Magnetic component core loss prediction method, device, equipment and storage medium

Abstract

The present invention provides a method, device, equipment and storage medium for predicting the core loss of a magnetic element, including obtaining the actual core loss value corresponding to the magnetic element when it works within the working temperature range; partitioning the working temperature range of the magnetic core of the magnetic element to construct a piecewise function that characterizes the core loss in each temperature range; constructing a core loss model based on the piecewise function; estimating the parameters of the core loss model with the goal of minimizing the sum of squares of the error between the predicted core loss value and the actual core loss value to obtain the core loss prediction model; based on the core loss prediction model, the prediction of the core loss of the magnetic element is achieved. By using the piecewise function, the temperature is converted into a qualitative variable, and its influence on the core loss is divided into different intervals. The core loss model is constructed based on the piecewise function, and the model can better capture the local changes of experimental data in different temperature ranges, thereby improving the fitting accuracy.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of magnetic element core loss calculation, and in particular to a magnetic element core loss prediction method, device, equipment and storage medium. Background Art

[0002] The core loss equation under sinusoidal excitation is the basic model in the study of core loss. Although this equation cannot be directly applied to complex non-sinusoidal conditions in practice, it can be used to construct the core loss equation at different temperatures under non-sinusoidal excitation through appropriate modification and extension. Therefore, it is extremely important to conduct in-depth analysis and research on the core loss equation under sinusoidal excitation. This not only lays the foundation for understanding the basic mechanism of core loss, but also provides a theoretical basis and method support for the improvement of the core loss equation under non-sinusoidal conditions, thereby promoting the design and optimization of cores in practical applications.

[0003] As the most commonly used core loss equation, the Steinmetz equation is a classic model for describing the core loss of ferromagnetic materials, especially suitable for predicting core loss under sinusoidal waveforms. This equation assumes that the size of the core loss is only related to the magnetic material, magnetic induction intensity and operating frequency, and ignores the influence of other variables such as the shape and temperature of the core on the core loss. This setting makes the equation simpler. Its basic form is:

[0004] ;

[0005] in, represents the core loss per unit volume, is a constant related to the core material, is the frequency of magnetic field change, is the peak value of the magnetic flux density, and They are the core coefficients related to the core material properties and working conditions. These parameters are usually determined by fitting analysis of experimental data and are suitable for estimating the loss of core materials under sinusoidal waveform excitation.

[0006] The Steinmetz equation is simple in form and only related to the peak value of frequency and flux density. It can provide an effective tool for engineers to quickly estimate core loss without considering temperature and other conditions with low accuracy requirements. The coefficients in this equation are obtained by fitting a large amount of experimental data, and have a certain degree of stability and reliability, thus having engineering application value in a large range. The simplicity and wide applicability of the Steinmetz equation have made it widely used in transformer design and core analysis of power electronic equipment. Especially in actual engineering design, this equation can significantly simplify the calculation process of core loss, reduce calculation complexity, and improve design efficiency.

[0007] Although the Steinmetz equation has been widely used to predict core loss under sinusoidal conditions, its limitations cannot be ignored in practical engineering, especially when dealing with more complex excitation waveforms or more stringent working conditions. The Steinmetz equation ignores the effect of temperature on core loss during its application. The loss characteristics of magnetic materials are very sensitive to temperature changes. Temperature increases can cause significant changes in parameters such as coercivity and permeability of core materials, thereby affecting the loss level. Especially in high-frequency and high-power density application scenarios, the temperature rise of the core is more significant, and the loss characteristic deviation caused by temperature changes will be more prominent. Therefore, the Steinmetz equation, which does not take the temperature effect into account, has limited prediction accuracy under these conditions and cannot meet the requirements of high-precision design. In response to the limitations of the Steinmetz equation, researchers have proposed a variety of correction models. The limitation of the existing methods is that due to the fact that temperature is a quantitative variable, continuous temperature changes will lead to complex calculations and model fitting, and it is difficult to capture the nonlinear effect of temperature on core loss. Summary of the invention

[0008] Under the same core material and sinusoidal waveform, there are differences in the prediction effect of core loss for different temperature changes; based on the original Steinmetz equation, how to add the temperature factor to adapt to different temperature changes and make the prediction effect of core loss better. In view of this technical problem existing in the prior art, the present invention proposes a method, device, equipment and storage medium for predicting the core loss of a magnetic component.

[0009] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0010] In one aspect, the present invention provides a method for predicting core loss of a magnetic component, comprising:

[0011] Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range;

[0012] Divide the operating temperature range of the magnetic core of the magnetic component into different parts and construct a piecewise function to characterize the core loss in each temperature range.

[0013] Construct a core loss model based on piecewise function;

[0014] With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model;

[0015] Based on the core loss prediction model, the core loss of magnetic components can be predicted.

[0016] In one embodiment, a core loss model is constructed based on a piecewise function as follows:

[0017] ;

[0018] in, is the temperature interval coefficient corresponding to each temperature interval to be fitted, i=1,...,n, is the piecewise function corresponding to the ith temperature interval, is the frequency of magnetic field change, is the peak value of the magnetic flux density, and They are the core coefficients related to the core material properties and working conditions.

[0019] On the other hand, a device for implementing a method for predicting core loss of a magnetic component is provided, comprising:

[0020] The first module is used to obtain the actual core loss value corresponding to the magnetic component when it works within the working temperature range;

[0021] The second module is used to divide the operating temperature range of the magnetic core of the magnetic component into different zones and construct a piecewise function to characterize the core loss in each temperature zone;

[0022] The third module is used to construct a core loss model based on piecewise functions;

[0023] The fourth module is used to estimate the parameters of the core loss model with the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the actual value of the core loss, so as to obtain the core loss prediction model;

[0024] The fifth module is used to predict the core loss of magnetic components based on the core loss prediction model.

[0025] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range;

[0027] Divide the operating temperature range of the magnetic core of the magnetic component into different parts and construct a piecewise function to characterize the core loss in each temperature range.

[0028] Construct a core loss model based on piecewise function;

[0029] With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model;

[0030] Based on the core loss prediction model, the core loss of magnetic components can be predicted.

[0031] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the following steps:

[0032] Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range;

[0033] Divide the operating temperature range of the magnetic core of the magnetic component into different parts and construct a piecewise function to characterize the core loss in each temperature range.

[0034] Construct a core loss model based on piecewise function;

[0035] With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model;

[0036] Based on the core loss prediction model, the core loss of magnetic components can be predicted.

[0037] Compared with the prior art, the technical effects of the present invention are:

[0038] The present invention proposes a method, device, equipment and storage medium for predicting the core loss of a magnetic element, including obtaining the actual core loss value corresponding to the magnetic element when it works within the working temperature range; partitioning the working temperature range of the magnetic core of the magnetic element, constructing a piecewise function that characterizes the core loss in each temperature range; constructing a core loss model based on the piecewise function; estimating the parameters of the core loss model with the goal of minimizing the square sum of the error between the predicted core loss value and the actual core loss value, and obtaining the core loss prediction model; based on the core loss prediction model, the prediction of the core loss of the magnetic element is realized. By using the piecewise function, the temperature is converted into a qualitative variable, and its influence on the power loss is divided into different intervals. The material may show relatively consistent loss characteristics within a specific temperature range, while significant jumps or nonlinear changes occur in other temperature ranges. The core loss model is constructed based on the piecewise function, and the model can better capture the local changes of experimental data in different temperature ranges, thereby improving the fitting accuracy. The model can more accurately describe the core loss characteristics of each temperature range and adapt to complex working environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0040] Figure 1is a flow chart of a method for predicting core loss of a magnetic component in one embodiment;

[0041] Figure 2 This is a bar chart comparing the experimental results of different models in an example. Figure 2 (a) is the MSE comparison bar chart. Figure 2 (b) is the MAE comparison bar chart. Figure 2 (c) is R 2 Compare histograms;

[0042] Figure 3 This is a comparison chart of the fitting effects of different models in an example, where Figure 3 (a) is the fitting effect diagram of the original model. Figure 3 (b) is the fitting effect diagram of the linear model. Figure 3 (c) is the fitting effect diagram of the polynomial model. Figure 3 (d) is the fitting effect diagram of the power function model. Figure 3 (e) is the fitting effect diagram of the qualitative variable model. Figure 3 (f) is the fitting effect diagram of the exponential model;

[0043] Figure 4 This is a comparison chart of the residual results of different models in an example, where Figure 4 (a) is the residual result diagram of the original model. Figure 4 (b) is the residual result diagram of the linear model. Figure 4 (c) is the residual result diagram of the polynomial model. Figure 4 (d) is the residual result diagram of the power function model. Figure 4 (e) is the residual result diagram of the qualitative variable model. Figure 4 (f) is the residual result diagram of the exponential model;

[0044] Figure 5 Schematic diagram of the structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Reference Figure 1 In one embodiment of the present invention, a method for predicting core loss of a magnetic component is provided, comprising:

[0047] Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range;

[0048] Divide the operating temperature range of the magnetic core of the magnetic component into different parts and construct a piecewise function to characterize the core loss in each temperature range.

[0049] Construct a core loss model based on piecewise function;

[0050] With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model;

[0051] Based on the core loss prediction model, the core loss of magnetic components can be predicted.

[0052] Considering that in some temperature ranges, the core loss of the magnetic component may show relatively consistent loss characteristics, while in other temperature ranges, the core loss of the magnetic component undergoes significant jumps or nonlinear changes. That is, in different temperature ranges, the change of the core loss is different. The present invention converts the temperature into a qualitative variable by using a piecewise function, and divides the operating temperature range of the magnetic core of the magnetic component into different ranges according to the influence of temperature on the core loss.

[0053] Specifically, first obtain the actual core loss value of the magnetic component within the operating temperature range, and draw a curve of the change in temperature and actual core loss. According to the influence of temperature on core loss, the operating temperature range of the magnetic component is divided into several intervals. By considering temperature as a qualitative variable, a piecewise function can be constructed for each temperature interval. , thereby capturing the impact of different temperature ranges on core losses.

[0054] The formula for the piecewise function is as follows:

[0055] ;

[0056] Specifically, assume that the temperature range is divided into intervals, each temperature interval corresponds to a piecewise function , piecewise function Characterizes the state in the i-th temperature interval, i=1,...,n. Fall into In the temperature range, ;otherwise, This way, each region of the temperature range will have a corresponding weighted contribution to the loss calculation.

[0057] As an example, Defined as:

[0058] ;

[0059] In another embodiment, the core loss model is constructed based on a piecewise function as follows:

[0060] ;

[0061] in, is the temperature interval coefficient corresponding to each temperature interval to be fitted, i=1,...,n, is the piecewise function corresponding to the ith temperature interval, is the frequency of magnetic field change, is the peak value of the magnetic flux density, and They are the core coefficients related to the core material properties and working conditions.

[0062] Magnetic flux density refers to the distribution of magnetic flux per unit area, and its unit is Tesla (T). It reflects the strength and direction of the magnetic field in space. The formula for magnetic flux density is:

[0063] ;

[0064] in, is the magnetic flux, is the area perpendicular to the direction of the magnetic field.

[0065] The core loss is measured using the double winding method using AC power, with the excitation winding wound evenly on the core. and the induction winding , the high-frequency excitation source applies a certain excitation AC magnetic field through the excitation coil, and samples the excitation voltage and current The instantaneous value of the two is multiplied in one cycle. The average power is obtained by averaging , and the core loss is obtained:

[0066] ;

[0067] in, is the effective volume or unit volume.

[0068] In one embodiment, a method for estimating the core loss model parameters is provided, comprising:

[0069] Construct a linear model with multiple independent variables:

[0070] ;

[0071] in: is the target variable, Is The independent variable matrix is ​​composed of is the regression coefficient vector, is the error term;

[0072] The regression coefficient vector is estimated using the least squares method with the goal of minimizing the sum of squared errors between the predicted core loss and the true core loss. ;

[0073] ;

[0074] This can be solved directly using the normal equations for linear regression:

[0075] ;

[0076] in, yes The transposed matrix of is its inverse matrix.

[0077] Through the above steps, the temperature range coefficient is obtained as well as and The estimated value of .

[0078] Using the parameters estimated above, the core loss of magnetic components can be predicted;

[0079] ;

[0080] in represents the predicted value of core loss, .

[0081] Further, using the core loss prediction value and the true value of core loss , calculate the loss of the model (such as mean squared error):

[0082] ;

[0083] in, is the number of sample data, It is The true value of the core loss of the data is It is The core loss prediction value of the data.

[0084] To verify the effectiveness of the present invention, in one example, the core loss data of the magnetic element under given working conditions are measured. Based on the same original data, the core loss model (referred to as the qualitative variable model) in the magnetic element core loss prediction method proposed in the present invention is compared with the original Steinmetz equation (referred to as the original model), the Steinmetz correction equation of the temperature linear term (referred to as the linear model), the Steinmetz correction equation of the temperature quadratic term (referred to as the polynomial model), the Steinmetz correction equation in the form of the temperature power function (referred to as the power function model), and the Steinmetz correction equation in the form of the temperature exponential (referred to as the exponential model) to verify the effectiveness of the invention.

[0085] The influencing factors in this example are: temperature is 25, 50, 70, and 90°C, frequency is between 50000 Hz and 500000 Hz, excitation waveform type is sine wave, peak value of magnetic flux density is the maximum value of samples with the same spacing within one cycle time, and the unit of core loss is watt per cubic meter.

[0086] The experimental results are compared in the bar graph. Figure 2 As shown, Figure 2 (a) is the MSE comparison bar chart. Figure 2 (b) is the MAE comparison bar chart. Figure 2 (c) is R 2 Compare the bar graphs. Figure 2 It can be seen that the qualitative variable model has the best fitting effect, with MSE and MAE of 0.13×10 9 and 0.68×10 4 , which is significantly lower than the original model’s 1.85×10 9 and 1.99×10 4 This is because the qualitative variable model can more flexibly adapt to the mutation points and discrete features in the data by introducing discrete categorical variables. Therefore, the qualitative variable model has stronger adaptability and generalization ability when dealing with complex and multi-dimensional experimental data. In addition, the model can better capture the local changes of experimental data under different temperature ranges, thereby improving the fitting accuracy.

[0087] The fitting effect of the polynomial model is also relatively good, and its MAE value is slightly higher than that of the qualitative variable model, but still better than the power function and exponential function models. Its higher accuracy can be attributed to the powerful ability of the polynomial model in capturing nonlinear relationships. Since the polynomial model can fit more complex data relationships by increasing the order, it can effectively handle the nonlinear trends of some data. However, as the order of the polynomial increases, the model may be at risk of overfitting, especially when the data is noisy, the generalization ability of the model may be affected. This explains to some extent why its MAE is slightly higher than that of the qualitative variable model, but the overall performance is still excellent.

[0088] Among all the compared models, the fitting effects of the power function model and the exponential model are relatively poor. Although their error values ​​are smaller than those of the original model, they are still significantly higher than those of the polynomial model and the qualitative variable model. This shows that these two models cannot fully capture the changing trend of the data. The main reason is that these two types of models are not adaptable enough when dealing with highly nonlinear or volatile data scenarios. The power function model fits exponentially growing data better, but in this experiment, the growth trend of the data does not fully conform to the assumptions of the power function, so the model performs poorly. Although the exponential function model can describe some exponential growth relationships, in physical systems such as temperature data with upper and lower limit constraints, the exponential model often leads to overfitting of certain data points, resulting in a large overall fitting error.

[0089] In particular, the coefficient of determination R 2 Models with a p / s lower than 0.99 also show that they are insufficient in explaining the overall trend of data changes. This may be because these models rely too much on certain specific functional forms, while in actual experimental data, the relationship between temperature changes and other variables is not a single power or exponential relationship. Therefore, although these models can achieve good fitting results in some local areas, they do not perform well on global data.

[0090] In order to more intuitively demonstrate the fitting effect of the model, the prediction results of each model are compared with the actual observed values. The visualization results are consistent with the quantitative indicators. The polynomial model and the qualitative variable model have the best fitting effect. Figure 3 As shown, Figure 3 This is a comparison chart of the fitting effects of different models. Figure 3 (a) is the fitting effect diagram of the original model. Figure 3 (b) is the fitting effect diagram of the linear model. Figure 3 (c) is the fitting effect diagram of the polynomial model. Figure 3 (d) is the fitting effect diagram of the power function model. Figure 3 (e) is the fitting effect diagram of the qualitative variable model. Figure 3 (f) is the fitting effect diagram of the exponential model. The sample points are closely distributed on the diagonal line y=x. In contrast, the sample points of the power function model and the exponential function model deviate from this straight line, indicating that these models have significant errors in some areas. Similarly, referring to Figure 4 , Figure 4 This is a comparison chart of the residual results of different models, where Figure 4 (a) is the residual result diagram of the original model. Figure 4 (b) is the residual result diagram of the linear model. Figure 4 (c) is the residual result diagram of the polynomial model. Figure 4 (d) is the residual result diagram of the power function model. Figure 4 (e) is the residual result diagram of the qualitative variable model. Figure 4 (f) is the residual result diagram of the exponential model. Figure 4 The residuals in show the same effect more clearly, with the polynomial set qualitative variable model having very small residuals and the other three models having larger residuals.

[0091] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for predicting the core loss of a magnetic component is implemented.

[0092] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0093] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0094] Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range;

[0095] Divide the operating temperature range of the magnetic core of the magnetic component into different parts and construct a piecewise function to characterize the core loss in each temperature range.

[0096] Construct a core loss model based on piecewise function;

[0097] With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model;

[0098] Based on the core loss prediction model, the core loss of magnetic components can be predicted.

[0099] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0100] Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range;

[0101] Divide the operating temperature range of the magnetic core of the magnetic component into different parts and construct a piecewise function to characterize the core loss in each temperature range.

[0102] Construct a core loss model based on piecewise function;

[0103] With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model;

[0104] Based on the core loss prediction model, the core loss of magnetic components can be predicted.

[0105] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0106] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above-mentioned embodiments only express several implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for predicting core loss of a magnetic component, characterized in that: include: Obtain the actual core loss value corresponding to the magnetic component when it works within the operating temperature range; The operating temperature range of the magnetic core of the magnetic component is divided into sections, and a piecewise function is constructed to characterize the core loss in each temperature range: Assume that the temperature range is divided into intervals, each temperature interval corresponds to a piecewise function , piecewise function Characterize the state in the i-th temperature interval, i=1,...,n, piecewise function as follows: When the temperature Fall into In the temperature range, ; otherwise, ; The core loss model is constructed based on the piecewise function as follows: Where P is the true value of core loss, is the temperature interval coefficient corresponding to each temperature interval to be fitted, i=1,...,n, is the piecewise function corresponding to the i-th temperature interval, is the frequency of magnetic field variation, is the peak value of the magnetic flux density, and They are the core coefficients related to the core material properties and working conditions; With the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the true value of the core loss, the parameters of the core loss model are estimated to obtain the core loss prediction model; Based on the core loss prediction model, the core loss of magnetic components can be predicted.

2. The method for predicting core loss of a magnetic component according to claim 1, characterized in that: Estimate the parameters of the core loss model, including: Construct a linear model with multiple independent variables: in: is the target variable, Is The independent variable matrix is ​​composed of is the regression coefficient vector, is the error term; The regression coefficient vector is estimated using the least squares method with the goal of minimizing the sum of squared errors between the predicted core loss and the true core loss. ; Through the above steps, the temperature range coefficient is obtained as well as and The estimated value of .

3. The method for predicting core loss of a magnetic component according to claim 2, characterized in that: Based on the core loss prediction model, the core loss of magnetic components can be predicted; in represents the predicted value of core loss, .

4. A device for implementing the method for predicting the core loss of a magnetic component as claimed in claim 1, characterized in that: The device comprises: The first module is used to obtain the actual core loss value corresponding to the magnetic component when it works within the working temperature range; The second module is used to partition the operating temperature range of the magnetic core of the magnetic component and construct a piecewise function to characterize the core loss in each temperature range: Assume that the temperature range is divided into intervals, each temperature interval corresponds to a piecewise function , piecewise function Characterize the state in the i-th temperature interval, i=1,...,n, piecewise function as follows: When the temperature Fall into In the temperature range, ;otherwise, ; The third module is used to build a core loss model based on piecewise functions, as follows: in, is the temperature interval coefficient corresponding to each temperature interval to be fitted, i=1,...,n, is the piecewise function corresponding to the i-th temperature interval, is the frequency of magnetic field variation, is the peak value of the magnetic flux density, and They are the core coefficients related to the core material properties and working conditions; The fourth module is used to estimate the parameters of the core loss model with the goal of minimizing the sum of squares of the errors between the predicted value of the core loss and the actual value of the core loss, so as to obtain the core loss prediction model; The fifth module is used to predict the core loss of magnetic components based on the core loss prediction model.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for predicting the core loss of a magnetic component according to claim 1 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the core loss of a magnetic element according to claim 1 are implemented.

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