformer to be optimized according to an embodiment of the present disclosure is shown schematically.

[0027] Figure 6 A schematic diagram of a simulation model of a transformer to be optimized according to an embodiment of the present disclosure is shown.

[0028] Figure 7 A schematic diagram illustrating the simulated losses of a transformer to be optimized according to an embodiment of the present disclosure is shown.

[0029] Figure 8 A schematic diagram illustrating the temperature distribution during operation of the transformer to be optimized according to an embodiment of the present disclosure is shown.

[0030] Figure 9 A schematic diagram illustrating the temperature distribution of a transformer under unidirectional coupling according to an embodiment of the present disclosure is shown.

[0031] Figure 10 A schematic diagram of a simulation model after meshing according to an embodiment of the present disclosure is shown.

[0032] Figure 11 A schematic diagram illustrating the mesh division of the magnetic core and winding portions according to an embodiment of the present disclosure is shown.

[0033] Figure 12 A schematic diagram illustrating the detailed winding mesh subdivision according to an embodiment of the present disclosure is shown.

[0034] Figure 13 A flowchart illustrating the generation of a target population according to an embodiment of the present disclosure is shown schematically;

[0035] Figure 14 A schematic diagram illustrates the structural block diagram of a high-frequency transformer optimization parameter determination device based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization according to an embodiment of the present disclosure; and

[0036] Figure 15 A block diagram of an electronic device suitable for implementing a method for determining high-frequency transformer optimization parameters based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization, according to an embodiment of the present disclosure, is shown schematically. Detailed Implementation

[0037] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0039] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0040] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0041] Figure 1 The illustration shows an application scenario of the high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization according to an embodiment of the present disclosure.

[0042] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0043] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0044] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0045] For example, a user can use a first terminal device 101, a second terminal device 102, and a third terminal device 103 to send the fixed parameters of the transformer to be optimized and the optimization variables for the first iteration to the server 105: the initial optimization variables.

[0046] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0047] For example, the following operations can be repeated on server 105 until preset conditions are met: In the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, multiple sets of losses of the transformer to be optimized under non-sinusoidal excitation signals are calculated. The optimization variables for the nth iteration include multiple sets of optimization variable parameters, and the optimization variable parameters correspond one-to-one with the losses. n is an integer greater than or equal to 1, and the optimization variables for the 1st iteration are the initial optimization variables. From the multiple sets of optimization variable parameters, the target for the transformer to be optimized is determined: the insulation distance satisfies the insulation constraint, the leakage inductance value satisfies the leakage inductance constraint, and the loss is minimized. The optimization variable parameters are obtained by performing electromagnetic-thermal bidirectional coupling simulation on the transformer to be optimized based on the fixed parameters and target optimization variable parameters of the transformer to be optimized, thus obtaining the temperature rise result of the transformer to be optimized. Then, if it is determined that the temperature rise result of the transformer to be optimized does not meet the preset conditions, multi-objective optimization is performed on the optimization variable of the nth iteration to obtain the optimization variable for the (n+1)th iteration, so as to execute the (n+1)th iteration using the optimization variable of the (n+1)th iteration. If it is determined that the temperature rise result of the transformer to be optimized meets the preset conditions, the target optimization variable parameters for the temperature rise result are determined as the optimization parameters of the transformer to be optimized.

[0048] It should be noted that the high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization provided in this disclosure embodiment can generally be executed by server 105. Correspondingly, the high-frequency transformer optimization parameter determination device based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization provided in this disclosure embodiment can generally be located in server 105. The high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization provided in this disclosure embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the high-frequency transformer optimization parameter determination device based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization provided in this disclosure embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0049] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0050] The following will be based on Figure 1 The described scene, through Figures 2-10 The present disclosure provides a detailed description of the method for determining the optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization.

[0051] Figure 2 The flowchart illustrates a method for determining the optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization according to an embodiment of the present disclosure.

[0052] like Figure 2 As shown, the method 200 includes operations S210 to S260.

[0053] In operation S210, during the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, multiple sets of losses of the transformer to be optimized under non-sinusoidal excitation signals are calculated.

[0054] The optimization variables in the nth iteration include multiple sets of optimization variable parameters. The optimization variable parameters and the loss are in one-to-one correspondence. n is an integer greater than or equal to 1. The optimization variables for the first iteration are the initial optimization variables.

[0055] According to the embodiments of this disclosure, since the temperature rise of the transformer to be optimized will also affect the performance of the transformer, the impact of the temperature rise of the transformer to be optimized on the performance needs to be considered during the optimization design process. That is, it is necessary to determine whether the optimization variable parameters corresponding to the temperature rise result meet the optimization design requirements for the transformer to be optimized based on the temperature rise result of the transformer to be optimized.

[0056] Based on this, the process of determining the optimization parameters for the transformer to be optimized is iterative until the temperature rise result of the transformer to be optimized meets the optimization design requirements for the transformer to be optimized.

[0057] According to embodiments of this disclosure, the temperature rise of the transformer to be optimized can be determined based on fixed parameters and optimized variable parameters for the transformer. However, in addition to the temperature rise affecting the performance of the transformer, the losses of the transformer also affect its performance.

[0058] Therefore, since the loss of the transformer to be optimized is determined based on the fixed parameters and optimization variable parameters for the transformer to be optimized, and the optimization variables include multiple sets of optimization variable parameters, a portion of the optimization variable parameters can be screened based on the loss of the transformer to be optimized calculated for each set of optimization variable parameters to ensure that the optimization variable parameters used to calculate the loss of the transformer to be optimized meet the loss requirements of the transformer to be optimized.

[0059] According to embodiments of this disclosure, in the nth iteration, the loss of the transformer under a non-sinusoidal excitation signal can be calculated based on the fixed parameters of the transformer to be optimized and the parameters of each set of optimization variables in the optimization variables for the nth iteration. However, the temperature rise result of the transformer to be optimized determined for the (n-1)th iteration does not meet the optimization design requirements of the transformer to be optimized.

[0060] In one embodiment, in the first iteration, the optimization variables for the first iteration are initially set; the input signal of the transformer to be optimized is a non-sinusoidal excitation signal.

[0061] In one embodiment, the transformer to be optimized can be a high-capacity high-frequency transformer; the optimization variable parameters may include the core window width, core window height, distance from the high-voltage winding to the core, and interlayer insulation distance of the high-voltage winding of the transformer to be optimized; the fixed parameters may include the distance from the low-voltage winding to the core, the interlayer insulation distance of the low-voltage winding, etc.

[0062] For example, high-frequency transformers typically operate in the frequency range of 20kHz to 1MHz.

[0063] In operation S220, the target optimization variable parameters are determined from multiple sets of optimization variable parameters to ensure that the insulation distance of the transformer to be optimized meets the insulation constraint, the leakage inductance value meets the leakage inductance constraint, and the loss is minimized.

[0064] According to embodiments of this disclosure, in addition to the losses of the transformer to be optimized affecting its performance, the insulation distance and leakage inductance value of the transformer to be optimized also affect its performance.

[0065] Therefore, based on the fixed parameters of the transformer to be optimized and the insulation distance, leakage inductance value and loss of the transformer to be optimized determined by each set of optimization variable parameters, the optimized transformer parameters used for subsequent temperature rise result calculation can be determined from multiple sets of optimization variable parameters.

[0066] Specifically, the insulation distance of the transformer to be optimized is the insulation distance between the high-voltage winding and the insulation distance between the low-voltage winding in the optimization variable parameters; the leakage inductance value of the transformer to be optimized is determined based on the core window width and core window height in the optimization variable parameters.

[0067] Therefore, based on any one set of optimized variable parameters from multiple sets, it can be determined whether the insulation distance, leakage inductance value, and loss determined based on the fixed parameters and that set of optimized variable parameters meet the requirements. Specifically, the requirements may include the insulation distance meeting the insulation constraint, the leakage inductance value meeting the leakage inductance constraint, and the loss meeting the requirement of minimizing loss.

[0068] In one embodiment, the insulation constraint can characterize an insulation distance greater than or equal to a preset insulation distance, and the leakage inductance constraint can characterize a leakage inductance value greater than or equal to a preset leakage inductance value. The preset insulation distance and preset leakage inductance value are set as needed.

[0069] Therefore, based on the insulation distance, leakage inductance value, and loss of the transformer to be optimized determined for multiple sets of optimization variable parameters, the target optimization variable parameters are determined from the multiple sets of optimization variable parameters to satisfy the insulation constraint, the leakage inductance value satisfies the leakage inductance constraint, and the loss is minimized.

[0070] According to embodiments of this disclosure, the transformer to be optimized has the lowest loss, which means the transformer to be optimized has the highest efficiency.

[0071] In operation S230, based on the fixed parameters of the transformer to be optimized and the target optimization variable parameters, an electromagnetic-thermal bidirectional coupling simulation is performed on the transformer to be optimized to obtain the temperature rise result of the transformer to be optimized.

[0072] According to embodiments of this disclosure, during actual operation, the transformer to be optimized generates a large amount of heat due to electromagnetic induction, hysteresis loss, eddy current loss, etc. The increase in temperature, in turn, affects the electromagnetic properties of the transformer material. Therefore, by employing electromagnetic-thermal bidirectional coupling simulation, the actual operating state of the transformer can be simulated more accurately by simultaneously considering the feedback effects between the electromagnetic and thermal fields, thus resulting in a more accurate temperature rise result for the transformer to be optimized.

[0073] Therefore, based on the target optimization variable parameters determined by the above operations S210 and S220, electromagnetic-thermal bidirectional coupling simulation can be performed on the transformer to be optimized based on the fixed parameters of the transformer to be optimized and the target optimization variable parameters to obtain the temperature rise result of the transformer to be optimized.

[0074] In operation S240, determine whether the temperature rise result of the transformer to be optimized meets the preset conditions.

[0075] According to embodiments of this disclosure, the preset condition can characterize that the temperature rise result is less than or equal to the maximum allowable temperature rise of the transformer to be optimized. If the temperature rise result of the transformer to be optimized does not meet the preset condition, operation S250 is executed; if the temperature rise result of the transformer to be optimized meets the preset condition, operation S260 is executed.

[0076] In operation S250, if the temperature rise result of the variable to be optimized does not meet the preset conditions, multi-objective optimization is performed on the optimization variable of the nth iteration to obtain the optimization variable for the (n+1)th iteration, so as to use the optimization variable of the (n+1)th iteration to execute the (n+1)th iteration.

[0077] According to the embodiments of this disclosure, when the temperature rise result of the transformer to be optimized does not meet the preset conditions, that is, when the temperature rise result of the transformer to be optimized is greater than the maximum allowable temperature rise, the target optimization variable parameters for the transformer to be optimized do not meet the optimization design requirements for the transformer to be optimized. Therefore, it is necessary to perform multi-objective optimization on the optimization variables of the nth iteration to obtain the optimization variables for the (n+1)th iteration.

[0078] Based on this, the (n+1)th iteration can be performed based on the optimization variables of the (n+1)th iteration, that is, the above operations S210 to S240 can be performed based on the optimization variables of the (n+1)th iteration.

[0079] According to embodiments of this disclosure, the optimization design of the transformer to be optimized is a complex multi-objective problem involving multiple conflicting optimization objectives such as loss, volume, weight, efficiency, and cost. Therefore, for example, a multi-objective optimization algorithm can be employed to perform multi-objective optimization on the optimization variables of the nth iteration, so that while ensuring the temperature rise result obtained based on the optimization variables of the (n+1)th iteration meets preset conditions, a balance can be found among the multiple conflicting objectives.

[0080] In operation S260, if the temperature rise result of the transformer to be optimized meets the preset conditions, the target optimization variable parameter for the temperature rise result is determined as the optimization parameter of the transformer to be optimized.

[0081] According to embodiments of this disclosure, when it is determined that the temperature rise result of the transformer to be optimized meets preset conditions, the target optimization variable parameter corresponding to the temperature rise result can be determined as the optimization parameter of the transformer to be optimized. The determined optimization parameter of the transformer to be optimized can be used to optimize the transformer, that is, the determined optimization parameter of the transformer to be optimized meets the optimization design requirements for the transformer to be optimized.

[0082] According to embodiments of this disclosure, in the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, multiple sets of losses of the transformer to be optimized under a non-sinusoidal excitation signal are first calculated. This allows for the determination of target optimization variable parameters from these multiple sets of optimization variable parameters, ensuring that the insulation distance satisfies the insulation constraint, the leakage inductance value satisfies the leakage inductance constraint, and the loss is minimized. This achieves the screening of multiple sets of optimization variable parameters based on insulation constraints, leakage inductance constraints, and losses. Based on this, an electromagnetic-thermal bidirectional coupling simulation is performed on the transformer to be optimized based on the target optimization variable parameters. This allows for a more accurate simulation of the actual operating state of the transformer by simultaneously considering the feedback effect between the electromagnetic field and the thermal field, resulting in a more accurate temperature rise result for the transformer to be optimized. Therefore, if the temperature rise result of the transformer to be optimized does not meet the preset conditions, multi-objective optimization is performed on the optimization variables of the nth iteration to consider multiple objectives involved in the optimization design of the transformer to be optimized. This ensures that the target optimization variable parameters corresponding to the condition where the temperature rise result of the transformer to be optimized meets the preset conditions satisfy the optimization design requirements of the transformer to be optimized.

[0083] Figure 3 A flowchart illustrating the process of obtaining the loss of the transformer to be optimized according to an embodiment of the present disclosure is shown.

[0084] like Figure 3 As shown, the method 300 includes operations S310 to S330.

[0085] Among them, the set of losses of the transformer to be optimized includes core loss and winding loss for any set of optimization variable parameters.

[0086] According to embodiments of this disclosure, the losses of the transformer to be optimized include core losses and winding losses. Core losses typically include hysteresis losses and eddy current losses, collectively referred to as iron losses. Winding losses mainly include conductor resistance losses and additional losses caused by the skin effect and proximity effect, collectively referred to as copper losses. .

[0087] Therefore, the loss of the transformer to be optimized can be regarded as the sum of the core loss and the winding loss, that is, the loss of the transformer to be optimized can be represented by the following formula (1).

[0088] (1)

[0089] In operation S310, the core loss of the transformer to be optimized is calculated based on the loss coefficient related to the transformer core material, the duty cycle of the non-sinusoidal excitation signal, the frequency of the non-sinusoidal excitation signal, and the peak value of the magnetic induction intensity of the non-sinusoidal excitation signal in the fixed parameters.

[0090] In engineering, the Steinmetz Equation (SE) is often used to calculate the core loss under sinusoidal excitation. The core loss can be expressed by the following formula (2).

[0091] (2)

[0092] in, The frequency of the non-sinusoidal excitation signal; The peak value of the magnetic flux density is the magnetic flux density of the non-sinusoidal excitation signal. , and The loss factor is determined based on the core material of the transformer to be optimized.

[0093] Based on SE, modified formulas such as MSE (Modified Steinmetz Equation), GSE (Generalized Steinmetz Equation), IGSE (Improved Generalized Steinmetz Equation), and WCSE (Waveform-coefficient Steinmetz Equation) were proposed for calculating core losses under non-sinusoidal excitation.

[0094] MSE (Medium-Switching Sequence) represents the magnetization rate by introducing the switching frequency without introducing new variable parameters, making its calculation relatively simple. It is universal for windings with different square window core structures (such as EE type, U type, etc.), but it suffers from insufficient calculation accuracy. GSE (Gross-Switching Sequence) takes into account the influence of hysteresis loops and uses the peak-to-peak value of magnetic flux density instead of the instantaneous magnetic flux density, but this formula contains a large number of numerical integrals, which greatly increases the calculation time. WCSE (Wavelength Coefficient Sequence) introduces the waveform coefficient FWC, which correlates non-sinusoidal waveforms with sinusoidal waveforms under the same magnetic induction intensity. Core loss is estimated by multiplying the waveform coefficient by the SE calculation formula. It is suitable for square wave voltage excitation and triangular wave voltage excitation, but the magnetic flux density integral does not change when the duty cycle changes, limiting its application to 50% duty cycle conditions.

[0095] Based on the above, the prior art typically uses IGSE to calculate core loss, while the improved general-purpose IGSE provided in this disclosure can more conveniently and quickly calculate core loss under high-frequency non-sinusoidal excitation waveforms. Therefore, the core loss calculation formula of this disclosure can be shown in the following formula (3).

[0096] (3)

[0097] in, D is the duty cycle of the non-sinusoidal excitation signal; , and It is the loss coefficient related to the transformer core material; The frequency of the non-sinusoidal excitation signal; The peak value of the magnetic flux density is the magnetic flux density of the non-sinusoidal excitation signal.

[0098] Therefore, in operation S310, the core loss of the transformer to be optimized can be calculated using the above formula (3).

[0099] In one embodiment, nanocrystalline alloys are magnetic core materials with a special structure, exhibiting an amorphous or nanocrystalline atomic structure. These materials possess excellent soft magnetic properties, such as high permeability, low loss, and good high-frequency characteristics. Therefore, the transformer to be optimized in this disclosure can be selected using nanocrystalline materials as the magnetic core material.

[0100] Based on this, the empirical coefficients (loss coefficients) of IGSE for nanocrystalline materials can be shown in Table 1 below.

[0101] Table 1

[0102]

[0103] In operation S320, for any set of optimization variable parameters in the nth iteration, based on the fixed parameters and the parameters related to the Litz wire winding in any set of optimization variable parameters, the Litz wire winding of the transformer to be optimized is subjected to area equivalence processing to obtain the equivalent diameter of the single-strand round conductor in the Litz wire of the primary winding and the secondary winding of the transformer to be optimized.

[0104] According to embodiments of this disclosure, unlike conventional transformers, high-frequency transformers (transformers to be optimized) typically use Litz wire as the winding material to reduce winding losses. Litz wire is composed of multiple strands of surface-insulated fine wires twisted together. By performing area equivalence on the Litz wire windings of the transformer to be optimized, the distributed current path of the Litz wire strands can be simplified to a planar current distribution of copper foil, making the magnetic field distribution conform to the one-dimensional assumption.

[0105] Based on this, the winding losses of the transformer to be optimized can be calculated by taking into account the equivalent diameter of the single-strand round conductor in the Litz line of the primary and secondary windings of the transformer to be optimized after area equivalence.

[0106] Figure 4 A schematic diagram illustrating the Litz wire winding area equivalence process of the transformer to be optimized according to an embodiment of the present disclosure is shown.

[0107] like Figure 4 As shown, following the direction of the arrows, from left to right, is the process of equivalence of the Litz wire winding area of ​​the transformer to be optimized.

[0108] Where H refers to the height of the core window, hw1 refers to the bus height of the primary winding, and D... L ds1 refers to the diameter of each turn of the winding wire, ds2 refers to the diameter of each winding wire (Litz wire) in the primary winding, dseq1 refers to the equivalent length of each winding wire in the primary winding, dseq2 refers to the equivalent length of each winding wire in the secondary winding, and hweq1 refers to the total height of the equivalent winding.

[0109] According to embodiments of this disclosure, the parameters related to the Litz wire winding in the fixed parameters and any set of optimization variables include winding height, number of Litz wire strands per turn, and average turn length of the winding. Therefore, based on the parameters related to the Litz wire winding in the fixed parameters and any set of optimization variables, the Litz wire winding of the transformer to be optimized can be subjected to area equivalence processing.

[0110] According to embodiments of this disclosure, after performing area equivalence processing on the Litz wire winding of the transformer to be optimized, the equivalent diameter of the single-strand round conductor in the Litz wire of both the primary and secondary windings of the transformer to be optimized can be obtained. This equivalent diameter, for example... Figure 4The diagram shows the diameter ds1 of each winding wire in the primary winding and the diameter ds2 of each winding wire in the secondary winding.

[0111] In operation S330, the winding loss of the transformer to be optimized is calculated based on the equivalent diameter of a single-strand round conductor in the Litz line, fixed parameters, and any set of optimization variable parameters.

[0112] According to embodiments of this disclosure, after performing area equivalence processing on the Litz wire winding of the transformer to be optimized, the equivalent diameter of a single-strand round conductor in the Litz wire can be obtained. Based on this, the winding loss of the transformer to be optimized can be calculated using the equivalent diameter of the single-strand round conductor in the Litz wire, fixed parameters, and any set of optimization variable parameters.

[0113] According to embodiments of this disclosure, the losses of the transformer to be optimized include core losses and winding losses. Based on the loss coefficient related to the transformer core material and the duty cycle and frequency of the non-sinusoidal excitation signal, the core losses of the transformer to be optimized can be calculated quickly and accurately. The winding material of the transformer to be optimized is Litz wire. By performing area equivalence on the Litz wire winding of the transformer to be optimized, the distributed current path of the Litz wire strands is simplified to a planar current distribution of copper foil, making the magnetic field distribution conform to the one-dimensional assumption. Therefore, based on the equivalent diameter of a single-strand round conductor in the Litz wire, fixed parameters, and any set of optimization variable parameters, the winding losses of the transformer to be optimized can be calculated more accurately.

[0114] Figure 5 A flowchart illustrating the process of obtaining the winding losses of a transformer to be optimized according to an embodiment of the present disclosure is shown.

[0115] like Figure 5 As shown, the method 500 includes operations S510 to S550.

[0116] In operation S510, the skin depth is calculated based on the frequency of the non-sinusoidal excitation signal, the permeability of the vacuum, and the conductivity of the Litz wire in the fixed parameters.

[0117] According to embodiments of this disclosure, skin depth The calculation formula can be shown in formula (4) below.

[0118] (4)

[0119] Where μ0 is the free permeability and f is the frequency of the non-sinusoidal excitation signal. Let be the conductivity of the line.

[0120] Based on the above formula (4), the skin depth of the primary winding of the transformer to be optimized is the same as that of the secondary winding.

[0121] In operation S520, the harmonic AC resistance factor of the primary and secondary windings is calculated based on the equivalent diameter of the single-strand round conductor in the Litz wire of the primary and secondary windings, as well as the diameter, skin depth, number of strands, and number of winding layers of the single-strand round conductor in the Litz wire of the primary and secondary windings, respectively, in the fixed parameters.

[0122] According to embodiments of this disclosure, the harmonic AC resistance factor F of the Litz wire winding is... r The calculation formula can be shown in formula (5) below.

[0123] (5)

[0124] in, ; The number of strands in the Lids wire winding; This refers to the number of winding layers. The diameter of the single-strand round conductor in the Leeds line; This is the equivalent diameter of a single-strand round conductor in the Leeds line.

[0125] Therefore, based on the above formula (5), the harmonic AC resistance factors of the primary winding and the secondary winding can be calculated respectively.

[0126] In operation S530, the DC resistance of the primary and secondary windings is calculated based on the fixed parameters, namely the number of winding layers, the number of turns per winding layer, the average turn length of the winding, the conductivity of the Litz wire, the diameter of the single-strand round conductor in the Litz wire, and the number of strands of the Litz wire.

[0127] According to embodiments of this disclosure, the DC resistance of the winding It can be calculated using the following formula (6).

[0128] (6)

[0129] Where MLT is the average turn length of the winding. The conductivity of the Lids line, This refers to the number of winding layers. This refers to the number of turns in each layer of windings. The number of shares in the Leeds Line; This is the diameter of the single-strand round conductor in the Leeds line.

[0130] Therefore, based on the above formula (6), the DC resistance of the primary winding and the secondary winding can be calculated respectively.

[0131] In operation S540, for the mth harmonic in the non-sinusoidal excitation signal, the effective current value of the mth harmonic of each of the primary and secondary windings is calculated based on the leakage inductance value of the primary and secondary windings, the harmonic order, and the winding voltage, minimum phase shift angle, DC-DC conversion ratio, and frequency of the non-sinusoidal excitation signal for the primary and secondary windings, respectively.

[0132] The leakage inductance value is determined based on any set of optimization variable parameters.

[0133] According to embodiments of this disclosure, in a DC-DC converter, the input DC voltage is controlled by switching devices to form a series of pulse waveforms. These pulse waveforms undergo voltage transformation via a high-frequency transformer (the transformer to be optimized), thereby achieving energy transfer and conversion. The input signal of this pulse waveform is typically a non-sinusoidal wave, and due to the increased frequency, the skin effect and proximity effect of the conductors within the transformer increase winding losses. Therefore, calculating the winding losses of a high-frequency transformer is complex, requiring consideration of the AC resistance of the windings at various harmonic frequencies.

[0134] Therefore, the effective value of the current in the primary winding under the mth harmonic. It can be calculated using the following formula (7).

[0135] (7)

[0136] in, Where φ is the primary winding voltage, φ is the minimum phase shift angle, and d is the DC-DC conversion ratio. This is the leakage inductance value; for the m-th harmonic, the harmonic number is m; The frequency of the non-sinusoidal excitation signal.

[0137] Therefore, the effective value of the primary winding current under the mth harmonic can be calculated using the above formula (7). Similarly, based on the winding voltage, minimum phase shift angle, DC-DC conversion ratio, frequency of the non-sinusoidal excitation signal, leakage inductance value, and harmonic order associated with the secondary winding, the effective value of the current in the secondary winding under the m-th harmonic can also be calculated using the above formula (7). .

[0138] By operating the S550, the winding loss of the transformer to be optimized is calculated based on the harmonic AC resistance factor, DC resistance, and effective current value for the primary and secondary windings under different harmonics in the non-sinusoidal excitation signal.

[0139] According to embodiments of this disclosure, the winding losses of the transformer to be optimized It can be calculated using the following formula (8).

[0140] (8)

[0141] Where m is the harmonic order of the current, I rms1,m and I rms2,m F is the effective value of the current for the m-th harmonic. r1 and F r2 These are the AC resistance factors of the nth harmonic of the primary winding and the secondary winding, respectively. and These are the DC resistances of the primary and secondary windings, respectively; M is the total harmonic order under a non-sinusoidal excitation signal.

[0142] Therefore, based on the above formula (8), the winding loss of the transformer to be optimized can be calculated.

[0143] According to the embodiments of this disclosure, the winding loss of the transformer to be optimized can be calculated by the above formulas (4) to (8). Considering the AC resistance factor under different harmonics, the calculation of the winding loss of the transformer to be optimized is more accurate.

[0144] According to embodiments of this disclosure, the optimized variable parameters include the insulation distance of the transformer to be optimized, with the insulation constraint being that the insulation distance of the transformer to be optimized is greater than or equal to a preset insulation distance; and the leakage inductance constraint being that the leakage inductance value of the transformer to be optimized is greater than or equal to a preset leakage inductance value.

[0145] According to embodiments of this disclosure, the insulation constraint is that the insulation distance of each part of the transformer to be optimized meets the minimum insulation requirement, that is, the insulation distance of the transformer to be optimized is greater than or equal to a preset insulation distance, such as... ,in, For the insulation distance of the transformer to be optimized, This is the preset insulation distance.

[0146] According to embodiments of this disclosure, the leakage inductance constraint is that the leakage inductance meets a minimum leakage inductance requirement, that is, the leakage inductance value of the transformer to be optimized is greater than or equal to a preset leakage inductance value, such as... Among them, the leakage inductance value of the transformer to be optimized is The preset leakage inductance value is .

[0147] According to embodiments of this disclosure, based on insulation constraints and leakage inductance constraints, it is ensured that the determined optimization variable parameters for optimizing the transformer to be optimized can take into account both leakage inductance and insulation constraints, so that the performance of the transformer to be optimized after optimization based on the target optimization variable parameters is better.

[0148] According to embodiments of this disclosure, based on the fixed parameters and target optimization variable parameters of the transformer to be optimized, an electromagnetic-thermal bidirectional coupling simulation is performed on the transformer to be optimized to obtain the temperature rise result of the transformer to be optimized. This includes: establishing a simulation model for the transformer to be optimized based on the fixed parameters and target optimization variable parameters of the transformer to be optimized; using the simulation model, performing electromagnetic simulation on the transformer to be optimized to obtain the simulation loss of the transformer to be optimized; and based on the simulation loss of the transformer to be optimized, performing thermal simulation on the transformer to be optimized through the simulation model to obtain the temperature rise result of the transformer to be optimized.

[0149] According to embodiments of this disclosure, high-frequency transformers generate a large amount of heat during operation due to electromagnetic induction, hysteresis losses, eddy current losses, etc. The increase in temperature, in turn, affects the electromagnetic properties of the transformer materials. Unidirectional coupling cannot account for this interdependence, while bidirectional coupling can simultaneously consider the feedback effect between the electromagnetic field and the thermal field, thereby more accurately simulating the actual operating state of the transformer. Therefore, electromagnetic-thermal bidirectional coupling simulation is used for the transformer to be optimized to obtain the temperature rise results of the transformer to be optimized.

[0150] According to embodiments of this disclosure, a simulation model for the transformer to be optimized can be established based on the fixed parameters of the transformer to be optimized and the target optimization variable parameters.

[0151] For example, consider a 160kW / 20kHz high-frequency transformer. The parameters and electrical specifications of this transformer are shown in Table 2 below.

[0152] Table 2

[0153]

[0154] Figure 6 A schematic diagram of a simulation model of a transformer to be optimized according to an embodiment of the present disclosure is shown.

[0155] like Figure 6 As shown, this can be a simulation model established based on the parameters shown in Table 2 above.

[0156] In one embodiment, the transformer core can adopt a nanocrystalline alloy laminate structure, which has high saturation magnetic flux density (1.25T) and low iron loss characteristics; the primary and secondary windings of the transformer can be wound with Litz wire to suppress high-frequency eddy current losses, and a polyimide insulating film is provided between the layers.

[0157] In one embodiment, the ANSYS Workbench platform can be used to perform joint simulations of Maxwell and Icepak to complete the electromagnetic-thermal bidirectional coupling calculations of the high-frequency transformer. For example, electromagnetic simulations of the transformer to be optimized can be performed using Maxwell; thermal simulations can be performed using Icepak.

[0158] Specifically, thanks to ANSYS Workbench, Maxwell and Icepak can exchange data bidirectionally: the simulation loss calculated in Maxwell is passed to Icepak as a heat source input, and Icepak calculates the temperature rise result.

[0159] Figure 7 A schematic diagram illustrating the simulated loss of a transformer to be optimized according to an embodiment of the present disclosure is shown.

[0160] like Figure 7 As shown, when the transformer to be optimized is a high-frequency transformer, the core loss and winding loss of the high-frequency transformer can be obtained in Maxwell.

[0161] from Figure 7 As can be seen, the core loss exhibits significant peaks in each cycle, which is related to the transformer's operating frequency and the hysteresis characteristics of the core material. The peak values ​​of the core loss are approximately at 25μs, 50μs, and 75μs per cycle, indicating that the magnetic flux density changes most drastically at these times, leading to substantial energy loss. The winding loss, on the other hand, is relatively stable, but its value increases slightly over time. This is likely due to the increased winding resistance with rising temperature, leading to increased losses.

[0162] Figure 8 A schematic diagram illustrating the temperature distribution during operation of the transformer to be optimized according to an embodiment of the present disclosure is shown.

[0163] like Figure 8 As shown, the temperature distribution of a high-frequency transformer during operation can be obtained in Icepak.

[0164] from Figure 8 As can be seen, the highest temperature of the transformer reaches 43.319°C, located in the upper part of the magnetic core, while the lowest temperature is 28.786°C, occurring in the bottom region of the windings. A significant temperature gradient exists within the transformer, with heat transferred from the bottom to the top, making the upper region a hotspot. In the upper region of the magnetic core, the higher electromagnetic field strength results in greater electromagnetic losses, and hindered heat dissipation, leading to a temperature increase. The convection and radiation cooling effects within the transformer also influence the temperature distribution; the bottom region of the windings is more conducive to convection and radiation cooling, thus lowering the temperature in that area.

[0165] Figure 9 A schematic diagram illustrating the temperature distribution of a transformer under unidirectional coupling according to an embodiment of the present disclosure is shown.

[0166] Therefore, combined Figure 8 The temperature distribution of the transformer under bidirectional coupling is shown. Figure 9 The temperature distribution of the transformer under unidirectional coupling is shown. By comparing and analyzing the temperature rise calculation results of bidirectional coupling and unidirectional coupling, it was found that the hot spot temperature in the upper part of the transformer core is higher under bidirectional coupling, and the temperature gradient between the winding and the core is steeper. In contrast, the hot spot temperature under unidirectional coupling is only 40.112℃, a difference of 3.207℃. This is because bidirectional coupling reproduces the dynamic process of "temperature rise → change in core permeability / winding resistance → change in electromagnetic loss → further affecting temperature rise"; while unidirectional coupling only transmits losses in one direction and ignores the reverse effect of temperature on electromagnetic characteristics, resulting in a lower calculated temperature value and a smoother distribution.

[0167] According to embodiments of this disclosure, by employing electromagnetic-thermal bidirectional coupling simulation, the temperature rise result of the transformer to be optimized is calculated, thereby improving the accuracy of the calculated temperature rise result. This makes it more consistent with the actual situation of the transformer to be optimized when judging whether the preset conditions are met based on the temperature rise result.

[0168] According to embodiments of this disclosure, based on the simulation losses of the transformer to be optimized, thermal simulation is performed on the transformer to be optimized using a simulation model to obtain the temperature rise result for the transformer to be optimized. This includes: performing tetrahedral meshing on non-critical areas in the simulation model, performing local refinement and hexahedral mesh mapping on the winding and core portions in the simulation model, and performing local refinement and hexahedral mesh mapping on the bent portion of the Litz wire in the simulation model to obtain a meshed simulation model; based on the simulation losses of the transformer to be optimized, thermal simulation is performed on the transformer to be optimized using the meshed simulation model to obtain the temperature rise result for the transformer to be optimized.

[0169] In the thermal simulation of transformers for optimization, mesh generation is a crucial step in the finite element simulation process, directly affecting the convergence, accuracy, and efficiency of the simulation. Due to the complex geometry and fine structure of the Litz wire winding, multiphysics simulations require very fine mesh generation to capture the detailed characteristics of the winding, especially at high frequencies where the skin effect and proximity effect are more pronounced. This necessitates a denser mesh structure at the edges.

[0170] When performing thermal simulations on the simulation model, if free meshing is used, the mesh at the edge of the Litz wire winding will be severely out of sync. Local mesh refinement can improve this problem to some extent, but the resulting mesh size is huge, which will greatly increase the computation time and complexity, and may also affect mesh convergence.

[0171] Therefore, this disclosure proposes a new hybrid meshing method to address these problems, specifically: performing tetrahedral meshing on non-critical areas in the simulation model, performing local refinement and hexahedral mesh mapping on the winding and core parts in the simulation model, and performing local refinement and hexahedral mesh mapping on the bent parts of the Litz wire in the simulation model.

[0172] Figure 10 A schematic diagram of a simulation model after meshing according to an embodiment of the present disclosure is shown.

[0173] like Figure 10 As shown, for the winding and core sections where hot spot temperatures need to be monitored, as well as the bent sections of the Litz wire, local mesh refinement and hexahedral mesh mapping are used; for non-critical areas in the simulation model, such as the air domain, tetrahedral meshing is used.

[0174] Figure 11 A schematic diagram of the mesh division of the magnetic core and winding portions according to an embodiment of the present disclosure is shown.

[0175] like Figure 11 As shown, Figure 11 The left side shows the magnetic core after grid partitioning. Figure 11 The right side shows the windings after grid partitioning.

[0176] Figure 12 A schematic diagram illustrating a detailed representation of a winding mesh partition according to an embodiment of the present disclosure is provided.

[0177] like Figure 12 As shown, Figure 12 The right side shows the meshing of the bent portion of the winding Litz wire. Specifically, the bent portion of the winding Litz wire is meshed using local refinement and hexahedral mapping.

[0178] Therefore, a hybrid network partitioning is performed on the simulation model to obtain a meshed simulation model. Based on the simulation losses of the transformer to be optimized, thermal simulation of the transformer can be performed using the meshed simulation model to obtain the temperature rise results for the transformer to be optimized.

[0179] According to embodiments of this disclosure, by locally refining and mapping the mesh in key areas of the transformer to be optimized (such as the bent portion of the Litz wire), the consistency and high quality of the mesh shape are ensured while also helping to control the overall mesh size. For non-critical areas in the simulation model, free tetrahedral meshing is used to simplify the calculation process. Since the windings and core are areas where hotspot temperatures and temperature rise are of concern, hexahedral meshing is used. The refined meshing in the bent portion of the Litz wire makes it more closely aligned with the surface and better mitigates skin effect, proximity effect, and edge effect. The remaining air domain uses free tetrahedral meshing to reduce the mesh size and save computational resources.

[0180] From the perspective of meshing principles and physical field solution mechanisms, the hybrid meshing method disclosed in this paper employs structured or mapped meshing for key electromagnetic-thermal coupling regions such as windings and magnetic cores, while using free meshing for non-critical regions such as the air domain. This differentiated strategy can accurately fit the Litz line arrangement and magnetic core geometry, reduce mesh distortion in key regions, and make the calculation of electromagnetic losses (skin effect, proximity effect, etc.) more accurate, providing high-precision heat source input for thermal analysis. At the same time, regular meshes improve the numerical stability of the heat conduction equation solution, with small calculation errors in temperature gradient and heat flow direction, and clear temperature stratification in windings and magnetic cores, accurately capturing local thermal characteristics. In contrast, the free meshing method is prone to poor mesh quality in complex geometric regions, resulting in distorted simulation of current distribution for skin effect and proximity effect during electromagnetic loss calculation, and large deviations between heat source distribution and reality. The mesh will exhibit a blocky distribution, and poor-quality meshes will amplify the discrete errors in thermal solution, making local details of heat conduction and temperature field distribution relatively blurred and averaged, making it difficult to accurately represent the thermal characteristics of key parts.

[0181] The number of grids, computation time, and computation results of several mesh generation methods are compared, and the comparison results are shown in Table 3 below.

[0182] Table 3

[0183]

[0184] As shown in Table 3 above, it can be seen that if the simulation model is fully densified, the number of meshes reaches over 20 million, resulting in the longest computation time and extremely low efficiency. After local densification, the number of meshes is reduced, but it is still over 13 million, with a computation time of 1605 seconds, indicating room for optimization. However, using the hybrid meshing method, the number of meshes is only 5.67 million, far lower than both full and local densification, with a computation time of 443 seconds, close to the time of free meshing. But by comparing the calculation results, the accuracy is much higher than that of free meshing, and the hotspot temperature results are not much different from the other two meshing methods. Therefore, the hybrid meshing method disclosed in this paper achieves a good balance between accuracy and efficiency.

[0185] According to embodiments of this disclosure, multi-objective optimization is performed on the optimization variables of the nth iteration to obtain optimization variables for the (n+1)th iteration, including: generating an initial population based on the optimization variables of the nth iteration, wherein individuals in the initial population represent a set of optimization variable parameters; performing optimization analysis on the optimization variables of the nth iteration based on multiple objective optimization functions and the initial population to generate a target population; and determining the optimization variables for the (n+1)th iteration based on the target population.

[0186] In one embodiment, an improved MOEA / D (Multi-Objective Evolutionary Algorithm based on Decomposition) algorithm can be used to perform multi-objective optimization on the optimization variables of the nth iteration.

[0187] According to embodiments of this disclosure, the optimal design of a high-frequency transformer is a complex multi-objective problem involving multiple conflicting optimization objectives such as loss, volume, weight, efficiency, and cost. For example, increasing the operating frequency can reduce transformer volume and increase power density, but may increase losses and reduce efficiency; increasing current density can reduce winding volume and weight, but may lead to heat dissipation difficulties. Furthermore, adjustments to the inter-layer spacing and insulation distance of the transformer windings also affect the magnitude of losses and leakage inductance, and are subject to insulation constraints. Therefore, employing a multi-objective optimization algorithm can find a balance among these conflicting objectives, achieving a globally optimal solution.

[0188] Taking a step-up transformer as an example, since the insulation requirements for the low-voltage side winding are relatively low, the distance d from the low-voltage (primary) winding to the magnetic core in the winding structure is... 1core The interlayer insulation distance d1 between the primary winding and the secondary winding is a fixed parameter, while the distance d between the secondary winding and the core is... 2core The interlayer distance d2 of the secondary winding is selected as an optimization variable parameter. Furthermore, since the core size affects transformer losses and volume, the height H and width l of the core window are chosen as optimization variable parameters.

[0189] According to embodiments of this disclosure, the optimization variables are based on the nth iteration, which include multiple sets of optimization variable parameters. Using one set of optimization variable parameters as an individual in the population, the initial population can be obtained.

[0190] According to embodiments of this disclosure, the target optimization function may include a maximum efficiency function and a maximum power density function. The maximum efficiency function may be shown in Equation (9) below, and the maximum power density function may be shown in Equation (10) below.

[0191] (9)

[0192] (10)

[0193] Therefore, based on the objective optimization function and the initial population, the optimization variables of the nth iteration can be optimized to obtain the target population.

[0194] In this context, an individual in the target population is also a set of optimization variable parameters.

[0195] Based on this, and using the target population, optimization variables can be determined for the (n+1)th iteration, to be used in the (n+1)th iteration.

[0196] According to embodiments of this disclosure, during the multi-objective optimization of the optimization variables in the nth iteration, an initial population is generated from the optimization variables in the nth iteration. This allows for the optimization analysis of the optimization variables in the nth iteration using a multi-objective optimization algorithm, based on multiple objective optimization functions and the initial population, to generate a target population. Based on this target population, optimization variables for the (n+1)th iteration can be determined for use in subsequent (n+1)th iterations.

[0197] According to embodiments of this disclosure, the above-mentioned method for determining the optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization further includes: obtaining multiple weight vectors based on multiple objective optimization functions, wherein each weight vector corresponds one-to-one with an individual in the initial population; calculating the function value of each individual in the initial population for each of the multiple objective optimization functions based on the multiple objective optimization functions; determining the optimal value for any objective optimization function based on the function value of each individual in the initial population for any objective optimization function for any objective optimization function; and determining the individual corresponding to the optimal value for any objective optimization function as the ideal individual for any objective optimization function.

[0198] According to embodiments of this disclosure, initialization is required before performing optimization analysis on the optimization variables of the nth iteration.

[0199] Specifically, the initialization process includes: obtaining multiple weight vectors based on multiple objective optimization functions; calculating the function values ​​of each individual in the initial population for each of the multiple objective optimization functions; determining the optimal value for any objective optimization function based on the function values ​​of each individual in the initial population for any objective optimization function; and determining the individual corresponding to the optimal value for any objective optimization function as the ideal individual for any objective optimization function.

[0200] According to embodiments of this disclosure, multiple weight vectors are obtained based on multiple objective optimization functions. The dimension of each weight vector is the same as the number of objective optimization functions, and the weight of each objective optimization function in the weight vector represents its importance to the overall optimization objective. Thus, the weight vectors are obtained based on the number of objective optimization functions and the characteristics of the optimization objective, thereby transforming a multi-objective problem into multiple single-objective optimization sub-problems.

[0201] Based on this, the Euclidean distance between each pair of weight vectors is calculated, and for each weight vector, the T weight vectors closest to it are found based on the Euclidean distance. These T weight vectors form the neighborhood B(i) of the weight vector. ,in, arrive The indices of the T nearest neighbor weight vectors.

[0202] In one embodiment, the neighborhood serves to limit the scope of information exchange between subproblems, providing a basis for solution updates in subsequent iterations. Therefore, the neighborhood can serve as the primary local range for individuals in the population to reference and interact with during subsequent iterations.

[0203] According to the embodiments of this disclosure, for each individual in the initial population, based on the optimization variable parameters corresponding to each individual, the function value of each individual for each objective optimization function is calculated by the above formulas (9) and (10).

[0204] Based on this, for any one of the multiple objective optimization functions, the optimal value for that objective optimization function is determined based on the function value of each individual in the initial population for that objective optimization function.

[0205] In one embodiment, the optimal value for the objective optimization function can represent the maximum function value for the objective optimization function. The individual corresponding to the optimal value for any objective optimization function is then determined as the ideal individual for any objective optimization function.

[0206] According to embodiments of this disclosure, before performing optimization analysis on the optimization variables of the nth iteration, multiple weight vectors for multiple objective optimization functions, the optimal value for any objective optimization function, and the ideal individual are obtained first, for use in the subsequent optimization analysis process of the optimization variables.

[0207] Figure 13 A flowchart illustrating the generation of a target population according to an embodiment of the present disclosure is shown schematically.

[0208] like Figure 13 As shown, the method 1300 includes operations S1310 to S1370.

[0209] In operation S1310, if it is determined that the number of iterations does not meet the termination condition, for any individual in the first group, the neighborhood of any individual in the first group is determined.

[0210] In the first cycle, the first population is the initial population.

[0211] According to embodiments of this disclosure, the termination condition can characterize the number of loops reaching the maximum number of iterations, wherein the maximum number of iterations is set as needed.

[0212] According to embodiments of this disclosure, for any individual in the first group, the domain of that individual is determined from the first group. Specifically, the P individuals closest to that individual are found from the first group.

[0213] In operation S1320, the first and second individuals are selected from the neighborhood of any individual.

[0214] According to embodiments of this disclosure, a first individual and a second individual are randomly selected from the neighborhood of any individual.

[0215] In operation S1330, based on preset parameters, genetic operations are performed on the first and second individuals to obtain the third individual.

[0216] According to embodiments of this disclosure, preset parameters can characterize parameters related to genetic operators, such as crossover probability, mutation probability, etc.

[0217] According to embodiments of this disclosure, based on preset parameters, a third individual is obtained by performing genetic operations (such as crossover and mutation) on a first individual and a second individual. The third individual represents a new individual.

[0218] In operation S1340, based on leakage inductance constraints and insulation constraints, the third body is optimized to obtain the fourth body.

[0219] According to embodiments of this disclosure, a heuristic method can be applied to optimize a third individual based on leakage inductance constraints and insulation constraints to obtain a fourth individual. The fourth individual represents the optimized individual.

[0220] In operation S1350, the first population is updated based on the fourth individual and multiple objective optimization functions to obtain the second population.

[0221] The second population represents the new first population.

[0222] According to embodiments of this disclosure, based on a fourth individual and multiple objective optimization functions, the function values ​​of the fourth individual for each of the multiple objective optimization functions can be calculated. Based on the function values ​​of the fourth individual for each of the multiple objective optimization functions, the individuals that need to be replaced in the first group can be determined, thereby updating the first group and obtaining the second group.

[0223] In operation S1360, it is determined whether the number of loop iterations meets the termination condition.

[0224] According to an embodiment of this disclosure, if the number of iterations does not meet the termination condition, operations S1310 to S1350 are performed; if the number of iterations meets the termination condition, operation S1370 is performed.

[0225] In operation S1370, the second population obtained when the number of iterations meets the termination condition is determined as the target population.

[0226] According to embodiments of this disclosure, a new individual (a third individual) is obtained by performing genetic operations on a first individual and a second individual randomly selected from the neighborhood of any individual; and the third individual is optimized based on leakage inductance constraints and insulation constraints to improve the quality of the obtained third individual. Thus, the first population can be updated based on the function values ​​of the fourth individual for each of the multiple objective optimization functions until the number of iterations meets the termination condition, at which point the target population is determined, and the target population can be used in the subsequent (n+1)th iteration.

[0227] According to embodiments of this disclosure, the termination condition can also be that the convergence accuracy of the population reaches a preset standard.

[0228] According to embodiments of this disclosure, updating the first group to obtain a second group based on a fourth individual and multiple objective optimization functions includes: for any one of the multiple objective optimization functions, determining a predetermined number of weight vectors closest to the fourth individual as the neighborhood of the fourth individual based on the Euclidean distance between the fourth individual and each of the multiple weight vectors; calculating the function value of the fifth individual corresponding to each weight vector in the neighborhood of the fourth individual for any objective optimization function; identifying a target individual among the multiple fifth individuals whose function value for any objective optimization function is less than the function value of the fourth individual for the objective optimization function; adjusting the target individual based on the ideal individual for any objective optimization function to obtain a new target individual; and replacing the target individual in the first group with the new target individual to obtain the second group.

[0229] According to embodiments of this disclosure, for any objective optimization function, the Euclidean distance between the fourth individual and each of the multiple weight vectors is calculated, thereby selecting a preset number of weight vectors that are closest to the fourth individual. The determined preset objective weight vectors can characterize the neighborhood of the fourth individual.

[0230] According to embodiments of this disclosure, since there is a one-to-one correspondence between the weight vectors in the neighborhood and the individuals in the population, the fifth individual in the first population corresponding to each weight vector in the neighborhood of the fourth individual can be determined first. Then, the function value of the fifth individual for any objective optimization function can be calculated.

[0231] Since the function value of the fifth individual for any objective function is less than the function value of the fourth individual for the same objective function, it indicates that the fourth individual performs better than the fifth individual. Therefore, we determine the target individual among the multiple fifth individuals whose function value for any objective function is less than the function value of the fourth individual for the same objective function.

[0232] Based on this, the target individuals are adjusted according to the ideal individuals for any objective optimization function to obtain new target individuals. Thus, the target individuals in the first group can be replaced with the new target individuals to obtain the second group.

[0233] In one embodiment, after obtaining the fourth individual, the function value of the fourth individual with respect to any objective optimization function can be calculated, and it can be determined whether the function value of the fourth individual with respect to any objective optimization function is greater than the function value of the ideal individual with respect to any objective optimization function. If the function value of the fourth individual with respect to any objective optimization function is greater than the function value of the ideal individual with respect to any objective optimization function, the ideal individual with respect to any objective optimization function is updated based on the fourth individual, resulting in a new ideal individual, i.e., the new ideal individual is the fourth individual. If the function value of the fourth individual with respect to any objective optimization function is less than or equal to the function value of the ideal individual with respect to any objective optimization function, then there is no need to update the ideal individual.

[0234] According to embodiments of this disclosure, a neighborhood for the fourth individual is determined based on the Euclidean distance between the fourth individual and each of the multiple weight vectors. The neighborhood solution is then updated based on the function value of any objective optimization function among the multiple fifth individuals, thus updating the first population and obtaining the second population. Therefore, updating the first population with multiple objective optimization functions as the core objective allows for faster convergence to the optimal frontier. Furthermore, since the ideal individual guides the overall convergence direction of the population, and the neighborhood solution is used for refined searching under the guidance of the ideal individual, both are centered on the function value of the objective optimization function. Their cooperation allows the algorithm to converge to the optimal solution set while maintaining solution diversity.

[0235] According to embodiments of this disclosure, based on the second population, the crowding degree of the second population can be calculated to measure the density of the solution distribution in the target space, and sorted by crowding degree. Based on the sorting result, new values ​​are assigned to the preset parameters of each subproblem from the preset parameter set (regions with high crowding degree may be assigned parameters that are more conducive to exploration, and regions with low crowding degree may be assigned parameters that are more conducive to convergence).

[0236] In one embodiment, based on the electromagnetic and thermal theoretical formulas of high-frequency transformers, the reasonable range of values ​​for key parameters is derived, and then "theoretically feasible parameter combinations" are selected in combination with engineering specifications, thereby forming a preset parameter set.

[0237] In one embodiment, the crowding level and preset parameters can be recalculated at preset intervals of a predetermined number of cycles to adapt to changes in population distribution. For example, the preset number of cycles could be 50.

[0238] Based on the above, a multi-objective optimization algorithm is used to optimize the variables of the nth iteration. By retaining high-quality individuals in the current generation and dynamically adjusting the search direction, the algorithm can converge to the optimal frontier more quickly, thus improving the convergence speed. By optimizing the local search direction and progress, a more uniform and diverse non-dominated solution set can be generated, providing decision-makers with more choices and enhancing the diversity of the solution set. An optimization mechanism is introduced to dynamically adjust the search speed of individuals, further improving the algorithm's global search capability, avoiding getting trapped in local optima, and enhancing the global search capability.

[0239] This disclosure presents a method for determining optimal parameters of high-frequency transformers based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization. The method focuses on the temperature rise calculation and optimization design of large-capacity high-frequency transformers. It constructs an electromagnetic-thermal bidirectional coupling simulation model based on ANSYS. Workbench enables data interaction between Maxwell and Icepak, revealing the variation patterns of core and winding losses and temperature, thus improving computational accuracy compared to unidirectional coupling. The proposed hybrid mesh partitioning employs hexahedral mapping and local refinement for key regions such as windings and cores, while using free tetrahedral partitioning for the air domain. This effectively controls the total mesh size while capturing fine features such as the skin effect and proximity effect at the Litz line edge, significantly improving the efficiency and accuracy of electromagnetic-thermal bidirectional coupling simulation. The simulation results accurately represent the temperature distribution characteristics of the transformer, with the upper part of the core being a hotspot region, consistent with loss distribution and heat dissipation conditions. With efficiency and power density as core objectives, while considering leakage inductance and insulation constraints, the algorithm achieves faster convergence and a Pareto front that is closer to the ideal state by dynamically adjusting the search direction and enhancing solution set diversity. The optimized transformer exhibits a synergistic improvement in efficiency and power density, validating the superiority of this algorithm in multi-objective optimization of high-frequency transformers.

[0240] Therefore, the method for determining the optimization parameters of high-frequency transformers based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization disclosed herein provides a complete and effective solution for temperature rise prediction and performance optimization of large-capacity high-frequency transformers, and has important reference significance for improving the design level and engineering application value of high-frequency transformers.

[0241] Based on the above-mentioned method for determining the optimization parameters of high-frequency transformers using electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization, this disclosure also provides a device for determining the optimization parameters of high-frequency transformers using electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization. The following will combine... Figure 14 The device is described in detail.

[0242] Figure 14 The diagram illustrates the structure of a high-frequency transformer optimization parameter determination device based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization according to an embodiment of the present disclosure.

[0243] like Figure 14 As shown, the high-frequency transformer optimization parameter determination device 1400 based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization in this embodiment includes a calculation module 1410, a first determination module 1420, a simulation module 1430, an optimization module 1440, and a second determination module 1450.

[0244] The calculation module 1410 is used in the nth iteration to calculate multiple sets of losses of the transformer to be optimized under a non-sinusoidal excitation signal, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration. The optimization variables for the nth iteration include multiple sets of optimization variable parameters, and each optimization variable parameter corresponds one-to-one with a loss. n is an integer greater than or equal to 1, and the optimization variables for the first iteration are the initial optimization variables. In one embodiment, the calculation module 1410 can be used to perform the operation S210 described above, which will not be repeated here.

[0245] The first determining module 1420 is used to determine, from multiple sets of optimization variable parameters, the target optimization variable parameters of the transformer to be optimized, which satisfy the insulation distance constraint, the leakage inductance value constraint, and minimize the loss. In one embodiment, the first determining module 1420 can be used to perform the operation S220 described above, which will not be repeated here.

[0246] The simulation module 1430 is used to perform electromagnetic-thermal bidirectional coupling simulation of the transformer to be optimized based on the fixed parameters and target optimization variable parameters of the transformer to be optimized, and to obtain the temperature rise result of the transformer to be optimized. In one embodiment, the simulation module 1430 can be used to execute the operation S230 described above, which will not be repeated here.

[0247] The optimization module 1440 is used to perform multi-objective optimization on the optimization variables of the nth iteration when it is determined that the temperature rise result of the transformer to be optimized does not meet the preset conditions, so as to obtain the optimization variables for the (n+1)th iteration, and then use the optimization variables of the (n+1)th iteration to execute the (n+1)th iteration. In one embodiment, the optimization module 1440 can be used to execute the operation S250 described above, which will not be repeated here.

[0248] The second determining module 1450 is used to determine the target optimization variable parameter for the temperature rise result as the optimization parameter of the transformer to be optimized when the temperature rise result of the transformer to be optimized meets the preset conditions. In one embodiment, the second determining module 1450 can be used to perform the operation S260 described above, which will not be repeated here.

[0249] According to embodiments of this disclosure, the calculation module includes a first calculation submodule, an area equivalence submodule, and a second calculation submodule.

[0250] The first calculation submodule is used to calculate the core loss of the transformer to be optimized based on the loss coefficient related to the transformer core material, the duty cycle of the non-sinusoidal excitation signal, the frequency of the non-sinusoidal excitation signal, and the peak value of the magnetic induction intensity of the non-sinusoidal excitation signal in the fixed parameters.

[0251] The area equivalence submodule is used to perform area equivalence processing on the Litz wire winding of the transformer to be optimized for any set of optimization variable parameters in the nth iteration, based on fixed parameters and parameters related to the Litz wire winding in any set of optimization variable parameters, to obtain the equivalent diameter of the single-strand round conductor in the Litz wire of the primary winding and secondary winding of the transformer to be optimized.

[0252] The second calculation submodule is used to calculate the winding loss of the transformer to be optimized based on the equivalent diameter of a single-strand round conductor in the Litz line, fixed parameters, and any set of optimization variable parameters; wherein, the set of losses of the transformer to be optimized includes core loss and winding loss for any set of optimization variable parameters.

[0253] According to embodiments of this disclosure, the second computing submodule includes a first computing unit, a second computing unit, a third computing unit, a fourth computing unit, and a fifth computing unit.

[0254] The first calculation unit is used to calculate the skin depth based on the frequency of the non-sinusoidal excitation signal, the permeability of the vacuum, and the conductivity of the Litz line in the fixed parameters.

[0255] The second calculation unit is used to calculate the harmonic AC resistance factor of the primary and secondary windings based on the equivalent diameter of the single-strand round conductor in the Litz wire of the primary and secondary windings, as well as the diameter, skin depth, number of strands, and number of winding layers of the single-strand round conductor in the Litz wire of the primary and secondary windings, respectively, in the fixed parameters.

[0256] The third calculation unit is used to calculate the DC resistance of the primary and secondary windings based on the fixed parameters, namely, the number of winding layers, the number of turns per winding layer, the average turn length of the winding, the conductivity of the Litz wire, the diameter of the single-strand round conductor in the Litz wire, and the number of strands of the Litz wire.

[0257] The fourth calculation unit is used to calculate the effective current value of the mth harmonic of the primary and secondary windings based on the leakage inductance value of the primary and secondary windings, the harmonic order, and the winding voltage, minimum phase shift angle, DC-DC conversion ratio, and frequency of the non-sinusoidal excitation signal for the primary and secondary windings, respectively. The leakage inductance value is determined based on any set of optimized variable parameters.

[0258] The fifth calculation unit is used to calculate the winding loss of the transformer to be optimized based on the harmonic AC resistance factor, DC resistance and RMS current value of the primary and secondary windings under different harmonics in the non-sinusoidal excitation signal.

[0259] According to embodiments of this disclosure, simulation module 1430 includes an establishment submodule, a first simulation submodule, and a second simulation submodule.

[0260] A submodule is established to create a simulation model for the transformer to be optimized, based on the fixed parameters of the transformer to be optimized and the target optimization variable parameters.

[0261] The first simulation submodule is used to perform electromagnetic simulation of the transformer to be optimized using a simulation model, and obtain the simulation loss of the transformer to be optimized.

[0262] The second simulation submodule is used to perform thermal simulation of the transformer to be optimized based on the simulation loss of the transformer to be optimized, and obtain the temperature rise result of the transformer to be optimized.

[0263] According to embodiments of this disclosure, the second simulation submodule includes mesh partitioning units and simulation units.

[0264] Mesh partitioning elements are used to perform tetrahedral mesh partitioning on non-critical areas of the simulation model, local refinement and hexahedral mesh mapping on the winding and core parts of the simulation model, and local refinement and hexahedral mesh mapping on the bent parts of the Litz wire in the simulation model, to obtain the mesh partitioned simulation model.

[0265] The simulation unit is used to perform thermal simulation of the transformer to be optimized based on the simulation loss of the transformer to be optimized, and obtain the temperature rise results of the transformer to be optimized through the simulation model after meshing.

[0266] According to embodiments of this disclosure, the optimization module 1440 includes a first generation submodule, a second generation submodule, and a first determination submodule.

[0267] The first generation submodule is used to generate an initial population based on the optimization variables of the nth iteration, wherein the individuals in the initial population represent a set of optimization variable parameters.

[0268] The second generation submodule is used to perform optimization analysis on the optimization variables of the nth iteration based on multiple objective optimization functions and the initial population, and generate the target population.

[0269] The first determination submodule is used to determine the optimization variables for n+1 iterations based on the target population.

[0270] According to embodiments of this disclosure, the optimization module further includes an acquisition submodule, a third calculation submodule, a second determination submodule, and a third determination submodule.

[0271] The submodule is used to obtain multiple weight vectors based on multiple objective optimization functions, where each weight vector corresponds one-to-one with an individual in the initial population.

[0272] The third calculation submodule is used to calculate the function value of each individual in the initial population for each of the multiple objective optimization functions.

[0273] The second determination submodule is used to determine the optimal value for any objective optimization function among multiple objective optimization functions, based on the function values ​​of each individual in the initial population for any objective optimization function.

[0274] The third determination submodule is used to determine the individual corresponding to the optimal value for any objective optimization function as the ideal individual for any objective optimization function.

[0275] According to embodiments of this disclosure, the second generation submodule includes a first determining unit, a selecting unit, an obtaining unit, an optimizing unit, an updating unit, and a second determining unit.

[0276] The first determining unit is used to determine the neighborhood of any individual in the first population when the number of determined cycles does not meet the termination condition; wherein, in the first cycle, the first population is the initial population.

[0277] The selection unit is used to select the first and second individuals from the neighborhood of any individual.

[0278] The acquisition unit is used to perform genetic operations on the first and second individuals based on preset parameters to obtain the third individual.

[0279] An optimization unit is used to optimize the third body based on leakage inductance constraints and insulation constraints to obtain the fourth body.

[0280] The update unit is used to update the first population based on the fourth individual and multiple objective optimization functions to obtain the second population, where the second population represents the new first population.

[0281] The second determining unit is used to determine the second population obtained when the number of iterations meets the termination condition as the target population.

[0282] According to embodiments of this disclosure, the updating unit includes a first determining subunit, a calculation submodule, a second determining subunit, an adjustment subunit, and a replacement subunit.

[0283] The first determining sub-unit, for any of the multiple objective optimization functions, determines the neighborhood of the fourth individual based on the Euclidean distance between the fourth individual and each of the multiple weight vectors, and the number of weight vectors closest to the fourth individual.

[0284] The computational subunit is used to calculate the function value of the fifth individual for any objective optimization function, corresponding to each weight vector in the neighborhood of the fourth individual.

[0285] The second determining subunit is used to determine the target individual among multiple fifth individuals whose function value for any objective optimization function is less than the function value of the fourth individual for the objective optimization function;

[0286] The adjustment sub-unit is used to adjust the target individual based on the ideal individual for any objective optimization function, so as to obtain a new target individual;

[0287] The replacement subunit is used to replace the target individual in the first group with a new target individual to obtain the second group.

[0288] According to embodiments of this disclosure, any plurality of modules among the calculation module 1410, the first determining module 1420, the simulation module 1430, the optimization module 1440, and the second determining module 1450 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least a portion of the functionality of one or more of these modules may be combined with at least a portion of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the calculation module 1410, the first determining module 1420, the simulation module 1430, the optimization module 1440, and the second determining module 1450 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the calculation module 1410, the first determining module 1420, the simulation module 1430, the optimization module 1440, and the second determining module 1450 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0289] Figure 15 A block diagram of an electronic device suitable for implementing a method for determining high-frequency transformer optimization parameters based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization, according to embodiments of the present disclosure, is illustrated.

[0290] like Figure 15 As shown, an electronic device 1500 according to an embodiment of the present disclosure includes a processor 1501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1502 or a program loaded from a storage portion 1508 into a random access memory (RAM) 1503. The processor 1501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1501 may also include onboard memory for caching purposes. The processor 1501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0291] RAM 1503 stores various programs and data required for the operation of electronic device 1500. Processor 1501, ROM 1502, and RAM 1503 are interconnected via bus 1504. Processor 1501 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 1502 and / or RAM 1503. It should be noted that the programs may also be stored in one or more memories other than ROM 1502 and RAM 1503. Processor 1501 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0292] According to embodiments of this disclosure, the electronic device 1500 may further include an input / output (I / O) interface 1505, which is also connected to a bus 1504. The electronic device 1500 may also include one or more of the following components connected to the input / output (I / O) interface 1505: an input section 1506 including a keyboard, mouse, etc.; an output section 1507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1508 including a hard disk, etc.; and a communication section 1509 including a network interface card such as a LAN card, modem, etc. The communication section 1509 performs communication processing via a network such as the Internet. A drive 1510 is also connected to the input / output (I / O) interface 1505 as needed. A removable medium 1511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1510 as needed so that computer programs read from it can be installed into the storage section 1508 as needed.

[0293] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0294] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 1502 and / or RAM 1503 and / or one or more memories other than ROM 1502 and RAM 1503 described above.

[0295] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the high-frequency transformer optimization parameter determination method based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization provided in embodiments of this disclosure.

[0296] When the computer program is executed by the processor 1501, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0297] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1509, and / or installed from the removable medium 1511. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0298] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 1509, and / or installed from the removable medium 1511. When the computer program is executed by the processor 1501, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0299] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0300] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0301] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0302] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A method for determining optimization parameters of a high-frequency transformer based on electromagnetic-thermal bidirectional coupling simulation and multi-objective optimization, comprising repeatedly performing the following operations until a preset condition is met: In the nth iteration, based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, calculating multiple sets of losses of the transformer to be optimized under a non-sinusoidal excitation signal, wherein, The optimization variables for the nth iteration include multiple sets of optimization variable parameters. The optimization variable parameters are in one-to-one correspondence with the loss. n is an integer greater than or equal to 1. The optimization variables for the first iteration are the initial optimization variables. From the multiple sets of optimization variable parameters, determine the target optimization variable parameters for the transformer to be optimized, which satisfy the insulation constraint for insulation distance, the leakage inductance value for leakage inductance constraint, and the minimum loss. Based on the fixed parameters and target optimization variable parameters of the transformer to be optimized, an electromagnetic-thermal bidirectional coupling simulation is performed on the transformer to be optimized to obtain the temperature rise result of the transformer to be optimized. If the temperature rise result of the transformer to be optimized does not meet the preset condition, multi-objective optimization is performed on the optimization variables of the nth iteration to obtain the optimization variables for the (n+1)th iteration, so as to use the optimization variables of the (n+1)th iteration to execute the (n+1)th iteration. If the temperature rise result of the transformer to be optimized is determined to meet the preset conditions, the target optimization variable parameter for the temperature rise result is determined as the optimization parameter of the transformer to be optimized.

2. The method according to claim 1, wherein, Based on the fixed parameters of the transformer to be optimized and the optimization variables for the nth iteration, multiple sets of losses of the transformer to be optimized under non-sinusoidal excitation signals are calculated, including: The core loss of the transformer to be optimized is calculated based on the loss coefficient related to the transformer core material, the duty cycle of the non-sinusoidal excitation signal, the frequency of the non-sinusoidal excitation signal, and the peak value of the magnetic induction intensity of the non-sinusoidal excitation signal in the fixed parameters. For any set of optimization variable parameters in the nth iteration, based on the fixed parameters and the parameters related to the Litz wire winding in any set of optimization variable parameters, the Litz wire winding of the transformer to be optimized is subjected to area equivalence processing to obtain the equivalent diameter of the single-strand round conductor in the Litz wire of the primary winding and the secondary winding of the transformer to be optimized. Based on the equivalent diameter of a single-strand round conductor in the Litz line, the fixed parameters, and any set of optimization variable parameters, the winding loss of the transformer to be optimized is calculated. The set of losses of the transformer to be optimized includes the core loss and the winding loss for any set of optimization variable parameters.

3. The method according to claim 2, wherein, The winding loss of the transformer to be optimized is calculated based on the equivalent diameter of a single-strand round conductor in the Litz line, the fixed parameters, and the set of optimization variable parameters, including: The skin depth is calculated based on the frequency of the non-sinusoidal excitation signal, the vacuum permeability, and the conductivity of the Litz wire in the fixed parameters. Based on the equivalent diameter of the single-strand round conductor in the Litz wire of the primary and secondary windings, and the diameter, skin depth, number of strands, and number of winding layers of the single-strand round conductor in the Litz wire of the primary and secondary windings respectively, the harmonic AC resistance factor of the primary and secondary windings is calculated. Based on the fixed parameters, namely the number of winding layers, the number of turns per winding layer, the average turn length of the winding, the conductivity of the Litz wire, the diameter of the single-strand round conductor in the Litz wire, and the number of strands of the Litz wire, the DC resistance of the primary winding and the secondary winding is calculated. For the m-th harmonic in the non-sinusoidal excitation signal, the effective current value of the m-th harmonic of each of the primary and secondary windings is calculated based on the leakage inductance value of the primary winding and the secondary winding, the harmonic order, and the winding voltage, minimum phase shift angle, DC-DC conversion ratio, and frequency of the non-sinusoidal excitation signal for the primary and secondary windings, respectively, in the fixed parameters. The leakage inductance value is determined based on any set of optimized variable parameters. The winding loss of the transformer to be optimized is calculated based on the harmonic AC resistance factor, DC resistance, and effective current value for the primary and secondary windings under different harmonics in the non-sinusoidal excitation signal.

4. The method according to claim 3, wherein, The optimization variable parameters include the insulation distance of the transformer to be optimized, the insulation constraint being that the insulation distance of the transformer to be optimized is greater than or equal to a preset insulation distance; and the leakage inductance constraint being that the leakage inductance value of the transformer to be optimized is greater than or equal to a preset leakage inductance value.

5. The method according to claim 1, wherein, The method involves performing an electromagnetic-thermal bidirectional coupling simulation on the transformer to be optimized based on its fixed parameters and target optimization variable parameters, to obtain the temperature rise results of the transformer to be optimized, including: Based on the fixed parameters and target optimization variable parameters of the transformer to be optimized, a simulation model for the transformer to be optimized is established. Using the simulation model, electromagnetic simulation is performed on the transformer to be optimized to obtain the simulated loss of the transformer to be optimized. Based on the simulated loss of the transformer to be optimized, a thermal simulation of the transformer to be optimized is performed using the simulation model to obtain the temperature rise result of the transformer to be optimized.

6. The method according to claim 5, wherein, The process involves performing thermal simulations on the transformer to be optimized based on the simulation losses, using the simulation model to obtain the temperature rise results for the transformer to be optimized, including: The non-critical areas in the simulation model are subjected to tetrahedral meshing, the winding and core parts in the simulation model are subjected to local refinement and hexahedral mesh mapping, and the bent parts of the Litz wire in the simulation model are subjected to local refinement and hexahedral mesh mapping, to obtain the meshed simulation model. Based on the simulation loss of the transformer to be optimized, thermal simulation is performed on the transformer to be optimized using the simulation model after mesh partitioning, and the temperature rise result of the transformer to be optimized is obtained.

7. The method according to claim 1, wherein, The multi-objective optimization of the optimization variables in the nth iteration to obtain the optimization variables for the (n+1)th iteration includes: Based on the optimization variables of the nth iteration, an initial population is generated, wherein each individual in the initial population represents a set of optimization variable parameters; Based on multiple objective optimization functions and the initial population, the optimization variables of the nth iteration are optimized and analyzed to generate the target population; Based on the target population, the optimization variables for the (n+1)th iteration are determined.

8. The method according to claim 7, wherein, Also includes: Based on the multiple objective optimization functions, multiple weight vectors are obtained, wherein each weight vector corresponds one-to-one with an individual in the initial population; Based on the plurality of objective optimization functions, calculate the function value of each individual in the initial population for each of the plurality of objective optimization functions; For any one of the plurality of objective optimization functions, the optimal value for any one objective optimization function is determined based on the function value of each individual in the initial population for that objective optimization function. The individual corresponding to the optimal value for any of the objective optimization functions is determined as the ideal individual for any of the objective optimization functions.

9. The method according to claim 8, wherein the step of performing optimization analysis on the optimization variables of the nth iteration based on multiple objective optimization functions and the initial population to generate the target population includes repeatedly performing the following operations until the termination condition is met: If the termination condition is not met after a certain number of iterations, for any individual in the first group... Determine the neighborhood of any individual from the first population; wherein, In the first cycle, the first population is the initial population; Select the first individual and the second individual from the neighborhood of any one of the individuals; Based on preset parameters, genetic operations are performed on the first individual and the second individual to obtain a third individual; Based on leakage inductance constraints and insulation constraints, the third body is optimized to obtain the fourth body; Based on the fourth individual and the plurality of objective optimization functions, the first population is updated to obtain a second population, wherein the second population represents the new first population; The second population obtained when the number of iterations meets the termination condition is determined as the target population.

10. The method according to claim 9, wherein, The step of updating the first population based on the fourth individual and the plurality of objective optimization functions to obtain the second population includes: for any one of the plurality of objective optimization functions... Based on the Euclidean distance between the fourth individual and each of the multiple weight vectors, a preset number of weight vectors that are closest to the fourth individual are determined as the neighborhood of the fourth individual; Calculate the function value of the fifth individual for any objective optimization function corresponding to each weight vector in the neighborhood of the fourth individual; Identify a target individual among multiple fifth individuals whose function value for any objective optimization function is less than the function value of the fourth individual for the objective optimization function; Based on the ideal individual for any of the objective optimization functions, the objective individual is adjusted to obtain a new objective individual; The target individuals in the first population are replaced with the new target individuals to obtain the second population.

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