CN112600375A - Multi-objective optimization method of novel alternating-pole brushless hybrid excitation motor - Google Patents

Multi-objective optimization method of novel alternating-pole brushless hybrid excitation motor Download PDF

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CN112600375A
CN112600375A CN202011475772.2A CN202011475772A CN112600375A CN 112600375 A CN112600375 A CN 112600375A CN 202011475772 A CN202011475772 A CN 202011475772A CN 112600375 A CN112600375 A CN 112600375A
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hybrid excitation
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樊英
杨灿
梅叶依
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Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K16/00—Machines with more than one rotor or stator
    • H02K16/04—Machines with one rotor and two stators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K1/00—Details of the magnetic circuit
    • H02K1/06—Details of the magnetic circuit characterised by the shape, form or construction
    • H02K1/12—Stationary parts of the magnetic circuit
    • H02K1/16—Stator cores with slots for windings
    • H02K1/165—Shape, form or location of the slots
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K1/00—Details of the magnetic circuit
    • H02K1/06—Details of the magnetic circuit characterised by the shape, form or construction
    • H02K1/22—Rotating parts of the magnetic circuit
    • H02K1/27—Rotor cores with permanent magnets
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K3/00—Details of windings
    • H02K3/04—Windings characterised by the conductor shape, form or construction, e.g. with bar conductors
    • H02K3/28—Layout of windings or of connections between windings
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K2201/00—Specific aspects not provided for in the other groups of this subclass relating to the magnetic circuits
    • H02K2201/03—Machines characterised by aspects of the air-gap between rotor and stator
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K2213/00—Specific aspects, not otherwise provided for and not covered by codes H02K2201/00 - H02K2211/00
    • H02K2213/03—Machines characterised by numerical values, ranges, mathematical expressions or similar information

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Abstract

The invention discloses a multi-objective optimization method of a novel alternating-pole brushless hybrid excitation motor, which relates to the field of excitation motors. The optimized scheme effectively improves the output torque of the motor and increases the magnetic modulation coefficient of the motor. The novel alternating-pole brushless hybrid excitation motor is complex in topology and large in number of design parameters, the optimization scheme combining the intelligent optimization algorithm and the single-parameter independent optimization effectively solves the problems that in the optimization process of the motor, due to the fact that the number of the design parameters is large and the design parameters are mutually coupled, conflicts are easy to occur among optimization targets, and a global optimal solution is difficult to obtain, and meanwhile optimization efficiency is improved.

Description

Multi-objective optimization method of novel alternating-pole brushless hybrid excitation motor
Technical Field
The invention relates to the field of hybrid excitation motors, in particular to a multi-objective optimization method of a novel alternating-pole brushless hybrid excitation motor.
Background
The permanent magnet synchronous motor is widely applied to the field of electric automobile driving systems due to the advantages of high power density, high efficiency, flexible driving and the like, but the air gap field adjusting range of the permanent magnet synchronous motor is limited and the risk of irreversible demagnetization of the permanent magnet exists. The hybrid excitation motor integrates the advantages of a permanent magnet motor and an electric excitation motor, the speed regulation range of the motor can be expanded, and meanwhile, an electric excitation source can bear the main excitation function under the condition of demagnetization of a permanent magnet, so that the fault tolerance of a motor driving system is enhanced. The novel double-stator alternate-pole brushless hybrid excitation motor combines an alternating-current excitation winding and an alternate-pole structure to realize brushless hybrid magnetic regulation, and meanwhile, the armature winding and the excitation winding are respectively placed on the outer stator and the inner stator, so that the partition isolation of the winding is realized, and the utilization rate of the internal space of the motor is improved. The motor is suitable for application occasions such as motors for driving electric automobiles and the like which need high torque density, wide speed regulation range and high reliability, but the motor is relatively complex in topological structure, has more design variables influencing optimization targets and different influence degrees, has interactive influence among the design variables, and has an important problem of how to balance optimization effect and optimization efficiency, thereby becoming the optimal design of the motor with the complex topological structure.
At present, single-parameter independent optimization is widely applied to the field of motor design optimization, and is characterized by simple steps and low calculation cost, and a good optimization effect can be obtained when the number of design variables to be optimized is small, but when the number of design variables is large and the design variables have interaction, the method easily causes conflict among optimization targets, and only a local optimal solution can be obtained. In recent decades, some modern intelligent optimization algorithms are rapidly popularized and applied in the field of motor optimization design, such as genetic algorithms, particle swarm optimization, simulated annealing methods and the like. The intelligent algorithm is utilized, an objective function needs to be constructed firstly, and the objective function is easy to obtain when single-objective optimization is carried out; when multi-objective optimization is performed, a method of adding weighting factors is usually adopted to convert a multi-objective optimization problem into a single-objective optimization problem for solving, but the method needs to manually set the weighting factors, and when the weighting factors are changed, optimization needs to be performed again.
The Pareto optimal concept comes from an economic problem, and aims to achieve the effect of obtaining the maximum benefit at the minimum cost through reasonable allocation of resources. In the multi-objective optimization of the motor, when the design variable changes, any optimization target can not be guaranteed to be improved on the premise that the optimization target is not deteriorated, and the solution at the moment is called a Pareto optimal solution. By combining an intelligent algorithm and Pareto optimization, multi-objective optimization can be realized without setting a weighting factor, so that a global optimal solution is obtained, but the scheme has the advantages of high calculation cost, long optimization time and low optimization efficiency. Therefore, a set of solutions needs to be provided for the multi-objective optimization problem of the novel alternating-pole brushless hybrid excitation motor.
Disclosure of Invention
In order to solve the above mentioned drawbacks in the background art, the present invention provides a multi-objective optimization method for a novel alternating-pole brushless hybrid excitation motor.
The purpose of the invention can be realized by the following technical scheme:
a multi-objective optimization method for a novel alternating-pole brushless hybrid excitation motor comprises the following steps:
step one, determining the optimized target of the novel alternating-pole brushless hybrid excitation motor as increasing the torque output TaAnd magnetic coefficient of regulation KfDegree d of non-uniformity of outer air gap, and arc angle a of iron core pole1Outer stator yoke width hjOuter stator slot width Bs0Length of inner air gap deltaiOptimizing design variables;
secondly, sensitivity calculation is carried out on each design variable based on a Taguchi method, the design variables are sorted according to the sensitivity and are distributed with different priorities;
establishing a response surface model of each optimization target for the design variable with high priority, and replacing a finite element model of the motor with the response surface model to reduce the calculation cost of subsequent optimization;
step four, solving a Pareto solution set through an NSGA-II algorithm based on the value range of the design variables and a second-order response surface regression model of each design target;
and step five, for the design variable with low priority, because the influence on the optimization target is small, single-parameter independent optimization is adopted in sequence according to the sensitivity order.
Furthermore, the novel alternating-pole brushless hybrid excitation motor comprises an outer stator and an inner stator, wherein an armature winding, an iron core pole, a permanent magnet and an excitation winding are sequentially arranged between the outer stator and the inner stator.
Further, the third step includes the following specific steps:
designing and constructing a sample space by using a central composite surface, calculating values of two optimization targets through finite elements, fitting a second-order response surface regression model of the optimization targets by using Design-Expert software, and analyzing the effectiveness and the accuracy of the model, wherein the established model is as follows:
Figure BDA0002835294110000031
Figure BDA0002835294110000032
further, the fourth step includes the following specific steps:
establishing an initial generation population P by taking a design variable value range as a constraint condition0Generating offspring population Q by selection, crossover and mutation operations0(ii) a Calculating P0And Q0The fitness function of the middle individual is a second-order response surface regression model of two optimization targets, and the better individual is selected through non-dominated sorting and crowding distance comparison, and the initial generation population is updated; obtaining a group of Pareto solution sets after the set iteration times; and 4 solutions are selected from the Pareto solution set for finite element calculation, and solutions with finite element results similar to the algorithm results are selected for optimization of other design variables.
Further, the fifth step includes the following specific steps:
when a certain variable is optimized, other design variables are kept unchanged, the initial value of the optimized variable is replaced by the optimal value of the optimized variable, and the influence of each design variable on the optimization target in the optimization process is obtained through finite element calculation.
The invention has the beneficial effects that:
the invention takes the torque output and the magnetic modulation coefficient of the novel alternating-pole brushless hybrid excitation motor as optimization targets, combines a single-parameter independent optimization method and a global intelligent optimization algorithm, carries out layered optimization on the design parameters of the motor, and adopts a response surface model to replace a finite element model of the motor, thereby reducing the calculation cost, effectively improving the torque output of the motor and increasing the magnetic modulation coefficient. The optimization method effectively solves the problems that the number of design variables of the complex topology motor is large, interaction exists among the design variables, and high-efficiency optimization is difficult to realize. In addition, the method is not only suitable for novel alternating-pole brushless hybrid excitation motors, but also suitable for other motor optimization occasions with more design variables.
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The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a structural diagram of a novel alternating-pole brushless hybrid excitation motor provided in an embodiment of the invention;
FIG. 2 is a schematic illustration of a non-uniform outer air gap provided by an embodiment of the present invention;
FIG. 3 is a schematic view of an arc angle of a pole of a core provided by an embodiment of the present invention;
FIG. 4 is a flow chart of multi-objective optimization of the novel alternating-pole brushless hybrid excitation motor according to the embodiment of the present invention;
FIG. 5 is a Pareto front solved by the NSGA-II algorithm provided by the embodiment of the present invention;
FIG. 6 is a graph of optimization objectives as a function of inner air gap length provided by an embodiment of the present invention;
FIG. 7 is a graph of optimization objectives as a function of outer stator slot width provided by an embodiment of the present invention.
In the figure: 1. an outer stator; 2. an armature winding; 3. an inner stator; 4. an excitation winding; 5. an iron core pole; 6. and a permanent magnet.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A multi-objective optimization method of a novel alternate-pole brushless hybrid excitation motor is disclosed, and as shown in figure 1, the novel alternate-pole brushless hybrid excitation motor comprises an outer stator 1 and an inner stator 3, wherein an armature winding 2, an iron core pole 5, a permanent magnet 6 and an excitation winding 4 are sequentially arranged between the outer stator 1 and the inner stator 3.
The optimization method comprises the following specific steps:
firstly, determining the optimization target and the value of a design variable of a motor. As shown in fig. 2-4, as a motor for driving an electric vehicle, the torque output of a novel alternating-pole brushless hybrid excitation motor is one of key indexes to be optimized, and an important feature of the motor is that the magnetic field can be adjusted to realize wide-range speed regulation of the motor, so as to select an optimization target to be the torque output TaAnd magnetic coefficient of regulation KfDegree d of non-uniformity of outer air gap, and arc angle a of iron core pole1Outer stator yoke width hjOuter stator slot width Bs0Length of inner air gap deltaiOptimized for design variables, in which the magnetic coefficient K is adjustedfAnd the degree of outer-air-gap unevenness d are as follows, respectively:
Figure BDA0002835294110000053
Figure BDA0002835294110000051
in the formula, #H+Applying a no-load flux linkage peak value when rated pure boosting current is applied to the excitation winding; psiH-When rated pure weak magnetic current is applied to the exciting windingThe no-load flux linkage peak value; h is the eccentricity of the rotor; daIs the armature diameter; theta is an eccentric angle; deltamaxThe value at which the outer air gap length is maximum; deltaminThe value at which the length of the outer air gap is the smallest.
The value intervals of the design variables are shown in table 1:
table 1 design variable value intervals
Figure BDA0002835294110000052
Figure BDA0002835294110000061
And secondly, carrying out sensitivity analysis on each design variable based on a Taguchi method. Setting 3 influencing factors for each design variable, and selecting L according to Taguchi method9(35) Orthogonal table and establishing experiment matrix, wherein the matrix is tested for 9 times totally, and each time of torque output T can be obtained by finite element simulationaAnd magnetic regulation coefficient KfExperimental values of (2). In order to quantitatively calculate the influence degree of each design variable on the optimization target, the Taguchi method carries out mean value analysis and variance analysis according to an experimental matrix and a corresponding finite element calculation result, and the sensitivity calculation method comprises the following steps:
Figure BDA0002835294110000062
wherein x represents a design variable, S represents each optimization objective,
Figure BDA0002835294110000063
representing the average value of S when x is a certain influence factor, m (S) representing the average value of S in all experiments, according to the calculation result and the influence degree of each design variable on two optimization targets, the classification condition is priority 1[ a ]1，d，hj]Priority 2[ delta ]i，Bs0]。
Establishing a response surface model of each optimization target for the design variables with high priority, which comprises the following specific steps: firstly, designing and constructing an experimental plan table by using a central composite surface, calculating a corresponding optimization objective function value through a finite element, and then fitting a second-order response surface regression model of an optimization objective by using Design-Expert software as follows:
Figure BDA0002835294110000064
Figure BDA0002835294110000065
in the formula x1、x2、x3Are respectively a1、d、hjThe method for converting the actual value and the coded value is as follows:
Figure BDA0002835294110000071
wherein x is the actual value of the design variable; x is the number ofcCode values for design variables; x is the number ofhTo a high level value of the design variable; x is the number oflTo design low level values of the variables.
Using the corrected determined coefficients
Figure BDA0002835294110000072
The accuracy of the model is quantitatively analyzed, and the calculation method is as follows:
Figure BDA0002835294110000073
wherein y is an observed value, and y is,
Figure BDA0002835294110000074
for the values of the fit to be obtained,
Figure BDA0002835294110000075
is the average of the observations, N is the number of observations, and P is the total number of terms of the fitted regression equation. The calculation result is TaIs/are as follows
Figure BDA0002835294110000076
KfIs/are as follows
Figure BDA0002835294110000077
Are closer to 1 and therefore the response surface model is efficient and accurate.
And fourthly, solving a Pareto solution set through an NSGA-II algorithm based on the value range of the design variables and a second-order response surface regression model of each design target, setting the population number to be 100 and the iteration number to be 500, and obtaining a Pareto front edge formed by the Pareto solution set as shown in FIG. 5. And 4 solutions are selected from the Pareto solution set for finite element calculation, and solutions [20, 0.1 and 6.53] are selected for next optimization.
And fifthly, for the design variables of the priority 2, sequentially adopting single-parameter independent optimization according to the sensitivity order. The method comprises the following specific steps: when a certain variable is optimized, other design variables are kept unchanged, the initial value of the optimized variable is replaced by the optimal value of the optimized variable, and the influence of each design variable on the optimization target in the optimization process is obtained through finite element calculation, as shown in fig. 6 and 7.
In order to verify the effectiveness of the optimization method, the motor finite element models before and after optimization are compared and analyzed, and the results are shown in table 2:
TABLE 2 effects of hierarchical optimization
Figure BDA0002835294110000078
Figure BDA0002835294110000081
After the layered optimization, the torque output is improved by 15.4%, the magnetic modulation coefficient is increased by 16.0%, the optimization effect is good, and the effectiveness of the optimization method is verified.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (5)

1. A multi-objective optimization method for a novel alternating-pole brushless hybrid excitation motor is characterized by comprising the following steps:
step one, determining the optimized target of the novel alternating-pole brushless hybrid excitation motor as increasing the torque output TaAnd magnetic coefficient of regulation KfDegree d of non-uniformity of outer air gap, and arc angle a of iron core pole1Outer stator yoke width hjOuter stator slot width Bs0Length of inner air gap deltaiOptimizing design variables;
secondly, sensitivity calculation is carried out on each design variable based on a Taguchi method, the design variables are sorted according to the sensitivity and are distributed with different priorities;
establishing a response surface model of each optimization target for the design variable with high priority, and replacing a finite element model of the motor with the response surface model to reduce the calculation cost of subsequent optimization;
step four, solving a Pareto solution set through an NSGA-II algorithm based on the value range of the design variables and a second-order response surface regression model of each design target;
and step five, for the design variable with low priority, because the influence on the optimization target is small, single-parameter independent optimization is adopted in sequence according to the sensitivity order.
2. The method as claimed in claim 1, wherein the novel alternating-pole brushless hybrid excitation motor comprises an outer stator and an inner stator, and an armature winding, an iron core pole, a permanent magnet and an excitation winding are sequentially arranged between the outer stator and the inner stator.
3. The novel multi-objective optimization method for the alternating-pole brushless hybrid excitation motor according to claim 1, wherein the third step comprises the following specific steps:
designing and constructing a sample space by using a central composite surface, calculating values of two optimization targets through finite elements, fitting a second-order response surface regression model of the optimization targets by using Design-Expert software, and analyzing the effectiveness and the accuracy of the model, wherein the established model is as follows:
Figure FDA0002835294100000021
Figure FDA0002835294100000022
4. the novel multi-objective optimization method for the alternating-pole brushless hybrid excitation motor according to claim 1, wherein the step four comprises the following specific steps:
establishing an initial generation population P by taking a design variable value range as a constraint condition0Generating offspring population Q by selection, crossover and mutation operations0(ii) a Calculating P0And Q0Fitness function of medium individualCounting, namely, a second-order response surface regression model of two optimization targets, selecting better individuals through non-dominated sorting and crowding distance comparison, and updating the initial generation population; obtaining a group of Pareto solution sets after the set iteration times; and 4 solutions are selected from the Pareto solution set for finite element calculation, and solutions with finite element results similar to the algorithm results are selected for optimization of other design variables.
5. The novel multi-objective optimization method for the alternating-pole brushless hybrid excitation motor according to claim 1, wherein the step five comprises the following specific steps:
when a certain variable is optimized, other design variables are kept unchanged, the initial value of the optimized variable is replaced by the optimal value of the optimized variable, and the influence of each design variable on the optimization target in the optimization process is obtained through finite element calculation.
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CN113472261A (en) * 2021-06-07 2021-10-01 江苏大学 Layered multi-objective optimization design method based on hybrid permanent magnet synchronous motor
CN113657009A (en) * 2021-10-20 2021-11-16 山东神力索具有限公司 Method, device and equipment for optimizing finite element model of rigging product
CN113726033A (en) * 2021-09-07 2021-11-30 浙江大学先进电气装备创新中心 Method for designing robustness of magnetic pole structure of low-torque-fluctuation continuous pole permanent magnet synchronous motor
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CN116383912A (en) * 2023-06-02 2023-07-04 深蓝(天津)智能制造有限责任公司 Micro motor structure optimization method and system for improving control precision

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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113472261A (en) * 2021-06-07 2021-10-01 江苏大学 Layered multi-objective optimization design method based on hybrid permanent magnet synchronous motor
CN113726033A (en) * 2021-09-07 2021-11-30 浙江大学先进电气装备创新中心 Method for designing robustness of magnetic pole structure of low-torque-fluctuation continuous pole permanent magnet synchronous motor
CN113657009A (en) * 2021-10-20 2021-11-16 山东神力索具有限公司 Method, device and equipment for optimizing finite element model of rigging product
CN113657009B (en) * 2021-10-20 2022-02-18 山东神力索具有限公司 Finite element model optimization method, device and equipment for rigging product
CN116207892A (en) * 2023-05-04 2023-06-02 成都理工大学 Mixed excitation motor
CN116383912A (en) * 2023-06-02 2023-07-04 深蓝(天津)智能制造有限责任公司 Micro motor structure optimization method and system for improving control precision
CN116383912B (en) * 2023-06-02 2023-08-11 深蓝(天津)智能制造有限责任公司 Micro motor structure optimization method and system for improving control precision

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