A brushless direct current motor optimization method based on multi-objective grey wolf algorithm

Abstract

The application discloses a brushless direct current motor optimization method based on a multi-objective grey wolf algorithm. The method comprises the following steps: establishing an initial finite element model of a brushless direct current motor, and establishing a target function meeting multi-objective optimization conditions and optimization constraint conditions of design parameters of the brushless direct current motor; according to the target function, using the multi-objective grey wolf algorithm to obtain an optimal solution of the target function as optimal design parameters of the brushless direct current motor; optimizing the initial finite element model according to the optimal design parameters to obtain a finite element model of the brushless direct current motor after optimization; and obtaining an optimized brushless direct current motor according to the finite element model of the brushless direct current motor after optimization, so as to realize optimization of the brushless direct current motor. The method selects the multi-objective grey wolf algorithm (MOGWO) to be applied to the brushless direct current motor design problem, so that the solving precision and high convergence speed can be effectively improved.

Description

Technical Field

[0001] This invention relates to an optimization method for brushless DC motors, specifically an optimization method for brushless DC motors based on a multi-objective gray wolf algorithm. Background Technology

[0002] Brushless DC motors are widely used in various industries, and proper design is required for practical applications to improve motor efficiency. However, the optimization problem of brushless DC motors is a highly nonlinear problem, typically solved using finite element analysis (FEA). But relying solely on this method is not only time-consuming and labor-intensive, but also produces results that are not always accurate. Summary of the Invention

[0003] To address the problems existing in the background technology, this invention provides a brushless DC motor optimization method based on the multi-objective gray wolf algorithm. The Gray Wolf Optimization Algorithm (MOGWO) is a novel metaheuristic algorithm inspired by the hunting behavior of gray wolves. The pack consists of four wolves: α, β, θ, and ω. The wolves in a pack are ranked according to their leadership qualities. The α wolf dominates all wolves in the pack and acts as the decision-maker; the other wolves have lower dominance and are called followers. Based on dominance levels, the order of the wolves is α, β, θ, and ω. Applying the multi-objective gray wolf algorithm MOGWO to the brushless DC motor design problem can effectively improve the solution accuracy and achieve high convergence speed.

[0004] The technical solution adopted in this invention is:

[0005] The brushless DC motor optimization method of the present invention includes the following steps:

[0006] Step 1: Based on the preset design parameters of the brushless DC motor, establish the initial finite element model of the brushless DC motor using Ansys Maxwell, i.e., establish the geometric model; establish the objective function that satisfies the multi-objective optimization conditions and optimization constraints of the design parameters of the brushless DC motor, i.e., determine the motor objective function, optimization parameters and constraints.

[0007] Step 2: Based on the objective function, use the multi-objective gray wolf algorithm to obtain the optimal solution of the objective function as the optimal design parameters of the brushless DC motor. Optimize the initial finite element model based on the optimal design parameters to obtain the optimized finite element model of the brushless DC motor. Obtain the optimized brushless DC motor based on the optimized finite element model of the brushless DC motor, thus realizing the optimization of the brushless DC motor.

[0008] In step 1, the objective function that satisfies the multi-objective optimization conditions and optimization constraints for the design parameters of the brushless DC motor is established as follows:

[0009]

[0010] Where f1 represents the efficiency objective function; f2 represents the quality objective function; η() max Denotes the efficiency-related function; x represents the optimization parameter matrix; M() min D represents the quality correlation function; s D represents the stator outer diameter of a brushless DC motor. r D represents the outer diameter of the rotor of a brushless DC motor. d δ represents the diameter of the winding coil of the brushless DC motor; I represents the air gap length of the brushless DC motor; max Indicates the maximum current of the brushless DC motor; M tot T represents the total mass of the brushless DC motor. a This represents the temperature of the brushless DC motor. The optimization objective is to achieve maximum efficiency and minimum mass for the brushless DC motor under the optimal parameter matrix.

[0011] In step 1, the multi-objective optimization conditions for the design parameters of the brushless DC motor specifically include the main dimensional conditions, rotor structural dimensional conditions, and stator structural dimensional conditions of the brushless DC motor. Based on the multi-objective optimization conditions for the design parameters, the stator tooth width, stator yoke width, and stator outer diameter of the brushless DC motor can be determined. Finally, based on the optimal design parameters and the determined stator tooth width, stator yoke width, and stator outer diameter of the brushless DC motor, the initial finite element model is optimized to obtain the optimized finite element model of the brushless DC motor.

[0012] The main dimensional requirements for the brushless DC motor are the stator inner diameter and stacking length, as detailed below:

[0013]

[0014]

[0015] P N =2EI

[0016] Among them, L stk λ represents the stacking length of the brushless DC motor; λ represents the ratio of the core length to the pole pitch of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; p represents the number of pole pairs of the brushless DC motor; P N This represents the electromagnetic power of a brushless DC motor; α′ p K represents the calculated pole arc coefficient of a brushless DC motor. Nm K represents the waveform coefficient of the air gap magnetic field in a brushless DC motor. dp1 A1 represents the fundamental frequency coefficient of the stator winding of the brushless DC motor; B represents the electrical load of the brushless DC motor; δ This represents the maximum value of the air gap magnetic flux density of a brushless DC motor; n NThe motor's rated speed is represented by E; the back electromotive force (EMF) of the brushless DC motor is represented by E; and the current of the brushless DC motor is represented by I. For a three-phase square wave current brushless DC motor, the electromagnetic power is constant at a constant speed.

[0017] The rotor structure dimensional conditions include the air gap length condition and the permanent magnet dimensional condition of the brushless DC motor, as detailed below:

[0018] a) Air gap length requirements for brushless DC motors:

[0019]

[0020] Where δ represents the air gap length of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; L stk This indicates the stacking length of the brushless DC motors. The air gap length of the brushless DC motor can be calculated with reference to that of the three-phase asynchronous induction motor. Since the air gap length of the brushless DC motor is generally larger than that of the three-phase asynchronous induction motor, a certain value should be appropriately added to the calculated air gap length to obtain the air gap length of the brushless DC motor.

[0021] b) Permanent magnet size requirements for brushless DC motors:

[0022]

[0023] A m =W PM l m

[0024] hP M =k s K α b m0 S[(1-b m0 )σ0]

[0025] Among them, B δ This represents the maximum value of the air gap magnetic flux density of a brushless DC motor; α′ p The value represents the calculated pole arc coefficient of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; L represents the calculated pole arc coefficient of the brushless DC motor. stk represents the stacking length of the brushless DC motor; p represents the number of pole pairs of the brushless DC motor; b m0 Indicates the no-load operating point of the permanent magnet in a brushless DC motor; B r A represents the residual magnetic flux density of the permanent magnet in a brushless DC motor; m σ0 represents the cross-sectional area of ​​the permanent magnet in a brushless DC motor; σ0 represents the no-load leakage flux coefficient of the brushless DC motor; l m This indicates the axial length of the permanent magnet in a brushless DC motor; w PM h represents the width of the permanent magnet in a brushless DC motor. PMK represents the thickness of the permanent magnet in a brushless DC motor. s K represents the saturation coefficient of a brushless DC motor. α This represents the rotor structure coefficient of a brushless DC motor.

[0026] The stator structure dimensional conditions include the effective area condition of the stator slots and the slot fill factor condition of the stator slots, as detailed below:

[0027] a) Stator slot effective area condition:

[0028]

[0029] Among them, A ef A represents the effective area of ​​the stator slots in a brushless DC motor. s A represents the total area within the stator slots of a brushless DC motor; i represents the internal insulation area of ​​the stator of the brushless DC motor; r represents the radius of the stator slot tail of the brushless DC motor; b1 represents the width of the stator slot head of the brushless DC motor; h 12 C represents the slot depth of the stator slots in a brushless DC motor. i This indicates the stator slot insulation thickness of a brushless DC motor;

[0030] The motor designed in this invention is a small motor, and the stator slots used are pear-shaped slots.

[0031] b) Slot fill factor condition for stator slots:

[0032] K f =N s N b (D d +h d ) 2 / A ef

[0033] N s =2maN / Q

[0034] N=A1πD / (2mI N )

[0035] J d =I N / [aπN b (D d / 2) 2 ]

[0036] Among them, K f N represents the slot fill factor of the stator slots in a brushless DC motor. s N represents the number of conductors per slot in the stator of a brushless DC motor. b D represents the number of parallel windings of each coil conductor in the stator slot of a brushless DC motor;d This indicates the diameter of the enameled wire in the stator slots of a brushless DC motor; h d The values ​​represent the thickness of the insulation varnish on both sides of the stator slot of the brushless DC motor; m represents the number of phases of the stator slot; a represents the number of parallel branches per phase of the stator slot; N represents the number of series turns per phase of the stator slot; Q represents the number of stator slots; A1 represents the electrical load of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; I N J represents the phase current of the stator slots of a brushless DC motor; d This indicates the electrical density value of the stator slots in a brushless DC motor.

[0037] Based on the electrical load A1 of the brushless DC motor determined in the main size conditions of the brushless DC motor, the number of turns in series per phase of the winding can be determined, the number of coils per slot can be determined, the coil diameter can be determined, and finally the slot fill factor of the stator slot can be determined.

[0038] The beneficial effects of this invention are:

[0039] This invention selects the multi-objective gray wolf algorithm (MOGWO) for the design problem of brushless DC motors, which can effectively improve the solution accuracy and convergence speed. Attached Figure Description

[0040] Figure 1 This is a flowchart of the method of the present invention;

[0041] Figure 2 This is a schematic diagram of the shape of the motor stator slot of the present invention. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] like Figure 1 As shown, the brushless DC motor optimization method of the present invention includes the following steps:

[0044] Step 1: Based on the preset design parameters of the brushless DC motor, establish the initial finite element model of the brushless DC motor using Ansys Maxwell, i.e., establish the geometric model; establish the objective function that satisfies the multi-objective optimization conditions and optimization constraints of the design parameters of the brushless DC motor, i.e., determine the motor objective function, optimization parameters and constraints.

[0045] In step 1, the objective function that satisfies the multi-objective optimization conditions and optimization constraints for the design parameters of the brushless DC motor is established as follows:

[0046]

[0047] Where f1 represents the efficiency objective function; f2 represents the quality objective function; η() max Denotes the efficiency-related function; x represents the optimization parameter matrix; M() min D represents the quality correlation function; s D represents the stator outer diameter of a brushless DC motor. r D represents the outer diameter of the rotor of a brushless DC motor. d δ represents the diameter of the winding coil of the brushless DC motor; I represents the air gap length of the brushless DC motor; max Indicates the maximum current of the brushless DC motor; M tot T represents the total mass of the brushless DC motor. a This represents the temperature of the brushless DC motor. The optimization objective is to achieve maximum efficiency and minimum mass for the brushless DC motor under the optimal parameter matrix.

[0048] In step 1, the multi-objective optimization conditions for the design parameters of the brushless DC motor specifically include the main dimensional conditions, rotor structural dimensional conditions, and stator structural dimensional conditions of the brushless DC motor. Based on the multi-objective optimization conditions for the design parameters, the stator tooth width, stator yoke width, and stator outer diameter of the brushless DC motor can be determined. Finally, based on the optimal design parameters and the determined stator tooth width, stator yoke width, and stator outer diameter of the brushless DC motor, the initial finite element model is optimized to obtain the optimized finite element model of the brushless DC motor.

[0049] The main dimensional requirements for brushless DC motors are the stator inner diameter and stacking length, as detailed below:

[0050]

[0051]

[0052] P N =2EI

[0053] Among them, L stk λ represents the stacking length of the brushless DC motor; λ represents the ratio of the core length to the pole pitch of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; p represents the number of pole pairs of the brushless DC motor; PN represents the electromagnetic power of the brushless DC motor; α′ p This represents the calculated pole arc coefficient of a brushless DC motor. If the motor core is unsaturated, it is generally taken as 0.637; if the motor core is saturated, it is generally 0.66–0.71. K Nm This represents the waveform coefficient of the air gap magnetic field in a brushless DC motor. If the motor core is unsaturated, it is generally taken as 1.11; if the motor core is saturated, the value decreases as the degree of saturation increases. K dp1This represents the fundamental frequency coefficient of the stator winding of the brushless DC motor. In the initial design phase, if a double-layer short-pitch winding is selected, it is generally taken as 0.92; if a single-layer winding is selected, it is generally taken as 0.96. A1 represents the electrical load of the brushless DC motor; for small motors, the value ranges from 15000 to 50000 A / m. B δ This represents the maximum value of the air gap magnetic flux density of a brushless DC motor, ranging from 0.5 to 0.8 T; n N The motor's rated speed is represented by E; the back electromotive force (EMF) of the brushless DC motor is represented by E; and the current of the brushless DC motor is represented by I. For a three-phase square wave current brushless DC motor, the electromagnetic power is constant at a constant speed.

[0054] The rotor structure dimensions include the air gap length and permanent magnet dimensions of the brushless DC motor, as detailed below:

[0055] a) Air gap length requirements for brushless DC motors:

[0056]

[0057] Where δ represents the air gap length of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; L stk This indicates the stacking length of the brushless DC motors. The air gap length of the brushless DC motor can be calculated with reference to that of the three-phase asynchronous induction motor. Since the air gap length of the brushless DC motor is generally larger than that of the three-phase asynchronous induction motor, a certain value should be appropriately added to the calculated air gap length to obtain the air gap length of the brushless DC motor.

[0058] b) Permanent magnet size requirements for brushless DC motors:

[0059]

[0060] A m =W PM l m

[0061] h PM =k s K α b m0 S / [(1-b m0 )σ0]

[0062] Among them, B δ This represents the maximum value of the air gap magnetic flux density of a brushless DC motor; α′ p The value represents the calculated pole arc coefficient of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; L represents the calculated pole arc coefficient of the brushless DC motor. stk represents the stacking length of the brushless DC motor; p represents the number of pole pairs of the brushless DC motor; b m0This indicates the no-load operating point of the permanent magnet in a brushless DC motor, typically taken as 0.85; B r A represents the residual magnetic flux density of the permanent magnet in a brushless DC motor; m σ0 represents the cross-sectional area of ​​the permanent magnet in a brushless DC motor; σ0 represents the no-load leakage flux coefficient of the brushless DC motor, typically taken as 1.3; l m This represents the axial length of the permanent magnets in a brushless DC motor, and is generally equal to the stacking length; w PM h represents the width of the permanent magnet in a brushless DC motor. PM K represents the thickness of the permanent magnet in a brushless DC motor. s K represents the saturation coefficient of a brushless DC motor, with a value ranging from 1.05 to 1.3. α This represents the rotor structure coefficient of a brushless DC motor, with a value ranging from 0.7 to 1.2.

[0063] The stator structural dimensional conditions include the effective area condition of the stator slots and the slot fill factor condition, as detailed below:

[0064] a) Stator slot effective area condition:

[0065]

[0066] Among them, A ef A represents the effective area of ​​the stator slots in a brushless DC motor. s A represents the total area within the stator slots of a brushless DC motor; i represents the internal insulation area of ​​the stator of the brushless DC motor; r represents the radius of the stator slot tail of the brushless DC motor; b1 represents the width of the stator slot head of the brushless DC motor; h 12 C represents the slot depth of the stator slots in a brushless DC motor. i This indicates the thickness of the stator slot insulation in a brushless DC motor.

[0067] The motor designed in this invention is a small motor, and the stator slots used are pear-shaped slots.

[0068] b) Slot fill factor condition for stator slots:

[0069] K f =N s N b (D d +h d ) 2 / A ef

[0070] N s ==2maN / Q

[0071] N = A l πD / (2mI N )

[0072] J d =I N / [aπN b (D d / 2) 2 ]

[0073] Among them, K f N represents the slot fill factor of the stator slots in a brushless DC motor. s N represents the number of conductors per slot in the stator of a brushless DC motor. b D represents the number of parallel windings of each coil conductor in the stator slot of a brushless DC motor; d This indicates the diameter of the enameled wire in the stator slots of a brushless DC motor; h d The values ​​represent the thickness of the insulation varnish on both sides of the stator slot of the brushless DC motor; m represents the number of phases of the stator slot; a represents the number of parallel branches per phase of the stator slot; N represents the number of series turns per phase of the stator slot; Q represents the number of stator slots; A1 represents the electrical load of the brushless DC motor; D represents the stator inner diameter of the brushless DC motor; I N J represents the phase current of the stator slots of a brushless DC motor; d This indicates the electrical density value of the stator slots in a brushless DC motor.

[0074] Based on the electrical load A1 of the brushless DC motor determined in the main size conditions of the brushless DC motor, the number of turns in series per phase of the winding can be determined, the number of coils per slot can be determined, the coil diameter can be determined, and finally the slot fill factor of the stator slot can be determined.

[0075] Step 2: Based on the objective function, use the multi-objective gray wolf algorithm to obtain the optimal solution of the objective function as the optimal design parameters of the brushless DC motor. Optimize the initial finite element model based on the optimal design parameters to obtain the optimized finite element model of the brushless DC motor. Obtain the optimized brushless DC motor based on the optimized finite element model of the brushless DC motor, thus realizing the optimization of the brushless DC motor.

[0076] In step 2, before optimization begins, a set of preset random solutions is generated as the initial gray wolf population. The gray wolf population and initial positions are initialized, and the first, second, and third parameters a, A, and C of the multi-objective gray wolf algorithm are initialized. The archive size of the multi-objective gray wolf algorithm is set to 100, and the archive is used to store the optimal solutions of the multi-objective gray wolf algorithm. The maximum number of iterations is 6000, and the population size is 100. A non-dominated optimal solution is found, denoted as α wolf, and temporarily excluded from the archive. The search continues for a non-dominated optimal solution, denoted as β wolf, and temporarily excluded from the archive. The search continues for another non-dominated optimal solution, denoted as δ wolf, and these are stored in the archive. The archive is responsible for storing the non-dominated optimal solutions obtained so far. The leader selection strategy assists in selecting α, β, and δ wolves as leaders for the hunting process obtained from the archive.

[0077] Calculate and update the position of each ω wolf (search agent), while the first parameter a and the second parameter A decrease linearly during the iteration. Calculate the fitness value (objective function value). The position update formula is as follows:

[0078]

[0079]

[0080]

[0081] in, and These represent the distances between α, β, and δ wolves and ω wolf, respectively. and These represent the first, second, and third coefficient factors of the second coefficient, respectively; and These represent the first, second, and third optimal positions of the prey, i.e., the positions of the optimal solution; Indicates the location of the gray wolf; and These represent the positions of α, β, and δ wolves after one iteration; and These represent the first, second, and third coefficient factors of the first coefficient, respectively; This represents the position vector of the (l+1)th iteration; l represents the current iteration number.

[0082] when At this time, the ω wolf tends to deviate from its prey, when At that time, ω_wolf tends to converge toward its prey. Simultaneously, the vector... As the exploration group, generate random values ​​within [0,2]. Emphasizing the random weight of prey, if This reduces the random weight of the prey.

[0083] After completing a position update, a non-dominated optimal solution is found and the archive is initialized. When the archive is not full, the iteration count is set to l = l + 1, and the steps are repeated until the maximum iteration count termination condition is met, at which point the algorithm stops.

[0084] During the iteration process, a new ω-wolf is dominated by at least one wolf in an archive. In this case, the ω-wolf is not allowed to enter the archive. If a new ω-wolf dominates one or more wolves in an archive, the dominated wolf in the archive is ignored, and the new wolf is added to the archive. If the archive is full, the grid mechanism should be run first to delete an archive, save the new solution, rearrange the partition of the target space, find the most crowded region, and ignore one wolf in it. Then, the new wolf is inserted into the least crowded region to improve the diversity of the final approximate Pareto optimal front. When any newly added solution to the archive is not outside the hypercube, the iteration count is set to l = l + 1. When any newly added solution to the archive is outside the hypercube, the grid is updated to protect the new solution.

[0085] The leader selection strategy chooses the least crowded region in the search space and assigns one of its non-dominated solutions as an α, β, or δ wolf. Selection is made using a roulette wheel method, with each hypercube having a probability P. i as follows:

[0086]

[0087] Where C represents a constant greater than 1; N i Indicates the i-th th The number of Pareto optimal solutions obtained in the part;

[0088] Because the leader selection mechanism favors the least crowded hypercube, the search always proceeds towards unexplored or unexposed regions in the search space. If there are not enough leaders (less than 3) in the least crowded region, leaders from different regions are provided. After reaching the maximum number of iterations, the position and fitness value of the α wolf are returned as the optimal solution in the overall optimization process, resulting in a set of non-dominated optimal solutions. Based on this optimal solution, the initial finite element model is modified using Ansys Maxwell.

[0089] This invention reduces the motor design cycle by designing an initial motor geometric model and then using the multi-objective gray wolf algorithm to solve the motor optimization problem. Compared with other common algorithms such as NSGA-II, MOBA, and MOPSO, it also has better competitiveness and robustness.

Claims

1. A brushless DC motor optimization method based on the multi-objective gray wolf algorithm, characterized in that: The method includes the following steps: Step 1: Establish the initial finite element model of the brushless DC motor based on the preset design parameters of the brushless DC motor; establish the objective function that satisfies the multi-objective optimization conditions and optimization constraints of the design parameters of the brushless DC motor. Step 2: Based on the objective function, use the multi-objective gray wolf algorithm to obtain the optimal solution of the objective function as the optimal design parameters of the brushless DC motor. Optimize the initial finite element model based on the optimal design parameters to obtain the optimized finite element model of the brushless DC motor. Obtain the optimized brushless DC motor based on the optimized finite element model of the brushless DC motor, thus realizing the optimization of the brushless DC motor. In step 1, the objective function that satisfies the multi-objective optimization conditions and optimization constraints for the design parameters of the brushless DC motor is established as follows: in, This represents the objective function to maximize efficiency. This represents the function that minimizes the quality objective. Represents the efficiency-related function; Represents the optimization parameter matrix; Represents the quality-related function; This indicates the stator outer diameter of the brushless DC motor; This indicates the outer diameter of the rotor of a brushless DC motor; This indicates the diameter of the winding coil of a brushless DC motor; This indicates the air gap length of the brushless DC motor; This indicates the maximum current of the brushless DC motor. This indicates the total mass of the brushless DC motor; This indicates the temperature of the brushless DC motor; In step 1, the multi-objective optimization conditions for the design parameters of the brushless DC motor specifically include the main dimensional conditions, rotor structural dimensional conditions, and stator structural dimensional conditions of the brushless DC motor. Based on the multi-objective optimization conditions for the design parameters, the stator tooth width, stator yoke width, and stator outer diameter of the brushless DC motor can be determined. Finally, based on the optimal design parameters and the determined stator tooth width, stator yoke width, and stator outer diameter of the brushless DC motor, the initial finite element model is optimized to obtain the optimized finite element model of the brushless DC motor. The stator structure dimensional conditions include the effective area condition of the stator slots and the slot fill factor condition of the stator slots, as detailed below: a) Stator slot effective area condition: in, This represents the effective area of ​​the stator slots in a brushless DC motor. This represents the total area within the stator slots of a brushless DC motor. This indicates the area of ​​the internal insulation of the stator of a brushless DC motor; This indicates the radius of the end of the stator slot in a brushless DC motor. This indicates the width of the head portion of the stator slot in a brushless DC motor. This indicates the slot depth of the stator slots in a brushless DC motor. This indicates the stator slot insulation thickness of a brushless DC motor; b) Slot fill factor condition for stator slots: in, This indicates the slot fill factor of the stator slots in a brushless DC motor. This indicates the number of conductors per slot in the stator of a brushless DC motor. This indicates the number of parallel windings of each coil conductor in the stator slot of a brushless DC motor; This indicates the diameter of the enameled wire in the stator slots of a brushless DC motor; This indicates the thickness of the insulating varnish on both sides of the enameled wire in the stator slot of a brushless DC motor. This indicates the number of phases in the stator slots of a brushless DC motor; This indicates the number of parallel branches per phase in the stator slots of a brushless DC motor; This indicates the number of turns in series per phase of the stator slot of a brushless DC motor; This indicates the number of stator slots in a brushless DC motor; This indicates the electrical load of the brushless DC motor; This indicates the stator inner diameter of a brushless DC motor; This represents the phase current of the stator slots in a brushless DC motor. This indicates the electrical density value of the stator slots in a brushless DC motor.

2. The brushless DC motor optimization method based on the multi-objective gray wolf algorithm according to claim 1, characterized in that: The main dimensional requirements for the brushless DC motor are the stator inner diameter and stacking length, as detailed below: in, This indicates the stacking length of brushless DC motors; This represents the ratio of the core length to the pole pitch of a brushless DC motor. This indicates the stator inner diameter of a brushless DC motor; This indicates the number of pole pairs in a brushless DC motor. This indicates the electromagnetic power of the brushless DC motor. This represents the calculated pole arc coefficient of a brushless DC motor. The waveform coefficients representing the air gap magnetic field of a brushless DC motor; This represents the fundamental frequency coefficient of the stator winding of a brushless DC motor. This indicates the electrical load of the brushless DC motor; This represents the maximum value of the air gap magnetic flux density of a brushless DC motor; This indicates the rated speed of the brushless DC motor; This represents the back electromotive force of a brushless DC motor. This indicates the current of the brushless DC motor.

3. The brushless DC motor optimization method based on the multi-objective gray wolf algorithm according to claim 1, characterized in that: The rotor structure dimensional conditions include the air gap length condition and the permanent magnet dimensional condition of the brushless DC motor, as detailed below: a) Air gap length requirements for brushless DC motors: in, This indicates the air gap length of the brushless DC motor; This indicates the stator inner diameter of a brushless DC motor; This indicates the stacking length of brushless DC motors; b) Permanent magnet size requirements for brushless DC motors: in, This represents the maximum value of the air gap magnetic flux density of a brushless DC motor; This represents the calculated pole arc coefficient of a brushless DC motor. This indicates the stator inner diameter of a brushless DC motor; This indicates the stacking length of brushless DC motors; This indicates the number of pole pairs in a brushless DC motor. This indicates the no-load operating point of the permanent magnet in a brushless DC motor. This represents the residual magnetic flux density of the permanent magnet in a brushless DC motor. This represents the cross-sectional area of ​​the permanent magnet in a brushless DC motor. This represents the no-load leakage flux coefficient of a brushless DC motor; This indicates the axial length of the permanent magnet in a brushless DC motor. This indicates the width of the permanent magnet in a brushless DC motor; This indicates the thickness of the permanent magnet in a brushless DC motor. This represents the saturation coefficient of a brushless DC motor. This represents the rotor structure coefficient of a brushless DC motor.

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