1. A motor parameter design method based on multi-objective optimization is characterized by comprising the following steps:

s101: constructing a motor parameter prediction model, and constructing a motor optimization objective function according to the motor parameter prediction model;

s102: setting an objective function of a random optimization algorithm according to the motor optimization objective function, and optimizing parameters to be optimized by adopting the random optimization algorithm;

s103: and calculating an optimization result of the parameters to be optimized according to a random optimization algorithm to obtain the optimal parameters of the motor which meet the minimum motor optimization objective function value.

2. The method for designing the motor parameter based on the multi-objective optimization as claimed in claim 1, wherein the step of constructing the motor parameter prediction model and the step of constructing the motor optimization objective function according to the motor parameter prediction model specifically comprise:

s201: constructing a motor optimization mathematical model:

（1）；

wherein the content of the first and second substances,is the ith parameter to be optimized,is the set of parameters to be optimized,is a linear multi-objective function of the system,is the k-th optimization objective and,is the weight coefficient of the kth optimization objective, satisfies， Is the jth constraint that is imposed on the system,；is the number of design parameters that can be used,is the number of optimization objectives that are to be met,is the number of constraints;

s202: for m parameters to be optimized, taking p groups of different values, wherein each group comprises values of the m parameters to be optimized, and calculating by adopting a finite element method to obtain actual values of p optimization targets;

s203: calculating regression coefficients according to the actual values of the p optimization targets and the p groups of parameters to be optimized by using a second-order regression method, and constructing a parameter model as shown in the following formula:

（2）；

wherein the formula (2) represents the optimization objectiveWith m parameters to be optimizedThe actual values of p optimization targets and p groups of parameters to be optimized are substituted into the formula (2) for calculation to obtain，Is a constant term that is used to determine,are the regression coefficients of the primary term, the secondary term and the cross term respectively,in order to observe the error, the error is observed,；

s204: taking q groups of different values for the m parameters to be optimized, wherein each group comprises values of the m parameters to be optimized, and calculating a predicted value and an actual value of an optimization target: calculating to obtain q actual values of the optimization targets by adopting a finite element method, and calculating to obtain q actual values by using a parameter modelAs a pre-of q optimization objectivesMeasuring;

s205: and calculating a prediction coefficient according to the actual value and the predicted value of the optimization target by adopting the following formula:

（3）；

wherein the content of the first and second substances,is a prediction coefficient of the motion vector,in order to optimize the actual value of the target,in order to optimize the predicted value of the target,is composed ofThe mean of the actual values of the individual optimization objectives;

s206: determining whether the prediction coefficient is < coefficient threshold; according to the relation between the prediction coefficient and the coefficient threshold, either of the following cases is performed:

a) when predicting the coefficientIf the value is smaller than the coefficient threshold value, the step S204 is returned to, the value of the parameter to be optimized is different from the previous value, and the obtained value is usedThe group data were used as samples of the computational parametric model,the number of times calculated for the cycle;

b) when predicting the coefficientIf the coefficient is greater than or equal to the coefficient threshold, step S207 is executed;

s207: acquiring a parameter prediction model; when predicting the coefficientThe parameter model when the coefficient threshold value is larger than or equal to the coefficient threshold value is a parameter prediction model;

s208: according to the parameter prediction model and the optimization direction to be achieved, the optimization targets are divided into two types, and a motor optimization target function is constructed according to the classification of the optimization targets; the optimization targets are divided into two types, specifically:

a) the first type: the optimization direction is the optimization target of obtaining the initial value or moreA is marked as；

b) The second type: the optimization direction is to obtain an optimization target smaller than the initial valueA is marked as；

The number of the two types of optimization targets respectively,；

and constructing a motor optimization objective function in the following form according to the classification of the optimization objective:

（4）；

wherein the content of the first and second substances,is an optimization objective function of the motor,andare the initial values for two types of optimization objectives.

3. The method for designing the motor parameters based on the multi-objective optimization as claimed in claim 1, wherein the objective function of the random optimization algorithm is set according to the motor optimization objective function, the parameters to be optimized are optimized by the random optimization algorithm, the optimization result of the parameters to be optimized is obtained by calculation according to the random optimization algorithm, and the specific steps of obtaining the motor optimal parameters which satisfy the minimum motor optimization objective function value are as follows:

s301: setting variables and variation intervals of a random optimization algorithm according to parameters and ranges to be optimized; setting a target function of a random optimization algorithm according to the constructed motor optimization target function;

s302: setting population quantity, iteration times and error precision, and calculating under the condition of minimum objective function value;

s303: and obtaining the value of the variable, namely the optimization result of the parameter to be optimized, namely the motor optimal parameter meeting the minimum motor optimization objective function value according to the result of the algorithm convergence.

Background

Motors are widely used in various fields of industrial life. The generator generates huge electric energy, and the motor plays an extremely important role in the fields of production, manufacturing and transportation. The motor directly influences production and life, and the design of the high-performance motor has very important significance.

Because the motor structure is complex and all parameters are mutually influenced, the traditional design method cannot accurately design the motor parameters with the best performance based on the experience of designers. The existing optimization method mainly builds a mathematical model of an optimization target and optimizes the mathematical model through an algorithm, the number of optimized parameters is limited, the optimization is not verified through simulation, the reliability is insufficient, the relationship between the optimization target and the optimized parameters cannot be obtained, the calculation time is long, the efficiency is low, and the requirement of multi-target optimization of the motor cannot be met.

The Chinese patent document '201410836987.0' establishes a motor optimization mathematical model, and adopts a genetic algorithm to perform multi-objective optimization design of an asynchronous motor, so that the design period of the motor is shortened. The chinese patent document "201910650018.9" performs experimental design by the field method, and explores the influence of motor parameters on the output voltage of the motor to obtain an optimal design scheme. However, the above method cannot give consideration to both the progress and speed of multi-objective optimization and simulation calculation, and cannot obtain the relationship between the optimization objective and the optimization parameter.

In consideration of the problems, the motor parameter design method based on multi-objective optimization is provided.

Disclosure of Invention

The invention aims to solve the technical problems of the existing motor optimization method, provides a motor parameter design method based on multi-objective optimization, overcomes the defects of long time consumption and low efficiency of the existing optimization method, solves the problem of multi-objective optimization of the motor, improves the speed and the precision of the optimization, and promotes the efficient optimization design of the motor.

In order to solve the technical problems, the technical scheme provided by the invention is as follows:

the invention provides a motor parameter design method based on multi-objective optimization, which comprises the following steps:

s101: constructing a motor parameter prediction model, and constructing a motor optimization objective function according to the motor parameter prediction model;

s102: setting an objective function of a random optimization algorithm according to the motor optimization objective function, and optimizing parameters to be optimized by adopting the random optimization algorithm;

s103: and calculating an optimization result of the parameters to be optimized according to a random optimization algorithm to obtain the optimal parameters of the motor which meet the minimum motor optimization objective function value.

Further, the specific steps of constructing the motor parameter prediction model and constructing the motor optimization objective function according to the motor parameter prediction model are as follows:

s201: constructing a motor optimization mathematical model:

（1）；

wherein the content of the first and second substances,is the ith parameter to be optimized,is the set of parameters to be optimized,is a linear multi-objective function of the system,is the k-th optimization objective and,is the weight coefficient of the kth optimization objective, satisfies，Is the jth constraint that is imposed on the system,；is the number of design parameters that can be used,is the number of optimization objectives that are to be met,is the number of constraints;

s202: for m parameters to be optimized, taking p groups of different values, wherein each group comprises values of the m parameters to be optimized, and calculating by adopting a finite element method to obtain actual values of p optimization targets;

s203: calculating regression coefficients according to the actual values of the p optimization targets and the p groups of parameters to be optimized by using a second-order regression method, and constructing a parameter model as shown in the following formula:

（2）；

wherein the formula (2) represents the optimization objectiveWith m parameters to be optimizedThe actual values of p optimization targets and p groups of parameters to be optimized are substituted into the formula (2) for calculation to obtain，Is a constant term that is used to determine,are the regression coefficients of the primary term, the secondary term and the cross term respectively,in order to observe the error, the error is observed,；

s204: taking q groups of different values for the m parameters to be optimized, wherein each group comprises values of the m parameters to be optimized, and calculating a predicted value and an actual value of an optimization target: calculating to obtain q actual values of the optimization targets by adopting a finite element method, and calculating to obtain q actual values by using a parameter modelThe value of (a) is used as the predicted value of q optimization targets;

s205: and calculating a prediction coefficient according to the actual value and the predicted value of the optimization target by adopting the following formula:

（3）；

wherein the content of the first and second substances,is a prediction coefficient of the motion vector,in order to optimize the actual value of the target,in order to optimize the predicted value of the target,is composed ofThe mean of the actual values of the individual optimization objectives;

s206: determining whether the prediction coefficient is < coefficient threshold; according to the relation between the prediction coefficient and the coefficient threshold, either of the following cases is performed:

a) when predicting the coefficientIf the value is smaller than the coefficient threshold value, the step S204 is returned to, the value of the parameter to be optimized is different from the previous value, and the obtained value is usedThe group data were used as samples of the computational parametric model,the number of times calculated for the cycle;

b) when predicting the coefficientIf the coefficient is greater than or equal to the coefficient threshold, step S207 is executed;

s207: acquiring a parameter prediction model; when predicting the coefficientThe parameter model when the coefficient threshold value is larger than or equal to the coefficient threshold value is a parameter prediction model;

s208: according to the parameter prediction model and the optimization direction to be achieved, the optimization targets are divided into two types, and a motor optimization target function is constructed according to the classification of the optimization targets; the optimization targets are divided into two types, specifically:

a) the first type: the optimization direction is the optimization target of obtaining the initial value or moreA is marked as；

b) The second type: the optimization direction is to obtain an optimization target smaller than the initial valueA is marked as；

The number of the two types of optimization targets respectively,；

and constructing a motor optimization objective function in the following form according to the classification of the optimization objective:

（4）；

wherein the content of the first and second substances,is an optimization objective function of the motor,andare the initial values for two types of optimization objectives.

Further, the specific steps of setting a target function of a random optimization algorithm according to the motor optimization target function, optimizing the parameters to be optimized by adopting the random optimization algorithm, calculating an optimization result of the parameters to be optimized according to the random optimization algorithm, and obtaining the motor optimal parameters meeting the minimum motor optimization target function value are as follows:

s301: setting variables and variation intervals of a random optimization algorithm according to parameters and ranges to be optimized; setting a target function of a random optimization algorithm according to the constructed motor optimization target function;

s302: setting population quantity, iteration times and error precision, and calculating under the condition of minimum objective function value;

s303: and obtaining the value of the variable, namely the optimization result of the parameter to be optimized, namely the motor optimal parameter meeting the minimum motor optimization objective function value according to the result of the algorithm convergence.

The invention provides a motor parameter design method based on multi-objective optimization aiming at the problems of long optimization time consumption and low efficiency of motor parameter design, and the method mainly has the following advantages:

(1) in the optimization process, a finite element method is adopted to obtain the relation between an optimization target and an optimization parameter, and on the basis of establishing a parameter prediction model, a random optimization algorithm is adopted for calculation, so that the accuracy of a result is ensured;

(2) the reliability of prediction is improved, the calculation time is greatly shortened, and both the precision and the efficiency are considered.

Drawings

Fig. 1 is a flowchart of a motor parameter design method based on multi-objective optimization according to an embodiment of the present invention.

Fig. 2 is a flowchart of a method for constructing a motor parameter prediction model and an objective function according to an embodiment of the present invention.

Detailed Description

The present invention will be further described with reference to the following examples and drawings, but the present invention is not limited thereto.

The invention aims to solve the technical problems of the existing motor optimization method, provides a motor parameter design method based on multi-objective optimization, overcomes the defects of long time consumption and low efficiency of the existing optimization method, solves the problem of multi-objective optimization of the motor, improves the speed and the precision of the optimization, and promotes the efficient optimization design of the motor.

The invention provides a motor parameter design method based on multi-objective optimization, which comprises the following steps as shown in figure 1:

s101: constructing a motor parameter prediction model, and constructing a motor optimization objective function according to the motor parameter prediction model;

s102: setting an objective function of a random optimization algorithm according to the motor optimization objective function, and optimizing parameters to be optimized by adopting the random optimization algorithm;

s103: and calculating an optimization result of the parameters to be optimized according to a random optimization algorithm to obtain the optimal parameters of the motor which meet the minimum motor optimization objective function value.

Further, as shown in fig. 2, the specific steps of constructing the motor parameter prediction model and the motor optimization objective function are as follows:

s201: constructing a motor optimization mathematical model:

（1）；

wherein the content of the first and second substances,is the ith parameter to be optimized,is the set of parameters to be optimized,is a linear multi-objective function of the system,is the k-th optimization objective and,is the weight coefficient of the kth optimization objective, satisfies，Is the jth constraint that is imposed on the system,；is the number of design parameters that can be used,is the number of optimization objectives that are to be met,is the number of constraints;

s202: for m parameters to be optimized, taking p groups of different values, wherein each group comprises values of the m parameters to be optimized, and calculating by adopting a finite element method to obtain actual values of p optimization targets;

s203: calculating regression coefficients according to the actual values of the p optimization targets and the p groups of parameters to be optimized by using a second-order regression method, and constructing a parameter model as shown in the following formula:

（2）；

wherein the formula (2) represents the optimization objectiveWith m parameters to be optimizedThe actual values of p optimization targets and p groups of parameters to be optimized are substituted into the formula (2) for calculation to obtain，Is a constant term that is used to determine,are the regression coefficients of the primary term, the secondary term and the cross term respectively,in order to observe the error, the error is observed,；

s204: taking q groups of different values for the m parameters to be optimized, wherein each group comprises values of the m parameters to be optimized, and calculating a predicted value and an actual value of an optimization target:calculating to obtain q actual values of the optimization targets by using a finite element method, and calculating to obtain q actual values by using a parameter modelThe value is used as the predicted value of q optimization targets;

s205: and calculating a prediction coefficient according to the actual value and the predicted value of the optimization target by adopting the following formula:

（3）；

wherein the content of the first and second substances,is a prediction coefficient of the motion vector,in order to optimize the actual value of the target,in order to optimize the predicted value of the target,is composed ofThe mean of the actual values of the individual optimization objectives;

s206: determining whether the prediction coefficient is < coefficient threshold; according to the relation between the prediction coefficient and the coefficient threshold, either of the following cases is performed:

c) when predicting the coefficientIf the value is smaller than the coefficient threshold value, the step S204 is returned to, the value of the parameter to be optimized is different from the previous value, and the obtained value is usedThe group data were used as samples of the computational parametric model,the number of times calculated for the cycle;

d) when predicting the coefficientIf the coefficient is greater than or equal to the coefficient threshold, step S207 is executed;

s207: acquiring a parameter prediction model; when predicting the coefficientThe parameter model when the coefficient threshold value is larger than or equal to the coefficient threshold value is a parameter prediction model;

s208: according to the parameter prediction model and the optimization direction to be achieved, the optimization targets are divided into two types, and a motor optimization target function is constructed according to the classification of the optimization targets; the optimization targets are divided into two types, specifically:

c) the first type: the optimization direction is the optimization target of obtaining the initial value or moreA is marked as；

d) The second type: the optimization direction is to obtain an optimization target smaller than the initial valueA is marked as；

The number of the two types of optimization targets respectively,；

and constructing a motor optimization objective function in the following form according to the classification of the optimization objective:

（4）；

wherein the content of the first and second substances,is an optimization objective function of the motor,andare the initial values for two types of optimization objectives.

Further, the design parameters in S201 include, but are not limited to, stator and rotor structural parameters, winding parameters, magnetic steel specifications, and the like, and the optimization targets include, but are not limited to, average torque, torque ripple, efficiency, and the like.

Further, the finite element method described in S202 is implemented by software simulation calculation such as Ansoft Maxwell, ANSYS Electronics Desktop, and the like.

Further, the specific steps of setting the objective function of the random optimization algorithm according to the motor optimization objective function, optimizing the parameter to be optimized by using the random optimization algorithm, and calculating the optimization result of the parameter to be optimized according to the random optimization algorithm in steps S102 and S103 to obtain the motor optimal parameter that satisfies the minimum motor optimization objective function value are as follows:

s301: setting variables and variation intervals of a random optimization algorithm according to parameters and ranges to be optimized; setting a target function of a random optimization algorithm according to the constructed motor optimization target function;

s302: setting population quantity, iteration times and error precision, and calculating under the condition of minimum objective function value;

s303: and obtaining the value of the variable, namely the optimization result of the parameter to be optimized, namely the motor optimal parameter meeting the minimum motor optimization objective function value according to the result of the algorithm convergence.

The random optimization algorithm in step S102 and step S103 is the prior art, and includes a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm, and the like.

The motor parameter design method based on multi-objective optimization provided by the invention is described in detail, and the implementation description is only used for helping to understand the method and the core idea of the method; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.