Design method of built-in permanent magnet synchronous motor and motor obtained by method

Abstract

The invention discloses a design method of a built-in permanent magnet synchronous motor and a motor obtained by the method, and aims to solve the problems of magnetic field modeling precision and efficiency imbalance, multivariable optimization dimension redundancy and performance index imbalance of the existing motor. According to the method, a stator slotting equivalent air gap magnetic permeability model is constructed through a magnetic potential penetration method, and rapid and accurate calculation of a magnetic field is realized by coupling permanent magnet magnetomotive force harmonic expansion; analyzing a quantitative variable interaction effect in combination with variance, and compressing an experiment dimension from 32768 dimension to 32 dimension through an L32 mixed horizontal orthogonal array; and constructing a multi-performance evaluation system based on entropy weight-TOPSIS. After implementation, the magnetic field calculation efficiency is improved by 92%, and the fundamental wave error is lt; the method has the advantages of high power density and high stability, the calculation resource consumption is reduced by 37%, the motor torque ripple is reduced by 42.7%, the cogging torque is inhibited by 85%, the comprehensive efficiency is improved by 1.5%, and the requirements of high power density and high stability of scenes such as new energy automobile driving and the like can be met.

Description

Technical Field

[0001] This invention belongs to the field of embedded permanent magnet synchronous motor (IPMSM) design technology, and more specifically, relates to a design method for an embedded permanent magnet synchronous motor and the motor obtained by the method. Background Technology

[0002] V-type built-in permanent magnet synchronous motors are widely used in new energy vehicles, industrial drives, and other fields due to their high power density and high efficiency. Their magnetic field distribution is closely related to the rotor structure parameters. Accurately calculating the air gap magnetic flux density and optimizing rotor design parameters are key to improving motor torque performance and reducing torque ripple.

[0003] In existing technologies, magnetic field calculations mostly employ the finite element method, which, while highly accurate, suffers from time-consuming calculations and low iteration efficiency, making it difficult to meet the needs of rapid design. Meanwhile, motor rotor design involves multiple structural variables (such as rotor rib width, permanent magnet slot angle, etc.), and the influence of each variable and its interaction effects on motor performance (average torque, torque ripple, etc.) is complex. Traditional multi-objective optimization methods suffer from drawbacks such as numerous experiments, dimensional redundancy, and inaccurate comprehensive evaluation, making it impossible to efficiently achieve a balance between performance and structure optimization.

[0004] Therefore, there is an urgent need for a motor design method that balances computational efficiency and accuracy and can efficiently handle multivariate optimization, thus addressing the pain points of traditional technologies. Summary of the Invention

[0005] 1. The problem to be solved To address the problems of time-consuming traditional finite element methods, low efficiency of multivariate optimization, and lack of scientific comprehensive evaluation indicators, this invention provides a design method for an embedded permanent magnet synchronous motor and the motor obtained by this method.

[0006] 2. Technical Solution To solve the above problems, the technical solution adopted by the present invention is as follows: The present invention discloses a design method for an embedded permanent magnet synchronous motor, which includes the following steps: (1) Stator slotted equivalent air gap magnetic permeability modeling: based on the number of stator slots z The corresponding periodic characteristics allow the air gap permeability to be expanded using a Fourier series as follows: ; (2) Equivalent permanent magnet magnetomotive force modeling: The magnetomotive force of the V-shaped permanent magnet is expanded into ; (3) Synthesis of unloaded air gap magnetic flux density: based on the fundamental laws of magnetic fields By coupling the magnetic permeability of step (1) with the magnetomotive force of step (2), we obtain ; (4) Multi-objective collaborative optimization: Quantify the contribution of design variables using ANOVA. An L32 orthogonal array dimensionality reduction experiment was conducted. After triangular fuzzy membership degree normalization and entropy weighting, a comprehensive index was calculated. , and select the optimal structural parameters.

[0007] Preferably, in step (1), the magnetic permeability harmonic order Choose from 1 to 12, and pay special attention to the 6th and 12th harmonics that are related to the number of stator slots.

[0008] Preferably, in step (4), the sensitive variables screened by ANOVA need to meet the contribution requirements. Significant interactive combinations include rotor magnetic bridge height Height of spline control points .

[0009] Preferably, in step (4), the optimal value of the triangular fuzzy membership function is... This represents the maximum or minimum value of each performance objective in the orthogonal experiment.

[0010] Preferably, in step (4), the step of determining the weights using the entropy weight method is as follows: first calculate the entropy of each target information. Then through Obtain the weights.

[0011] Preferably, in step (1), the magnetic permeability harmonic amplitude The results were obtained through finite element simulation fitting, with a fitting error not exceeding 5%.

[0012] Preferably, in step (4), the orthogonal array is The hybrid horizontal array reduces the number of experiments by more than 99% compared to the full factorial experiment.

[0013] Preferably, in step (4), the signal-to-noise ratio SNR The calculation method is as follows: ,in This represents the number of times the experiment was repeated.

[0014] Preferably, in step (4), the value range of the comprehensive index MPCI is 0-1. When MPCI≥0.45, the comprehensive performance of the motor meets the application requirements of medium and high power density.

[0015] Preferably, the rated power of the built-in permanent magnet synchronous motor is 50kW-200kW, suitable for new energy vehicle drive or industrial transmission scenarios; the design variables include 9 key parameters, and the value range of each parameter is as follows: rotor rib width ω r 0.45-0.50mm; Rotor rib height h r1.2-1.8mm; height of spline curve fitting control points h sr1 0.30-0.60mm h sr2 1.3-1.8mm h sr3 : 0-1.8mm; spline curve fitting control angle a sr1 8-15° a sr2 5-10°; Permanent magnet slot spacing ω g : 2.5-5.0mm; Permanent magnet slot angle a R : 110-130°.

[0016] The present invention also provides an embedded permanent magnet synchronous motor, which is designed by any of the above-mentioned design methods.

[0017] 3. Beneficial effects Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The magnetic field modeling of the present invention is accurate and efficient, solving the problem of the imbalance between accuracy and efficiency of traditional methods: The present invention uses the magnetic potential penetration method to construct the stator slot equivalent air gap magnetic permeability model, combined with permanent magnet magnetomotive force harmonic expansion and coupling calculation, which not only ensures the high accuracy of the fundamental wave error of the unloaded air gap magnetic flux density <3.5%, but also shortens the magnetic field calculation time from several hours of the traditional finite element method to minutes, improving efficiency by more than 92%, perfectly adapting to the needs of rapid iterative design of motors.

[0018] (2) The multivariate optimization of this invention achieves efficient dimensionality reduction, breaking through the "combinatorial explosion" bottleneck of traditional schemes: by quantifying the second-order interaction effect of design variables through analysis of variance (ANOVA), the coupling effect of key variables is accurately captured, combined with L32 (4 8 ×8) Hybrid horizontal orthogonal arrays compress the theoretical and experimental dimensions of the nine design variables from 32,768 to 32, reducing computational resource consumption by 37%, while avoiding the problem of insufficient engineering applicability caused by traditional optimization methods ignoring variable interaction effects.

[0019] (3) The multi-objective evaluation of this invention is scientific and comprehensive, achieving a leap in the overall performance of the motor: Based on the entropy weight-TOPSIS, a multi-performance evaluation system is constructed. Different dimensional objectives are unified through the triangular fuzzy membership function, and reasonable weights are given to core indicators such as average torque and torque ripple. Ultimately, the motor torque ripple is reduced by 42.7%, cogging torque is suppressed by 85%, and the overall efficiency is improved by 1.5%. It takes into account high power density, low vibration and noise, and high reliability, meeting the stringent requirements of scenarios such as new energy vehicle drive. Attached Figure Description

[0020] Figure 1 This is a flowchart of the IPMSM unloaded magnetic field analysis of the present invention; Figure 2 shows the equivalent solution model of IPMSM of the present invention; (a) is the slotless model of the equivalent MMF, and (b) is the slotted model of the equivalent air gap permeation. Figure 3 The analysis and calculation results of the equivalent magnetomotive force of the present invention are shown in (a) periodic distribution and (b) harmonic content. Figure 4 The equivalent air gap magnetic permeability analysis and calculation results of the present invention are shown in (a) effective air gap length and (b) magnetic permeability harmonic content. Figure 5 These are the key design variables for the rotor of this invention; Figure 6 The Pearson correlation coefficient between the variables and the target of this invention; Figure 7 This is a schematic diagram of the MPCI based on design variables at various levels according to the present invention; Figure 8 This is a schematic diagram illustrating the adjustment of unimportant variables in this invention; Figure 9 This is a schematic diagram illustrating the adjustment of key variables in this invention; Figure 10 shows a comparison of the analytical and finite element results of the air gap magnetic flux density under different pole-slot combinations of the present invention: (a) 4 poles and 36 slots; (b) 6 poles and 36 slots; (c) 6 poles and 9 slots; (d) 8 poles and 9 slots. Figure 11 This is a torque comparison diagram under different design schemes of the present invention; Figure 12 These are schematic diagrams illustrating the overall sound pressure level of different embodiments of the present invention; Figure 13 Mechanical strength analysis of the present invention before and after optimization (8000 rpm): (a) Original design; (b) Improved and optimized design. Detailed Implementation

[0021] The more detailed description of embodiments of the invention below is not intended to limit the scope of the claimed invention, but is merely illustrative and does not limit the description of the features and characteristics of the invention, in order to suggest the best mode for carrying out the invention and to enable those skilled in the art to practice the invention. However, it should be understood that various modifications and variations can be made without departing from the scope of the invention as defined by the appended claims. The detailed description should be considered illustrative only and not restrictive, and any such modifications and variations shall fall within the scope of the invention described herein. Furthermore, the background art is intended to illustrate the current state of research and development and significance of the technology, and is not intended to limit the invention or the scope of application of this application.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0023] The present invention discloses a design method for an embedded permanent magnet synchronous motor, employing an integrated design scheme of "rapid magnetic field calculation using the magnetomotive force penetration method + multi-objective collaborative optimization". It achieves rapid magnetic flux density calculation through magnetic permeability-magnetomotive force coupling modeling, and combines sensitive variable identification, experimental dimensionality reduction, and comprehensive evaluation to complete the motor structure optimization. The main process and key components are as follows: Figure 1 (The flowchart for IPMSM unloaded magnetic field analysis is shown.) It specifically includes the following steps: 1. Magnetic field rapid calculation module based on magnetic potential penetration method Core logic: By utilizing the periodic characteristics of the stator slotted air gap magnetic permeability and the harmonic characteristics of the permanent magnet magnetomotive force, the unloaded air gap magnetic flux density is obtained through coupled calculation, avoiding the tedious iteration of the traditional finite element method.

[0024] Step 1.1 Stator slotted equivalent air gap magnetic permeability modeling Stator slotting leads to air gap length Rotor position angle θ Stator tooth-pole angle α It exhibits periodic changes, with a period of... (z represents the number of stator slots). For the slotted equivalent air gap magnetic permeability model in Figure 2(b), the uniform distribution characteristic of the stator tooth air gap (tooth magnetic flux density) is utilized. B δtP (Spatial constant), deriving the magnetomotive force of the teeth. F δt = B δtP · δ / μ 0 ( δ The physical air gap length, μ 0 is the permeability of free space (according to the definition of permeability). ,because Due to its periodicity, the magnetic permeability needs to be expanded using a Fourier series into the sum of a constant component and harmonic components, i.e.: Derivation basis: The periodic variation of the air gap length causes the magnetic permeability to fluctuate in the same period, and the Fourier series can accurately describe the characteristics of this fluctuation. For the permeability constant component, z The number of stator slots a The angle between the stator teeth and the center line of the magnetic poles. The amplitude of the nth harmonic (obtained by finite element simulation fitting, its distribution law is as follows) Figure 4 (b) As shown in the equivalent air gap magnetic permeability harmonic content, the 6th and 12th harmonics are the main interference components. n Harmonic order (and the number of stator slots) z (Strong correlation).

[0025] Step 1.2 Equivalent permanent magnet magnetomotive force modeling The V-shaped internal permanent magnets are spatially periodically distributed, generating a magnetomotive force. Rotation with rotor (electric angular velocity) ω The magnetomotive force exhibits time-varying characteristics. Based on the slotless equivalent magnetomotive force model in Figure 2 (IPMSM equivalent solution model), considering the spatial periodic distribution of the permanent magnet, it is also expanded using Fourier series: in, ω It is the electric angular frequency. ω= 2 πf ( f (Power supply frequency) p This represents the number of pole pairs of the motor. v For harmonic orders, Fv For the first v The amplitude of the second harmonic (determined by the remanence and size of the permanent magnet, the calculation results are as follows) Figure 3 (b) As shown in the equivalent magnetomotive force harmonic content, the fundamental frequency accounts for over 90%.

[0026] Step 1.3 Synthesis of Unloaded Air Gap Magnetic Flux Density According to the fundamental laws of magnetic fields , combined ( l (where is the length of the magnetic circuit), we can obtain ( (For magnetic permeability). Couple the magnetic permeability of Equation 1 with the magnetomotive force of Equation 2 to obtain the air gap magnetic flux density: Derivation: Equation 3 is the direct coupling result of Equation 1 and Equation 2. Through this equation, the fundamental magnetic flux density and slot harmonic interference (such as the 6th and 12th harmonics) can be quickly separated, and the calculation time is only 1 / 10 of that of the traditional finite element method.

[0027] By combining the magnetic potential penetration method with the equivalent air gap magnetic permeability model of stator slotting, as shown in Figure 10 (ad represents the comparison of air gap magnetic flux density for 4 poles with 36 slots, 6 poles with 36 slots, 6 poles with 9 slots, and 8 poles with 9 slots respectively), the fundamental error of the analytical solution and the finite element solution under different pole and slot combinations is <3.5%, and the maximum relative error is <5%. Moreover, the calculation time for the unloaded magnetic field is reduced from 2 hours in the traditional finite element method to 9.6 minutes, with an efficiency improvement of 92%, solving the technical pain point of "low accuracy or low efficiency".

[0028] 2. Multi-objective collaborative optimization module Core logic: Through dimensionality reduction via variable sensitivity analysis, load reduction via orthogonal experiments, and quantification via comprehensive evaluation, the average torque is achieved. T avg Torque pulsation ( T rip The optimization balances multiple performance aspects, including "sensitive variable identification, interaction effect quantification, experimental dimensionality reduction, and multi-performance evaluation." A highly efficient optimization framework is constructed around this core, with key components such as... Figure 5 (Key design variables for rotor) Figure 6 (Pearson correlation coefficient between the variable and the target) Figure 7 (The MPCI diagram based on design variables at each level is shown.) Specific steps are as follows: Step 2.1 Variable sensitivity and interaction effect quantification (ANOVA) The Pearson correlation coefficient was used to identify sensitive variables, as shown in Figure 7, including rotor rib width. ω r (A) Spline control point height h sr1 (C) Permanent magnet slot angle a R (I) has a correlation coefficient with an absolute value exceeding 0.7, making it a core sensitive variable; variable h r (B) and h sr1 (C) a sr2 (G) and ω g The interaction effect of (H) contributes more than 10%, making it a significant interaction combination. Furthermore, in Table 1, D represents the height of the spline curve fitting control points, and F represents the control points used for spline curve fitting. In this study, we found that the interaction effect of D and F is relatively small, therefore they are not selected for subsequent parameter optimization. The aforementioned other parameters are preferred.

[0029] Analysis of variance (ANOVA) was used to quantify the contribution of each design variable and interaction effect to the performance target. The formula is as follows: Derivation basis: a. Sum of squares relation: Total sum of squares SS Total =Sum of squares of target effect SS i + Sum of squared errors SS e ,in SS i The sum of squares of a certain variable / interaction effect. SS e This is the sum of squares caused by experimental error; b. Degrees of freedom calculation: Total degrees of freedom df Total =Total number of samples - 1, Degrees of freedom for the target effect df i (Single variable) df i =1, interaction between two variables df i =1), error degrees of freedom ; c. Mean square error This characterizes the average fluctuation of experimental error.

[0030] Sensitive variables with a contribution rate >10% (such as rotor rib width) are screened using Equation 4. ω r spline control point height h sr1 ) and significant interaction combinations (such as h r and h sr1 This achieves variable dimensionality reduction.

[0031] Step 2.2 Experimental Design and Data Acquisition Based on the sensitivity variables after dimensionality reduction, L32(4) is adopted. 8 ×8) Mixed-level orthogonal array (as shown in Table 1), reducing the number of full factorial experiments from 4 8 ×8=32768 times compressed to 32 times, and data collected from each group of experiments. T avg , T rip Data, and calculate the signal-to-noise ratio of each target. .

[0032] Table 1. Performance at each design variable level Differentiated adjustment strategies are adopted based on the characteristics of different variables: for insensitive variables (such as...) h sr3 (E)), according to Figure 8 (Diagram showing adjustment of unimportant variables) Compressed into double horizontals (spacing ( d 2<2 d 1)); for sensitive variables (such as ( ω r (A), according to Figure 9 (Important variable adjustment diagram) Maintain four levels (interval (3) d 2≤2 d 1)) Enables refined search of the design space.

[0033] Step 2.3 Comprehensive Evaluation of Multiple Performance Aspects (Entropy Weighted TOPSIS) 1. Unification of Target Dimensions: As shown in Figure 7 (MPCI schematic diagram based on design variables at various levels), the target dimensions are unified through the triangular fuzzy membership function (Equation 5), and torque pulsation is assigned ( T rip (0.3), average torque ( T avg The target weights (e.g., 0.2) are used to select the optimal combination of variables (e.g., A2, B1, C4, etc., Table 2).

[0034] Purpose of derivation: To eliminate dimensional differences and enable direct comparison of different performance targets, among which... Let $j$ be the minimum value of the $j$-th objective. This is the optimal value.

[0035] Table 2. Design variables for different design schemes 2. Calculation of the Comprehensive Index (MPCI): The weights of each objective are determined using the entropy weight method. ω j (The smaller the information entropy, the greater the weight), combined with SNR j and μ j This yields a comprehensive set of performance indicators: Derivation of the relationship: In Equation 6 ω j For the first j The weights of each optimization objective, SNR j For the first j Signal-to-noise ratio of each target m jLet be the triangular fuzzy membership function of the j-th target, and ⊗ be the fuzzy composition operator. m To optimize the total number of targets m =5, including average torque T avg Torque pulsation T rip Cogging torque T cog Total loss P loss Air gap magnetic flux density fundamental wave amplitude B r . ω j Calculated using the entropy weight method ( , For the first j The higher the MPCI value (information entropy of each target), the better the overall performance of the motor.

[0036] To ensure the feasibility of the optimized plan, three major constraint criteria are established: 1. Electromagnetic performance constraints: Slot fill factor ≥ 80%, air gap magnetic flux density harmonic distortion rate < 5% (based on Figure 10 (comparison of air gap magnetic flux density under different pole-slot combinations), the error between analytical solution and finite element solution distortion rate is < 2%). 2. Mechanical strength constraint: At the limiting speed of 8000 rpm, the maximum equivalent stress of the rotor is less than the yield strength of 35W250 silicon steel sheet (250 MPa). Figure 13 (Mechanical strength analysis before and after optimization) shows that the maximum stress after optimization is 230MPa, which meets the requirements; 3. Manufacturing process constraints: silicon steel sheet stacking coefficient ≥ 0.95, permanent magnet assembly gap ≤ 0.1mm, suitable for industrialized production processes.

[0037] The present invention will be further described below with reference to specific embodiments. Specifically, to avoid redundancy in the embodiments, only three typical applications are taken as examples: 6-pole 36-slot, 8-pole 9-slot, and 6-pole 9-slot V-type built-in permanent magnet synchronous motors (IPMSM). This is not a limitation on the type of motor of the present invention. For example, Figure 10 also includes 4-pole 36-slot motors, etc. The embodiments of the present invention share the above steps 1.1-1.3 and 2.1-2.3. The specific implementation methods (exemplary and comparative examples) are described in detail below: Example 1 (6-pole 36-slot V-type IPMSM) This embodiment provides a design method for an embedded permanent magnet synchronous motor. Detailed modeling and formulas are described in steps 1.1-1.3 and 2.1-2.3 above, and will not be repeated here. The detailed calculation process and parameters are as follows: (1) Determination of basic motor parameters; Application scenario: For driving new energy vehicles, rated power 150kW, rated speed 3000rpm; Stator: 36 slots, core outer diameter 280mm, inner diameter 180mm, using 35W250 silicon steel sheets (100mm thick), distributed short-pitch windings (pitch 1-6), slot fill factor 85%; Rotor: 6 poles, outer diameter 179.8mm (air gap) δ =0.1mm), the V-shaped permanent magnet is NdFeB N52 (remanence 1.48T, coercivity 950kA / m), and the isolation magnetic bridge width is 1.2mm; Initial range of design variables: rotor rib width ω r 0.45-0.50mm, permanent magnet slot angle a R 110-130°.

[0038] (2) Implementation of magnetic field modeling using the magnetic potential penetration method; 2.1 Calculation of equivalent magnetomotive force: Ignoring stator slots, the fundamental amplitude of the air gap magnetic flux density is obtained from the initial finite element simulation. B 0( θ ) = 1.2T, substitute into F ( θ )≈ B 0( θ )· δ / μ 0, yielding a fundamental magnetomotive force amplitude of 800A; Fourier decomposition reveals that the fundamental frequency accounts for 92%, the third harmonic accounts for 5%, and the fifth harmonic accounts for 3%. 2.2 Calculation of equivalent air gap magnetic permeability for slotted sections: Measure the stator slot width (5mm) and slot depth (15mm), and fit the magnetic permeability curve using Equation 1. λ 0 = 1.1 × 10 -6 H / m, λ 1 = 0.2 × 10 -6 H / m (1st harmonic) λ 6 = 0.05 × 10 -6 H / m (6th harmonic, corresponding to 36 slots); 2.3 Verification of unloaded air gap magnetic flux density: Calculation B ( t , θ )=F( t , θ )· λ ( θ , a Compared with finite element simulation, the fundamental amplitude error is 2.8%, and the harmonic distortion rate error is 1.2%; the calculation time is reduced from 2 hours in traditional finite element simulation to 9.6 minutes (efficiency improvement of 92%).

[0039] (3) Multi-objective collaborative optimization implementation 3.1 Interaction Effect and Sensitivity Analysis: ANOVA calculations yielded... a R (Angle between permanent magnet slots) and ω g The interaction effect of "(permanent magnet slot spacing)" contributed the most (12.5%); the Pearson correlation coefficient showed... ω r (0.82) h sr1 (0.78) a R (0.75) is a sensitive variable; 3.2 Orthogonal experimental design: L32 (4 8 ×8) The array design includes 32 sets of experiments. For example, the variable combination for the 17th set is: ( ω r =0.50mm), ( h r =1.30mm), ( h sr1 =0.50mm\), ( a R =120°), the test result is ( T avg =130N·m), ( T rip =29.00), ( T cog =0.288 N·m); 3.3 Multi-performance evaluation: Calculate the SNR of each target, such as ( T avg The SNR of ) is -10lg [1 / 32×Σ(130⁻²)] = 37.0dB. T rip The SNR of ) is -10lg [1 / 32×Σ(29.00²)] = -28.1dB; the weights are determined by the entropy weight method. T rip ): 0.3, ( T avg ): 0.2, ( T cog ): 0.2, ( P loss ): 0.2, ( B r ): 0.1), calculate MPCI=0.571 (group 17 is the optimal); 3.4 Parameter Space Adjustment: Adjusting Sensitive Variables ( ω r ), ( h sr1 ), ( a R Maintain four horizontal levels, and adjust the spacing to 0.01mm (original spacing 0.05mm, satisfying (3) d 2 = 0.03 ≤ 2 d 1=0.1); for insensitive variables ( h sr3 The compression is done to two horizontal lines (0.9mm, 1.2mm) with a spacing of 0.3mm (meeting the requirements of...). d 2 = 0.3 < 2 d 1=0.6).

[0040] The core performance data and advantages of the built-in permanent magnet synchronous motor designed in this embodiment are recorded in Table 3 for comparison.

[0041] Example 2 (8-pole 9-slot V-type IPMSM) This embodiment provides a design method for an embedded permanent magnet synchronous motor. Detailed modeling and formulas are described in steps 1.1-1.3 and 2.1-2.3 above, and will not be repeated here. The detailed calculation process and parameters are as follows: (1) Determination of basic parameters of motor Application scenario: Industrial transmission, rated power 50kW, rated speed 1500rpm; Stator: 9 slots, 150mm outer diameter and 80mm inner diameter of iron core, 60mm thick silicon steel sheet, 1-3 winding pitch; Rotor: 8 poles, outer diameter 79.5mm (air gap) δ =0.15mm), the permanent magnet is NdFeB N48 (remanence 1.45T), and the isolation magnetic bridge width is 1.0mm; Initial range of design variables: a R 95-105° ω r 0.40-0.45mm.

[0042] (2) Implementation of magnetic field modeling using the magnetic potential penetration method Equivalent magnetomotive force calculation: finite element preliminary simulation B 0( θ =1.1T, fundamental magnetomotive force amplitude 650A, Fourier decomposition fundamental frequency accounts for 90%; Slotted magnetic permeability calculation: Stator slot width 4mm, fitted result λ 0 = 1.0 × 10 -6 H / m, 3rd harmonic λ 3 = 0.15 × 10- 6 H / m; Magnetic flux density verification: B ( t , θ The deviation from the finite element method was 3.1%, and the calculation time was 7.2 minutes (the traditional finite element method takes 1.5 hours).

[0043] (3) Multi-objective collaborative optimization implementation Sensitive variables: a R (Correlation coefficient 0.76) ω g (0.72); Orthogonal Experiment: Group 22 of Variables in L32 Array a R =100° ω r =0.42mm; Performance results: After optimization T rip =25.1, overall efficiency 94.8%, MPCI=0.536.

[0044] The core performance data and advantages of the built-in permanent magnet synchronous motor designed in this embodiment are recorded in Table 3 for comparison.

[0045] Example 3 (6-pole 9-slot V-type IPMSM) This embodiment provides a design method for an embedded permanent magnet synchronous motor. Detailed modeling and formulas are described in steps 1.1-1.3 and 2.1-2.3 above, and will not be repeated here. The detailed calculation process and parameters are as follows: (1) Determination of basic parameters of motor Application scenario: For driving small equipment, rated power 20kW, rated speed 2000rpm; Stator: 9 slots, iron core outer diameter 120mm, inner diameter 60mm, stack thickness 40mm; Rotor: 6 poles, outer diameter 59.8mm (air gap) δ =0.12mm), rotor rib height h r Initial value: 1.5-1.7 mm; Initial range of design variables: h r 1.5-1.7mm a R 115-125°.

[0046] (2) Implementation of magnetic field modeling using the magnetic potential penetration method Equivalent magnetomotive force calculation: Fundamental magnetomotive force amplitude 500A, fundamental frequency accounts for 91%; Slotted magnetic permeability calculation: λ 0 = 0.9 × 10 -6 H / m, second harmonic λ 2 = 0.12 × 10 -6 H / m; Magnetic flux density verification: calculation time was 5.8 minutes (traditional finite element method takes 1 hour).

[0047] (3) Multi-objective collaborative optimization implementation Sensitive variables: h r (Correlation coefficient 0.74) a R (0.70); Orthogonal Experiment: Group 14 Variables h r =1.6mm a R =120°; Performance results: After optimization T cog =0.29 N·m, computational resource consumption reduced by 35%, MPCI=0.508.

[0048] The core performance data and advantages of the built-in permanent magnet synchronous motor designed in this embodiment are recorded in Table 3 for comparison.

[0049] Comparative Example 1 (Original design: 6-pole 36-slot IPMSM) This comparative example also provides a design method for a built-in permanent magnet synchronous motor. The motor designed by this method is the same as that in Example 1. The main difference is that the original design scheme of the traditional finite element method without any optimization is adopted. Variable combinations: ω r =0.48mm h r =23.0mm h sr1 =0.80mm a R =110°; performance: T avg =125 N·m T rip =35.2、 T cog =0.35 N·m, MPCI=0.445; Magnetic field calculation time: 2 hours (traditional finite element method).

[0050] The core performance data of the built-in permanent magnet synchronous motor in this comparative design are recorded in Table 3 for comparison.

[0051] Comparative Example 2 (Orthogonal design: 6-pole 36-slot IPMSM) This comparative example also provides a design method for an embedded permanent magnet synchronous motor. The motor designed by this method is the same as that in Example 1, with the main difference being that the optimal solution is selected only through an L32 orthogonal array (without multi-objective comprehensive evaluation). Variable combinations: ω r =0.50mm h r =23.0mm h sr1 =0.60mm a R =120°; performance: T avg =128 N·m T rip =31.7、 T cog =0.32 N·m, MPCI=0.471; Magnetic field calculation time: 1 hour (simplified finite element method).

[0052] The core performance data of the built-in permanent magnet synchronous motor in this comparative design are recorded in Table 3 for comparison.

[0053] Comparative Example 3 (GA design: genetic algorithm optimization, 6-pole 36-slot IPMSM) This comparative example also provides a design method for an embedded permanent magnet synchronous motor. The motor designed by this method is the same as that in Example 1, with the main difference being that it is optimized only by a genetic algorithm (rapid modeling without magnetic potential penetration method). Variable combinations: ω r =0.50mm h r =22.8mm h sr1 =0.50mm a R =120°; performance: T avg =129 N·m T rip =27.5、 T cog =0.30 N·m, MPCI=0.512; Magnetic field calculation time: 1.5h (iterative finite element method).

[0054] The core performance data of the built-in permanent magnet synchronous motor in this comparative design are recorded in Table 3 for comparison.

[0055] Table 3. Comparison of Performance Data between Examples and Comparative Examples Scheme type Motor parameters (pole / slot) Magnetic field calculation time Core performance indicators MPCI value Key advantages Example 1 6 / 36 9.6min <![CDATA[ T avg =130N·m、 T rip =29.00]]> 0.571 High-power scenarios, optimal balance of multiple objectives Example 2 8 / 9 7.2min <![CDATA[ T rip =25.1, Overall efficiency 94.8% 0.536 High efficiency in medium power scenarios Example 3 6 / 9 5.8min <![CDATA[ T cog =0.29 N·m, resources reduced by 35% 0.508 Lowest computational cost in low-power scenarios Comparative Example 1 (Original Design) 6 / 36 2h <![CDATA[ T avg =125N·m、 T rip =35.2]]> 0.445 No optimization, worst performance Comparative Example 2 (Orthogonal Design) 6 / 36 1h <![CDATA[ T avg =128N·m、 T rip =31.7]]> 0.471 Experimental dimensionality reduction but no comprehensive evaluation Comparative Example 3 (GA Design) 6 / 36 1.5h <![CDATA[ T avg =129N·m、 T rip =27.5]]> 0.512 Single algorithm optimization is time-consuming. Comparative Examples 1-3 and 1-3: Figure 11 The torque comparison chart shown illustrates different design schemes. After optimization, the motor torque ripple decreased from 48.01% to 27.53%, a reduction of 42.7%, and the cogging torque decreased from 2.13 N·m to 0.32 N·m, a suppression rate of 85%, thus resolving the problem of poor dynamic stability. Figure 12 The schematic diagrams of the overall sound pressure level of different schemes show that the A-weighted sound pressure level at 1m decreases from 48.2dB(A) to 22.5dB(A), meeting the "low noise, high efficiency" requirements of new energy vehicles; for example... Figure 13 As shown, the total deformation of the optimized rotor is <0.05mm, and the maximum equivalent stress is <250MPa. It still maintains mechanical strength under lightweight design and is suitable for high-speed operation.

[0056] In summary, Examples 1-3 demonstrate comprehensive technical advantages over Comparative Examples 1-3. They employ the magnetic potential penetration method instead of the traditional finite element method, reducing magnetic field modeling time to only 1 / 10 to 1 / 12.5 of the comparative examples. For instance, Example 1 (9.6 min) shows a 92% efficiency improvement over Comparative Example 1 (2 h), significantly reducing design time costs. Regarding core performance, the examples achieve multi-dimensional optimization, including increased average torque, reduced torque ripple, and reduced cogging torque. Example 1 shows an average torque (130 N·m) 4% higher than the original design, with a 17.6% reduction in torque ripple (29.0%). Example 3 shows a 17.1% reduction in cogging torque (0.29 N·m) and a 35% reduction in computational resource consumption. Example 2 achieves a comprehensive efficiency of 94.8%, suitable for industrial transmission requirements. Through entropy weighting... The TOPSIS multi-objective evaluation system shows that the MPCI value of the example is significantly better than all comparative examples. Example 1 (0.571) is 28.3%, 21.2%, and 11.5% higher than the original design, orthogonal design, and GA design, respectively, avoiding the performance imbalance of single-objective optimization. At the same time, the example covers all scenarios of high-power new energy vehicle drive, medium-power industrial transmission, and low-power small equipment drive, and each scenario has its own specific advantages. In contrast, the comparative examples can only be adapted to a single scenario and have single performance, which cannot meet the differentiated needs of multiple scenarios. This fully demonstrates the comprehensive leadership of the example in terms of computational efficiency, performance balance, comprehensive evaluation, and scenario adaptability.

[0057] The present invention has been described in detail above with reference to specific exemplary embodiments. However, it should be understood that various modifications and variations can be made without departing from the scope of the invention as defined by the appended claims. The detailed description should be considered illustrative only and not restrictive, and any such modifications and variations shall fall within the scope of the invention described herein. Furthermore, the background art is intended to illustrate the current state of development and significance of the technology and is not intended to limit the present invention or the scope of application of the present application.

[0058] More specifically, although exemplary embodiments of the invention have been described herein, the invention is not limited to these embodiments, but includes any and all embodiments modified, omitted, such as combinations between various embodiments, adaptive changes, and / or substitutions, as would be apparent to those skilled in the art from the foregoing detailed description. The limitations in the claims are to be interpreted broadly as used in the language of the claims and are not limited to the examples described in the foregoing detailed description or during the implementation of this application, which should be considered non-exclusive. Any step listed in any method or process claim may be performed in any order and is not limited to the order set forth in the claims. Therefore, the scope of the invention should be determined solely by the appended claims and their legal equivalents, and not by the description and examples given above.

[0059] Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. In case of conflict, the definitions in this specification shall prevail. When flow rate, power, refractive index, time, or other values ​​or parameters are expressed as ranges, preferred ranges, or a series of upper and lower preferred values, this should be understood as specifically disclosing all ranges formed by any pair of any upper or preferred value with any lower or preferred value, regardless of whether such range is disclosed individually. For example, the range 1-50 should be understood to include any number, combination of numbers, or subrange selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50, as well as all decimal values ​​between the integers mentioned above, such as 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, and 1.9. Regarding subranges, specifically consider "nested subranges" extending from any endpoint of the range. For example, nested sub-ranges of the exemplary range 1-50 may include 1-10, 1-20, 1-30 and 1-40 in one direction, or 50-40, 50-30, 50-20 and 50-10 in another direction.

Claims

1. A design method for a built-in permanent magnet synchronous motor, characterized in that, Includes the following steps: (1) Stator slotted equivalent air gap magnetic permeability modeling: based on the number of stator slots z The corresponding periodic characteristics allow the air gap permeability to be expanded using a Fourier series as follows: ; (2) Equivalent permanent magnet magnetomotive force modeling: The magnetomotive force of the V-shaped permanent magnet is expanded into ; (3) Synthesis of unloaded air gap magnetic flux density: based on the fundamental laws of magnetic fields By coupling the magnetic permeability of step (1) with the magnetomotive force of step (2), we obtain ; (4) Multi-objective collaborative optimization: Quantify the contribution of design variables using ANOVA. An L32 orthogonal array dimensionality reduction experiment was conducted. After triangular fuzzy membership degree normalization and entropy weighting, a comprehensive index was calculated. , and select the optimal structural parameters.

2. The design method of an embedded permanent magnet synchronous motor according to claim 1, characterized in that, In step (1), the order of magnetic permeability harmonics Choose from 1 to 12, and pay special attention to the 6th and 12th harmonics that are related to the number of stator slots.

3. The design method of an embedded permanent magnet synchronous motor according to claim 1, characterized in that, In step (4), the sensitive variables selected by ANOVA must meet the contribution requirements. Significant interactive combinations include rotor magnetic bridge height Height of spline control points .

4. The design method of a built-in permanent magnet synchronous motor according to claim 1, characterized in that, In step (4), the optimal value of the triangular fuzzy membership function is... This represents the maximum or minimum value of each performance objective in the orthogonal experiment.

5. The design method of a built-in permanent magnet synchronous motor according to claim 1, characterized in that, In step (4), the steps for determining the weights using the entropy weight method are as follows: first, calculate the entropy of each target information. Then through Obtain the weights.

6. The design method of a built-in permanent magnet synchronous motor according to claim 1, characterized in that, In step (4), the orthogonal array is The hybrid horizontal array reduces the number of experiments by more than 99% compared to the full factorial experiment.

7. The design method of a built-in permanent magnet synchronous motor according to claim 1, characterized in that, In step (4), the signal-to-noise ratio SNR The calculation method is as follows: ,in n This represents the number of times the experiment was repeated.

8. The design method of a built-in permanent magnet synchronous motor according to claim 1, characterized in that, In step (4), the value range of the comprehensive index MPCI is 0-1. When MPCI≥0.45, the comprehensive performance of the motor meets the application requirements of medium and high power density.

9. The design method of a built-in permanent magnet synchronous motor according to claim 1, characterized in that, The built-in permanent magnet synchronous motor has a rated power of 50kW-200kW and is suitable for new energy vehicle drive or industrial transmission scenarios; the design variables include 9 key parameters, and the value range of each key parameter is as follows: rotor rib width ω r 0.45-0.50mm; Rotor rib height h r 1.2-1.8mm; height of spline curve fitting control points h sr1 0.30-0.60mm h sr2 1.3-1.8mm h sr3 : 0-1.8mm; spline curve fitting control angle a sr1 8-15° a sr2 5-10°; Permanent magnet slot spacing ω g : 2.5-5.0mm; Permanent magnet slot angle a R : 110-130°.

10. A built-in permanent magnet synchronous motor, characterized in that, The motor is designed by the design method of any one of claims 1-9.

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