Permanent magnet synchronous motor optimization method and system based on novel topological structure

Abstract

The invention relates to a permanent magnet synchronous motor optimization method and system based on a novel topological structure. The method comprises the steps that parametric modeling is carried out through preset magnetic pole block parameters, preset magnetic barrier groove geometric parameters and preset asymmetric air gap parameters, and a variable topology model library is generated; through multi-physics field coupling simulation, dynamic eddy current distribution, a local demagnetization risk coefficient and a high-frequency vibration mode of the permanent magnet synchronous motor are calculated; constructing a multi-target optimization model for optimization through dynamic eddy current distribution, a local demagnetization risk coefficient and a high-frequency vibration mode, and generating a Pareto optimal solution set; and based on the manufacturing tolerance sensitivity parameter and demagnetization risk coefficient distribution in the solution set, constructing a topology fault-tolerant structure and obtaining a dynamic current compensation strategy, and generating a final optimization scheme. According to the method, through comprehensive application of multi-physical field coupling simulation, multi-objective optimization and a dynamic compensation strategy, the precision and scientificity of motor optimization are improved, and effective technical support is provided for design and manufacturing of the motor.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and particularly relates to an optimization method and system for a permanent magnet synchronous motor based on a novel topological structure. Background Art

[0002] With the continuous development of the new energy vehicle and industrial automation fields, as a key power component, how to further optimize the performance of the permanent magnet synchronous motor has become the focus of research. Traditional permanent magnet synchronous motors are limited by symmetric magnetic poles and uniform magnetic circuit designs, suffering from problems such as bottlenecks in efficiency improvement, high torque ripple, and insufficient flux-weakening ability. However, with the increasing demand for high performance in new energy vehicles, industrial drives, aerospace, and other fields for permanent magnet synchronous motors, optimization technologies for permanent magnet synchronous motors based on novel topological structures have emerged. This technology can further improve torque density, reduce losses, and expand flux-weakening ability through asymmetric magnetic poles, segmented permanent magnets, or composite magnetic circuit designs. However, although the optimization technology for permanent magnet synchronous motors based on novel topological structures can improve performance, it also introduces new challenges, such as extremely high manufacturing precision requirements and the excitation problem of high-frequency vibration modes. Moreover, existing optimization methods for permanent magnet synchronous motors based on novel topological structures mostly rely on single electromagnetic field simulations, lacking the collaborative optimization of multi-physical field coupling effects and engineering feasibility, resulting in a disconnection between theoretical design and practical application. Summary of the Invention

[0003] Based on this, it is necessary to provide an optimization method and system for a permanent magnet synchronous motor based on a novel topological structure to reduce the risk of local demagnetization and the influence of high-frequency vibration modes, thereby improving the overall performance and reliability of the motor and meeting the requirements for high-performance motors in new energy vehicles, industrial drives, aerospace, and other fields.

[0004] In a first aspect, the present application provides an optimization method for a permanent magnet synchronous motor based on a novel topological structure, the method comprising:

[0005] Performing parametric modeling on a permanent magnet synchronous motor based on a novel topological structure through preset magnetic pole segmentation parameters, preset magnetic barrier slot geometric parameters, and preset asymmetric air gap parameters to generate a variable topology model library;

[0006] According to the variable topology model library, calculating the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration modes of the permanent magnet synchronous motor through multi-physical field coupling simulation based on electromagnetic fields, temperature fields, and structural mechanics;

[0007] Constructing a multi-objective optimization model through the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration modes, and performing collaborative optimization using a multi-objective genetic algorithm to generate a Pareto optimal solution set;

[0008] Construct a topological fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, and generate a dynamic current compensation strategy through the distribution of the demagnetization risk coefficient in the Pareto optimal solution set;

[0009] Combine the topological fault-tolerant structure and the dynamic demagnetization compensation strategy to generate the final optimization plan, which is used to guide the manufacturing process of the permanent magnet synchronous motor with a new topological structure.

[0010] In one embodiment, parametric modeling of the permanent magnet synchronous motor based on the new topological structure is performed by presetting the pole block parameters, the magnetic barrier slot geometric parameters, and the preset asymmetric air gap parameters to generate a variable topological model library, including:

[0011] According to the preset pole block parameters, the magnetic barrier slot geometric parameters, and the preset asymmetric air gap parameters, select the number of permanent magnet blocks, the width gradient of the magnetic barrier slot, and the asymmetric air gap eccentricity as the core parameters;

[0012] Use the Morris global sensitivity screening method to perform sensitivity analysis on the core parameters to generate a reduced-dimension parameter set with the top 20% sensitivity rankings;

[0013] According to the reduced-dimension parameter set, generate a sampling point matrix covering the full parameter space through orthogonal experimental design;

[0014] Based on the sampling point matrix, generate an initial variable topological model library through a parametric script. The initial variable topological model library includes the position of the segmented permanent magnet, the gradually changing profile of the magnetic barrier slot, and the eccentric air gap distribution;

[0015] Perform geometric rationality verification on the initial variable topological model library to obtain the variable topological model library.

[0016] In one embodiment, according to the variable topological model library, through multi-physics field coupling simulation based on electromagnetic field, temperature field, and structural mechanics, calculate the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor, including:

[0017] Perform transient electromagnetic field analysis on each model in the variable topological model library through finite element simulation to obtain the transient electromagnetic field analysis results, and the transient electromagnetic field analysis results include magnetic field data;

[0018] According to the transient electromagnetic field analysis results, calculate the dynamic eddy current distribution on the stator core and the surface of the permanent magnet, and generate a high-risk area mark where the eddy current density exceeds 10A / mm 2 to obtain the dynamic eddy current distribution;

[0019] Based on the dynamic eddy current distribution, simulate the temperature field distribution of the permanent magnet synchronous motor after continuous operation for 1 hour in the temperature field simulation module, and calculate the demagnetization field strength of the permanent magnet according to the temperature field distribution and magnetic field data, generate the area markings where the demagnetization risk coefficient exceeds the threshold, and obtain the local demagnetization risk coefficient;

[0020] For each model, calculate the displacement of the segmented permanent magnet under high-speed centrifugal force through structural mechanics simulation, and eliminate the models with displacement exceeding the preset threshold to obtain the high-frequency vibration mode.

[0021] In one embodiment, construct a multi-objective optimization model through the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, and use the multi-objective genetic algorithm for collaborative optimization. The generated Pareto optimal solution set includes:

[0022] Based on the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, maximize the efficiency, minimize the torque ripple, and minimize the permanent magnet usage as optimization objectives, and construct a multi-objective optimization model in combination with the constraint conditions;

[0023] Train the Kriging surrogate model according to the sample data in the variable topology model library, generate the mapping relationship between the input parameters and the optimization objectives, and obtain the trained Kriging surrogate model;

[0024] Use the multi-objective genetic algorithm to iteratively solve the multi-objective optimization model through the trained Kriging surrogate model, and perform penalties through the process constraint penalty function during the iteration process to generate the initial solution set. The process constraint penalty function is used to penalize the solutions with manufacturing tolerance sensitivity exceeding the preset limit.

[0025] Verify the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of each solution in the initial solution set through finite element simulation, eliminate the abnormal solutions with simulation errors exceeding 5%, and select the optimal solution with the highest comprehensive score based on the entropy weight TOPSIS decision method to generate the Pareto optimal solution set.

[0026] In one embodiment, construct a topology fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, and generate a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set, including:

[0027] Extract the manufacturing tolerance sensitivity parameters from the Pareto optimal solution set, and identify the sensitivity-exceeding solutions. The sensitivity-exceeding solutions are the solutions with sensitivity to the magnet position and air gap non-uniformity exceeding the sensitivity threshold;

[0028] According to the sensitivity-exceeding solutions, design a floating mounting groove structure on the rotor surface to construct a topology fault-tolerant structure. The topology fault-tolerant structure is used to make the segmented permanent magnet radially adaptively displace based on the preset displacement.

[0029] According to the distribution of the demagnetization risk coefficient in the Pareto optimal solution set, identify the permanent magnet region where the demagnetization risk coefficient is greater than 0.8;

[0030] Obtain the real-time temperature data at the corresponding positions of the permanent magnet region, generate a closed-loop control instruction for dynamically adjusting the q-axis current component, and based on the closed-loop control instruction, reduce the local demagnetizing field strength by a preset percentage to obtain a dynamic current compensation strategy.

[0031] In one of the embodiments, the novel topological structure is any one of the following:

[0032] A transverse flux topology combining segmented permanent magnets and an asymmetric magnetic barrier slot;

[0033] An axial flux topology with a double-rotor eccentric air gap;

[0034] A hybrid excitation and Halbach array composite topology.

[0035] In a second aspect, the present application also provides a permanent magnet synchronous motor optimization system based on a novel topological structure. The system includes:

[0036] A topology model construction module for parametric modeling of a permanent magnet synchronous motor based on a novel topological structure through preset pole segmentation parameters, preset magnetic barrier slot geometric parameters, and preset asymmetric air gap parameters to generate a variable topology model library;

[0037] A physical coupling simulation module for calculating the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor through multi-physical field coupling simulation based on electromagnetic field, temperature field, and structural mechanics according to the variable topology model library;

[0038] A target optimization analysis module for constructing a multi-objective optimization model through the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, and performing collaborative optimization using a multi-objective genetic algorithm to generate a Pareto optimal solution set;

[0039] An optimization scheme generation module for:

[0040] Constructing a topology fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, and generating a dynamic current compensation strategy through the distribution of the demagnetization risk coefficient in the Pareto optimal solution set;

[0041] Combining the topology fault-tolerant structure and the dynamic demagnetization compensation strategy to generate a final optimization scheme, and the final optimization scheme is used to guide the manufacturing process of the permanent magnet synchronous motor with the novel topological structure.

[0042] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the first aspect are implemented.

[0043] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the first aspect are implemented.

[0044] The above-mentioned optimization method and system for a permanent magnet synchronous motor based on a new topology structure perform parametric modeling by presetting pole block parameters, magnetic barrier slot geometric parameters, and asymmetric air gap parameters to generate a variable topology model library, providing a model basis for subsequent multi-physics field coupling simulation. Moreover, by using multi-physics field coupling simulation to calculate dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration modes, it not only overcomes the limitations of traditional single-physics field simulation methods but also can comprehensively consider the complex working conditions of the motor during actual operation, thus providing more accurate data support for subsequent optimization design. Secondly, this method constructs a multi-objective optimization model through dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration modes, and uses a multi-objective genetic algorithm for collaborative optimization to generate a Pareto optimal solution set, thereby being able to balance multiple objectives such as the efficiency, torque ripple, and manufacturing cost of the motor and finding a globally optimal solution. In addition, a topology fault-tolerant structure is constructed based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, which can effectively reduce the impact of manufacturing tolerances on the motor performance, improve the manufacturing fault tolerance rate of the motor, and reduce the scrap rate and cost during the production process. And this method generates a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set, which can adjust the current distribution of the motor in real time, further reduce the local demagnetization risk, and improve the reliability and stability of the motor. Finally, combining the topology fault-tolerant structure and the dynamic demagnetization compensation strategy to generate the final optimization plan for guiding the manufacturing process of the permanent magnet synchronous motor with a new topology structure can not only improve the performance and efficiency of the motor but also enhance its adaptability and reliability under different working conditions.

[0045] Compared with the traditional permanent magnet synchronous motor optimization method, this method significantly improves the accuracy and scientificity of motor optimization through the comprehensive application of multi-physics field coupling simulation, multi-objective optimization, and dynamic compensation strategy, providing effective technical support for the design and manufacturing of high-performance permanent magnet synchronous motors. Description of the Drawings

[0046] To more clearly illustrate the technical solutions in the embodiments of the present application or in the related art, the following will briefly introduce the drawings required for use in the description of the embodiments or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0047] Figure 1 Flowchart of an optimization method for a permanent magnet synchronous motor based on a novel topology structure provided for an exemplary embodiment of the present invention;

[0048] Figure 2 Schematic diagram of the structure of an optimization system for a permanent magnet synchronous motor based on a novel topology structure provided for an exemplary embodiment of the present invention. Detailed implementation manners

[0049] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the following further details the present application in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] In one embodiment, as Figure 1 shown, an optimization method for a permanent magnet synchronous motor based on a novel topology structure is provided. In this embodiment, the application of this method to a terminal is taken as an example for illustration. It can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0051] S101: Perform parametric modeling on a permanent magnet synchronous motor based on a novel topology structure through preset pole block parameters, preset magnetic barrier slot geometric parameters, and preset asymmetric air gap parameters to generate a variable topology model library.

[0052] Specifically, the preset pole block parameters determine the segmentation method and quantity of the permanent magnets in the motor. Different pole block methods will significantly affect the distribution characteristics of the magnetic field inside the motor. Schematically, reasonable pole block can optimize the air gap magnetic field waveform, reduce the harmonic content, and improve the motor efficiency. The preset magnetic barrier slot geometric parameters include information such as the shape, width, depth, and distribution of the magnetic barrier slots. Among them, designing the magnetic barrier slots can change the permeability distribution inside the motor, play a role in regulating the magnetic field and suppressing torque ripple. The preset asymmetric air gap parameters can change the uniformity of the air gap magnetic field by setting parameters such as the air gap eccentricity, and then affect performance indicators such as the inductance and torque of the motor. By the preset pole block parameters, preset magnetic barrier slot geometric parameters, and preset asymmetric air gap parameters, multiple different motor structure variants can be generated to form a variable topology model library.

[0053] S102: According to the variable topology model library, through multi-physics field coupling simulation based on electromagnetic field, temperature field and structural mechanics, calculate the dynamic eddy current distribution, local demagnetization risk coefficient and high-frequency vibration mode of the permanent magnet synchronous motor.

[0054] Specifically, based on the variable topology model library, the performance of the motor can be further evaluated through multi-physics field coupling simulation. Schematically, multi-physics field coupling simulation means simultaneously considering the interactions and influences of three aspects: electromagnetic field, temperature field and structural mechanics. Through this simulation, the dynamic eddy current distribution of the motor under different working conditions can be calculated, that is, the distribution of eddy currents generated in the motor materials due to the change of the electromagnetic field during the operation of the motor. And the local demagnetization risk coefficient can also be calculated. This coefficient can reflect the possibility of demagnetization of the permanent magnet under high temperature or high magnetic field intensity. In addition, the calculation of the high-frequency vibration mode involves the vibration characteristics of the motor under high-frequency operating conditions and is a participating parameter for evaluating the stability and life of the motor.

[0055] S103: Construct a multi-objective optimization model through the dynamic eddy current distribution, local demagnetization risk coefficient and high-frequency vibration mode, and use the multi-objective genetic algorithm for collaborative optimization to generate a Pareto optimal solution set.

[0056] Specifically, according to the obtained data such as the dynamic eddy current distribution, local demagnetization risk coefficient and high-frequency vibration mode, a multi-objective optimization model can be constructed. This model can balance and optimize multiple competing objectives, such as improving the efficiency of the motor, reducing torque ripple, and reducing manufacturing costs, etc., and use the multi-objective genetic algorithm for collaborative optimization. Schematically, this multi-objective genetic algorithm can handle multiple optimization objectives simultaneously and find the best compromise among different objectives. After the optimization iteration process, finally a Pareto optimal solution set can be generated. Each solution in this solution set can represent a motor design scheme that achieves an optimal balance among multiple objectives, providing a diverse selection for the subsequent selection of optimization schemes.

[0057] S104: Based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, construct a topology fault-tolerant structure, and generate a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set.

[0058] Specifically, manufacturing tolerances are inevitable factors in the actual production process, and different topological structures have different sensitivities to manufacturing tolerances. The manufacturing tolerance sensitivity parameter in the Pareto optimal solution set can reflect the sensitivity of the motor design to the possible tolerance changes during the manufacturing process. Based on this parameter, a topological fault-tolerant structure can be constructed, that is, a motor structure that can still maintain good performance in the presence of a certain degree of manufacturing tolerances can be designed. By analyzing the manufacturing tolerance sensitivity parameters corresponding to each solution in the Pareto optimal solution set, topological structure features or parameter combinations that are insensitive to manufacturing tolerances can be found. And the dynamic current compensation strategy can be generated using the demagnetization risk coefficient distribution in the Pareto optimal solution set. This strategy can further reduce the possibility of demagnetization occurring in high-risk areas by adjusting the input current of the motor in real time, thereby improving the reliability and stability of the motor. Schematically, corresponding current compensation schemes can be formulated according to the demagnetization risk coefficient distribution of the motor under different working conditions. When it is detected that the motor is operating in a region with a high demagnetization risk, the input current of the motor is dynamically adjusted through the control system to increase the reverse magnetic field to offset part of the demagnetizing magnetic field, thereby reducing the demagnetization risk of the permanent magnet and improving the reliability and stability of the motor operation.

[0059] S105: Combine the topological fault-tolerant structure and the dynamic demagnetization compensation strategy to generate the final optimization plan, which is used to guide the manufacturing process of the permanent magnet synchronous motor with a new topological structure.

[0060] Specifically, combine the topological fault-tolerant structure and the generated dynamic current compensation strategy to form the final optimization plan. This plan comprehensively considers the tolerance factors during the manufacturing process of the motor and the demagnetization risk during the operation process, and can comprehensively improve the performance of the permanent magnet synchronous motor with a new topological structure. And this final optimization plan can be presented in the form of detailed technical documents and parameter setting instructions, etc., for guiding the manufacturing process of the permanent magnet synchronous motor with a new topological structure. During the manufacturing process, engineers can then perform the machining and assembly of the motor according to the topological structure design requirements in the optimization plan to ensure that the geometric dimensions and structural accuracy of the motor meet the design standards. And in the programming and debugging of the motor control system, the dynamic current compensation strategy is embedded so that the motor can automatically adjust the current according to the real-time working conditions during the actual operation process to achieve effective control of the demagnetization risk.

[0061] In the above method, a variable topology model library is generated through parametric modeling by presetting parameters such as magnetic pole segmentation, magnetic barrier groove geometry, and asymmetric air gap, which provides a diversified model basis for subsequent analysis. Secondly, by using multi-physics field coupling simulation to calculate the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, the limitations of traditional single-physics field simulation methods are overcome, and the operating characteristics of the motor can be comprehensively understood. And by constructing a multi-objective optimization model and using a multi-objective genetic algorithm for collaborative optimization to generate a Pareto optimal solution set, it helps to find a balance among multiple performance indicators. In addition, based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, a topology fault-tolerant structure is constructed, which can reduce the impact of manufacturing tolerances on the motor performance, improve the fault-tolerant ability of motor manufacturing, and reduce the scrap rate and cost in the production process; and according to the distribution of the demagnetization risk coefficient, a dynamic current compensation strategy is generated, which can adjust the current distribution of the motor in real time, reduce the local demagnetization risk, and improve the reliability and stability of the motor. Finally, by combining the topology fault-tolerant structure and the dynamic demagnetization compensation strategy to generate the final optimization scheme, the performance of the permanent magnet synchronous motor can be improved, the manufacturing process can be optimized, and the production efficiency and product quality can be enhanced.

[0062] In one embodiment, by presetting the magnetic pole segmentation parameters, the magnetic barrier groove geometry parameters, and the asymmetric air gap parameters, parametric modeling is performed on the permanent magnet synchronous motor based on the new topology structure to generate a variable topology model library, including:

[0063] According to the preset magnetic pole segmentation parameters, the magnetic barrier groove geometry parameters, and the asymmetric air gap parameters, the number of permanent magnet blocks, the magnetic barrier groove width gradient, and the asymmetric air gap eccentricity are selected as the core parameters;

[0064] The Morris global sensitivity screening method is used to perform sensitivity analysis on the core parameters to generate a reduced-dimension parameter set with the top 20% sensitivity ranking;

[0065] According to the reduced-dimension parameter set, a sampling point matrix covering the full parameter space is generated through orthogonal experimental design;

[0066] Based on the sampling point matrix, an initial variable topology model library is generated through a parametric script. The initial variable topology model library includes the positions of segmented permanent magnets, the gradually changing profile of the magnetic barrier groove, and the eccentric air gap distribution;

[0067] The initial variable topology model library is subjected to geometric rationality verification to obtain the variable topology model library.

[0068] Specifically, the number of permanent magnet segments, the width gradient of the magnetic barrier groove, and the eccentricity of the asymmetric air gap can be selected as the core parameters according to the preset magnetic pole segmentation parameters, the preset geometric parameters of the magnetic barrier groove, and the preset asymmetric air gap parameters. The preset magnetic pole segmentation parameters define the way and the number range of the permanent magnet segmentation, which affect the magnetic field distribution and performance of the motor. The preset geometric parameters of the magnetic barrier groove include aspects such as the shape and size of the magnetic barrier groove. And the width gradient of the magnetic barrier groove, as one of the core parameters of the preset geometric parameters of the magnetic barrier groove, can reflect the change of the magnetic barrier groove width at different positions, and plays an important role in the magnetic permeability modulation and torque characteristics of the motor. The preset asymmetric air gap parameters involve the asymmetric setting of the air gap. Among them, the eccentricity of the asymmetric air gap can reflect the degree of asymmetry and can change the distribution of the air gap magnetic field of the motor, thereby affecting the motor performance.

[0069] Secondly, the Morris global sensitivity screening method is a method used to evaluate the influence degree of model input parameters on the output results. By performing a series of samplings and model calculations on the parameters, the change of the influence of each parameter on the output under different values can be analyzed. In this embodiment, for the three core parameters of the number of permanent magnet segments, the width gradient of the magnetic barrier groove, and the eccentricity of the asymmetric air gap selected, this method is used for comprehensive analysis, and the parameters with high sensitivity to the motor performance can be selected to form a reduced-dimensional parameter set with the top 20% in sensitivity ranking. This process can identify the parameters that have the most significant influence on the motor performance, reduce the complexity of subsequent analysis and calculation, and improve the modeling efficiency. In addition, the orthogonal experimental design is an efficient experimental design method that can obtain relatively comprehensive parameter combination information with fewer experimental times. For each parameter in the reduced-dimensional parameter set, according to its value range and change law, the principle and method of orthogonal experimental design are used to reasonably arrange the combinations of different parameter values. The points corresponding to the combinations then form a sampling point matrix. This matrix can comprehensively cover the parameter space composed of the reduced-dimensional parameters, and thus ensure that when generating the model subsequently, the influence of different parameter combinations on the motor performance can be fully considered, improving the accuracy and comprehensiveness of the model.

[0070] Finally, the parameterized script is pre-written program code that can generate corresponding model structures according to given parameters. By sequentially inputting each parameter value in the sampling point matrix into the parameterized script, the script can precisely determine the positions of the segmented permanent magnets in the motor, depict the gradual contour of the magnetic barrier slots, and set the distribution of the eccentric air gap. By processing all parameter combinations in the sampling point matrix, a series of motor models with different topological structures are generated, constituting the initial variable topology model library. In addition, the geometric rationality check mainly examines whether the geometric structures of the models in the initial variable topology model library meet the requirements of actual manufacturing and motor operation. For example, it can be checked whether there is interference between the segmented permanent magnets, whether the shape and size of the magnetic barrier slots are within the reasonable manufacturing process range, and whether the setting of the eccentric air gap will cause mechanical failures during motor operation. For models that do not meet the geometric rationality, they are corrected or removed. Through this verification process, unreasonable models can be removed, that is, the remaining models can form the final variable topology model library. The models in this model library are all reasonable in geometric structure and can provide a reliable basis for subsequent performance research and optimization design of permanent magnet synchronous motors based on new topological structures.

[0071] In one embodiment, according to the variable topology model library, through multi-physics field coupling simulation based on electromagnetic field, temperature field, and structural mechanics, the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor are calculated, including:

[0072] Perform transient electromagnetic field analysis on each model in the variable topology model library through finite element simulation to obtain the transient electromagnetic field analysis results, and the transient electromagnetic field analysis results include magnetic field data;

[0073] According to the transient electromagnetic field analysis results, calculate the dynamic eddy current distribution on the stator core and the surface of the permanent magnet, and generate a high-risk area marker where the eddy current density exceeds 10A / mm 2 to obtain the dynamic eddy current distribution;

[0074] Based on the dynamic eddy current distribution, simulate the temperature field distribution of the permanent magnet synchronous motor after continuous operation for 1 hour in the temperature field simulation module, and according to the temperature field distribution and magnetic field data, calculate the demagnetizing magnetic field strength of the permanent magnet, and generate a regional marker where the demagnetization risk coefficient exceeds the threshold to obtain the local demagnetization risk coefficient;

[0075] For each model, calculate the displacement of the segmented permanent magnet under high-speed centrifugal force through structural mechanics simulation, and remove the models whose displacement exceeds the preset threshold to obtain the high-frequency vibration mode.

[0076] Specifically, finite element simulation can divide the motor model into numerous tiny units, and by discretely solving the electromagnetic equations for each unit, simulate the operating state of the motor in a complex electromagnetic environment. During this process, an excitation source that conforms to the actual working conditions, such as three-phase alternating current, can be applied to the model to simulate the electromagnetic input when the motor is operating normally. And as time progresses, gradually calculate the changes in the electromagnetic field at each point inside the model, and finally obtain the transient electromagnetic field analysis results. This result contains rich magnetic field data, such as the magnetic field strength and direction at the air gap of the motor at different times, and the magnetic flux density distribution in each region inside the motor, etc. This magnetic field data can intuitively reflect the dynamic change characteristics of the magnetic field during the operation of the motor.

[0077] Secondly, since excessive eddy current density will cause additional energy loss, intensify the heating of the motor, and may even affect the normal operation of the motor, accurately determining the dynamic eddy current distribution and high-risk areas is of great significance for evaluating the efficiency and reliability of the motor. Therefore, based on the transient electromagnetic field analysis results obtained above, the dynamic eddy current distribution on the surface of the stator core and permanent magnet can be further calculated. Schematically, when the motor is operating, the changing magnetic field will induce eddy currents in conductors such as the surface of the stator core and permanent magnet. The electromagnetic induction law can be used, combined with the magnetic field data obtained from the transient electromagnetic field analysis, to solve the distribution of eddy currents through numerical calculation methods. And during the calculation process, physical properties such as the conductivity of the material can be considered to accurately calculate the magnitude and direction of the eddy current density at different positions. Through the calculation and analysis of the entire model surface, further mark the area where the eddy current density exceeds 10A / mm 2 , that is, the high-risk area. Based on the determined dynamic eddy current distribution, the temperature field distribution after the permanent magnet synchronous motor operates continuously for 1 hour can be simulated in a dedicated temperature field simulation module. Specifically, in the temperature field simulation, a detailed heat transfer model can be established, considering the process of heat transfer inside the motor through conduction, convection, and radiation, etc., to simulate the propagation path and temperature distribution of heat inside the motor, and obtain the temperature field distribution.

[0078] Subsequently, the demagnetizing magnetic field strength of the permanent magnet can be calculated by combining the magnetic field data obtained from the previous transient electromagnetic field analysis. Schematically, the demagnetization of the permanent magnet is closely related to temperature and magnetic field. When the temperature rises and the magnetic field strength reaches a certain level, the magnetic properties of the permanent magnet will decline or even undergo irreversible demagnetization. According to relevant magnetic theories and material properties, by establishing a suitable mathematical model, the demagnetizing magnetic field strength of the permanent magnet in the current temperature field and magnetic field environment can be calculated. And on this basis, the demagnetization risk coefficient can be further calculated. This coefficient comprehensively considers factors such as the material properties, working temperature, and magnetic field strength of the permanent magnet, and can quantitatively evaluate the possibility of the permanent magnet demagnetizing. And by setting a reasonable threshold, mark the area where the demagnetization risk coefficient exceeds the threshold, that is, the area with a relatively high local demagnetization risk.

[0079] Specifically, when the permanent magnet synchronous motor operates at high speed, the segmented permanent magnets will displace due to the centrifugal force generated by their own mass and high-speed rotation. Therefore, in structural mechanics simulation, an accurate mechanical model can be established, considering factors such as the mass, shape, mechanical properties of the material of the segmented permanent magnets, and the rotational speed of the motor. The mechanical analysis method is used to calculate the magnitude of the centrifugal force, and the displacement of the segmented permanent magnets under the action of the centrifugal force is solved according to the mechanical constitutive relationship of the material. During the calculation process, the segmented permanent magnets of each model are analyzed in detail to obtain the displacement data at different positions. And a reasonable preset threshold can be set, and the models with displacement exceeding the threshold are regarded as non-conforming models and eliminated. By analyzing the remaining models and combining the structural dynamics theory, the high-frequency vibration mode of the motor is further determined. This high-frequency vibration mode reflects the vibration characteristics of the motor during high-speed operation, including information such as vibration frequency, vibration amplitude, and spatial distribution of vibration.

[0080] In one of the embodiments, a multi-objective optimization model is constructed through dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, and a multi-objective genetic algorithm is used for collaborative optimization to generate a Pareto optimal solution set including:

[0081] Based on the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, maximizing efficiency, minimizing torque ripple, and minimizing the permanent magnet usage are taken as optimization objectives, and a multi-objective optimization model is constructed in combination with constraint conditions;

[0082] According to the sample data in the variable topology model library, the Kriging surrogate model is trained to generate the mapping relationship between input parameters and optimization objectives, and the trained Kriging surrogate model is obtained;

[0083] The multi-objective genetic algorithm is used to iteratively solve the multi-objective optimization model through the trained Kriging surrogate model, and a penalty is imposed through the process constraint penalty function during the iteration process to generate an initial solution set. The process constraint penalty function is used to penalize the solutions with manufacturing tolerance sensitivity exceeding the preset limit.

[0084] The dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of each solution in the initial solution set are verified through finite element simulation, the abnormal solutions with simulation errors exceeding 5% are eliminated, and based on the entropy weight TOPSIS decision-making method, the optimal solution with the highest comprehensive score is selected to generate the Pareto optimal solution set.

[0085] Specifically, the efficiency of the motor is directly related to the energy utilization efficiency. Higher efficiency means lower energy consumption and operating costs, so maximizing the efficiency is one of the important goals. The dynamic eddy current distribution affects the eddy current loss of the motor, and thus affects the efficiency. Therefore, it is necessary to optimize the relevant parameters to reduce the eddy current loss and then improve the efficiency. In addition, torque ripple will cause the motor to operate unstably, generate vibration and noise, affecting the service life and working performance of the motor. That is, it is necessary to minimize the torque ripple. Moreover, minimizing the amount of permanent magnets used as the goal can effectively reduce the manufacturing cost of the motor. And to make the optimization model meet the actual engineering requirements, a multi-objective optimization model can be constructed by combining a series of constraints. These constraints can be set through aspects such as the electrical performance, mechanical structure, and manufacturing process of the motor. For example, current limit, voltage limit, etc. in terms of electricity; size limit, material strength limit, etc. in terms of mechanical structure; manufacturing tolerance, processing feasibility, etc. in terms of manufacturing process. By comprehensively considering these goals and constraints, a multi-objective optimization model that can comprehensively reflect the motor performance and actual application requirements is constructed, providing a basic framework for subsequent optimization calculations.

[0086] Specifically, the Kriging surrogate model is a statistics-based approximation model that can establish a mapping relationship between input parameters and optimization objectives. In this embodiment, a rich set of sample data is first extracted from the variable topology model library. This sample data includes various parameter settings of the motor under different topologies, as well as performance index data such as the corresponding dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode. Using these sample data to train the Kriging surrogate model, the potential relationship between input parameters and optimization objectives can be explored through learning the sample data, and a corresponding mathematical expression can be constructed to describe this relationship, ultimately obtaining a trained Kriging surrogate model. This surrogate model can replace the complex motor physical model for rapid calculation in subsequent optimization calculations, greatly improving the optimization efficiency. The multi-objective genetic algorithm is an optimization algorithm that simulates the biological evolution process and is suitable for solving multi-objective optimization problems. In this embodiment, the multi-objective genetic algorithm is adopted, and with the help of the trained Kriging surrogate model, the multi-objective optimization model is iteratively solved to generate a set of initial solutions, where each solution represents a combination of topology structure parameters of a motor. Then, the optimization objective values corresponding to each solution, such as efficiency, torque ripple, and permanent magnet usage, are quickly calculated through the trained Kriging surrogate model. In addition, during the iteration process, a process constraint penalty function is introduced to penalize the solutions whose manufacturing tolerance sensitivity exceeds the preset limit. If the manufacturing tolerance sensitivity corresponding to a certain solution exceeds the preset limit, it means that the performance of this solution may be significantly degraded due to tolerance issues during the actual manufacturing process. By penalizing such solutions through the process constraint penalty function, their survival probability in the population can be reduced, prompting the algorithm to evolve in the direction of meeting the manufacturing process requirements in subsequent iterations. After multiple rounds of iteration, the algorithm can continuously update the population and gradually screen out the solutions that achieve a better balance among multiple optimization objectives, generating an initial solution set.

[0087] For each solution in the initial solution set, its dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode can be further verified through finite element simulation. Finite element simulation can perform accurate physical field analysis on the motor model, and can more accurately reflect the actual performance of the motor compared to the approximate calculation of the Kriging surrogate model. And for the remaining solutions, comprehensive evaluation is carried out based on the entropy weight TOPSIS decision-making method. Among them, the entropy weight method can be used to determine the weights of various evaluation indicators such as efficiency, torque ripple, and permanent magnet consumption, and reflect the degree of disorder of the index information by calculating the entropy value of the index data. If the degree of information disorder is higher and the entropy value is larger, the weight of this index in the evaluation is lower, and vice versa. The TOPSIS method can rank according to the distances of each solution from the positive ideal solution and the negative ideal solution. Among them, the positive ideal solution is the solution where each evaluation index reaches the optimal value, and the negative ideal solution is the solution where each evaluation index reaches the worst value. The advantages and disadvantages of each solution can be comprehensively evaluated by calculating the distances of each solution from the positive and negative ideal solutions, and the optimal solution with the highest comprehensive score is selected. Finally, these verified and selected optimal solutions can be combined to generate a Pareto optimal solution set. This solution set contains a series of optimal solutions under multiple optimization objectives and constraint conditions, providing multiple feasible solution options for the optimal design of the motor.

[0088] In one embodiment, based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, a topological fault-tolerant structure is constructed, and a dynamic current compensation strategy is generated through the demagnetization risk coefficient distribution in the Pareto optimal solution set, including:

[0089] Extract the manufacturing tolerance sensitivity parameters from the Pareto optimal solution set, and identify the solutions with sensitivity exceeding the limit. The solutions with sensitivity exceeding the limit are the solutions whose sensitivities to the magnetic block position and air gap unevenness exceed the sensitivity threshold;

[0090] According to the solutions with sensitivity exceeding the limit, a floating mounting groove structure is designed on the rotor surface to construct a topological fault-tolerant structure, which is used to radially adaptively displace the segmented permanent magnet based on a preset displacement amount;

[0091] According to the demagnetization risk coefficient distribution in the Pareto optimal solution set, identify the permanent magnet regions where the demagnetization risk coefficient is greater than 0.8;

[0092] Obtain the real-time temperature data at the corresponding positions of the permanent magnet regions, generate a closed-loop control instruction for dynamically adjusting the q-axis current component, and reduce the local demagnetizing field strength by a preset percentage based on the closed-loop control instruction to obtain a dynamic current compensation strategy.

[0093] Specifically, the manufacturing tolerance sensitivity parameter reflects the sensitivity of different motor topologies to the tolerance changes during the manufacturing process. For example, during the motor manufacturing process, the magnetic block position tolerance and the air gap non-uniformity tolerance are important factors affecting the motor performance. The sensitivity threshold is preset according to the actual manufacturing process level and the acceptable fluctuation range of the motor performance. By comparing the extracted manufacturing tolerance sensitivity parameter with the sensitivity threshold, the sensitivity over-limit solutions can be identified. For example, for the magnetic block position, if the performance indicators (such as torque ripple, efficiency, etc.) of the motor show large fluctuations beyond the acceptable range when the topology corresponding to a certain solution has a small deviation in the magnetic block position, then this solution is the solution with over-limit sensitivity to the magnetic block position. Based on the identified sensitivity over-limit solutions, a floating mounting groove structure can be designed on the rotor surface to construct a topology fault-tolerant structure. Schematically, the floating mounting groove structure can provide a certain radial movement space for the segmented permanent magnet. Based on the preset displacement amount, this structure can enable the segmented permanent magnet to perform radial adaptive displacement according to the actual manufacturing tolerance and operating conditions during the motor operation. For example, when there is a magnetic block position deviation during the motor manufacturing process, the segmented permanent magnet can automatically adjust its position in the floating mounting groove to compensate for the influence caused by the magnetic block position tolerance. The preset displacement amount can be designed according to the estimation of the manufacturing tolerance range and the response characteristics of the motor performance to the magnetic block position adjustment, so as to effectively reduce the influence of the manufacturing tolerance on the motor performance without affecting the normal operation of the motor. Through the design of this topology fault-tolerant structure, even if there are certain tolerances during the motor manufacturing process, the motor can still maintain good performance through the adaptive displacement of the segmented permanent magnet, improve the manufacturing fault-tolerant ability of the motor, reduce the risk of performance degradation caused by manufacturing errors, and make the performance of the motor more stable and reliable in actual production.

[0094] Specifically, the demagnetization risk coefficient is calculated by comprehensively considering factors such as the temperature, magnetic field intensity, and permanent magnet material properties during the operation of the motor, and can quantitatively evaluate the possibility of demagnetization of the permanent magnet. The permanent magnet region with a demagnetization risk coefficient greater than 0.8 is set as the high demagnetization risk region. When the demagnetization risk coefficient reaches this value, the probability of demagnetization of the permanent magnet during the operation of the motor is relatively high, which may seriously affect the performance and service life of the motor. During the operation of the motor, temperature is one of the key factors affecting the demagnetization of the permanent magnet. Temperature sensors and other devices can be installed based on the identified permanent magnet region with a demagnetization risk coefficient greater than 0.8, and the collected temperature data can be transmitted to the control system in real time. Based on this real-time temperature data, a closed-loop control instruction for dynamically adjusting the q-axis current component can be generated. Schematically, in a permanent magnet synchronous motor, the q-axis current component has an important influence on the magnetic field of the motor. According to the operating principle of the motor and the relationship between demagnetization risk and magnetic field, the q-axis current component can be adjusted through the control system to generate a reverse magnetic field to offset part of the demagnetizing magnetic field, thereby reducing the demagnetization risk of the permanent magnet. The closed-loop control instruction can be generated based on the feedback control principle, that is, the q-axis current component can be continuously adjusted according to the real-time temperature data and the operating state of the motor to achieve the goal of reducing the local demagnetizing magnetic field strength by a preset percentage, such as 10%-15%. Through this dynamic current compensation strategy, the demagnetization risk of the permanent magnet during the operation of the motor can be monitored and addressed in real time, improving the reliability and stability of the motor operation, extending the service life of the motor, and providing a strong guarantee for the stable operation of the permanent magnet synchronous motor with a new topology structure.

[0095] Schematically, the new topology structure is any one of the following:

[0096] The transverse flux topology of a segmented permanent magnet combined with an asymmetric magnetic barrier slot;

[0097] The axial flux topology of a double-rotor eccentric air gap;

[0098] The hybrid excitation and Halbach array composite topology.

[0099] Based on the same inventive concept, as Figure 2 shown, the present application also provides an optimization system 200 for a permanent magnet synchronous motor based on a new topology structure. The system includes:

[0100] A topology model construction module 201, configured to perform parametric modeling on a permanent magnet synchronous motor based on a new topology structure through preset pole segmentation parameters, preset magnetic barrier slot geometric parameters, and preset asymmetric air gap parameters, and generate a variable topology model library;

[0101] The physical coupling simulation module 202 is used to calculate the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor through multi-physical field coupling simulation based on the electromagnetic field, temperature field, and structural mechanics according to the variable topology model library;

[0102] The target optimization analysis module 203 is used to construct a multi-objective optimization model through the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, and perform collaborative optimization using the multi-objective genetic algorithm to generate a Pareto optimal solution set;

[0103] The optimization scheme generation module 204 is used for:

[0104] Construct a topology fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, and generate a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set;

[0105] Combine the topology fault-tolerant structure and the dynamic demagnetization compensation strategy to generate a final optimization scheme, and the final optimization scheme is used to guide the manufacturing process of the permanent magnet synchronous motor with a new topology structure.

[0106] In the above-mentioned permanent magnet synchronous motor optimization system 200 based on a new topology structure, the topology model construction module 201 performs parametric modeling on the permanent magnet synchronous motor based on the new topology structure by presetting the magnetic pole segmentation parameters, magnetic barrier slot geometric parameters, and asymmetric air gap parameters, generating a variable topology model library. This model library includes various structural variants, ensuring the comprehensiveness and diversity of the subsequent optimization process. The physical coupling simulation module 202 can, according to the variable topology model library, calculate the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor through multi-physics field coupling simulation based on the electromagnetic field, temperature field, and structural mechanics, capable of comprehensively considering the complex working conditions of the motor during actual operation, thereby providing more accurate data support for the subsequent optimization design. The target optimization analysis module 203 can construct a multi-objective optimization model through the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode, and perform collaborative optimization using a multi-objective genetic algorithm to generate a Pareto optimal solution set. This solution set can find a balance among multiple conflicting performance indicators, providing multiple feasible solution options for the optimization design of the motor, ensuring the scientificity and practicality of the optimization results. The optimization scheme generation module 204 can construct a topology fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, thereby enhancing the fault-tolerant ability of the motor during the manufacturing process and reducing the risk of performance degradation caused by manufacturing errors; and generate a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set, which can effectively reduce the demagnetization risk of the motor during operation and improve the reliability and stability of the motor. In addition, this module combines the topology fault-tolerant structure and the dynamic demagnetization compensation strategy to generate the final optimization scheme, thereby being able to optimize the manufacturing process of the permanent magnet synchronous motor, improve production efficiency and product quality, and ensure that the motor has good operating performance and reliability in actual applications.

[0107] Furthermore, the topology model construction module 201 includes:

[0108] A data processing sub-unit, used for:

[0109] According to the preset magnetic pole segmentation parameters, preset magnetic barrier slot geometric parameters, and preset asymmetric air gap parameters, select the number of permanent magnet segments, magnetic barrier slot width gradient, and asymmetric air gap eccentricity as core parameters;

[0110] Adopt the Morris global sensitivity screening method to perform sensitivity analysis on the core parameters, generating a set of dimensionality-reduced parameters with the top 20% sensitivity rankings;

[0111] According to the set of dimensionality-reduced parameters, generate a sampling point matrix covering the full parameter space through orthogonal experimental design;

[0112] A parametric modeling sub-unit, used for:

[0113] Based on the sampling point matrix, an initial variable topology model library is generated through a parameterized script. The initial variable topology model library includes the position of the block permanent magnet, the gradient contour of the magnetic barrier slot, and the distribution of the eccentric air gap.

[0114] The geometric rationality of the initial variable topology model library is checked to obtain the variable topology model library.

[0115] Furthermore, the physical coupling simulation module 202 includes:

[0116] The electromagnetic field analysis subunit is used to perform transient electromagnetic field analysis on each model in the variable topology model library through finite element simulation to obtain transient electromagnetic field analysis results, which include magnetic field data;

[0117] Parameter calculation subunit, used for:

[0118] Based on the transient electromagnetic field analysis results, the dynamic eddy current distribution on the stator core and permanent magnet surface is calculated, and the generated eddy current density exceeds 10A / mm 2 High-risk areas are marked to obtain dynamic eddy current distribution;

[0119] Based on the dynamic eddy current distribution, the temperature field distribution of the permanent magnet synchronous motor after continuous operation for 1 hour is simulated in the temperature field simulation module. According to the temperature field distribution and magnetic field data, the demagnetization magnetic field intensity of the permanent magnet is calculated, and the area mark where the demagnetization risk coefficient exceeds the threshold is generated to obtain the local demagnetization risk coefficient.

[0120] For each model, the displacement of the block permanent magnet under high-speed centrifugal force is calculated through structural mechanics simulation, and the models with displacement exceeding the preset threshold are eliminated to obtain the high-frequency vibration mode.

[0121] Furthermore, the target optimization analysis module 203 includes:

[0122] Model processing subunit, used to:

[0123] According to the dynamic eddy current distribution, local demagnetization risk factor and high-frequency vibration mode, the efficiency maximization, torque pulsation minimization and permanent magnet usage minimization are taken as optimization goals, and a multi-objective optimization model is constructed in combination with constraint conditions.

[0124] According to the sample data in the variable topology model library, the Kriging proxy model is trained to generate a mapping relationship between input parameters and optimization objectives, and a trained Kriging proxy model is obtained;

[0125] Target optimization subunit, used to:

[0126] The multi-objective genetic algorithm is adopted to iteratively solve the multi-objective optimization model through the trained Kriging surrogate model, and penalty is imposed through the process constraint penalty function during the iteration process to generate an initial solution set. The process constraint penalty function is used to penalize the solutions whose manufacturing tolerance sensitivity exceeds the preset limit value.

[0127] The dynamic eddy current distribution, local demagnetization risk coefficient and high-frequency vibration mode of each solution in the initial solution set are verified through finite element simulation, the abnormal solutions with simulation errors exceeding 5% are eliminated, and based on the entropy weight TOPSIS decision-making method, the optimal solution with the highest comprehensive score is selected to generate the Pareto optimal solution set.

[0128] Further, the optimization scheme generation module 204 includes:

[0129] The structure design sub-unit is used for:

[0130] Extracting the manufacturing tolerance sensitivity parameters from the Pareto optimal solution set, and identifying the solutions with sensitivity exceeding the limit. The solutions with sensitivity exceeding the limit are the solutions whose sensitivities to the magnet position and air gap non-uniformity exceed the sensitivity threshold;

[0131] According to the solutions with sensitivity exceeding the limit, a floating mounting groove structure is designed on the rotor surface to construct a topological fault-tolerant structure, which is used to radially adaptively displace the segmented permanent magnet based on the preset displacement amount;

[0132] The current compensation sub-unit is used for:

[0133] Identifying the permanent magnet regions with demagnetization risk coefficient greater than 0.8 according to the distribution of demagnetization risk coefficients in the Pareto optimal solution set;

[0134] Obtaining the real-time temperature data at the corresponding positions of the permanent magnet regions, generating a closed-loop control instruction for dynamically adjusting the q-axis current component, and reducing the local demagnetizing magnetic field strength by a preset percentage based on the closed-loop control instruction to obtain a dynamic current compensation strategy.

[0135] Further, the new topological structure is any one of the following:

[0136] The transverse flux topology combined with segmented permanent magnets and asymmetric magnetic barrier grooves;

[0137] The axial flux topology with double-rotor eccentric air gaps;

[0138] The hybrid excitation and Halbach array composite topology.

[0139] In an exemplary embodiment, the present invention further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of an optimization method for a permanent magnet synchronous motor based on a new topology structure of the present application are implemented. A multi-core processor is preferably used to improve the parallel processing ability of the system. Memory: Provide sufficient temporary storage space to support the operation of the program and the processing of data. The memory capacity should be large enough to accommodate a large amount of supply information and computing tasks.

[0140] In an exemplary embodiment, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of an optimization method for a permanent magnet synchronous motor based on a new topology structure of the present application are implemented. The computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), solid state drive (SSD, Solid State Drives), or optical disc, etc. Among them, the random access memory may include resistive random access memory (ReRAM, Resistance Random Access Memory) and dynamic random access memory (DRAM, Dynamic Random Access Memory).

[0141] The above-described embodiments merely represent several implementation manners of the embodiments of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several deformations and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. An optimization method for a permanent magnet synchronous motor based on a novel topological structure, characterized in that, The method includes: Parametrically modeling a permanent magnet synchronous motor based on a novel topological structure by preset magnetic pole segmentation parameters, preset magnetic barrier groove geometric parameters, and preset asymmetric air-gap parameters to generate a variable topological model library; According to the variable topological model library, calculating the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor through multi-physics field coupling simulation based on electromagnetic field, temperature field, and structural mechanics; Constructing a multi-objective optimization model through the dynamic eddy current distribution, the local demagnetization risk coefficient, and the high-frequency vibration mode, and performing collaborative optimization using a multi-objective genetic algorithm to generate a Pareto optimal solution set; Based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, constructing a topological fault-tolerant structure, and generating a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set; Combining the topological fault-tolerant structure and the dynamic demagnetization compensation strategy to generate a final optimization plan, which is used to guide the manufacturing process of the permanent magnet synchronous motor with a novel topological structure.

2. The method according to claim 1, wherein The parametrically modeling of a permanent magnet synchronous motor based on a novel topological structure by preset magnetic pole segmentation parameters, preset magnetic barrier groove geometric parameters, and preset asymmetric air-gap parameters to generate a variable topological model library includes: Selecting the number of permanent magnet segments, magnetic barrier groove width gradient, and asymmetric air-gap eccentricity as core parameters according to the preset magnetic pole segmentation parameters, preset magnetic barrier groove geometric parameters, and preset asymmetric air-gap parameters; Performing sensitivity analysis on the core parameters using the Morris global sensitivity screening method to generate a reduced-dimension parameter set with the top 20% sensitivity ranking; Generating a sampling point matrix covering the full parameter space through orthogonal experimental design according to the reduced-dimension parameter set; Based on the sampling point matrix, generating an initial variable topological model library through a parametric script, where the initial variable topological model library includes the positions of segmented permanent magnets, the gradually changing profile of magnetic barrier grooves, and the eccentric air-gap distribution; Performing geometric rationality verification on the initial variable topological model library to obtain the variable topological model library.

3. The method according to claim 1, characterized in that The calculating the dynamic eddy current distribution, local demagnetization risk coefficient, and high-frequency vibration mode of the permanent magnet synchronous motor through multi-physics field coupling simulation based on electromagnetic field, temperature field, and structural mechanics according to the variable topological model library includes: Performing transient electromagnetic field analysis on each model in the variable topological model library through finite element simulation to obtain transient electromagnetic field analysis results, where the transient electromagnetic field analysis results include magnetic field data; According to the transient electromagnetic field analysis results, calculate the dynamic eddy current distribution on the surface of the stator core and the permanent magnet, and generate a high-risk area mark where the eddy current density exceeds 10 A / mm 2 to obtain the dynamic eddy current distribution; Based on the dynamic eddy current distribution, simulating the temperature field distribution of the permanent magnet synchronous motor after continuous operation for 1 hour in the temperature field simulation module, and calculating the demagnetizing magnetic field strength of the permanent magnet according to the temperature field distribution and the magnetic field data to generate a region mark where the demagnetization risk coefficient exceeds the threshold, obtaining the local demagnetization risk coefficient; For each model, calculating the displacement of the segmented permanent magnet under high-speed centrifugal force through structural mechanics simulation, and removing the models with the displacement exceeding the preset threshold to obtain the high-frequency vibration mode.

4. The method according to claim 1, characterized in that Constructing a multi-objective optimization model through the dynamic eddy current distribution, the local demagnetization risk coefficient, and the high-frequency vibration mode, and performing collaborative optimization using a multi-objective genetic algorithm to generate a Pareto optimal solution set, including: Taking the maximization of efficiency, the minimization of torque ripple, and the minimization of the permanent magnet usage as optimization objectives according to the dynamic eddy current distribution, the local demagnetization risk coefficient, and the high-frequency vibration mode, and constructing the multi-objective optimization model in combination with constraint conditions; Training a Kriging surrogate model based on the sample data in the variable topology model library to generate a mapping relationship between input parameters and optimization objectives, and obtaining a trained Kriging surrogate model; Using the multi-objective genetic algorithm to iteratively solve the multi-objective optimization model through the trained Kriging surrogate model, and performing penalties through a process constraint penalty function during the iteration to generate an initial solution set, where the process constraint penalty function is used to penalize solutions with a manufacturing tolerance sensitivity exceeding a preset limit. Verifying the dynamic eddy current distribution, the local demagnetization risk coefficient, and the high-frequency vibration mode of each solution in the initial solution set through finite element simulation, eliminating abnormal solutions with a simulation error exceeding 5%, and selecting the optimal solution with the highest comprehensive score based on the entropy weight TOPSIS decision method to generate the Pareto optimal solution set.

5. The method according to claim 1, wherein Constructing a topology fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, and generating a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set, including: Extracting the manufacturing tolerance sensitivity parameters from the Pareto optimal solution set and identifying sensitivity-exceeding solutions, where the sensitivity-exceeding solutions are solutions with a sensitivity to the magnet block position and air gap non-uniformity exceeding the sensitivity threshold; Designing a floating mounting groove structure on the rotor surface according to the sensitivity-exceeding solutions to construct the topology fault-tolerant structure, where the topology fault-tolerant structure is used to radially adaptively displace the segmented permanent magnets based on a preset displacement; Identifying the permanent magnet regions with a demagnetization risk coefficient greater than 0.8 according to the demagnetization risk coefficient distribution in the Pareto optimal solution set; Obtaining the real-time temperature data at the corresponding positions of the permanent magnet regions, generating a closed-loop control instruction for dynamically adjusting the q-axis current component, and reducing the local demagnetizing magnetic field strength by a preset percentage based on the closed-loop control instruction to obtain the dynamic current compensation strategy.

6. The method according to claim 1, characterized in that, The novel topology structure is any one of the following: A transverse flux topology combining segmented permanent magnets and an asymmetric magnetic barrier groove; An axial flux topology with a double-rotor eccentric air gap; A hybrid excitation and Halbach array composite topology.

7. A permanent magnet synchronous motor optimization system based on a novel topology structure, characterized in that, The system includes: A topology model construction module for parametrically modeling a permanent magnet synchronous motor based on a novel topology structure through preset pole segmentation parameters, preset magnetic barrier groove geometric parameters, and preset asymmetric air gap parameters to generate a variable topology model library; A physical coupling simulation module for calculating the dynamic eddy current distribution, the local demagnetization risk coefficient, and the high-frequency vibration mode of the permanent magnet synchronous motor through multi-physical field coupling simulation based on electromagnetic fields, temperature fields, and structural mechanics according to the variable topology model library; The target optimization analysis module is used to construct a multi-objective optimization model through the dynamic eddy current distribution, the local demagnetization risk coefficient, and the high-frequency vibration mode, and perform collaborative optimization using a multi-objective genetic algorithm to generate a Pareto optimal solution set; The optimization scheme generation module is used for: Construct a topological fault-tolerant structure based on the manufacturing tolerance sensitivity parameters in the Pareto optimal solution set, and generate a dynamic current compensation strategy through the demagnetization risk coefficient distribution in the Pareto optimal solution set; Combine the topological fault-tolerant structure and the dynamic demagnetization compensation strategy to generate a final optimization scheme, and the final optimization scheme is used to guide the manufacturing process of the new topological structure permanent magnet synchronous motor.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 6.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 6.

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