CN108020427A - A kind of pure electric automobile shift quality evaluation method based on GA-BP neutral nets - Google Patents
A kind of pure electric automobile shift quality evaluation method based on GA-BP neutral nets Download PDFInfo
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Abstract
The invention discloses a kind of pure electric automobile shift quality evaluation method based on GA BP neural networks, include the following steps：Step 1: collection evaluation index data, including shift time t, shift gears success rate η, shift shock degree j, longitudinal velocity change rate ω；Step 2: determining gauge angle value to These parameters data, the weight progress quantification treatment to the evaluation index draws weights；Step 3: comprehensive score calculating, comprehensive score W=∑s W are carried out according to the gauge angle value and the weighted valueiRi, in formula, WiFor the score of i-th of scoring item, RiThe weights of i-th of scoring item, W are comprehensive score；Step 4: being modified to above-mentioned comprehensive score W, revised evaluation score W ' is obtained；Evaluation model is established based on three layers of GA BP neural networks Step 5: establishing, obtains shift quality.
Description
Technical field
The present invention relates to electric automobile gearshift quality evaluation field, and in particular to a kind of based on the pure of GA-BP neutral nets
Electric automobile gearshift method for evaluating quality.
Background technology
Evaluated based on shift quality of automobile for a long time using human subjective, lack showing for objectivity and stability
Shape, and lack the system and method that a kind of ripe shift quality specifically for pure electric vehicle is evaluated, electric automobile is with passing
System automobile is compared：1st, the noise of engine eliminates, and is just exaggerated from the noise of gearbox and vibration；2nd, electric automobile by
In motor and the reason power sexual deviation of power supply, therefore gearshift selection is critically important, and the evaluation of gearshift will more take into account dynamic property；3rd, it is electric
Electrical automobile discharge is 0, therefore without the concern for its emission problem；As shown in Figure 1, the shift process of electric car AMT using by means of
The characteristic of motor is helped, by controlling motor mode to realize same step gear shifting, detailed process is divided into following several stages：1st, request gearshift
Stage, TCU are judged by the signal such as accelerator pedal and speed and current shift, in the case of meeting to shift gears, are sent to VCU
Gearshift request, VCU judge its backward mandate, and TCU according to circumstances decides whether to shift gears；2nd, the motor drop torsion stage, due to using nothing
Clutch gear-shift, here in order to smoothly pluck gear, motor carries out drop torsion, prepares for gearshift；3rd, pluck the gear stage, motor drop, which is turned round, to be completed
Afterwards, speed changer starts to pluck gear；4th, the electric machine speed regulation stage, after the completion of plucking gear, for the completion of gearshift, will make two of goal mesh
The linear velocity of tooth is equal, reaches synchronous regime, therefore to carry out the speed governing to motor；5th, put into gear the stage, when speed discrepancy meets necessarily
During scope, TCU control gearshifts are shifted gears, but at this time may due to the effect of surplus torque, may be at this time opposite plus
Velocity contrast is larger, and gearshift can be made to produce greater impact, or even failure；6th, torque Restoration stage, after shift of transmission success
Meet the power demand of target gear, therefore to recover moment of torsion as early as possible.Here shift quality is exactly according to shift process and electronic
Automobile feature and shift quality require what is formulated.
In the Chinese patent application of Application No. 200710056088.9, inventor is in order to overcome vehicle shift quality master
See the deficiency of evaluation, there is provided by neural network model training data sample, shift quality is evaluated, but in this application
In, evaluation of the inventor to output progress is simply artificial to be evaluated in real time by driver, by test ride give a mark
Go out output data, but due to very strong subjectivity, objective can not be fully assessed to the quality of shift quality.
Therefore, based on above-mentioned the problems of the prior art, it is necessary to propose a kind of for pure electric automobile no-clutch AMT's
The index and method of shift quality evaluation.
The content of the invention
The present invention has designed and developed pure electric automobile shift quality evaluation method, and goal of the invention of the invention is to pass through subjectivity
Evaluation of estimate is modified, and can carry out more accurate optimizing evaluation to pure electric automobile shift quality.
Technical solution provided by the invention is：
A kind of pure electric automobile shift quality evaluation method based on GA-BP neutral nets, includes the following steps：
Step 1: collection evaluation index data, including shift time t, shift gears success rate η, shift shock degree j, longitudinal direction speed
Spend change rate ω；
Step 2: determining gauge angle value to These parameters data, quantification treatment is carried out to the weight of the evaluation index
Draw weights；
Step 3: carrying out comprehensive score calculating according to the gauge angle value and the weights, comprehensive score formula is W=
∑WiRi, in formula, WiFor the score of i-th of scoring item, RiThe weights of i-th of scoring item, W are comprehensive score；
Step 4: being modified to above-mentioned comprehensive score W, the correction formula for obtaining revised evaluation score W ', W ' is
In formula, t is shift time, and V is speed of operation, V1、V2、V3Speed, R are demarcated for experience1、R2、R3Demarcated for experience
Constant, r are radius of wheel, and n is output shaft of gear-box rotating speed；
Three layers of GA-BP neural network evaluation models are based on Step 5: establishing, obtain shift quality；Wherein it is determined that three
Input layer vector x={ x of layer GA-BP neutral nets1,x2,x3,x4, obtain output layer vector y={ y1, input layer vector reflects
It is mapped to intermediate layer, the intermediate layer vector o={ o1,o2,…,om, in formula, m is middle layer node number, x1For shift time
Coefficient, x2For successfully rate coefficient, the x of shifting gears3For shift shock rate coefficient, x4Change rate coefficient, y for longitudinal velocity1To be revised
Evaluate score.
Preferably, in the step 5, the weights in GA-BP neutral nets and threshold value are optimized, including such as
Lower step：
Step a, the weights and threshold value in BP neural network are initialized；
Step b, fitness calculating is carried out to the weights and threshold value, fitness calculation formula is
In formula, ykIt is the desired output of BP neural network,Be BP neural network prediction it is defeated
Go out, z is the number of training sample；
Step c, last weights and threshold value are obtained when fitness result of calculation meets output condition；Tied when fitness calculates
When fruit is unsatisfactory for output condition, genetic manipulation calculating is carried out, until making fitness result of calculation meet output condition；
Step d, the error of weights and threshold value is calculated, carries out the renewal of weights and threshold value, obtains output result.
Preferably, the genetic manipulation includes：
Selection operation calculates, and calculation formula isIn formula, fiFor the fitness value of individual i, n is population number；
Crossover operation calculates, calculation formula amj=(1-b) amj+anjB, amj=(1-b) amj+anjB, in formula, one assembles
To chromosome amAnd anSingle-point crossover operation computing is carried out in j-th of gene position, b is the random number between 0~1；And
Mutation operation calculates, and calculation formula is In formula, Xmin,XmaxRespectively XkBound, r2Random number, g
For current iteration number, GmaxFor maximum evolution number, R is the random number between 0~1.
Preferably, the standard scale table makes the data Normal Distribution, and falls the number in normal state section
Value goes out score value using interpolation calculation.
Preferably, use interpolation calculation formula for
In formula, x0,x1,x2It is interpolation point, y0,y1,y2For the value of interpolation point, x is point to be evaluated, and L (x) is after interpolation calculation
Score.
Preferably, the weight progress quantification treatment to the evaluation index includes：Determine the evaluation index data
Relative importance, neighbouring two are compared, and write out each weights from bottom to up, and place is normalized to the weights
Reason.
Preferably, in the step 5, by shift time t, shift gears success rate η, shift shock degree j, longitudinal velocity
The formula that change rate ω is normalized is：
In formula, xkIt is evaluation index data t, η, j, ω of collection, k=1,2,3,4 respectively；xmin,xmaxIt is respectively corresponding
Minimum value and maximum in evaluation index data monitoring parameter.
Preferably, in the step 5, the middle layer node number is 7.
Beneficial effect of the present invention：
1st, arbitrary nonlinear mapping can be fitted in face of nonlinear evaluation index and evaluation subjective criterion, use of the present invention
BP neural network evaluation, optimized using genetic algorithm, the intelligent of evaluation and automation adapted to, after genetic algorithm optimization
Neutral net there is fast convergence rate, the advantages of predictive ability is strong；
2nd, in order to make the weights of shift quality index more objective and quantify with more preferable, here using Gu Linfa to its amount
Change, the comprehensive score of weight computing is modified, and mutually corrected with the subjective index of expert survey, as training network
Output, be shift quality evaluation it is more fully accurate.
Brief description of the drawings
Fig. 1 is electric automobile conventional shift schematic diagram.
Fig. 2 is that the neural metwork training that is modified to evaluated shift quality weight distribution and subjective synthesis is prepared signal
Figure.
Fig. 3 is the flow chart of the neutral net of genetic algorithm optimization.
Fig. 4 is the neural network structure figure of evaluation index.
Fig. 5 is electric automobile gearshift quality evaluation model figure.
Embodiment
The present invention is described in further detail below in conjunction with the accompanying drawings, to make those skilled in the art with reference to specification text
Word can be implemented according to this.
As shown in Fig. 2, the present invention provides a kind of pure electric automobile shift quality evaluation side based on GA-BP neutral nets
Method, shift quality refer to ensureing power performance and in the case of the powertrain service life, can quickly and smoothly shift gears
Degree, embody a concentrated reflection of reliability, dynamic property, comfort and durability.These qualities are to intercouple to have plenty of mutually
Restrict.Since AMT shift processes are complicated, the time is longer, and impact is big, therefore the evaluation of its shift quality is increasingly complex.More
Extensive shift quality evaluation further relates to vibration and noise, economy, discharge, due to be electric car here without considering discharge, and
From the viewpoint of the conservation of energy, the small just meaning preferably economy of impact, and be preferably comfort.The noise of smaller and shake
It is dynamic.
Therefore the index of shift quality evaluation will can inherently embody these features, and with can preferably obtain
Property.
AMT shift processes are more more complicated than AT, and the interruption in gearshift for power can make dynamic property insufficient, the long period
Power interruption can more cause the larger amplitude of speed to decline, easily cause alternately shift gears, make troubles to gearshift.Therefore gearshift is held
The continuous time should be an important shift quality index.
The success rate of gearshift should be ensured primarily at the same time, therefore success rate of shifting gears should be also a shift quality evaluation
Index.
Shift shock degree is a good table of motor torque change rate speed as the amount closely related with acceleration
Value indicative, can cause the spot corrosion of the gear of gearbox, wear and fracture, and cause larger noise, and have preferable measurability,
Pretend as an important index.
Research shows that the longitudinal velocity fluctuation of automobile can bring very big influence to comfort, therefore the longitudinal velocity of automobile becomes
Rate equally should be a more important index.
Therefore evaluation index selects：Shift time t, gearshift success rate η, shift shock degree j, longitudinal velocity change rate ω.
The present invention provides pure electric automobile AMT shift quality evaluation methods to specifically comprise the following steps：
Step 1: experiment obtains data, according to service condition, mainly municipal highway, (needs to select according to use here
Test condition) use gear each to electric automobile on the good road surface of identical road conditions each with 20km/h, 40km/h and 60km/
The speed of h respectively traveling 30 minutes, and gathered data；
Step 2: using expert survey, evaluation criterion scale table is formulated to each shift quality according to actual conditions and made
Its Normal Distribution of trying one's best, is fallen the value in section and is then carried out calculating its score value using interpolation, select in section at any 3 points
It is respectively x for interpolation point0,x1,x2, determine the value y of interpolation point0,y1,y2, according to mathematic interpolation formulaInterpolation result L (x) is obtained,
Score value as to be asked；
Wherein, the difference of the Stringency required due to evaluation can suitably be changed, here only with symbolic indication, such as the institute of table 1
Show；
1 evaluation criterion scale table of table
Step 3: according to the characteristics of pure electric automobile and actual importance, the weight of each index is carried out using A. Gu Linfa
Quantification treatment, the weight of so each evaluation index is more fine and accurate, as shown in table 2, specific as follows：
1st, the relative importance of each index is determined, neighbouring two are compared；
2nd, each weights is write out from bottom to up；
3rd, weights are normalized；
2 Gu Linfa of table seeks weights
Here WjFor the weights of every gearshift index, RjFor one above relatively one following ratio weights, KjFor items
Weights after the normalization of shift quality index；
Step 4: its comprehensive score can be calculated according to every score of weights and gained of tabling look-up to each each speed of gear, so
Its final weighted comprehensive score W is calculated afterwards, calculation formula is W=∑s WiRi, in formula, WiFor i-th scoring item
Point, RiThe weights of i-th of scoring item, W are comprehensive score；
Step 5: the scoring criterion of subjective assessment is carried out mutually evaluation with the above results corrects subjective evaluation, as a result
As shown in table 3；
3 shift quality subjective synthesis evaluation table of table
Step 6: evaluated using the improved neutral net of genetic algorithm (abbreviation GA-BP), by this four evaluations
Project measure value normalization after as input, the comprehensive score that the data of collection are weighted using preceding step, and
And be modified comprehensive score, the data after revised evaluation Score Normalization are as output, in the network knot determined
In structure, carry out network training, a kind of mapping relations output and input, recycle the Neural Network Toolbox of MATLAB into
Row modeling, can obtain evaluation model；
Wherein, comprehensive score W is modified by equation below, obtains the amendment of revised evaluation score W ', W '
Formula is
In formula, t is shift time, unit s；V is speed of operation, unit km/h；V1、V2、V3For experience calibration vehicle
Speed, unit km/h；R1、R2、R3Constant, unit s are demarcated for experience2；R is radius of wheel, unit m；N is defeated for gearbox
Go out rotating speed, unit r/min；In the present embodiment, V1=10km/h, V2=30km/h, V3=50km/h, R1=0.014s2,
R2=0.114s2, R3=0.214s2, π values 3.14.
In another embodiment, the weights and threshold value of neutral net are optimized using genetic algorithm, its mistake
Journey includes：Input data pre-processes, and BP neural network structure determines, the optimization of genetic algorithm and the training four of BP neural network
A part；
(1) data prediction, including the selection of the sample of data and the normalized of data, data are gathered from experiment
Data select after treatment, the target of output is to be weighted score by the data that gather and revised evaluated
Point；The normalized of data is all synchronously to integrate data to be dealt into (0,1) section, prevents the numerical value quantity due to each index
Error caused by level difference is too big, is using minimax method, calculation formula hereIn formula, xmin,
xmax,xkIt is certain value in the minimum value and maximum and sample each evaluated in sample respectively, in the present embodiment, determines three
Input layer vector x={ x of layer GA-BP neutral nets1,x2,x3,x4, x1For shift time coefficient, x2For gearshift success rate system
Number, x3For shift shock rate coefficient, x4Change rate coefficient for longitudinal velocity；
(2) determining for neural network structure is carried out, there are four evaluation indexes here therefore there are four inputs, there is Comprehensive Assessment knot
One output of fruit, and double stealthy neural network structures can approach any one nonlinear function, selection here is single implicit
Rotating fields, the number of nodes of hidden layer according to programming obtain it is each calculate expected result and actual result mean error it is minimum when
Corresponding number of nodes, selects 7, as seen in figures 3-5 here；
(3) weights and threshold value of genetic algorithm optimization BP network, including genetic coding are determined, individual adaptation degree is calculated and lost
Pass operation；Wherein, genetic manipulation is calculated including selection operation, and crossover operation calculates and mutation operation calculates；
Fitness calculates function：In formula, ykIt is the desired output of BP neural network,It is BP nerves
The prediction output of network, z are the number of training sample；
Last weights and threshold value are obtained when fitness result of calculation meets output condition；When fitness result of calculation is discontented with
During sufficient output condition, genetic manipulation calculating is carried out, until making fitness result of calculation meet output condition；
Even if genetic manipulation is calculated including selection operation, crossover operation calculates and mutation operation calculates：
Selection operation calculates：The select probability of each individual i：In formula, fiFor the fitness value of individual i, n
For population number；
Crossover operation calculates：Using real number single-point cross method, a set of paired chromosome amAnd anIn j-th of gene position
Carry out single-point crossover operation computing：amj=(1-b) amj+anjB, anj=(1-b) anj+amjB, in formula, b be 0~1 between it is random
Number；
Mutation operation calculates：Using mutation operation heterogeneous, to the k-th gene x of certain chromosomekCarry out heterogeneous
Mutation operation has：
In formula, Xmin,XmaxRespectively XkBound, r2Random number, g are current iteration number, GmaxFor maximum evolution number, R for 0~
1 random number；
Operation can be by BP network trainings be carried out, using trained network, with reference to the nerve in MATLAB more than
The electric automobile that the function that network tool case and GAs Toolbox provide establishes the BP neural network based on genetic algorithm changes
Keep off the objective models of quality evaluation.
In another embodiment, according to service condition with Gu Linfa to the weights of each gear and each gear each
The use of speed carries out quantization and determines, big weight (as described in step 2) is used to the shift quality of the common speed of common gear,
Its comprehensive score is drawn using the method for weighted sum.
Although embodiment of the present invention is disclosed as above, it is not restricted in specification and embodiment listed
With it can be applied to various suitable the field of the invention completely, can be easily for those skilled in the art
Realize other modification, therefore under the universal limited without departing substantially from claim and equivalency range, it is of the invention and unlimited
In specific details and shown here as the legend with description.
Claims (8)
1. a kind of pure electric automobile shift quality evaluation method based on GA-BP neutral nets, it is characterised in that including following step
Suddenly：
Step 1: collection evaluation index data, including shift time t, shift gears success rate η, shift shock degree j, and longitudinal velocity becomes
Rate ω；
Step 2: determining gauge angle value to These parameters data, the weight progress quantification treatment to the evaluation index is drawn
Weights；
Step 3: carrying out comprehensive score calculating according to the gauge angle value and the weights, comprehensive score formula is W=∑s
WiRi, in formula, WiFor the score of i-th of scoring item, RiThe weights of i-th of scoring item, W are comprehensive score；
Step 4: being modified to above-mentioned comprehensive score W, the correction formula for obtaining revised evaluation score W ', W ' is
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In formula, t is shift time, and V is speed of operation, V1、V2、V3Speed, R are demarcated for experience1、R2、R3Constant is demarcated for experience,
R is radius of wheel, and n is output shaft of gear-box rotating speed；
Three layers of GA-BP neural network evaluation models are based on Step 5: establishing, obtain shift quality；Wherein it is determined that three layers
Input layer vector x={ x of GA-BP neutral nets1,x2,x3,x4, obtain output layer vector y={ y1, input layer DUAL PROBLEMS OF VECTOR MAPPING
To intermediate layer, the intermediate layer vector o={ o1,o2,…,om, in formula, m is middle layer node number, x1For shift time system
Number, x2For successfully rate coefficient, the x of shifting gears3For shift shock rate coefficient, x4Change rate coefficient, y for longitudinal velocity1Commented to be revised
Valency score.
2. the pure electric automobile shift quality evaluation method based on GA-BP neutral nets, its feature exist as claimed in claim 1
In in the step 5, optimizing, include the following steps to the weights in GA-BP neutral nets and threshold value：
Step a, the weights and threshold value in BP neural network are initialized；
Step b, fitness calculating is carried out to the weights and threshold value, fitness calculation formula is
In formula, ykIt is the desired output of BP neural network,It is the prediction output of BP neural network, z is
The number of training sample；
Step c, last weights and threshold value are obtained when fitness result of calculation meets output condition；When fitness result of calculation not
When meeting output condition, genetic manipulation calculating is carried out, until making fitness result of calculation meet output condition；
Step d, the error of weights and threshold value is calculated, carries out the renewal of weights and threshold value, obtains output result.
3. the pure electric automobile shift quality evaluation method based on GA-BP neutral nets, its feature exist as claimed in claim 2
In the genetic manipulation includes：
Selection operation calculates, and calculation formula isIn formula, fiFor the fitness value of individual i, n is population number；
Crossover operation calculates, calculation formula amj=(1-b) amj+anjB, amj=(1-b) amj+anjB, it is a set of paired in formula
Chromosome amAnd anSingle-point crossover operation computing is carried out in j-th of gene position, b is the random number between 0~1；And
Mutation operation calculates, and calculation formula is In formula, Xmin,XmaxRespectively XkBound, r2Random number, g
For current iteration number, GmaxFor maximum evolution number, R is the random number between 0~1.
4. the pure electric automobile shift quality evaluation side based on GA-BP neutral nets as any one of claim 1-3
Method, it is characterised in that the standard scale table makes the data Normal Distribution, and the numerical value fallen in normal state section is adopted
Go out score value with interpolation calculation.
5. the pure electric automobile shift quality evaluation method based on GA-BP neutral nets, its feature exist as claimed in claim 4
In, use interpolation calculation formula for
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In formula, x0,x1,x2It is interpolation point, y0,y1,y2For the value of interpolation point, x is point to be evaluated, and L (x) is obtaining after interpolation calculation
Point.
6. the pure electric automobile shift quality evaluation method based on GA-BP neutral nets as described in claim 5, its feature
It is, the weight progress quantification treatment to the evaluation index includes：Determine the relative importance of the evaluation index data, on
Under adjacent two be compared, write out each weights from bottom to up, the weights be normalized.
7. the pure electric automobile shift quality evaluation method based on GA-BP neutral nets, its feature exist as claimed in claim 6
In in the step 5, by shift time t, shift gears success rate η, and shift shock degree j, longitudinal velocity change rate ω are returned
One change formula be：
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In formula, xkIt is evaluation index data t, η, j, ω of collection, k=1,2,3,4 respectively；xmin,xmaxRespectively corresponding evaluation refers to
Mark the minimum value and maximum in data monitoring parameter.
8. the pure electric automobile shift quality evaluation side based on GA-BP neutral nets as any one of claim 5-7
Method, it is characterised in that in the step 5, the middle layer node number is 7.
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| CN109979035A (en) * | 2019-04-03 | 2019-07-05 | 吉林大学 | A kind of static shift quality evaluation method for carrying hydraulic mechanical type vehicle with automatic transmission |
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| CN112924189B (en) * | 2021-01-27 | 2022-02-01 | 东风汽车股份有限公司 | Durability test method for automobile transmission system |
| CN113742841A (en) * | 2021-08-20 | 2021-12-03 | 麦格纳动力总成(江西)有限公司 | Gear shifting performance testing method and device for automobile, storage medium and computer equipment |
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