CN113468682A - Permanent magnet motor robust optimization design method considering magnetic material uncertainty - Google Patents
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Abstract
The invention discloses a permanent magnet motor robust optimization design method considering uncertainty of magnetic materials, which comprises the following steps: the design requirement of the motor is determined; performing initial design on the motor according to design requirements, and determining design parameters and performance to be optimized; constructing a motor steady optimization model considering uncertainty factors of magnetic materials, setting relevant parameters of residual magnetism, magnetization direction, size and the like of permanent magnetic materials into a random number set obeying a certain distribution rule, and setting expectation and standard deviation of motor performance as design targets; solving the steady multi-target optimization model by using an evolutionary algorithm and a proxy model; and determining a processing scheme after evaluating the performance of the motor. According to the design method of the permanent magnet motor, potential influence of uncertain factors existing in material characteristics and later-stage processing of magnetic materials on motor performance is considered in the design stage, the robustness of the design scheme of the permanent magnet motor is improved through a design means, and the problems that a prototype and the design of a traditional permanent magnet motor are poor in consistency and the like are solved.
Description
Technical Field
The invention relates to a permanent magnet motor robust optimization design method, in particular to a permanent magnet motor robust optimization design method considering uncertainty of magnetic materials, and belongs to the technical field of permanent magnet motors.
Background
In the technical field of motors, permanent magnet motors are widely applied due to the advantages of high power density, high efficiency and the like. In general, for the design and processing of a permanent magnet motor, the design process of a conventional induction motor is usually used for reference: firstly, selecting a motor type and designing an initial structure according to application requirements; then, obtaining an optimal motor design scheme of the motor through design optimization; and finally, verifying the design by a processing test prototype.
However, unlike induction motors, permanent magnet motors use permanent magnet materials as excitation sources, and the quality of the permanent magnet materials directly affects the performance of the motor. The current motor design often ignores a plurality of uncertain factors of permanent magnet materials in material properties and later processing, so that the real performance of a motor prototype often cannot reach the designed performance. For example: the magnetization characteristic deviation of the permanent magnet material in the remanence and magnetization directions and the size deviation in the processing and assembling process can directly influence the performance of the motor. Therefore, it is necessary to research a robust optimization design method suitable for a permanent magnet motor, which can consider uncertainty factors existing in magnetic materials and improve the reliability of a permanent magnet motor design scheme.
Disclosure of Invention
The invention provides a permanent magnet motor robust optimization method considering uncertainty of magnetic materials in order to overcome the defects in the prior art. According to the method, uncertain factors existing in the magnetic materials are introduced into the motor optimization design process, so that the motor performance is improved, and meanwhile, the robustness of the motor design scheme is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a permanent magnet motor robust optimization method considering magnetic material uncertainty comprises the following steps:
A. determining the design requirement of the motor: according to the application occasions of the permanent magnet motor, the design requirements and indexes of the motor are determined.
B. Initial design of a motor: according to the design requirements of the motor, the type selection of the motor is realized, part of important parameters of the motor are fixed, and the basic structure of the motor is determined. And determining the design parameters and the performance to be optimized of the motor according to the design indexes of the motor.
C. Constructing a steady optimization model: randomly selecting material characteristic parameters and geometric parameters of the permanent magnet according to a certain distribution rule, and simulating the uncertainty distribution of related parameters of the magnetic material; and simultaneously taking the mean value (mu) and the standard deviation (sigma) of the motor performance to be optimized as design targets, and constructing a steady multi-target optimization model.
D. Robust multi-objective optimization: and D, solving the robust multi-objective optimization model constructed in the step C by using a robust multi-objective optimization method. Firstly, obtaining a limited number of sample points through experimental design and finite element calculation, then constructing an approximate proxy model, carrying out Monte Carlo analysis statistics on the mean value (mu) and the standard deviation (sigma) of the motor performance on the model, and finally, searching for the motor steady optimal design scheme by using an evolutionary algorithm.
E. Electromagnetic performance and distribution evaluation: and (3) simulating the electromagnetic performance of the motor design scheme and the distribution condition of the motor performance under the disturbance of certain magnetic material parameters through the finite element, and judging the effectiveness of the design scheme.
F. Machining and testing a permanent magnet motor prototype: after the design scheme is simulated through simulation, prototype machining is carried out according to the design scheme, relevant tests are carried out, and the motor structure is further verified.
Specifically, the uncertainty of the magnetic material characteristics in the step C includes, but is not limited to, the deviation of the remanence and the magnetizing direction of the permanent magnet, and the uncertainty of post-processing includes the dimension deviation of the permanent magnet and the assembly deviation of the motor caused by the dimension deviation of the permanent magnet.
In particular, the step D further comprises: in the optimization process by using the evolutionary algorithm, each individual in the algorithm needs to statistically design a target mean value and a standard deviation by using Monte Carlo analysis. Meanwhile, the evolutionary algorithm includes, but is not limited to, a genetic algorithm, an ant colony algorithm, a particle swarm algorithm, an immune algorithm, and the like.
In particular, the distribution of the motor performance under the parameter disturbance of the certain magnetic material in the step E refers to: and applying a large amount of random disturbance to the permanent magnet of the motor structure corresponding to the design scheme, calculating the design performance of the motor under different disturbances by using a finite element, and counting the performance distribution.
The invention has the beneficial effects that:
1. the permanent magnet motor robustness optimization design method considering the uncertainty of the magnetic material can consider the potential influence of the uncertainty factor of the magnetic material on the performance of the motor in the material characteristic and later processing in the design stage of the permanent magnet motor, improves the robustness of the design scheme of the permanent magnet motor through the design means, and solves the problems that the model machine and the design consistency of the traditional permanent magnet motor design method are poor and the like.
2. The robust optimization model adopted by the invention can effectively and comprehensively simulate and analyze the influence of uncertainty factors of magnetic materials on the motor performance by setting relevant parameters of residual magnetism, magnetization direction, size and the like of the permanent magnetic materials into a random number set which obeys a certain distribution rule and counting the mean value and standard deviation of the motor performance.
3. The steady multi-objective optimization method adopts experimental design and a proxy model to replace finite element calculation, overcomes the problem of high computation cost of Monte Carlo analysis in uncertain analysis of magnetic materials, and provides a rapid computation means for solving the steady multi-objective optimization model by an evolutionary algorithm.
Drawings
FIG. 1 is a flow chart of a permanent magnet motor robust optimization design method considering magnetic material uncertainty according to the present invention.
Fig. 2 is a topology structure of a dual permanent magnet motor according to an embodiment of the present invention.
Wherein: 1 is a stator, 2 is an outer rotor module, 3-bit inner rotor, 4 is an armature winding, 5 is a neodymium iron boron permanent magnet and 6-bit ferrite permanent magnet.
FIG. 3 illustrates uncertainty factors of magnetic materials according to an embodiment of the present invention.
FIG. 4 is a flowchart illustrating a robust optimization design according to an embodiment of the present invention.
FIG. 5 is a magnetic material uncertainty factor approximation model according to an embodiment of the present invention.
Wherein: 1 is neodymium iron boron permanent magnet, 2-position ferrite and 3 is an air gap between the ferrite and the iron core.
FIG. 6 shows multi-objective optimization results according to an embodiment of the present invention.
Fig. 7 is a comparison of motor performance before and after optimization according to an embodiment of the present invention.
FIG. 8 is a comparison of performance distributions before and after optimization according to an embodiment of the present invention.
Detailed Description
The invention will be further explained with reference to the drawings.
Example (b): a robust optimization design of a double-permanent magnet brushless motor is provided. Referring to fig. 1, fig. 1 is a flowchart of a robust optimization design method of a permanent magnet motor considering uncertainty of a magnetic material according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a topology of a dual permanent magnet brushless motor according to an embodiment, in which 1 is a stator, 2 is an outer rotor module, 3 is an inner rotor, 4 is an armature winding, 5 is an ndfeb permanent magnet, and 6-bit ferrite permanent magnet. The motor used in the embodiment of the invention is a 12-slot/10-pole double-permanent-magnet brushless motor, and has a structure of a double-layer rotor and an outer stator; in the rotor, neodymium iron boron permanent magnets and ferrite permanent magnets are simultaneously used as excitation sources, and the neodymium iron boron permanent magnets and the ferrite permanent magnets are arranged side by side on each rotor pole along the radial direction, wherein two ferrites are split, and a magnetic bridge is arranged in the middle; in the stator, a three-phase centralized winding arrangement mode is adopted. Fig. 3 shows uncertainty factors of magnetic materials, including material diversity, machining error, and assembly error, which need to be considered in the motor of the embodiment. FIG. 4 is a flowchart illustrating an embodiment of a robust optimization design.
The specific implementation steps are as follows:
s1, determining the design requirement of a motor. Taking the design case as an example, the average output torque, the torque ripple and the cogging torque of the permanent magnet motor are optimized according to the common performance indexes, and the average output torque, the torque ripple and the cogging torque of a final machining prototype are required to be ensured to be higher than 30Nm, lower than 10% of the torque ripple and lower than 2 Nm. The variables to be optimized include: width w of Nd-Fe-B permanent magnetNdFeBLength of permanent magnet of ferriteferriteWidth w of permanent magnet of ferriteferriteAir gap width g, stator slot size (H)s0,Hs1,Hs2,ws0,ws1,ws2)。
S2, implementing initial design on the motor. Determining basic key parameters of the motor according to a motor power equation, wherein the motor power equation is as follows:
wherein DSIIs the stator bore, l is the axial length, P is the rated power, η is the efficiency, αiIs the polar arc coefficient, kdIs the magnetic leakage coefficient, keIs the winding factor, n is the rated speed, A is the electrical load, BδIs the maximum value of the air gap flux density. The basic specifications of the motor and the initial values of the design parameters according to the initial design are shown in table 1:
TABLE 1
| Basic parameters | Value of | Design parameters | Initial value |
| Rated power Pout | 5kw | Width w of Nd-Fe-B permanent magnetNdFeB | 2mm |
| Rated speed nrate | 1200rpm | Length l of permanent magnet of ferriteferrite | 50mm |
| Rated voltage vrate | 120VDC | Permanent magnet width w of ferriteferrite | 10mm |
| Iron core stacking length ls | 65mm | Air gap width g | 0.5mm |
| Stator outside diameter DSO | 250mm | Depth H of outer notch of stator slots0 | 2mm |
| Stator bore DSI | 160mm | Depth H of inner notch of stator slots1 | 2mm |
| Stator slot filling factor | 0.5 | Stator slot depth Hs2 | 25mm |
| Type of Nd-Fe-B permanent-magnet material | N35 | Stator slot outer slot opening width ws0 | 10deg |
| FerriteType of permanent magnet material | Y30 | Width w of inner slot opening of stator slots1 | 20deg |
| Dosage of single neodymium iron boron block VNdFeB | 1300mm3 | Stator slot width ws2 | 30deg |
And S3, constructing a motor robust optimization model considering magnetic material uncertainty. According to the design requirement of the motor, the average torque, the torque ripple and the cogging torque of the motor are taken as optimization targets, and a performance optimization model of the motor is as follows:
wherein f istor，frip，fctRepresenting torque, torque ripple and cogging torque, respectively, JarmtureRepresenting current density, VNdFeBRepresenting the amount of Nd-Fe-B permanent magnet, XlAnd XuRepresenting the upper and lower limits of the design parameter.
In order to consider the uncertainty factors of the magnetic materials in the motor of the embodiment, the performance optimization model of the motor is adjusted, the related parameters of the neodymium iron boron permanent magnet and the ferrite permanent magnet are set to be random number sets which obey a certain distribution rule, and the optimization target is set to be the mean value and standard deviation of the torque, the torque ripple and the cogging torque. FIG. 5 shows two magnetic material uncertainty factor approximation models according to embodiments of the present invention. The magnetic characteristic parameters of the neodymium iron boron and the ferrite are assumed to follow a two-dimensional normal distribution, the distribution comprises two dimensions of the remanence and the magnetization direction of the permanent magnet, and the standard deviation corresponding to each dimension is set to 1/3 of the corresponding tolerance respectively. For the magnetic property processing tolerance of the magnetic material, the remanence is always set to 2% of the nominal value, and the magnetization direction is set to +/-1 degree of the theoretical direction; in addition, the ferrite geometry parameters are assumed to follow a Weber distribution with the distribution shape parameter set to 1 and the scale parameter set to 1/3 of the ferrite machining tolerance. Adjusted, the robust optimization model of the motor is as follows:
wherein μ (f)tor)，μ(frip)，μ(fct) Represents the average values of torque, torque ripple and cogging torque, σ (f)tor)，σ(frip)，σ(fct) Represents the standard deviation, mu, of torque, torque ripple and cogging torque, respectivelyB，μθ，σB，σθRho is the expectation of each dimension of two-dimensional normal distribution, standard deviation and correlation coefficient of two dimensions, lambdaw，kwShape parameter and scale parameter B for Weber distributionNdFeBAnd thetaNdFeBRespectively representing remanence and magnetization angle of Nd-Fe-B and ferrite, wFerriteAnd
representing the actual and nominal values of the ferrite width, respectively. In addition, the deviation in the ferrite dimension causes an air gap to occur between the ferrite and the silicon steel sheet, and therefore Δ w is usedFerriteIndicating the air gap width.
And S4, solving the robust optimization model by adopting a robust multi-objective optimization method. Due to the fact that statistical analysis on uncertainty of magnetic materials is added in the optimization process, calculation cost is huge due to the fact that the model is directly solved. Therefore, there is a need for statistical motor performance distribution by using a proxy model instead of finite element calculations for monte carlo analysis, the optimization method comprising the steps of:
s41, sampling the motor by using an experimental design method, and analyzing the design performance of all sample points by using a finite element to obtain a parameter set X ═ { X ═ X1，x2，…，xnAnd its corresponding response Y ═ Y1，y2，…，yn}。
And S42, fitting and constructing a Krigin proxy model by using the samples (X, Y), quantizing the precision of the model by using the root mean square error of the test point, and judging whether the prediction precision of the proxy model reaches the standard or not. Root Mean Square Error (RMSE) is defined as follows:
wherein, yiAnd yiAnd respectively representing the finite element calculated value and the proxy model predicted value of the test point, wherein N is the number of the test points. And when the root mean square error is less than 1%, the prediction accuracy of the proxy model is considered to meet the subsequent calculation requirement.
S43, searching a motor steady optimal design scheme based on the agent model by using an evolutionary algorithm. In the optimization process, a Monte Carlo analysis is applied to each individual in the algorithm for analyzing the influence of statistical magnetic material uncertainty factors on each individual design objective. Specifically, for the motor structure corresponding to each individual, assuming that the related parameters of the neodymium iron boron and the ferrite are subjected to the probability distribution described in (2), a large number of samples are generated by using monte carlo analysis, the corresponding torque, torque ripple and cogging torque of the sample points are calculated through a proxy model, and the corresponding mean value and standard deviation are counted. In the evolutionary algorithm, the mean value and the standard deviation are used as optimization targets, the pareto frontier optimal solution set is obtained through continuous iteration on the basis of selection, intersection and mutation operators, and a final scheme of motor design is selected from the pareto frontier optimal solution set.
Wherein, the Monte Carlo analysis is a simulation statistical method, which is estimated by random sampling statistics, and the algorithm in the invention comprises the following specific processes: for each analysis individual, 10000 random test sample points are randomly selected, then the performance of the 10000 sample points is calculated by using an agent model, and finally the performance is counted. The purpose of this step is: by carrying out calculation statistics on the test sample points, the random distribution characteristics (mean and standard deviation) of the corresponding performance of the individual are obtained.
The evolutionary algorithm adopts a fast non-dominated evolutionary algorithm (NSGA-II) with an elite strategy, and combines the object of the invention, and the specific process of the evolutionary algorithm is as follows:
s421, initializing the first generation population randomly, and calculating the expectation and standard deviation of the performance of each individual in the statistical population.
S422, carrying out non-dominant sorting on individuals in the population, and grading the individuals according to a sorting result, wherein the individuals with lower non-dominant grades have higher priority; for individuals of the same non-dominant rank, further ranking is performed according to the crowdedness among individuals, wherein individuals farther away have higher priority.
And S423, carrying out evolution updating on the individuals with higher priority, and generating N individuals of filial generations through operations such as crossing, mutation and the like.
And S424, merging the parent-child population, wherein the population number is 2N, and performing S422 non-dominant sorting operation again.
And S425, selecting the N individuals with higher priority in the population S424, and performing crossover and mutation operations to generate new filial generations with the population size of N.
And S426, judging whether optimization is converged or not by judging whether the difference value of the iteration results of the previous iteration and the next iteration meets the set precision or whether the iteration frequency reaches the maximum iteration frequency. If not, the process returns to step S424.
The specific parameters of the algorithm in the invention are set as follows: the population size is 100, the cross probability is 0.9, the mutation probability is 1/6, and the maximum iteration number is 200.
Fig. 6 shows the robust multi-objective optimization results of the embodiment, where (a) is the pareto frontier solution set of six design objectives in parallel coordinate system, and (b) is the pareto frontier subspace of the motor performance objectives, and the final optimization values are as follows: the mean torque was 38.21Nm with a variance of 2.65, the torque ripple was 0.0878, the variance was 0.0067, the cogging torque was 1.05Nm, and the variance was 0.27.
And S5, evaluating the electromagnetic performance and distribution of the determined motor design scheme through finite element simulation. FIG. 7 is a comparison of motor performance before and after optimization for the examples, where (a) the cogging torque for the motor before and after optimization is reduced from 2.74Nm to 1.12 Nm; (b) in order to optimize comparison between ideal output torque of the motor before and after optimization and output torque under the condition of certain magnetic material parameter disturbance, the final actual torque value under the disturbance condition is improved from 35.87Nm to 38.02Nm, and the torque ripple is reduced from 11.26% to 8.93. FIG. 8 is a comparison of performance distributions before and after optimization of examples, where (a) is mean torque, (b) is torque ripple, and (c) is cogging torque. The standard deviation of the torque performance is reduced from 4.47 to 2.65, the standard deviation of the torque ripple is reduced from 0.0186 to 0.0067, and the cogging torque is reduced from 0.79 to 0.27.
And S6, machining a prototype according to the design scheme, carrying out related tests, and further verifying the motor structure.
The above-listed series of detailed descriptions are merely specific illustrations of possible embodiments of the present invention, and they are not intended to limit the scope of the present invention, and all equivalent means or modifications that do not depart from the technical spirit of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A permanent magnet motor robust optimization design method considering magnetic material uncertainty is characterized by comprising the following steps:
s1, determining the design requirement of a motor: determining the design requirement and index of the motor according to the application occasion of the permanent magnet motor;
s2, initial design of a motor: according to the design requirements of the motor, the type selection of the motor is realized, part of important parameters of the motor are fixed, the basic structure of the motor is determined, and then according to the design indexes of the motor, the design parameters and the performance to be optimized of the motor are determined;
s3, constructing a robust optimization model: randomly selecting material characteristic parameters and geometric parameters of the permanent magnet according to a certain distribution rule, and simulating the uncertainty distribution of related parameters of the magnetic material; simultaneously taking the mean value (mu) and the standard deviation (sigma) of the motor performance to be optimized as design targets, and constructing a steady multi-target optimization model;
s4, steady multi-objective optimization: solving the robust multi-objective optimization model constructed in the step S3 through a robust multi-objective optimization method; firstly, obtaining a limited number of sample points through experimental design and finite element calculation, then constructing an approximate proxy model, carrying out Monte Carlo analysis statistics on the mean value (mu) and the standard deviation (sigma) of the motor performance on the model, and finally, searching for the motor steady optimal design scheme by using an evolutionary algorithm.
2. The permanent magnet motor robust optimization design method considering uncertainty of magnetic materials according to claim 1, wherein the uncertainty of magnetic materials includes uncertainty of magnetic material characteristics, uncertainty of post-processing; the uncertainty of the characteristics of the magnetic materials comprises the deviation of the remanence and the magnetizing direction of the permanent magnets, and the uncertainty of the post-processing comprises the size deviation of the permanent magnets and the motor assembly deviation caused by the size deviation of the permanent magnets.
3. The permanent magnet motor robust optimization design method considering uncertainty of magnetic materials according to claim 1, wherein the motor adopts a 12-slot/10-pole double permanent magnet brushless motor, double-layer rotor and outer stator structure; in the rotor, neodymium iron boron permanent magnets and ferrite permanent magnets are simultaneously used as excitation sources, and the neodymium iron boron permanent magnets and the ferrite permanent magnets are arranged side by side on each rotor pole along the radial direction, wherein two ferrites are split, and a magnetic bridge is arranged in the middle; in the stator, a three-phase centralized winding arrangement mode is adopted.
4. The robust optimization design method of permanent magnet motor considering uncertainty of magnetic material according to claim 3, wherein the specific process of S2 is as follows:
determining key parameters of the motor according to a motor power equation, wherein the motor power equation is as follows:
wherein DSIIs the stator bore, l is the axial length, P is the rated power, η is the efficiency, αiIs the polar arc coefficient, kdIs the magnetic leakage coefficient, keIs the winding factor, n is the rated speed, A is the electrical load, BδIs the maximum value of the air gap flux density.
The initial values of the key parameters of the designed motor are shown in table 1 below:
TABLE 1
5. The robust optimization design method of permanent magnet motor considering uncertainty of magnetic material according to claim 3, wherein the specific process of S3 is as follows:
taking the average torque, the torque ripple and the cogging torque of the motor as optimization targets, designing a performance optimization model of the motor as follows:
wherein f istor，frip，fctRepresenting torque, torque ripple and cogging torque, X, respectivelyuAnd XlRepresenting the upper and lower limits of the design parameter; considering uncertainty factors of magnetic materials in the motor, adjusting a performance optimization model of the motor, wherein related parameters of neodymium iron boron and ferrite are set to be a random number set which obeys a certain distribution rule, an optimization target is set to be a mean value and a standard deviation of torque, torque ripple and cogging torque, remanence and magnetization directions of the neodymium iron boron and the ferrite are assumed to obey two-dimensional normal distribution, the width of the ferrite is assumed to obey Weber distribution, and the width of an air gap between the ferrite and an iron core is a difference value between nominal width and real width of the ferrite;
adjusted, the robust optimization model of the motor is as follows:
wherein B and θ represent remanence and magnetization angle, wFerriteAnd
actual and nominal values, Δ w, representing the width of the ferriteFerriteShowing the width of the gas gap between the ferrite and the silicon steel sheet.
6. The robust optimization design method of permanent magnet motor considering uncertainty of magnetic material according to claim 5, wherein the specific process of S4 is as follows:
s41, sampling the motor, analyzing the design performance of all sample points by using a finite element, and obtaining a parameter set X ═ { X ═ X }1，x2，…，xnAnd its corresponding response y1，y2，…，yn}；
S42, fitting and constructing a Krigin proxy model by using the samples (X, y), quantizing the precision of the model by using a root mean square error, and judging whether the prediction precision of the proxy model reaches the standard or not;
s43, searching for a motor steady optimal design scheme based on a proxy model by using an evolutionary algorithm, wherein in the optimization process, Monte Carlo analysis is applied to each individual in the algorithm and used for analyzing and counting the influence of uncertainty factors of magnetic materials on each individual design target, specifically, for a motor structure corresponding to each individual, assuming that related parameters of neodymium iron boron and ferrite are subjected to probability distribution described in a formula (2), generating a large number of samples by using Monte Carlo analysis, and calculating the mean value and standard deviation of torque, torque ripple and cogging torque corresponding to the individual through the proxy model; and taking the mean value and the standard deviation as optimization targets, obtaining the pareto frontier optimal solution set through continuous iteration based on selection, intersection and mutation operators, and selecting a final scheme of motor design from the pareto frontier optimal solution set.
7. The permanent magnet motor robust optimization design method considering uncertainty of magnetic materials according to claim 6, wherein the evolutionary algorithm in S43 can adopt genetic algorithm, ant colony algorithm, particle swarm algorithm, immune algorithm;
when a genetic algorithm is adopted, the algorithm is a rapid non-dominated genetic algorithm (NSGA-II) with an elite strategy, and the specific process of the algorithm is as follows:
s421, randomly initializing a first generation population, and calculating the expectation and standard deviation of the performance of each individual in the statistical population;
s422, carrying out non-dominant sorting on individuals in the population, and grading the individuals according to a sorting result, wherein the individuals with lower non-dominant grades have higher priority; for individuals with the same non-dominant level, further grading according to crowdedness among individuals, wherein the more distant individuals have higher priority;
s423, carrying out evolution updating on the individuals with higher priority, and generating N individuals of filial generations through operations such as crossing, mutation and the like;
s424, merging parent-child population, wherein the population number is 2N, and performing S422 non-dominated sorting operation again;
s425, selecting N individuals with higher priority in the S424 population, and performing crossover and mutation operations to generate new filial generations with the population scale of N;
and S426, judging whether optimization is converged or not by judging whether the difference value of the iteration results of the previous iteration and the next iteration meets the set precision or whether the iteration frequency reaches the maximum iteration frequency. If not, the process returns to step S424.
8. The permanent magnet motor robust optimization design method considering uncertainty of magnetic materials is characterized in that the algorithm is set as follows according to specific parameters of the invention: the population size is 100, the crossover probability is 0.9, the mutation probability is 1/6, and the maximum number of iterations is 200.
9. The permanent magnet motor robust optimization design method considering uncertainty of magnetic materials according to claim 1, further comprising S5. electromagnetic performance and distribution evaluation: the electromagnetic performance of the motor design scheme and the distribution condition of the motor performance under the parameter disturbance of the magnetic material are simulated through finite elements, and the effectiveness of the design scheme is judged; the distribution of the motor performance under the parameter disturbance of the magnetic materials is as follows: and applying a large amount of random disturbance to the permanent magnet of the motor structure corresponding to the design scheme, calculating the design performance of the motor under different disturbances by using a finite element, and counting the performance distribution.
10. The permanent magnet motor robust optimization design method considering uncertainty of magnetic materials according to claim 1, further comprising S6. permanent magnet motor prototype machining and testing: after the design scheme is simulated through simulation, prototype machining is carried out according to the design scheme, relevant tests are carried out, and the motor structure is further verified.
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