Design method for T-shaped block iron core of motor

Abstract

The invention discloses a method for designing a T-shaped block iron core of a motor, which belongs to the field of motor design, and comprises the steps of determining initial parameters of the T-shaped block iron core, determining variables to be optimized, optimizing the variables to be optimized and the like, improving the structural size design of the T-shaped block, and considering the pole-arc coefficient, the circumference of a yoke part, the pole distance, the pole shoe angle and the change degree of the pole-arc angle. The above parameters are used as variables to optimize an optimization target, modeling simulation is carried out after optimization, yoke radial magnetization and depth change analysis are introduced, magnetic flux uniformity is accurately evaluated, magnetic flux adjustment is guided, magnetic flux density and air gap flux density waveforms meeting design requirements are met, motor magnetic resistance is reduced, EMC risks are reduced, and according to different design requirements, the design requirements are met. The optimal design scheme is determined through variables, the optimal design is achieved, and according to the optimization result, the magnetic resistance of the motor can be reduced by 15%-30%, the efficiency can be improved by 3%-5%, and the EMC can be improved by 20%-35%.

Description

Technical Field

[0001] The invention relates to the field of motor design, and in particular to a motor T-block core design method. Background Art

[0002] The T-core is a core component commonly used in power transformers and motors. It has good magnetic permeability and low hysteresis loss, can effectively conduct magnetic fields, and has high magnetic permeability. The original motor stator of the T-core is divided into multiple groups of T-shaped silicon steel sheets, which are reassembled into a complete stator after winding. Since its technology disassembles the stator into a single tooth-shaped part, the winding of its enameled coil must be centralized, which effectively reduces the loss of end winding resistance, thereby providing higher efficiency. In addition, the single tooth winding also reduces adverse factors and potential interference in the production process, further improving the efficiency of the motor.

[0003] The existing T-type core is designed using electromagnetic calculation methods. First, a preliminary design is performed based on the structure and design requirements of the T-type core in the classic motor to obtain a core block that meets the performance requirements. The core block is then tested and the size of the core block is repeatedly modified based on the test results until the T-type core meets the design requirements. This design method has a long test and modification cycle and low efficiency. Summary of the invention

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

[0005] One of the purposes of the present invention is achieved by the following technical solution:

[0006] A method for designing a T-block core of a motor comprises the following steps:

[0007] Determine the initial parameters of the T-block core: Calculate the radial design parameters of the T-block based on the rated power, rated voltage, maximum torque, target efficiency and outer diameter parameters of the motor;

[0008] Determine the 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 radial magnetization thickness of the yoke;

[0009] Optimize the variables to be optimized: spatially divide the optimization variables according to the range of the variables to be optimized to obtain a spatial division plane, obtain sample data in the spatial division plane according to the sampling value, calculate the Pareto optimal solution of the sample data, and establish the boundary of the optimization variable according to the Pareto optimal solution, obtain new sample data according to the boundary of the optimization variable, and iterate the optimization algorithm according to the new sample data and the corresponding objective function and constraint function.

[0010] Furthermore, in the step of optimizing the variables to be optimized, spatially dividing the optimization variables according to the range of the variables to be optimized to obtain the spatial division plane specifically includes the following steps:

[0011] Dividing the optimization range of the optimization variable according to centralization to obtain two sampling spaces;

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

[0013] Orthogonal minimization is performed based on the sum of data to obtain an orthogonal sampling space.

[0014] Furthermore, 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 includes the following steps:

[0015] The initial sample of the initial population is randomly ordered;

[0016] Select samples after iterative calculation;

[0017] Perform Pareto dominance sorting on the remaining samples to obtain the global optimal individual of the initial population;

[0018] According to the global optimal individual, each sample in the remaining samples is dominance-ranked in turn;

[0019] Perform crossover and mutation on the remaining samples to generate offspring populations;

[0020] Perform Pareto dominance sorting on the offspring population to obtain the global optimal individual of the offspring population;

[0021] According to the global optimal individual of the offspring population, the initial sample and the offspring population are sorted by Pareto dominance.

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

[0023] Group the initial sample set to obtain an initial solution set;

[0024] Group the offspring population to obtain the initial solution set of the offspring population;

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

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

[0027] Furthermore, the motor T-block core design method further includes a simulation verification step, which is located after the step of optimizing the variables to be optimized, and the simulation verification step is specifically as follows:

[0028] The pole slot module model is established in the finite element software using the optimized variables, and the pole slot module model is simulated;

[0029] The air gap magnetic flux waveform is obtained by simulation;

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

[0031] Furthermore, in the step of optimizing the variables to be optimized, obtaining new sample data according to the boundaries of the optimized variables specifically comprises: uniformly sampling on the boundaries of the optimized variables to obtain new sample data.

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

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

[0034] Compared with the prior art, the motor T-block core design method of the present invention designs the motor T-block core through the steps of determining the initial parameters of the T-block core, determining the variables to be optimized, optimizing the variables to be optimized, etc., so that the motor T-block core design efficiency is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The figure is a flow chart of the method for designing the T-block core of the motor according to the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may be another intermediate component through which it is fixed. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may be another intermediate component at the same time. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be another intermediate component at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0039] See also Figure 1 , a motor T-block core design method, comprising the following steps:

[0040] Determine the initial parameters of the T-block core: Calculate the radial design parameters of the T-block based on the rated power, rated voltage, maximum torque, target efficiency and outer diameter parameters of the motor;

[0041] Determine the 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 radial magnetization thickness of the yoke;

[0042] Optimize the variables to be optimized: divide the optimization variables into spaces according to the range of the variables to be optimized to obtain the space division plane, obtain sample data according to the sampling values ​​in the space division plane, calculate the Pareto optimal solution of the sample data, establish the boundaries of the optimization variables according to the Pareto optimal solution, obtain new sample data according to the boundaries of the optimization variables, and iterate the optimization algorithm according to 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 motor-related parameters of the T-block of the motor. The motor-related parameters include data related to design requirements and constraints, wherein the design requirements include the rated power, rated voltage, maximum torque, target efficiency and motor outer diameter parameters of the motor. 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 rotor coil length and the stator coil length of the motor are determined to obtain the stator pole arc coefficient, the rotor pole arc coefficient, the stator stack thickness per slot and the rotor stack thickness per slot. The constraints are the motor flux density, the motor torque density, the motor air gap flux density waveform and the motor electromagnetic torque waveform.

[0044] The specific steps of determining the variables to be optimized are as follows: referring to the initial parameters of the T-block core, establishing the original pole slot model in the finite element electromagnetic software, and optimizing the radial design parameters of the motor according to the original pole slot model and the initial total magnetic flux density waveform of the motor T-block motor.

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

[0046] In the step of optimizing the variables to be optimized, the optimization variables are spatially divided according to the range of the variables to be optimized, and obtaining the spatial division plane specifically includes the following steps:

[0047] Divide the optimization range of the optimization variable according to the centralization to obtain two sampling spaces;

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

[0049] Orthogonal minimization is performed based on the sum of data to obtain an orthogonal sampling space.

[0050] Orthogonal minimization based on the sum of data is specifically as follows: in the optimization variable sampling space, take any point on the X-axis to obtain two sampling spaces, namely Q1 and Q2; calculate the sum of the data of the samples in Q1 and Q2; fix Q1, calculate the center of gravity of Q2 to obtain the center of gravity position; calculate the rotation angle based on the center of gravity position; calculate the deviation value between Q2 and Q1, and when the deviation value is the smallest, minimize the centralized orthogonal.

[0051] In the step of optimizing the optimization variable, the Pareto optimal solution of the sample data is calculated by performing Pareto dominance sorting on the sample data. The Pareto dominance sorting on the sample data includes the following steps:

[0052] The initial sample of the initial population is randomly ordered;

[0053] Select samples after iterative calculation;

[0054] Perform Pareto dominance sorting on the remaining samples to obtain the global optimal individual of the initial population;

[0055] According to the global optimal individual, each sample in the remaining samples is dominance-ranked in turn;

[0056] Perform crossover and mutation on the remaining samples to generate offspring populations;

[0057] Perform Pareto dominance sorting on the offspring population to obtain the global optimal individual of the offspring population;

[0058] According to the global optimal individual of the offspring population, the initial sample and the offspring population are sorted by Pareto dominance.

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

[0060] Group the initial sample set to obtain an initial solution set;

[0061] Group the offspring population to obtain the initial solution set of the offspring population;

[0062] The Pareto optimal solution set is updated according to 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. The new sample data is obtained according to the boundary of the optimization variable. Specifically, the new sample data is obtained by uniformly sampling on the boundary of the optimization variable.

[0064] The optimization process of the optimized variables is as follows: the total magnetic flux density of the pole slot is calculated according to the pole shoe angle, pole pitch, pole arc coefficient and radial magnetization thickness of the yoke. Combined with the actual total magnetic flux density waveform curve of finite element modeling simulation, the calculation formula of the total magnetic flux density of the pole slot is corrected. According to the rotor, stator pole pitch, pole shoe angle, rotor pole arc coefficient and stator pole arc coefficient, the relative relationship expression of the T-block structure and the magnetic flux of the motor is calculated. By performing multi-factor variance calculation on the thickness of the yoke area, the relative relationship expression of the thickness of the relative yoke area and the magnetic flux is obtained. The magnetic flux distribution calculation formula is calculated by the rotor, stator pole pitch, pole shoe angle, rotor pole arc coefficient and stator pole arc coefficient of the motor. Through the optimized radial design parameters, the modified pole slot module is re-established. Through the obtained magnetic flux distribution calculation formula, the distribution of the magnetic flux of the magnetic core is calculated according to the axial parameters of the magnetic core size, and the axial parameters of the magnetic core size are corrected to obtain the final design data.

[0065] The motor T-block core design method also includes a simulation verification step, which is located after the step of optimizing the variables to be optimized. The simulation verification step is specifically as follows:

[0066] The pole slot module model is established in the finite element software using the optimized variables, and the pole slot module model is simulated;

[0067] The air gap magnetic flux waveform is obtained by simulation;

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

[0069] Compared with the prior art, the motor T-block core design method of the present invention designs the motor T-block core through the steps of determining the initial parameters of the T-block core, determining the variables to be optimized, optimizing the variables to be optimized, etc., so that the motor T-block core design efficiency is high.

[0070] The motor T-block core design method of this application takes the rated voltage of 22V DC, the motor outer diameter of 20cm, and the rated power of 25W as an example. Under the motor design requirements, the design process is as follows:

[0071] Stator coil length 7.5cm;

[0072] Stator pole arc coefficient 0.25;

[0073] Rotor pole arc coefficient 0.8;

[0074] Rotor and stator pole arc angle (rotor pole arc coefficient × 360 / number of pole pairs) 40°, 70°;

[0075] Stator tooth pitch, rotor tooth pitch 14mm, 7mm;

[0076] The stack thickness of each stator slot and the stack thickness of each rotor slot (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 slot model is calculated by the above parameters, and the air gap magnetic flux waveform of the initial pole slot model is obtained by modeling and simulation. The variables to be optimized are optimized, and the magnetic flux distribution curve is calculated by the optimized radial parameters, and the model is modeled and simulated to verify.

[0078] The motor T-block core design method of the present application improves the structural dimension design of the T-block, considers the variation degree of the pole arc coefficient, the circumference of the yoke, the pole pitch, the pole shoe angle, and the pole arc angle, optimizes the optimization target by using the above parameters as variables, and performs modeling and simulation after optimization, introduces radial magnetization of the yoke and depth variation analysis, accurately evaluates the uniformity of the magnetic flux, guides the adjustment of the magnetic flux, meets the design requirements of the magnetic flux density and the air gap magnetic density waveform, reduces the magnetic resistance of the motor, reduces the EMC risk, and determines the optimal design scheme through variables according to different design requirements to achieve the optimal design. The optimization result can reduce the magnetic resistance of the motor by 15%-30%, improve the efficiency by 3%-5%, and improve the EMC by 20-35%.

[0079] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which are equivalent modifications and improvements made to the above embodiments based on the essential technology of the present invention, and all of them belong to the protection scope of the present invention.

Claims

1. A method for designing a T-block core of a motor, characterized in that: The following steps are involved: Determine the initial parameters of the T-block core: Calculate the radial design parameters of the T-block based on the rated power, rated voltage, maximum torque, target efficiency and outer diameter parameters of the motor; Determine the 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 radial magnetization thickness of the yoke; Optimize the variables to be optimized: spatially divide the optimization variables according to the range of the variables to be optimized to obtain a spatial division plane, obtain sample data in the spatial division plane according to the sampling value, calculate the Pareto optimal solution of the sample data, and establish the boundary of the optimization variable according to the Pareto optimal solution, obtain new sample data according to the boundary of the optimization variable, and iterate the optimization algorithm according to the new sample data and the corresponding objective function and constraint function.

2. The motor T-block core design method according to claim 1, characterized in that: In the step of optimizing the variables to be optimized, spatially dividing the optimization variables according to the range of the variables to be optimized to obtain the spatial division plane specifically includes the following steps: Dividing the optimization range of the optimization variable according to centralization to obtain two sampling spaces; Calculate the data sum of samples in two sampling spaces; Orthogonal minimization is performed based on the sum of data to obtain an orthogonal sampling space.

3. The motor T-block core design method according to claim 1, characterized in that: 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 includes the following steps: The initial sample of the initial population is randomly ordered; Select samples after iterative calculation; Perform Pareto dominance sorting on 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 dominance-ranked in turn; Perform crossover and mutation on the remaining samples to generate offspring populations; Perform Pareto dominance sorting on the offspring population to obtain the global optimal individual of the offspring population; According to the global optimal individual of the offspring population, the initial sample and the offspring population are sorted by Pareto dominance.

4. The method for designing a T-block core of a motor according to claim 3, characterized in that: Performing Pareto dominance sorting on the initial sample and the offspring population includes the following steps: Group the initial sample set to obtain an initial solution set; Group the offspring population to obtain the initial solution set of the offspring population; The Pareto optimal solution set is updated according to the initial solution set and the initial solution set of the offspring population.

5. The method for designing a T-block core of a motor according to claim 1, characterized in that: The optimization algorithm used iteratively is a multi-objective genetic algorithm.

6. The motor T-block core design method according to claim 1, characterized in that: The motor T-block core design method further includes a simulation verification step, which is located after the step of optimizing the variables to be optimized, and the simulation verification step is specifically: The pole slot module model is established in the finite element software using the optimized variables, and the pole slot module model is simulated; The air gap magnetic flux waveform is obtained by simulation; Determine whether the magnetic flux density and air gap magnetic flux waveform meet the design requirements.

7. The motor T-block core design method according to claim 1, characterized in that: In the step of optimizing the variables to be optimized, obtaining new sample data according to the boundaries of the optimized variables specifically includes: uniformly sampling on the boundaries of the optimized variables to obtain new sample data.

8. The method for designing a T-block core of a motor according to claim 1, characterized in that: The steps for determining the initial parameters of the T-block core are specifically as follows: the rated speed of the motor is calculated according to the rated power, rated voltage, maximum torque and target efficiency of the motor; the number of pole pairs of the motor is calculated according to the rated speed of the motor, the outer diameter of the motor and the number of stator slots of the motor; the rotor coil length and the stator coil length of the motor are determined; and the stator pole arc coefficient, the rotor pole arc coefficient, the stator stack thickness per slot and the rotor stack thickness per slot are obtained.

9. The method for designing a T-block core of a motor according to claim 8, characterized in that: The step of determining the variables to be optimized is specifically as follows: establishing an original pole slot model in finite element electromagnetic software with reference to the initial parameters of the T-block core, and optimizing the radial design parameters of the motor according to the original pole slot model and the initial total magnetic flux density waveform of the motor T-block motor.

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