Motor multi-objective optimization method and system based on improved dung beetle optimization algorithm

Abstract

The invention is suitable for the technical field of permanent magnet synchronous motor performance improvement, and provides a motor multi-objective optimization method and system based on an improved dung beetle optimization algorithm, and the method comprises the steps: building a mathematical model of the torque ripple and dynamic response capability of a motor, and determining initial design parameters which affect the torque ripple and dynamic response capability; according to the simulation data of the stator skewed slot of the motor, determining an optimal angle meeting a first constraint condition so as to establish a motor model; based on the motor model, motor structure parameters influencing the initial design parameter sensitivity are analyzed, and the obtained motor structure parameters include the stator outer diameter, the permanent magnet thickness, the tooth width and the air gap length; according to the invention, research is carried out from the angle of a stator skewed slot, and an optimal angle with large torque pulsation reduction and small dynamic response capability reduction is obtained; the initial design parameters are subjected to sensitivity analysis to obtain structure parameters, algorithm optimization is carried out after fitting, and multi-objective optimization of the motor is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of permanent magnet synchronous motor performance improvement, and specifically discloses a motor multi-objective optimization method and system based on an improved dung beetle optimization algorithm. Background Art

[0002] Due to its special structure with the rotor outside and the stator inside, the outer rotor permanent magnet synchronous motor has the advantages of large output torque and high power density, and is widely used in the field of robot joints. However, due to the existence of cogging torque and large rotor inertia, the motor has large torque pulsation and poor dynamic response capability. Higher torque pulsation increases electromagnetic noise and system instability; lower dynamic response capability will lead to increased torque pulsation and increase mechanical vibration. Therefore, how to reduce the motor torque pulsation and improve the dynamic response capability is an urgent problem to be solved.

[0003] In order to reduce torque pulsation, the motor structure is usually improved by stator slot optimization, stator skew, rotor skew and pole slot matching. However, the above methods effectively reduce the motor cogging torque, but do not take into account the impact of structural improvement on dynamic response capability. In general, research on improving the dynamic response capability of motors is mainly focused on rotor structure design, the application of new materials, etc. However, the dynamic response capability of the motor depends not only on the rotor inertia, but also on the torque. In order to improve the performance of the motor, the commonly used method is single-objective optimization. However, it is often ignored because there is a conflict between the selected optimization objective and other objectives. Therefore, it is necessary to design a multi-objective optimization design method. Summary of the invention

[0004] The purpose of the present invention is to provide a motor multi-objective optimization method and system based on an improved dung beetle optimization algorithm, aiming at the problem mentioned in the background technology. The present invention selects a suitable stator skew angle to reduce torque pulsation while considering the factors affecting dynamic response capability, improves dynamic response capability by optimizing the mechanical time constant, and uses an improved dung beetle optimization algorithm based on non-dominated sorting to optimize the parameters of the motor, thereby improving the convergence speed and optimization accuracy of the algorithm, and finally obtaining a more superior Pareto optimal solution set.

[0005] The present invention is implemented in this way: a motor multi-objective optimization method based on an improved dung beetle optimization algorithm, the method comprising the following steps:

[0006] Establish a mathematical model of motor torque pulsation and dynamic response capability, and determine the initial design parameters that affect torque pulsation and dynamic response capability;

[0007] According to the simulation data of the stator skew slot of the motor, an optimal angle satisfying a first constraint condition is determined to establish a motor model; wherein the first constraint condition is to make the torque pulsation drop greatly while the dynamic response capability drop slightly;

[0008] Based on the motor model, the motor structural parameters that affect the sensitivity of the initial design parameters are analyzed, and the motor structural parameters are obtained as the stator outer diameter, the permanent magnet thickness, the tooth width, and the air gap length;

[0009] The fitting model of the design goal is constructed by response surface method, and the design goal is to reduce the torque pulsation of the motor and improve the dynamic response capability;

[0010] Based on the fitting model, the improved dung beetle optimization algorithm is used to optimize the motor structural parameters.

[0011] Another object of the present invention is to provide a motor multi-objective optimization system based on an improved dung beetle optimization algorithm, the system comprising:

[0012] Mathematical model input module, used to establish a mathematical model of motor torque pulsation and dynamic response capability, and determine the initial design parameters affecting torque pulsation and dynamic response capability;

[0013] A simulation optimization module, used to determine an optimal angle satisfying a first constraint condition according to simulation data of a stator skew slot of the motor, so as to establish a motor model; wherein the first constraint condition is to make the torque pulsation drop greatly while the dynamic response capability drop little;

[0014] A sensitivity analysis module, for analyzing the motor structural parameters that affect the sensitivity of the initial design parameters based on the motor model, and obtaining the motor structural parameters as the stator outer diameter, permanent magnet thickness, tooth width, and air gap length;

[0015] The target solution module is used to construct a fitting model of the design target through the response surface method. The design target is to reduce the torque pulsation of the motor and improve the dynamic response capability;

[0016] A parameter optimization module is used to optimize the motor structure parameters based on the fitting model by using an improved dung beetle optimization algorithm.

[0017] The present invention provides a motor multi-objective optimization method based on an improved dung beetle optimization algorithm. The method conducts simulation research from the perspective of stator skew slots. While significantly reducing torque pulsation, the increase in mechanical time constant is minimized, which helps to maintain a good speed response capability of the motor. In this way, the motor can run smoothly and respond quickly to speed changes, improving the overall performance of the motor. The improved dung beetle optimization algorithm adopted by the present invention is a non-dominated sorting dung beetle optimization algorithm, which optimizes the parameters of the fitting model, and can simultaneously take into account the design goals of low torque pulsation and high dynamic response capability of the motor and obtain non-dominated solutions. The excellent global search capability of the algorithm enables it to avoid local optimality in high-dimensional space and approach global optimal parameters. The unique mechanism can effectively avoid premature convergence, continuously explore new solution space, and finally accurately optimize the fitting model parameters to achieve the optimization of motor structural parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A principle flow chart of a motor multi-objective optimization method based on an improved dung beetle optimization algorithm provided in an embodiment of the present invention;

[0019] Figure 2 This is a result diagram of the optimization target sensitivity analysis in an embodiment of the present invention;

[0020] Figure 3 It is the optimization target response surface diagram in the embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of a non-dominated sorting principle in an embodiment of the present invention;

[0022] Figure 5 Schematic diagram of optimization steps based on the non-dominated sorting dung beetle optimization algorithm in an embodiment of the present invention;

[0023] Figure 6 is the optimized Pareto frontier graph in the embodiment of the present invention;

[0024] Figure 7 This is a torque pulsation comparison diagram before and after optimization in an embodiment of the present invention;

[0025] Figure 8 A comparison diagram of dynamic response capabilities before and after optimization in an embodiment of the present invention;

[0026] Fig. 9 A schematic diagram of the structure of an outer rotor permanent magnet synchronous motor applied in an embodiment of the present invention;

[0027] Fig.10 Schematic diagram of a simulation model of a stator skew slot in an embodiment of the present invention;

[0028] Fig.11A flowchart of a motor multi-objective optimization method based on an improved dung beetle optimization algorithm provided in an embodiment of the present invention;

[0029] Fig.12 A structural block diagram of a motor multi-objective optimization system based on an improved dung beetle optimization algorithm provided in an embodiment of the present invention.

[0030] In the accompanying drawings: 101 - stator; 102 - stator skew slot; 103 - rotor; 104 - permanent magnet. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] In fact, however, the dynamic response capability of the motor depends not only on the rotor inertia, but also on the torque. The dynamic response capability of the outer rotor permanent magnet synchronous motor includes the torque response capability and the speed response capability, which are respectively related to the electrical time constant and the mechanical time constant. Since the mechanical time constant of the outer rotor permanent magnet synchronous motor is much larger than the electrical time constant, the embodiment of the present invention chooses to optimize only the mechanical time constant.

[0033] In order to improve the performance of the outer rotor permanent magnet synchronous motor, this embodiment adopts a multi-objective optimization design method to optimize the design objectives. The motor multi-objective optimization method of this embodiment generally adopts a linear weighted sum method to convert the multi-objective function into a single objective function. When looking for a trade-off solution for the Pareto optimal solution, the use of a multi-objective optimization algorithm can reduce the optimization process.

[0034] Fig. 9 A schematic diagram of the structure of an outer rotor permanent magnet synchronous motor applied in an embodiment of the present invention; Fig.10 Schematic diagram of the simulation model of the stator skew slot in the embodiment of the present invention; wherein the outer rotor permanent magnet synchronous motor includes a stator 101, a stator skew slot 102 opened on the outer peripheral surface of the stator 101, for installing the coil, and a rotor 103 sleeved outside the stator 101, and a permanent magnet 104 fixed on the inner surface of the outer rotor permanent magnet synchronous motor. The motor parameters are selected as an outer rotor permanent magnet synchronous motor with 20 poles and 24 slots, and the rated speed is 3000rpm. The motor adopts a surface-mounted structure, which is conducive to reducing the rotor inertia and improving the dynamic response capability of the motor.

[0035] Figure 1 A principle flow chart of a motor multi-objective optimization method based on an improved dung beetle optimization algorithm provided in an embodiment of the present invention specifically includes:

[0036] Determine design variables and optimization objectives;

[0037] Conduct sensitivity analysis;

[0038] Determine whether it is significant, if not, perform single parameter optimization;

[0039] If so, the experimental points were selected based on Box-Behnken (response surface methodology);

[0040] Calculate motor response based on finite element analysis;

[0041] The response surface methodology was used to construct a fitting model for the design objectives;

[0042] Multi-objective optimization based on improved dung beetle optimization algorithm and response surface model;

[0043] Get the optimal design from the Pareto solution set;

[0044] Build finite element analysis models and test performance.

[0045] Fig.11 In this embodiment, a flowchart of a motor multi-objective optimization method based on an improved dung beetle optimization algorithm is provided; the following steps S101 to S105 are included;

[0046] Step S101, establishing a mathematical model of motor torque pulsation and dynamic response capability, and determining initial design parameters affecting torque pulsation and dynamic response capability;

[0047] In an outer rotor permanent magnet synchronous motor, the dq axis inductance is not much different, so the cogging torque T cog It has a great influence on torque ripple. The cogging torque expression is as follows:

[0048]

[0049] Among them, L a is the axial length; Z is the number of slots; μ0 is the vacuum permeability; R3 is the rotor inner diameter; R2 is the stator outer diameter; n is the is an integer; α is the angle between the pole centerline and the tooth centerline; G n is the Fourier decomposition coefficient of the square of relative air gap permeance; is the Fourier decomposition coefficient of the air gap magnetic flux;

[0050]

[0051] Among them, h m is the thickness of the permanent magnet; g is the air gap length; θ s0 Indicates the arc value corresponding to the stator slot width; α p is the polar arc coefficient; B r is the distribution of the remanent magnetism of the permanent magnet along the circumferential direction, and 2p is the number of poles.

[0052] In this embodiment, the mathematical model of the motor torque pulsation is:

[0053]

[0054] Among them, T max is the output torque T N Maximum value; T min is the output torque T N Minimum value; T av is the average torque value.

[0055] The mathematical model of the motor's dynamic response capability is:

[0056]

[0057] Among them, ρ w is the conductor resistivity; L E is the average half-turn length; N is the number of turns in series; A 01 is the cross-sectional area of ​​the enameled wire; a1 is the number of parallel branches; B av is the average magnetic flux density; D a is the diameter of the magnetic steel surface; l is the length of the conductor in the magnetic field; ρ m is the permanent magnet density; R1 is the rotor outer diameter; ρ Fe is the rotor core density; h m is the thickness of the permanent magnet; g is the air gap length; R2 is the stator outer diameter.

[0058] From the above equations (2) to (5), we can know that the stator outer diameter R2 and the permanent magnet thickness h can be selected as m 、Notch width B s0 、Tooth width B s1 , air gap length g, pole arc coefficient α p , Groove depth H s0 , slot height H s1 Table 1 gives the reasonable value range of the initial design parameters, which is used for the subsequent sensitivity analysis.

[0059] Table 1 Initial design parameter values

[0060]

[0061] Step S102, determining an optimal angle that satisfies a first constraint condition based on simulation data of the stator skew slot of the motor to establish a motor model; wherein the first constraint condition is to make the torque pulsation drop more and the dynamic response capability drop less;

[0062] Among them, the simulation data of the stator skew slot is simulated by establishing a finite element model; the finite element model is solved under different skew slot degrees, and the torque pulsation and mechanical time constant obtained according to the stator skew slot model of the motor are shown in Table 2.

[0063] Table 2 Torque ripple and mechanical time constant when the skew angle is 0-10°

[0064]

[0065] According to Table 2, it can be found that the torque ripple decreases with the increase of the skew degree, and the mechanical time constant increases with the increase of the skew degree. Since the mechanical time constant increases significantly with the increase of the skew degree after 10°, it is not considered.

[0066] In order to make the torque ripple drop more and the mechanical time constant increase less, Among them, β1=ΔT1-ΔT2 represents the torque pulsation decrease value, and β2=τ2-τ1 represents the mechanical time constant increase value. The larger it is, the better the effect is. For details, please refer to Table 3;

[0067] Table 3 Variation of different skew degrees

[0068]

[0069] It can be seen from Table 3 that when the skew degree is 2 0 becomes 3 0 hour, This indicates that the use of 3 0 The skew degree can minimize the mechanical time constant while minimizing the torque ripple, so a skew degree of 3 is selected. 0 Then proceed to build the subsequent model.

[0070] Step S103, based on the motor model, analyzing the motor structural parameters that affect the sensitivity of the initial design parameters, and obtaining the motor structural parameters as the stator outer diameter, permanent magnet thickness, tooth width, and air gap length;

[0071] In the step of analyzing the motor structural parameters that affect the sensitivity of the initial design parameters based on the motor model, Sobol sensitivity analysis is adopted; the number of subsequent tests can be reduced and the complexity of calculation can be alleviated.

[0072] Among them, in Table 1, sensitivity analysis is performed on several optimization parameters, and the final sensitivity analysis results are as follows: Figure 2 As shown;

[0073] The higher the absolute value of the sensitivity factor, the greater the influence of the optimization variable on the optimization objective, and vice versa. Figure 2It can be seen that the significant parameters that have a greater impact on the two optimization objectives are R2 and h m , B s1 and g, and the above four parameters are taken as motor structure parameters.

[0074] The significant variable levels after sensitivity analysis are shown in Table 4. The finite element simulation software was used for the experiment, and the Box-Behnken (BBD, response surface method) design of four variables required 29 experiments. Table 4 gives the code and level value of each design variable;

[0075] Table 4 Significant variable levels

[0076]

[0077] The final regression equation for the optimization objective can be expressed as:

[0078] ΔT=29.82901-0.803543×R2-3.2429×h m -2.28548×B s1 -9.2697×g+0.095288×R2×h m -0.175492×R2×B s1 -0.050172×R2×g-1.07744×h m ×B s1 +0.676725×h m ×g-0.61125×B s1 ×g+0.010817×R2 2 -0.060553×h m 2 +3.50163×B s1 2 +5.22456×g 2 (6);

[0079] τ m =6.43702-0.13528×R2-3.48663×h m -0.6747×B s1 +2.88003×g+0.0555×R2×h m -0.1395×R2×B s1 -0.063281×R2×g-0.524×h m ×B s1 -1.195×h m ×g+0.84×B s1 ×g+0.004898×R2 2 +0.862×h m2 +1.562×B s1 2 +1.14766×g 2 (7);

[0080] Step S104, constructing a fitting model of the design target by using the response surface method, wherein the design target is to reduce the torque pulsation of the motor and improve the dynamic response capability;

[0081] In step S104, the step of constructing a fitting model of the design target by using the response surface method specifically includes:

[0082] Obtain the significant variable level table after sensitivity analysis, conduct finite element simulation tests, and record each group of optimization parameters and the corresponding response variable observation values;

[0083] Construct a response surface regression equation and use the recorded experimental data to fit and solve the response surface regression equation:

[0084] Among them, when performing fitting solution, the second-order polynomial regression method is used to establish the fitting model, and the established fitting model is:

[0085]

[0086] Among them: y is the optimization target; b0 is the constant term; x i 、x ij corresponds to different optimization parameters; b i , b ij and b ii are the first and second order coefficients of the optimization parameters; ε is the random error;

[0087] In this step, the approximation of the fitted model is determined by the multiple correlation coefficient R 2 express;

[0088] The regression equation of the optimization target is analyzed by significance test; the approximation degree of the fitting model is determined by the multiple correlation coefficient R 2 express;

[0089] R 2 =1-S SE / S ST (9);

[0090]

[0091] where Y i is the true value of the test set, y i is the fitting value, n is the number of experiments, S SE is the residual sum of squares, S ST is the total dispersion squared. R 2The closer the value of is to 1, the closer the test data of the fitted model is to the actual value. The correlation coefficients of the two responses of torque pulsation and mechanical time constant are named

[0092] and The values ​​of are 0.973 and 0.996 respectively. It can be seen that the R 2 The values ​​of are all greater than 0.9, which have good applicability and good correlation.

[0093] The response surface diagram of the optimization objective is as follows: Figure 3 As shown in Figure 1, when a single parameter is changed, the torque ripple and mechanical time constant will change at the same time. Therefore, in order to avoid conflicts between design goals, a multi-objective optimization algorithm is used.

[0094] Step S105: Based on the fitting model, an improved dung beetle optimization algorithm is used to optimize the motor structural parameters.

[0095] In order to achieve the design goals of low torque pulsation and high dynamic response capability in this embodiment, the DBO algorithm (Dung Beetle Optimizer) is used to iteratively optimize the fitting model. However, the original DBO algorithm (Dung Beetle Optimizer) has certain deficiencies in local search capability and processing multi-objective optimization problems. In order to overcome these deficiencies, this embodiment combines the DBO algorithm with the non-dominated sorting strategy to obtain an improved Dung Beetle Optimizer; that is, the Dung Beetle Optimizer (NSDBO) based on non-dominated sorting. This algorithm can effectively improve the convergence speed and optimization accuracy of the algorithm, and ultimately obtain a more superior Pareto optimal solution set.

[0096] In this embodiment, the method of this embodiment further includes: combining the dung beetle optimization algorithm with a non-dominated sorting strategy to obtain an improved dung beetle optimization algorithm; the non-dominated sorting strategy is configured as follows:

[0097] For each solution, calculate the number of solutions it is dominated by;

[0098] Solutions that are not dominated by any solution are classified as the first layer;

[0099] Among the solutions in the first layer, remove all solutions that dominate other solutions and assign the remaining solutions to the second layer;

[0100] Repeat the above process until all solutions are classified into different non-dominated layers;

[0101] In the solutions of the same layer, they are sorted by the degree of crowding, which is the density of any solution from other solutions.

[0102] Output the solutions in all non-dominated layers as the Pareto optimal solution set.

[0103] In this embodiment, the non-dominated sorting is performed according to the degree of dominance of the Pareto optimal solution, such as Figure 4 As shown in Figure 1, solutions that are not dominated by any solution are assigned order 1, solutions dominated by only one solution are assigned order 2, solutions dominated by only two solutions are assigned order 3, and so on. Then, solutions are selected according to their order to improve the quality of the dung beetle population and maintain population diversity through crowding calculation.

[0104] like Figure 5 As shown, the algorithm optimization specifically includes steps S1) to S7);

[0105] S1) Initialize the population: Initialize the dung beetle population N = 200, and the initial maximum iteration P = 500;

[0106] S2) Calculate fitness: Calculate the fitness value of the individual according to the objective function;

[0107] S3) Screening the population by fast non-dominated sorting and crowding calculation to obtain the first N individuals as a new population;

[0108] S4) The position update formula for rolling dung beetles, breeding dung beetles, foraging dung beetles and thief dung beetles is:

[0109] X i t+1 =X i t +βkX i t-1 +b|X i t -X worst t | (12);

[0110] X i t+1 =X i t +tanθ|X i t -X i t-1 | (13);

[0111] B i t+1 =X gbest t +b1(B i t -Lb * )+b2(B i t -Ub* ) (14);

[0112] X i t+1 =X i t +C1(X i t -Lb l )+C2(X i t -Ub l ) 15);

[0113] X i t+1 =X lbest t +M(|X i t -X gbest t |+|X i t -X lbest t |) (16);

[0114] Formula (12) and (13) are the formulas for the behavior stage of the rolling dung beetle, and formula (14) to (16) are the formulas for the reproductive behavior stage, foraging behavior stage, and stealing behavior stage, respectively. Where t represents the current iteration number; X i t represents the position of the i-th dung beetle in the population at the t-th iteration; X worst t Indicates the worst position in the current population; |X i t -X worst t |Used to simulate the changes in light intensity; B i t+1 is the position of individual iteration in the foraging process; b1 and b2 are two independent random vectors of size 1×D, D represents the dimension of the problem, and M is a constant;

[0115] S5) recalculating the fitness value of each individual in the new population, determining the dominance relationship between individuals based on the non-dominated sorting and crowding distance calculation, and selecting the first N individuals as the parent population;

[0116] S6) Whether the algorithm termination condition is met, if yes, stop iteration, otherwise repeat steps S2)-S5);

[0117] S7) The first N individuals are the Pareto solution set.

[0118] The optimized Pareto frontier is as follows Figure 6As shown in the figure, according to the set constraints and combined with the goals of low torque ripple and high dynamic response capability of the motor, the obtained Pareto frontier is screened and finally selected Figure 6 The solution marked with a triangle in the middle is the optimal solution. The final values ​​of the motor optimization parameters are shown in Table 5;

[0119] Table 5 Final values ​​of optimized parameters

[0120]

[0121] The comparison of torque ripple and dynamic response capability before and after optimization is shown in the figure below. Figure 7 , Figure 8 shown.

[0122] In another embodiment, Fig.12 As shown, a motor multi-objective optimization system based on an improved dung beetle optimization algorithm is used in the method described above, and the system includes:

[0123] The mathematical model input module 100 is used to establish a mathematical model of the motor torque pulsation and dynamic response capability, and determine the initial design parameters that affect the torque pulsation and dynamic response capability;

[0124] The simulation optimization module 200 is used to determine the optimal angle that satisfies the first constraint condition according to the simulation data of the stator skew slot of the motor, so as to establish a motor model; wherein the first constraint condition is to make the torque pulsation drop greatly and the dynamic response capability drop less;

[0125] A sensitivity analysis module 300 is used to analyze the motor structural parameters that affect the sensitivity of the initial design parameters based on the motor model, and obtain the motor structural parameters as the stator outer diameter, permanent magnet thickness, tooth width, and air gap length;

[0126] The target solving module 400 is used to construct a fitting model of the design target by using the response surface method, wherein the design target is to reduce the torque pulsation of the motor and improve the dynamic response capability;

[0127] The parameter optimization module 500 is used to optimize the motor structure parameters by using an improved dung beetle optimization algorithm based on the fitting model.

[0128] The above embodiment provides a motor multi-objective optimization method based on an improved dung beetle optimization algorithm, and based on the motor multi-objective optimization method based on the improved dung beetle optimization algorithm, a motor multi-objective optimization system based on the improved dung beetle optimization algorithm is provided. The motor multi-objective optimization method based on the improved dung beetle optimization algorithm performs simulation analysis on different stator skew angles of the stator, and uses a dung beetle optimization algorithm based on non-dominated sorting (i.e., an improved dung beetle optimization algorithm) to optimize the motor structural parameters; it can more specifically solve the problem of motor speed response and improve the overall performance of the motor; analyze the optimal angle of the stator skew slot, while significantly reducing the torque pulsation, minimize the increase in the mechanical time constant, and help maintain the motor's good speed response capability; in this way, the motor can run smoothly and respond quickly to speed changes, thereby improving the motor's comprehensive performance. The improved dung beetle optimization algorithm is used to optimize the parameters of the fitting model, which can simultaneously take into account the design goals of low torque pulsation and high dynamic response capability of the motor and obtain non-dominated solutions; the algorithm has excellent global search capabilities, which enables it to avoid local optimality in high-dimensional space and approach global optimal parameters. It can effectively avoid premature convergence, continuously explore new solution space, and ultimately accurately optimize the fitting model parameters to achieve optimization of motor structure parameters.

[0129] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0130] 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 protection scope of the present invention.

Claims

1. A motor multi-objective optimization method based on an improved dung beetle optimization algorithm, characterized in that: The method comprises: Establish a mathematical model of motor torque pulsation and dynamic response capability, and determine the initial design parameters that affect torque pulsation and dynamic response capability; According to the simulation data of the stator skew slot of the motor, an optimal angle satisfying a first constraint condition is determined to establish a motor model; wherein the first constraint condition is to make the torque pulsation drop greatly while the dynamic response capability drop slightly; Based on the motor model, the motor structural parameters that affect the sensitivity of the initial design parameters are analyzed, and the motor structural parameters are obtained as the stator outer diameter, the permanent magnet thickness, the tooth width, and the air gap length; The fitting model of the design goal is constructed by response surface method, and the design goal is to reduce the torque pulsation of the motor and improve the dynamic response capability; Based on the fitting model, the improved dung beetle optimization algorithm is used to optimize the motor structural parameters.

2. The method according to claim 1, characterized in that The mathematical model of the motor torque pulsation is: Among them, T max is the output torque T N Maximum value; T min is the output torque T N Minimum value; T av is the average torque value.

3. The method according to claim 2, characterized in that The mathematical model of the motor dynamic response capability is: Among them, ρ w is the conductor resistivity; L E is the average half-turn length; N is the number of turns in series; A 01 is the cross-sectional area of ​​the enameled wire; a1 is the number of parallel branches; B av is the average magnetic flux density; D a is the diameter of the magnetic steel surface; l is the length of the conductor in the magnetic field; ρ m is the permanent magnet density; R1 is the rotor outer diameter; ρ Fe is the rotor core density; h m is the thickness of the permanent magnet; g is the air gap length; R2 is the stator outer diameter.

4. The method according to claim 1, characterized in that: The steps of constructing a fitting model for the design objective by response surface methodology include: Obtain the significant variable level table after sensitivity analysis, conduct finite element simulation tests, and record each group of optimization parameters and the corresponding response variable observation values; Construct a response surface regression equation and use the recorded test data to fit and solve the response surface regression equation; Among them, when performing fitting solution, the second-order polynomial regression method is used to establish the fitting model, and the established fitting model is: Among them: y is the optimization target; b0 is the constant term; x i 、x ij corresponds to different optimization parameters; b i , b ij and b ii are the first and second order coefficients of the optimization parameters; ε is the random error; The regression equation of the optimization target is analyzed by significance test; the approximation degree of the fitting model is determined by the multiple correlation coefficient R 2 express; R 2 =1-S SE / S ST ; where Y i is the true value of the test set, y i is the fitting value, n is the number of experiments, S SE is the residual sum of squares, S ST is the total dispersion squared.

5. The method according to claim 1, characterized in that The method further includes: combining the dung beetle optimization algorithm with a non-dominated sorting strategy to obtain an improved dung beetle optimization algorithm; the non-dominated sorting strategy is configured as follows: For each solution, calculate the number of solutions it is dominated by; Solutions that are not dominated by any solution are classified as the first layer; Among the solutions in the first layer, remove all solutions that dominate other solutions and assign the remaining solutions to the second layer; Repeat the above process until all solutions are classified into different non-dominated layers; In the solutions of the same layer, they are sorted by the degree of crowding, which is the density of any solution from other solutions. Output the solutions in all non-dominated layers as the Pareto optimal solution set.

6. The method according to claim 1, characterized in that In the step of analyzing the motor structural parameters that affect the sensitivity of the initial design parameters based on the motor model, Sobol sensitivity analysis is adopted.

7. A motor multi-objective optimization system based on an improved dung beetle optimization algorithm, used in the method according to any one of claims 1 to 6, characterized in that: The system includes: Mathematical model input module, used to establish a mathematical model of motor torque pulsation and dynamic response capability, and determine the initial design parameters affecting torque pulsation and dynamic response capability; A simulation optimization module, used to determine an optimal angle satisfying a first constraint condition according to simulation data of a stator skew slot of the motor, so as to establish a motor model; wherein the first constraint condition is to make the torque pulsation drop greatly while the dynamic response capability drop little; A sensitivity analysis module, for analyzing the motor structural parameters that affect the sensitivity of the initial design parameters based on the motor model, and obtaining the motor structural parameters as the stator outer diameter, permanent magnet thickness, tooth width, and air gap length; The target solution module is used to construct a fitting model of the design target through the response surface method. The design target is to reduce the torque pulsation of the motor and improve the dynamic response capability; A parameter optimization module is used to optimize the motor structure parameters based on the fitting model by using an improved dung beetle optimization algorithm.

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