Modeling and Parameter Optimization Method and System of Hybrid Magnetic Pole Sinusoidal Permanent Magnet Motor

Abstract

The present invention relates to the technical field of permanent magnet motors, and provides a modeling and parameter optimization method and system for a hybrid pole sinusoidal permanent magnet motor. The analytical modeling method includes calculating the no-slot air-gap flux density of the motor under no-load conditions of the motor permanent magnet assembly with different remanences and different sector angles according to the remanence distribution of the motor permanent magnet assembly and the air-gap magnetic field boundary conditions; establishing a relationship between the relative air-gap permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtaining the relative air-gap permeability with slots through Fourier decomposition calculation; calculating the air-gap flux density with slots of the motor under no-load conditions in the complex coordinate system according to the no-slot air-gap flux density and the relative air-gap permeability with slots; calculating the cogging torque of the motor by the energy method based on the air-gap flux density with slots of the motor under no-load conditions; at the same time, calculating the no-load back electromotive force of the motor according to the winding distribution and the air-gap flux density with slots, and calculating the output torque by combining the no-load back electromotive force of the motor with the actual driving current to obtain the average torque, torque ripple and torque cost.

Description

Technical Field

[0001] The present invention relates to the technical field of permanent magnet motors, and in particular, to a method and system for modeling and parameter optimization of a hybrid pole sinusoidal permanent magnet motor. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] The magnetic circuit of a hybrid magnet permanent magnet synchronous motor is complex and variable, involving various magnet materials, magnetic circuit connection methods, and magnetic barrier structures. These complex magnetic circuit structures not only increase the difficulty of magnetic circuit analysis, but also make the performance prediction of the motor more complex. The traditional finite element method (FEM) electromagnetic performance calculation method requires high-precision meshes and complex iterations, resulting in a large amount of calculation and long calculation time for such motors. Performance indicators of hybrid pole permanent magnet synchronous motors such as average torque, torque ripple, and pole cost have gradually become the key to optimization design. However, there are often mutual constraints between these diverse performance indicators, making multi-objective optimization extremely difficult, and it is necessary to find a balance between multiple objectives to achieve the optimal comprehensive performance. At the same time, the optimization based on the traditional finite element method is time-consuming, has low optimization efficiency, and has high requirements for the host computer, further restricting the optimization efficiency. Summary of the Invention

[0004] In order to solve the technical problems existing in the above background technique, the present invention provides a method and system for modeling and parameter optimization of a hybrid pole sinusoidal permanent magnet motor. The present invention can not only reduce torque ripple, including cogging torque and torque ripple, but also save pole cost.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The first aspect of the present invention provides a modeling method for a hybrid pole sinusoidal permanent magnet motor.

[0007] A modeling method for a hybrid pole sinusoidal permanent magnet motor includes:

[0008] Calculating the no-slot air-gap flux density of the motor under no-load conditions at different remanences and different sector angles of the motor permanent magnet assembly according to the remanence distribution of the motor permanent magnet assembly and the air-gap magnetic field boundary conditions;

[0009] Establishing a relational expression between the relative air-gap permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtaining the relative air-gap permeability with slots through Fourier decomposition calculation;

[0010] Calculating the air-gap flux density with slots of the motor under no-load conditions in the complex coordinate system according to the no-slot air-gap flux density and the relative air-gap permeability with slots;

[0011] Based on the slotted air-gap flux density under no-load condition of the motor, the cogging torque of the motor is calculated according to the energy method; meanwhile, the no-load back electromotive force of the motor is calculated according to the winding distribution and the slotted air-gap flux density, and the output torque is calculated by combining the no-load back electromotive force of the motor with the actual driving current, so as to obtain the average torque, torque ripple and torque cost.

[0012] Further, the no-load back electromotive force of the motor is expressed by the following formula:

[0013]

[0014] where E a is the no-load back electromotive force of phase A of the motor, R s is the inner diameter of the stator of the motor, l ef is the shaft length of the motor, ω r is the angular velocity, K d and K p are the distribution coefficient and pitch coefficient of the winding respectively, B sr is the radial air-gap flux density considering stator slotting, n is the order of each order after Fourier decomposition (n = 1, 3, 5...), and t is time.

[0015] Further, the output torque is expressed by the following formula:

[0016] T e =(E a i a +E b i b +E c i c ) / ω r +T cog

[0017]

[0018] where T e is the output torque, T cog is the cogging torque of the motor; E a , E b and E c are the no-load back electromotive forces of the three phases of the motor respectively; i a , i b and i c are the stator currents of the three phases of the motor respectively; ω r is the angular velocity of the rotor of the motor; μ0 is the permeability of free space; R s and R r are the inner diameter of the stator and the outer diameter of the rotor core (excluding the permanent magnet) respectively; n is the order of each order after Fourier decomposition (n = 1, 2, 3…); N Lis the least common multiple of the number of rotor poles and the number of stator slots; G nNL and B nNL are respectively the amplitudes of each order of the relative air-gap permeability after Fourier decomposition corresponding to the N L order and the amplitudes of each order of the radial air-gap flux density without considering the slotting effect; α is the angular difference between the center line of the magnet and the center line of the stator tooth.

[0019] Furthermore, the hybrid pole sinusoidal permanent magnet motor includes a stator and a motor rotor. A plurality of combined sinusoidal poles are evenly arranged on the end plate of the motor rotor. The magnetization directions of adjacent combined sinusoidal poles are opposite. The arrangement of the permanent magnet components on each pole is symmetrically arranged from low to high and then from high to low according to the remanence performance. The permanent magnet with the highest remanence performance is arranged in the middle of each pole. The permanent magnets with lower remanence and the permanent magnet with the lowest remanence are symmetrically distributed with the permanent magnet with the highest remanence performance as the central axis, and the permanent magnet with lower remanence is arranged between the permanent magnet with the highest remanence performance and the permanent magnet with the lowest remanence.

[0020] Even further, the permanent magnet with the highest remanence performance and the permanent magnets with lower remanence both adopt neodymium iron boron permanent magnets, and the permanent magnet with the lowest remanence adopts a ferrite permanent magnet.

[0021] The second aspect of the present invention provides a modeling system for a hybrid pole sinusoidal permanent magnet motor.

[0022] A modeling system for a hybrid pole sinusoidal permanent magnet motor includes:

[0023] A slotless air-gap flux density calculation module, which is configured to calculate the slotless air-gap flux density of the motor under no-load conditions of the motor permanent magnet components at different remanences and different sector angles according to the remanence distribution of the motor permanent magnet components and the air-gap magnetic field boundary conditions;

[0024] A slotted air-gap relative permeability calculation module, which is configured to establish a relationship between the air-gap relative permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtain the slotted air-gap relative permeability through Fourier decomposition calculation;

[0025] A slotted air-gap flux density calculation module, which is configured to calculate the slotted air-gap flux density of the motor under no-load conditions in the complex coordinate system according to the slotless air-gap flux density and the slotted air-gap relative permeability;

[0026] An output torque calculation module, which is configured to calculate the cogging torque of the motor based on the slotted air-gap flux density of the motor under no-load conditions according to the energy method; at the same time, calculate the no-load back electromotive force of the motor according to the winding distribution and the slotted air-gap flux density, and calculate the output torque by combining the no-load back electromotive force of the motor with the actual driving current, and obtain the average torque, torque ripple and torque cost.

[0027] The third aspect of the present invention provides a parameter optimization method for a hybrid magnetic pole sinusoidal permanent magnet motor.

[0028] A parameter optimization method for a hybrid magnetic pole sinusoidal permanent magnet motor includes:

[0029] Taking the sector angle and thickness of each permanent magnet component in each pole as optimization variables, and using the average torque, torque ripple, and torque cost obtained in the modeling method of the hybrid magnetic pole sinusoidal permanent magnet motor described in the first aspect as objective functions;

[0030] Based on the objective functions, the global optimization of the motor performance is carried out by using the manta ray intelligent optimization algorithm to obtain the optimal sector angle and thickness of each permanent magnet component;

[0031] Substituting the optimal sector angle and thickness into the finite element model for solution, and comparing the average torque, torque ripple, and torque cost obtained from the solution with the average torque, torque ripple, and torque cost obtained in the modeling method of the hybrid magnetic pole sinusoidal permanent magnet motor described in the first aspect to check whether the performance matches.

[0032] Further, the objective functions are expressed by the following formulas:

[0033]

[0034] In the formula, x i is the optimization variable, f(x i ) is the value of the optimization objective function corresponding to the change of the optimization variable x i , i = 1, 2, 3.... T av0 , T r0 , T c0 are the initial values of the average torque, torque ripple, and torque cost respectively, T av (x i ), T r (x i ), T c (x i ) are the average torque, torque ripple, and torque cost corresponding to the optimization variable x i respectively, and λ1, λ2, and λ3 are the weight coefficients of the average torque, torque ripple, and torque cost respectively, and satisfy λ1 + λ2 + λ3 = 1.

[0035] Further, the process of globally optimizing the motor performance using the manta ray intelligent optimization algorithm to obtain the optimal sector angles and thicknesses of each permanent magnet component includes: dividing the range of each input optimization variable into intervals, and performing a random sampling observation of the input variables within each interval; substituting the sampled sector angles and thicknesses of the permanent magnet components into the modeling method of the hybrid pole sinusoidal permanent magnet motor to solve for the average torque, torque ripple, and torque cost.

[0036] The fourth aspect of the present invention provides a parameter optimization system for a hybrid pole sinusoidal permanent magnet motor.

[0037] A parameter optimization system for a hybrid pole sinusoidal permanent magnet motor includes:

[0038] An objective function construction module, which is configured to: use the sector angles and thicknesses of each permanent magnet component in each pole as optimization variables, and use the average torque, torque ripple, and torque cost obtained from the modeling method of the hybrid pole sinusoidal permanent magnet motor in the first aspect as the objective function;

[0039] An optimization module, which is configured to: based on the objective function, globally optimize the motor performance using the manta ray intelligent optimization algorithm to obtain the optimal sector angles and thicknesses of each permanent magnet component;

[0040] A comparison output module, which is configured to: substitute the optimal sector angles and thicknesses into the finite element model for solution, and compare the average torque, torque ripple, and torque cost obtained from the solution with the average torque, torque ripple, and torque cost obtained from the modeling method of the hybrid pole sinusoidal permanent magnet motor in the first aspect to check whether the performance matches.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] The present invention proposes a modeling method and system for a hybrid pole sinusoidal permanent magnet motor. By calculating the electromagnetic performance of the motor based on the analytical method, it can solve the no-load slotted air-gap flux density, cogging torque, back electromotive force, output torque, and torque cost of the hybrid pole sinusoidal permanent magnet motor more quickly and accurately compared with the finite element simulation results. It can effectively reduce the motor design cycle and lower the motor simulation calculation cost.

[0043] The present invention proposes a parameter optimization method and system for a hybrid magnetic pole sinusoidal permanent magnet motor. Through a global multi-objective optimization method combining the analytical method and the manta ray intelligent algorithm, the calculation time and the upper computer memory requirement can be effectively reduced compared with the traditional three-dimensional finite element calculation. The proposed optimization scheme can not only make the air-gap magnetic density waveform and the motor back electromotive force tend to be sinusoidal, reduce the torque ripple while ensuring a high electromagnetic torque, but also optimize the permanent magnet configuration and reduce the cost consumed by the magnetic poles.

[0044] The present invention proposes a hybrid magnetic pole sinusoidal permanent magnet motor, which can effectively reduce the air-gap magnetic density and back electromotive force harmonics, and at the same time minimize the cogging torque and torque ripple. During the process of eliminating harmonics, the iron loss of the motor is also significantly reduced, which helps the motor to maintain an efficient operating state. In addition, combined with the design of permanent magnets of different materials, both the motor cost and loss are effectively reduced, and this design does not require special permanent magnet forming processes, simplifies the production process, and reduces the manufacturing cost. Brief Description of the Drawings

[0045] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0046] Figure 1 It is a flowchart of the modeling method of the hybrid magnetic pole sinusoidal permanent magnet motor in the embodiment of the present invention;

[0047] Figure 2 It is a structural diagram of the stator in the hybrid magnetic pole sinusoidal permanent magnet motor in the embodiment of the present invention;

[0048] Figure 3 It is a structural diagram of the motor rotor in the hybrid magnetic pole sinusoidal permanent magnet motor in the embodiment of the present invention;

[0049] Figure 4 It is a flowchart of the parameter optimization method of the hybrid magnetic pole sinusoidal permanent magnet motor in the embodiment of the present invention;

[0050] Figure 5 It is a three-dimensional modeling diagram of the average torque, torque ripple, and relative torque cost in the embodiment of the present invention;

[0051] Figure 6 It is a comparison diagram of the effects of the prior art and the modeling method and parameter optimization method proposed by the present invention in the embodiment of the present invention;

[0052] Among them, 1. Stator, 1-1. Stator core, 1-2. Motor winding, 2. Motor rotor, 2-1. Rotor core, 2-2. Neodymium iron boron S-pole permanent magnet with the highest remanence at the first position, 2-3. Neodymium iron boron S-pole permanent magnet with lower remanence at the second position, 2-4. Ferrite S-pole permanent magnet with the lowest remanence at the third position, 2-5. Neodymium iron boron N-pole permanent magnet with the highest remanence at the first position, 2-6. Neodymium iron boron N-pole permanent magnet with lower remanence at the second position, 2-7. Ferrite N-pole permanent magnet with the lowest remanence at the third position. Specific embodiments

[0053] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0054] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which the present invention belongs.

[0055] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0056] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code may include one or more executable instructions for implementing the logical functions defined in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Similarly, it should be noted that each block in the flowchart and / or block diagram, and the combinations of blocks in the flowchart and / or block diagram, may be implemented using a dedicated hardware-based system for performing the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions.

[0057] Embodiment 1

[0058] As Figure 1 shown, this embodiment provides a modeling method for a hybrid magnetic pole sinusoidal permanent magnet motor, including the following steps:

[0059] According to the residual magnetism distribution of the motor permanent magnet assembly and the air-gap magnetic field boundary conditions, calculate the no-slot air-gap flux density under no-load conditions of the motor for the motor permanent magnet assembly under different residual magnetisms and different sector angles;

[0060] Establish a relational expression between the relative air-gap permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtain the relative air-gap permeability with slots through Fourier decomposition calculation;

[0061] Under the complex coordinate system, calculate the air-gap flux density with slots under no-load conditions of the motor according to the no-slot air-gap flux density and the relative air-gap permeability with slots;

[0062] Based on the air-gap flux density with slots under no-load conditions of the motor, calculate the cogging torque of the motor according to the energy method. At the same time, calculate the no-load back electromotive force of the motor according to the winding distribution and the air-gap flux density with slots, and calculate the output torque by combining the back electromotive force with the actual driving current to obtain the average torque, torque ripple and torque cost.

[0063] In some embodiments, the calculation method for the no-slot air-gap flux density of the hybrid-magnet sinusoidal permanent magnet motor under no-load is as follows:

[0064] According to Figure 1 the residual magnetism distribution of the permanent magnets therein, it can be assumed that within the range of one pole, the radial magnetization vector M r and the tangential magnetization vector M θ are:

[0065]

[0066] where B r1 is the residual magnetism of the ferrite permanent magnet with the lowest residual magnetism symmetrically arranged at the third position, X1 is the opening angle of the ferrite permanent magnet with the lowest residual magnetism symmetrically arranged at the first position, B r2 is the residual magnetism of the NdFeB permanent magnet with relatively low residual magnetism symmetrically arranged at the second position, X2 is the opening angle of the NdFeB permanent magnet with relatively low residual magnetism symmetrically arranged at the second position, B r3 is the residual magnetism of the NdFeB permanent magnet with the lowest residual magnetism symmetrically arranged at the first position, X3 is the opening angle of the NdFeB permanent magnet with the lowest residual magnetism symmetrically arranged at the first position, and μ0 is the air-gap permeance.

[0067] Ignoring the interaction between permanent magnets, the Fourier decomposition superposition of each permanent magnet's formula (1) can obtain the Fourier series radial magnetization vector M r and the tangential magnetization vector M θ within the range of a unit magnetic pole:

[0068]

[0069] where M rn1 corresponds to Br1 Magnetization vector function; M rn2 Corresponding to B r2 And B r1 Magnetization vector function difference of; M rn1 +M rn2 Used to obtain the magnetization vector distribution of the permanent magnets at the first and second positions, M rn3 Corresponding to B r3 And B r1 Magnetization vector function difference of; M rn1 +M rn3 Used to obtain the magnetization vector distribution of the permanent magnets at the first and third positions, M rn1 +M rn2 +M rn3 Used to obtain the magnetization vector distribution of the permanent magnets at the first and third positions, p is the number of pole pairs of the motor, θ is the rotor position angle, and n is the order after Fourier decomposition (n = 1, 3, 5...).

[0070]

[0071] In addition, the slotless air-gap magnetic density of the proposed hybrid-magnet sinusoidal permanent-magnet motor can be obtained:

[0072]

[0073] Wherein, B r (r, θ) and B θ (r, θ) are the radial component and the tangential component of the slotless air-gap magnetic density of the motor respectively; r is the sampling radius of the air-gap magnetic density; θ is the rotor position angle; n is the order after Fourier decomposition (n = 1, 3, 5...); K B (n), f Br (r) and f Bθ (r) are transition parameters respectively, as shown below:

[0074]

[0075]

[0076]

[0077] Wherein, μ l is the relative permeability of the permanent magnet; p is the number of pole pairs; r is the sampling radius of the air-gap magnetic density; R s , R m , R r are the inner diameter of the stator, the outer diameter of the permanent magnet and the outer diameter of the rotor core of the motor respectively.

[0078] In some embodiments, the calculation method of the no-load relative permeability and the slotted air-gap magnetic density of the hybrid-magnet sinusoidal permanent-magnet motor is:

[0079] The relative permeability of the air gap of the proposed motor is solved by using conformal transformation.

[0080]

[0081] Among them, λ r is the radial relative permeability; λ0 is the basic component of the radial relative permeability, and λ μr is the Fourier component of the radial relative permeability; λ θ is the tangential relative permeability; λ μr is the Fourier component of the tangential relative permeability.

[0082]

[0083]

[0084] Among them, Q s is the number of slots of the motor, and K a , K b are the air gap adjustment coefficients of the motor, K c is the Carter coefficient, and t is equal to b0Q s / 2πR s .

[0085] In some embodiments, the air gap slotting magnetic density can be obtained by the following formula:

[0086]

[0087] Among them, B sr and B sθ are the radial component and the tangential component of the air gap magnetic density after the motor is slotted, respectively.

[0088] In some embodiments, the calculation method of the no-load back electromotive force of the hybrid pole sinusoidal permanent magnet motor is

[0089]

[0090] Among them, E a is the no-load back electromotive force of phase A of the motor, R s is the inner diameter of the stator of the motor; l ef is the shaft length of the motor; ω r is the angular velocity; K d and K p are the distribution coefficient and the pitch coefficient of the winding, respectively; B sr is the air gap radial magnetic flux density considering stator slotting; n is the order of each order after Fourier decomposition (n = 1, 3, 5...); t is the time.

[0091] In some embodiments, the calculation methods of the cogging torque and the output torque of the hybrid pole sinusoidal permanent magnet motor are:

[0092]

[0093] Among them, T e is the output torque; T cog is the cogging torque of the motor; μ0 is the magnetic permeability of vacuum; R s and R r are the inner diameter of the stator and the outer diameter of the rotor core (excluding the permanent magnet), respectively; n is the order after Fourier decomposition (n = 1, 2, 3...); N L is the least common multiple of the number of rotor poles and the number of stator slots; G nNL and B nNL are the amplitudes of each order of the relative air-gap magnetic permeability after Fourier decomposition corresponding to the N L th order and the amplitudes of each order of the radial air-gap magnetic flux density without considering the slotting effect, respectively; α is the angular difference between the center line of the rotor magnet and the center line of the stator tooth.

[0094] In some embodiments, by introducing a driving current, the total torque of the motor can be obtained:

[0095] T e =(E a i a +E b i b +E c i c ) / ω + T cog (14)

[0096] Among them, E a , E b and E c are the back electromotive forces of the three phases of the motor on no load; i a , i b and i c are the stator currents of the three phases of the motor; ω r is the angular velocity of the motor rotor.

[0097] As Figure 2 , Figure 3 shown, a sinusoidal hybrid magnetic pole permanent magnet motor includes: a stator 1, a stator core 1-1, a motor winding 1-2, a motor rotor 2, a rotor core 2-1, a neodymium iron boron S-pole permanent magnet 2-2 with the highest remanence at the first position, a neodymium iron boron S-pole permanent magnet 2-3 with a lower remanence at the second position, a ferrite S-pole permanent magnet 2-4 with the lowest remanence at the third position, a neodymium iron boron N-pole permanent magnet 2-5 with the highest remanence at the first position, a neodymium iron boron N-pole permanent magnet 2-6 with a lower remanence at the second position, and a ferrite N-pole permanent magnet 2-7 with the lowest remanence at the third position.

[0098] A plurality of combined sine magnetic poles are evenly arranged on the end disc of the motor rotor. The magnetization directions of adjacent combined sine magnetic poles are opposite, that is, each combined sine magnetic pole is arranged alternately as an N pole and an S pole; the arrangement of the permanent magnet components on each pole is symmetrically arranged from low to high and then from high to low according to the remanence performance, and the permanent magnet with the highest remanence performance is arranged in the middle of each pole; the ferrite permanent magnet with the lowest remanence is arranged at the third position, the neodymium iron boron permanent magnet with a relatively low remanence is arranged at the second position, and the neodymium iron boron permanent magnet with the highest remanence is arranged at the first position; each permanent magnet is closely attached in sequence, and by designing the remanence of each permanent magnet, a sinusoidal magnetic pole in the same magnetization direction is obtained.

[0099] Embodiment 2

[0100] This embodiment provides a modeling system for a hybrid magnetic pole sinusoidal permanent magnet motor.

[0101] A modeling system for a hybrid magnetic pole sinusoidal permanent magnet motor includes:

[0102] A slotless air-gap flux density calculation module, which is configured to calculate the slotless air-gap flux density of the motor under no-load conditions of the motor permanent magnet assembly at different remanences and different sector angles according to the remanence distribution of the motor permanent magnet assembly and the air-gap magnetic field boundary conditions;

[0103] A slotted air-gap relative permeability calculation module, which is configured to establish a relationship between the air-gap relative permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtain the slotted air-gap relative permeability through Fourier decomposition calculation;

[0104] A slotted air-gap flux density calculation module, which is configured to calculate the slotted air-gap flux density of the motor under no-load conditions in the complex coordinate system according to the slotless air-gap flux density and the slotted air-gap relative permeability;

[0105] An output torque calculation module, which is configured to calculate the cogging torque of the motor based on the slotted air-gap flux density of the motor under no-load conditions according to the energy method; at the same time, calculate the no-load back electromotive force of the motor according to the winding distribution and the slotted air-gap flux density, and calculate the output torque by combining the no-load back electromotive force of the motor with the actual driving current, and obtain the average torque, torque ripple and torque cost.

[0106] It should be noted here that the above slotless air-gap flux density calculation module, slotted air-gap relative permeability calculation module, slotted air-gap flux density calculation module and output torque calculation module have the same examples and application scenarios as the steps in Embodiment 1, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules can be executed in a computer system such as a set of computer executable instructions as part of the system.

[0107] Embodiment 3

[0108] As Figure 4 shown, this embodiment provides a parameter optimization method for a hybrid magnetic pole sinusoidal permanent magnet motor, including:

[0109] Taking the sector angle and thickness of each permanent magnet component in each pole as optimization variables, and taking the average torque, torque ripple, and torque cost as optimization objectives; and calculating the average torque, torque ripple, and torque cost in combination with Embodiment 1.

[0110] Using the manta ray intelligent optimization algorithm to globally optimize the motor performance to obtain the optimal sector angle and thickness of each permanent magnet component.

[0111] Substitute the obtained optimal permanent magnet sector angle and thickness into the finite element model for solution, and compare the average torque, torque ripple, and torque cost obtained from the solution with the average torque, torque ripple, and torque cost obtained in Embodiment 1.

[0112] In this embodiment, taking the sector angle and thickness of each permanent magnet component in each pole as optimization variables, determining a reasonable range of optimization variables, and taking the average torque, torque ripple, and torque cost as optimization objectives; designing an optimization scheme to optimize the sector angle and thickness of each permanent magnet component.

[0113] In order to improve the optimization efficiency and save the optimization cost, the optimization scheme of this embodiment is specifically as follows:

[0114] (1) Taking the sector angle and thickness of each permanent magnet component in each pole as optimization variables, determining a reasonable range of optimization variables, and taking the average torque, torque ripple, and torque cost as optimization objectives;

[0115] Since there are constraints that the multiple optimization objectives cannot be optimal at the same time, and the importance degrees of the three optimization objectives are also different, a weight coefficient λ is introduced to establish an optimization objective function, and the objective function covers the average torque, torque ripple, and torque cost, as shown specifically below:

[0116]

[0117] In the formula, x i is the optimization variable, f(x i ) is the value of the optimization objective function corresponding to the change of the optimization variable x i , i = 1, 2, 3.... T av0 , T r0 , T c0 are respectively the initial values of the average torque, torque ripple, and torque cost, T av (x i ), T r (x i ), T c(x i ) are the average torque, torque ripple, and torque cost corresponding to the optimization variable x i respectively. λ1, λ2, and λ3 are the weight coefficients of the average torque, torque ripple, and torque cost respectively, and satisfy λ1 + λ2 + λ3 = 1. Since the hybrid magnetic pole sinusoidal permanent magnet motor takes into account both the average torque performance and torque ripple, the weight coefficient λ1 of the average torque is set to 0.4, the weight coefficient λ2 of the torque ripple is set to 0.4, and the weight coefficient λ3 of the torque cost is set to 0.2.

[0118] (2) Establish an analytical model of the magnetic field of the sinusoidal hybrid magnetic pole permanent magnet motor using the analytical method. By calculating the no-load magnetic field of the motor, the slotted air-gap magnetic density and the back electromotive force of the motor are obtained, and the torque waveform is further solved to obtain the effective average torque, torque ripple, and torque cost;

[0119] (3) Use the manta ray foraging algorithm to globally optimize the motor performance to obtain the optimal sector angle and thickness of the permanent magnet.

[0120] The range of each input optimization variable is divided into intervals, and a random sampling observation of the input variable is performed within each interval; the sector angle and thickness of the permanent magnet assembly obtained by sampling are brought into the motor analytical model for solution to obtain the output results of the average torque, torque ripple, and torque cost.

[0121] (4) Optimize the objective function established by the analytical method through manta ray optimization and obtain the optimal size parameters. In order to verify the accuracy of the optimization of the analytical model, the size parameters of the optimal analytical model are substituted into the finite element model for simulation analysis to obtain the average torque, torque ripple, and torque cost of the finite element simulation, and compare and verify them with the average torque, torque ripple, and torque cost calculated by the analytical method to check whether the performance matches.

[0122] Figure 5 For the responses of torque, torque ripple, and torque cost during the optimization process, it can be seen that there is an obvious Pareto front among the three, which is convenient for finding the optimal solution.

[0123] Figure 6 For the comparison of the torque performance of the optimal solution of the parameters of the hybrid magnetic pole sinusoidal permanent magnet motor, it can be seen that the calculation results of the analytical modeling and the finite element simulation are basically the same. Compared with the traditional motor model, the hybrid magnetic pole sinusoidal permanent magnet motor used has a larger average torque and a smaller torque ripple.

[0124] The parameter optimization method of this embodiment has the advantages of high optimization accuracy, strong algorithm adaptability, short calculation time, etc. The rotor magnetomotive force, air-gap magnetic density, and back electromotive force of the obtained optimization results have high sinusoidality, significantly reducing the torque ripple and improving the torque performance.

[0125] Example 4

[0126] This embodiment provides a parameter optimization system for a hybrid magnetic pole sinusoidal permanent magnet motor.

[0127] A parameter optimization system for a hybrid magnetic pole sinusoidal permanent magnet motor includes:

[0128] An objective function construction module configured to use the sector angle and thickness of each permanent magnet component in each pole as optimization variables, and use the average torque, torque ripple, and torque cost obtained from the modeling method of the hybrid magnetic pole sinusoidal permanent magnet motor described in Embodiment 1 as the objective function;

[0129] An optimization module configured to globally optimize the motor performance based on the objective function using the manta ray intelligent optimization algorithm to obtain the optimal sector angle and thickness of each permanent magnet component;

[0130] A comparison and output module configured to substitute the optimal sector angle and thickness into the finite element model for solution, and compare the average torque, torque ripple, and torque cost obtained from the solution with the average torque, torque ripple, and torque cost obtained from the modeling method of the hybrid magnetic pole sinusoidal permanent magnet motor described in Embodiment 1 to check whether the performance matches.

[0131] It should be noted here that the above objective function construction module, optimization module, and comparison and output module have the same examples and application scenarios as the steps in Embodiment 3, but are not limited to the content disclosed in the above Embodiment 1. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0132] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A parameter optimization method for a hybrid magnetic pole sinusoidal permanent magnet motor, characterized in that, Including: Taking the sector angle and thickness of each permanent magnet component in each pole as optimization variables, and taking the average torque, torque ripple and torque cost obtained from the modeling method of the hybrid pole sinusoidal permanent magnet motor as the objective function; Based on the objective function, using the manta ray intelligent optimization algorithm to globally optimize the motor performance, and obtaining the optimal sector angle and thickness of each permanent magnet component; Substituting the optimal sector angle and thickness into the finite element model for solution, and comparing the average torque, torque ripple and torque cost obtained from the solution with the average torque, torque ripple and torque cost obtained from the modeling method of the hybrid pole sinusoidal permanent magnet motor to check whether the performance matches; Among them, the modeling method of the hybrid pole sinusoidal permanent magnet motor includes: calculating the no-slot air-gap flux density of the motor under no-load conditions with different remanences and different sector angles according to the remanence distribution of the motor permanent magnet components and the air-gap magnetic field boundary conditions; establishing a relationship between the relative air-gap permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtaining the relative air-gap permeability with slots through Fourier decomposition calculation; in the complex coordinate system, calculating the air-gap flux density with slots of the motor under no-load conditions according to the no-slot air-gap flux density and the relative air-gap permeability with slots; based on the air-gap flux density with slots of the motor under no-load conditions, calculating the cogging torque of the motor by the energy method; at the same time, calculating the no-load back electromotive force of the motor according to the winding distribution and the air-gap flux density with slots, and calculating the output torque by combining the no-load back electromotive force of the motor with the actual driving current, and obtaining the average torque, torque ripple and torque cost; The objective function is expressed by the following formula: Wherein, x i is the optimization variable, f(x i ) is the corresponding optimal objective function value when the optimization variable x i changes, i = 1, 2, 3...; T av0 , T r0 , T c0 are respectively the initial values of the average torque, torque ripple and torque cost, T av (x i ), T r (x i ), T c (x i ) are respectively the average torque, torque ripple and torque cost corresponding to the optimization variable x i , and λ1, λ2, and λ3 are respectively the weight coefficients of the average torque, torque ripple and torque cost, and satisfy λ1 + λ2 + λ3 = 1.

2. The parameter optimization method of the hybrid magnetic pole sinusoidal permanent magnet motor according to claim 1, characterized in that The no-load back electromotive force of the motor is expressed by the following formula: Among them, E a is the no-load back electromotive force of the A-phase of the motor, R s is the inner diameter of the stator of the motor, l ef is the shaft length of the motor, ω r is the angular velocity, K d and K p are respectively the distribution coefficient and pitch coefficient of the winding, B sr is the radial air-gap flux density considering stator slotting, n is the order of each order after Fourier decomposition, and t is the time.

3. The parameter optimization method of the hybrid magnetic pole sinusoidal permanent magnet motor according to claim 1, characterized in that The output torque is expressed by the following formula: T e = (E a i a + E b i b + E c i c ) / ω + T cog Among them, T e is the output torque, and T cog is the cogging torque of the motor. E a , E b and E c are the back electromotive forces of the three phases of the motor respectively. i a , i b and i c are the stator currents of the three phases of the motor respectively. ω r is the angular velocity of the motor rotor; where μ0 is the magnetic permeability of vacuum, R s and R r are the inner diameter of the stator and the outer diameter of the rotor core respectively, and n is the order after Fourier decomposition; N L is the least common multiple of the number of rotor poles and the number of stator slots; G nNL and B nNL are the amplitudes of each order of the relative air-gap permeability after Fourier decomposition corresponding to the N L order and the amplitudes of each order of the radial air-gap flux density without considering the slotting effect respectively; α is the angle difference between the center line of the magnet and the center line of the stator tooth.

4. The parameter optimization method of the hybrid magnetic pole sinusoidal permanent magnet motor according to claim 1, characterized in that The hybrid pole sinusoidal permanent magnet motor includes a stator and a motor rotor. A plurality of combined sinusoidal poles are uniformly arranged on the end plate of the motor rotor. The magnetization directions of adjacent combined sinusoidal poles are opposite. The arrangement of the permanent magnet components on each pole is symmetrically arranged from low to high and then from high to low according to the remanence performance. The permanent magnet with the highest remanence performance is arranged in the middle of each pole. The permanent magnets with lower remanence and the permanent magnet with the lowest remanence are symmetrically distributed with the permanent magnet with the highest remanence performance as the central axis, and the permanent magnet with lower remanence is arranged between the permanent magnet with the highest remanence performance and the permanent magnet with the lowest remanence.

5. The parameter optimization method of the hybrid magnetic pole sinusoidal permanent magnet motor according to claim 4, characterized in that Both the permanent magnet with the highest remanence performance and the permanent magnet with lower remanence are made of neodymium iron boron permanent magnets, and the permanent magnet with the lowest remanence is made of ferrite permanent magnets.

6. The parameter optimization method of the hybrid magnetic pole sinusoidal permanent magnet motor according to claim 1, characterized in that The process of using the manta ray intelligent optimization algorithm to globally optimize the motor performance and obtaining the optimal sector angle and thickness of each permanent magnet component includes: dividing the range of each input optimization variable into intervals, and performing a random sampling observation on the input variables within each interval; substituting the sampled sector angle and thickness of the permanent magnet component into the modeling method of the hybrid pole sinusoidal permanent magnet motor for solution, and obtaining the average torque, torque ripple and torque cost.

7. A parameter optimization system for a hybrid magnetic pole sinusoidal permanent magnet motor, characterized in that, Including: A target function construction module, which is configured to: use the sector angle and thickness of each permanent magnet component in each pole as optimization variables, and use the average torque, torque ripple, and torque cost obtained from the modeling method of the hybrid pole sinusoidal permanent magnet motor as the target function; An optimization module, which is configured to: based on the target function, globally optimize the motor performance using the manta ray intelligent optimization algorithm to obtain the optimal sector angle and thickness of each permanent magnet component; A comparison and output module, which is configured to: substitute the optimal sector angle and thickness into the finite element model for solution, and compare the average torque, torque ripple, and torque cost obtained from the solution with the average torque, torque ripple, and torque cost obtained from the modeling method of the hybrid pole sinusoidal permanent magnet motor to check whether the performance matches; Among them, the modeling method of the hybrid pole sinusoidal permanent magnet motor includes: calculating the no-slot air-gap flux density of the motor under no-load conditions with different remanences and different sector angles of the motor permanent magnet components according to the remanence distribution of the motor permanent magnet components and the air-gap magnetic field boundary conditions; establishing a relationship between the relative air-gap permeability and the slot opening under no-load conditions according to the stator slot model of the motor, and obtaining the relative air-gap permeability with slots through Fourier decomposition calculation; in the complex coordinate system, calculating the air-gap flux density with slots of the motor under no-load conditions according to the no-slot air-gap flux density and the relative air-gap permeability with slots; based on the air-gap flux density with slots of the motor under no-load conditions, calculating the cogging torque of the motor by the energy method; at the same time, calculating the no-load back electromotive force of the motor according to the winding distribution and the air-gap flux density with slots, and calculating the output torque by combining the no-load back electromotive force of the motor with the actual driving current to obtain the average torque, torque ripple, and torque cost; The target function is expressed by the following formula: where x i is the optimization variable, f(x i ) is the value of the optimization objective function corresponding to the change of the optimization variable x i , i = 1, 2, 3...; T av0 , T r0 , T c0 are the initial values of the average torque, torque ripple, and torque cost respectively, T av (x i ), T r (x i ), T c (x i ) are the average torque, torque ripple, and torque cost corresponding to the optimization variable x i respectively, and λ1, λ2, and λ3 are the weight coefficients of the average torque, torque ripple, and torque cost respectively, and satisfy λ1 + λ2 + λ3 = 1.

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