CN117540643A - Method for realizing high-density winding of tooth slot motor by adopting intelligent optimization algorithm - Google Patents
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Abstract
The invention relates to the technical field of motor windings, in particular to a method for realizing a high-density winding of a cogging motor by adopting an intelligent optimization algorithm, which comprises the following steps: generating design variables of high-density windings of the tooth space motor; establishing a mathematical model according to design variables of the tooth slot motor; optimizing the data model based on a particle swarm algorithm to obtain optimal design parameters; processing and winding the stator based on the optimal design parameters, monitoring winding data at the same time, and returning monitoring data; the winding design is adjusted based on the monitoring data. Therefore, the winding mode of the tooth slot motor can be designed more conveniently based on the target parameters, so that the processing quality of the winding mode is monitored in the actual processing process, and then the processing technology or the design parameters are modified based on the monitoring data, so that the design quality is improved or the winding technology is optimized, and the overall winding quality is improved.
Description
Technical Field
The invention relates to the technical field of motor windings, in particular to a method for realizing a high-density winding of a cogging motor by adopting an intelligent optimization algorithm.
Background
The high power density winding of the motor means that the winding has higher resistance and higher magnetic field strength through the optimal design, thereby realizing higher output power. Such designs typically include increasing the number of turns of the winding, changing the cross-sectional area and shape of the wire, using multiple layers of windings, and the like. In order to increase the power density of the motor, it is sometimes also desirable to employ new materials and processes to improve the performance of the windings. The design of the high power density winding plays an important role in improving the overall performance of the motor and reducing the energy consumption. The following are some features and advantages related to the high density windings of the motor:
the volume is smaller: the high-density winding can make the number of the internal elements of the motor more and the volume smaller, so the volume of the motor can be greatly reduced, and the motor is convenient to use in various occasions.
The power is higher: through the high-density winding, the energy conversion efficiency of the motor can be improved, and therefore higher output power is obtained.
The heat dissipation is better: because the high-density winding can enable electronic elements in the motor to be more closely arranged together, the high-density winding is favorable for quick dissipation of heat in the motor, and the motor is more stable and reliable in operation.
The noise is lower: the high-density winding technology can also reduce noise generated in the running process of the motor, so that the motor is quieter in the running process.
The existing motor winding mode is inconvenient to correspondingly adjust along with a winding processing technology, so that the subsequent winding processing quality is reduced.
Disclosure of Invention
The invention aims to provide a method for realizing a high-density winding of a tooth slot motor by adopting an intelligent optimization algorithm, which aims to more conveniently design a winding mode of the tooth slot motor based on target parameters so as to monitor the processing quality of the winding mode in the actual processing process, and then modify the processing technology or design parameters based on monitoring data so as to improve the design quality or optimize the winding technology, thereby improving the overall winding quality.
In order to achieve the above purpose, the invention provides a method for realizing the high-density winding of the cogging motor by adopting an intelligent optimization algorithm, which comprises the steps of generating design variables of the high-density winding of the cogging motor;
establishing a mathematical model according to design variables of the tooth slot motor;
optimizing the data model based on a particle swarm algorithm to obtain optimal design parameters;
processing and winding the stator based on the optimal design parameters, monitoring winding data at the same time, and returning monitoring data;
the winding design is adjusted based on the monitoring data.
Wherein the design variables include an output performance index including power and efficiency, a physical constraint of the motor including a slot number and a coil width, and a design constraint including a current density and thermal coupling.
The establishing the mathematical model according to the design variable of the tooth slot motor comprises the following steps:
determining an objective function according to the motor design variable;
determining constraint conditions according to motor design variables;
and normalizing the objective function and the constraint condition.
The specific steps for optimizing the data model based on the particle swarm algorithm to obtain the optimal design parameters comprise the following steps:
representing design parameters of the motor high-density winding as position vectors of particles;
generating an adaptability function according to the motor design target and the constraint condition;
generating a subgroup of particles, and generating fitness of each particle according to the fitness function;
updating the position and speed of each particle according to the updating rule of the particle swarm optimization algorithm, and repeating the steps until the preset iteration times are reached or the stopping condition is met;
and obtaining optimal design parameters according to the result of the optimization iteration.
The specific mode for generating the fitness function according to the motor design target and the constraint condition is as follows:
converting and combining constraint conditions into an objective function by using a penalty function method;
the fitness function uses the value of the objective function as fitness to maximize the objective function.
The specific modes of processing and winding the stator based on the optimal design parameters, monitoring winding data and returning monitoring data are as follows:
processing and winding the stator based on the optimal design parameters;
monitoring winding force to obtain wire force data;
and detecting the winding precision to obtain wire precision data.
The specific mode for monitoring the winding force and obtaining the wire force data comprises the step of monitoring the tension of the wire in the winding process in real time by using a tension sensor.
The specific mode for detecting the winding precision and obtaining the wire precision data comprises the step of using displacement sensing equipment to monitor the position change of a winding tool or a wire in the winding process and obtaining the wire precision data.
The specific steps for adjusting the winding design based on the monitoring data comprise:
acquiring winding force data and precision position data;
carrying out statistics and analysis on the acquired data;
and optimizing the motor winding design according to the data analysis result.
The invention discloses a method for realizing a high-density winding of a tooth slot motor by adopting an intelligent optimization algorithm, which comprises the following steps: generating design variables of high-density windings of the tooth space motor; establishing a mathematical model according to design variables of the tooth slot motor; optimizing the data model based on a particle swarm algorithm to obtain optimal design parameters; processing and winding the stator based on the optimal design parameters, monitoring winding data at the same time, and returning monitoring data; the winding design is adjusted based on the monitoring data. Therefore, the winding mode of the tooth slot motor can be designed more conveniently based on the target parameters, so that the processing quality of the winding mode is monitored in the actual processing process, and then the processing technology or the design parameters are modified based on the monitoring data, so that the design quality is improved or the winding technology is optimized, and the overall winding quality is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flow chart of a method for implementing a high-density winding of a cogging motor using an intelligent optimization algorithm according to the present invention.
FIG. 2 is a flow chart of the present invention for modeling a mathematical model based on design variables of a cogging motor.
FIG. 3 is a flow chart of the present invention for optimizing a data model based on a particle swarm algorithm to obtain optimal design parameters.
Fig. 4 is a flow chart of the present invention for processing windings of a stator based on optimal design parameters while monitoring winding data and returning the monitored data.
Fig. 5 is a flow chart of the present invention for adjusting a winding design based on monitoring data.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1 to 5, fig. 1 is a flowchart of a method for implementing a high-density winding of a cogging motor using an intelligent optimization algorithm according to the present invention. FIG. 2 is a flow chart of the present invention for modeling a mathematical model based on design variables of a cogging motor. FIG. 3 is a flow chart of the present invention for optimizing a data model based on a particle swarm algorithm to obtain optimal design parameters. Fig. 4 is a flow chart of the present invention for processing windings of a stator based on optimal design parameters while monitoring winding data and returning the monitored data. Fig. 5 is a flow chart of the present invention for adjusting a winding design based on monitoring data.
The invention provides a method for realizing a high-density winding of a tooth slot motor by adopting an intelligent optimization algorithm, which comprises the following steps:
s101, generating design variables of a high-density winding of the cogging motor;
the design variables include output performance metrics including power and efficiency, physical constraints of the motor including slot number and coil width, and design constraints including current density and thermal coupling.
Corresponding performance indexes, physical constraints and design limiting conditions can be selected according to requirements, and specifically, historical design data can be adopted to train through a deep learning model, wherein the historical design data comprises motor parameters, performance indexes, structural parameters and the like. Such data may be obtained from enterprise databases, papers, patents, etc., and the collected data may be divided into training sets, validation sets, and test sets. And then selecting a convolutional neural network to obtain a matching model of the design variables, inputting the preprocessed training set data into the constructed model, and optimizing model parameters through a back propagation algorithm. In the training process, performance indexes such as loss function, accuracy and the like on the verification set are focused to judge whether the model is over-fitted or under-fitted, and corresponding adjustment is carried out. For example, under certain sizing conditions, the power is maximized to match a corresponding set of design variable ranges.
S102, establishing a mathematical model according to design variables of the tooth slot motor;
comprising the following steps:
s201, determining an objective function according to motor design variables;
in motor design, the objective function is a key indicator for measuring motor performance, which reflects the impact of design variables on motor performance. Different objective functions may be determined according to different design goals and requirements. Taking the power density as an example here, the power density is the output power per unit volume of the motor, which may reflect the compactness and efficiency of the motor. Increasing power density is an important goal of motor design. The objective function can be expressed as:
P = P_out / V；
wherein P_out is the output power of the motor, and V is the volume of the motor.
S202, determining constraint conditions according to motor design variables;
constraints include motor type, rated power, motor speed, motor efficiency, size and weight, etc.: the choice of motor type is influenced by factors such as the application environment, load characteristics, power requirements, etc. For example, in an electric vehicle, it may be desirable to select a high torque, high efficiency permanent magnet synchronous motor; in the household appliances, the induction motor with low cost and low noise is more suitable.
Rated power: the rated power of the motor needs to meet the power requirement required in actual operation. Too high or too low a rated power may cause the motor to fail or waste energy.
Motor speed: the rotating speed of the motor is closely related to the application scene. For example, for a fan or compressor rotating at high speed, a high speed motor needs to be selected; and for heavy machinery, it may be desirable to select a low speed, high torque motor.
Motor efficiency: the efficiency of an electric machine is an important measure for its energy conversion capability. Generally, the higher the efficiency of the motor, the better, but this also entails higher manufacturing costs and complexity.
Size and weight: the size and weight of the motor are limited by factors such as installation space, handling difficulty, etc. Generally, a small, lightweight design is more popular, but this may also affect the power density and efficiency of the motor.
S203, normalizing the objective function and the constraint condition.
In order to make different objective functions and constraints have the same dimensions, they may be normalized. This can be achieved by normalizing or scaling the objective function and constraints so that they are all optimized within similar dimensions.
S103, optimizing a data model based on a particle swarm algorithm to obtain optimal design parameters;
the method comprises the following specific steps:
s301, representing design parameters of a motor high-density winding as position vectors of particles;
the position vector of each particle contains design variables of the motor windings, such as coil cross-sectional area, winding pattern.
S302, generating an adaptability function according to a motor design target and constraint conditions;
in motor designs, a fitness function is used to evaluate the fitness of each particle to determine its position and velocity updates in the particle swarm optimization algorithm, such as where the objective function is maximizing motor power, the fitness function may be the value of the objective function.
S303, generating a particle swarm, and generating fitness of each particle according to the fitness function;
the specific mode is as follows:
converting and combining constraint conditions into an objective function by using a penalty function method; the fitness function uses the value of the objective function as fitness to maximize the objective function. For each constraint, a penalty function is introduced to quantify the degree of violation of the constraint. The penalty function is a non-negative function whose value increases as the degree of violation of the constraint increases. The penalty function is then added to the objective function to form a new objective function. The coefficients of the penalty function may be adjusted based on the importance of the constraint. Optimization algorithms (e.g., gradient descent, newton's method, etc.) may also be used to minimize or maximize the new objective function. The goal is to find a variable value that causes the new objective function to take a minimum or maximum value.
By converting constraints into penalty functions and incorporating them into objective functions, both objectives and constraints can be considered in the optimization process. The advantage of the penalty function method is that the trade-off between the objective and the constraint can be balanced by appropriate penalty function coefficients so that the optimization result satisfies the constraint condition.
S304, updating the position and the speed of each particle according to the updating rule of the particle swarm optimization algorithm, and repeating the steps until the preset iteration times are reached or the stop condition is met.
And updating the speed of each particle according to the current position and speed, the group optimal solution and the individual optimal solution. The update rule of the speed is as follows:
speed = inertial weight speed + learning factor 1 random number 1 (individual optimal solution-current position) +learning factor 2 random number 2 (population optimal solution-current position)
Wherein the inertial weight is an important parameter for balancing the particle motion, the learning factors 1 and 2 are parameters for adjusting the influence of individuals and groups on the particle motion, and the random numbers 1 and 2 are random numbers between 0 and 1. And then updating the individual optimal solution and the group optimal solution of each particle according to the new fitness value. The individual optimal solution is the optimal solution encountered by the particle itself, and the population optimal solution is the optimal solution in the whole particle swarm.
Updating the position: the position of each particle is updated according to the new velocity. The update rule of the position is:
position = position + velocity.
And S305, obtaining optimal design parameters according to the result of the optimization iteration.
S104, processing and winding the stator based on the optimal design parameters, monitoring winding data and returning monitoring data;
the specific mode is as follows:
s401, processing and winding the stator based on the optimal design parameters;
s402, monitoring winding force to obtain wire force data;
the specific mode includes using a tension sensor to monitor the tension of the wire in real time during the winding process. A tension sensor is a sensor that measures the force or tension of an object. The device is generally composed of an elastic element, a force measuring bridge and a signal processing circuit, so that wire force data can be detected more conveniently.
S403, detecting winding precision to obtain wire precision data.
The specific mode includes that displacement sensing equipment is used for monitoring the position change of a winding tool or a wire in the winding process, and wire precision data are obtained. The displacement sensor is mounted on the winding device or near the wire so that the change in position can be accurately measured. The sensor can be contact type or non-contact type, a proper sensor type is selected according to specific requirements, and the displacement sensor needs to be calibrated before winding is started so as to ensure accurate and reliable measurement results. The zero point and the measuring range can be determined in the calibration process, and corresponding adjustment and calibration are carried out,
after the winding process is started, the displacement sensor monitors the position change of the tool or the wire in real time. By means of the signals output by the sensors, data about the position, such as displacement, velocity, acceleration, etc., can be obtained. And recording and analyzing the data output by the displacement sensor. The data may be processed using a data logging device or computer software to better understand the position changes during the winding process. The winding device or operation may be adjusted in time based on the data provided by the displacement sensor to ensure that the position of the tool or wire is maintained within a predetermined range. This helps to improve winding quality and efficiency and reduce the occurrence of undesirable products.
S105 adjusts the winding design based on the monitoring data.
The method comprises the following specific steps:
s501, winding force data and precision position data are obtained;
s502, counting and analyzing winding force data and precision position data;
and S503, optimizing the motor winding design according to the data analysis result.
And analyzing the collected data to know the force change and position deviation in the winding process. By statistical and trend analysis of the data, the problems present in the windings and the direction of improvement can be determined. And optimizing the motor winding design according to the data analysis result. The method can be improved in aspects of adjusting a force control mode of a winding tool, adjusting operation parameters of winding equipment or improving a winding process. The key point is to reduce the fluctuation of winding force and improve the precision of winding position through the optimal design. After the optimization design, experimental verification was performed to evaluate the improvement effect. A part of motors can be selected for winding, and data of winding force and position accuracy can be measured. By comparing with the previous data, the effect of the optimum design is evaluated and necessary adjustments and improvements are made.
The invention discloses a method for realizing a high-density winding of a tooth slot motor by adopting an intelligent optimization algorithm, which comprises the following steps: generating design variables of high-density windings of the tooth space motor; establishing a mathematical model according to design variables of the tooth slot motor; optimizing the data model based on a particle swarm algorithm to obtain optimal design parameters; processing and winding the stator based on the optimal design parameters, monitoring winding data at the same time, and returning monitoring data; the winding design is adjusted based on the monitoring data. Therefore, the winding mode of the tooth slot motor can be designed more conveniently based on the target parameters, so that the processing quality of the winding mode is monitored in the actual processing process, and then the processing technology or the design parameters are modified based on the monitoring data, so that the design quality is improved or the winding technology is optimized, and the overall winding quality is improved.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (9)
1. The method for realizing the high-density winding of the tooth slot motor by adopting the intelligent optimization algorithm is characterized in that,
comprising the following steps: generating design variables of high-density windings of the tooth space motor;
establishing a mathematical model according to design variables of the tooth slot motor;
optimizing the data model based on a particle swarm algorithm to obtain optimal design parameters;
processing and winding the stator based on the optimal design parameters, monitoring winding data at the same time, and returning monitoring data;
the winding design is adjusted based on the monitoring data.
2. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 1, wherein,
the design variables include output performance metrics including power and efficiency, physical constraints of the motor including slot number and coil width, and design constraints including current density and thermal coupling.
3. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 2, wherein,
the establishing the mathematical model according to the design variable of the tooth slot motor comprises the following steps:
determining an objective function according to the motor design variable;
determining constraint conditions according to motor design variables;
and normalizing the objective function and the constraint condition.
4. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 3, wherein,
the specific steps for optimizing the data model based on the particle swarm algorithm to obtain the optimal design parameters comprise the following steps:
representing design parameters of the motor high-density winding as position vectors of particles;
generating an adaptability function according to the motor design target and the constraint condition;
generating a subgroup of particles, and generating fitness of each particle according to the fitness function;
updating the position and speed of each particle according to the updating rule of the particle swarm optimization algorithm, and repeating the steps until the preset iteration times are reached or the stopping condition is met;
and obtaining optimal design parameters according to the result of the optimization iteration.
5. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 4, wherein,
the specific mode for generating the fitness function according to the motor design target and the constraint condition is as follows:
converting and combining constraint conditions into an objective function by using a penalty function method;
the fitness function uses the value of the objective function as fitness to maximize the objective function.
6. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 5, wherein,
the specific modes of processing and winding the stator based on the optimal design parameters, monitoring winding data and returning monitoring data are as follows:
processing and winding the stator based on the optimal design parameters;
monitoring winding force to obtain wire force data;
and detecting the winding precision to obtain wire precision data.
7. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 6, wherein,
the specific mode for monitoring the winding force and obtaining the wire force data comprises the step of monitoring the tension of the wire in the winding process in real time by using a tension sensor.
8. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 7, wherein,
the specific mode for detecting the winding precision and obtaining the wire precision data comprises the step of using displacement sensing equipment to monitor the position change of a winding tool or a wire in the winding process and obtaining the wire precision data.
9. The method for realizing the high-density winding of the cogging motor by adopting the intelligent optimization algorithm according to claim 8, wherein,
the specific steps for adjusting the winding design based on the monitoring data comprise:
acquiring winding force data and precision position data;
counting and analyzing the winding force data and the precision position data;
and optimizing the motor winding design according to the data analysis result.
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