Double-layer parameter optimization method of doubly salient motor

Abstract

The invention relates to a double-layer parameter optimization method for a doubly salient motor, and the method comprises the steps: obtaining important structure parameters which affect the torque ripple of the motor; determining an objective function according to the lowest torque ripple; performing first-layer parameter preliminary optimization based on the important structure parameters to obtain a first-layer parameter preliminary optimization result; performing genetic algorithm global optimization according to the target function and the first-layer parameter optimization result to obtain an optimal parameter; a torque ripple result is verified according to the optimal parameter, preliminary optimization is carried out by adopting an orthogonal test, the optimal range of the structural parameter is rapidly screened out, the search space of a subsequent genetic algorithm is reduced, and the effect of improving the optimization efficiency is achieved; and based on the primary result of the orthogonal table interactivity test of the first-layer optimization, second-layer global optimization is carried out by using a genetic algorithm, mutual influence among structural parameters is fully considered, a global optimal solution of the torque pulsation is found, and the effects of effectively reducing the torque pulsation of the motor and reducing computing resources are achieved.

Description

Technical Field

[0001] The present invention relates to the field of motor optimization, and in particular to a double-layer parameter optimization method for a double-salient-pole motor. Background Art

[0002] Traditional design methods for doubly salient motors include the simple magnetic circuit method, the variable parameter network method, and the finite element method. Although these methods can meet the design requirements to a certain extent, they have many significant defects such as being time-consuming and inefficient.

[0003] With the development of computers, artificial intelligence, and the emergence of algorithms, motor design has reached a new level. The application of intelligent algorithms in motor design has had a significant impact. Traditional intelligent algorithms, such as genetic algorithms and particle swarm optimization, can precisely adjust motor structural parameters to achieve a global optimal solution, significantly reducing torque ripple and improving motor performance.

[0004] However, in practical applications, algorithm convergence speed and optimization efficiency still need to be considered. Although traditional intelligent algorithms can obtain the optimal solution for motors, they consume enormous computing resources and take a lot of time.

[0005] Therefore, based on this, a double-layer parameter optimization method for doubly salient motor is proposed. Summary of the Invention

[0006] Based on the above problems existing in the prior art, an object of an embodiment of the present invention is to provide a double-layer parameter optimization method for a doubly salient-pole motor.

[0007] To achieve the above object, the technical solution adopted by the present invention is: a double-layer parameter optimization method for a doubly salient motor, comprising:

[0008] Step S1, obtaining important structural parameters that affect motor torque ripple;

[0009] Step S2, determining the objective function according to the lowest torque ripple;

[0010] Step S3, performing preliminary optimization of the first layer parameters based on the important structural parameters to obtain preliminary optimization results of the first layer parameters;

[0011] Step S4, performing global optimization using a genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain the optimal parameters;

[0012] Step S5: verify the torque pulsation result according to the optimal parameters.

[0013] Furthermore, in S1, the important structural parameters that affect the motor torque pulsation are obtained, including changing the air gap magnetic circuit inside the DSEM and determining the important structural parameters that affect the motor torque pulsation. The important structural parameters are the stator yoke height hsy , stator pole arc coefficient α st , rotor yoke height h ry , rotor pole arc coefficient α rt .

[0014] Furthermore, in S2, determining the objective function according to the lowest torque pulsation includes: taking the difference between the maximum value and the minimum value of the torque pulsation as the objective function.

[0015] Furthermore, step S3 , performing preliminary optimization of first-layer parameters based on important structural parameters to obtain preliminary optimization results of first-layer parameters, includes step S31 , establishing a first-layer parameter optimization interval based on important structural parameters.

[0016] Furthermore, in step S32, the sensitivity of each parameter to be optimized is analyzed according to the optimization interval to determine the factor level of the orthogonal array;

[0017] Specifically, the stator yoke height change rate function is defined, and the absolute value of the derivative is calculated as the change rate function:

[0018]

[0019] Among them, T c is the torque, h sy is the stator yoke height, T c.t 、h sy.t Indicates the current value of torque and stator yoke height, T c.t-1 、h sy.t-1 Indicates the value of the torque and stator yoke height at the previous moment, by calculating T c With h sy The difference between the current moment and the previous moment gives the current rate of change.

[0020] Furthermore, in step S33, a suitable orthogonal test table is constructed according to the factor levels;

[0021] Step S34 , according to each set of structural parameter combinations in the orthogonal test interaction table, finite element analysis software is used to perform parameterized modeling and analysis on the DSEM to obtain the torque pulsation value corresponding to each set of structural parameters.

[0022] Furthermore, in step S4, a genetic algorithm global optimization is performed based on the objective function and the first-layer parameter optimization results to obtain the optimal parameters, including:

[0023] Step S41, parameter values are taken according to the objective function f best (X) = min[T max (X)-T min(X)], X∈R, based on the first-level orthogonal test optimization, the two factor levels with the best quality characteristics of each factor are selected in the minimum or optimal way, and the interval between factor levels is appropriately reduced to continue to take values nearby;

[0024] Among them, f best (X) is the minimum objective function, that is, the optimal solution, T max is the maximum value of periodic torque pulsation, T min is the minimum value of periodic torque pulsation. sy ,α st ,h ry ,α rt ] represents the four motor structural parameter variables to be optimized, among which h sy is the stator yoke height, α st is the stator pole arc coefficient, h ry is the rotor yoke height, α rt is the rotor pole arc coefficient.

[0025] Furthermore, in step S45 , the selection operation adopts roulette wheel selection to select individuals with higher fitness from the current population as parents.

[0026] Furthermore, in step S46, a crossover operation is performed on the selected parent individuals to exchange some chromosome genes with a certain crossover probability to generate new individuals.

[0027] Furthermore, step S5 verifies the torque pulsation result according to the optimal parameters, including: inputting the optimized parameters into a finite element method for analysis to obtain optimized output torque data, and comparing and verifying the optimized output torque data with the data before optimization.

[0028] The beneficial effects of the present invention are:

[0029] The present invention provides a double-layer parameter optimization method for a double-pole motor, which obtains important structural parameters that affect the torque pulsation of the motor; determines the objective function according to the lowest torque pulsation; performs preliminary optimization of the first-layer parameters based on the important structural parameters to obtain preliminary optimization results of the first-layer parameters; performs global optimization using a genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain optimal parameters; verifies the torque pulsation results based on the optimal parameters, and performs preliminary optimization through orthogonal experiments to quickly screen out a better range of structural parameters, reducing the search space of subsequent genetic algorithms and achieving the effect of improving optimization efficiency; then, based on the preliminary results of the orthogonal table interactivity test of the first-layer optimization, performs a second-layer global optimization using a genetic algorithm, fully considering the mutual influence between the structural parameters, and finding the global optimal solution to the torque pulsation, thereby effectively reducing the torque pulsation of the motor and reducing computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below with reference to the accompanying drawings and examples.

[0031] In the picture:

[0032] Figure 1 This is a flow chart of a double-layer parameter optimization method for a double-salient-pole motor in the present invention.

[0033] Figure 2 This is the initial structure diagram of DSEM in the present invention.

[0034] Figure 3 Schematic diagram of the electric vehicle planning problem of the present invention.

[0035] Figure 4 Schematic diagram of the high sensitivity of the DSEM stator yoke of the present invention.

[0036] Figure 5 This is a diagram of the genetic algorithm encoding method of the present invention.

[0037] Figure 6 Schematic diagram of the crossover operation of the present invention.

[0038] Figure 7 This is the genetic algorithm flow chart of the present invention DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] An embodiment of the present invention provides a two-layer parameter optimization method for a double-pole motor, which obtains important structural parameters that affect the torque pulsation of the motor; determines the objective function according to the lowest torque pulsation; performs preliminary optimization of the first-layer parameters based on the important structural parameters to obtain preliminary optimization results of the first-layer parameters; performs global optimization of the genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain optimal parameters; verifies the torque pulsation results based on the optimal parameters, and performs preliminary optimization by adopting orthogonal experiments to quickly screen out the optimal range of structural parameters, reducing the search space of subsequent genetic algorithms, and achieving the effect of improving optimization efficiency; then, based on the preliminary results of the orthogonal table interactivity test of the first-layer optimization, uses the genetic algorithm to perform the second-layer global optimization, fully considering the mutual influence between the structural parameters, and finding the global optimal solution of the torque pulsation, thereby effectively reducing the torque pulsation of the motor and reducing computing resources.

[0041] The following is a detailed description of the implementation details of a double-layer parameter optimization method for a double-salient-pole motor in this embodiment. The following content is only for the convenience of understanding the implementation details and is not necessary for the implementation of this solution.

[0042] See Figure 1 and Figure 2 , step S1, obtaining important structural parameters that affect the motor torque pulsation.

[0043] Specifically, by changing the air gap magnetic circuit inside the DSEM, the important structural parameters that affect the torque ripple of the motor are determined, namely, the stator yoke height h sy , stator pole arc coefficient α st , rotor yoke height h ry , rotor pole arc coefficient α rt .

[0044] Among them, the specific stator pole arc coefficient α st Expressed as

[0045]

[0046] In the above formula, W slot Indicates the stator pole width, D si represents the inner diameter of the stator, and p represents the number of stator poles.

[0047] Rotor pole arc coefficient α rt Expressed as

[0048]

[0049] In the above formula, D ro Indicates the outer diameter of the rotor, W rlot Indicates that the rotor is extremely wide.

[0050] See Figure 3 The air gaps of a DSEM are: first air gap A1, second air gap A2, and third air gap A3. First air gap A1 is the distance between the outer surface of a rotor tooth and the inner surface of a stator tooth; second air gap A2 is the distance between the bottom of a rotor slot and the surface of a stator tooth; and third air gap A3 is the distance between the side faces of a stator tooth and the side faces of a rotor tooth when the stator pole and the centerline of the rotor slot coincide. The air gaps, rotor slots, stator slots, stator poles, and rotor poles together form the magnetic flux circuit of the DSEM. Stator is the stator, and Rotor is the rotor.

[0051] Step S2: determining the objective function according to the lowest torque ripple.

[0052] Specifically, the difference between the maximum and minimum torque pulsation values is used as the objective function, and the objective function is f best (X) = min[T max (X)-T min(X)], X∈R. The minimum objective function is the optimal one, T max is the maximum value of periodic torque pulsation, T min is the minimum value of periodic torque pulsation. sy ,α st ,h ry ,α rt ] represents the four motor structural parameter variables to be optimized, among which h sy is the stator yoke height, α st is the stator pole arc coefficient, h ry is the rotor yoke height, α rt is the rotor pole arc coefficient, and the variable value is all real numbers that satisfy the motor slot fill rate interval. The slot fill rate is expressed as

[0053]

[0054] S d1 is the total cross-sectional area of all conductors in the slot, S d is the effective area in the slot that can be used to place the conductor. a is the number of turns of the armature winding, and b is the number of turns of the excitation winding. A wd Indicates the armature winding wire diameter, F wd Indicates the wire diameter of the excitation winding. h st Indicates the stator tooth height, W slot Indicates the stator slot width. Usually the slot fill rate of a motor is between 40% and 60%.

[0055] Step S3: Preliminary optimization of the first layer parameters is performed based on the important structural parameters to obtain preliminary optimization results of the first layer parameters.

[0056] Step S31: establishing a first-layer parameter optimization interval based on important structural parameters.

[0057] Specifically, according to the design requirements and slot filling rate requirements of DSEM, the stator yoke height h sy , stator pole arc coefficient α st , rotor yoke height h ry , rotor pole arc coefficient α rt Take the first layer optimization interval.

[0058] Set the upper limit h of the yoke height optimization interval sy.up , lower limit h sy.low The upper limit of the stator pole arc coefficient α st.up , lower limit α st.low . Rotor yoke height upper limit h ry.up , lower limit h ry.low The upper limit of the rotor pole arc coefficient α rt.up , lower limit α rt.low .

[0059] Step S32: Analyze the sensitivity of each parameter to be optimized according to the optimization interval to determine the factor level of the orthogonal array.

[0060] Specifically, the sensitivity of a parameter to the target is determined by changing one parameter while fixing the other parameters. Based on the change trend of the sensitivity, the equal change rate segmentation method is used to select 5 points in proportion with the change rate (derivative) as the weight, so that the factor level points are more dense in the area with a large change rate and more sparse in the area with a small change rate. Taking the stator yoke height as an example, the stator yoke height parameter sensitivity curve can be found in Figure 4 . The stator yoke height 5 parameter levels are set as [H sy1 , H sy2 , H sy3 , H sy4 , H sy5 ]

[0061] First, define the stator yoke height change rate function, and calculate the absolute value of the derivative as the change rate function:

[0062]

[0063] Where T c is the torque, h sy is the stator yoke height, T c.t 、h sy.t Indicates the current value of torque and stator yoke height, T c.t-1 、h sy.t-1 Indicates the value of the torque and stator yoke height at the previous moment, by calculating T c With h sy The difference between the current moment and the previous moment gives the current rate of change.

[0064] Then divide the total change rate into 4 parts. Taking n sampling points as an example, the change rate of each part is

[0065]

[0066] Where h sy1 is the first sampling point of the stator yoke height, h syn The last sampling point of the stator yoke height is finally found to make the rate of change of each portion reach the value of the horizontal coordinate Δg(x) as the horizontal factor

[0067]

[0068] H sy.1 =h sy.low

[0069] Where i = 2, 3, 4, 5. Finally, the factor level of 5 of the stator yoke height [H sy1 , H sy2 , H sy3, H sy4 , H sy5 ].

[0070] The horizontal values of the other three structural parameters are obtained by analogy, and the stator pole arc coefficient is set to The rotor yoke height is set to [H ry1 , H ry2 , H ry3 , H ry4 , H ry5 ], the rotor pole arc coefficient is set to Step S33: construct a suitable orthogonal test table according to the factor levels and arrange the test plan.

[0071] The orthogonal test table is as follows.

[0072]

[0073] An orthogonal experiment interaction table is constructed based on the above orthogonal experiment table. The orthogonal experiment interaction table is as follows.

[0074]

[0075] Step S34 , according to each set of structural parameter combinations in the orthogonal test interaction table, finite element analysis software is used to perform parameterized modeling and analysis on the DSEM to obtain torque pulsation values Rusult1 to Rusult25 corresponding to each set of structural parameters.

[0076] Step S35, analyzing the orthogonal test results: Based on the analysis method of the orthogonal table interaction test and the finite element analysis results of step S34, the average values of the factors at different levels are calculated and analyzed to obtain a better optimization factor combination. The calculation formula is

[0077]

[0078] where m dj is the average value of the quality attribute at the j factor level of the d factor, z is the number of trials for a single level factor, S dj1 ~S djn is the quality characteristic value of z experiments of factor d at factor level j.

[0079] Step S36, the average value m of the quality attribute analyzed in step S35 dj Select the parameters of the optimal level of a single level factor, taking the stator yoke height h as st For example, the average value m of the quality attribute at levels x and y dj Minimum, then select h st.x and h st.y is the optimization interval for the next step.

[0080] Step S4: Perform global optimization using a genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain optimal parameters.

[0081] Step S41, parameter values are taken according to the objective function f best (X) = min[T max (X)-T min (X)], X∈R, based on the first level of orthogonal test optimization, the two factor levels with the best quality characteristics of each factor are selected with the minimum or optimal method, and the interval between the factor levels is appropriately reduced to continue to take values nearby. st For example, two minimum values are selected through the objective function, namely h st.best1 、h st.best2 The upper and lower ranges of the value range are as follows

[0082]

[0083] h sy.min =h sy.n i=1

[0084] h sy.max =h sy.n i=5

[0085] Where h sy.min 、h st.max is the lower and upper intervals of the algorithm optimization interval, h st.best1 and h st.best2 The value of the factor level corresponding to the orthogonal test table in step 2, |h sy.best2 -h sy.best1 | corresponds to the interval between the best two factor levels in the orthogonal test table.

[0086] Step S42: Encode the value range after preliminary optimization.

[0087] See Figure 5 Specifically, the stator yoke height h after preliminary optimization is sy , stator pole arc coefficient α st , rotor yoke height h ry , rotor pole arc coefficient α rt The value range of is encoded, and the four structural parameters are converted into binary and then combined together as the chromosome encoding form in the genetic algorithm.

[0088] Step S43, initializing the population: randomly generating a certain number of individuals (populations), each individual representing a set of structural parameter combinations.

[0089] Step S44, taking torque pulsation as fitness function, that is, the fitness value is inversely proportional to the torque pulsation value, the smaller the torque pulsation, the higher the fitness value. The specific objective function is: f best (X) = min[T max (X)-T min (X)],X∈R.

[0090] Among them, f best (X) is the minimum objective function, that is, the optimal solution, T max is the maximum value of periodic torque pulsation, T min is the minimum value of periodic torque pulsation. sy ,α st ,h ry ,α rt ] represents the four motor structural parameter variables to be optimized, among which h sy is the stator yoke height, α st is the stator pole arc coefficient, h ry is the rotor yoke height, α rt is the rotor pole arc coefficient.

[0091] Step S45: Roulette wheel selection is used to select individuals with higher fitness from the current population as the parent generation. The probability of each individual entering the next generation is equal to the ratio of its fitness value to the sum of the fitness values of individuals in the entire population. The fitness value r of individual x in the previous generation is x The proportion retained to the next generation is Specifically expressed as the proportion of the fitness value of individual x to the fitness values of all individuals in the previous generation.

[0092] Step S46: Perform a crossover operation on the selected parent individuals, exchanging some chromosome genes with a certain crossover probability to generate new individuals.

[0093] See Figure 6 Specifically, a crossover point is randomly set in the coding strings of two adjacent individuals, and then part of the chromosomes of the two paired individuals are exchanged at this point.

[0094] Step S47: Mutate the chromosome genes of the new individual with a certain mutation probability to increase the diversity of the population. The individual code string is bitwise inverted, that is, from 0 to 1 or from 1 to 0, with a certain mutation probability and random bits.

[0095] Step S48, iterative optimization repeats the selection, crossover, and mutation operations until a preset number of iterations is reached or convergence conditions are met, thereby obtaining a globally optimized structural parameter combination.

[0096] Specifically, by initially setting the first generation population G=0, the selection, crossover, and mutation operations are repeated until the preset number of iterations Gmax is reached or the convergence condition is met, and the globally optimized structural parameter combination is obtained.

[0097] See Figure 7 , genetic algorithm implementation steps. Finally, after the genetic algorithm optimization is completed, four DSEM optimal parameter combinations are obtained, namely the stator yoke height optimal parameter h sy.best , optimal parameter α of stator pole arc coefficient st.best , the optimal parameter h of rotor yoke height ry.best , the optimal parameter α of the rotor pole arc coefficient rt.best .

[0098] Step S5: verify the torque pulsation result according to the optimal parameters.

[0099] Specifically, to verify the optimization results, the globally optimized structural parameter combination was applied to the actual DSEM design. Through finite element parametric modeling, the optimized parameters were input into the finite element analysis to obtain the optimized output torque data. This data was then compared with the pre-optimization data to verify the effective reduction of torque ripple.

[0100] An embodiment of the present invention provides a two-layer parameter optimization method for a double-pole motor, which obtains important structural parameters that affect the torque pulsation of the motor; determines the objective function according to the lowest torque pulsation; performs preliminary optimization of the first-layer parameters based on the important structural parameters to obtain preliminary optimization results of the first-layer parameters; performs global optimization of the genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain optimal parameters; verifies the torque pulsation results based on the optimal parameters, and performs preliminary optimization by adopting orthogonal experiments to quickly screen out the optimal range of structural parameters, reducing the search space of subsequent genetic algorithms, and achieving the effect of improving optimization efficiency; then, based on the preliminary results of the orthogonal table interactivity test of the first-layer optimization, uses the genetic algorithm to perform the second-layer global optimization, fully considering the mutual influence between the structural parameters, and finding the global optimal solution of the torque pulsation, thereby effectively reducing the torque pulsation of the motor and reducing computing resources.

[0101] The steps of the various methods above are divided only for the purpose of clear description. During implementation, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0102] The above is only an embodiment of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A double-layer parameter optimization method for a doubly salient motor, characterized in that: include: Step S1, obtaining important structural parameters that affect motor torque ripple; Step S2, determining the objective function according to the lowest torque ripple; Step S3, performing preliminary optimization of the first layer parameters based on the important structural parameters to obtain preliminary optimization results of the first layer parameters; Step S4, performing global optimization using a genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain optimal parameters; Step S5: verify the torque pulsation result according to the optimal parameters.

2. The double-layer parameter optimization method for a doubly salient motor according to claim 1, characterized in that: In S1, the important structural parameters that affect the motor torque pulsation are obtained, including changing the air gap magnetic circuit inside the DSEM and determining the important structural parameters that affect the motor torque pulsation. The important structural parameters are stator yoke height h sy , stator pole arc coefficient α st , rotor yoke height h ry , rotor pole arc coefficient α rt .

3. The double-layer parameter optimization method for a doubly salient motor according to claim 1, characterized in that: In S2, the objective function is determined according to the lowest torque pulsation, including: taking the difference between the maximum value and the minimum value of the torque pulsation as the objective function.

4. The double-layer parameter optimization method for a doubly salient motor according to claim 1, characterized in that: Step S3, performing preliminary optimization of first-layer parameters based on important structural parameters to obtain preliminary optimization results of first-layer parameters, includes step S31, establishing a first-layer parameter optimization interval based on important structural parameters.

5. The double-layer parameter optimization method for a doubly salient motor according to claim 4, characterized in that: Step S32, analyzing the sensitivity of each parameter to be optimized according to the optimization interval to determine the factor level of the orthogonal array; Specifically, the stator yoke height change rate function is defined, and the absolute value of the derivative is calculated as the change rate function: Among them, T c is the torque, h sy is the stator yoke height, T c.t 、h sy.t Indicates the current value of torque and stator yoke height, T c.t-1 、h sy.t-1 Indicates the value of the torque and stator yoke height at the previous moment, by calculating T c With h sy The difference between the current moment and the previous moment gives the current rate of change.

6. The double-layer parameter optimization method for a doubly salient motor according to claim 3, characterized in that: Step S33, constructing a suitable orthogonal test table according to the factor levels; Step S34 , according to each set of structural parameter combinations in the orthogonal test interaction table, finite element analysis software is used to perform parameterized modeling and analysis on the DSEM to obtain the torque pulsation value corresponding to each set of structural parameters.

7. The double-layer parameter optimization method for a doubly salient motor according to claim 1, characterized in that: Step S4, performing global optimization of the genetic algorithm based on the objective function and the first-layer parameter optimization results to obtain the optimal parameters, including: Step S41, parameter values are taken according to the objective function f best (X) = min[T max (X)-T min (X)], X∈R, based on the first-level orthogonal test optimization, the two factor levels with the best quality characteristics of each factor are selected in the minimum or optimal way, and the interval between factor levels is appropriately reduced to continue to take values nearby; Among them, f best (X) is the minimum objective function, that is, the optimal solution, T max is the maximum value of periodic torque pulsation, T min is the minimum value of periodic torque pulsation. sy ,α st ,h ry ,α rt ] represents the four motor structural parameter variables to be optimized, among which h sy is the stator yoke height, α st is the stator pole arc coefficient, h ry is the rotor yoke height, α rt is the rotor pole arc coefficient.

8. The double-layer parameter optimization method for a doubly salient motor according to claim 7, characterized in that: Step S45: Roulette wheel selection is used in the selection operation to select individuals with higher fitness from the current population as parents.

9. The double-layer parameter optimization method for a doubly salient motor according to claim 8, characterized in that: Step S46: Perform a crossover operation on the selected parent individuals, exchanging some chromosome genes with a certain crossover probability to generate new individuals.

10. The double-layer parameter optimization method for a doubly salient motor according to claim 1, characterized in that: Step S5, verifying the torque pulsation result according to the optimal parameters, including: inputting the optimized parameters into the finite element method for analysis to obtain the optimized output torque data, and comparing and verifying it with the data before optimization.

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