A method and system for evaluating the conversion efficiency of a light-weight medium-high frequency transformer

Abstract

The present application relates to the technical field of transformer evaluation, and particularly relates to a light-weight medium-high frequency transformer conversion efficiency evaluation method and system, which comprises the following steps: obtaining structure winding parameters, light-weight parameters and operation condition parameters of a transformer to be evaluated, and constructing an evaluation data set; constructing an efficiency evaluation model, which at least comprises an iron loss sub-model and a copper loss sub-model, and setting model parameters of the iron loss sub-model and the copper loss sub-model as to-be-identified parameters; obtaining test data corresponding to the transformer to be evaluated, and performing parameter calibration on the to-be-identified parameters; inputting the evaluation data set into the calibrated efficiency evaluation model, outputting loss decomposition results and efficiency results of each working condition point, and obtaining conversion efficiency evaluation results. Through the present application, the problem that the efficiency evaluation of a light-weight medium-high frequency transformer in the prior art relies on experience or single-point testing and is difficult to model and calibrate in multiple working conditions, thereby leading to large deviation, is effectively solved, and accurate evaluation of multiple working condition loss decomposition and conversion efficiency is realized.

Description

Technical Field

[0001] This invention relates to the field of transformer evaluation technology, and in particular to a lightweight method and system for evaluating the conversion efficiency of medium- and high-frequency transformers. Background Technology

[0002] With the development of power electronics technology, medium- and high-frequency transformers are widely used in solid-state transformers (SST), on-board chargers (OBC), DC / DC converters, and energy storage conversion systems. Compared with power frequency transformers, medium- and high-frequency transformers have significantly higher operating frequencies, which can effectively reduce the size of magnetic components and increase the power density of the system. Therefore, lightweight medium- and high-frequency transformers have become an important basic component for realizing equipment miniaturization and high power density.

[0003] However, the transformer loss mechanism under medium- and high-frequency conditions differs significantly from that at power frequency: core losses exhibit strong nonlinear changes with increasing frequency, winding AC losses are significantly amplified by the skin effect and proximity effect, and lightweight designs are typically accompanied by structural changes such as reduced insulation thickness and more compact winding spacing, making parasitic effects more prominent and further complicating the variation patterns of iron and copper losses. Therefore, accurately evaluating the conversion efficiency of transformers under lightweight structural constraints and multiple operating conditions at medium- and high frequencies is a key issue in current engineering design and performance optimization. Existing technologies for efficiency evaluation of lightweight medium- and high-frequency transformers typically employ empirical estimation or single-condition testing, making it difficult to effectively model and calibrate iron and copper losses at discrete operating points at medium- and high frequencies. This results in significant deviations between the evaluation results and actual operating efficiency, failing to meet the need for accurate conversion efficiency evaluation of lightweight medium- and high-frequency transformers under multiple operating conditions. Summary of the Invention

[0004] This invention provides a method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers, the method comprising: Obtain the structural winding parameters, lightweighting parameters, and operating condition parameters of the transformer to be evaluated, and construct an evaluation dataset containing several medium- and high-frequency discrete operating condition points; An efficiency evaluation model is constructed based on the evaluation dataset. The efficiency evaluation model includes at least an iron loss model and a copper loss model, and the model parameters of the iron loss model and the copper loss model are set as parameters to be identified. Obtain test data corresponding to the transformer to be evaluated, and perform parameter calibration on the parameters to be identified based on the test data; The evaluation dataset is input into the calibrated efficiency evaluation model, which outputs the loss decomposition results and efficiency results for each operating point, and the conversion efficiency evaluation results are obtained.

[0006] Furthermore, the iron loss sub-model dynamically adjusts the weighting coefficients of the high-frequency eddy current loss term based on lightweight structural parameters, wherein the lightweight structural parameters include at least the insulation layer thickness parameter; the copper loss sub-model generates a proximity effect compensation factor based on the winding compaction spacing, and corrects the winding loss based on the proximity effect compensation factor.

[0007] Furthermore, the weighting coefficients of the high-frequency eddy current loss term are dynamically adjusted based on the lightweight structural parameters, including: Establish a mapping relationship between insulation layer reduction thickness and eddy current loss sensitivity; Based on the mapping relationship, calculate the adjustment amount of the weighting coefficient for the high-frequency eddy current loss term; The weight coefficients are updated based on the weight coefficient adjustment amount and the preset ratio parameter.

[0008] Furthermore, a proximity effect compensation factor is generated based on the compacted winding spacing, and the winding loss is corrected based on the proximity effect compensation factor, including: Obtain the winding compaction pitch and determine the intensity of the proximity effect based on the winding compaction pitch parameter; Based on the influence intensity of the proximity effect, a proximity effect compensation factor related to the frequency parameter is generated; The AC loss component of the winding is calculated based on the proximity effect compensation factor to obtain the corrected winding loss value.

[0009] Furthermore, the test data includes at least no-load test data and short-circuit test data, which are used to calibrate the parameters of the iron loss sub-model and the copper loss sub-model, respectively.

[0010] Furthermore, the evaluation dataset is input into the calibrated efficiency evaluation model, which outputs the loss decomposition results and efficiency results for each operating point, and the conversion efficiency evaluation results are obtained, including: For each of the aforementioned operating points, the iron loss value and the copper loss value are output respectively, and the efficiency value of the corresponding operating point is calculated based on the iron loss value and the copper loss value. Based on the efficiency values ​​of each medium- and high-frequency discrete operating point, discrete efficiency distribution data is generated, and the discrete efficiency distribution data is interpolated and reconstructed to generate efficiency map data covering a preset frequency range and a preset load range. Based on the iron loss value and copper loss value, copper loss distribution data and iron loss distribution data are generated, and the iron loss distribution data and copper loss distribution data are interpolated and reconstructed to generate iron loss map data and copper loss map data. The dominant loss type at each operating point is determined based on the iron loss map data and the copper loss map data. Based on the efficiency map data, the target operating condition range that meets the preset efficiency threshold is determined, and the target operating condition range and its corresponding dominant loss type are output as the conversion efficiency evaluation result.

[0011] Further, based on the efficiency map data, the target operating condition range that meets the preset efficiency threshold is determined, including: Extract the efficiency value corresponding to each operating point from the efficiency map data; The efficiency value is compared with the preset efficiency threshold to filter a set of candidate operating points whose efficiency value is not less than the preset efficiency threshold; Perform connectivity analysis on the candidate operating point set to determine at least one continuous operating condition region that satisfies the preset efficiency threshold; The at least one continuous operating condition region is defined as the target operating condition range.

[0012] Furthermore, parameter calibration is performed on the parameter to be identified based on the test data, including: The no-load test data is input into the iron loss sub-model. Under the corresponding no-load condition, the objective function is to minimize the error between the iron loss value calculated by the iron loss sub-model and the iron loss value measured in the test. The parameters to be identified in the iron loss sub-model are then optimized and solved. The short-circuit test data is input into the copper loss sub-model. Under the corresponding short-circuit condition, the objective function is to minimize the error between the copper loss value calculated by the copper loss sub-model and the copper loss value measured by the test, and to optimize and solve the parameters to be identified in the copper loss sub-model. The calibrated iron loss model is combined with the copper loss model to obtain the calibrated efficiency evaluation model.

[0013] A lightweight medium- and high-frequency transformer conversion efficiency evaluation system, the system comprising: The evaluation dataset construction module obtains the structural winding parameters, lightweighting parameters, and operating condition parameters of the transformer to be evaluated, and constructs an evaluation dataset containing several medium- and high-frequency discrete operating condition points. The evaluation model construction module constructs an efficiency evaluation model based on the evaluation dataset. The efficiency evaluation model includes at least an iron loss sub-model and a copper loss sub-model, and sets the model parameters of the iron loss sub-model and the copper loss sub-model as parameters to be identified. The parameter calibration module acquires test data corresponding to the transformer to be evaluated, and performs parameter calibration on the parameter to be identified based on the test data. The evaluation result generation module inputs the evaluation dataset into the calibrated efficiency evaluation model, outputs the loss decomposition results and efficiency results for each operating point, and obtains the conversion efficiency evaluation results.

[0014] Furthermore, the evaluation result generation module includes: The loss decomposition unit outputs iron loss value and copper loss value for each operating point, and calculates the efficiency value of the corresponding operating point based on the iron loss value and copper loss value. The efficiency reconstruction unit generates discrete efficiency distribution data based on the efficiency values ​​of each medium- and high-frequency discrete operating point, and performs interpolation reconstruction on the discrete efficiency distribution data to generate efficiency map data covering a preset frequency range and a preset load range. The loss reconstruction unit generates copper loss distribution data and iron loss distribution data based on the iron loss value and copper loss value, and performs interpolation reconstruction on the iron loss distribution data and copper loss distribution data to generate iron loss map data and copper loss map data. The dominant determination unit determines the dominant loss type for each of the operating points based on the iron loss map data and the copper loss map data. The operating condition output unit determines the target operating condition range that meets the preset efficiency threshold based on the efficiency map data, and outputs the target operating condition range and its corresponding dominant loss type as the conversion efficiency evaluation result.

[0015] The technical solution of this invention can achieve the following technical effects: Compared with existing technologies, this invention constructs a discrete operating condition evaluation dataset for medium- and high-frequency transformers by acquiring structural winding parameters, lightweighting parameters, and operating condition parameters. It establishes an efficiency evaluation model that includes at least an iron loss sub-model and a copper loss sub-model, and calibrates the model parameters by combining experimental data. This enables accurate prediction of transformer iron and copper losses and conversion efficiency under multiple operating conditions at medium and high frequencies, significantly reducing the deviation between efficiency evaluation results and actual operating efficiency, and meeting the high-precision evaluation requirements for conversion efficiency of lightweight medium- and high-frequency transformers.

[0016] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers; Figure 2 A flowchart illustrating the dynamic adjustment of the weighting coefficients for the high-frequency eddy current loss term; Figure 3 A schematic diagram of the process for generating a proximity effect compensation factor and correcting winding losses; Figure 4 A flowchart illustrating the process of outputting loss decomposition and efficiency results at each operating point and obtaining conversion efficiency evaluation results; Figure 5 A flowchart illustrating the process for determining the target operating condition range that meets a preset efficiency threshold. Figure 6 This is a schematic diagram of the process for calibrating the parameters to be identified based on experimental data. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] Example 1: like Figure 1 As shown, this application provides a method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers, the method comprising: S1: Obtain the structural winding parameters, lightweighting parameters, and operating condition parameters of the transformer to be evaluated, and construct an evaluation dataset containing several medium- and high-frequency discrete operating condition points; Specifically, the structural winding parameters preferably include key parameters that characterize the transformer's geometry and winding arrangement, such as core dimensions, number of turns, conductor cross-sectional dimensions, number of winding layers, winding connection method, and winding arrangement method. These parameters reflect the fundamental information on the impact of structural design and winding implementation on losses in lightweight medium- and high-frequency transformers. The lightweight parameters preferably include parameters that characterize the degree of lightweighting and its structural constraints, such as the overall transformer mass, core mass, winding mass, transformer external volume, or winding envelope volume. These parameters reflect the lightweight design goals and their constraints on material usage and structural arrangement. The operating condition parameters preferably include at least one of the following: frequency parameters, input and output voltage and current parameters, load level parameters, and temperature parameters. These parameters describe the electrical and thermal environmental conditions under which the transformer operates in actual operation. The frequency parameters are limited to the medium- and high-frequency range to ensure that the evaluation dataset can cover the efficiency variation patterns under medium- and high-frequency operating conditions. Regarding the parameter acquisition method, structural winding parameters and lightweighting parameters are preferably obtained from transformer design drawings, 3D modeling data, bill of materials, or prototype measurement data, while operating condition parameters are preferably obtained from system design indicators, control strategy settings, or test plan settings. In terms of constructing the evaluation dataset, the operating condition parameters are preferably discretized according to the preset medium- and high-frequency range and load variation range to generate several medium- and high-frequency discrete operating condition points. Each operating condition point is associated with and stored with the corresponding structural winding parameters and lightweighting parameters to form an evaluation dataset that can be directly used for subsequent efficiency evaluation model calls.

[0022] S2: Construct an efficiency evaluation model based on the evaluation dataset. The efficiency evaluation model includes at least an iron loss model and a copper loss model, and set the model parameters of the iron loss model and the copper loss model as parameters to be identified. Specifically, the efficiency evaluation model preferably adopts a hierarchical modeling structure, consisting of an iron loss sub-model and a copper loss sub-model. The iron loss sub-model characterizes the hysteresis loss and eddy current loss generated by the transformer core under medium- and high-frequency operating conditions, while the copper loss sub-model characterizes the conductor loss generated by the winding under current and its AC loss characteristics under medium- and high-frequency conditions. During model construction, the input variables for the iron loss sub-model and the copper loss sub-model are first determined based on the structural winding parameters and operating condition parameters included in the evaluation dataset, enabling each sub-model to independently calculate the corresponding loss value at different medium- and high-frequency discrete operating points. Subsequently, the iron loss value output by the iron loss sub-model and the copper loss value output by the copper loss sub-model are combined to characterize the total loss level of the transformer at the corresponding operating point, serving as the basis for efficiency calculation. Regarding model parameter settings, to avoid evaluation bias caused by relying on empirically fixed parameters, the loss-related parameters in both the iron loss sub-model and the copper loss model are preferably uniformly set as parameters to be identified, allowing for calibration with experimental data in subsequent steps, thus making the model closer to the actual loss characteristics of a specific transformer.

[0023] S3: Obtain test data corresponding to the transformer to be evaluated, and perform parameter calibration based on the test data for the parameters to be identified; Specifically, the test data preferably comes from bench tests or prototype tests conducted on the lightweight medium-high frequency transformer to be evaluated. The testing process is carried out within a preset medium-high frequency range and covers typical input voltage, current, and load variation conditions to ensure that the test data can reflect the actual laws of iron loss and copper loss changes with operating conditions during medium-high frequency operation. The test data collection preferably includes at least loss-related voltage, current, input power, output power, and temperature information. Among them, temperature information is used to reflect the trend of the impact of thermal environment changes on losses under lightweight design, which facilitates the improvement of calibration consistency and stability. During parameter calibration, the test data is preferably aligned with the operating condition descriptions in the evaluation dataset to ensure that each set of test records corresponds to one or more discrete medium-high frequency operating condition points. Subsequently, under the corresponding operating condition, the efficiency evaluation model is called to calculate the predicted loss, and the predicted loss is compared with the loss information measured in the test to form the error quantity used for calibration. In order to make the calibration process repeatable and engineering feasible, iterative update is preferably used to adjust the parameters to be identified: each iteration updates the parameters to be identified according to the magnitude and trend of the error quantity, so that the error quantity gradually decreases until the error meets the preset accuracy requirements or the iteration reaches the preset termination condition. Through the above calibration process, the efficiency evaluation model can more accurately reflect the iron loss and copper loss levels of the transformer under medium and high frequency multi-operating conditions, thus providing a reliable basis for the subsequent steps to output the loss decomposition results and efficiency results at each operating point.

[0024] S4: Input the evaluation dataset into the calibrated efficiency evaluation model, output the loss decomposition results and efficiency results for each operating point, and obtain the conversion efficiency evaluation results.

[0025] Specifically, the evaluation dataset is first fed into the calibrated efficiency evaluation model for calculation. Each discrete operating point in the evaluation dataset corresponds to a set of operating parameters, which are then correlated with the transformer's structural winding parameters and lightweighting parameters. During calculation, the efficiency evaluation model calls the iron loss sub-model and copper loss sub-model at each operating point to obtain the iron loss value and copper loss value, thus forming the loss decomposition result for that operating point. The loss decomposition result is preferably output in a traceable record format, allowing for a direct assessment of the contribution ratio of iron loss and copper loss under different operating conditions. Based on the above loss decomposition result, a corresponding efficiency result is further generated for each operating point. The efficiency result is preferably output in a list format corresponding one-to-one with the operating point, and is bound to the iron loss value and copper loss value for subsequent comparative analysis and visualization. To ensure that the evaluation output better aligns with the actual selection and operation strategies of medium- and high-frequency transformers, the preferred embodiment of this invention organizes the efficiency results under multiple operating conditions into an efficiency distribution covering a preset frequency range and a preset load range. Based on this efficiency distribution, it identifies the target operating condition range that meets a preset efficiency threshold, thereby avoiding the problem of outputting only discrete efficiency values ​​that are difficult to guide engineering applications. Simultaneously, by combining the loss decomposition results, it determines the dominant loss type within the target operating condition range, indicating whether iron loss or copper loss has a greater influence on efficiency within that range, thus providing a basis for lightweight structure optimization. For example, if the evaluation results show a significant increase in the proportion of iron loss in a higher frequency and medium load region, this region can be marked as an iron loss-dominated region, prompting subsequent design phases to prioritize core loss suppression; if the evaluation results show a significant increase in the proportion of copper loss in a high-current, high-load region, this region can be marked as a copper loss-dominated region, prompting subsequent design phases to prioritize winding AC loss suppression. Through the above multi-condition calculations, loss decomposition output, efficiency result organization, and target operating condition range identification, this step ultimately forms the conversion efficiency evaluation result, making the evaluation output not only numerically accurate, but also usable and interpretable for the design verification and operation selection of lightweight medium- and high-frequency transformers.

[0026] As a preferred embodiment of the above, the iron loss sub-model dynamically adjusts the weighting coefficients of the high-frequency eddy current loss term based on lightweight structural parameters, and the lightweight structural parameters include at least the insulation layer thickness parameter; the copper loss sub-model generates a proximity effect compensation factor based on the compact winding spacing, and corrects the winding loss based on the proximity effect compensation factor.

[0027] Specifically, when calculating core losses, the iron loss sub-model not only considers the influence of operating parameters on magnetic flux changes, but also introduces the insulation layer thickness parameter as an important input to reflect changes in lightweight structure. This parameter is used to dynamically adjust the weighting coefficient of the high-frequency eddy current loss term in the iron loss calculation. This is because in lightweight design, the reduction of insulation layer thickness is often accompanied by changes in the coupling conditions between the core and windings and the local magnetic field distribution, which affects the proportion of high-frequency eddy current loss. By associating the insulation layer thickness parameter with the eddy current loss weight, the iron loss sub-model can more closely approximate the actual loss characteristics under high-frequency conditions. On the other hand, when calculating winding losses, the copper loss sub-model preferably introduces the winding compaction spacing, a structural parameter that can directly characterize the lightweight winding arrangement. Based on this parameter, a proximity effect compensation factor is generated to correct the AC losses caused by the significantly enhanced proximity effect due to the reduced winding spacing under medium and high frequency conditions. In specific implementation, the smaller the winding compaction spacing, the more obvious the impact of the proximity effect on the uneven distribution of conductor current, and the greater the correction range of the generated proximity effect compensation factor on winding losses, thereby avoiding the problem of underestimation or overestimation of high-frequency copper losses caused by using fixed empirical coefficients.

[0028] As a preferred embodiment of the above, such as Figure 2 As shown, the weighting coefficients of the high-frequency eddy current loss term are dynamically adjusted based on the lightweight structural parameters, including: A10: Establish the mapping relationship between insulation layer thickness reduction and eddy current loss sensitivity; A20: Calculate the adjustment amount of the weighting coefficient of the high-frequency eddy current loss term based on the mapping relationship; A30: Update the weight coefficients based on the weight coefficient adjustment amount and preset ratio parameters.

[0029] Specifically, firstly, the reference thickness of the insulation layer is determined. This reference thickness is preferably the standard design thickness before lightweight design or the recommended thickness value that meets electrical safety specifications. The actual insulation layer thickness of the transformer to be evaluated is then obtained. The difference between the two is used to determine the degree of insulation layer reduction. Preferably, this reduction degree is converted into a standardized proportional parameter to unify comparisons between products of different specifications. Subsequently, a mapping relationship between "reduction ratio - eddy current loss sensitivity" is established through finite element simulation analysis or comparative test data from multiple prototypes. This mapping relationship is preferably obtained using piecewise fitting or smooth regression to reflect the amplified loss trend caused by increased local magnetic field coupling, concentrated magnetic flux distribution, and changes in eddy current paths when the insulation layer thickness decreases. In step A20, the sensitivity coefficient corresponding to the current reduction ratio is retrieved or calculated based on the mapping relationship. Using the eddy current loss weight coefficient in the original iron loss model as the reference value, the weight adjustment relative to the reference value is determined by combining the sensitivity coefficient. This ensures that the weight remains unchanged when the insulation layer is not reduced, while the weight increases accordingly when the reduction ratio increases. The high weighting makes the proportion of eddy current components in the iron loss model closer to the actual operating state. To avoid model instability caused by sudden weight changes, the sensitivity data is preferably smoothed or constrained at the upper limit when calculating the adjustment amount. In step A30, a preset proportional parameter is introduced to adjust the weight adjustment amount. This proportional parameter is used to control the update intensity, and its value is preferably limited to between zero and one. The adjustment amount after proportional parameter adjustment is superimposed on the original weight coefficient to obtain the updated high-frequency eddy current loss weight coefficient. The proportional parameter can be set according to different frequency ranges or different lightweighting degrees, or it can be optimized and solved in combination with experimental data during the model calibration stage. Through the above continuous processing, a complete dynamic adjustment link of "insulation layer reduction degree acquisition - sensitivity mapping establishment - weight adjustment amount calculation - controlled update" is realized, so that the iron loss sub-model can automatically correct the contribution ratio of high-frequency eddy current loss components with changes in lightweight structure, thereby significantly improving the accuracy of iron loss prediction under medium and high frequency conditions and enhancing the model's adaptability to different lightweighting design schemes.

[0030] As a preferred embodiment of the above, such as Figure 3 As shown, a proximity effect compensation factor is generated based on the compacted winding spacing, and the winding loss is corrected based on the proximity effect compensation factor, including: B10: Obtain the winding compaction pitch and determine the intensity of the proximity effect based on the winding compaction pitch parameters; B20: A proximity effect compensation factor related to frequency parameters, generated based on the intensity of the proximity effect. B30: Calculate the AC loss component of the winding based on the proximity effect compensation factor to obtain the corrected winding loss value.

[0031] Specifically, firstly, the winding compaction spacing parameters are obtained. The winding compaction spacing can be defined as the actual insulation distance or equivalent average spacing between adjacent conductors. This parameter can be obtained from structural design drawings, 3D modeling data, or prototype measurement data, and compared with the baseline spacing before lightweight design to determine the spacing compression ratio. Based on this, the influence intensity of the proximity effect is determined according to the spacing compression ratio. The influence intensity is preferably obtained through a pre-established mapping relationship between the spacing ratio and the degree of enhancement of the proximity effect. This mapping relationship can be obtained through electromagnetic field simulation analysis or fitting the AC loss test results of prototypes with different spacings. It is used to reflect the trend of enhanced magnetic field interaction between conductors, increased uneven current distribution, and increased additional losses when the winding spacing decreases. In step B20, a proximity effect compensation factor related to the frequency parameter is generated based on the influence intensity of the proximity effect. In specific implementation, it is preferred to use the influence intensity and the current operating frequency as input variables. The corresponding compensation coefficient is determined by table lookup, interpolation calculation, or data fitting, so that the compensation factor can simultaneously reflect the superposition of structural compactness and frequency increase. The compensation amplitude is relatively small in the low-frequency range and significantly larger in the mid-to-high-frequency range to reflect the physical characteristic that the proximity effect intensifies with increasing frequency. In step B30, the proximity effect compensation factor is applied to the AC loss component in the original copper loss model to amplify and correct the AC loss component. This correction is then superimposed with the DC resistance loss to obtain the corrected total winding loss value. It is preferable to set a reasonable upper limit range for the compensation factor to avoid non-physical over-amplification under extreme parameter conditions. For example, in the design of a lightweight planar transformer, when the winding interlayer spacing is reduced by a certain proportion compared to the benchmark design and the operating frequency is in the mid-to-high frequency range, the proximity effect is determined to be strong through mapping relationship. After generating the corresponding compensation factor, the AC loss is corrected, which can significantly improve the copper loss prediction value and make the prediction result closer to the measured result. Through the complete adjustment link formed by the above steps, the copper loss sub-model can adaptively adjust the AC loss component according to the winding compactness and operating frequency changes, thereby significantly improving the accuracy of copper loss prediction of lightweight mid-to-high frequency transformers under multiple operating conditions and enhancing the engineering applicability of the overall efficiency evaluation model.

[0032] As a preferred embodiment of the above, the test data includes at least no-load test data and short-circuit test data, which are used to calibrate the parameters of the iron loss model and the copper loss model, respectively.

[0033] Specifically, to improve the prediction accuracy of the efficiency evaluation model under multiple operating conditions, it is preferable to perform targeted calibration of the iron loss sub-model and copper loss sub-model using no-load test data and short-circuit test data respectively, thereby achieving independent identification and accurate correction of loss components. When acquiring test data, multiple sets of tests can be carried out on the prototype test platform according to a preset frequency range. The no-load test is conducted with the secondary open circuit state. By adjusting the input voltage, the magnetic core is placed at different magnetic flux density levels, and data such as input voltage, input current, and input power are recorded. Since the winding current is small and the copper loss ratio is extremely low at this time, the difference between the input power and the theoretical output power mainly reflects the magnetic core loss, thus serving as the basic data for iron loss calibration. It is preferable to conduct no-load tests at different frequency points to obtain the actual curve of iron loss changing with frequency, and then... The test results are compared with the predicted values ​​of the iron loss sub-model under the corresponding operating conditions. By iteratively adjusting the parameters to be identified in the iron loss model, the predicted values ​​are gradually brought closer to the measured values. When the error is reduced to within the preset accuracy range, the parameter calibration of the iron loss sub-model is completed. In the short-circuit test stage, the secondary winding can be short-circuited, and the input current is gradually increased to the rated current range under low voltage conditions. At the same time, the input power and current data are recorded. Since the magnetic flux density is low and the proportion of iron loss is relatively small, the input power mainly reflects the winding loss at this time, so it can be used as the basis for the calibration of the copper loss sub-model. It is preferable to obtain multiple sets of short-circuit test data under different current levels and different frequencies, and compare the predicted results of the copper loss sub-model with the measured copper loss. By adjusting the relevant parameters of AC additional loss and the resistance correction coefficient, the model output is made consistent with the test results.

[0034] As a preferred embodiment of the above, such as Figure 4 As shown, in step S4, the evaluation dataset is input into the calibrated efficiency evaluation model, which outputs the loss decomposition results and efficiency results for each operating point, and obtains the conversion efficiency evaluation results, including: S41: For each operating point, output the iron loss value and copper loss value respectively, and calculate the efficiency value of the corresponding operating point based on the iron loss value and copper loss value. S42: Generate discrete efficiency distribution data based on the efficiency values ​​of each medium- and high-frequency discrete operating point, and perform interpolation and reconstruction on the discrete efficiency distribution data to generate efficiency map data covering the preset frequency range and preset load range. S43: Generate copper loss distribution data and iron loss distribution data based on iron loss and copper loss values, and perform interpolation reconstruction on the iron loss distribution data and copper loss distribution data to generate iron loss map data and copper loss map data; S44: Determine the dominant loss type at each operating point based on iron loss map data and copper loss map data; S45: Based on the efficiency map data, determine the target operating condition range that meets the preset efficiency threshold, and output the target operating condition range and its corresponding dominant loss type as the conversion efficiency evaluation result.

[0035] Specifically, firstly, each discrete operating point of the mid-to-high frequency range in the evaluation dataset is sequentially input into the calibrated efficiency evaluation model. The iron loss sub-model outputs the iron loss value for the corresponding operating point, and the copper loss sub-model outputs the copper loss value for the corresponding operating point. The two types of losses are then summarized to obtain the total loss value. The efficiency value is then calculated by combining this with the output power at that operating point. Simultaneously, the iron loss, copper loss, and efficiency results are bound and stored in a one-to-one correspondence, thus forming a complete loss decomposition data table. Preferably, frequency parameters and load parameters are simultaneously recorded in this data table to facilitate subsequent multidimensional analysis. Then, a two-dimensional efficiency distribution matrix is ​​constructed based on the efficiency values ​​of all discrete operating points. One dimension represents the frequency range, and the other the load range. Discrete data is then smoothly reconstructed using interpolation algorithms. Ideally, bilinear interpolation or surface fitting is used to generate a continuous efficiency distribution map, thus obtaining efficiency map data covering the preset frequency and load ranges, making the efficiency change trend continuously visible throughout the operating range. Next, using the same data structure as the efficiency map, corresponding two-dimensional distribution matrices are constructed for iron loss and copper loss values, respectively. Iron loss map data and copper loss map data are then generated through interpolation reconstruction, allowing the distribution patterns of the two types of losses under different frequency and load conditions to be intuitively presented. In step S44, through... At the same operating point, the iron loss map data and copper loss map data are numerically compared to determine which type of loss has a higher proportion, thereby identifying the dominant loss type at that operating point. Preferably, a judgment rule can be set such that when the proportion of a certain type of loss in the total loss exceeds a preset proportion, it is identified as a dominant loss area, and the dominant type is associated with the operating point coordinates. Finally, based on the efficiency map data, a set of all operating points with efficiency values ​​not lower than a preset efficiency threshold is extracted, and regional connectivity analysis is performed on this set to identify continuously distributed high-efficiency operating areas. This continuous area is determined as the target operating range. Simultaneously, combined with the dominant loss type determined in step S44, the target operating range and its... For example, when a continuous high-efficiency zone is formed in a medium-load area within a certain frequency range, and the proportion of iron loss is low while the proportion of copper loss is high, it can be determined that this operating range is suitable as the preferred operating range, and it can be suggested that the subsequent optimization direction should focus on winding loss control. Through the above continuous processing flow, a complete analysis link is realized from single-point loss calculation to efficiency map construction, loss map construction, dominant loss determination and high-efficiency operating zone identification. This makes the conversion efficiency evaluation results not only numerically accurate, but also visually expressible and provide guidance for engineering decisions, thereby significantly improving the practical value of efficiency evaluation for lightweight medium and high frequency transformers.

[0036] As a preferred embodiment of the above, such as Figure 5 As shown, the target operating condition range that meets the preset efficiency threshold is determined based on efficiency map data, including: C10: Extract the efficiency value corresponding to each working point from the efficiency map data; C20: Compare the efficiency value with a preset efficiency threshold and filter the set of candidate operating points whose efficiency value is not less than the preset efficiency threshold; C30: Perform connectivity analysis on the candidate operating point set to determine at least one continuous operating condition region that meets the preset efficiency threshold; C40: Define at least one continuous operating condition area as the target operating condition range.

[0037] Specifically, firstly, the efficiency values ​​corresponding to each operating point are extracted from the generated efficiency map data. This efficiency map data covers a preset frequency range and load range. Each operating point has unique frequency and load coordinates and an efficiency value calculated through the aforementioned efficiency evaluation model, thus forming a two-dimensional grid-like efficiency distribution. Next, the extracted efficiency values ​​of each operating point are compared with a preset efficiency threshold. This preset efficiency threshold can be set according to the transformer design goals or application requirements, for example, 98%. By comparing each value individually, all operating points with efficiency values ​​not less than the preset efficiency threshold are selected and included in the candidate operating point set. Subsequently, step C30 is executed to perform connectivity analysis on the candidate operating point set, that is, based on each candidate... The adjacent relationships of operating points in a two-dimensional coordinate system with frequency as the x-axis and load as the y-axis are selected. Operating points that are adjacent or connected in the four directions (up, down, left, and right) are divided into the same region. By traversing all candidate operating points, at least one connected region consisting of continuous operating points is identified. These connected regions indicate that the efficiency of the transformer meets the preset efficiency threshold requirements within the corresponding frequency and load range, and there are no discontinuities with efficiency values ​​below the threshold between operating points within the region. Finally, at least one continuous operating region determined by the connected region analysis is identified as the target operating range. This target operating range reflects the frequency and load combination range in which the transformer can operate efficiently in the form of continuous intervals, providing an intuitive and quantitative basis for subsequent optimization design or operation strategy formulation.

[0038] As a preferred embodiment of the above, such as Figure 6 As shown, parameter calibration is performed on the parameters to be identified based on experimental data, including: D10: Input the no-load test data into the iron loss sub-model. Under the corresponding no-load conditions, take the minimum error between the iron loss value calculated by the iron loss sub-model and the iron loss value measured by the test as the objective function, and optimize the parameters to be identified in the iron loss sub-model. D20: Input the short-circuit test data into the copper loss sub-model. Under the corresponding short-circuit condition, take the minimum error between the copper loss value calculated by the copper loss sub-model and the copper loss value measured by the test as the objective function, and optimize the parameters to be identified in the copper loss sub-model. D30: Combine the calibrated iron loss model with the copper loss model to obtain the calibrated efficiency evaluation model.

[0039] Specifically, firstly, the acquired no-load test data is input into the iron loss sub-model. This no-load test data includes no-load loss values, i.e., iron loss values, measured at different medium and high frequency points. Under the corresponding no-load conditions, the objective function is to minimize the sum of squared errors between the iron loss values ​​calculated by the iron loss sub-model based on the current parameters to be identified and the measured iron loss values. A nonlinear least squares algorithm or intelligent optimization algorithm is used to iteratively optimize and solve the parameters to be identified in the iron loss sub-model until the objective function converges or reaches the preset number of iterations, ensuring a high degree of agreement between the calculated results of the iron loss sub-model and the measured results. Next, the acquired short-circuit test data is input into the copper loss sub-model. This short-circuit test data includes short-circuit loss values ​​measured at different medium and high frequency points. The circuit loss value, also known as the copper loss value, is calculated under the corresponding short-circuit condition. The objective function is to minimize the sum of squared errors between the copper loss value calculated by the copper loss sub-model based on the current parameters to be identified and the experimentally measured copper loss value. The same optimization algorithm is used to iteratively optimize and solve the parameters to be identified in the copper loss sub-model, so that the copper loss sub-model can accurately reflect the AC loss characteristics of the winding under actual operating conditions. Finally, the iron loss sub-model and the copper loss sub-model after parameter calibration are combined, that is, the input and output structures and calibrated parameter values ​​of the two sub-models are retained, so that the two can work together to form a complete calibrated efficiency evaluation model. This model can be used in subsequent steps to accurately calculate the iron loss, copper loss and efficiency at different operating points.

[0040] Example 2: Based on the same inventive concept as the lightweight medium-high frequency transformer conversion efficiency evaluation method in the foregoing embodiments, the present invention also provides a lightweight medium-high frequency transformer conversion efficiency evaluation system, comprising: The evaluation dataset construction module obtains the structural winding parameters, lightweighting parameters, and operating condition parameters of the transformer to be evaluated, and constructs an evaluation dataset containing several medium- and high-frequency discrete operating condition points. The evaluation model construction module constructs an efficiency evaluation model based on the evaluation dataset. The efficiency evaluation model includes at least an iron loss model and a copper loss model, and sets the model parameters of the iron loss model and the copper loss model as parameters to be identified. The parameter calibration module acquires test data corresponding to the transformer to be evaluated and calibrates the parameters to be identified based on the test data. The evaluation result generation module inputs the evaluation dataset into the calibrated efficiency evaluation model, outputs the loss decomposition results and efficiency results for each operating point, and obtains the conversion efficiency evaluation results.

[0041] As a preferred embodiment of the above, the evaluation result generation module includes: The loss decomposition unit outputs iron loss and copper loss values ​​for each operating point, and calculates the efficiency value for the corresponding operating point based on the iron loss and copper loss values. The efficiency reconstruction unit generates discrete efficiency distribution data based on the efficiency values ​​of each medium- and high-frequency discrete operating point, and performs interpolation reconstruction on the discrete efficiency distribution data to generate efficiency map data covering a preset frequency range and a preset load range. The loss reconstruction unit generates copper loss distribution data and iron loss distribution data based on iron loss and copper loss values, and performs interpolation reconstruction on the iron loss distribution data and copper loss distribution data to generate iron loss map data and copper loss map data. The dominant determination unit determines the dominant loss type at each operating point based on iron loss map data and copper loss map data. The operating condition output unit determines the target operating condition range that meets the preset efficiency threshold based on efficiency map data, and outputs the target operating condition range and its corresponding dominant loss type as the conversion efficiency evaluation result.

[0042] The evaluation system described above in this invention can effectively realize the evaluation method for the conversion efficiency of lightweight medium and high frequency transformers, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0043] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0044] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Thus, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for evaluating the conversion efficiency of a lightweight medium-to-high frequency transformer, characterized in that, The method includes: Obtain the structural winding parameters, lightweighting parameters, and operating condition parameters of the transformer to be evaluated, and construct an evaluation dataset containing several medium- and high-frequency discrete operating condition points; An efficiency evaluation model is constructed based on the evaluation dataset. The efficiency evaluation model includes at least an iron loss model and a copper loss model, and the model parameters of the iron loss model and the copper loss model are set as parameters to be identified. Obtain test data corresponding to the transformer to be evaluated, and perform parameter calibration on the parameters to be identified based on the test data; The evaluation dataset is input into the calibrated efficiency evaluation model, which outputs the loss decomposition results and efficiency results for each operating point, and the conversion efficiency evaluation results are obtained.

2. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 1, characterized in that, The iron loss sub-model dynamically adjusts the weighting coefficients of the high-frequency eddy current loss term based on lightweight structural parameters, which include at least the insulation layer thickness parameter; the copper loss sub-model generates a proximity effect compensation factor based on the compact winding spacing, and corrects the winding loss based on the proximity effect compensation factor.

3. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 2, characterized in that, The weighting coefficients of the high-frequency eddy current loss term are dynamically adjusted based on lightweight structural parameters, including: Establish a mapping relationship between insulation layer reduction thickness and eddy current loss sensitivity; Based on the mapping relationship, calculate the adjustment amount of the weighting coefficient for the high-frequency eddy current loss term; The weight coefficients are updated based on the weight coefficient adjustment amount and the preset ratio parameter.

4. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 2, characterized in that, A proximity effect compensation factor is generated based on the compacted winding spacing, and the winding loss is corrected based on the proximity effect compensation factor, including: Obtain the winding compaction pitch and determine the intensity of the proximity effect based on the winding compaction pitch parameter; Based on the influence intensity of the proximity effect, a proximity effect compensation factor related to the frequency parameter is generated; The AC loss component of the winding is calculated based on the proximity effect compensation factor to obtain the corrected winding loss value.

5. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 1, characterized in that, The test data includes at least no-load test data and short-circuit test data, which are used to calibrate the parameters of the iron loss sub-model and the copper loss sub-model, respectively.

6. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 1, characterized in that, The evaluation dataset is input into the calibrated efficiency evaluation model, which outputs the loss decomposition results and efficiency results for each operating point, and the conversion efficiency evaluation results are obtained, including: For each of the aforementioned operating points, the iron loss value and the copper loss value are output respectively, and the efficiency value of the corresponding operating point is calculated based on the iron loss value and the copper loss value. Based on the efficiency values ​​of each medium- and high-frequency discrete operating point, discrete efficiency distribution data is generated, and the discrete efficiency distribution data is interpolated and reconstructed to generate efficiency map data covering a preset frequency range and a preset load range. Based on the iron loss value and copper loss value, copper loss distribution data and iron loss distribution data are generated, and the iron loss distribution data and copper loss distribution data are interpolated and reconstructed to generate iron loss map data and copper loss map data. The dominant loss type at each operating point is determined based on the iron loss map data and the copper loss map data. Based on the efficiency map data, the target operating condition range that meets the preset efficiency threshold is determined, and the target operating condition range and its corresponding dominant loss type are output as the conversion efficiency evaluation result.

7. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 6, characterized in that, Based on the efficiency map data, the target operating condition range that meets the preset efficiency threshold is determined, including: Extract the efficiency value corresponding to each operating point from the efficiency map data; The efficiency value is compared with the preset efficiency threshold to filter a set of candidate operating points whose efficiency value is not less than the preset efficiency threshold; Perform connectivity analysis on the candidate operating point set to determine at least one continuous operating condition region that satisfies the preset efficiency threshold; The at least one continuous operating condition region is defined as the target operating condition range.

8. The method for evaluating the conversion efficiency of lightweight medium- and high-frequency transformers according to claim 1, characterized in that, Based on the test data, parameter calibration of the parameter to be identified is performed, including: The no-load test data is input into the iron loss sub-model. Under the corresponding no-load condition, the objective function is to minimize the error between the iron loss value calculated by the iron loss sub-model and the iron loss value measured in the test. The parameters to be identified in the iron loss sub-model are then optimized and solved. The short-circuit test data is input into the copper loss sub-model. Under the corresponding short-circuit condition, the objective function is to minimize the error between the copper loss value calculated by the copper loss sub-model and the copper loss value measured by the test, and to optimize and solve the parameters to be identified in the copper loss sub-model. The calibrated iron loss model is combined with the copper loss model to obtain the calibrated efficiency evaluation model.

9. A lightweight medium-to-high frequency transformer conversion efficiency evaluation system, characterized in that, The system includes: The evaluation dataset construction module obtains the structural winding parameters, lightweighting parameters, and operating condition parameters of the transformer to be evaluated, and constructs an evaluation dataset containing several medium- and high-frequency discrete operating condition points. The evaluation model construction module constructs an efficiency evaluation model based on the evaluation dataset. The efficiency evaluation model includes at least an iron loss sub-model and a copper loss sub-model, and sets the model parameters of the iron loss sub-model and the copper loss sub-model as parameters to be identified. The parameter calibration module acquires test data corresponding to the transformer to be evaluated, and performs parameter calibration on the parameter to be identified based on the test data. The evaluation result generation module inputs the evaluation dataset into the calibrated efficiency evaluation model, outputs the loss decomposition results and efficiency results for each operating point, and obtains the conversion efficiency evaluation results.

10. The lightweight medium-high frequency transformer conversion efficiency evaluation system according to claim 9, characterized in that, The evaluation result generation module includes: The loss decomposition unit outputs iron loss value and copper loss value for each operating point, and calculates the efficiency value of the corresponding operating point based on the iron loss value and copper loss value. The efficiency reconstruction unit generates discrete efficiency distribution data based on the efficiency values ​​of each medium- and high-frequency discrete operating point, and performs interpolation reconstruction on the discrete efficiency distribution data to generate efficiency map data covering a preset frequency range and a preset load range. The loss reconstruction unit generates copper loss distribution data and iron loss distribution data based on the iron loss value and copper loss value, and performs interpolation reconstruction on the iron loss distribution data and the copper loss distribution data to generate iron loss map data and copper loss map data. The dominant determination unit determines the dominant loss type for each of the operating points based on the iron loss map data and the copper loss map data. The operating condition output unit determines the target operating condition range that meets the preset efficiency threshold based on the efficiency map data, and outputs the target operating condition range and its corresponding dominant loss type as the conversion efficiency evaluation result.

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