Electromagnetic induction heating optimization method and device and electronic equipment

Abstract

The invention relates to an electromagnetic induction heating optimization method and device and electronic equipment, and relates to the technical field of electromagnetic induction heating, and the method comprises the steps: obtaining temperature distribution data and thermal deformation displacement data generated when a target object is heated in an electromagnetic induction heating process; based on the temperature distribution data and the thermal deformation displacement data, the excitation current of electromagnetic induction heating is optimized with the temperature distribution uniformity and the thermal deformation uniformity of the target object as optimization targets, and an optimization result is obtained; the optimization result represents the dynamic excitation current. The temperature is accurately controlled, the phenomena of temperature fluctuation and instantaneous overheating are avoided, it is ensured that the temperature is within the target range, and uniform distribution of thermal deformation of a heated object is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of electromagnetic induction heating, and in particular to an optimization method, device and electronic equipment for electromagnetic induction heating. Background Art

[0002] Electromagnetic induction heating is a widely used technology in motor shrink-wrap applications. Uneven heating of the shrink-wrap housing can lead to insufficient deformation in some areas, causing scraping during assembly. Alternatively, uneven deformation can cause excessive deformation in some areas, making assembly impossible. Therefore, achieving uniform temperature distribution and uniform thermal deformation after transient heating of the motor shrink-wrap is crucial for practical industrial applications.

[0003] In related technologies, the optimization of electromagnetic induction heating is usually achieved by experimental methods and numerical calculations. The advantage of the experimental method is that it can obtain real-time temperature change data based on the test positions of the temperature sensors. The disadvantage is that it cannot directly perform comprehensive optimization and improvement based on the current heating device. When the design of the heating device is unreasonable, the heating coil must be modified or remade, and repeated disassembly and assembly and verification tests of multiple sets of heating schemes must be carried out, which requires high experimental costs and a long debugging and production cycle. Moreover, due to the limited number of temperature sensor locations, the location settings can only be made based on experience, and there is a problem of unclear optimization goals. The application of this technology to optimize the heating device has a certain degree of blindness and trial and error.

[0004] Existing numerical calculation methods all use a constant current source heating approach. This involves setting a constant current excitation as input to calculate the transient temperature field distribution or thermal deformation under a specific heating device. As the temperature of the heated object rises, the resistance increases, potentially causing temperature fluctuations and making precise temperature control difficult. Furthermore, when approaching the target temperature, a continuous, high current can cause the heated object to overheat momentarily. Summary of the Invention

[0005] The present application provides an optimization method, device and electronic equipment for electromagnetic induction heating to achieve precise temperature control, avoid temperature fluctuations and instantaneous overheating, ensure that the temperature is within the target range, and achieve uniform distribution of thermal deformation of the heated object.

[0006] According to a first aspect of the present application, a method for optimizing electromagnetic induction heating is provided, the method comprising:

[0007] Acquiring temperature distribution data and thermal deformation displacement data generated by the target object being heated during electromagnetic induction heating;

[0008] Based on the temperature distribution data and the thermal deformation displacement data, the excitation current of electromagnetic induction heating is optimized with the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization goals to obtain an optimization result; the optimization result represents the dynamic excitation current.

[0009] According to the above technical means, optimization targets for the uniformity of temperature distribution and thermal deformation of the target object during electromagnetic induction heating are set, and the excitation current is optimized during the heating process to achieve uniform temperature distribution and uniform thermal deformation of the target object during the heating process.

[0010] This allows precise temperature control, avoiding temperature fluctuations and instantaneous overheating, ensuring the temperature remains within the target range and improving heating efficiency. Furthermore, evenly distributing the thermal deformation of the target object during heating ensures that the interference fit remains within the target range, improving assembly accuracy.

[0011] In one possible implementation, obtaining temperature distribution data generated by a target object during electromagnetic induction heating includes:

[0012] constructing a three-dimensional simulation model of the heating device and the target object;

[0013] Based on the three-dimensional simulation models of the heating device and the target object, respectively, a transient heating simulation is performed on the target object, and eddy current losses generated during the transient heating simulation process are calculated;

[0014] The temperature distribution data is calculated based on the eddy current loss.

[0015] In one possible implementation, calculating the temperature distribution data based on the eddy current loss includes:

[0016] Calculating transient heating temperature field data based on the eddy current loss and thermal boundary conditions;

[0017] The temperature data of the temperature monitoring points are determined from the transient heating temperature field data as the temperature distribution data.

[0018] In one possible implementation, obtaining thermal deformation displacement data includes:

[0019] determining thermal deformation parameters and displacement constraint boundary conditions of the target object;

[0020] Calculating thermal deformation displacement field data of the target object based on the temperature distribution data, the thermal deformation parameters and the displacement constraint boundary conditions;

[0021] The displacement data of the displacement monitoring point is determined from the thermal deformation displacement field data as the thermal deformation displacement data.

[0022] In one possible implementation, optimizing the excitation current of electromagnetic induction heating based on the temperature distribution data and the thermal deformation displacement data, with the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization targets, includes:

[0023] Obtaining temperature distribution data and thermal deformation displacement data calculated based on the current excitation current, and determining whether the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity, respectively, during a heating cycle;

[0024] If not, the current excitation current is optimized to obtain a new excitation current, and the step of obtaining the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current is re-executed until the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity respectively during the heating cycle.

[0025] In a possible implementation, the temperature distribution uniformity is characterized by a difference between the upper temperature limit data and the lower temperature limit data in the temperature distribution data being less than a first temperature threshold;

[0026] The thermal deformation uniformity indicates that a difference between an upper displacement limit and a lower displacement limit in the thermal deformation displacement data is greater than a first displacement threshold and less than a second displacement threshold.

[0027] According to a second aspect of the present application, there is provided an optimization device for electromagnetic induction heating, the device comprising:

[0028] An acquisition module, configured to acquire temperature distribution data and thermal deformation displacement data generated when a target object is heated during electromagnetic induction heating;

[0029] An optimization module is used to optimize the excitation current of electromagnetic induction heating based on the temperature distribution data and the thermal deformation displacement data, taking the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization goals, and obtain an optimization result; the optimization result represents the dynamic excitation current.

[0030] In one possible implementation, a module is obtained, specifically for:

[0031] constructing a three-dimensional simulation model of the heating device and the target object;

[0032] Based on the three-dimensional simulation models of the heating device and the target object, respectively, a transient heating simulation is performed on the target object, and eddy current losses generated during the transient heating simulation process are calculated;

[0033] The temperature distribution data is calculated based on the eddy current loss.

[0034] In one possible implementation, a module is obtained, specifically for:

[0035] Calculating transient heating temperature field data based on the eddy current loss and thermal boundary conditions;

[0036] The temperature data of the temperature monitoring points are determined from the transient heating temperature field data as the temperature distribution data.

[0037] In one possible implementation, a module is obtained, specifically for:

[0038] determining thermal deformation parameters and displacement constraint boundary conditions of the target object;

[0039] Calculating thermal deformation displacement field data of the target object based on the temperature distribution data, the thermal deformation parameters and the displacement constraint boundary conditions;

[0040] The displacement data of the displacement monitoring point is determined from the thermal deformation displacement field data as the thermal deformation displacement data.

[0041] In one possible implementation, the optimization module is specifically configured to:

[0042] Obtaining temperature distribution data and thermal deformation displacement data calculated based on the current excitation current, and determining whether the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity, respectively, during a heating cycle;

[0043] If not, the current excitation current is optimized to obtain a new excitation current, and the step of obtaining the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current is re-executed until the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity respectively during the heating cycle.

[0044] In a possible implementation, the temperature distribution uniformity is characterized by a difference between the upper temperature limit data and the lower temperature limit data in the temperature distribution data being less than a first temperature threshold;

[0045] The thermal deformation uniformity indicates that a difference between an upper displacement limit and a lower displacement limit in the thermal deformation displacement data is greater than a first displacement threshold and less than a second displacement threshold.

[0046] According to the third aspect provided by the present application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the above-mentioned first aspect and any possible implementation method thereof.

[0047] According to the fourth aspect provided by the present application, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by the processor of an electronic device, the electronic device is enabled to execute the method in the above-mentioned first aspect and any possible implementation method thereof.

[0048] According to the fifth aspect provided by the present application, a computer program product is provided, which includes computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the method of the above-mentioned first aspect and any possible implementation method thereof.

[0049] Beneficial effects of this application:

[0050] (1) Achieve precise temperature control. By dynamically adjusting the current curve, the temperature of the heated object can be precisely controlled, avoiding temperature fluctuations and instantaneous overheating, and ensuring that the temperature is within the target range, such as between 160°C and 200°C.

[0051] (2) Improved temperature uniformity: By real-time monitoring of temperature distribution and dynamic adjustment of current, the temperature of the heated object is uniformly distributed, temperature non-uniformity is reduced, and heating efficiency is improved.

[0052] (3) Optimize thermal deformation uniformity. Through thermal deformation analysis and dynamic adjustment of the excitation current, the deformation interference is ensured to be within the target range, achieving thermal deformation uniformity of the heated object, reducing thermal deformation non-uniformity, and improving assembly accuracy.

[0053] (4) Enhanced flexibility and adaptability. This method can be dynamically adjusted according to different heating requirements and the characteristics of the heated object. It has high flexibility and adaptability and is suitable for a variety of motor heat sleeve process scenarios.

[0054] It should be noted that the technical effects brought about by any implementation method in the second to fifth aspects can refer to the technical effects brought about by the corresponding implementation method in the first aspect, and will not be repeated here.

[0055] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification are used to explain the principles of the present application, and do not constitute an improper limitation on the present application.

[0057] Figure 1 is a flow chart showing an optimization method for electromagnetic induction heating according to an exemplary embodiment;

[0058] Figure 2 is a structural schematic diagram of an electromagnetic induction heating simulation system according to an exemplary embodiment;

[0059] Figure 3 is a flow chart showing a method of determining temperature distribution data according to an exemplary embodiment;

[0060] Figure 4 is a flow chart illustrating a method for optimizing electromagnetic induction heating according to an exemplary embodiment;

[0061] Figure 5 is a block diagram of an optimization device for electromagnetic induction heating according to an exemplary embodiment;

[0062] Figure 6 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0063] In order to enable ordinary people in the art to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0064] It should be noted that the terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0065] In related technologies, experimental methods and numerical calculations are commonly used to optimize electromagnetic induction heating. The experimental method requires repeated disassembly and assembly and verification tests of multiple heating schemes, which requires high experimental costs and a long debugging and production cycle. The numerical calculation method uses a constant current source heating method. As the temperature of the heated object increases, the resistance increases, which may cause temperature fluctuations, making it difficult to achieve precise temperature control. At the same time, when approaching the target temperature, the continuous constant high current may cause the heated object to overheat momentarily.

[0066] In order to solve the above problems, the present application proposes an optimization method, device and electronic equipment for electromagnetic induction heating.

[0067] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0068] For ease of understanding, the optimization method for electromagnetic induction heating provided in this application is specifically introduced below with reference to the accompanying drawings.

[0069] The present invention provides an optimization method for electromagnetic induction heating. Figure 1 As shown, the optimization method of electromagnetic induction heating includes the following steps:

[0070] S101: Acquire temperature distribution data and thermal deformation data of a target object generated during electromagnetic induction heating.

[0071] In the embodiments of the present application, electromagnetic induction heating simulation software can be used to simulate the process of heating a target object via electromagnetic induction. During the simulation, the excitation current is optimized to ensure uniform temperature distribution and thermal deformation. For ease of description, the electromagnetic induction heating simulation software will be referred to as heating simulation software. The heating simulation software can be ANSYS, for example. ANSYS is a large-scale, general-purpose finite element analysis software that can be used for electromagnetic induction heating simulation.

[0072] In the embodiment of the present application, the heating simulation software supports the construction and simulation of multiple physical fields, see Figure 2 The electromagnetic induction heating simulation system may include an electromagnetic field module, a transient temperature field module, and a structural field module. In addition, since the excitation current of the electromagnetic induction heating is optimized in the embodiment of the present application, an optimization module may also be provided.

[0073] The following introduces the functions of each module in the simulation framework and the information interaction between modules.

[0074] See also Figure 2 The Electromagnetic Field Module is used to create a three-dimensional digital model of electromagnetic induction heating during the simulation, including information about the properties of the heating device and the heated object. The Electromagnetic Field Module is also used to execute the simulation and calculate eddy current losses. These eddy current losses are imported as heat source data into the Transient Temperature Field Module.

[0075] The transient temperature field module calculates the temperature distribution of the heated object during the heating process and its temporal changes, sending the results to the structural field module. The structural field module calculates the thermal deformation data of the heated object, which can include displacement data from multiple monitoring points.

[0076] The optimization module receives temperature distribution data and thermal deformation data from the transient temperature field module and the structural field module, respectively. Using the uniformity of the temperature distribution and thermal deformation of the heated object as optimization objectives, it optimizes the excitation current and sends current feedback data to the electromagnetic field module. The electromagnetic field module then reruns the simulation based on the current feedback data. This process iterates until an optimized result that meets the optimization objectives is obtained, namely, a dynamic excitation current that satisfies both temperature distribution and thermal deformation uniformity. Dynamic excitation current refers to an excitation current whose magnitude varies over time.

[0077] In the embodiment of the present application, during the simulation of electromagnetic induction heating, temperature distribution data and thermal deformation displacement data of a target object generated by heating are obtained, wherein the target object is the object to be heated, such as a motor housing.

[0078] See also Figure 3 In some embodiments of the present application, obtaining temperature distribution data generated by a target object being heated during electromagnetic induction heating may specifically include the following steps:

[0079] S301: Constructing a three-dimensional simulation model of the heating device and the target object.

[0080] Specifically, a three-dimensional digital model of the electromagnetic induction heating system is imported or created in the heating simulation software, including a heating device (coil and magnetic core) and a heated object. The heated object is taken as a motor housing as an example.

[0081] Subsequently, the material of the heating coil (for example, copper), the material of the magnetic core (for example, ferrite), and the material of the motor housing (for example, aluminum) can be set. The electrical conductivity, thermal conductivity, specific heat capacity and other parameters of the material are further set to change with temperature. For example, the conductivity of the aluminum shell is set as a function that changes with temperature: if(Temp<=22,1,1 / (1+0.0039*(Temp-22))), a connection related to temperature feedback is established. When the temperature is lower than the set temperature of 22°C, the thermal attribute is 1. When the temperature exceeds the set temperature, the thermal attribute is 1 / (1+0.0039*(Temp-22). Wherein, Temp represents temperature.

[0082] Furthermore, the excitation current may be set, for example, by selecting an end face of the heating coil and setting the current direction and initial current value.

[0083] S302: Based on the three-dimensional simulation models of the heating device and the target object, a transient heating simulation is performed on the target object, and eddy current losses generated during the transient heating simulation process are calculated.

[0084] Specifically, the solver type, iteration number, relative tolerance, and AC frequency can be further set in the heating simulation software. For example, the solver type is eddy current solver, the iteration number is 200, the relative tolerance is 0.0001, and the AC frequency is 23kHz.

[0085] Furthermore, you can select the adaptive meshing function to automatically adjust the mesh density.

[0086] As you can understand, since numerical simulation of electromagnetic induction heating can be considered a finite element analysis, meshing is necessary. Meshing in electromagnetic induction heating directly impacts the accuracy and efficiency of solving the electromagnetic, temperature, and coupled fields. Heating simulation software can provide automated meshing capabilities.

[0087] After completing the above settings, run the solution. Using the calculation function of the heating simulation software, you can calculate the eddy current losses generated during the simulated heating process.

[0088] Eddy current loss refers to the loss caused by eddy currents induced in a heated object in a changing external magnetic field. Since eddy current loss is a form of energy loss, the heat generated by the heated object during the heating process is also affected by eddy current loss. Therefore, the temperature of the heated object can be calculated based on eddy current loss. The magnitude of eddy current loss is related to factors such as the geometry, magnetic permeability, and electrical conductivity of the heated object.

[0089] S303: Calculate temperature distribution data based on eddy current loss.

[0090] Specifically, the eddy current loss is transferred to the transient temperature field module in the heating simulation software, and then the transient heating temperature field data is calculated.

[0091] In some embodiments of the present application, calculating temperature distribution data based on eddy current loss may include the following steps: calculating transient heating temperature field data based on eddy current loss and thermal boundary conditions; determining temperature data of temperature monitoring points from the transient heating temperature field data as temperature distribution data.

[0092] Specifically, the calculation can be performed using heating simulation software. The following settings need to be made in the heating simulation software:

[0093] (1) Import model and material properties.

[0094] The established three-dimensional simulation model and material properties are transferred to the transient temperature field module in the heating simulation software.

[0095] (2) Set thermal boundary conditions.

[0096] Thermal boundary conditions are expressions used in heat transfer problems to describe how heat is exchanged between the surface of an object and the external environment.

[0097] For example, taking the heated object as an aluminum shell, parameters such as thermal conductivity, specific heat capacity, density and initial reference temperature of the aluminum shell at different temperatures are set.

[0098] (3) Introducing a heat source.

[0099] The eddy current loss is imported as a heat source into the transient temperature field module in the heating simulation software.

[0100] (4) Grid division.

[0101] In the embodiment of the present application, automatic meshing can be achieved with the help of the functions provided by the heating simulation software.

[0102] (5) Run the solution.

[0103] Run the transient temperature field module to calculate the transient heating temperature field data based on eddy current loss and thermal boundary conditions. Transient heating temperature field data refers to the real-time data of the temperature change of the heated object over time during the heating process.

[0104] (6) Temperature monitoring.

[0105] Specifically, for a heated object, it is not necessary to focus on the temperature and thermal deformation of every location on the heated object. For example, if the heated object is a motor housing including a base, the temperature and thermal deformation of the base do not require special attention. Therefore, in the embodiments of the present application, temperature monitoring points can be set, and during the subsequent optimization process, only the temperatures at these temperature monitoring points are focused on, and the temperatures at these temperature monitoring points are used as temperature distribution data.

[0106] Exemplarily, a temperature monitoring point may be set in the transient temperature field module. The temperature monitoring point is a key position of the heated object, so that the temperature of the key position of the heated object can be obtained in real time.

[0107] In some embodiments of the present application, thermal deformation displacement data of the heated object may be further calculated based on the transient heating temperature field data calculated by running the transient temperature field module.

[0108] Since the transient heating temperature field data changes with time, the thermal deformation displacement data will also evolve with time. For example, the calculation can be performed in multiple time steps, with the transient heating temperature field data of each time step as input, and the thermal deformation displacement data is gradually updated.

[0109] For the specific calculation of thermal deformation displacement data, please see below.

[0110] S102: Based on the temperature distribution data and the thermal deformation displacement data, the excitation current of the electromagnetic induction heating is optimized with the temperature distribution uniformity and the thermal deformation uniformity of the target object as optimization goals to obtain an optimization result; the optimization result represents the dynamic excitation current.

[0111] In an embodiment of the present application, in order to dynamically optimize the excitation current, an optimization module can be set up. The optimization module is connected to the transient temperature field module and the structural field module. During the simulation process, temperature feedback and displacement feedback are received, the excitation current used in the simulation process is optimized, and the optimized excitation current is fed back to the electromagnetic field module, instructing the electromagnetic field module to re-execute a new round of iterative optimization based on the optimized excitation current.

[0112] In the embodiment of the present application, corresponding conditions for temperature distribution uniformity and thermal deformation uniformity can be set in advance based on multiple factors such as the characteristics of the heated object and the assembly requirements of the heated object.

[0113] In some embodiments of the present application, temperature distribution uniformity is characterized by the difference between the upper and lower temperature limits in the temperature distribution data being less than a first temperature threshold. Thermal deformation uniformity is characterized by the difference between the upper and lower displacement limits in the thermal deformation displacement data being greater than a first displacement threshold and less than a second displacement threshold.

[0114] For example, optimization targets for temperature distribution uniformity and thermal deformation uniformity can be set in the optimization module. For example, for temperature distribution uniformity, define (Tmax - Tmin) < 40°C. Tmax and Tmin represent the upper and lower temperature limits in the temperature distribution data, respectively. Furthermore, for the lower temperature limit, you can also define (Tmin - 160°C) > 0.

[0115] For example, for thermal deformation uniformity, it is defined as 0.18 mm < (Deformation_max - Deformation_min) < 0.43 mm, where Deformation_max and Deformation_min represent the upper limit and lower limit of the thermal deformation displacement data, respectively.

[0116] Furthermore, constraints on the excitation current and heating time can be set. The heating time can be understood as the time used to optimize the excitation current and can include multiple rounds of iterations. For example, the excitation current can be set to less than 725A and the heating time to no more than 80s.

[0117] In the embodiments of the present application, during the optimization process, the temperature feedback signal (i.e., temperature distribution data) and the deformation feedback signal (i.e., thermal deformation displacement data) are monitored, and an optimization module is executed. The optimization module automatically adjusts the current variable according to the set optimization objectives and constraints. The specific optimization algorithm can be selected as needed. For example, the current variable can be optimized based on a gradient descent method.

[0118] In some embodiments of the present application, based on the temperature distribution data and the thermal deformation displacement data, the excitation current of the electromagnetic induction heating is optimized with the temperature distribution uniformity and thermal deformation uniformity of the target object as the optimization goal, which may specifically include:

[0119] Obtaining temperature distribution data and thermal deformation displacement data calculated based on the current excitation current, and determining whether the temperature distribution data and thermal deformation displacement data during the heating cycle meet temperature distribution uniformity and thermal deformation uniformity, respectively;

[0120] If not, the current excitation current is optimized to obtain a new excitation current, and the steps of obtaining the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current are re-executed until the temperature distribution data and thermal deformation displacement data satisfy the temperature distribution uniformity and thermal deformation uniformity respectively during the heating cycle.

[0121] Specifically, the optimization of the excitation current can be performed iteratively. It is understood that during the simulation process, only when the temperature distribution data and thermal deformation displacement data of the target object meet the temperature distribution uniformity and thermal deformation uniformity within a certain duration, can the target object be considered to meet the temperature distribution uniformity and thermal deformation uniformity.

[0122] For example, if the heating cycle for heating the target object is 10 seconds, the optimization goal is: within 10 seconds of starting the simulation, the temperature distribution data and thermal deformation data of the target object continue to meet the temperature distribution uniformity and thermal deformation uniformity respectively.

[0123] Therefore, during the optimization process, the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current are obtained. A determination is made as to whether the temperature distribution data and thermal deformation displacement data during the heating cycle meet the requirements for temperature distribution uniformity and thermal deformation uniformity, respectively. If so, the current excitation current is used as the excitation current that meets the optimization objective.

[0124] It can be understood that the above-mentioned excitation current is dynamic, that is, the magnitude of the excitation current may vary with time.

[0125] If the temperature distribution data and thermal deformation displacement data during the heating cycle do not meet the requirements of temperature distribution uniformity and thermal deformation uniformity respectively, the current excitation current is optimized to obtain a new excitation current, which is sent to the electromagnetic field module. The new excitation current is re-imported and the simulation is run. The iterative optimization is continued until it is determined that the temperature distribution data and thermal deformation displacement data during the heating cycle meet the requirements of temperature distribution uniformity and thermal deformation uniformity respectively. The current excitation current is used as the optimization result, that is, the dynamic excitation current that can meet the requirements of temperature distribution uniformity and thermal deformation uniformity.

[0126] For example, after the optimization module completes the optimization, the heating simulation software can display the optimization results, including the optimal current curve, i.e., the dynamic current curve, which can meet the requirement of uniform heating. If the optimization target is not met during the optimization process, the optimization module can provide a suboptimal solution.

[0127] After obtaining a dynamic excitation current that meets the requirements through simulation, this current can be set during the actual heating of the target object. Compared to constant current heating, this method can precisely control the temperature, avoid temperature fluctuations and transient overheating, ensure that the temperature is within the target range, and achieve uniform temperature distribution over the heated object, reducing temperature non-uniformity and improving heating efficiency. Furthermore, it achieves uniform distribution of thermal deformation of the heated object, ensuring that the deformation interference is within the target range, thereby improving assembly accuracy.

[0128] In some embodiments of the present application, obtaining thermal deformation displacement data may specifically include: determining thermal deformation parameters and displacement constraint boundary conditions of the target object; calculating thermal deformation displacement field data of the target object based on temperature distribution data, thermal deformation parameters, and displacement constraint boundary conditions; and determining displacement data of a displacement monitoring point from the thermal deformation displacement field data as the thermal deformation displacement data.

[0129] Specifically, before calculating thermal deformation, thermal deformation parameters are set. These parameters represent engineering parameters of the target object related to thermal deformation. For example, the temperature-dependent linear expansion coefficient α (20°C to 300°C) is set to 2.27E-5 / °C, the elastic modulus E = 71.0 GPa, the shear modulus G = 26.5 GPa, and the Poisson's ratio μ = 0.33.

[0130] Furthermore, constraints can be set based on the actual working conditions of the target object. For example, a displacement constraint boundary condition can be set based on the actual assembly situation, specifically a frictionless constraint boundary condition. A frictionless constraint boundary condition applies normal constraints to selected faces of a geometric object, while leaving the tangential direction unconstrained. This constraint has the same effect as a symmetry constraint and is often used to simulate the behavior of objects in a frictionless environment.

[0131] Subsequently, the thermal deformation displacement field data of the target object is calculated based on the temperature distribution data, thermal deformation parameters and displacement constraint boundary conditions.

[0132] Exemplarily, the temperature distribution data is imported into the structural field module, and the thermal deformation parameters and displacement constraint boundary conditions are set in the structural field module. The structural field module runs the simulation to obtain the thermal deformation displacement field data of the target object, that is, the displacement field data generated by thermal deformation.

[0133] It's understandable that during electromagnetic induction heating, the electromagnetic field generates eddy currents, which are converted into heat energy, causing changes in the temperature field. This temperature field, in turn, affects the stress and displacement fields through thermal expansion and changes in mechanical properties. The structural field module can include algorithms for solving the displacement field, such as implicit numerical integration methods.

[0134] Similar to the temperature field, for a heated object, it's not necessary to focus on the displacement caused by thermal deformation at every location on the object; only the thermal deformation displacement at specific locations related to assembly is important. Therefore, in the present embodiment, displacement monitoring points can be set up. During subsequent optimization, only the displacement data from these monitoring points is considered. The displacements of these monitoring points are combined and used as thermal deformation displacement data for subsequent evaluation of thermal deformation uniformity.

[0135] As can be seen, specific locations related to assembly can be set as displacement monitoring points. Displacement data from these key points can be used to assess thermal deformation uniformity. This allows for dynamic adjustments based on the characteristics of the heated object, providing high flexibility and adaptability. Compared to constant current source heating, this reduces thermal deformation non-uniformity and improves assembly accuracy.

[0136] In order to enable those skilled in the art to further understand the optimization method of electromagnetic induction heating in the embodiment of the present application, a detailed description is given below in conjunction with specific embodiments.

[0137] Specifically, such as Figure 4 As shown in FIG, the overall step includes four branch steps. The first branch step is used to solve the eddy current loss, the second branch step is used to solve the temperature distribution data, the third branch step is used to solve the thermal deformation displacement data, and the fourth branch step is used to perform related settings for current optimization.

[0138] The first branch of steps includes:

[0139] A1: Build a model.

[0140] That is, a three-dimensional simulation model of the heating device and the target object is established.

[0141] A2: Set material properties.

[0142] Specifically, it may include the material of the heating coil, the material of the magnetic core, and the material of the motor housing.

[0143] A3: Set incentive variables.

[0144] That is, the excitation current is set, and the initial current value can be predetermined.

[0145] A4: Set the solution type.

[0146] For example, the solution type may be an eddy current solution.

[0147] A5: Mesh division.

[0148] Automatic meshing can be achieved with the help of the functions provided by the heating simulation software.

[0149] A6: Solve for eddy current loss.

[0150] The second branch of steps includes:

[0151] B1: Import the model.

[0152] That is, the three-dimensional simulation models of the heating device and the target object are imported into the transient temperature field module.

[0153] B2: Set thermal boundary conditions.

[0154] B3: Introduce heat source.

[0155] Specifically, after executing A6, the eddy current loss obtained in A6 can be introduced into the transient temperature field module as a heat source.

[0156] B4: Mesh division.

[0157] B5: Set the temperature data monitoring point.

[0158] B6: Solve for transient thermal temperature distribution.

[0159] B7: Monitoring point temperature feedback.

[0160] The transient thermal temperature distribution can be understood as transient heating temperature field data, from which the temperature data of the temperature monitoring point is determined for subsequent judgment.

[0161] The third branch of steps includes:

[0162] C1: Import the model.

[0163] The 3D simulation models of the heating device and target object are imported into the structural field module.

[0164] C2: Set temperature-related deformation parameters.

[0165] C3: Set constraints.

[0166] C4: Import transient temperature distribution data.

[0167] C5: Set the displacement data monitoring point.

[0168] C6: Solve for transient structural displacement data.

[0169] C7: Monitoring point displacement feedback.

[0170] Among them, the transient structural displacement data can be understood as thermal deformation displacement field data, from which the displacement data of the displacement monitoring point is determined for subsequent judgment.

[0171] The fourth branch step includes:

[0172] D1: Set variable current.

[0173] D2: Define optimization goals.

[0174] Specifically, it can include: 1. Meeting the uniformity of transient temperature distribution. 2. Meeting the uniformity of thermal deformation.

[0175] D3: Set constraints.

[0176] D4: Select optimization algorithm.

[0177] After executing B7 and C7, it can be determined whether the temperature distribution uniformity and thermal deformation uniformity meet the preset conditions, such as Figure 4 As shown, when either the temperature distribution uniformity or the thermal deformation uniformity does not meet the corresponding conditions, the step of dynamically adjusting the current curve is performed, and the excitation variable is reset through current feedback control.

[0178] When the temperature distribution uniformity and thermal deformation uniformity are met at the same time, the optimization process ends.

[0179] It can be seen that the embodiment of the present application provides a constant temperature variable current control method for electromagnetic induction uniform heating, including establishing a multi-physical field coupling model. In addition, on the basis of establishing the multi-physical field coupling model, an optimization module dynamic control is added to establish a current and temperature dynamic feedback adjustment mode between the electromagnetic field, transient temperature field, and structural field and the optimization module; at the same time, a bidirectional feedback iterative coupling interface between the electromagnetic field and the temperature field is established, and a bidirectional coupling link relationship between the electromagnetic field and the temperature field is established.

[0180] Compared to constant-current heating, this method can precisely control temperature, avoiding temperature fluctuations and instantaneous overheating, ensuring the temperature is within the target range. It also achieves uniform temperature distribution across the heated object, reducing temperature non-uniformity and improving heating efficiency. Furthermore, it achieves uniform thermal deformation distribution across the heated object, ensuring the deformation interference is within the target range, improving assembly accuracy.

[0181] In some embodiments, as Figure 5 As shown, the electromagnetic induction heating optimization device may include: an acquisition module 501 for acquiring temperature distribution data and thermal deformation displacement data generated by the target object during electromagnetic induction heating; an optimization module 502 for optimizing the excitation current of the electromagnetic induction heating based on the temperature distribution data and the thermal deformation displacement data, with the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization targets, to obtain an optimization result; the optimization result represents the dynamic excitation current.

[0182] In one possible implementation, the acquisition module is specifically configured to:

[0183] constructing a three-dimensional simulation model of the heating device and the target object;

[0184] Based on the three-dimensional simulation models of the heating device and the target object, respectively, a transient heating simulation is performed on the target object, and eddy current losses generated during the transient heating simulation process are calculated;

[0185] The temperature distribution data is calculated based on the eddy current loss.

[0186] In one possible implementation, the acquisition module is specifically configured to:

[0187] determining thermal deformation parameters and displacement constraint boundary conditions of the target object;

[0188] Calculating thermal deformation displacement field data of the target object based on the temperature distribution data, the thermal deformation parameters and the displacement constraint boundary conditions;

[0189] The displacement data of the displacement monitoring point is determined from the thermal deformation displacement field data as the thermal deformation displacement data.

[0190] In one possible implementation, the optimization module is specifically configured to: obtain temperature distribution data and thermal deformation displacement data calculated based on a current excitation current, and determine whether the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity, respectively, during a heating cycle;

[0191] If not, the current excitation current is optimized to obtain a new excitation current, and the step of obtaining the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current is re-executed until the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity respectively during the heating cycle.

[0192] In a possible implementation, the temperature distribution uniformity is characterized by a difference between the upper temperature limit data and the lower temperature limit data in the temperature distribution data being less than a first temperature threshold;

[0193] The thermal deformation uniformity indicates that a difference between an upper displacement limit and a lower displacement limit in the thermal deformation displacement data is greater than a first displacement threshold and less than a second displacement threshold.

[0194] The present application also provides an electronic device, such as Figure 6 As shown, the electronic device includes but is not limited to: a processor 601 and a memory 602 .

[0195] The memory 602 is used to store executable instructions of the processor 601. It is understandable that the processor 601 is configured to execute instructions to implement the electromagnetic induction heating optimization method in the above embodiment.

[0196] It should be noted that those skilled in the art can understand that Figure 6 The electronic device structure shown in the figure does not limit the electronic device, and the electronic device may include Figure 6 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0197] The processor 601 is the control center of the electronic device, connecting the various parts of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 602 and accessing data stored in the memory 602, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. The processor 601 may include one or more processing units. Optionally, the processor 601 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 601.

[0198] The memory 602 can be used to store software programs and various data. The memory 602 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and application programs required by at least one functional module (such as a determination unit, a processing unit, etc.). Furthermore, the memory 602 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0199] In an exemplary embodiment, a computer-readable storage medium including instructions is further provided, such as a memory 602 including instructions. The instructions may be executed by a processor 601 of an electronic device to implement the method in the above embodiment.

[0200] Optionally, the computer-readable storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0201] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, which can be executed by the processor 601 of the electronic device to implement the method in the above embodiment.

[0202] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0203] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete the full classification or partial functions described above.

[0204] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0205] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0206] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0207] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks or optical disks.

[0208] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing electromagnetic induction heating, characterized in that: The method comprises: Acquire temperature distribution data and thermal deformation displacement data generated by the target object during electromagnetic induction heating; Based on the temperature distribution data and the thermal deformation displacement data, the excitation current of electromagnetic induction heating is optimized with the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization goals to obtain an optimization result; the optimization result represents the dynamic excitation current.

2. The method according to claim 1, characterized in that The temperature distribution data is obtained by: constructing a three-dimensional simulation model of the heating device and the target object; Based on the three-dimensional simulation models of the heating device and the target object, respectively, a transient heating simulation is performed on the target object, and eddy current losses generated during the transient heating simulation process are calculated; The temperature distribution data is calculated based on the eddy current loss.

3. The method according to claim 2, characterized in that The calculating the temperature distribution data based on the eddy current loss includes: Calculating transient heating temperature field data based on the eddy current loss and thermal boundary conditions; The temperature data of the temperature monitoring points are determined from the transient heating temperature field data as the temperature distribution data.

4. The method according to any one of claims 1 to 3, characterized in that The thermal deformation displacement data is obtained by: determining thermal deformation parameters and displacement constraint boundary conditions of the target object; Calculating thermal deformation displacement field data of the target object based on the temperature distribution data, the thermal deformation parameters and the displacement constraint boundary conditions; The displacement data of the displacement monitoring point is determined from the thermal deformation displacement field data as the thermal deformation displacement data.

5. The method according to claim 1, wherein The method of optimizing the excitation current of electromagnetic induction heating based on the temperature distribution data and the thermal deformation displacement data and taking the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization targets includes: Obtaining temperature distribution data and thermal deformation displacement data calculated based on the current excitation current, and determining whether the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity, respectively, during a heating cycle; If not, the current excitation current is optimized to obtain a new excitation current, and the step of obtaining the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current is re-executed until the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity respectively during the heating cycle.

6. The method according to claim 1, wherein The temperature distribution uniformity indicates that the difference between the upper temperature limit data and the lower temperature limit data in the temperature distribution data is less than a first temperature threshold; The thermal deformation uniformity indicates that a difference between an upper displacement limit and a lower displacement limit in the thermal deformation displacement data is greater than a first displacement threshold and less than a second displacement threshold.

7. An optimization device for electromagnetic induction heating, characterized in that: The device comprises: An acquisition module, configured to acquire temperature distribution data and thermal deformation displacement data generated when a target object is heated during electromagnetic induction heating; An optimization module is used to optimize the excitation current of electromagnetic induction heating based on the temperature distribution data and the thermal deformation displacement data, taking the temperature distribution uniformity and thermal deformation uniformity of the target object as optimization goals, and obtain an optimization result; the optimization result represents the dynamic excitation current.

8. The device according to claim 7, characterized in that The acquisition module is specifically used to: constructing a three-dimensional simulation model of the heating device and the target object; Based on the three-dimensional simulation models of the heating device and the target object, respectively, a transient heating simulation is performed on the target object, and eddy current losses generated during the transient heating simulation process are calculated; The temperature distribution data is calculated based on the eddy current loss.

9. The device according to claim 7 or 8, characterized in that The acquisition module is specifically used to: determining thermal deformation parameters and displacement constraint boundary conditions of the target object; Calculating thermal deformation displacement field data of the target object based on the temperature distribution data, the thermal deformation parameters and the displacement constraint boundary conditions; The displacement data of the displacement monitoring point is determined from the thermal deformation displacement field data as the thermal deformation displacement data.

10. The device according to claim 7, characterized in that The optimization module is specifically configured to obtain temperature distribution data and thermal deformation displacement data calculated based on the current excitation current, and determine whether the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity, respectively, during a heating cycle; If not, the current excitation current is optimized to obtain a new excitation current, and the step of obtaining the temperature distribution data and thermal deformation displacement data calculated based on the current excitation current is re-executed until the temperature distribution data and the thermal deformation displacement data satisfy the temperature distribution uniformity and the thermal deformation uniformity respectively during the heating cycle.

11. The device according to claim 7, characterized in that The temperature distribution uniformity indicates that the difference between the upper temperature limit data and the lower temperature limit data in the temperature distribution data is less than a first temperature threshold; The thermal deformation uniformity indicates that a difference between an upper displacement limit and a lower displacement limit in the thermal deformation displacement data is greater than a first displacement threshold and less than a second displacement threshold.

12. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6.

Images