CN108984975A - A kind of hub drive system efficient light optimum design method - Google Patents
A kind of hub drive system efficient light optimum design method Download PDFInfo
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Abstract
The present invention discloses a kind of hub drive system efficient light optimum design method, and its technical solution is as follows: establishing hub drive system finite element model and carries out electromagnetic field analysis, calculates the response variables such as electromagnetic torque, each component deterioration and each component weight；The sensitivity analysis of hub drive system structural parameters is carried out, determines therefrom that optimization design variable；According to determining design variable and response variable, design and simulation experiment, and calculate the response variable value of each group experiment；The regression model of response variable and design variable is established, and carries out accuracy test；Hub drive system efficient light mathematical optimization models are established using regression model, are optimized；Optimum results are verified, if not satisfied, then more new variables re-starts optimization design, if meeting the requirements, obtain final design scheme.Method proposed by the present invention can preferably solve hub drive system torque density and cooling constraint conflict and nonspring carried mass increase caused by ride comfort deterioration problem.
Description
Technical field
The invention belongs to electric car design field more particularly to a kind of hub drive system efficient light optimization design sides
Method.
Technical background
The chassis structure of hub motor driven electric vehicle concentrates motor-driven vehicle chassis structure dramatically different with tradition,
Its by motor, deceleration mechanism, brake etc. height be integrated in wheel, this chassis structure make its vehicle general arrangement structure,
Obvious technical advantage in terms of chassis active control and manipulation convenience.But also bring a series of new problems, novel bottom
While the reference of dish structure makes vehicle unsprung mass increase have an adverse effect vehicle ride comfort and riding comfort, by
The limitation in space and running environment, simultaneously because driving torque required when low speed is very big, therefore, hub drive system is needed
Higher torque density and power density run to meet vehicle.But the pursuit of the high power density to hub motor, makes in function
In the case that rate is constant, motor volume reduces, although this makes its power density be improved, correspondingly, its loss density
Also it increased, thus will bring motor overheating.Winding insulation can not only be damaged, reduce the service life, permanent magnet can also be caused
Irreversible demagnetization drastically influences the safe operation of driving motor and vehicle.It can be seen that the special knot of hub motor drive system
Structure and working environment, so that the design of hub drive system is in terms of quality (volume), torque density, efficiency and temperature (loss)
There is certain restricting relation, the raising of a certain performance may be to sacrifice other performances as cost.Therefore, how to combine
The multinomial performances such as its quality, torque density, efficiency and temperature (loss) carry out the design of hub drive system, are wheel hub driving electricity
Electrical automobile development has to one of critical issue solved, and has important engineering application value.
For the deficiency in terms of existing hub drive system designing technique, the present invention proposes that a kind of hub drive system is efficient
Lightweight optimum design method can combine quality (volume), torque density, efficiency and the temperature of hub drive system
Multiple performance factors such as (loss), the optimization for realizing that torque maximizes, loss minimizes and quality minimizes to the greatest extent are set
Meter.The torque density that method proposed by the present invention can preferably solve hub drive system conflicts with efficiency with temperature (loss)
And ride comfort deterioration problem caused by nonspring carried mass increase.
Summary of the invention
It is an object of the invention to for current hub drive system design present in quality (volume), torque density,
The properties collision problem such as efficiency and temperature (loss), proposes a kind of hub drive system efficient light optimization design side
Method.This method is maximized with torque, loss minimizes and quality is minimised as optimization aim, combines FInite Element and regression analysis
Method, and hub drive system is optimized using certain optimization algorithm.Using optimization design side proposed by the present invention
Hub drive system designed by method can combine quality (volume), torque density, efficiency and the temperature of hub drive system
Properties demands such as (losses) is spent, realizes the efficient light design of hub drive system to the greatest extent.
The purpose of the present invention is achieved through the following technical solutions:
According to hub drive system specific structure, electromagnetic field analysis is carried out establishing hub drive system finite element model
On the basis of, calculate the response variables such as electromagnetic torque, each component deterioration and each component weight；Carry out hub drive system structural parameters
Sensitivity analysis determines therefrom that optimization design variable；According to determining design variable and response variable, design and simulation is tested, and
Calculate the response variable value of each group experiment；The regression model of response variable and design variable is established, and carries out accuracy test；It utilizes
Regression model establishes efficient light mathematical optimization models, is optimized using suitable method；Optimum results are tested
Card, if not satisfied, then more new variables re-starts optimization design, if meeting the requirements, obtains final design scheme.
The invention belongs to electric car design field more particularly to a kind of hub drive system efficient light optimization design sides
Method.This method can combine quality (volume), the torque density, effect of hub drive system in hub drive system design
Multiple performance factors such as rate and temperature (loss) realize that hub drive system torque maximizes, loss minimizes to the greatest extent
And the optimization design that quality minimizes.This method be solve the torque density of hub drive system with it is cooling constrain conflict and it is non-
Ride comfort deterioration problem provides referential scheme, method caused by spring carried mass increase.
Detailed description of the invention
The present invention will be further described with embodiment with reference to the accompanying drawing.
Fig. 1 is the flow chart of hub drive system efficient light optimum design method of the present invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples, but embodiments of the present invention are unlimited
In this.
As shown in Fig. 1 flow chart, a kind of hub drive system efficient light optimum design method proposed by the present invention, step
It suddenly include: the analysis of S1 hub motor drive system specific structure；The foundation of S2 hub motor drive system finite element model；S3 electricity
Magnetic field analysis；S4 structural parameters sensitivity analysis；The determination of S5 optimization design variable；S6 Design of Simulation；The emulation of S7 each group
The calculating of experiment is analyzed；The foundation of S8 response variable and design variable regression model；The inspection of S9 model accuracy；S10 is efficiently light
The foundation of of fine qualityization design object model；S11 optimization design；Whether S12 reaches maximum number of iterations or meets optimization aim letter
Numerical lower limits；S13 exports global optimum and its optimization object function value；S14 design variable updates；S15 terminates.
A further preferred embodiment of the present invention is:
1, step S1 is that have to structure, arrangement, the connection type of wheel hub power drive system etc. of concrete application object
Body analysis.
2, step S2 is according to the concrete analysis of the step S1 as a result, establishing the finite element mould of hub motor drive system
Type.
3, step S3 is to carry out electromagnetic field analysis, and based on the analysis results using the built finite element model of step S2
Calculate each response variable value, specifically include: S31 electromagnetic torque calculates, each component deterioration of S32 calculates and each component weight meter of S33
It calculates.
4, step S4 is to calculate different hub drive system different structures using the built finite element model of the step S2 to join
Several each response variable values to the step S3, and structural parameters sensitivity analysis is carried out, structural parameters are obtained to each response
The effect tendency of variable.The hub drive system structural parameters refer to hub motor stator in hub drive system, rotor, forever
The specific structure of the parameters of structural dimension of the main components such as magnet, shell and support frame, structural parameters and hub drive system has
It closes, different structures can have different structural parameters.
5, step S5 is affected as a result, finding to each response variable according to the analysis of the step S4 parametric sensitivity
Structural parameters, and determine it as optimization design variable.The number of the optimization design variable can be according to hub drive system
Specific structure and analysis result are determined.
6, step S6 is the response variable progress of the optimization design variable according to determined by the step S5 and the step 3
Design of Simulation.The experimental design method can be orthogonal experiment method, Latin―Square design, response surface design experimental design, complete
Various orthogonal experimental design methods and the factorials design methods such as factorial experiment design.
7, step S7 is the emulation experiment according to designed by the step S6, the finite element established using the step 2
Model analysis calculates and obtains each response variable value of the step S3.
8, step S8 is the data using the obtained each group emulation experiment of the step S7, establishes each response variable and sets
Count the regression analysis model of variable.The regression analysis model is the functional relation being fitted between design variable and response variable,
It can be by polynomial regression (Polynomial Regression), successive Regression (Stepwise Regression), ridge regression
(Ridge Regression) and lasso trick return analysis methods such as (Lasso Regression) and obtain.The recurrence of the present embodiment
Analysis model is more preferably the even experiment design with cross term in polynomial regression analysis, embody as
Under:
Wherein: β is regression coefficient, and x is design variable, and ε is a random error vector.
It is write the model of receptance function Y as matrix form, can be denoted as:
Y=X β+ε
X is design variable matrix.
Using least square method, to estimate regression vector β.Least square function are as follows:
ε ' is the transposed matrix of ε.
Function L is minimized about β, least squares estimatorIt must satisfy:
Then least squares estimator and fit regression model can be obtained are as follows:
According to above-mentioned calculation method, the second order regression equation coefficient vector β can be obtained, then obtain each response variable
Second order regression equation Y.
9, step S9 is to carry out accuracy test to each response variable regression equation obtained by the step S8.The present embodiment
Preferred embodiment is to calculate the residual standard deviation of regression model, is tested to the conspicuousness of model, conspicuousness is good, then mould
Type precision is high.It is specific as follows:
The estimated value that regression model calculates each response variable in the step S6 is obtained using the step S8It can benefit
The residual standard deviation that regression model is calculated with following formula, tests to the precision of model.
Wherein, n is sample point number, and p is the number of arguments.
10, step S10 is the foundation of efficient light optimization design object module.The efficient light mathematical optimization models are
The mathematic(al) representation that efficiency maximizes and quality minimizes can be described, there can be different expression ways.
The efficient light optimization design object module of the present embodiment is further preferred and is described as following form:
In above formula, X is the vector of optimization design variable composition；Ω is solution space；xiFor a design to be optimized
Variable, n are the number of optimization design variable；Gj (x) >=0 is constraint condition, represents certain performance requirement；J(X)To optimize mesh
Scalar functions.T (x) is electromagnetic torque, PlossIt (x) is the summation of each component deterioration, W (x) is the summation of each component weight.T(x),
Ploss(x), the regression equation of W (x) can be obtained by the step 8.
11, step S11 is the optimization design that hub motor drive system is carried out using certain optimization method.The optimization
Method can be the intelligent algorithms such as local search, simulated annealing, genetic algorithm, neural network, particle swarm algorithm, can also be with
It is the constrained optimization methods such as Monte Carlo analysis, SSLE method, penalty function method, sequential quadratic programming.
12, step 12 is the optimization object function value that is calculated according to the step 11 to judge whether the number of iterations reaches
Whether the optimization object function value of maximum number of iterations or the step 10 to setting converges to minimum value.
If 13, step 13 is the judging result of the step 12 are as follows: the number of iterations reaches maximum number of iterations or optimization mesh
Offer of tender numerical value has converged to minimum value, exports global optimum and optimization object function value at this time.
If 14, step 14 is the judging result of the step 12 are as follows: do not reach maximum number of iterations or optimization aim letter
Numerical value does not converge to minimum value, then updates design variable according to optimization object function value, re-start optimization, analyze and set
Meter.
15, step 15 is that optimization design reaches maximum number of iterations or optimization object function value has converged to minimum value,
Optimization terminates.
Claims (8)
1. the present invention discloses a kind of hub drive system efficient light optimum design method, which comprises the steps of:
(1) hub drive system specific structure is analyzed, and specifically structure, arrangement, connection type of hub drive system etc. carry out
Concrete analysis；
(2) according to hub motor drive system specific structure, hub drive system finite element model is established；
(3) using built finite element model, electromagnetic field is carried out, each response variable value is calculated, comprising: electromagnetic torque, each component damage
Consumption and each component weight；
(4) sensitivity analysis of hub drive system structural parameters is carried out to each response variable, found to each response variable shadow
Ring biggish design variable；
(5) according to sensitivity analysis as a result, determining final optimization pass design variable；
(6) it according to determining response variable and design variable, is tested using certain experimental design method design and simulation；
(7) analytical calculation is carried out to designed each group emulation experiment using the finite element model established, obtains each group experiment
Corresponding response variable；
(8) using the above-mentioned each group emulation experiment calculated result being calculated, the recurrence of each response variable and design variable is established
Analysis model；
(9) the corresponding method of inspection is utilized, is tested to the precision of the step (8) obtained regression model；
(10) hub drive system efficient light mathematical optimization models are established using regression model；
(11) mathematical optimization models established according to the step (10), are optimized using certain optimum design method；
(12) judge whether the number of iterations reaches setting according to the optimization object function value that the step (11) is calculated
Whether the suitable optimization object function value of maximum number of iterations or the step (10) converges to minimum value.
(13) step (12) if judging result: the number of iterations has reached maximum number of iterations or optimization object function value
Minimum value is converged to, exports global optimum and optimization object function value at this time.
(14) step (12) if judging result the number of iterations: do not reach maximum number of iterations or optimization object function
Value does not converge to minimum value, then updates design variable according to optimization object function value, re-start optimization, analysis and design.
(15) until the step (13) output global optimum and optimization object function value, process of optimization terminate.
2. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(4) in, the hub drive system structural parameters refer to hub motor stator, rotor, permanent magnet, shell in hub drive system
And the parameters of structural dimension of the main components such as support frame, structural parameters are related with the specific structure of hub drive system, different
Structure can have different structural parameters.
3. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(5) in, the optimization design variable be obtained according to sensitivity analysis result to response variable have larger impact structure join
The number of number, design variable can be determined according to hub drive system specific structure and analysis result.
4. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(6) in, the experimental design method can be orthogonal experiment method, Latin―Square design, response surface design experimental design, total divisor experiment
Various orthogonal experimental design methods and the factorials design methods such as design.
5. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(8) in, the regression analysis model is the functional relation being fitted between design variable and response variable, can be by polynomial regression
(Polynomial Regression), successive Regression (Stepwise Regression), ridge regression (Ridge
Regression) analysis methods such as (Lasso Regression) are returned and are obtained with lasso trick.
6. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(9) in, the method for inspection is corresponding, used recurrence with regression analysis employed in the step (8)
Method is different, and the corresponding method of inspection is also different.
7. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(10) in, the efficient light mathematical optimization models are can to describe the mathematic(al) representation that efficiency maximizes and quality minimizes,
There can be different expression ways.
8. according to claim 1, the hub drive system efficient light optimum design method, which is characterized in that the step
(11) in, the optimization method can be local search, simulated annealing, genetic algorithm, neural network, particle swarm algorithm etc.
Intelligent algorithm is also possible to the calculation of the constrained optimizations such as Monte Carlo analysis, SSLE method, penalty function method, sequential quadratic programming
Method.
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Application publication date: 20181211 |