Temperature prediction and control method, system and equipment of LLC transformer and medium

Abstract

The invention relates to an LLC transformer temperature prediction and control method, system, device and medium, and the method comprises the steps: obtaining the structure parameters and material attributes of an LLC transformer, and carrying out the parameterized three-dimensional modeling, and obtaining a transformer three-dimensional geometric model comprising complete material attributes; non-sinusoidal current waveforms of the LLC transformer during working are acquired through circuit simulation, key characteristic parameters of the current waveforms are extracted, and magnetic core loss is calculated by utilizing an IGSE formula based on the key characteristic parameters and material attributes; acquiring environment parameters of an environment where the LLC transformer is located, and calculating heat transfer key parameters between the surface of the LLC transformer and air based on the environment parameters; based on the magnetic core loss and the heat transfer key parameters, performing steady-state thermal simulation and transient thermal simulation by using the three-dimensional geometric model of the transformer to obtain a temperature distribution condition, and generating an LLC transformer temperature management strategy based on the temperature distribution condition; the system, the equipment and the medium are used for realizing the method. Compared with the prior art, more accurate transformer temperature prediction is realized.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to a method, system, device and medium for temperature prediction and control of LLC transformers. Background Technology

[0002] LLC resonant converters are widely used in power electronics due to their excellent resonant characteristics, enabling high-efficiency energy conversion and maintaining good current and voltage stability over a wide range of input voltage and load conditions. However, as power electronic devices develop towards higher power density, the heat generation of LLC resonant converters becomes increasingly significant during high-power operation. The transformer, as the core component for energy transfer in the LLC resonant converter, carries a large amount of power, making its temperature rise issue particularly prominent. Furthermore, the performance of magnetic materials is extremely sensitive to operating temperature. For example, the permeability of magnetic materials exhibits a complex trend of first increasing and then decreasing with rising temperature, reaching a peak and then rapidly decreasing, eventually approaching zero at high temperatures. The temperature corresponding to the maximum permeability of most magnetic materials differs from the temperature at which their permeability drops to zero by only about 20 degrees Celsius. If the temperature rise of the magnetic core in the LLC resonant converter is not properly controlled, the core may experience a sharp drop in permeability, leading to a series of problems such as reduced magnetic circuit performance, increased core losses, and deviations of the LLC resonant characteristics from design values, ultimately causing the entire converter system to fail. Furthermore, the saturation magnetic flux density of magnetic materials is significantly affected by temperature, decreasing as temperature rises. When a magnetic material enters magnetic saturation, the significantly reduced permeability leads to waveform distortion, current overload, and increased core heat loss. Simultaneously, temperature significantly impacts the loss characteristics of magnetic materials. Core losses primarily include hysteresis and eddy current losses, which act as heat, further exacerbating temperature rise. Excessive core loss and temperature rise significantly affect material performance, manifesting as decreased permeability and reduced saturation magnetic flux density. This can trigger a vicious cycle between loss and temperature rise, weakening the performance and stability of the magnetic material. The core loss exhibits complex changes with increasing temperature: initially, losses may decrease slightly, but subsequently increase significantly with further temperature increases.

[0003] Therefore, considering the magnetic effect is essential when performing transformer temperature analysis. For example, Chinese patent application CN119918353A provides a method for calculating transformer tank temperature using electromagnetic induction heating. Although it introduces the electromagnetic field principle in the temperature solution process, it still has the following problems: it calculates eddy currents and Joule heating based on the electromagnetic induction principle, but does not consider the non-sinusoidal current waveform in LLC resonant converters. The accuracy of traditional loss calculation methods is low under this waveform, resulting in a large deviation in the core loss prediction, which in turn affects the accuracy of temperature prediction.

[0004] Therefore, how to achieve more accurate core loss prediction and thus more accurate temperature prediction is a technical problem that needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a method, system, device and medium for temperature prediction and control of LLC transformers. It aims to achieve accurate prediction of the temperature of LLC resonant converter transformers through precise modeling, simulation analysis and experimental verification, and to formulate effective temperature management strategies accordingly to ensure that the transformer operates within the normal operating temperature range and improve its stability and reliability.

[0006] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for temperature prediction and control of an LLC transformer is provided, the method comprising: Obtain the structural parameters and material properties of the LLC transformer and perform parametric 3D modeling to obtain a 3D geometric model of the transformer that includes complete material properties; The non-sinusoidal current waveform of the LLC transformer during operation was acquired by circuit simulation, and key characteristic parameters of the current waveform were extracted. Based on the key characteristic parameters and material properties, the core loss was calculated using the IGSE formula. Obtain the environmental parameters of the environment in which the LLC transformer is located, and calculate the key parameters of heat transfer between its surface and the air based on the environmental parameters. Based on the core loss and key heat transfer parameters, steady-state thermal simulation and transient thermal simulation are performed using the three-dimensional geometric model of the transformer to obtain the temperature distribution. Based on the temperature distribution, an LLC transformer temperature management strategy is generated.

[0007] As a preferred technical solution, the steady-state thermal simulation is as follows: The core loss is applied as the volume heat generation rate to the three-dimensional geometric model of the transformer. Boundary conditions are determined based on the aforementioned key heat transfer parameters and applied to the three-dimensional geometric model of the transformer. The key heat transfer parameters include natural convection heat dissipation parameters, heat conduction parameters, and radiation parameters. The convection boundary of the outer surface of the transformer in contact with air is set based on the natural convection heat dissipation parameters; the contact thermal resistance boundary between various components of the transformer is determined based on the heat conduction parameters; and the surface emissivity boundary is determined based on the radiation parameters. The three-dimensional geometric model of the transformer after applying the volumetric heat generation rate and boundary conditions is divided into a thermal analysis mesh. The steady-state thermal balance is calculated using a steady-state thermal simulation solver to generate the steady-state thermal simulation temperature distribution.

[0008] As a preferred technical solution, the transient thermal simulation is as follows: A random initial temperature and a time-varying load curve are set for the three-dimensional geometric model of the transformer; For each load point in the time-varying load curve, key parameters of the current waveform of the LLC transformer at the corresponding load point are collected, and the corresponding core loss is calculated. Based on the core loss at each load point, a time variation curve is plotted. The time-varying curve and boundary conditions are applied to the three-dimensional geometric model of the transformer, and the solver is used to calculate the complete process of the transformer temperature changing with time to obtain the transient temperature distribution; the boundary conditions are determined based on the key heat transfer parameters.

[0009] As a preferred technical solution, the LLC transformer temperature management strategy includes a temperature monitoring strategy, an early warning strategy, and heat dissipation measures; The temperature monitoring strategy includes temperature monitoring locations determined based on the temperature distribution and key areas that need to be monitored. The aforementioned early warning strategy includes multi-level temperature threshold settings and trend early warning strategies; The heat dissipation measures include core structure optimization, heat dissipation structure optimization, improved heat dissipation airflow, and active heat dissipation control strategies. Core structure optimization includes adjusting the core air gap length, core cross-sectional area, and optimizing the winding arrangement. Heat dissipation structure optimization includes increasing the number or area of ​​heat sinks.

[0010] According to a second aspect of the present invention, a temperature prediction and control system for an LLC transformer is provided, comprising: The 3D geometric model construction module performs parametric 3D modeling based on the structural parameters and material properties of LLC transformers, generating a 3D geometric model of the transformer that includes complete material properties. The core loss calculation module acquires the non-sinusoidal current waveform of the LLC transformer during operation through circuit simulation, extracts key characteristic parameters of the current waveform, and calculates the core loss based on the key characteristic parameters and material properties using the IGSE formula. The heat transfer key parameter calculation module calculates the key heat transfer parameters between the surface of the LLC transformer and the air based on the environmental parameters of the environment in which the transformer is located. The temperature management strategy generation module, based on the aforementioned core loss and key heat transfer parameters, uses a three-dimensional geometric model of the transformer to perform steady-state thermal simulation and transient thermal simulation to obtain the temperature distribution, and generates an LLC transformer temperature management strategy based on the temperature distribution.

[0011] As a preferred technical solution, the steady-state thermal simulation is as follows: The core loss is applied as the volume heat generation rate to the three-dimensional geometric model of the transformer. Boundary conditions are determined based on the aforementioned key heat transfer parameters and applied to the three-dimensional geometric model of the transformer. The key heat transfer parameters include natural convection heat dissipation parameters, heat conduction parameters, and radiation parameters. The convection boundary of the outer surface of the transformer in contact with air is set based on the natural convection heat dissipation parameters; the contact thermal resistance boundary between various components of the transformer is determined based on the heat conduction parameters; and the surface emissivity boundary is determined based on the radiation parameters. The three-dimensional geometric model of the transformer after applying the volumetric heat generation rate and boundary conditions is divided into a thermal analysis mesh. The steady-state thermal balance is calculated using a steady-state thermal simulation solver to generate the steady-state thermal simulation temperature distribution.

[0012] As a preferred technical solution, the transient thermal simulation is as follows: A random initial temperature and a time-varying load curve are set for the three-dimensional geometric model of the transformer; For each load point in the time-varying load curve, key parameters of the current waveform of the LLC transformer at the corresponding load point are collected, and the corresponding core loss is calculated. Based on the core loss at each load point, a time variation curve is plotted. The time-varying curve and boundary conditions are applied to the three-dimensional geometric model of the transformer, and the solver is used to calculate the complete process of the transformer temperature changing with time to obtain the transient temperature distribution; the boundary conditions are determined based on the key heat transfer parameters.

[0013] As a preferred technical solution, the LLC transformer temperature management strategy includes a temperature monitoring strategy, an early warning strategy, and heat dissipation measures; The temperature monitoring strategy includes temperature monitoring locations determined based on the temperature distribution and key areas that need to be monitored. The aforementioned early warning strategy includes multi-level temperature threshold settings and trend early warning strategies; The heat dissipation measures include core structure optimization, heat dissipation structure optimization, improved heat dissipation airflow, and active heat dissipation control strategies. Core structure optimization includes adjusting the core air gap length, core cross-sectional area, and optimizing the winding arrangement. Heat dissipation structure optimization includes increasing the number or area of ​​heat sinks.

[0014] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described thereon.

[0015] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described thereon.

[0016] Compared with existing technologies, this invention acquires the non-sinusoidal current waveform of an LLC transformer during operation through circuit simulation, and extracts key characteristic parameters of the current waveform, including current amplitude, frequency, and harmonic components. Then, based on these characteristic parameters and material properties, the core loss is calculated using the IGSE formula. By incorporating the harmonic components and transient characteristics of the non-sinusoidal waveform into the loss calculation, the method adopted in this invention more accurately reflects the loss mechanism under actual operating conditions compared with traditional methods, thereby improving the accuracy of core loss prediction and achieving accurate prediction of transformer temperature. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is the front view of the three-dimensional geometric model of the transformer of the present invention; Figure 3 This is a front view of the steady-state thermal simulation magnetic core of the present invention; Figure 4 This is a view of the bottom surface of the magnetic core in steady-state thermal simulation according to the present invention. Figure 5 This is a temperature change curve of LLC according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0020] Example 1 This invention provides a simulation-based method for predicting and managing transformer temperature in LLC resonant converters. This method, through precise modeling, steady-state thermal performance simulation, and transient thermal performance simulation, can accurately predict the transformer's temperature distribution and propose effective temperature management measures based on the prediction results. The process is as follows: Figure 1 As shown, it includes: S1. Obtain the structural parameters and material properties of the LLC transformer and perform parametric 3D modeling to obtain a 3D geometric model of the transformer including complete material properties, such as... Figure 2 As shown.

[0021] Using 3D finite element analysis software, a 3D model of the transformer is established based on its actual structural and material properties. During the modeling process, key components such as the transformer windings, core, frame, and heat sink are fully considered to ensure the accuracy of the model. In addition, the influence of winding losses on temperature must also be considered during modeling. Although the heat generated by winding losses is difficult to transfer directly to the core, its impact on the transformer's temperature rise is still significant. Therefore, the influence of winding losses on the transformer's temperature distribution should be fully considered during modeling and simulation, and its current density distribution and the generated heat should be accurately simulated.

[0022] S2. The non-sinusoidal current waveform of the LLC transformer during operation is acquired through circuit simulation, and the key characteristic parameters of the current waveform are extracted. Based on the key characteristic parameters and material properties, the core loss is calculated using the IGSE formula.

[0023] Core loss is one of the main sources of transformer heat generation. Since the current waveform in an LLC resonant converter is not a sinusoidal waveform but rather a triangular waveform, the traditional Steinmetz empirical formula cannot be directly applied to this system. To solve this problem, this invention uses IGSE to calculate core loss. This formula can more accurately reflect the core loss under non-sinusoidal waveforms. The core loss calculated based on key data such as the transformer's resonant cavity parameters, core geometry, and performance parameters can be further analyzed to assess the transformer's heat generation.

[0024] S3. Obtain the environmental parameters of the LLC transformer's environment, and calculate the key parameters of heat transfer between its surface and the air based on the environmental parameters.

[0025] The heat dissipation conditions of a transformer have a significant impact on its temperature distribution. This invention provides a detailed analysis of the heat dissipation conditions of transformers, including natural convection heat dissipation, heat conduction heat dissipation, and possible heat dissipation auxiliary measures (such as cooling fans, heat sinks, etc.). By analyzing the heat dissipation conditions, we can understand the heat dissipation capacity of the transformer under different operating conditions, providing a basis for subsequent simulation analysis and the formulation of temperature management strategies.

[0026] S4. Based on core loss and key heat transfer parameters, steady-state thermal simulation and transient thermal simulation are performed using the three-dimensional geometric model of the transformer to obtain the temperature distribution. Based on the temperature distribution, an LLC transformer temperature management strategy is generated.

[0027] S41. Temperature distribution analysis.

[0028] S411, Steady-state thermal simulation.

[0029] The calculated core loss is applied as a heat source to the three-dimensional geometric model of the transformer to simulate its steady-state thermal performance. During the simulation, the transformer's heat dissipation conditions, including the effects of natural convection, heat conduction, and heat sinks, are fully considered. Through simulation analysis, the temperature distribution of the transformer under steady-state operating conditions can be obtained. The detailed steps are as follows: i) Apply the core loss as the volumetric heat generation rate to the three-dimensional geometric model of the transformer, and consider the thermal conduction coupling between the core, winding, and frame.

[0030] ii) Determine boundary conditions based on key heat transfer parameters and apply them to the three-dimensional geometric model of the transformer. Key heat transfer parameters include natural convection heat dissipation parameters, heat conduction parameters, and radiation parameters. Set the convection boundary of the outer surface of the transformer in contact with air based on the natural convection heat dissipation parameters; determine the contact thermal resistance boundary between various components of the transformer based on the heat conduction parameters; and determine the surface emissivity boundary based on the radiation parameters.

[0031] iii) After applying the volumetric heat generation rate and boundary conditions, the 3D geometric model of the transformer is meshed for thermal analysis. A steady-state thermal equilibrium calculation is performed using a steady-state thermal simulation solver to generate the steady-state thermal simulation temperature distribution, including key parameters such as the maximum temperature and temperature gradient. The final result is shown below. Figure 3 and Figure 4 As shown.

[0032] S412, Transient Simulation.

[0033] In addition to steady-state thermal simulation analysis, the embodiments of the present invention also perform transient thermal simulation analysis. Transient thermal simulation analysis can simulate the temperature changes of the transformer under dynamic operating conditions such as load changes and input voltage fluctuations. Through transient thermal simulation analysis, the temperature response characteristics of the transformer under dynamic operating conditions can be understood, providing a more comprehensive basis for formulating temperature management strategies.

[0034] i) Set a random initial temperature and a time-varying load curve for the three-dimensional geometric model of the transformer.

[0035] ii) For each load point in the time-varying load curve, collect the key parameters of the current waveform of the LLC transformer at the corresponding load point, calculate the corresponding core loss, and plot the time variation curve based on the core loss of each load point.

[0036] iii) Apply the time-varying curve and boundary conditions (determined based on key heat transfer parameters) to the three-dimensional geometric model of the transformer, and use the solver to calculate the complete process of the transformer temperature changing with time, obtaining the transient temperature distribution as follows: Figure 5 As shown in the attached figure, CH01 represents the transformer core temperature curve, CH02 represents the transformer winding temperature curve, CH03 represents the resonant inductor core temperature curve, CH04 represents the resonant inductor winding temperature curve, CH05 represents the resonant capacitor temperature curve, CH06 represents the half-bridge switching transistor temperature curve, CH07 represents the secondary rectifier transistor temperature curve, and CH08 represents the room temperature curve.

[0037] In addition, the transformer core temperature, transformer winding temperature, resonant inductor core temperature, resonant inductor winding temperature, resonant capacitor temperature, half-bridge switch temperature curve, secondary rectifier tube temperature, and room temperature were collected at preset intervals, as shown in Table 1.

[0038] Table 1 Temperature data of various components at different times Furthermore, after conducting simulation experiments, this invention also requires real-world experiments to verify the simulation results. An LLC resonant converter experimental platform is built, and temperature measurement experiments are performed on the transformer. A multi-channel temperature meter is used to monitor and record the temperature at different locations such as the transformer core and windings in real time. The experimental data is compared and analyzed with the simulation results to verify the accuracy and reliability of the simulation model. Based on the comparative analysis results, the simulation model is optimized. The parameter settings in the model are adjusted to more accurately reflect the actual working conditions of the transformer. Simultaneously, the heat dissipation conditions are further analyzed and optimized to improve the transformer's heat dissipation efficiency.

[0039] S42, LLC transformer temperature management strategy generation.

[0040] Based on simulation analysis and experimental verification results, a temperature management strategy for LLC resonant converter transformers is formulated. In this invention, the LLC transformer temperature management strategy includes a temperature monitoring strategy, an early warning strategy, and heat dissipation measures. The temperature monitoring strategy includes determining the temperature monitoring locations based on temperature distribution and identifying key areas requiring focused monitoring. The early warning strategy includes multi-level temperature threshold settings and trend warning strategies. Heat dissipation measures include core structure optimization, heat dissipation structure optimization, improved heat dissipation ducts, and active heat dissipation control strategies. Core structure optimization includes adjusting the core air gap length and / or core cross-sectional area to reduce core losses and temperature, optimizing winding arrangement, and considering the use of materials with better thermal conductivity to improve the transformer's heat dissipation effect. Heat dissipation structure optimization includes increasing the number or area of ​​heat sinks.

[0041] Furthermore, the actual operating environment of a transformer may include adverse factors such as high temperature, humidity, and dust, all of which can affect the transformer's heat dissipation. Therefore, when conducting temperature prediction and management, the impact of these factors on transformer temperature should be fully considered, and corresponding measures should be taken to improve its heat dissipation.

[0042] The established temperature management strategy is applied to actual production and continuously optimized. The effectiveness of the temperature management strategy is evaluated by regularly monitoring the transformer's temperature. Based on the evaluation results, the strategy is adjusted and optimized to improve the stability and reliability of the transformer.

[0043] In summary, the method provided by this invention, through precise modeling, steady-state thermal performance simulation, and experimental verification, can accurately predict the temperature distribution of a transformer and propose effective temperature management measures based on the prediction results. These measures can effectively reduce the temperature of the transformer, improve its stability and reliability, and provide strong technical support for the application of LLC resonant converters. Specifically, these measures are reflected in the following aspects: (1) Accurate temperature prediction capability: This invention employs a core loss calculation method based on IGSE (Generalized Steinmetz Formula), combined with simulation software, to accurately predict the temperature of the transformer in an LLC resonant converter. This method considers the complexity of core material properties changing with temperature, as well as various loss factors in actual transformer operation, thus ensuring the accuracy of the prediction results. In contrast, traditional methods often ignore these complex factors, leading to significant deviations between the predicted results and actual conditions.

[0044] (2) Comprehensive thermal management strategy: This invention not only addresses the temperature rise caused by core losses but also comprehensively considers the influence of winding losses, heat dissipation conditions, and the PCB board on heat conduction. By optimizing design parameters such as core structure, air gap length, and cross-sectional area, and by rationally arranging the windings, this invention effectively improves the transformer's heat dissipation capacity and reduces the maximum core temperature. This comprehensive thermal management strategy helps ensure the stability and reliability of the transformer under long-term high-power operation.

[0045] (3) Combining experimental verification with simulation: This invention verifies the accuracy of theoretical predictions by conducting temperature experiments on LLC transformers based on IGSE calculations. During the experiment, a multi-channel temperature measuring instrument was used to measure the temperature at different locations on the transformer core, obtaining thermal distribution data for each region. This method, combining experimentation and simulation, not only improves the reliability of the prediction results but also provides strong data support for the thermal design of transformers.

[0046] (4) Efficient design optimization process: Compared to traditional design processes, this invention, through precise calculations and simulation analysis, can quickly identify and resolve temperature management issues. This avoids the cumbersome and time-consuming process of importing PCB designs into simulations, saving significant time and costs. Furthermore, the optimized method provided by this invention is applicable to simplifying the extraction of complex PCB designs under any circumstances, and has broad application prospects.

[0047] (5) Targeted solutions: After the technical solution of this invention is transformed, resonant energy can be analyzed specifically according to the specific frequency band used by the microwave product and the specific processing method of the PCB board. By selecting the most suitable capacitor value and placement, the resonant energy and impedance in the board can be reduced to the greatest extent, thereby improving electromagnetic compatibility. This targeted solution helps to meet the specific needs of different application scenarios.

[0048] Example 2 This invention also provides a temperature prediction and control system for LLC transformers, the system comprising: The 3D geometric model construction module performs parametric 3D modeling based on the structural parameters and material properties of LLC transformers, generating a 3D geometric model of the transformer that includes complete material properties.

[0049] The core loss calculation module acquires the non-sinusoidal current waveform of the LLC transformer during operation through circuit simulation, extracts key characteristic parameters of the current waveform, and calculates the core loss based on the key characteristic parameters and material properties using the IGSE formula.

[0050] The heat transfer key parameter calculation module calculates the key heat transfer parameters between the surface of the LLC transformer and the air based on the environmental parameters of the environment in which the transformer is located.

[0051] The temperature management strategy generation module uses the transformer's three-dimensional geometric model to perform steady-state and transient thermal simulations based on core loss and key heat transfer parameters to obtain the temperature distribution, and then generates the LLC transformer temperature management strategy based on the temperature distribution.

[0052] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0053] Furthermore, the present invention provides an electronic device including a central processing unit (CPU) that can perform various appropriate actions and processes according to computer program instructions stored in read-only memory (ROM) or loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The CPU, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0054] Multiple components in the device are connected to the I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0055] The processing unit executes the various methods and processes described above, such as methods S1 to S4. For example, in some embodiments, methods S1 to S4 may be implemented as computer software programs tangibly contained in a machine-readable medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded and / or installed on the device via ROM and / or a communication unit. When the computer program is loaded into RAM and executed by the CPU, one or more steps of methods S1 to S4 described above may be performed. Alternatively, in other embodiments, the CPU may be configured to execute methods S1 to S4 by any other suitable means (e.g., by means of firmware).

[0056] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0057] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0058] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0059] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for temperature prediction and control of an LLC transformer, characterized in that, The methods include: Obtain the structural parameters and material properties of the LLC transformer and perform parametric 3D modeling to obtain a 3D geometric model of the transformer that includes complete material properties; The non-sinusoidal current waveform of the LLC transformer during operation was acquired by circuit simulation, and key characteristic parameters of the current waveform were extracted. Based on the key characteristic parameters and material properties, the core loss was calculated using the IGSE formula. Obtain the environmental parameters of the environment in which the LLC transformer is located, and calculate the key parameters of heat transfer between its surface and the air based on the environmental parameters. Based on the core loss and key heat transfer parameters, steady-state thermal simulation and transient thermal simulation are performed using the three-dimensional geometric model of the transformer to obtain the temperature distribution. Based on the temperature distribution, an LLC transformer temperature management strategy is generated.

2. The method for temperature prediction and control of an LLC transformer according to claim 1, characterized in that, The steady-state thermal simulation is as follows: The core loss is applied as the volume heat generation rate to the three-dimensional geometric model of the transformer. Boundary conditions are determined based on the aforementioned key heat transfer parameters and applied to the three-dimensional geometric model of the transformer. The key heat transfer parameters include natural convection heat dissipation parameters, heat conduction parameters, and radiation parameters. The convection boundary of the outer surface of the transformer in contact with air is set based on the natural convection heat dissipation parameters; the contact thermal resistance boundary between various components of the transformer is determined based on the heat conduction parameters; and the surface emissivity boundary is determined based on the radiation parameters. The three-dimensional geometric model of the transformer after applying the volumetric heat generation rate and boundary conditions is divided into a thermal analysis mesh. The steady-state thermal balance is calculated using a steady-state thermal simulation solver to generate the steady-state thermal simulation temperature distribution.

3. The method for temperature prediction and control of an LLC transformer according to claim 1, characterized in that, The transient thermal simulation is as follows: A random initial temperature and a time-varying load curve are set for the three-dimensional geometric model of the transformer; For each load point in the time-varying load curve, key parameters of the current waveform of the LLC transformer at the corresponding load point are collected, and the corresponding core loss is calculated. Based on the core loss at each load point, a time variation curve is plotted. The time-varying curve and boundary conditions are applied to the three-dimensional geometric model of the transformer, and the solver is used to calculate the complete process of the transformer temperature changing with time to obtain the transient temperature distribution; the boundary conditions are determined based on the key heat transfer parameters.

4. The method for temperature prediction and control of an LLC transformer according to claim 1, characterized in that, The LLC transformer temperature management strategy includes a temperature monitoring strategy, an early warning strategy, and heat dissipation measures; The temperature monitoring strategy includes temperature monitoring locations determined based on the temperature distribution and key areas that need to be monitored. The aforementioned early warning strategy includes multi-level temperature threshold settings and trend early warning strategies; The heat dissipation measures include core structure optimization, heat dissipation structure optimization, improved heat dissipation airflow, and active heat dissipation control strategies. Core structure optimization includes adjusting the core air gap length, core cross-sectional area, and optimizing the winding arrangement. Heat dissipation structure optimization includes increasing the number or area of ​​heat sinks.

5. A temperature prediction and control system for an LLC transformer, characterized in that, The system includes: The 3D geometric model construction module performs parametric 3D modeling based on the structural parameters and material properties of LLC transformers, generating a 3D geometric model of the transformer that includes complete material properties. The core loss calculation module acquires the non-sinusoidal current waveform of the LLC transformer during operation through circuit simulation, extracts key characteristic parameters of the current waveform, and calculates the core loss based on the key characteristic parameters and material properties using the IGSE formula. The heat transfer key parameter calculation module calculates the key heat transfer parameters between the surface of the LLC transformer and the air based on the environmental parameters of the environment in which the transformer is located. The temperature management strategy generation module, based on the aforementioned core loss and key heat transfer parameters, uses a three-dimensional geometric model of the transformer to perform steady-state thermal simulation and transient thermal simulation to obtain the temperature distribution, and generates an LLC transformer temperature management strategy based on the temperature distribution.

6. The temperature prediction and control system for an LLC transformer according to claim 5, characterized in that, The steady-state thermal simulation is as follows: The core loss is applied as the volume heat generation rate to the three-dimensional geometric model of the transformer. Boundary conditions are determined based on the aforementioned key heat transfer parameters and applied to the three-dimensional geometric model of the transformer. The key heat transfer parameters include natural convection heat dissipation parameters, heat conduction parameters, and radiation parameters. The convection boundary of the outer surface of the transformer in contact with air is set based on the natural convection heat dissipation parameters; the contact thermal resistance boundary between various components of the transformer is determined based on the heat conduction parameters; and the surface emissivity boundary is determined based on the radiation parameters. The three-dimensional geometric model of the transformer after applying the volumetric heat generation rate and boundary conditions is divided into a thermal analysis mesh. The steady-state thermal balance is calculated using a steady-state thermal simulation solver to generate the steady-state thermal simulation temperature distribution.

7. The temperature prediction and control system for an LLC transformer according to claim 5, characterized in that, The transient thermal simulation is as follows: A random initial temperature and a time-varying load curve are set for the three-dimensional geometric model of the transformer; For each load point in the time-varying load curve, key parameters of the current waveform of the LLC transformer at the corresponding load point are collected, and the corresponding core loss is calculated. Based on the core loss at each load point, a time variation curve is plotted. The time-varying curve and boundary conditions are applied to the three-dimensional geometric model of the transformer, and the solver is used to calculate the complete process of the transformer temperature changing with time to obtain the transient temperature distribution; the boundary conditions are determined based on the key heat transfer parameters.

8. The temperature prediction and control system for an LLC transformer according to claim 5, characterized in that, The LLC transformer temperature management strategy includes a temperature monitoring strategy, an early warning strategy, and heat dissipation measures; The temperature monitoring strategy includes temperature monitoring locations determined based on the temperature distribution and key areas that need to be monitored. The aforementioned early warning strategy includes multi-level temperature threshold settings and trend early warning strategies; The heat dissipation measures include core structure optimization, heat dissipation structure optimization, improved heat dissipation airflow, and active heat dissipation control strategies. Core structure optimization includes adjusting the core air gap length, core cross-sectional area, and optimizing the winding arrangement. Heat dissipation structure optimization includes increasing the number or area of ​​heat sinks.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 4.

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