A method and device for optimizing torque coefficient of a permanent magnet torque motor

Abstract

This invention provides a method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor, relating to the field of permanent magnet motor design optimization technology. The method includes determining the range of values ​​for the parameters to be optimized in the permanent magnet torque motor; determining an optimization objective function based on the torque coefficient equation and torque ripple equation; minimizing the optimization objective function as the objective, using the range of values ​​as constraints, and obtaining the parameters to be optimized by training a surrogate model and performing multi-objective optimization. The apparatus executes the above method. The method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor provided by this invention can accelerate the optimization convergence speed while taking into account torque ripple changes.

Description

Technical Field

[0001] This invention relates to the field of permanent magnet motor design optimization technology, specifically to a method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor. Background Technology

[0002] Permanent magnet torque motors are a type of magnetic synchronous motor capable of providing continuous and stable torque output at low speeds or even in stalled conditions. They are widely used in high-torque applications such as vehicle electromechanical braking systems, robot joints, and machine tool feed systems. The torque coefficient characterizes the motor's output torque per unit current and is a crucial indicator of permanent magnet torque motors. Optimizing the torque coefficient of a permanent magnet torque motor can effectively improve the system's dynamic performance and overload capacity, which has significant engineering implications.

[0003] Searching for the optimal number of winding turns and structural parameters using intelligent algorithms is a common method for optimizing the torque coefficient of permanent magnet torque motors. However, in the optimization process, process constraints such as slot fill factor are usually used as constraints of the optimization problem, which is a post-hoc verification of the design scheme. This leads to redundant calculations of a large number of design schemes that are invalid due to process impracticality, slowing down the optimization convergence speed. On the other hand, increasing the torque coefficient will, to some extent, exacerbate the magnetic saturation of the permanent magnet torque motor and aggravate the torque ripple of the motor. The torque ripple of permanent magnet motors is quite sensitive to parameters such as pole arc coefficient. Conventional optimization methods usually search within a relatively wide range of values, lacking guidance, and thus it is difficult to obtain a high torque coefficient while taking into account the changes in torque ripple. Summary of the Invention

[0004] To address the problems in the prior art, embodiments of the present invention provide a method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor, which can at least partially solve the problems existing in the prior art.

[0005] On the one hand, this invention proposes a method for optimizing the torque coefficient of a permanent magnet torque motor, comprising: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0006] The parameters to be optimized include the pole arc coefficient; correspondingly, determining the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The reference pole arc coefficient is determined based on the number of poles and slots of the permanent magnet torque motor, as well as the number of pole pairs of the permanent magnet torque motor. The range of values ​​for the polar arc coefficient is determined based on the reference polar arc coefficient and the preset value.

[0007] The parameters to be optimized include the stator slot depth, tooth width, tooth tip height, and tooth wedge height of the permanent magnet torque motor; correspondingly, the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The range of values ​​corresponding to the stator slot depth, tooth width, tooth tip height, and tooth wedge height are determined based on the dimensional parameters of the permanent magnet torque motor.

[0008] The objective function for optimization is determined based on the torque coefficient equation and the torque ripple equation, including: The torque coefficient equation and the torque pulsation equation are normalized respectively; The optimization objective function is determined based on the normalized torque coefficient equation, the normalized torque ripple equation, and their corresponding weighting coefficients.

[0009] The determination of the number of winding turns based on the winding slot fill factor that meets process requirements includes: The number of winding turns is calculated based on the stator slot area, the winding slot fill factor, and the winding wire cross-sectional area of ​​the permanent magnet torque motor.

[0010] The surrogate model is a Kriging model with the parameter to be optimized as the independent variable, and includes a global trend term and a stochastic process function with the parameter to be optimized as the independent variable.

[0011] On one hand, the present invention proposes a torque coefficient optimization device for a permanent magnet torque motor, comprising: The determination unit is used to determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and to determine the optimization objective function based on the torque coefficient equation and the torque pulsation equation. Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; An optimization unit is used to obtain the parameters to be optimized by training a proxy model and performing multi-objective optimization, with the goal of minimizing the optimization objective function and the range of values ​​as constraints. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0012] In another aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the following method: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0013] This invention provides a computer-readable storage medium, comprising: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0014] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the following method: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0015] The present invention provides a method and apparatus for optimizing the torque coefficient of a permanent magnet torque motor. The method determines the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determines the objective function based on the torque coefficient equation and the torque ripple equation. Both the torque coefficient equation and the torque ripple equation use the parameters to be optimized as independent variables. The objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a surrogate model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets process requirements, which can accelerate the optimization convergence speed and take into account torque ripple changes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart illustrating a method for optimizing the torque coefficient of a permanent magnet torque motor according to an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the topology and optimized parameters of the permanent magnet torque motor provided in an embodiment of the present invention.

[0018] Figure 3 This is a flowchart illustrating a method for optimizing the torque coefficient of a permanent magnet torque motor according to another embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram illustrating the multi-objective optimization results of the permanent magnet torque motor provided in an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram illustrating the comparison of torque waveforms before and after optimization of the permanent magnet torque motor provided in this embodiment of the invention.

[0021] Figure 6 This is a schematic diagram of the structure of a permanent magnet torque motor torque coefficient optimization device provided in an embodiment of the present invention.

[0022] Figure 7 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and descriptions of the present invention are used to explain the present invention, but are not intended to limit the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other.

[0024] Figure 1 This is a flowchart illustrating a method for optimizing the torque coefficient of a permanent magnet torque motor according to an embodiment of the present invention, as shown below. Figure 1 As shown, the method for optimizing the torque coefficient of a permanent magnet torque motor provided in this embodiment of the invention includes: Step S1: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; The torque coefficient equation and the torque ripple equation both use the parameter to be optimized as the independent variable.

[0025] Step S2: With the goal of minimizing the optimization objective function and the range of values ​​as constraints, obtain the parameters to be optimized by training a surrogate model and conducting multi-objective optimization; The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0026] In step S1 above, the device determines the range of values ​​for the parameters to be optimized of the permanent magnet torque motor, and determines the objective function based on the torque coefficient equation and the torque pulsation equation. In this application, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable. The device can be a computer that executes this method. The acquisition, storage, use, and processing of data in this application's technical solution all comply with relevant regulations.

[0027] In step S2 above, the device aims to minimize the optimization objective function and uses the range of values ​​as a constraint condition. It obtains the parameters to be optimized by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0028] Before step S1, the cross-sectional area of ​​the winding wire can be calculated using the following formula. :

[0029] in, This is the maximum phase current of the permanent magnet torque motor system. The maximum allowable current density to meet insulation and cooling requirements.

[0030] The parameters to be optimized include the pole arc coefficient; correspondingly, determining the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The reference pole arc coefficient is determined based on the number of poles and slots of the permanent magnet torque motor, as well as the number of pole pairs of the permanent magnet torque motor. The range of values ​​for the polar arc coefficient is determined based on the reference polar arc coefficient and the preset value.

[0031] The parameters to be optimized also include the stator slot depth, tooth width, tooth tip height, and tooth wedge height of the permanent magnet torque motor; correspondingly, the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The range of values ​​corresponding to the stator slot depth, tooth width, tooth tip height, and tooth wedge height are determined based on the dimensional parameters of the permanent magnet torque motor.

[0032] like Figure 2 As shown, the stator slot depth of a permanent magnet torque motor is... Tooth width Tooth tip height Tooth wedge height and polar arc coefficient As parameters to be optimized, The range of values ​​for should be determined through the following process: Calculate the maximum polar arc coefficient that satisfies the following formula. As a reference polar arc coefficient:

[0033] in, It is the least common multiple of the number of poles and slots in a permanent magnet torque motor. This refers to the number of pole pairs in the permanent magnet torque motor. The preset value can be selected as 0.1, which is the pole arc coefficient. The range of values ​​is as follows .

[0034] , , and The range of values ​​can be determined based on the size of the motor.

[0035] The objective function for optimization is determined based on the torque coefficient equation and the torque ripple equation, including: The torque coefficient equation and the torque pulsation equation are normalized respectively; The optimization objective function is determined based on the normalized torque coefficient equation, the normalized torque ripple equation, and their corresponding weighting coefficients. The optimization objective function is... As shown in the following formula:

[0036] in, As a weighting coefficient, it can be selected by balancing the emphasis on torque coefficient and torque ripple in practical engineering. These are the normalized torque coefficient equation and torque ripple equation. For example, when k=1, it represents the torque coefficient equation; when k=2, it represents the torque ripple equation.

[0037] The specific normalization method is shown in the following formula:

[0038] in, and These represent the maximum and minimum values ​​of the torque coefficient equation or torque pulsation equation in the non-dominated solution, respectively. Detailed explanations are as follows:

[0039] in, The torque coefficient, For torque pulsation, the specific formula is as follows:

[0040] in, and They are respectively The maximum and minimum values ​​of the torque waveform of the driven permanent magnet torque motor.

[0041] Based on Latin hypercube sampling method , , , and The five-dimensional parameter space formed by these five optimization parameters is used to obtain the characteristic sample scheme model of the permanent magnet torque motor. The number of characteristic sample schemes should be no less than 5×30.

[0042] The number of winding turns is determined based on the winding slot fill factor that meets process requirements, including: The number of winding turns is calculated based on the stator slot area, the winding slot fill factor, and the winding wire cross-sectional area of ​​the permanent magnet torque motor.

[0043] Establish finite element analysis models for various permanent magnet torque motor characteristic sample schemes, with the number of winding turns in the model. Calculated using the following formula:

[0044] in, Let be the stator slot area of ​​the permanent magnet torque motor design. To meet the winding slot fill factor requirements of the process, [ ] indicates rounding down.

[0045] The finite element analysis model of the aforementioned characteristic sample scheme is used in... Simulation calculations are performed under the drive to obtain their torque waveforms and utilize... Calculation formula and Calculation formula and Furthermore, based on the parameter values ​​of the five optimization parameters of the feature sample scheme, and and The calculation results are used to construct a kriging model, which can be a standard kriging model, as shown in the following expression:

[0046] in, This is a global trend item. It is a random process function.

[0047] The ordinary Kriging model described above is trained using a Gaussian function as the kernel function to obtain a surrogate model for predicting the torque coefficient and torque ripple performance of a permanent magnet torque motor. The expression for the Gaussian function is:

[0048] in, The spatial distance between two points These are the hyperparameters of the model.

[0049] The trained surrogate model is used to perform multi-objective optimization on the objective function based on a multi-objective intelligent optimization algorithm to obtain non-dominated solutions. The final optimization scheme of the permanent magnet torque motor is determined by making a decision on the non-dominated solutions using the following formula:

[0050] The specific explanation of this formula will not be repeated here.

[0051] Furthermore, the optimization method for the torque coefficient of the permanent magnet torque motor can be tested. Specifically, finite element models of the permanent magnet torque motor before and after optimization are established, and simulation calculations are performed on the two motors. The output torque waveform under drive, according to Calculation formula The improvement level of the torque coefficient of the permanent magnet torque motor before and after optimization was compared.

[0052] like Figure 3 As shown, the method of the present invention will be specifically explained using a 16-pole, 18-slot permanent magnet torque motor as an example: Step 1, the maximum phase current of the permanent magnet torque motor system selected in this example. 12A, maximum allowable current density 15A / mm 2 Substitute the winding cross-sectional area of ​​the permanent magnet torque motor Calculation formula:

[0053] Solving for the results =0.8 mm 2 .

[0054] Step 2, based on the stator slot depth of the permanent magnet torque motor Tooth width Tooth tip height Tooth wedge height and polar arc coefficient As optimization parameters, the model and parameter definitions of the permanent magnet torque motor selected in this example are as follows: Figure 2 As shown.

[0055] The range of values ​​for the polar arc coefficient is determined through the following process: Calculate the maximum polar arc coefficient that satisfies the following formula as the reference polar arc coefficient:

[0056] in, It is the least common multiple of the number of poles and slots in a permanent magnet torque motor. This refers to the number of pole pairs of the permanent magnet torque motor. In this example, the permanent magnet torque motor selected... and The values ​​are 144 and 8 respectively. Substituting them into the above equation, we get... =0.89.

[0057] The range for selecting the polar arc coefficient is: =[0.79,0.99].

[0058] stator slot depth Tooth width Tooth tip height Tooth wedge height The value range is appropriately selected based on the size of the permanent magnet torque motor used in this example. The final value range of each optimized parameter of the permanent magnet torque motor used in this example is shown in Table 1: Table 1

[0059] Constructing the torque coefficient of a permanent magnet torque motor With torque pulsation Optimization equation:

[0060] in, and They are respectively The maximum and minimum values ​​of the torque waveform of the magnetic torque motor under drive.

[0061] Step 3, based on Latin hypercube sampling method , , , and These five optimized parameters form a five-dimensional parameter space to obtain a feature sample scheme for a permanent magnet torque motor. The number of feature sample schemes should be no less than 5 × 30, and in this example, we take 5 × 50 = 250.

[0062] Establish finite element analysis models for characteristic sample schemes of permanent magnet torque motors, and determine the number of winding turns in each characteristic sample scheme model. Calculated using the following formula:

[0063] in, Let be the stator slot area of ​​the permanent magnet torque motor design. To meet the winding slot fill factor constraints required by the process, [ The symbol ] indicates rounding down. In this example, a permanent magnet torque motor is selected. It is 0.3.

[0064] The finite element analysis model of the aforementioned characteristic sample scheme is used in... Simulation calculations are performed under the drive to obtain their torque waveforms and utilize... Calculation formula and Calculation formula and Furthermore, based on the parameter values ​​of the five optimization parameters of the feature sample scheme, and and The calculation results are used to construct a kriging model, which can be a standard kriging model, as shown in the following expression:

[0065] in, This is a global trend item. It is a random process function.

[0066] The ordinary Kriging model described above is trained using a Gaussian function as the kernel function to obtain a surrogate model for predicting the torque coefficient and torque ripple performance of a permanent magnet torque motor. The expression for the Gaussian function is:

[0067] in, The spatial distance between two points These are the hyperparameters of the model.

[0068] Step 4: Utilize the trained surrogate model and a multi-objective intelligent optimization algorithm to perform multi-objective optimization on the objective function to obtain a non-dominated solution. In this example, a multi-objective genetic algorithm is chosen to perform multi-objective optimization on the selected permanent magnet torque motor. The group size and number of iterations are selected as 100 and 200, respectively. The optimization results are as follows: Figure 4 As shown, the optimal solution for the final permanent magnet torque motor is determined by using the following formula to make a decision on the non-dominated solution:

[0069] The specific explanation of this formula will not be repeated here.

[0070] in, The weighting coefficient is selected by balancing the emphasis on torque coefficient and torque ripple in practical engineering. To optimize the normalization result of the objective, it can be calculated according to the following formula:

[0071] in, and In the non-dominated solutions The maximum and minimum values. In this example... and Taking values ​​of 0.7 and 0.3 respectively, the final optimized scheme for the selected permanent magnet torque motor... , , , and The thicknesses are 8.75mm, 20.97mm, 1.01mm, 1.86mm and 0.93mm respectively.

[0072] Step 5: Establish finite element models of the permanent magnet torque motors before and after optimization, and simulate and calculate the performance of the two motors. The output torque waveform under drive, the permanent magnet torque motor selected in this example, is shown in the following figure. The torque waveforms before and after optimization under the drive are as follows: Figure 5 As shown, substitute the torque calculation results into... Calculation formula to calculate its Therefore, the permanent magnet torque motor selected in this example, after optimization, is... The torque output increased from 2.68 Nm / A to 3.10 Nm / A, an improvement of 15.67%, without a significant increase in torque ripple.

[0073] The method for optimizing the torque coefficient of a permanent magnet torque motor provided in this invention has the following beneficial technical effects: 1. The slot fill factor constraint of the permanent magnet torque motor is directly introduced into the optimization for winding turn matching calculation, rather than being designed as an additional constraint condition for the optimization equation to perform a posteriori calculation on the optimization scheme. This avoids the occurrence of unreasonable design schemes from the design scheme generation stage, avoids unnecessary and invalid calculations, and accelerates the convergence speed of the optimization process.

[0074] 2. In the design parameter selection stage of multi-objective optimization, the influence of the pole arc coefficient on torque ripple is directly considered to determine the selection range of the pole arc coefficient, so that the optimization takes into account both the torque coefficient and torque ripple of the permanent magnet torque motor, and reduces the search domain of the pole arc coefficient during the optimization process.

[0075] 3. In multi-objective optimization, train a Kriging surrogate model to replace the finite element model for predicting the performance of motor schemes, thereby reducing the computational load in the multi-objective optimization process.

[0076] The torque coefficient optimization method for permanent magnet torque motors provided in this invention determines the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determines the optimization objective function based on the torque coefficient equation and the torque ripple equation. Both the torque coefficient equation and the torque ripple equation use the parameters to be optimized as independent variables. The method aims to minimize the optimization objective function, using the range of values ​​as constraints, and obtains the parameters to be optimized by training a surrogate model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets process requirements, which can accelerate the optimization convergence speed and take into account torque ripple changes.

[0077] In the above optional embodiments, the parameter to be optimized includes the pole arc coefficient; correspondingly, determining the range of values ​​for the parameter to be optimized of the permanent magnet torque motor includes: The reference pole arc coefficient is determined based on the number of poles and slots of the permanent magnet torque motor, as well as the number of pole pairs of the permanent magnet torque motor; this can be referred to the above embodiments for explanation, and will not be repeated here.

[0078] The range of values ​​for the polar arc coefficient is determined based on the reference polar arc coefficient and the preset value. This can be referred to the above embodiments for further explanation and will not be repeated here.

[0079] In the above optional embodiments, the parameters to be optimized further include the stator slot depth, tooth width, tooth tip height, and tooth wedge height of the permanent magnet torque motor; correspondingly, determining the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The value ranges corresponding to the stator slot depth, tooth width, tooth tip height, and tooth wedge height are determined based on the dimensional parameters of the permanent magnet torque motor. This can be referred to the above embodiment for explanation, and will not be repeated here.

[0080] In the above optional embodiments, the optimization objective function is determined based on the torque coefficient equation and the torque ripple equation, including: The torque coefficient equation and the torque pulsation equation are normalized respectively; this can be referred to the above embodiments for explanation, and will not be repeated here.

[0081] The optimization objective function is determined based on the normalized torque coefficient equation, the normalized torque ripple equation, and their corresponding weighting coefficients. This can be referred to the above embodiments for further explanation, and will not be repeated here.

[0082] In the above optional embodiments, determining the number of winding turns based on the winding slot fill factor that meets process requirements includes: The number of winding turns is calculated based on the stator slot area, the winding slot fill factor, and the winding cross-sectional area of ​​the permanent magnet torque motor. This can be referred to the above embodiment for further explanation and will not be repeated here.

[0083] In the above optional embodiments, the surrogate model is a Kriging model with the parameter to be optimized as the independent variable, and includes a global trend term and a stochastic process function with the parameter to be optimized as the independent variable. Refer to the above embodiments for further details.

[0084] Figure 6 This is a schematic diagram of the structure of a permanent magnet torque motor torque coefficient optimization device provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the permanent magnet torque motor torque coefficient optimization device provided in this embodiment of the invention includes a determining unit 601 and an optimization unit 602, wherein: The determining unit 601 is used to determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and to determine the optimization objective function based on the torque coefficient equation and the torque ripple equation; wherein, both the torque coefficient equation and the torque ripple equation use the parameters to be optimized as independent variables; the optimization unit 602 is used to obtain the parameters to be optimized by training a surrogate model and performing multi-objective optimization with the goal of minimizing the optimization objective function and the range of values ​​as constraints; wherein, the number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0085] Specifically, the determining unit 601 in the device is used to determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and to determine the objective function based on the torque coefficient equation and the torque ripple equation; wherein, both the torque coefficient equation and the torque ripple equation use the parameters to be optimized as independent variables; the optimizing unit 602 is used to obtain the parameters to be optimized by training a surrogate model and conducting multi-objective optimization with the objective function as the goal and the range of values ​​as the constraint condition; wherein, the number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0086] The torque coefficient optimization device for permanent magnet torque motors provided in this invention determines the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determines the optimization objective function based on the torque coefficient equation and the torque ripple equation. Both the torque coefficient equation and the torque ripple equation use the parameters to be optimized as independent variables. The objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a surrogate model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets process requirements, which can accelerate the optimization convergence speed and take into account torque ripple changes.

[0087] The embodiments of the present invention provide a permanent magnet torque motor torque coefficient optimization device that can be used to execute the processing flow of the above-described method embodiments. Its functions will not be repeated here, but can be referred to the detailed description of the above-described method embodiments.

[0088] Figure 7 This is a schematic diagram of the physical structure of a computer device provided in an embodiment of the present invention, such as... Figure 7 As shown, the computer device includes: a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor 702. When the processor 702 executes the computer program, it implements the following method: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0089] This embodiment discloses a computer program product, which includes a computer program that, when executed by a processor, implements the following method: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0090] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the following method: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

[0091] Compared with existing technical solutions, the torque coefficient optimization method for permanent magnet torque motors provided in this invention determines the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determines the optimization objective function based on the torque coefficient equation and the torque ripple equation. Both the torque coefficient equation and the torque ripple equation use the parameters to be optimized as independent variables. The goal is to minimize the optimization objective function, with the range of values ​​as constraints. The parameters to be optimized are obtained by training a surrogate model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets process requirements, which can accelerate the optimization convergence speed and take into account torque ripple changes.

[0092] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the torque coefficient of a permanent magnet torque motor, characterized in that, include: Determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and determine the objective function based on the torque coefficient equation and the torque pulsation equation; Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; The optimization objective function is minimized, and the range of values ​​is used as a constraint. The parameters to be optimized are obtained by training a proxy model and performing multi-objective optimization. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

2. The method for optimizing the torque coefficient of a permanent magnet torque motor according to claim 1, characterized in that, The parameters to be optimized include the pole arc coefficient; correspondingly, determining the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The reference pole arc coefficient is determined based on the number of poles and slots of the permanent magnet torque motor, as well as the number of pole pairs of the permanent magnet torque motor. The range of values ​​for the polar arc coefficient is determined based on the reference polar arc coefficient and the preset value.

3. The method for optimizing the torque coefficient of a permanent magnet torque motor according to claim 2, characterized in that, The parameters to be optimized also include the stator slot depth, tooth width, tooth tip height, and tooth wedge height of the permanent magnet torque motor; correspondingly, the range of values ​​for the parameters to be optimized of the permanent magnet torque motor includes: The range of values ​​corresponding to the stator slot depth, tooth width, tooth tip height, and tooth wedge height are determined based on the dimensional parameters of the permanent magnet torque motor.

4. The method for optimizing the torque coefficient of a permanent magnet torque motor according to claim 1, characterized in that, The objective function for optimization is determined based on the torque coefficient equation and the torque ripple equation, including: The torque coefficient equation and the torque pulsation equation are normalized respectively; The optimization objective function is determined based on the normalized torque coefficient equation, the normalized torque ripple equation, and their corresponding weighting coefficients.

5. The method for optimizing the torque coefficient of a permanent magnet torque motor according to claim 1, characterized in that, The number of winding turns is determined based on the winding slot fill factor that meets process requirements, including: The number of winding turns is calculated based on the stator slot area, the winding slot fill factor, and the winding wire cross-sectional area of ​​the permanent magnet torque motor.

6. The method for optimizing the torque coefficient of a permanent magnet torque motor according to claim 1, characterized in that, The surrogate model is a Kriging model with the parameter to be optimized as the independent variable, and includes a global trend term and a stochastic process function with the parameter to be optimized as the independent variable.

7. A device for optimizing the torque coefficient of a permanent magnet torque motor, characterized in that, include: The determination unit is used to determine the range of values ​​for the parameters to be optimized in the permanent magnet torque motor, and to determine the optimization objective function based on the torque coefficient equation and the torque pulsation equation. Wherein, both the torque coefficient equation and the torque ripple equation use the parameter to be optimized as the independent variable; An optimization unit is used to obtain the parameters to be optimized by training a proxy model and performing multi-objective optimization, with the goal of minimizing the optimization objective function and the range of values ​​as constraints. The number of winding turns in the feature sample scheme model used to train the surrogate model is determined based on the winding slot fill factor that meets the process requirements.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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