A method and device for electromagnetic optimization of axial flux motor based on equivalent magnetic network

Abstract

The present application discloses an electromagnetic optimization method and device for an axial flux motor based on an equivalent magnetic network, and relates to the technical field of axial flux motors. The method includes: obtaining the parameters of the fixed parts and rotating parts of the motor, and establishing a three-dimensional geometric model of the motor; collecting the operating data and mechanical parameters of the motor, and importing them into the three-dimensional geometric model to obtain a three-dimensional model of the motor; establishing an air domain outside the radial boundary of the three-dimensional model, and setting the conductivity, magnetic permeability, velocity inlet, pressure outlet and wall surface for the air domain; using a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor; establishing an equivalent magnetic circuit for the mesh model, collecting and changing the preset values ​​of the operating data and mechanical parameters at a preset frequency, and calculating the magnetomotive force and magnetic resistance of each mesh to determine the optimal configuration. The present application simulates the magnetic field distribution of the motor more accurately by establishing an equivalent magnetic circuit, collecting operating data and mechanical parameters, and establishing a three-dimensional model.

Description

Technical Field

[0001] The present application relates to the technical field of axial flux motors, and in particular to an electromagnetic optimization method and device for an axial flux motor based on an equivalent magnetic network. Background Art

[0002] First of all, since the magnetic field distribution of the axial flux motor presents complex three-dimensional characteristics, it is difficult to construct its mathematical model and it is difficult to directly apply traditional classical analytical methods for fast and accurate solutions and calculations. Therefore, there are indeed few electromagnetic optimization methods for axial flux motors in the existing technology.

[0003] Secondly, the existing motor electromagnetic optimization strategies are mainly divided into two categories: one is to focus on the optimization of the motor's topological structure and structural parameters, and the other is to improve the motor's control strategy. However, the existing optimization methods often only consider the optimization of a single aspect of parameters, ignoring the interaction and influence between the various parameters of the motor system, and the interaction between the electromagnetic field and the structural field. This leads to mutual constraints in improving force density and suppressing force fluctuations, making it difficult to achieve the best balance between the two.

[0004] Through the above analysis, the problems and defects of the prior art are as follows:

[0005] The axial flux motor in the prior art ignores the interaction between the various parameters of the motor system and the interaction between the electromagnetic field and the structural field, making it difficult to achieve an optimal balance between the two. Summary of the invention

[0006] The embodiments of the present application provide an electromagnetic optimization method and device for an axial flux motor based on an equivalent magnetic network, which can solve the problem that the axial flux motor in the prior art ignores the interaction between the various parameters of the motor system and the interaction between the electromagnetic field and the structural field, making it difficult to achieve the optimal balance between the two.

[0007] In the first aspect, an embodiment of the present application provides an electromagnetic optimization method for an axial flux motor based on an equivalent magnetic network, the method comprising: obtaining parameters of the fixed parts and rotating parts of the motor, and establishing a three-dimensional geometric model of the motor, wherein the fixed parts include a stator core and a stator winding, and the rotating parts include a rotor core, a rotor winding and a permanent magnet; collecting the operating data and mechanical parameters of the motor, and importing them into the three-dimensional geometric model to obtain a three-dimensional model of the motor; establishing an air domain outside the radial boundary of the three-dimensional model, and setting conductivity, magnetic permeability, velocity inlet, pressure outlet and wall for the air domain; using a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor; establishing an equivalent magnetic circuit for the mesh model, collecting and changing the preset values ​​of the operating data and mechanical parameters at a preset frequency, and calculating the magnetomotive force and magnetic resistance of each mesh to determine the optimal configuration.

[0008] In one implementation of the present application, after collecting the operating data and mechanical parameters of the motor and importing them into a three-dimensional geometric model to obtain the three-dimensional model of the motor, the method also includes: dividing the three-dimensional model into multiple phases in the radial direction of the motor according to the periodicity of the motor in the circumferential direction, and verifying whether the magnetic field distribution of the multiple phases is uniform; when the magnetic field distribution is uniform, simplifying the three-dimensional model into one phase to obtain a simplified model of the three-dimensional model of the motor; when the magnetic field distribution is uneven, obtaining simplified models of multiple phases of the three-dimensional model respectively, and performing a visual display of the section.

[0009] In one implementation of the present application, an equivalent magnetic circuit is established for a grid model, operating data and preset values ​​of mechanical parameters are collected and changed at a preset frequency, and the magnetomotive force and magnetic resistance of each grid are calculated to determine the optimal configuration, specifically including: collecting operating data at a preset frequency, the operating data including rated power, peak power, rated torque, peak torque, armature current, terminal voltage, temperature, noise, duty cycle and frequency of PWM signals; collecting mechanical parameters and control strategies at a preset frequency, the mechanical parameters including magnetic flux density, weight and magnetization curve of permanent magnets, winding resistance and inductance, permanent magnet performance parameters, stator core outer diameter, air gap length, and permanent magnet thickness; control strategies including current waveform control and vector control; performing spectrum analysis on the armature current to identify harmonic components, as well as the frequency, amplitude and phase of each harmonic; and demonstrating and calculating the quantitative relationship between harmonic components and electromagnetic force fluctuations through a simplified model.

[0010] In one implementation of the present application, after demonstrating and calculating the quantitative relationship between harmonic components and electromagnetic force fluctuations through a simplified model, the method also includes: eliminating harmonic components through a filter, using an oscilloscope to collect the filtered armature current in real time, and verifying whether the quantitative relationship meets the preset standard; if the verification is passed, the electromagnetic force fluctuation after harmonic suppression is demonstrated again through the simplified model, and the operating data, mechanical parameters and control strategy are changed at a preset frequency, and the magnetomotive force and magnetic resistance are calculated; the simplified model is divided into a strong magnetic field region and a weak magnetic field region according to the magnetomotive force and magnetic resistance.

[0011] In one implementation of the present application, after dividing the simplified model into a strong magnetic field region and a weak magnetic field region according to magnetomotive force and magnetic resistance, the method also includes: for the strong magnetic field region, finding the optimal configuration of operating data and mechanical parameters through an optimization algorithm; for the weak magnetic field region, using sinusoidal current control, pulse width modulation current control, and maximum torque current ratio algorithm.

[0012] In one implementation of the present application, for strong magnetic field areas, an optimization algorithm is used to find the optimal configuration of operating data and mechanical parameters, specifically including: using the current operating data and mechanical parameters as the initialization population; substituting each individual in the initialization population into a simplified model for simulation, and calculating the fitness value of each individual, the fitness value including the weighted sum of the efficiency, torque density, and torque pulsation of the motor; sorting the individuals in the population according to the fitness value, selecting the individuals with preset fitness value rankings as the parents, performing crossover and mutation, and generating offspring individuals; substituting the generated offspring individuals into the simplified model for simulation, and calculating the fitness value, and selecting the individual with the highest fitness value as the optimal solution.

[0013] In one implementation of the present application, after the generated offspring individuals are substituted into a simplified model for simulation, and the fitness value is calculated, and the individual with the highest fitness value is selected as the optimal solution, the method also includes: building an experimental test platform, and installing current sensors, force sensors and temperature sensors on the test motor; setting experimental parameters according to the optimal solution, starting the test motor and running it for a preset time, and using an oscilloscope to collect armature current data and electromagnetic force fluctuation data in real time; comparing the experimental test results with the simulation results before optimization to verify the effectiveness of the genetic algorithm.

[0014] In one implementation of the present application, the method also includes: monitoring noise data, current, voltage and temperature, performing spectral analysis on the noise data, and identifying abnormal frequency components; comparing the abnormal frequency components with a preset fault characteristic frequency library to determine whether bearing wear and electromagnetic noise exist; obtaining the heat source distribution and heat dissipation channel of the motor, determining the external air temperature and convection coefficient of the motor, and determining whether the thermal performance of the motor is improved after the optimized configuration.

[0015] In one implementation of the present application, the method also includes: replacing materials and winding methods in a simplified model, and changing mechanical parameters according to the materials; changing the matching relationship between the number of poles and the number of slots, and determining the optimal combination of the motor's force density and force fluctuation suppression.

[0016] In the second aspect, an embodiment of the present application also provides an axial flux motor electromagnetic optimization device based on an equivalent magnetic network, the device comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain parameters of the fixed parts and rotating parts of the motor, and establish a three-dimensional geometric model of the motor, wherein the fixed parts include a stator core and a stator winding, and the rotating parts include a rotor core, a rotor winding and a permanent magnet; collect the operating data and mechanical parameters of the motor, import them into the three-dimensional geometric model, and obtain a three-dimensional model of the motor; establish an air domain outside the radial boundary of the three-dimensional model, and set the conductivity, magnetic permeability, velocity inlet, pressure outlet and wall surface for the air domain; use a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor; establish an equivalent magnetic circuit for the mesh model, collect and change the preset values ​​of the operating data and mechanical parameters at a preset frequency, and calculate the magnetomotive force and magnetic resistance of each mesh to determine the optimal configuration.

[0017] The embodiment of the present application provides an axial flux motor electromagnetic optimization method and device based on an equivalent magnetic network. By establishing an equivalent magnetic circuit, the magnetic field distribution and electromagnetic performance of the motor can be simulated and analyzed more accurately. By collecting the operating data and mechanical parameters of the motor, establishing a three-dimensional geometric model, and then meshing the model, the internal structure and magnetic field distribution of the motor can be described more carefully. This refined model helps to improve the accuracy of motor performance prediction. The method allows flexible adjustment of the parameters of the fixed and rotating parts of the motor, such as the size and material of the stator core, stator winding, rotor core, rotor winding and permanent magnet, which makes it possible to achieve parametric design of the motor. By performing equivalent magnetic circuit analysis on the grid model, multiple performance indicators of the motor, such as efficiency, torque density, cost, etc., can be comprehensively considered, thereby achieving multi-objective optimization. When establishing the three-dimensional geometric model, the heat dissipation design of the motor can be considered. By accurately simulating and analyzing the internal temperature field of the motor, the heat dissipation structure can be optimized, the heat dissipation performance of the motor can be improved, and the service life of the motor can be extended. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flow chart of an electromagnetic optimization method for an axial flux motor based on an equivalent magnetic network provided in an embodiment of the present application;

[0020] Figure 2A schematic diagram of the internal structure of an axial flux motor electromagnetic optimization device based on an equivalent magnetic network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0022] The embodiments of the present application provide an electromagnetic optimization method and device for an axial flux motor based on an equivalent magnetic network, which solves the problem that the axial flux motor in the prior art ignores the interaction between the various parameters of the motor system and the interaction between the electromagnetic field and the structural field, making it difficult to achieve an optimal balance between the two.

[0023] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0024] Figure 1 A flowchart of an electromagnetic optimization method for an axial flux motor based on an equivalent magnetic network is provided in an embodiment of the present application. Figure 1 As shown, an electromagnetic optimization method for an axial flux motor based on an equivalent magnetic network provided in an embodiment of the present application specifically includes the following steps:

[0025] Step 10: Obtain parameters of the fixed parts and rotating parts of the motor and establish a three-dimensional geometric model of the motor, wherein the fixed parts include the stator core and the stator winding, and the rotating parts include the rotor core, the rotor winding and the permanent magnet;

[0026] In this step, a three-dimensional modeling software, such as SolidWorks or AutoCAD, is used to draw a three-dimensional geometric model of the fixed motor according to these parameters.

[0027] Step 20: Collect the operation data and mechanical parameters of the motor, import them into the three-dimensional geometric model, and obtain the three-dimensional model of the motor;

[0028] In this step, temperature sensors, pressure sensors, displacement sensors, speed sensors, etc. are used to monitor the operating status of the motor, collect real-time data such as the motor's temperature, pressure, displacement, and speed, and obtain the motor's mechanical parameters, such as bearing clearance, rotor mass, and moment of inertia. The collected operating data and mechanical parameters are imported into the three-dimensional geometric model through a data interface or data conversion tool for subsequent simulation analysis.

[0029] Step 30: Establish an air domain outside the radial boundary of the three-dimensional model, and set the conductivity, magnetic permeability, velocity inlet, pressure outlet and wall surface for the air domain;

[0030] In this step, in order to accurately simulate the attenuation of the magnetic field in the air, an air boundary is established outside the radial boundary of the model. The velocity inlet is used to simulate the speed and direction of the external airflow entering the air domain, and the pressure outlet is used to simulate the pressure of the external airflow when leaving the air domain. The boundary of the air domain is set to the wall, which can be used to simulate the solid surface. The no-slip condition can be considered, that is, the velocity of the fluid on the solid surface is zero or the free slip condition, which can be set according to the actual situation.

[0031] Step 40: Use a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor;

[0032] In this step, mesh encryption is performed on key areas such as stator windings, rotor windings, and permanent magnets to improve the accuracy of simulation analysis. During the division process, the continuity and smoothness of the mesh are maintained to avoid mesh distortion or deformation.

[0033] Step 50: Establish an equivalent magnetic circuit for the grid model, collect and change the preset values ​​of operating data and mechanical parameters at a preset frequency, calculate the magnetomotive force and magnetic resistance of each grid, and determine the optimal configuration.

[0034] In this step, the grid model is subjected to equivalent magnetic circuit analysis, and multiple performance indicators of the motor, such as efficiency, torque density, cost, etc., can be comprehensively considered to achieve multi-objective optimization; the three-dimensional model is meshed, and the interior of the motor is divided into multiple grids. Each grid can be regarded as a small magnetic circuit unit, so that each grid can be calculated later. For a simple example, the magnetic circuit length of the grid is l=0.1m, the cross-sectional area is s=0.01m², and the magnetic permeability μ=1H / m. The magnetic resistance of the grid is calculated: Rm = l / μs = 0.1m / (1H / m * 0.01m²) = 10H^-1. If the magnetomotive force F=100At (ampere-turns) of the grid is known, the magnetic flux Φ can be calculated using a similar form of Ohm's law: Φ = F / Rm = 100At / 10H^-1 = 10Wb (Weber).

[0035] As an optional embodiment, an equivalent magnetic circuit is established for the grid model, and the preset values ​​of operating data and mechanical parameters are collected and changed at a preset frequency, and the magnetomotive force and magnetic resistance of each grid are calculated to determine the optimal configuration. Specifically, the following may be included: Step 501: collecting operating data at a preset frequency, and the operating data include rated power, peak power, rated torque, peak torque, armature current, terminal voltage, temperature, noise, duty cycle and frequency of PWM signal; Step 502: collecting mechanical parameters and control strategies at a preset frequency, and the mechanical parameters include magnetic flux density, weight and magnetization curve of permanent magnet, winding resistance and inductance, permanent magnet performance parameters, stator core outer diameter, air gap length, and permanent magnet thickness. The control strategies include current waveform control and vector control; Step 503: performing spectrum analysis on the armature current to identify harmonic components, as well as the frequency, amplitude and phase of each harmonic; Step 504: demonstrating and calculating the quantitative relationship between harmonic components and electromagnetic force fluctuations through a simplified model.

[0036] In this step, based on the equivalent magnetic network model, the equivalent magnetic circuit method and superposition method are used to calculate the key performance parameters of the motor, such as magnetic flux, back electromotive force, and electromagnetic torque. The influence of the armature current characteristic quantity on the electromagnetic thrust and the electromagnetic force fluctuation problem caused by current harmonics are analyzed to provide data support for subsequent optimization.

[0037] As an optional embodiment, after demonstrating and calculating the quantitative relationship between harmonic components and electromagnetic force fluctuations through a simplified model, the method may also include: Step 505: Eliminate the harmonic components through a filter, use an oscilloscope to collect the filtered armature current in real time, and verify whether the quantitative relationship meets the preset standard; Step 506: If the verification is passed, demonstrate the electromagnetic force fluctuation after harmonic suppression again through the simplified model, change the operating data, mechanical parameters and control strategy at a preset frequency, and calculate the magnetomotive force and magnetic resistance; Step 507: Divide the simplified model into a strong magnetic field area and a weak magnetic field area according to the magnetomotive force and magnetic resistance.

[0038] In this step, the magnetic field intensity H represents the ratio of the magnetic induction intensity to the magnetic permeability in a unit magnetic medium, that is, H=B / μ; the division criteria of the strong magnetic field area and the weak magnetic field area can be based on the magnetic field intensity H or magnetic flux density B For example, a magnetic field strength threshold can be set. Hthreshold , when the magnetic field strength in a certain area H > Hthreshold When , the area is considered to be a strong magnetic field area; otherwise, it is a weak magnetic field area.

[0039] As an optional embodiment, after the simplified model is divided into a strong magnetic field region and a weak magnetic field region according to the magnetomotive force and the magnetic resistance, the method may further include: Step 508: for the strong magnetic field region, finding the optimal configuration of the operating data and the mechanical parameters by an optimization algorithm;

[0040] In this step, intelligent optimization algorithms such as genetic algorithms are introduced to iteratively optimize the parameters in the equivalent magnetic network model to achieve an increase in the motor force density and a suppression of force fluctuations.

[0041] As an optional embodiment, for strong magnetic field areas, the optimal configuration of operating data and mechanical parameters is found through an optimization algorithm, which may specifically include: Step 5081: using the current operating data and mechanical parameters as the initialization population; Step 5082: substituting each individual in the initialization population into a simplified model for simulation, and calculating the fitness value of each individual, the fitness value including the weighted sum of the efficiency, torque density, and torque pulsation of the motor; Step 5083: sorting the individuals in the population according to the fitness value, selecting the individuals with preset fitness value rankings as the parents, performing crossover and mutation, and generating offspring individuals; Step 5084: substituting the generated offspring individuals into the simplified model for simulation, calculating the fitness value, and selecting the individual with the highest fitness value as the optimal solution.

[0042] In this step, the size, shape, position, number of turns of the winding, current density, etc. of the current permanent magnet are used as the initial population, and each individual can be represented by a vector, such as x i =[ Iarmature , Vterminal , f , B , R , L ,…],in i Indicates the first i The efficiency of the motor η , torque density ρT , torque ripple Δ T The weighted sum of other performance indicators, the fitness function can be expressed as: f (x i )= w 1 η (x i )+ w 2 ρT (x i )− w 3Δ T (x i ),in, w 1, w 2, w 3 is the weight coefficient, which is used to balance the importance of different performance indicators; assuming there are three performance indicators: efficiency η, torque density ρT and torque ripple Δ T The weight coefficients are w 1=0.5, w 2=0.3, w 3=0.2, for some individual x i , the simulation results are η (x i )=0.9, ρT (x i )=10Nm / kg,Δ T (x i )=0.05Nm, then the fitness value is: f (x i )=0.5×0.9+0.3×10-0.2×0.05=0.45+3-0.01=3.44. Assume that after a round of selection, crossover and mutation, a new offspring individual x is generated. j , the simulation results are η (x j )=0.92, ρT (x j )=10.5Nm / kg,Δ T (x j )=0.04Nm. Then the fitness value is: f (x j )=0.5×0.92+0.3×10.5-0.2×0.04=0.46+3.15-0.008=3.602, because f (x j )> f (x i ), so choose x j As the current optimal solution.

[0043] As an optional embodiment, after substituting the generated offspring individuals into the simplified model for simulation, calculating the fitness value, and selecting the individual with the highest fitness value as the optimal solution, the method may also include: Step 5085: building an experimental test platform, and installing current sensors, force sensors and temperature sensors on the test motor; Step 5086: setting experimental parameters according to the optimal solution, starting the test motor and running it for a preset time, and using an oscilloscope to collect armature current data and electromagnetic force fluctuation data in real time; Step 5087: comparing the experimental test results with the simulation results before optimization to verify the effectiveness of the genetic algorithm.

[0044] In this step, it is assumed that the armature current fluctuation obtained by simulation before optimization is Δ I armature,sim=0.1A, electromagnetic force fluctuation is Δ Fem,sim=0.5Nm; the armature current fluctuation obtained by experimental test is Δ I armature,exp=0.05A, electromagnetic force fluctuation is Δ F em,exp=0.25Nm. Then: the armature current fluctuation reduction percentage = (0.1-0.05) / 0.1×100%=50%, the electromagnetic force fluctuation reduction percentage = (0.5-0.25) / 0.5×100%=50%, which shows that the genetic algorithm is effective in optimizing motor performance.

[0045] Step 509: For the weak magnetic field region, adopt sinusoidal current control, pulse width modulation current control, and maximum torque current ratio algorithm.

[0046] In this step, the first thing to understand is that in the weak magnetic field area, the speed is high and the torque is low. In order to maintain the stable operation of the motor, the controller adopts a sinusoidal current control strategy. By accurately controlling the current amplitude and phase, the current waveform is matched with the motor's back electromotive force waveform, thereby achieving smooth operation of the motor; for example, in a PWM (PulseWidth Modulation) controlled PMSM (Permanent Magnet Synchronous Motor) system, the controller controls the motor current by adjusting the duty cycle of the PWM wave. When the motor runs in the weak magnetic field area, the controller will dynamically adjust the duty cycle of the PWM wave according to the motor's operating status and desired torque output to achieve precise current control.

[0047] Furthermore, in practical applications, sinusoidal current control, PWM current control and MTPA (Maximum Torque Per Ampere) algorithms can be combined to achieve more precise and efficient motor control: in a PMSM drive system, the controller first selects a suitable control strategy based on the operating status of the motor and the desired torque output; in the weak magnetic field area, the controller may use sinusoidal current control to ensure the smooth operation of the motor, and combine it with PWM current control to accurately adjust the current size and waveform. At the same time, in order to further improve the efficiency of the motor, the controller will also use the MTPA algorithm to calculate the optimal d-axis and q-axis current components.

[0048] As an optional embodiment, after collecting the operating data and mechanical parameters of the motor and importing them into a three-dimensional geometric model to obtain the three-dimensional model of the motor, the method may also include: dividing the three-dimensional model into multiple phases in the radial direction of the motor according to the periodicity of the motor in the circumferential direction, and verifying whether the magnetic field distribution of the multiple phases is uniform; when the magnetic field distribution is uniform, simplifying the three-dimensional model into one phase to obtain a simplified model of the three-dimensional model of the motor; when the magnetic field distribution is uneven, obtaining simplified models of multiple phases of the three-dimensional model respectively, and performing a visual display of the section.

[0049] In this step, the three-dimensional finite element model is reasonably simplified while maintaining the calculation accuracy, such as using periodic symmetric boundary conditions to reduce the calculation domain, or using the above-mentioned mapping grid, stretching and sweeping grid division technology to optimize the grid division, thereby improving the solution speed and efficiency, which helps to more accurately calculate the electromagnetic performance of the motor.

[0050] As an optional embodiment, the method may also include: monitoring noise data, current, voltage and temperature, performing spectral analysis on the noise data, and identifying abnormal frequency components; comparing the abnormal frequency components with a preset fault characteristic frequency library to determine whether bearing wear and electromagnetic noise exist; obtaining the heat source distribution and heat dissipation channels of the motor, determining the external air temperature and convection coefficient of the motor, and determining whether the thermal performance of the motor is improved after the optimized configuration.

[0051] In this step, the formula for spectrum analysis can be: X ( k )= n =0∑ N -1 x ( n ) e - j (2 π / N ) kn, Among them, X(k) is the frequency domain signal, x(n) is the time domain signal, N is the number of sampling points, and k is the frequency index. In the spectrum diagram, observe whether there are abnormal frequency components that are different from the normal motor operating frequency, establish a database containing the characteristic frequencies of various motor faults, compare the identified abnormal frequency components with the characteristic frequencies in the database, and determine whether there are faults such as bearing wear and electromagnetic noise.

[0052] Furthermore, by analyzing the structure and working principle of the motor, the heat source distribution (such as windings, cores, etc.) and heat dissipation channels (such as fans, heat sinks, etc.) of the motor are determined, and a thermometer is used to measure the external air temperature T of the motor. The convection coefficient (h) can be obtained through experimental measurement or empirical formula calculation, which represents the heat exchange capacity between the motor surface and the surrounding air. According to the heat source distribution, heat dissipation channels, external air temperature and convection coefficient of the motor, the temperature distribution and thermal resistance of the motor are calculated using methods such as thermal network model or finite element analysis (FEA). By comparing the temperature distribution and thermal resistance before and after the optimized configuration, it is determined whether the thermal performance of the motor is improved. The formula of the thermal network model may include the calculation of thermal resistance (R) and thermal capacity (C), such as: T ( t )= T +( Tinit - Tamb ) e - τt ,in, T ( t ) is the temperature that varies with time, Tamb is the ambient temperature, Tinit is the initial temperature, τ = RC is the time constant.

[0053] As an optional embodiment, the method may also include: replacing materials and winding methods in a simplified model, and changing mechanical parameters according to the materials; changing the matching relationship between the number of poles and the number of slots, and determining the optimal combination of the motor's force density and force fluctuation suppression.

[0054] In this step, new permanent magnet materials with higher magnetic energy product and lower loss are explored and adopted, such as improved versions of NdFeB permanent magnets or other high-performance permanent magnet materials; the number of poles determines the synchronous speed and torque characteristics of the motor; the more poles, the lower the motor speed, but the greater the torque. Therefore, when selecting the number of poles, it is necessary to weigh the application and load characteristics of the motor, study the matching relationship between different pole numbers and slot numbers, and determine the optimal combination to improve the force density of the motor and suppress force fluctuations.

[0055] That is to say, the embodiment of the present application takes into account the search for the optimal configuration through an optimization algorithm after harmonic suppression, simulation through a three-dimensional model, and further construction of a test platform for testing. It not only takes into account operating data and winding methods, but also takes into account new materials, the matching relationship between the number of poles and the number of slots, etc., and comprehensively calculates the interaction between the magnetic field and the structural field to achieve the best balance.

[0056] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides an axial flux motor electromagnetic optimization device based on an equivalent magnetic network, and its structure is as follows: Figure 2 shown.

[0057] Figure 2 The internal structure diagram of an axial flux motor electromagnetic optimization device based on an equivalent magnetic network provided in an embodiment of the present application. Figure 2 As shown, the device includes:

[0058] at least one processor 201;

[0059] and, a memory 202 communicatively connected to the at least one processor;

[0060] Among them, the memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 201 so that at least one processor 201 can: obtain parameters of fixed parts and rotating parts of the motor, and establish a three-dimensional geometric model of the motor, wherein the fixed parts include a stator core and a stator winding, and the rotating parts include a rotor core, a rotor winding and a permanent magnet; collect the operating data and mechanical parameters of the motor, import them into the three-dimensional geometric model, and obtain a three-dimensional model of the motor; establish an air domain outside the radial boundary of the three-dimensional model, and set the conductivity, magnetic permeability, velocity inlet, pressure outlet and wall surface for the air domain; use a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor; establish an equivalent magnetic circuit for the mesh model, collect and change the preset values ​​of the operating data and mechanical parameters at a preset frequency, and calculate the magnetomotive force and magnetic resistance of each mesh to determine the optimal configuration.

[0061] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the IoT device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0062] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0068] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0069] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0071] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An electromagnetic optimization method for an axial flux motor based on an equivalent magnetic network, characterized in that: The method comprises: Acquire parameters of the fixed parts and rotating parts of the motor and establish a three-dimensional geometric model of the motor, wherein the fixed parts include a stator core and a stator winding, and the rotating parts include a rotor core, a rotor winding and a permanent magnet; Collecting the operation data and mechanical parameters of the motor, and importing them into the three-dimensional geometric model to obtain a three-dimensional model of the motor; Establishing an air domain outside the radial boundary of the three-dimensional model, and setting conductivity, magnetic permeability, velocity inlet, pressure outlet and wall surface for the air domain; Using a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor; Establishing an equivalent magnetic circuit for the grid model, collecting and changing the preset values ​​of operating data and mechanical parameters at a preset frequency, and calculating the magnetomotive force and magnetic resistance of each grid to determine the optimal configuration; After collecting the operation data and mechanical parameters of the motor and importing them into the three-dimensional geometric model to obtain the three-dimensional model of the motor, the method further includes: According to the periodicity of the motor in the circumferential direction, the three-dimensional model is divided into a plurality of phases in the radial direction of the motor, and it is verified whether the magnetic field distribution of the plurality of phases is uniform; In the case of uniform magnetic field distribution, the three-dimensional model is simplified into one phase to obtain a simplified model of the three-dimensional model of the motor; In the case of uneven magnetic field distribution, simplified models of multiple phases of the three-dimensional model are obtained respectively, and a visual display of the sectioning is performed.

2. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 1 is characterized in that: An equivalent magnetic circuit is established for the grid model, operating data and preset values ​​of mechanical parameters are collected and changed at a preset frequency, and the magnetomotive force and magnetic resistance of each grid are calculated to determine the optimal configuration, specifically including: Collecting operating data at a preset frequency, the operating data including rated power, peak power, rated torque, peak torque, armature current, terminal voltage, temperature, noise, duty cycle and frequency of PWM signal; Collect mechanical parameters and control strategies at a preset frequency, the mechanical parameters including magnetic flux density, weight and magnetization curve of permanent magnet, winding resistance, inductance, permanent magnet performance parameters, stator core outer diameter, air gap length, permanent magnet thickness, and the control strategies including current waveform control and vector control; Performing spectrum analysis on the armature current to identify harmonic components, as well as the frequency, amplitude and phase of each harmonic; The simplified model is used to demonstrate and calculate the quantitative relationship between the harmonic components and the electromagnetic force fluctuations.

3. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 2, characterized in that: After demonstrating and calculating the quantitative relationship between the harmonic component and the electromagnetic force fluctuation through the simplified model, the method further includes: Eliminating the harmonic components through a filter, acquiring the filtered armature current in real time using an oscilloscope, and verifying whether the quantitative relationship meets the preset standard; If the verification is passed, the electromagnetic force fluctuation after harmonic suppression is demonstrated again through the simplified model, and the operating data, mechanical parameters and control strategy are changed at a preset frequency to calculate the magnetomotive force and magnetic resistance; The simplified model is divided into a strong magnetic field region and a weak magnetic field region according to the magnetomotive force and the magnetic resistance.

4. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 3 is characterized in that: After dividing the simplified model into a strong magnetic field region and a weak magnetic field region according to the magnetomotive force and the magnetic resistance, the method further includes: For the strong magnetic field region, finding the optimal configuration of the operating data and mechanical parameters through an optimization algorithm; For the weak magnetic field region, sinusoidal current control, pulse width modulation current control and maximum torque current ratio algorithm are adopted.

5. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 3, characterized in that: For the strong magnetic field region, an optimization algorithm is used to find the optimal configuration of the operating data and mechanical parameters, specifically including: Use the current operating data and mechanical parameters as the initialization population; Substituting each individual in the initialized population into the simplified model for simulation, and calculating the fitness value of each individual, wherein the fitness value includes a weighted sum of the efficiency, torque density, and torque ripple of the motor; Sort the individuals in the population according to the fitness values, select the individuals with preset fitness value rankings as parents, perform crossover and mutation, and generate offspring individuals; The generated offspring individuals are substituted into the simplified model for simulation, and the fitness values ​​are calculated, and the individual with the highest fitness value is selected as the optimal solution.

6. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 5, characterized in that: After substituting the generated offspring individuals into the simplified model for simulation, calculating the fitness value, and selecting the individual with the highest fitness value as the optimal solution, the method further includes: Build an experimental test platform and install current sensors, force sensors and temperature sensors on the test motor; Setting experimental parameters according to the optimal solution, starting the test motor and running it for a preset time, and using an oscilloscope to collect armature current data and electromagnetic force fluctuation data in real time; The experimental test results are compared with the simulation results before optimization to verify the effectiveness of the genetic algorithm.

7. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 6, characterized in that: The method further comprises: monitoring the noise data, current, voltage and temperature, performing spectrum analysis on the noise data, and identifying abnormal frequency components; Compare the abnormal frequency component with a preset fault characteristic frequency library to determine whether bearing wear and electromagnetic noise exist; Obtain the heat source distribution and heat dissipation channels of the motor, determine the external air temperature and convection coefficient of the motor, and determine whether the thermal performance of the motor is improved after the optimized configuration.

8. The electromagnetic optimization method of an axial flux motor based on an equivalent magnetic network according to claim 6, characterized in that: The method further comprises: replacing materials and winding methods in the simplified model and changing the mechanical parameters according to the materials; By changing the matching relationship between the number of poles and the number of slots, the optimal combination of the motor's force density and force fluctuation suppression can be determined.

9. An axial flux motor electromagnetic optimization device based on an equivalent magnetic network, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquire parameters of the fixed parts and rotating parts of the motor and establish a three-dimensional geometric model of the motor, wherein the fixed parts include a stator core and a stator winding, and the rotating parts include a rotor core, a rotor winding and a permanent magnet; Collecting the operation data and mechanical parameters of the motor, and importing them into the three-dimensional geometric model to obtain a three-dimensional model of the motor; Establishing an air domain outside the radial boundary of the three-dimensional model, and setting conductivity, magnetic permeability, velocity inlet, pressure outlet and wall surface for the air domain; Using a meshing tool to mesh the three-dimensional model and the air domain to obtain a mesh model of the motor; Establishing an equivalent magnetic circuit for the grid model, collecting and changing the preset values ​​of operating data and mechanical parameters at a preset frequency, and calculating the magnetomotive force, magnetic resistance, and temperature of each grid to determine the optimal configuration; After collecting the operation data and mechanical parameters of the motor and importing them into the three-dimensional geometric model to obtain the three-dimensional model of the motor, the method further includes: According to the periodicity of the motor in the circumferential direction, the three-dimensional model is divided into a plurality of phases in the radial direction of the motor, and it is verified whether the magnetic field distribution of the plurality of phases is uniform; In the case of uniform magnetic field distribution, the three-dimensional model is simplified into one phase to obtain a simplified model of the three-dimensional model of the motor; In the case of uneven magnetic field distribution, simplified models of multiple phases of the three-dimensional model are obtained respectively, and a visual display of the sectioning is performed.

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