Improved inverse j-a model based soft magnetic composite hysteresis simulation method

Abstract

The application discloses a soft magnetic composite material hysteresis simulation method based on an improved inverse J-A model and belongs to the field of electromagnetic characteristic analysis of magnetic elements. In view of the problems that the existing inverse J-A model has an unclosed small hysteresis loop, does not consider the influence of magnetization history, and the irreversible magnetization component control method is not applicable to soft magnetic composites, the application introduces a hysteresis loop proportion factor to correct the magnetic field strength expression to ensure the loop closure, introduces an advanced stack model to record the magnetization reversal point state, introduces a dissipation factor R and v to construct an adaptive irreversible magnetization component control method, and fuses the above improvements to form an improved inverse J-A model, so that different types of hysteresis loops of the soft magnetic composite material are simulated. The model parameters of the application can be extracted from a small amount of quasi-static symmetrical hysteresis loops, can accurately simulate the symmetrical hysteresis loops under different magnetic flux densities and the complex hysteresis loops containing offset small loops, and provide technical support for the loss calculation and optimization design of the magnetic core of a power electronic conversion system.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic characteristic analysis of magnetic components, specifically involving a hysteresis simulation method for soft magnetic composite materials based on an improved inverse JA model. Background Technology

[0002] Soft magnetic composite materials are made from soft magnetic powders such as iron powder, which are then surface-insulated and processed through powder metallurgy techniques including pressing and heat treatment. Due to their granular structure and surface insulating layer characteristics, soft magnetic composite materials offer advantages such as low high-frequency loss, high thermal stability, and flexible shape fabrication, making them a preferred material for the fabrication of magnetic cores for high-frequency magnetic components in power electronics. However, soft magnetic composite materials possess intrinsic hysteresis characteristics, which significantly affect the core loss and electromagnetic transient response of power electronic conversion systems. Therefore, accurately and rapidly simulating the hysteresis characteristics of these materials is crucial for improving core performance and optimizing system design.

[0003] Currently, based on magnetic flux density B For input, magnetic field strength H The inverse hysteresis model for output is more widely used because the electromagnetic problems of most electrical equipment cores are addressed by vector magnetic potential. A To facilitate the study of controlled variables, and because the system is typically powered by a voltage source, the existing inverse Jiles-Atherton (JA) hysteresis model is widely used due to its advantages of fewer parameters and high computational efficiency. However, current research has not considered the inherent structure and magnetization mechanism of soft magnetic composite materials, and has failed to address the inherent limitations of the JA model. For example:

[0004] (1) Reference 1 [Liu Ren, Gu Chaoyang, Sun Jiangdong, et al. Improvement of Jiles-Atherton hysteresis model and accurate simulation of complex hysteresis of soft magnetic materials under non-sinusoidal excitation [J]. Proceedings of the CSEE, 2025, 45(5): 2016-2027] introduces two dissipation factors to control the rate of change of irreversible magnetization to improve the simulation accuracy of the inverse JA model. Although the influence of the magnetization reversal point on the current magnetization state and model parameters is considered, it has not been verified in soft magnetic composite materials and cannot guarantee the closure of the hysteresis loop.

[0005] (2) Reference 2 [Ye Jianying, Liu Lei, Lin Bo, et al. Parameter identification of Jiles-Atherton hysteresis model based on particle swarm optimization and antlion hybrid optimization algorithm [J]. Electrical Appliances and Energy Efficiency Management Technology, 2026, (02): 12-18] proposed a hybrid algorithm to identify the parameters of the inverse JA model, but did not improve the model structure and could not solve the inherent defects of the model.

[0006] In summary, there is currently no inverse JA model that combines the structure and magnetization mechanism of soft magnetic composite materials, and the existing models still have the following inherent defects: (1) The model's own equation structure cannot guarantee the closure of the small hysteresis loop simulation; (2) The influence of the material's magnetization history on the model and parameters is not considered; (3) The existing improved model's irreversible magnetization component control method is not applicable to soft magnetic composite materials.

[0007] Therefore, researching a novel hysteresis simulation method for soft magnetic composite materials based on the inverse Jiles-Atherton model has significant engineering application value. Summary of the Invention

[0008] The purpose of this invention is to address the aforementioned problems by providing a hysteresis simulation method for soft magnetic composite materials based on an improved inverse JA model. First, to address the issue that existing inverse JA models cannot accurately simulate small hysteresis loops in soft magnetic composite materials (SMCs), the invention proposes a hysteresis loop scaling factor and a stack model considering the hysteresis process, and introduces these concepts into the inverse JA model. A method for controlling the irreversible magnetization component of the small hysteresis loop in the SMC of the inverse JA model is then constructed. Subsequently, the proposed concepts and methods are integrated to construct an improved SMC inverse JA model that considers the magnetization process and mechanism. Its dynamic and static parameters can be extracted using only a small number of conventional quasi-static symmetrical hysteresis loops. The improved SMC inverse JA model can simulate symmetrical hysteresis loops and complex hysteresis loops containing biased small loops in soft magnetic composite materials under different magnetic flux densities, solving the problem that existing technologies struggle to accurately simulate the quasi-static hysteresis characteristics of soft magnetic composite materials.

[0009] To achieve the above objectives, in a first aspect, the present invention provides a method for simulating the hysteresis of soft magnetic composite materials based on an improved inverse JA model, characterized by comprising the following steps: Step 1: Based on the magnetization state at the reversal point of the small hysteresis loop, introduce a small hysteresis loop scaling factor. v f The expressions for the magnetic field strength of the descending and ascending branches output by the inverse JA model are modified to make the various small hysteresis loops obtained by simulation have closure. Step 2: Introduce a last-in-first-out stack model into the inverse JA model, sequentially record the magnetic flux density and magnetic field strength state at each magnetization reversal point during the hysteresis process, and pop up the magnetization state of the corresponding reversal point at the end of the small hysteresis loop simulation, so as to provide a basis for the calculation of the hysteresis loop scaling factor and the updating of model parameters. Step 3: Dissipation factor R , v By introducing the inverse JA model and controlling the rate of change of irreversible magnetization through the dissipation factor, a method for controlling the irreversible magnetization component with small hysteresis loop suitable for soft magnetic composite materials is constructed, and the appropriate inverse JA model equation is derived. Step 4: Based on the measured quasi-static symmetric saturated hysteresis loop of soft magnetic composite material, the static parameters of the inverse JA model are identified by combining optimization algorithm; after fixing the static parameters, the dynamic parameters of the inverse JA model are extracted by the measured symmetric hysteresis loop under different magnetic flux density, and the functional relationship between the dissipation factor and the magnetic flux density amplitude of the hysteresis loop is fitted. Step 5: Based on steps 1-4, an improved inverse JA model considering the magnetization process and mechanism is formed. The dynamic parameters and hysteresis loop scaling factor are automatically updated according to the real-time state of the stack model to realize the simulation of various hysteresis loops of soft magnetic composite materials and obtain different types of hysteresis loops of soft magnetic composite materials.

[0010] Furthermore, a small hysteresis loop scaling factor is introduced in step 1. v f , (1) In the formula, H m1 , H m2 The magnetic field strengths at the first and second rotation points of the small hysteresis loop are respectively simulated using the inverse JA model. H ' m1 The magnetic field strength is calculated after the recovery segment of the small hysteresis loop returns to its initial magnetic flux density.

[0011] Based on the magnetization state at the reversal point of the small hysteresis loop, through the small hysteresis loop scaling factor v f Corrected expression for the output magnetic field strength of the inverse JA model: (2) In the formula, H ( B ( t )) for introduction v f The magnetic field strength was calculated subsequently; The magnetic field strength at each point on the descending branch in the original simulation of the inverse JA model.

[0012] Furthermore, in step 2, a stack model is introduced to record the historical magnetization process of the soft magnetic composite material. The stack model is characterized by last-in-first-out. Based on this characteristic, the stack model is used to sequentially record the magnetization state of each magnetization reversal point of the hysteresis loop, and the magnetization state of the magnetization reversal point is popped after the hysteresis loop ends.

[0013] Furthermore, in step 3, a dissipation factor is introduced into the traditional inverse JA model. R , v Controlling the rate of irreversible magnetization change: (3) In the formula, M irr 、H e , M an These are irreversible magnetization, effective magnetic field strength, and hysteresis-free magnetization, respectively. δ Indicates the direction coefficient. δ M This represents a limiting coefficient to prevent non-physical solutions from occurring; k The pinning coefficient is denoted as .

[0014] Therefore, the inverse JA model applicable to soft magnetic composite materials can be derived as follows: (4) In the formula, M The magnetization intensity; α This represents the average field parameters of the coupling within the magnetic domain. a , c These are the shape parameters and reversibility coefficients of the hysteresis-free magnetization curve, respectively. μ 0 is the vacuum permeability constant.

[0015] Step 4, the specific process of fitting the functional relationship between the dissipation factor and the magnitude of the magnetic flux density of the hysteresis loop, includes: (1) Based on the measured symmetrical saturated hysteresis loop of soft magnetic composite material and combined with intelligent optimization algorithm, all parameters of the inverse JA model of soft magnetic composite material are identified; (2) Fixed static parameters M s、 a , c , k , α ,in M s represents the saturation magnetization intensity, and the corresponding symmetric hysteresis loops under different magnetic flux density amplitudes are used to determine the... R , v The values ​​were obtained through fitting. R , v The functional relationship between the magnetic flux density amplitude of the hysteresis loop and the magnetic flux density amplitude is obtained. R , v Regarding the magnetic flux density at the magnetization reversal point B m The function.

[0016] Furthermore, in step 4, the specific process of fitting the functional relationship between the dissipation factor and the magnitude of the hysteresis loop magnetic flux density includes: (1) Based on the measured symmetrical saturated hysteresis loop of soft magnetic composite material and combined with intelligent optimization algorithm, all parameters of the inverse JA model of soft magnetic composite material are identified; (2) Fixed static parameters Ms、 a , c , k , α ,in M s represents the saturation magnetization intensity, and the corresponding symmetric hysteresis loops under different magnetic flux density amplitudes are used to determine the... R , v The values ​​were obtained through fitting. R , v The functional relationship between the magnetic flux density amplitude of the hysteresis loop and the magnetic flux density amplitude is obtained. R , v Regarding the magnetic flux density at the magnetization reversal point B m The function.

[0017] Preferably, step 5, which involves introducing the hysteresis loop scaling factor and stack model into the inverse JA model modeling process, specifically includes the following steps: (1) When the magnetization process reaches the starting point of the main hysteresis loop, the magnetization state at the starting point ( B 0, H 0) Push onto the stack, based on the magnetic flux density at the starting point. B 0. Determine the dynamic parameters of the improved inverse JA model, and then continue to simulate the main hysteresis loop based on the improved inverse JA model; (2) When the magnetization process reaches the reversal point of the first small hysteresis loop, the magnetization state at that point is ( B 1, H 1) Push onto the stack, based on the magnetic flux density at the inversion point of the first small hysteresis loop. B 1. Determine the dynamic parameters of the improved inverse JA model, and then continue to simulate the rising branch of the small hysteresis loop based on the improved inverse JA model; (3) When the magnetization process reaches the reversal point of the second small hysteresis loop, the magnetization state of that point is ( B 2, H 2) Push onto the stack, based on the magnetic flux density at the inversion point of the second small hysteresis loop. B 2. Determine the dynamic parameters of the improved inverse JA model, and then continue to simulate the descending branch of the small hysteresis loop based on the improved inverse JA model; (4) When the magnetic flux density decreases to be equal to or less than the magnetic flux density of the second element from the top of the stack, pop the two elements at the top of the stack, so that the hysteresis simulation returns to the main hysteresis loop, and determine the dynamic parameters of the improved inverse JA model based on the magnetization state of the current stack top element. (5) Repeat the above steps until the entire hysteresis simulation process is completed.

[0018] After determining the static parameter values ​​and dynamic parameter function relationships of the improved inverse JA model, the dynamic parameters are automatically updated based on the real-time state of the stack model during the hysteresis simulation. R , v With small hysteresis loop scaling factor v f Simulations were completed for symmetrical hysteresis loops and complex hysteresis loops containing biased small hysteresis loops in soft magnetic composite materials.

[0019] Secondly, the present invention provides a simulation system for the hysteresis characteristics of soft magnetic composite materials based on an improved inverse JA model, comprising: Scale factor calculation module: Used to calculate the scale factor of a small hysteresis loop based on the magnetization state at the hysteresis loop reversal point. v f and according to v f The expression for the magnetic field strength output by the inverse JA model is corrected; Stack model calculation module: It is used to push the magnetization state of each magnetization reversal point in the hysteresis process onto the stack, record the historical magnetization process, and pop the magnetization reversal point state at the top of the stack when the corresponding hysteresis loop simulation ends, so as to provide a basis for the calculation of the hysteresis loop scaling factor and the dynamic parameter update of the model. Dissipation factor calculation module: used to calculate the dissipation factor. R , v A traditional inverse JA model is introduced, and the rate of change of irreversible magnetization is controlled by the dissipation factor; Hysteresis simulation calculation module: It is used to integrate the output results of the scaling factor calculation module, stack model calculation module and dissipation factor calculation module, and combine them with the extracted static and dynamic parameter function relationships of the inverse JA model to realize the simulation of various hysteresis loops of soft magnetic composite materials.

[0020] Compared with the prior art, the beneficial effects of the present invention include: (1) This invention proposes the concepts of hysteresis loop scaling factor and stack model considering hysteresis process based on the inverse JA model, and introduces them for the first time into the correction of the inverse JA model. Then, it constructs a control method for the irreversible magnetization component of small hysteresis loop in soft magnetic composite materials based on the inverse JA model. Subsequently, it integrates the proposed concepts and methods to propose a novel inverse JA model that considers the magnetization process and mechanism. The dynamic and static parameters of this model can be extracted with only a small number of conventional quasi-static symmetric hysteresis loops. More importantly, this model can accurately simulate the quasi-static hysteresis loops of soft magnetic composite materials under different working conditions, solving the problem that existing technologies are unable to accurately simulate the quasi-static hysteresis characteristics of soft magnetic composite materials.

[0021] (2) The present invention can accurately and quickly simulate the symmetrical hysteresis loop and the complex hysteresis loop with bias small loop of soft magnetic composite material under different magnetic intensities. The relative error of core loss under sinusoidal excitation is up to 11.91%, which can provide reliable technical support for accurate calculation of core loss of soft magnetic composite material in power electronic conversion system, accurate analysis of electromagnetic transient response and system optimization design. Attached Figure Description

[0022] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0023] Figure 1 This is a framework diagram of the hysteresis simulation method for soft magnetic composite materials based on the improved inverse JA model according to an embodiment of the present invention.

[0024] Figure 2 Introduced in the embodiments of the present invention v f A schematic diagram of correcting the non-closure of small hysteresis loops.

[0025] Figure 3 This is a schematic diagram illustrating the real-time state update principle of the stack model in an embodiment of the present invention.

[0026] Figure 4 This is a flowchart illustrating the simulation of hysteresis loops in soft magnetic composite materials based on an improved inverse JA model, according to an embodiment of the present invention.

[0027] Figure 5 Parameters of soft magnetic composite material samples according to embodiments of the present invention R and v Regarding the peak magnetic flux density B p A graph showing the changes.

[0028] Figure 6 This is a comparison diagram of the symmetrical hysteresis loops simulated under different magnetic flux densities based on the improved inverse JA model in an embodiment of the present invention, and the corresponding measured hysteresis loops.

[0029] Figure 7 This is a comparison diagram of the hysteresis loop with bias obtained by simulation based on the improved inverse JA model in this embodiment of the invention and the corresponding measured hysteresis loop. Detailed Implementation

[0030] like Figure 1 As shown, the hysteresis simulation method for soft magnetic composite materials based on the improved inverse JA model specifically includes the following steps: Step 1: To address the issue of non-closed loops in existing inverse JA models simulating small hysteresis loops, a scaling factor is introduced here. v f ,like Figure 2As shown, the starting point of its simulation, which is the reversal point of the first small hysteresis loop, is ( B m1 , H m1 ), with magnetic flux density B As the inverse JA model increases, it gradually simulates the reversal point of the second small hysteresis loop. B m2 , H m2 After that, it reversed direction and then descended to the endpoint. B m1 , H m1 The magnetic field strength obtained by the inverse JA model is usually... H 1' does not equal H 1. This leads to the simulated small hysteresis loops not being closed. To ensure the closure of various small hysteresis loops simulated by the inverse JA model, a scaling factor for the descending / ascending branch of the hysteresis loop is defined based on the reversal point of the small hysteresis loop and the magnetic field strength calculated by the inverse JA model. v f The calculation formula is as follows: (1) Assume the descent branch points of the original simulation of the inverse JA model are... H '( B ( t Then, the scaling factor defined by equation (1) is used. v f right H '( B ( t After making corrections, the final output descent points can be obtained. H ( t )for: (2) According to equation (1), when the magnetic flux density of the descending branch decreases to B m1 At that time, the corresponding magnetic field strength is exactly equal to the magnetic field strength at the starting point. H m1 This ensures the closure of the small hysteresis loop simulated by the inverse JA model. Similarly, the same method can be used to ensure that the endpoint of the ascending branch coincides with the starting point of the inverse JA model simulation.

[0031] Therefore, the scaling factor v f By combining it with the inverse JA model, the closure of various small hysteresis loops simulated by the inverse JA model can be guaranteed, thereby improving the accuracy of its hysteresis simulation.

[0032] Step Two: The stack model records the material's historical magnetization process. The basic characteristic of a stack is last-in, first-out (LIFO). Based on this characteristic, it is used to sequentially record the magnetization state at each magnetization reversal point. B , H ), and pop up the magnetization status of the magnetization reversal point in a timely manner; Step 3: The inverse JA model is a semi-macroscopic physical model, which is composed of the domain wall motion mechanism of soft magnetic materials and the principle of energy conservation. Its traditional form is a scalar, that is, all field quantities are in the direction of the applied magnetic field, and the influence of direction on its hysteresis simulation behavior is not considered.

[0033] The traditional scalar inverse JA model is shown in equation (3): (3) In the formula, B , M, M an , H e These are the model input magnetic flux density, output magnetization, hysteresis-free magnetization, and effective magnetic field strength, respectively. M s , α , a , c , k These are the material's saturation magnetization, magnetic domain coupling coefficient, and M an Shape parameters, reversible magnetization coefficient, and pinning effect coefficient; μ 0 is the vacuum permeability constant; δ , δ M These represent the directional coefficient and the limiting coefficient set to prevent non-physical solutions, respectively.

[0034] Among them, the output magnetization M With reversible magnetization component M rev Irreversible magnetization component M irr The relationship between them is: (4) M an , M irr and H e The relationships between them are as follows: (5) (6) M rev and Man , M irr The following relationship must be satisfied: (7) The magnetic flux density during the magnetization process of magnetic materials B With magnetic field strength H Magnetization M The following relationship must be satisfied: (8) In equation (6), a dissipation factor is introduced. R , v Controlling the rate of change of irreversible magnetization: (9) By substituting equation (6) with equation (9), the inverse JA model applicable to soft magnetic composite materials is obtained as follows: (10) Step 4: Based on the measured static symmetric saturated hysteresis loop of soft magnetic composite material, and combined with optimization algorithms (e.g., a hybrid optimization algorithm based on genetic algorithm and Levenberg-Marquardt algorithm), identify the static parameters of the inverse JA model; after fixing the static parameters, use the measured symmetric hysteresis loop under different magnetic flux density to extract the dynamic parameters of the inverse JA model, and fit the functional relationship between the dissipation factor and the magnetic flux density amplitude of the hysteresis loop.

[0035] In the embodiments, the improved inverse JA model R , v The method for extracting all unknown parameters is as follows: 1) Identify all parameters of its inverse JA model based on the measured symmetrical saturated hysteresis loop of soft magnetic composite material and the optimization algorithm; 2) Under fixed static parameters ( M s、 a , c , k , α Based on this, the corresponding dynamic parameters are determined using measured symmetrical hysteresis loops under different magnetic flux densities. R , v And by fitting, the functional relationship between the magnetic flux density amplitude of the hysteresis loop and the magnetic flux density amplitude is obtained, thus obtaining... R , v Regarding the magnetic flux density at the magnetization reversal point B m The function.

[0036] Step 5: Introduce the hysteresis loop scaling factor and stack model into the modeling process of the inverse JA model to form an improved inverse JA model that considers the magnetization process and mechanism. The dynamic parameters and hysteresis loop scaling factor are automatically updated according to the real-time state of the stack model to simulate various hysteresis loops of soft magnetic composite materials and obtain different types of hysteresis loops of soft magnetic composite materials.

[0037] like Figure 3 As shown, in step five, the working principle of introducing the scaling factor, stack model, and dissipation factor into the inverse JA model is as follows: (1) First, when the magnetization process encounters the main ring reversal point ①, the magnetization state of that point is ( B 0, H 0) Absorb onto the stack, and based on this magnetic flux density B 0. Determine the dynamic parameters of the inverse JA model, and then continue to simulate the main loop based on the inverse JA model; (2) Next, when the simulation reaches the second reversal point ②, the magnetization state of that point ( B 1, H 1) Absorb onto the stack, and based on this magnetic flux density B 1. Determine the dynamic parameters of the inverse JA model, and then continue to simulate the rising branch of the small hysteresis loop based on the inverse JA model; (3) When the simulation reaches the third reversal point ③, determine the magnetization state of that point ( B 2, H 2) Absorb onto the stack, and based on this magnetic flux density B 2. Determine the dynamic parameters of the inverse JA model, and then continue to simulate the descending branch of the small hysteresis loop based on the inverse JA model; (4) When the magnetic flux density decreases to be equal to or less than the magnetic flux density of the second element in the stack B At point 1, that is, when the biased small hysteresis loop returns to the starting point ②, the first two elements of the stack model are popped, that is, the two magnetization reversal point states of the biased small hysteresis loop. B 1, H 1) ( B 2, H 2) This allows the hysteresis simulation to return to the main loop, based on the first element of the current stack, i.e., the main loop magnetization reversal point ① state ( B 0, H 0) Determine the dynamic parameters of the inverse JA model; (5) Repeat this process until the inverse JA model hysteresis simulation is completed.

[0038] As can be seen from the above process, the first and second elements of the proposed stack model store the data used to calculate the small hysteresis loop. v f Required H m1 ,H m2 Simultaneously, the magnetization history reversal point information stored in the stack can be used to determine whether the small hysteresis loop simulation has ended: for example, when encountering or passing through the second element from the top of the stack in the recovery segment ( B 1, H If 1) is encountered, the simulation of the small hysteresis loop is considered complete. At this point, the two magnetization reversal point information (the first two elements of the stack) of the small hysteresis loop are popped, the dynamic parameters of the inverse JA model are restored to the outer loop magnetization state, and the subsequent magnetization simulation process continues. This cycle repeats until a small hysteresis loop reversal point is encountered again.

[0039] After determining the static parameter values ​​and dynamic parameter functions of the inverse JA model for soft magnetic composite materials, the hysteresis loop of the soft magnetic composite material under arbitrary working conditions can be simulated based on this model. The specific implementation process is as follows: Figure 4 As shown. Clearly, when simulating different types of hysteresis loops based on the improved inverse JA model, the dynamic parameters can be automatically updated according to the real-time state of the stack model. R , v With small hysteresis loop scaling factor v f This enables accurate and rapid characterization of symmetrical hysteresis loops and complex hysteresis loops containing biased small hysteresis loops in soft magnetic composite materials.

[0040] To verify the accuracy of the improved inverse Jiles-Atherton hysteresis model provided by this invention, dynamic parameter functions extracted under different magnetic flux densities (such as...) were used. Figure 5 (As shown) Simulated hysteresis loops of soft magnetic composite materials under sinusoidal and non-sinusoidal excitation; and these simulation results are compared with measured data, such as... Figure 6 and Figure 7 As shown, its hysteresis characteristics are in high agreement with the measured values, and the core loss error under sinusoidal excitation is only 11.91% at most.

[0041] The aforementioned system for hysteresis simulation of soft magnetic composite materials based on the improved inverse JA model includes: Scale factor calculation module: Calculates the scale factor based on the reversal point of the small hysteresis loop. v f The closed-loop expression for the output magnetic field strength is then substituted into the inverse JA model to complete the small hysteresis loop closure correction. Stack model calculation module: Push the magnetization reversal point onto the stack, record the magnetization state, and calculate the scaling factor and update the parameters accordingly. Dissipation factor calculation module: introduced into the inverse JA model R , v By controlling the rate of change of irreversible magnetization, an inverse JA model applicable to soft magnetic composite materials is derived. Model calculation module: Integrates the outputs and extracted dynamic and static parameters of the model from the above three modules to complete the simulation of various hysteresis loops of soft magnetic composite materials.

Claims

1. A hysteresis simulation method for soft magnetic composite materials based on an improved inverse JA model, characterized in that, Includes the following steps: Step 1: Based on the magnetization state of the hysteresis loop reversal point, introduce the hysteresis loop scaling factor to modify the magnetic field strength expressions of the descending and ascending branches output by the inverse JA model, so that the simulated hysteresis loops have closure. Step 2: Introduce a last-in-first-out stack model into the inverse JA model, sequentially record the magnetic flux density and magnetic field strength state at each magnetization reversal point during the hysteresis process, and pop up the magnetization state of the corresponding reversal point at the end of the hysteresis loop simulation, providing a basis for the calculation of the hysteresis loop scaling factor and the updating of model parameters; Step 3: Introduce the dissipation factor into the inverse JA model, control the rate of change of irreversible magnetization through the dissipation factor, construct a hysteresis loop irreversible magnetization component control method suitable for soft magnetic composite materials, and derive the appropriate inverse JA model equation. Step 4: Based on the measured static symmetric saturated hysteresis loop of soft magnetic composite material, the static parameters of the inverse JA model are identified by combining optimization algorithm; after fixing the static parameters, the dynamic parameters of the inverse JA model are extracted by the measured symmetric hysteresis loop under different magnetic flux density, and the functional relationship between the dissipation factor and the magnetic flux density amplitude of the hysteresis loop is fitted. Step 5: Based on steps 1-4, the hysteresis loop scaling factor and stack model are introduced into the modeling process of the inverse JA model to form an improved inverse JA model that considers the magnetization process and mechanism. The dynamic parameters and hysteresis loop scaling factor are automatically updated according to the real-time state of the stack model to realize the simulation of various hysteresis loops of soft magnetic composite materials and obtain different types of hysteresis loops of soft magnetic composite materials.

2. The hysteresis simulation method for soft magnetic composite materials according to claim 1, characterized in that, In step 1, a small hysteresis loop scaling factor is introduced. v f , ； (1) In the formula, H m1 , H m2 The magnetic field strengths at the first and second rotation points of the small hysteresis loop are respectively simulated using the inverse JA model. H ' m1 The magnetic field strength is calculated after the recovery segment of the small hysteresis loop returns to its initial magnetic flux density.

3. The hysteresis simulation method for soft magnetic composite materials according to claim 2, characterized in that, In step 1, based on the magnetization state of the reversal point of the small hysteresis loop, the scaling factor of the small hysteresis loop is used. v f Corrected expression for the output magnetic field strength of the inverse JA model: ； (2) In the formula, H ( B ( t )) for introduction v f The magnetic field strength was calculated subsequently; The magnetic field strength at each point on the descending branch in the original simulation of the inverse JA model.

4. The hysteresis simulation method for soft magnetic composite materials according to claim 3, characterized in that, In step 2, a stack model is introduced to record the historical magnetization process of the soft magnetic composite material. The stack model is characterized by last-in-first-out. Based on this characteristic, the stack model is used to sequentially record the magnetization state of each magnetization reversal point of the hysteresis loop, and pops the magnetization state of the magnetization reversal point after the hysteresis loop ends.

5. The hysteresis simulation method for soft magnetic composite materials according to claim 4, characterized in that, In step 3, a dissipation factor is introduced into the traditional inverse JA model. R , v Controlling the rate of irreversible magnetization change: ； (3) In the formula, M irr 、H e , M an These are irreversible magnetization, effective magnetic field strength, and hysteresis-free magnetization, respectively. δ Indicates the direction coefficient. δ M This represents a limiting coefficient to prevent non-physical solutions from occurring; k The pinning coefficient is denoted as .

6. The hysteresis simulation method for soft magnetic composite materials according to claim 5, characterized in that, In step 3, the inverse JA model applicable to soft magnetic composite materials is derived: ； (4) In the formula, M The magnetization intensity, α This represents the average field parameters of the coupling within the magnetic domain. a , c These are the shape parameters and reversibility coefficients of the hysteresis-free magnetization curve, respectively. μ 0 is the vacuum permeability constant.

7. The hysteresis simulation method for soft magnetic composite materials according to claim 6, characterized in that, Step 4, the specific process of fitting the functional relationship between the dissipation factor and the magnitude of the magnetic flux density of the hysteresis loop, includes: (1) Based on the measured symmetrical saturated hysteresis loop of soft magnetic composite material and combined with intelligent optimization algorithm, all parameters of the inverse JA model of soft magnetic composite material are identified; (2) Fixed static parameters M s、 a , c , k , α ,in M s represents the saturation magnetization intensity, and the corresponding symmetric hysteresis loops under different magnetic flux density amplitudes are used to determine the... R , v The values ​​were obtained through fitting. R , v The functional relationship between the magnetic flux density amplitude of the hysteresis loop and the magnetic flux density amplitude is obtained. R , v Regarding the magnetic flux density at the magnetization reversal point B m The function.

8. The method for simulating hysteresis of soft magnetic composite materials according to claim 7, characterized in that, Step 5, which involves introducing the hysteresis loop scaling factor and stack model into the inverse JA model modeling process, specifically includes the following steps: (1) When the magnetization process reaches the starting point of the main hysteresis loop, the magnetization state at the starting point ( B 0, H 0) Push onto the stack, based on the magnetic flux density at the starting point. B 0. Determine the dynamic parameters of the improved inverse JA model, and then continue to simulate the main hysteresis loop based on the improved inverse JA model; (2) When the magnetization process reaches the reversal point of the first small hysteresis loop, the magnetization state at that point is ( B 1, H 1) Push onto the stack, based on the magnetic flux density at the inversion point of the first small hysteresis loop. B 1. Determine the dynamic parameters of the improved inverse JA model, and then continue to simulate the rising branch of the small hysteresis loop based on the improved inverse JA model; (3) When the magnetization process reaches the reversal point of the second small hysteresis loop, the magnetization state of that point is ( B 2, H 2) Push onto the stack, based on the magnetic flux density at the inversion point of the second small hysteresis loop. B 2. Determine the dynamic parameters of the improved inverse JA model, and then continue to simulate the descending branch of the small hysteresis loop based on the improved inverse JA model; (4) When the magnetic flux density decreases to be equal to or less than the magnetic flux density of the second element from the top of the stack, pop the two elements at the top of the stack, so that the hysteresis simulation returns to the main hysteresis loop, and determine the dynamic parameters of the improved inverse JA model based on the magnetization state of the current stack top element. (5) Repeat the above steps until the entire hysteresis simulation process is completed.

9. The method for simulating hysteresis of soft magnetic composite materials according to claim 8, characterized in that, In step 5, after determining the static parameter values ​​and dynamic parameter function relationship of the improved inverse JA model, the dynamic parameters are automatically updated based on the real-time state of the stack model during the hysteresis simulation. R , v With small hysteresis loop scaling factor v f Simulations were completed for symmetrical hysteresis loops and complex hysteresis loops containing biased small hysteresis loops in soft magnetic composite materials.

10. A system for implementing the hysteresis simulation method for soft magnetic composite materials based on the improved inverse JA model according to any one of claims 1-9, characterized in that, Includes the following modules: Scale factor calculation module: Used to calculate the scale factor of a small hysteresis loop based on the magnetization state at the hysteresis loop reversal point. v f and according to v f The expression for the magnetic field strength output by the inverse JA model is corrected; Stack model calculation module: It is used to push the magnetization state of each magnetization reversal point in the hysteresis process onto the stack, record the historical magnetization process, and pop the magnetization reversal point state at the top of the stack when the corresponding hysteresis loop simulation ends, so as to provide a basis for the calculation of the hysteresis loop scaling factor and the dynamic parameter update of the model. Dissipation factor calculation module: used to calculate the dissipation factor. R , v A traditional inverse JA model is introduced, and the rate of change of irreversible magnetization is controlled by the dissipation factor; Hysteresis simulation calculation module: It is used to integrate the output results of the scaling factor calculation module, stack model calculation module and dissipation factor calculation module, and combine them with the extracted static and dynamic parameter function relationships of the inverse JA model to realize the simulation of various hysteresis loops of soft magnetic composite materials.

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