Switched reluctance motor design optimization method and system based on cooperative game theory

Abstract

The invention discloses a switched reluctance motor design optimization method and system based on a cooperative game theory, which converts a multi-objective optimization problem of switched reluctance motor design into a game problem, avoids subjectivity of manual weight setting, enables an optimization result to be more objective and reasonable, and improves the optimization efficiency. The game model can explicitly describe the cooperation potential or conflict degree between different targets instead of simple weighting or compromise, the mutual influence relation between the design targets is considered through the game model, a more comprehensive optimal solution can be obtained, the optimization result is prevented from deviating from the actual demand, and the optimization efficiency is improved. The strategy space of each gaming party is automatically determined through fuzzy clustering analysis, the optimization efficiency is improved, compared with a traditional multi-objective optimization method, the method is higher in convergence speed and fewer in iteration times, a multi-objective optimization function is represented by a revenue function, the most suitable strategy set is a to-be-solved motor size combination, and the optimization efficiency is improved. The optimized switched reluctance motor can balance the contradiction between the average electromagnetic torque and the electromagnetic vibration while keeping high efficiency.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of motor design, and relates to a switched reluctance motor design optimization method and system based on a cooperative game theory. BACKGROUND

[0002] Switched reluctance motors (SRM) are widely used in industrial fields due to their simple structure, high reliability, and large power density. However, SRM has problems such as large torque ripple, electromagnetic vibration, and noise, which limit its further promotion. Traditional multi-objective design tasks often use a unified objective method to convert multiple objectives into a single objective, and then directly apply mature single-objective optimization algorithms such as gradient descent, genetic algorithm, or particle swarm optimization to solve the problem, which reduces the difficulty of solving. In the unified objective function, the designer's preference for different objectives (such as cost vs. performance) is reflected through weight distribution (such as the weighted sum method), which is suitable for scenarios where the importance of objectives is clear. However, if the weight is set unreasonably, the optimization result may deviate from the actual demand. In addition, the unified objective may hide the competitive relationship between objectives (such as increasing strength and reducing weight), causing the designer to overlook potential trade-offs. SUMMARY

[0003] The purpose of the present application is to solve the problem in the prior art that the potential trade-offs are ignored when optimizing the design of a switched reluctance motor, the weight is set unreasonably, and the optimization result may deviate from the actual demand, and to provide a switched reluctance motor optimization method and system based on a cooperative game theory.

[0004] To achieve the above-mentioned purpose, the following technical solutions are adopted in the present application:

[0005] A switched reluctance motor design optimization method based on a cooperative game theory, comprising the following steps:

[0006] A game model for switched reluctance motor optimization design is constructed, which includes optimization variables and optimization objectives of the switched reluctance motor;

[0007] The optimal solution of each single objective in the optimization objectives is calculated, the influence factor matrix of the optimization variables on the optimization objectives is calculated based on the optimal solution of each single objective, the influence factor matrix is subjected to fuzzy cluster analysis, and the game strategy space of the optimization objectives is determined;

[0008] A game payoff function of the optimization objectives is constructed according to the game strategy space, the game payoff function is solved, and the optimization result is obtained.

[0009] Further improvements of the present application are as follows:

[0010] The game model for switched reluctance motor optimization includes:

[0011] G={βs ,β r ,h s ,D r ;T avg ,|F max |}

[0012] Wherein, the stator pole arc coefficient β s , the rotor pole arc coefficient β r , the stator yoke thickness h s And the rotor outer diameter D r All are optimization variables;T avg Indicates the average electromagnetic torque value, |F max |Indicates the maximum radial force, T avg And |F max | are optimization objectives, as the game model of the game party.

[0013] Also includes the establishment of the game model of the constraint condition, the game model of the constraint condition includes the value range of each variable in the optimization variable.

[0014] The calculation of the influence factor matrix of the optimization variable on the optimization target, comprising:

[0015] Assuming that the influence factor of optimization variable x j On the game party f i (X) is Δ ji , and Δ ji Is solved as follows:

[0016]

[0017] In the formula: n is the number of design variables, the jth design variable x j The influence factor set of all m optimization objectives is Δ j , and the influence factor matrix Δ of the four design variables on the two optimization objectives is obtained as follows:

[0018]

[0019] Wherein, f i (X) is an implicit function, and the influence factor of each optimization variable on the optimization target can be calculated by discretization method.

[0020] The game strategy space of the optimization target is determined, comprising:

[0021] The influence factor matrix Δ is normalized to obtain the normalized influence factor matrix Δ1;

[0022] The Euclidean distance method is used to obtain the fuzzy similarity matrix R=(r ij ) 4×4 ;

[0023] The transmission closure t(R) is obtained by squaring the matrix R, and then the equivalent matrix is obtained

[0024]

[0025] The intercept separation coefficient set K = [1, r1, r2, r3] is obtained by selecting different elements in the equivalent matrix, and each element in the number set K is taken as the intercept separation coefficient k to obtain each element r in the equivalent matrix ij After intercepting, assuming 1>r3>r2>r1, four fuzzy clustering matrices R k are obtained

[0026]

[0027] When the separation coefficient is 1, the four optimization variables are divided into four categories;

[0028] When the separation coefficient is r1, the four optimization variables are all in one category;

[0029] When the separation coefficient is r2, the four optimization variables are divided into two categories, where the first optimization variable is in one category, and the remaining three optimization variables are in another category;

[0030] When the separation coefficient is r3, the four optimization variables are divided into three categories, where the first optimization variable is in one category, the second optimization variable is in two categories, and the remaining two optimization variables are in three categories;

[0031] When the separation coefficient is r2, the strategy set of game player 1 is {β s}, and the strategy set of game player 2 is {β r ,h s ,D r}.

[0032] The game payoff function of the optimization objective constructed according to the game strategy space includes:

[0033] The cooperative payoff function is defined as:

[0034]

[0035] In the formula: u i is the payoff function of each game player; m is the number of game players; w ii is the weight coefficient, w ii is related to the degree of competition of the game players, and the greater the value represents the more intense the competition; w ij represents the cooperative relationship; T i * represents the optimal solution set under single-objective optimization;

[0036] The optimization variable is lower bound to the initial policy set T. (0) ={T1 (0) T2 (0)}, T1 (0) T2 (0) These are the initial policy sets for T1 and T2, respectively, where T1 (0) ={β s (0)}, T2 (0) ={β r (0) ,h s (0) D r (0)};T1 (0) T2 (0) The complements are respectively

[0037] When w ii =1 and w ij When = 0, the total payoff u under the completely non-cooperative game model is:

[0038]

[0039] When w ii =0 and w ij When = 1, the total payoff u under the fully cooperative game model is:

[0040]

[0041] A switched reluctance motor is obtained by the optimization design method described in any one of the present invention.

[0042] A design optimization system for switched reluctance motors based on cooperative game theory includes:

[0043] The game model construction module is used to construct a game model for the optimization of a switched reluctance motor, wherein the game model includes the optimization variables and optimization objectives of the switched reluctance motor;

[0044] The game strategy space calculation module is used to calculate the optimal solution of each individual objective in the optimization objective, calculate the influence factor matrix of the optimization variables on the optimization objective based on the optimal solution of each individual objective, perform fuzzy clustering analysis on the influence factor matrix, and determine the game strategy space of the optimization objective.

[0045] The optimization module is used to construct the game payoff function of the optimization objective based on the game strategy space, solve the game payoff function, and obtain the optimization result.

[0046] A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of any of the present application when executing the computer program.

[0047] A computer readable storage medium storing a computer program, the computer program implementing the steps of the method of any of the present application when executed by a processor.

[0048] Compared with the prior art, the present application has the following beneficial effects:

[0049] The application discloses a switched reluctance motor optimization design method based on a cooperative game theory, converts a multi-objective optimization problem of a switched reluctance motor into a game problem, avoids subjectivity of artificial weight setting, makes an optimization result more objective and reasonable, and explicitly depicts cooperation potential or conflict degree between different objectives in a game model instead of simple weighting or compromise. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation to the scope, and other related drawings can also be obtained by those skilled in the art without creative labor on the premise of not paying.

[0051] Figure 1 A flow chart of an optimization solving algorithm used for a game benefit function of the present application;

[0052] Figure 2 A 2D plan view of an initial design of a switched reluctance motor;

[0053] Figure 3 A curve graph of an average electromagnetic torque obtained by an initial design;

[0054] Figure 4 A radial electromagnetic force waveform graph obtained by an initial design;

[0055] Figure 5To unify the solution process chart of particle swarm optimization algorithm of target method;

[0056] Figure 6 To solve the process of the payoff function of complete non-cooperative game;

[0057] Figure 7 To solve the process of the payoff function of complete cooperative game;

[0058] Figure 8 To obtain the two-dimensional design graph of motor under the complete non-cooperative game model;

[0059] Figure 9 To obtain the motor electromagnetic torque waveform graph under the complete non-cooperative game model;

[0060] Figure 10 To obtain the motor stator tooth radial electromagnetic force waveform graph under the complete non-cooperative game model;

[0061] Figure 11 To obtain the two-dimensional design graph of motor under the complete cooperative game model;

[0062] Figure 12 To obtain the motor electromagnetic torque waveform graph under the complete cooperative game model;

[0063] Figure 13 To obtain the motor stator tooth radial electromagnetic force waveform graph under the complete cooperative game model. DETAILED DESCRIPTION

[0064] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0065] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative work fall within the scope of protection of the present application.

[0066] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0067] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner" and the like indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, or the orientation or position relationship when the product of the present application is usually placed, which is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", etc. are only used for differentiation and cannot be understood as indicating or implying relative importance.

[0068] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0069] In the description of the embodiments of the present application, it should be noted that unless otherwise explicitly specified and limited, if the terms "arrangement", "installation", "connection", "connection" appear, they should be understood in a broad sense. For example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0070] The present application will be described in further detail below with reference to the accompanying drawings:

[0071] Referring to Figures 1 to 13 The embodiment of the present application discloses a switched reluctance motor design optimization method based on cooperative game theory, which converts the multi-objective motor structure design problem into a game problem, and provides a new solution for complex design optimization. The game model can explicitly depict the cooperation potential or conflict degree between different objectives (such as average electromagnetic torque and radial electromagnetic force), rather than simply weighting or compromising. The multi-objective optimization function is represented by a profit function, and the most suitable strategy set is the motor size combination to be solved.

[0072] The method comprises the following steps:

[0073] Step 1: establishing a motor multi-objective optimization model, determining optimization variables, optimization objectives and constraint conditions;

[0074] Specifically, the method comprises the following steps:

[0075] Determine the optimization variables: stator pole arc coefficient β s , rotor pole arc coefficient β r , stator yoke thickness h s , rotor outer diameter D r;

[0076] Objective: Maximize average electromagnetic torque T avg and minimize maximum absolute value of radial force |F max |.

[0077] Constraints: 0.2≤β s ≤0.6, 0.2≤β r ≤0.4, 15≤h s ≤40, 173≤D r ≤176.

[0078] The mathematical model of the multi-objective optimization problem thus established is shown in equation (1):

[0079]

[0080] Here, Ansys maxwell will be used to model the motor in two dimensions, and T avg and |F max | will be obtained through finite element analysis.

[0081] Step 2: Convert the objective optimization problem into a game problem and establish a game model.

[0082] The method of converting the multi-objective optimization problem into a game problem is as follows:

[0083] 1) m optimization objectives correspond to m game players;

[0084] 2) Each objective function f1(X), f2(X), …, f m (X) corresponds to a payoff function u1, u2, …, u m ;

[0085] 3) Design variables x1, x2, …, x n correspond to strategy spaces T1, T1, …, T m ;

[0086] 4) The value range of the design variables corresponds to the constraints in the game.

[0087] Accordingly, the average electromagnetic torque value T avg and the maximum value of the radial force F max constitute the game players, and the corresponding game model is as follows:

[0088]

[0089] In the formula: design variables β s , β r , h s and D r constitute the strategy space, and the objective function Tavg and |F max | is the payoff function, β s , β r , h s and D r are the ranges of the variables, which correspond to the constraints of the game.

[0090] β s , β r , h s , D r constitute the strategy space of the entire game problem, but the four variables and their different combinations have different effects on the game parties, and it is necessary to find the strategy that is strongly related to the game parties to constitute the strategy space of each game party. Specifically, it includes solving single-objective optimal solution, calculating influence factor, influence factor fuzzy clustering, and finally determining the strategy space.

[0091] Step 3: Solve the single-objective optimal solution;

[0092] Using Matlab and Ansys Maxwell joint simulation, the particle swarm algorithm is used to solve the combination of design variables that maximize T avg and minimize |F max . The solution flowchart of the particle swarm optimization algorithm is shown in the attached Figure 1 .

[0093] Step 4: Calculate the influence factor matrix of each design variable (i.e. optimization variable) on the optimization objective;

[0094] Assuming that the influence factor of design variable x j on game party f i (X) is Δ ji , the partial derivative of formula (3) is used to solve Δ ji :

[0095]

[0096] In the formula: n is the number of design variables, and the influence factor set of the jth design variable x j on all m optimization objectives is Δ j , and the influence factor matrix Δ of the four design variables on the two optimization objectives is:

[0097]

[0098] Here f i (X) is a hidden function, and the influence factor of each variable on the optimization objective can be calculated by discretization method to obtain formula (4).

[0099] Step 5: Determine the strategy space of each game party through fuzzy clustering analysis;

[0100] First, the influence factor matrix is standardized, and the influence factor matrix Δ is standardized by using the translation-range change method, to obtain the normalized influence factor matrix Δ1, as shown in formula (5):

[0101]

[0102] Then, the fuzzy similarity matrix R=(r ij ) 4×4 Formula (7).

[0103]

[0104] Wherein, c is the correlation coefficient, the greater the value, the more similar, here it is taken as 0.8:

[0105]

[0106] Next, the transmission closure t(R) is obtained by squaring R, and the equivalence matrix is obtained As shown in formula (8):

[0107]

[0108] Take the different elements in the equivalence matrix to form the intercept separation coefficient set K=[1,r1,r2,r3], and use each element in K as the intercept separation coefficient k to intercept each element r ij In the matrix k .

[0109]

[0110] Further, the determination of the strategy space of the game party:

[0111] The strategy space of the game party is determined by the intercept separation coefficient {1, r1, r2, r3}.

[0112] For the above four fuzzy clustering matrices R1, R2, R3, R4:

[0113] When the separation coefficient is 1, the four design variables are divided into four categories;

[0114] When the separation coefficient is r1, the four design variables are all in one category;

[0115]

[0116] ​When the separation coefficient is r2, the four design variables are divided into two categories: the first design variable is in one category, and the other three variables are in another category;

[0117] When the separation coefficient is r3, the four design variables are divided into three categories: the first variable is in category 1, the second variable is in category 2, and the other two variables are in category 3.

[0118] When the separation coefficient is r2, the strategy set of game player 1 is {β s}, and the strategy set of game player 2 is {β r ,h s ,D r}.

[0119] Step 6: Establish the game payoff function under complete non-cooperation and complete cooperation;

[0120] Step 6.1: Define the cooperative game payoff function:

[0121] According to the cooperative competition strategy, the payoff function of game player i is:

[0122]

[0123] In the formula: u i is the payoff function of each game player; m is the number of game players; w ii is the weight coefficient, which is related to the degree of competition of the game players. The greater the value, the more intense the competition. w ij has the opposite meaning, representing a cooperative relationship. T i * represents the optimal solution set under single-objective optimization.

[0124] The lower limit of the design variable is defined as the initial strategy set T (0) ={T1 (0) ,T2 (0)}, T1 (0) , T2 (0) are the initial strategy sets of T1 and T2, respectively, where T1 (0) ={β s (0)}, T2 (0) ={β r (0) ,h s (0) ,D r (0)}; The complements of T1 and T2 are (0) (0)

[0125] When w ii =1 and w ij ​​When = 0, the total payoff u under the completely non-cooperative game model is:

[0126]

[0127] When w ii =0 and w ij When = 1, the total payoff u under the fully cooperative game model is:

[0128]

[0129] In this step, the separation coefficient k for fuzzy clustering analysis is set to 0.26.

[0130] Step 7: Solve the game payoff function using the particle swarm optimization algorithm;

[0131] For the established multi-objective optimization design game model of motor, the optimal solution for the payoff function u can still be found using the particle swarm optimization algorithm. This algorithm has the advantages of fewer parameters, easy convergence and fast computation, and is suitable for global optimization problems and can meet the needs of practical engineering applications.

[0132] The formula for updating the d-th dimension velocity of particle i is:

[0133]

[0134] The formula for updating the d-th dimension position of particle i is:

[0135]

[0136] In the formula: q i c1 and c2 are weighting factors; c1 and c2 are acceleration factors; p id For the local optimum position; g d It is the global optimal solution.

[0137] Furthermore, in this step, the weighting factor q i =0.8, acceleration factor c1=c2=1.5.

[0138] Step 8: Verify the optimization results using finite element analysis.

[0139] The optimized motor's average electromagnetic torque, radial electromagnetic force, output power, and efficiency were verified through finite element analysis.

[0140] This embodiment also discloses a switched reluctance motor based on cooperative game theory, which is optimized and designed using the method described in this embodiment.

[0141] Its optimization parameter is β s =0.2, β r =0.24, h s =40mm, Dr = 174.74 mm.

[0142] Example 1

[0143] A 75kW three-phase 12 / 8-pole switched reluctance motor (SRM) was designed with rated DC voltage of 512V, rated speed of 6000rpm and rated efficiency of 90%. The following steps were implemented:

[0144] Step 1: Establishing multi-objective optimization function

[0145] According to the conventional motor design method, the main size parameters of the initial motor model were calculated, and the results are shown in Table 1. According to the results, the initial design 2D plan of the switched reluctance motor was obtained, as shown in FIG. 1. When the speed is 6000rpm, the efficiency is 90%, the rated output torque is 120N.m, and the output power is 75.42kW, which basically meets the design requirements. At this time, the motor torque ripple curve is shown in FIG. 2, the average electromagnetic torque is 124.41N.m, the torque ripple is 2.0603, the maximum radial electromagnetic force amplitude on the stator salient pole surface is 1291.87N, and the radial force waveform is shown in FIG. 3. Figure 2 Figure 3 Figure 4

[0146] Table 1: Geometric size values of the initial design SRM

[0147]

[0148] Maximum radial force value of the motor |F max | is similar to the order of magnitude of the average electromagnetic torque T avg , and the weight coefficients are each taken as 0.5, and the unified multi-objective function D is obtained as follows:

[0149] min D = -0.5T avg + 0.5|F max |

[0150] The particle swarm algorithm was used for solving, and the optimal motor design size obtained was: {0.6, 0.24, 40, 176}, at this time the optimal function value was 418.5035, the iteration number was 21, and the process is shown in FIG. 4. Under this size, the output power is 91.24kW, the rated output torque is 145N.m, the efficiency is 92.78%, the average electromagnetic torque is 165.9N.m, the electromagnetic radial force is 985.48N, and the torque ripple is 1.7465. Figure 5

[0151] Step 2: Determining the game model and optimization variables

[0152] The game model was constructed as follows:

[0153] G = {β​​​​s ,β r ,h s ,D r ;T avg ,|F max |}

[0154] The value range of the optimization variable is as follows:

[0155] 0.2≤β s ≤0.6、0.2≤β r ≤0.4、15≤h s ≤40、173≤D r ≤176。

[0156] The strategy space of each game party is further determined by a fuzzy clustering method.

[0157] Step 3: Solving each single-objective optimal solution

[0158] Maximize T avg The corresponding solution is: T avg = 311.0466 N.m;

[0159] Minimize |F max | The corresponding solution is: |F max | = 973.50 N;

[0160] Step 4: Calculate the influence factor matrix Δ:

[0161]

[0162] Step 5: Fuzzy clustering analysis

[0163] Calculate the normalized influence factor matrix Δ1

[0164]

[0165] When the correlation coefficient is 0.8, the fuzzy similarity matrix R is:

[0166]

[0167] The equivalence matrix

[0168]

[0169] When the separation coefficient is 1, 0.97, 0.26, and 0.22, respectively, the fuzzy clustering matrices obtained are:

[0170]

[0171] Step 6: Determining the strategy space of the players

[0172] When the classification coefficient is 0.22, all classes are classified as homologous {β}. s ,β r ,h s D r When the coefficient is 0.26, it can be divided into two categories: {β} s ,β r}, {h s D r When the coefficient is 0.96, it is divided into three categories: {β} s}, {β r}, {h s D r When the coefficient is 1, it is divided into four categories. Since there are two players, when the classification coefficient is 0.26, the strategy set T1 of player 1 is {β}. s ,β r}, the strategy set T2 of player 2 is {h s D r}

[0173] Step 7: Establish the game payoff function

[0174] Step 7.1, the payoff function for a completely non-cooperative game is as follows:

[0175]

[0176] This payoff function indicates that: within the set of feasible strategies for both parties {β} s ,β r ,h s D r In this context, the average electromagnetic torque should not be less than the result on the strategy set that includes its own optimal design variables, and the larger the better; the maximum electromagnetic radial force amplitude on the stator teeth should not be greater than the result on the strategy set that is most advantageous to itself, and the smaller the better. When the payoff function reaches its maximum value, it means that on a certain strategy set, both the average electromagnetic torque and the maximum electromagnetic radial force amplitude have achieved relatively good values, reaching a kind of equilibrium.

[0177] Step 7.2, the payoff function for the fully cooperative game is as follows:

[0178]

[0179] In the formula, The target value of the average electromagnetic torque is defined on a strategy set comprising the optimal design variables of the opposing force (maximum electromagnetic radial force amplitude). In this case, the average electromagnetic torque often yields a range value, which is the point of divergence. Similarly, The point of maximum electromagnetic radial force amplitude. The payoff function indicates that the equilibrium solution (Nash bargaining solution) after cooperation game is far away from the point of maximum electromagnetic radial force amplitude, and the result of both sides win. In combination with the design goal of this paper, the average electromagnetic torque and the maximum radial electromagnetic force amplitude are far away from the point of maximum electromagnetic radial force amplitude, and the maximum value of the payoff function is obtained when a balance is reached.

[0180] At this time, the maximum value of the payoff function obtained by the particle swarm optimization algorithm is 2.1993, and the strategy set is {0.6, 0.4, 40, 176}.

[0181] Step 8: Solve the game payoff function by using the particle swarm optimization algorithm

[0182] The maximum value of the completely non-cooperative game payoff function obtained by the particle swarm optimization algorithm is 2.006, and the corresponding strategy set is: {0.2, 0.24, 40, 174.74}, and the iteration number is 15 times. The iteration calculation process is shown in the attached Figure 6

[0183] The maximum value of the completely cooperative game payoff function obtained by the particle swarm optimization algorithm is 2.1993, and the corresponding strategy set is: {0.6, 0.4, 40, 176}, and the iteration number is 19 times. The iteration calculation process is shown in the attached Figure 7

[0184] Step 9: Finite element verification

[0185] The solution results {0.2, 0.24, 40, 174.74} of the completely non-cooperative game model are brought into the ansys Maxwell software, and the two-dimensional plane diagram of the motor obtained is shown in the attached Figure 8 At this time, the rated output torque is 124N.m, the output power is 77.62kW, the efficiency is 92.54%, the average electromagnetic torque is 125.46N.m, the torque ripple is 2.6779, as shown in the attached Figure 9 The maximum electromagnetic radial force amplitude of the stator tooth surface is 688N, as shown in the attached Figure 10

[0186] The solution results {0.6, 0.4, 40, 176} of the completely cooperative game model are brought into the software, and the two-dimensional plane diagram of the motor obtained is shown in the attached Figure 11 The rated output torque is 210N.m, the output power is 132kW, the efficiency is 90.61%, the average electromagnetic torque is 269N.m, the torque ripple is 1.7471, as shown in the attached Figure 12 The maximum electromagnetic radial force amplitude of the stator tooth is 1964N, as shown in the attached Figure 13

[0187] ​​​​Compared with the above results, under the premise of meeting the design index, comprehensive consideration of the average electromagnetic torque and the maximum electromagnetic radial force amplitude can show that the solution result of the completely non-cooperative game model is relatively appropriate, at this time, the efficiency of the motor is the highest, the maximum electromagnetic radial force amplitude is the smallest while having a higher electromagnetic torque, so that the electromagnetic vibration of the motor is smaller.

[0188] The embodiment also discloses a switched reluctance motor optimization design system based on a cooperative game theory, comprising:

[0189] A game model construction module is used for constructing a game model for optimization of the switched reluctance motor, and the game model comprises optimization variables and an optimization target of the switched reluctance motor.

[0190] A game strategy space calculation module is used for calculating optimal solutions of each single target in the optimization target, calculating an influence factor matrix of the optimization variables on the optimization target based on the optimal solutions of each single target, and performing fuzzy cluster analysis on the influence factor matrix to determine a game strategy space of the optimization target.

[0191] An optimization module is used for constructing a game benefit function of the optimization target according to the game strategy space, solving the game benefit function, and obtaining an optimization result.

[0192] The optimization method disclosed by the embodiment converts a multi-target optimization problem into a game problem through the game theory, avoids subjectivity of artificial setting of weights, and makes the optimization result more objective and reasonable. The cooperative game model is adopted, the mutual influence relationship between the design targets is considered, and a more comprehensive optimization solution can be obtained. The strategy space of each game party is automatically determined through fuzzy cluster analysis, and the optimization efficiency is improved. Compared with a traditional multi-target optimization method, the method has a faster convergence speed and a smaller iteration number. The optimized switched reluctance motor can balance the contradiction between the average electromagnetic torque and the electromagnetic vibration while maintaining high efficiency, and can also maintain high electromagnetic torque while minimizing the electromagnetic vibration.

[0193] An embodiment of the terminal device provided by the embodiment. The terminal device of the embodiment includes a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor implements the steps in each of the above method embodiments when executing the computer program. Alternatively, the processor implements the functions of each module / unit in each of the above device embodiments when executing the computer program.

[0194] The computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application.

[0195] The terminal device can be a desktop computer, a notebook computer, a cloud server, and the like, which has a strong computing capability. The terminal device can include, but is not limited to, a processor and a memory.

[0196] The processor is preferably a multi-core high-speed central processing unit (CPU).

[0197] The memory can be used to store the computer program and / or the module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or the module stored in the memory, and calling the data stored in the memory.

[0198] The modules / units integrated in the terminal device, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the computer-readable medium can include or exclude contents according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include an electrical carrier signal and a telecommunication signal.

[0199] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A switched reluctance motor design optimization method based on cooperative game theory, characterized in that, The method comprises the following steps: constructing a game model for the optimal design of the switched reluctance motor, the game model comprising optimization variables and optimization objectives of the switched reluctance motor; calculating optimal solutions of each single objective in the optimization objectives, calculating an influence factor matrix of the optimization variables on the optimization objectives based on the optimal solutions of each single objective, performing fuzzy clustering analysis on the influence factor matrix, and determining a game strategy space of the optimization objectives; constructing a game payoff function of the optimization objectives according to the game strategy space, solving the game payoff function, and obtaining an optimization result.

2. The method of claim 1, wherein, The game model for the optimal design of the switched reluctance motor comprises: G = {β s ,β r ,h s ,D r ; T avg , |F max |} Wherein, the stator pole arc coefficient β s , the rotor pole arc coefficient β r , the stator yoke thickness h s And the rotor outer diameter D r All are optimization variables; T avg Indicates the average electromagnetic torque value, |F max |Indicates the maximum radial force, T avg And |F max |As the optimization goal, as the game model of the game party.

3. The method of claim 2, wherein, The game model further comprises constraint conditions for establishing the game model, the constraint conditions comprising a value range of each variable in the optimization variables.

4. The method of claim 1, wherein, The influence factor matrix of the optimization variables on the optimization objectives comprises: Assume optimization variable x j The impact factor of game f i (X) is Δ ji , Δ ji is solved as: wherein: n is the number of design variables, defining the jth design variable x j The set of influence factors for all m optimization objectives is Δ j The influence factor matrix Δ for the four design variables on the two optimization objectives is obtained as: where f i (X) is an implicit function, and the influence factor of each optimization variable on the optimization objective can be calculated by a discretization method.

5. The method of claim 4, wherein, The game strategy space of the optimization objectives comprises: The influence factor matrix Δ is normalized to obtain a normalized influence factor matrix Δ1. The fuzzy similarity matrix R=(r ij ) 4×4 ; The transitive closure t(R) is obtained from the square of the matrix R, and then the equivalent matrix is obtained In the equivalent matrix, the different elements are selected to obtain the intercepting separation coefficient set K = [1, r1, r2, r3], and each element in the number set K is taken as the intercepting separation coefficient k to each element r in the equivalent matrix ij The intercepting is performed, assuming that 1 > r3 > r2 > r1, to obtain four fuzzy clustering matrices R k :​ When the separation coefficient is 1, the four optimization variables are divided into four categories. When the separation coefficient is r1, the four optimization variables are all in one category. When the separation coefficient is r2, the four optimization variables are divided into two categories, wherein the first optimization variable is in one category and the remaining three optimization variables are in another category. When the separation coefficient is r3, the four optimization variables are divided into three categories, wherein the first optimization variable is in one category, the second optimization variable is in two categories, and the remaining two optimization variables are in three categories. When the separation coefficient is r2, the strategy set of game player 1 is {β s}, and the strategy set of game player 2 is {β r ,h s ,D r}.

6. The method of claim 5, wherein the method is characterized by: The game payoff function of the optimization objectives according to the game strategy space comprises: a cooperative payoff function is defined as follows: wherein: u i is the payoff function of each player; m is the number of players; w ii is the weight coefficient, w ii is related to the degree of competition of the players, and the greater the value indicates the more intense the competition; w ij represents the cooperative relationship; T i * represents the optimal solution set under single-objective optimization; lower bounding the optimization variables to the initial strategy set T1 (0) , the initial strategy set of T1, T2, respectively, where T1 (0) , the complement of T1, T2, respectively, is When w ii = 1 and w ij = 0, the total revenue u under the complete non-cooperative game model is obtained as follows: When w ii = 0 and w ij = 1, the total revenue u under the complete cooperation game model is obtained as follows:

7. A switched reluctance motor characterised in that, obtained by the optimal design method of any one of claims 1-6.

8. A switched reluctance motor design optimization system based on cooperative game theory, characterized in that, The method comprises: a game model construction module configured to construct a game model for the optimal design of the switched reluctance motor, the game model comprising optimization variables and optimization objectives of the switched reluctance motor; a game strategy space calculation module configured to calculate optimal solutions of each single objective in the optimization objectives, calculate an influence factor matrix of the optimization variables on the optimization objectives based on the optimal solutions of each single objective, perform fuzzy clustering analysis on the influence factor matrix, and determine a game strategy space of the optimization objectives; an optimization module configured to construct a game payoff function of the optimization objectives according to the game strategy space, solve the game payoff function, and obtain an optimization result.

9. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1-6 when executing the computer program.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1-6.

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