A motor T-block core design method

Abstract

The application discloses a motor T-shaped block iron core design method and belongs to the motor design field. The method improves the T-shaped block structure size design, considers the variation degree of the pole arc coefficient, the yoke circumference length, the pole pitch, the pole shoe angle and the pole arc angle, optimizes the optimization target by taking the above parameters as variables, models and simulates after optimization, introduces yoke radial magnetization and depth variation analysis, accurately evaluates the magnetic flux uniformity, guides the magnetic flux adjustment, satisfies the design requirements of the magnetic flux density and the air gap magnetic density waveform, reduces the motor magnetic resistance, reduces the EMC risk, determines the optimal design scheme according to different design requirements, achieves the optimal design, and the optimization result can reduce the motor magnetic resistance by 15%-30%, improve the efficiency by 3%-5%, and improve the EMC by 20-35%.

Description

Technical Field

[0001] This invention relates to the field of motor design, and in particular to a design method for a T-shaped block iron core for a motor. Background Technology

[0002] T-core is a core component commonly used in power transformers and motors. It possesses good magnetic permeability and low hysteresis loss, effectively conducting magnetic fields and exhibiting high permeability. The original motor stator with a T-core was divided into multiple sets of T-shaped silicon steel sheets, which were then wound and reassembled into a complete stator. Because this technology disassembles the stator into individual tooth-like sections, the winding of its enameled coils must be centralized. This effectively reduces end-winding losses, thus providing higher efficiency. Furthermore, the single-tooth winding also reduces adverse factors and potential interference during the production process, further improving motor efficiency.

[0003] Existing T-shaped iron cores are designed using electromagnetic calculation methods. First, a preliminary design is performed based on the structure and design requirements of classic T-shaped iron cores in motors to obtain a core block that meets performance requirements. Then, the core block is tested, and its dimensions are repeatedly modified based on the test results until the T-shaped iron core block meets the design requirements. This design method has a long testing and modification cycle and is inefficient. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, one of the objectives of this invention is to provide a design method for a motor T-shaped block iron core with high design efficiency.

[0005] One of the objectives of this invention is achieved through the following technical solution:

[0006] A method for designing a T-shaped block iron core for an electric motor includes the following steps:

[0007] Determining the initial parameters of the T-block core: Calculate the radial design parameters of the T-block based on the motor's rated power, rated voltage, maximum torque, target efficiency, and outer diameter parameters.

[0008] Determine the variables to be optimized: The variables to be optimized include at least one of the following: pole shoe angle, pole pitch, pole arc coefficient and radial magnetization thickness of the yoke;

[0009] Optimize the variable to be optimized: Spatial partitioning of the variable to be optimized is performed according to its range to obtain a spatial partitioning plane. Sample data is obtained in the spatial partitioning plane based on the sampled values, and Pareto optimal solution of the sample data is calculated. Boundary of the variable to be optimized is established based on the Pareto optimal solution. New sample data is obtained based on the boundary of the variable to be optimized. The optimization algorithm is iterated based on the new sample data and the corresponding objective function and constraint function.

[0010] Furthermore, in the optimization step of the variable to be optimized, the spatial partitioning of the variable to be optimized according to its range to obtain the spatial partitioning plane specifically includes the following steps:

[0011] The optimization range of the optimization variables is divided according to centralization to obtain two sampling spaces;

[0012] Calculate the sum of data from samples within the two sampling spaces;

[0013] The orthogonal sampling space is obtained by orthogonally minimizing the sum of the data.

[0014] Furthermore, in the optimization step for the variable to be optimized, the Pareto optimal solution for the sample data is calculated by performing Pareto dominance ranking on the sample data. The Pareto dominance ranking of the sample data includes the following steps:

[0015] The initial samples of the initial population are randomly sorted;

[0016] Select samples after iterative calculation;

[0017] The remaining samples are sorted by Pareto dominance to obtain the globally optimal individual of the initial population;

[0018] Based on the globally optimal individual, the remaining samples are ranked sequentially according to their dominance.

[0019] The remaining samples are crossovered and mutated to generate a progeny population.

[0020] Pareto dominance sort is performed on the offspring population to obtain the globally optimal individual of the offspring population;

[0021] Pareto dominance sort is performed on the initial sample and the offspring population based on the globally optimal individual in the offspring population.

[0022] Furthermore, performing Pareto dominance sorting on the initial sample and the offspring population includes the following steps:

[0023] The initial sample set is grouped to obtain the initial solution set;

[0024] The offspring population is divided into groups to obtain the initial solution set of the offspring population;

[0025] The Pareto optimal solution set is updated based on the initial solution set and the initial solution set of the offspring population.

[0026] Furthermore, the optimization algorithm used in the iteration is a multi-objective genetic algorithm.

[0027] Furthermore, the motor T-block core design method also includes a simulation verification step, which is located after the optimization step of the variable to be optimized. The simulation verification step specifically includes:

[0028] The optimized variables were used to build a pole groove module model in the finite element software, and the pole groove module model was simulated.

[0029] The air gap magnetic flux density waveform was obtained through simulation;

[0030] Determine whether the magnetic flux density and air gap magnetic flux density waveforms meet the design requirements.

[0031] Furthermore, in the step of optimizing the variable to be optimized, obtaining new sample data based on the boundary of the variable to be optimized specifically involves: uniformly sampling along the boundary of the variable to obtain new sample data.

[0032] Furthermore, the specific steps for determining the initial parameters of the T-shaped block core are as follows: the rated speed of the motor is calculated based on the rated power, rated voltage, maximum torque and target efficiency of the motor; the number of pole pairs of the motor is calculated based on the rated speed of the motor, the outer diameter of the motor and the number of stator slots of the motor; the length of the rotor coil and the length of the stator coil of the motor are determined; and the stator pole arc coefficient, rotor pole arc coefficient, stator slot stack thickness and rotor slot stack thickness are obtained.

[0033] Furthermore, the step of determining the variables to be optimized specifically involves: establishing an original pole slot model in finite element electromagnetic software with reference to the initial parameters of the T-shaped block iron core; and optimizing the radial design parameters of the motor based on the original pole slot model and the initial total magnetic flux density waveform of the T-shaped block motor.

[0034] Compared with existing technologies, the motor T-block core design method of the present invention designs the motor T-block core through steps such as determining the initial parameters of the T-block core, determining the variables to be optimized, and optimizing the variables to be optimized, thereby making the motor T-block core design highly efficient. Attached Figure Description

[0035] Figure 1 This is a flowchart of the design method for the T-shaped block iron core of the motor according to the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or it can be fixed through another intermediate component. When a component is said to be "connected to" another component, it can be directly connected to the other component or it may be fixed through another intermediate component. When a component is said to be "set on" another component, it can be set directly on the other component or it may be set through another intermediate component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0038] 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 be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0039] Please see Figure 1 A design method for a T-shaped block iron core for an electric motor, comprising the following steps:

[0040] Determining the initial parameters of the T-block core: Calculate the radial design parameters of the T-block based on the motor's rated power, rated voltage, maximum torque, target efficiency, and outer diameter parameters.

[0041] Determine the variables to be optimized: The variables to be optimized include at least one of the following: pole shoe angle, pole pitch, pole arc coefficient and radial magnetization thickness of the yoke;

[0042] Optimize the variable to be optimized: Spatial partitioning of the variable to be optimized is performed according to its range to obtain a spatial partitioning plane. Sample data is obtained in the spatial partitioning plane based on the sampled values, and Pareto optimal solution of the sample data is calculated. Boundary of the variable to be optimized is established based on the Pareto optimal solution. New sample data is obtained based on the boundary of the variable to be optimized. The optimization algorithm is iterated based on the new sample data and the corresponding objective function and constraint function.

[0043] The specific steps for determining the initial parameters of the T-block core are as follows: Establish a finite element analysis model of the motor and import the relevant motor parameters of the T-block. These parameters include design requirements and constraints. Design requirements include the motor's rated power, rated voltage, maximum torque, target efficiency, and outer diameter. The rated speed is calculated based on these parameters. The number of pole pairs is calculated based on the rated speed, outer diameter, and number of stator slots. The rotor and stator coil lengths are then determined, resulting in the stator pole arc coefficient, rotor pole arc coefficient, stator slot thickness, and rotor slot thickness. Constraints include the motor's magnetic flux density, torque density, air gap magnetic flux density waveform, and electromagnetic torque waveform.

[0044] The specific steps for determining the variables to be optimized are as follows: 1. Establish the original pole slot model in the finite element electromagnetic software with reference to the initial parameters of the T-shaped block iron core. 2. Optimize the radial design parameters of the motor based on the original pole slot model and the initial total magnetic flux density waveform of the T-shaped block motor.

[0045] Among the variables to be optimized, the pole shoe angle is the circumferential angle between the tip of the rotor or stator tooth and the air gap. The pole pitch is the length of the air gap between two adjacent poles of the motor rotor and stator. The pole arc coefficient is the ratio of the pole arc angle of the motor rotor to the pole arc angle of the motor stator.

[0046] In the optimization process of the variable to be optimized, the spatial partitioning of the variable to be optimized, based on its range, to obtain the spatial partitioning plane, specifically includes the following steps:

[0047] The optimization range of the optimization variables is divided according to the centralization, resulting in two sampling spaces;

[0048] Calculate the sum of data from samples within the two sampling spaces;

[0049] The orthogonal sampling space is obtained by orthogonally minimizing the sum of the data.

[0050] The orthogonal minimization based on the sum of data is as follows: Within the sampling space of the optimization variable, take any point on the X-axis to obtain two sampling spaces, Q1 and Q2, respectively; calculate the sum of data of the samples in Q1 and Q2; fix Q1 and calculate the centroid position of Q2; calculate the rotation angle based on the centroid position; calculate the deviation between Q2 and Q1, and minimize the centering orthogonality when the deviation is minimized.

[0051] In the optimization step of the variable to be optimized, the Pareto optimal solution of the sample data is calculated by performing Pareto dominance ranking on the sample data. Pareto dominance ranking of the sample data includes the following steps:

[0052] The initial samples of the initial population are randomly sorted;

[0053] Select samples after iterative calculation;

[0054] The remaining samples are sorted by Pareto dominance to obtain the globally optimal individual of the initial population;

[0055] Based on the globally optimal individual, the remaining samples are ranked sequentially according to their dominance.

[0056] The remaining samples are crossovered and mutated to generate a progeny population.

[0057] Pareto dominance sort is performed on the offspring population to obtain the globally optimal individual of the offspring population;

[0058] Pareto dominance sort is performed on the initial sample and the offspring population based on the globally optimal individual in the offspring population.

[0059] Specifically, Pareto dominance sorting of the initial sample and offspring population includes the following steps:

[0060] The initial sample set is grouped to obtain the initial solution set;

[0061] The offspring population is divided into groups to obtain the initial solution set of the offspring population;

[0062] The Pareto optimal solution set is updated based on the initial solution set and the initial solution set of the offspring population.

[0063] Specifically, the optimization algorithm used in the iteration is a multi-objective genetic algorithm. Obtaining new sample data based on the boundaries of the optimization variables involves uniform sampling along the boundaries of the optimization variables to obtain new sample data.

[0064] The optimization process for the variables to be optimized is as follows: The total magnetic flux density of the pole slots is calculated based on the pole shoe angle, pole pitch, pole arc coefficient, and radial magnetization thickness of the yoke. Combining the actual total magnetic flux density waveform curve from finite element modeling simulation, the calculation formula for the total magnetic flux density of the pole slots is corrected. Based on the rotor and stator pole pitch, pole shoe angle, rotor pole arc coefficient, and stator pole arc coefficient, the relative relationship between the motor's T-block structure and magnetic flux is calculated. By performing multi-factor variance calculation on the thickness of the yoke region, the relative relationship between the thickness of the yoke region and magnetic flux is obtained. The magnetic flux distribution calculation formula is obtained using the motor's rotor and stator pole pitch, pole shoe angle, rotor pole arc coefficient, and stator pole arc coefficient. Using the optimized radial design parameters, a corrected pole slot module is re-established. Using the obtained magnetic flux distribution calculation formula, the magnetic flux distribution of the magnetic core is calculated based on the axial parameters of the magnetic core dimensions. The axial parameters of the magnetic core dimensions are then corrected to obtain the final design data.

[0065] The design method for the T-shaped block iron core of the motor also includes a simulation verification step. This simulation verification step follows the optimization step for the variables to be optimized. Specifically, the simulation verification step is as follows:

[0066] The optimized variables were used to build a pole groove module model in the finite element software, and the pole groove module model was simulated.

[0067] The air gap magnetic flux density waveform was obtained through simulation;

[0068] Determine whether the magnetic flux density and air gap magnetic flux density waveforms meet the design requirements.

[0069] Compared with existing technologies, the motor T-block core design method of the present invention designs the motor T-block core through steps such as determining the initial parameters of the T-block core, determining the variables to be optimized, and optimizing the variables to be optimized, thereby making the motor T-block core design highly efficient.

[0070] This application's motor T-type block iron core design method takes a rated voltage of 22V DC, a motor outer diameter of 20cm, and a rated power of 25W as an example. Under the motor design requirements, the design process is as follows:

[0071] The stator coil length is 7.5cm;

[0072] Stator pole arc coefficient: 0.25;

[0073] The rotor pole arc coefficient is 0.8;

[0074] Rotor and stator pole arc angles (rotor pole arc coefficient × 360 / number of pole pairs) are 40° and 70°, respectively.

[0075] Stator tooth pitch and rotor tooth pitch are 14mm and 7mm, respectively.

[0076] The stack thickness per slot of the stator and the stack thickness per slot of the rotor (stator tooth pitch / stator pole arc angle * stator coil length and rotor tooth pitch / rotor pole arc angle * rotor coil length) are 0.12 and 0.04, respectively.

[0077] The initial pole cell model is calculated using the above parameters, and the air gap magnetic flux density waveform of the initial pole cell model is obtained through modeling and simulation. The variables to be optimized are optimized, and the magnetic flux distribution curve is calculated using the optimized radial parameters. The model is then modeled and simulated to verify its performance.

[0078] This application presents a design method for a T-block iron core in a motor. By improving the structural dimensions of the T-block, it considers the variations in pole arc coefficient, yoke circumference, pole pitch, pole shoe angle, and pole arc angle. These parameters are used as variables to optimize the target. After optimization, modeling and simulation are performed, and radial magnetization and depth variation analysis of the yoke are introduced to accurately evaluate the magnetic flux uniformity, guide magnetic flux adjustment, and meet the design requirements for magnetic flux density and air gap magnetic flux density waveform. This reduces the motor's magnetic reluctance and EMC risk. Based on different design requirements, the optimal design scheme is determined through variables to achieve the optimal design. The optimization results can reduce the motor's magnetic reluctance by 15%-30%, improve efficiency by 3%-5%, and improve EMC by 20-35%.

[0079] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These are all equivalent modifications and improvements made to the above embodiments based on the essential technology of the present invention, and all of these fall within the protection scope of the present invention.

Claims

1. A method of designing a motor T-block core, characterized by, The method comprises the following steps: T block core initial parameter determination: according to the rated power, rated voltage, maximum torque and target efficiency of the motor, the rated speed of the motor is calculated, the pole pair number of the motor is calculated according to the rated speed of the motor and the motor outer diameter and the stator slot number of the motor, the rotor coil length and the stator coil length of the motor are determined, the stator pole arc coefficient, the rotor pole arc coefficient, the stator slot thickness and the rotor slot thickness are obtained; Determination of variables to be optimized: the variables to be optimized include at least one of the pole shoe angle, the pole pitch, the pole arc coefficient and the yoke radial magnetization thickness, the original pole slot model is established in the finite element electromagnetic software by referring to the T block core initial parameters, and the radial design parameters of the motor are optimized according to the original pole slot model and the initial motor T block motor total magnetic flux density waveform; Optimization of variables to be optimized: the optimization range of the optimization variables is divided according to centralization to obtain two sampling spaces; The data sum of the samples in the two sampling spaces is calculated; Orthogonal minimization is performed according to the data sum to obtain an orthogonal sampling space, sample data is obtained in the space division plane according to the sampling value, the Pareto optimal solution of the sample data is calculated, the boundary of the optimization variable is established according to the Pareto optimal solution, new sample data is obtained according to the boundary of the optimization variable, and optimization algorithm iteration is performed according to the new sample data and the corresponding objective function and constraint function; Simulation verification: the pole slot module model is established in the finite element software by using the optimized variables, and the pole slot module model is simulated; The air gap magnetic flux density waveform is obtained by simulation; whether the magnetic flux density and the air gap magnetic flux density waveform meet the design requirements is judged.

2. The motor T-block core design method of claim 1, wherein: In the step of optimizing the variables to be optimized, the Pareto optimal solution of the sample data is calculated by performing Pareto dominance sorting on the sample data, and the Pareto dominance sorting on the sample data comprises the following steps: Random sorting of initial samples of initial population; Selection of samples after iterative calculation; Pareto dominance sorting of the remaining samples to obtain the global optimal individual of the initial population; According to the global optimal individual, each sample in the remaining samples is sequentially sorted; Crossing and mutation are performed on the remaining samples to generate a child population; Pareto dominance sorting is performed on the child population to obtain the global optimal individual of the child population; According to the global optimal individual of the child population, the initial sample and the child population are sorted by Pareto dominance.

3. The motor T-block core design method of claim 2, wherein: The Pareto dominance sorting of the initial sample and the child population comprises the following steps: Grouping the initial sample set to obtain an initial solution set; Grouping the child population to obtain an initial solution set of the child population; Updating the Pareto optimal solution set according to the initial solution set and the initial solution set of the child population.

4. The motor T-block core design method of claim 1, wherein: The optimization algorithm used in the iteration is a multi-objective genetic algorithm.

5. The motor T-block core design method of claim 1, wherein: In the step of optimizing the variables to be optimized, the new sample data obtained according to the boundary of the optimization variable is: uniformly sampling on the boundary of the optimization variable to obtain the new sample data.

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