Comprehensive optimization design method for electromagnetic thermal performance of permanent magnet motor

Abstract

The invention discloses a comprehensive optimization design method for electromagnetic thermal performance of a permanent magnet motor, belongs to the technical field of optimization design of motors, and aims to solve the problem that the optimization speed and the optimization precision are difficult to consider at the same time according to the optimization design method for the electromagnetic thermal performance of the motor with multiple structural parameters. Optimization is carried out based on the incidence relation between the electromagnetic performance and the thermal performance, the electromagnetic performance is directly associated with the structural parameters, and the thermal performance is indirectly associated with the structural parameters. The optimization design comprises the following steps: constructing an overall optimization task of structural parameters and electromagnetic thermal performance; dividing into a plurality of sub optimization tasks; the optimization sequence of the sub-optimization tasks is determined and solved in sequence, the electromagnetic performance index is prior to the thermal performance index, and the no-load performance is prior to the light-load performance, the rated performance and the overload performance; and integrating sub-optimization results to form a final scheme. According to the method, a high-dimensional optimization problem is decomposed into low-dimensional sub-problems and the low-dimensional sub-problems are solved in order, so that the calculation complexity is reduced on the premise of ensuring the precision, and the optimization speed and the optimization precision are considered.

Description

Technical Field

[0001] This invention relates to the field of motor optimization design technology, and more specifically, to a comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor. Background Technology

[0002] With the rapid development of new energy power generation, electric vehicles, and high-end industrial electromechanical servo systems, permanent magnet motors are evolving towards higher power density, higher integration, and lower thermal resistance. This requires motor designs to simultaneously meet multiple mutually constraining performance indicators within a limited volume, including high torque output, low torque ripple, high-efficiency heat dissipation, and wide field weakening speed extension capability. Therefore, modern permanent magnet motor design has evolved into a typical strongly coupled optimization problem involving multiple parameters, multiple objectives, and multiple physics fields.

[0003] Currently, there are two main technical approaches and their inherent drawbacks for the comprehensive optimization of the electromagnetic and thermal performance of permanent magnet motors:

[0004] First, the traditional iterative optimization algorithm based on the finite element method (FEM).

[0005] These methods typically combine intelligent optimization algorithms such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) with high-precision finite element simulations, searching for the optimal solution by iteratively calling the simulation model. While the finite element method can accurately calculate the nonlinear magnetic field distribution, iron losses, and copper losses of a motor, ensuring optimization accuracy, it has significant drawbacks: the calculation of the electromagnetic and temperature fields of a permanent magnet motor is extremely time-consuming, and the population size required for multi-parameter optimization often grows exponentially. To search for the Pareto front, thousands or even tens of thousands of finite element simulations are usually required. This "precision-for-time" approach results in a single optimization cycle lasting weeks or even months, severely slowing down the R&D iteration speed of motor products.

[0006] Secondly, approximate optimization methods based on analytical models or surrogate models.

[0007] To overcome the computational burden of finite element methods, these approaches attempt to establish simplified analytical formulas using magnetic circuit methods and thermal network methods, or to construct surrogate models for finite element simulations using response surface methodology (RSM) and Kriging models. However, permanent magnet motors, especially those with built-in or complex topologies, exhibit high-dimensional, strongly nonlinear, and strongly coupled internal electromagnetic relationships. For example, magnetic saturation effects, cross-coupled flux linkages, and the feedback effect of magnetic field harmonics on temperature rise are difficult to accurately capture using low-order analytical formulas or static surrogate models. When structural parameters vary significantly or operating conditions change, the prediction accuracy of approximate models deteriorates sharply, causing optimization results to deviate from the true optimal solution and even fail to meet the basic constraints of engineering applications.

[0008] In summary, existing technologies face a core contradiction when addressing the electromagnetic-thermal integrated optimization problem of permanent magnet motors: high-precision methods (finite element method + intelligent algorithm) are computationally expensive, while high-efficiency methods (analytical / surrogate models) lack sufficient optimization accuracy. How to break down the barrier between optimization speed and accuracy under high-dimensional nonlinear and strongly coupled constraints, and develop a comprehensive design method that combines rapid convergence with accurate optimization capabilities, is a technical challenge that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0009] To address the challenge of simultaneously balancing optimization speed and accuracy in electromagnetic and thermal performance optimization design methods for motors with multiple structural parameters, this invention provides a comprehensive electromagnetic and thermal performance optimization design method for permanent magnet motors.

[0010] The present invention discloses a comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor. The method optimizes the electromagnetic performance based on the correlation between the electromagnetic performance and the thermal performance. The correlation is that the electromagnetic performance is directly related to the structural parameters of the motor, and the thermal performance is indirectly related to the structural parameters.

[0011] The optimization design includes the following steps:

[0012] S1. For the motor to be optimized, determine its structural parameters and electromagnetic thermal performance, limit the range of values ​​for the structural parameters, and build an overall optimization task describing the motor's structural parameters and electromagnetic thermal performance based on the structural parameters and electromagnetic thermal performance.

[0013] S2. The overall optimization task describing the motor structural parameters and electromagnetic thermal performance is divided into multiple sub-optimization tasks describing the relationship between a subset of structural parameters and a subset of electromagnetic thermal performance.

[0014] S3. Determine the optimization order of each sub-optimization task, and solve all sub-optimization tasks in sequence so that each sub-optimization task satisfies local optima;

[0015] S4. Integrate the optimization results of all sub-optimization tasks, adjust the optimization parameters according to the optimization order, and form the final optimization result of the permanent magnet motor.

[0016] Preferably, the structural parameters include: permanent magnet thickness, air gap length, stator tooth width, stator yoke thickness, and slot width;

[0017] The electromagnetic thermal properties include: no-load back EMF, cogging torque, slot area, torque ripple, and average torque.

[0018] Among them, the no-load back EMF, cogging torque, torque ripple and average torque are electromagnetic performance indicators, and the slot area is an indicator characterizing thermal performance.

[0019] Preferably, step S1 specifically includes the following steps:

[0020] S11, targeting Each structural parameter and For a motor whose electromagnetic and thermal properties need to be optimized, determine the set of structural parameters for the motor to be optimized. Combination of electromagnetic and thermal properties ;

[0021] S12. Based on the specific topology of the motor to be optimized, the set of structural parameters is defined. The range of values ​​for each structural parameter;

[0022] S13. Express the relationship between the motor structural parameters and electromagnetic thermal performance as an optimization task form:

[0023] .

[0024] Preferably, step S2 specifically includes:

[0025] S21. Based on the correlation between structural parameters and performance indicators, the set of structural parameters is... Divided into A subset of structural parameters: ,in For the first A subset of structural parameters;

[0026] S22. Based on the partitioning form of the structural parameter subset, the electromagnetic and thermal performance set is... Corresponding division into A subset of electromagnetic and thermal properties: ,in For the first A subset of electromagnetic and thermal properties;

[0027] S23. The subset of structural parameters covers all structural parameters: The electromagnetic thermal performance subset covers all performance indicators: ;

[0028] S24. Divide the overall optimization task into... A sub-optimization task that independently describes the relationship between a subset of structural parameters and a subset of electromagnetic and thermal properties: ;

[0029] S25. Intersections are allowed between the structural parameter subsets and between the electromagnetic and thermal performance subsets. When a parameter in a structural parameter subset directly affects the electromagnetic performance and indirectly affects the thermal performance, the corresponding electromagnetic and thermal performance subset simultaneously includes electromagnetic performance indicators and characteristic parameters characterizing the thermal performance.

[0030] Preferably, step S3 specifically includes:

[0031] S31. The optimization order of sub-optimization tasks satisfies the following constraints:

[0032] S311. When a subset of electromagnetic and thermal performance covers multi-physics field performance indicators in electricity, magnetism, and heat, the optimization order of electromagnetic performance indicators takes precedence over thermal performance indicators.

[0033] S312. When a subset of electromagnetic thermal performance covers electromagnetic performance under multiple different load conditions, the optimization order is as follows: no-load performance, light-load performance, rated performance, and overload performance.

[0034] Among them, the priority of constraint S311 is higher than that of constraint S312;

[0035] S32. Solve for the optimal solution for all sub-optimization tasks in sequence according to the optimization order.

[0036] Preferably, step S4 specifically includes:

[0037] S41. Integrate the optimization results of all sub-optimization tasks to form the final optimization result;

[0038] S42. When the same structural parameter belongs to different sub-optimization tasks and the optimization results obtained in different sub-optimization tasks are inconsistent, the optimization result of the sub-optimization task with the later optimization order shall be taken as the final value of the structural parameter.

[0039] The beneficial effects of this invention are:

[0040] 1. Optimize the balance between speed and accuracy

[0041] This invention decomposes a high-dimensional overall optimization task containing multiple structural parameters into several low-dimensional sub-optimization tasks. Each sub-optimization task contains only a small number of structural parameters and can be solved accurately using methods such as grid search and parameter scanning.

[0042] Taking the five-parameter, five-index motor in a specific implementation as an example, directly solving the overall optimization task requires... After sub-finite element simulation, using the method of this invention, it is divided into three sub-optimization tasks containing 2, 3, and 1 structural parameters, requiring only... This is the second simulation.

[0043] 2. Optimize and reduce complexity

[0044] This invention analyzes the physical mechanisms by which electromagnetic performance is directly related to structural parameters, thermal performance is directly related to heat sources in the form of losses, and indirectly related to structural parameters. This transforms the multi-objective optimization problem into multiple sub-optimization problems with clear physical meanings, thereby reducing the parameter space dimension of a single optimization.

[0045] 3. Optimization sequence under multiple operating conditions

[0046] This invention specifies the optimization order of sub-optimization tasks: when multi-physics performance indicators are covered, electromagnetic performance indicators take precedence over thermal performance indicators; when multiple different load conditions are covered, the order is no-load performance, light-load performance, rated performance, and overload performance.

[0047] When the same structural parameter belongs to different sub-optimization tasks and the optimization results are inconsistent, the result of the sub-optimization task that is optimized later in the order shall prevail.

[0048] 4. Engineering interpretability of optimization results

[0049] The optimization process of this invention is based on physical correlation and task decomposition, and the influence path and weight of each structural parameter on the performance index are traceable. After the final optimization result is formed, the feasibility of processing is confirmed. Attached Figure Description

[0050] Figure 1 This is a flowchart of a comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor as described in this invention;

[0051] Figure 2 This is a schematic diagram of the permanent magnet motor structure according to a specific embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram illustrating the division of the overall optimization task into sub-optimization tasks in this invention;

[0053] Figure 4 This is a schematic diagram illustrating the solution of a sub-optimization task in this invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0056] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0057] Specific Implementation Method 1: The following is combined with... Figures 1 to 4 This implementation method is described below.

[0058] The innovation of this invention lies in the following: by analyzing the direct influence of structural parameters on electromagnetic performance, and the indirect influence of structural parameters on thermal performance through their impact on heat sources in the form of losses, the complex comprehensive optimization problem of permanent magnet motor electromagnetic thermal performance is transformed into a simple combination of optimization problems involving subsets of structural parameters and subsets of electromagnetic thermal performance. On one hand, the optimization design of structural parameters and electromagnetic performance can be performed directly, and the optimization of structural parameters and thermal performance can be characterized through heat sources in the form of losses such as iron loss and copper loss. Therefore, the simple optimization problem of subsets of structural parameters and subsets of electromagnetic thermal performance can be solved quickly and accurately using methods such as parameter scanning, effectively ensuring the optimization effect of electromagnetic thermal performance. On the other hand, this method can significantly reduce the dimensionality of the optimization parameter space, improving the optimization speed of permanent magnet motor optimization design.

[0059] This implementation method focuses on the optimized design of an 8-pole, 9-slot surface-mount permanent magnet synchronous motor, such as... Figure 2 As shown. The permanent magnet motor rotor adopts a bread-shaped surface-mounted magnetic pole structure, and its key parameters include the thickness of the permanent magnet; the key parameters for optimizing the air gap region include the air gap length; the stator adopts a parallel tooth structure, and its key parameters include the stator tooth width, stator yoke thickness, and slot width.

[0060] First, we analyze the correlation between the electromagnetic and thermal properties of permanent magnet motors. Electromagnetic properties are directly related to the motor's structural parameters; thermal properties are indirectly related to structural parameters, specifically, the thermal properties are indirectly linked to structural parameters through heat sources such as iron losses and copper losses.

[0061] The optimized design in this embodiment includes the following steps S1 to S4.

[0062] S1. Establish the overall optimization task.

[0063] For the motor to be optimized, determine the structural parameters and specific electromagnetic and thermal performance, limit the range of values ​​for the structural parameters, and build an overall optimization task describing the motor's structural parameters and electromagnetic and thermal performance based on the structural parameters and electromagnetic and thermal performance.

[0064] S11, targeting Each structural parameter and For a motor whose electromagnetic and thermal properties need to be optimized, determine the set of structural parameters for the motor to be optimized. Combination of electromagnetic and thermal properties ;

[0065] Taking the optimization design of a motor with five structural parameters and five electromagnetic and thermal performance indicators as an example, and Both are 5:

[0066] ;

[0067] in, These represent five specific structural parameters: permanent magnet thickness, air gap length, stator tooth width, stator yoke thickness, and slot width. These represent five specific performance indicators: no-load back EMF, cogging torque, slot area, torque ripple, and average torque. Among them, no-load back EMF, cogging torque, torque ripple, and average torque are electromagnetic performance indicators, while slot area can be used to calculate electrical density and thus characterize copper loss, serving as an indirect performance indicator for thermal performance.

[0068] S12. Based on the specific topology of the motor to be optimized, the set of structural parameters is defined. The range of values ​​for each structural parameter;

[0069] The purpose of limiting the range of structural parameter values ​​is twofold: firstly, to ensure that the values ​​of each structural parameter are within the feasible range of the actual design and manufacturing of the motor, avoiding non-convergence or distortion of finite element simulation calculations due to parameter values ​​exceeding physical limits; secondly, to improve optimization efficiency by reducing the number of parameter combinations that need to be evaluated in subsequent optimization tasks by narrowing the optimization search space. A reasonable range of values ​​should be determined comprehensively based on the motor topology, material properties, processing technology, and design experience. For example, the thickness of the permanent magnet should be within the allowable range of mechanical strength, the air gap length should consider assembly tolerances and processing accuracy, and the slot width must meet the requirements of the winding insertion process.

[0070] S13. Express the relationship between the motor structural parameters and electromagnetic thermal performance as an optimization task form:

[0071] .

[0072] S2. Divide the overall optimization tasks.

[0073] The overall optimization task describing the structural parameters and electromagnetic and thermal performance of the motor is divided into multiple sub-optimization tasks that describe the relationship between subsets of structural parameters and subsets of electromagnetic and thermal performance.

[0074] S21. Based on the correlation between structural parameters and performance indicators, the set of structural parameters is... Divided into A subset of structural parameters, such as Figure 3 As shown: ,in For the first A subset of structural parameters In this embodiment, the number of subsets is [not specified]. Take 3;

[0075] S22. Based on the partitioning form of the structural parameter subset, the electromagnetic and thermal performance set is... Corresponding division into A subset of electromagnetic and thermal properties: ,in For the first A subset of electromagnetic and thermal properties;

[0076] S23. The subset of structural parameters covers all structural parameters, and the subset of electromagnetic and thermal performance should cover all performance indicators of the set of electromagnetic and thermal performance. ; ;

[0077] S24. Divide the motor structural parameters and electromagnetic thermal performance into... A sub-optimization task that independently describes the relationship between a subset of structural parameters and a subset of electromagnetic and thermal properties: ;

[0078] S25. Intersections are allowed between the structural parameter subsets and between the electromagnetic and thermal performance subsets. When a parameter in a structural parameter subset directly affects the electromagnetic performance and indirectly affects the thermal performance, the corresponding electromagnetic and thermal performance subset simultaneously includes electromagnetic performance indicators and characteristic parameters characterizing the thermal performance.

[0079] In this embodiment, the three structural parameter subsets and the three electromagnetic and thermal performance subsets are specifically: sub-optimization tasks. : Sub-optimization task : Sub-optimization task : .

[0080] S3. Determine the optimization order and solve them sequentially.

[0081] Based on the strength of the interaction between motor structural parameters and electromagnetic thermal performance during the optimization process, the optimization order of sub-optimization tasks is determined, and all sub-optimization tasks are solved in sequence to ensure that the sub-optimization tasks satisfy local optima.

[0082] The "strength of the mutual influence between electromagnetic and thermal properties" refers to the physical dependence between electromagnetic and thermal properties, and the changing patterns of performance indicators under different load conditions. Specifically, accurate calculation of thermal performance (losses) depends on accurate electromagnetic performance results as input; therefore, electromagnetic performance indicators have a significant unidirectional influence on thermal performance indicators, and optimization of electromagnetic performance indicators should take precedence over thermal performance indicators. Simultaneously, the saturation degree of the motor core deepens with increasing load. Performance under no-load conditions determines the foundation of performance under load conditions, and different core saturation degrees affect performance under light-load, rated, and overload conditions. Therefore, the optimization order should follow a progressive relationship of no-load, light-load, rated, and overload. This prioritization relationship is the concrete manifestation of the "strength of the mutual influence."

[0083] S31. Based on the intensity of the mutual influence between the electromagnetic and thermal performance of the motor during the optimization process, determine the optimization order of the sub-optimization tasks. The optimization order of the sub-optimization tasks should satisfy the following constraints, and the constraints of S311 should be satisfied first, compared with S312.

[0084] S311. When the subset of electromagnetic and thermal performance encompasses multiple physical field performance indicators such as electrical, magnetic, and thermal properties, the optimization order is generally considered to be electromagnetic performance indicators > thermal performance indicators; in this embodiment, the sub-optimization task... Kazuko optimization task Sub-optimization tasks for electromagnetic performance indicators Given the thermal performance metrics, the optimization order is therefore a series of sub-optimization tasks. Kazuko optimization task Prioritize sub-optimization tasks .

[0085] S312. When the subset of electromagnetic thermal performance covers electromagnetic performance under multiple different load conditions, the optimization order is as follows: no-load performance, light-load performance, rated performance, and overload performance. Here, no-load performance refers to the electromagnetic thermal performance when no current is applied to the motor; rated performance refers to the electromagnetic thermal performance when the rated current is applied; light-load performance refers to the electromagnetic thermal performance when the current is less than the rated value; and overload performance refers to the electromagnetic thermal performance when the current is several times the rated value. In this embodiment, the sub-optimization task... For no-load performance, sub-optimization tasks Kazuko optimization task To maintain rated performance, the optimization order is updated to sub-optimization tasks. Prioritize sub-optimization tasks Prioritize sub-optimization tasks .

[0086] The final optimization sequence of this implementation method is as follows: → → .

[0087] S32. Solve for the optimal solution for all sub-optimization tasks in sequence according to the optimization order.

[0088] For a subset of structural parameters With electromagnetic and thermal properties subset The sub-optimization task describes the optimization problem for no-load back EMF and cogging torque. The structural parameters to be optimized are permanent magnet thickness and air gap length. The optimal solution can be obtained by grid search method.

[0089] For the structural parameter subset X2 and the electromagnetic and thermal performance subset The sub-optimization task describes the optimization problem of slot area and cogging torque. The structural parameters to be optimized are air gap length, stator tooth width and stator yoke thickness. The optimal solution can be found by using intelligent algorithms such as genetic algorithms.

[0090] For a subset of structural parameters Subset of electromagnetic and thermal properties The sub-optimization task describes an optimization problem targeting average torque and torque ripple. The structural parameter to be optimized is the slot width. The optimal solution can be found using the parameter sweep method, such as... Figure 4 As shown. The subset of structural parameters analyzed. Only contains , The slot width. The subset of electromagnetic and thermal properties analyzed. Include Two kinds, These represent the average torque and torque ripple under rated load, respectively. A specific example will illustrate this: the parameter sweep method is used to solve this sub-optimization task, such as... Figure 4 As shown. A rated armature current of 100 amperes is applied to the three phases of the motor, at a slot width... Parameters are scanned within the range of values ​​to obtain different... The average torque of the motor under rated load. With torque pulsation The scanning results show that when When the value is 3.5, the average torque It reaches its maximum value, and at this point the torque fluctuation... At a relatively low level. When As the torque continues to increase, torque fluctuations occur. Although further reduced, the average torque Consequently, it decreases. Taking all factors into consideration... Two performance indicators determine the optimal slot width The value is 3.5.

[0091] In this embodiment, and All are 5, and the total optimization task includes 5 structural parameters and 5 electromagnetic and thermal performance indicators. =3, three subsets of structural parameters, three subsets of electromagnetic and thermal properties, for a total of three sub-optimization tasks: In the fixed-step optimization of structural parameters, it is assumed that each structural parameter has within the parameter constraints. The value of a fixed step size. Directly solving the overall optimization task requires... Finite element simulation of the motor is insufficient, and obtaining the optimal solution in the five-dimensional electromagnetic-thermal performance space is difficult to achieve intuitively and accurately. This invention transforms the overall optimization task into three sub-optimization tasks, reducing the number of finite element simulations required and providing a more intuitive and accurate optimal solution. For the sub-optimization tasks… The structural parameter subset includes two structural parameters. It is necessary to conduct Finite element simulation of the secondary motor; for sub-optimization tasks The structural parameter subset includes three structural parameters. It is necessary to conduct Finite element simulation of the secondary motor; for sub-optimization tasks The subset of structural parameters includes one structural parameter. It is necessary to conduct Finite element simulation of the secondary motor; 3 sub-optimization tasks need to be performed. Finite element simulation of the secondary motor is far less efficient than directly solving the overall optimization task, and the optimal solution obtained in the highest three-dimensional electromagnetic-thermal performance space is more intuitive and accurate.

[0092] In S31, this invention specifies the optimization order of sub-optimization tasks. In S311, electromagnetic performance indicators take precedence over thermal performance indicators. Since thermal performance optimization requires the loss portion of the electromagnetic performance optimization results as a heat source input, the optimization accuracy of thermal performance indicators will deteriorate if this order is not met. In S312, the optimization order of no-load performance, light-load performance, rated performance, and overload performance corresponds to the performance changes of the stator core as it gradually becomes saturated. If this order is not met, the final optimization result cannot guarantee the optimization accuracy of electromagnetic and thermal performance indicators when the core saturation level is high, such as overload performance.

[0093] S4, Integrated Optimization Results

[0094] The final optimization results of all sub-optimization tasks are integrated, and the optimization parameters are adjusted according to the optimization order of the sub-optimization tasks to form the final optimization result of the permanent magnet motor.

[0095] S41. Combine the optimal structure parameters obtained from each sub-optimization task: by solving the sub-optimization tasks. The optimal structural parameters can be obtained. By solving sub-optimization tasks The optimal structural parameters can be obtained. By solving sub-optimization tasks The optimal structural parameters can be obtained. .

[0096] S42. When the same structural parameter is optimized in multiple sub-optimization tasks and the optimal value is inconsistent, the value determined by the sub-optimization task with the later optimization order shall be taken as the final value of the parameter.

[0097] In this embodiment, structural parameters (Air gap length) also appears in the sub-optimization task and In the process, according to the optimization order determined in step S31: → → Sub-optimization task The optimization order is later, therefore, the sub-optimization tasks are optimized later. The optimal value obtained is the structural parameter. The final value of .

[0098] Based on the above steps, the final optimization results of all structural parameters are summarized, and after confirming the feasibility of processing, the final design scheme of the permanent magnet motor is formed.

[0099] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor, characterized in that, The method is optimized based on the correlation between electromagnetic performance and thermal performance. The correlation is that the electromagnetic performance is directly related to the structural parameters of the motor, and the thermal performance is indirectly related to the structural parameters. The optimization design includes the following steps: S1. For the motor to be optimized, determine its structural parameters and electromagnetic thermal performance, limit the range of values ​​for the structural parameters, and build an overall optimization task describing the motor's structural parameters and electromagnetic thermal performance based on the structural parameters and electromagnetic thermal performance. S2. The overall optimization task describing the motor structural parameters and electromagnetic thermal performance is divided into multiple sub-optimization tasks describing the relationship between a subset of structural parameters and a subset of electromagnetic thermal performance. S3. Determine the optimization order of each sub-optimization task, and solve all sub-optimization tasks in sequence so that each sub-optimization task satisfies local optima; S4. Integrate the optimization results of all sub-optimization tasks, adjust the optimization parameters according to the optimization order, and form the final optimization result of the permanent magnet motor.

2. The comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor according to claim 1, characterized in that, The structural parameters include: permanent magnet thickness, air gap length, stator tooth width, stator yoke thickness, and slot width; The electromagnetic thermal properties include: no-load back EMF, cogging torque, slot area, torque ripple, and average torque. Among them, the no-load back EMF, cogging torque, torque ripple and average torque are electromagnetic performance indicators, and the slot area is an indicator characterizing thermal performance.

3. The comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor according to claim 2, characterized in that, Step S1 specifically includes the following steps: S11, targeting Each structural parameter and For a motor whose electromagnetic and thermal properties need to be optimized, determine the set of structural parameters for the motor to be optimized. Combination of electromagnetic and thermal properties ; S12. Based on the specific topology of the motor to be optimized, the set of structural parameters is defined. The range of values ​​for each structural parameter; S13. Express the relationship between the motor structural parameters and electromagnetic thermal performance as an optimization task form: 。 4. The comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor according to claim 3, characterized in that, Step S2 specifically includes: S21. Based on the correlation between structural parameters and performance indicators, the set of structural parameters is... Divided into A subset of structural parameters: ,in For the first A subset of structural parameters; S22. Based on the partitioning form of the structural parameter subset, the electromagnetic and thermal performance set is... Corresponding division into A subset of electromagnetic and thermal properties: ,in For the first A subset of electromagnetic and thermal properties; S23. The subset of structural parameters covers all structural parameters: The electromagnetic thermal performance subset covers all performance indicators: ; S24. Divide the overall optimization task into... A sub-optimization task that independently describes the relationship between a subset of structural parameters and a subset of electromagnetic and thermal properties: ; S25. Intersections are allowed between the structural parameter subsets and between the electromagnetic and thermal performance subsets. When a parameter in a structural parameter subset directly affects the electromagnetic performance and indirectly affects the thermal performance, the corresponding electromagnetic and thermal performance subset simultaneously includes electromagnetic performance indicators and characteristic parameters characterizing the thermal performance.

5. The comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor according to claim 4, characterized in that, Step S3 specifically includes: S31. The optimization order of sub-optimization tasks satisfies the following constraints: S311. When a subset of electromagnetic and thermal performance covers multi-physics field performance indicators in electricity, magnetism, and heat, the optimization order of electromagnetic performance indicators takes precedence over thermal performance indicators. S312. When a subset of electromagnetic thermal performance covers electromagnetic performance under multiple different load conditions, the optimization order is as follows: no-load performance, light-load performance, rated performance, and overload performance. Among them, the priority of constraint S311 is higher than that of constraint S312; S32. Solve for the optimal solution for all sub-optimization tasks in sequence according to the optimization order.

6. The comprehensive optimization design method for the electromagnetic and thermal performance of a permanent magnet motor according to claim 5, characterized in that, Step S4 specifically includes: S41. Integrate the optimization results of all sub-optimization tasks to form the final optimization result; S42. When the same structural parameter belongs to different sub-optimization tasks and the optimization results obtained in different sub-optimization tasks are inconsistent, the optimization result of the sub-optimization task with the later optimization order shall be taken as the final value of the structural parameter.

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