CN110414818A - A kind of passenger car energy conservation competitiveness evaluation method and system - Google Patents
A kind of passenger car energy conservation competitiveness evaluation method and system Download PDFInfo
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- 238000004134 energy conservation Methods 0.000 title claims abstract description 24
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- 239000000446 fuel Substances 0.000 claims abstract description 15
- 239000011159 matrix material Substances 0.000 claims description 50
- 238000000513 principal component analysis Methods 0.000 claims description 11
- 238000007476 Maximum Likelihood Methods 0.000 claims description 7
- 238000000556 factor analysis Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 108010001267 Protein Subunits Proteins 0.000 claims description 2
- 238000005265 energy consumption Methods 0.000 abstract description 3
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- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Abstract
The present invention proposes a kind of passenger car energy conservation competitiveness evaluation method and system, and the data of several dimensions are chosen in terms of the space practicability in vehicle overall performance, fuel economy, power performance three；By the method for factorial analysis, the power saving factor that each technical parameter of passenger car implies behind is found, reasonable evaluation method is constructed.The present invention can effectively evade the limitation of single operating condition the criterion of the fuel consumption evaluation, will put together and vehicle energy consumption level is comprehensively compared in kerb weight, different capacity, various sizes of passenger car is not had to.
Description
Technical field
The invention belongs to automobile information fields, especially relate to a kind of evaluation side for passenger car energy conservation competitiveness
Method and evaluation system.
Background technique
Vehicle energy saving problem is both related to national energy security, is also related to the vital interests of consumer.Since is produced from market
Product diversification, new model emerge one after another, while the energy conservation of automobile product is due to being related to many technical parameters, bicycle fuel consumption
It measures this index and has been unable to satisfy consumption demand.Currently, the domestic common horizontal index of automobile fuel ecomomy has urban conditions
Several Xiang Zhibiao such as oil consumption, suburbs operating condition oil consumption, comprehensive operating condition oil consumption, 90km/h constant-speed fuel economy.Due to passenger car fuel economy
There is stronger correlation with kerb weight, power, external dimensions etc., therefore, is based only upon automobile kerb weight, space or size
More just seem more unilateral carry out energy-saving horizontal between vehicle, in this context, needs to construct comprehensive index system and comment
The energy saving competitiveness of valence automobile product, therefore how to select to construct reasonable evaluation method, scientific and reasonable guidance consumer green
Environmental protection consumption is as there is an urgent need for solve the problems, such as.
Summary of the invention
The present invention provides a kind of passenger car energy conservation competitiveness evaluation methods and system to be looked for by the method for factorial analysis
The power saving factor implied behind to each technical parameter of passenger car, carries out rational evaluation.
In order to achieve the above objectives, the technical scheme of the present invention is realized as follows:
A kind of passenger car energy conservation competitiveness evaluation method, comprising:
S1, the space practicability from vehicle overall performance, fuel economy, choose in terms of power performance three it is several
The data of dimension；
S2, vehicle is divided, by the data normalization of each dimension of each vehicle；
S3, factorial analysis is done for the data after standardization, obtains Factor load-matrix, and choose power saving factor；
S4, it is linearly calculated using power saving factor score coefficient, obtains the power saving factor score of each vehicle.
Further, data described in step S1 include: complete vehicle curb weight, length, spatial volume, comprehensive operating condition oil
Consumption, urban conditions oil consumption, suburbs operating condition oil consumption, max. speed, torque, power, specific power；Wherein the specific power be power/
Kerb weight.
Further, the method for data normalization described in step S2 includes but is not limited to: normal standardized, codes and standards
Change.
Further, the method for factorial analysis described in step S3 includes:
S3.1, according to standardized data, acquire Factor load-matrix, the method for acquiring Factor load-matrix includes but unlimited
In: principal component analysis, principal factor method, maximum-likelihood method；
S3.2, Factor load-matrix pass through orthogonal rotation, obtain variance maximum Factor load-matrix；
S3.3, power saving factor is chosen according to variance maximum Factor load-matrix.
Further, the method that power saving factor score coefficient described in step S4 calculates includes but is not limited to: weighting minimum two
Multiplication, the Return Law.
Another aspect of the present invention additionally provides a kind of passenger car energy conservation competitiveness evaluation system, comprising:
Data decimation module, from three the space practicability in vehicle overall performance, fuel economy, power performance sides
Choose the data of several dimensions in face；
Standardized module, for dividing vehicle, by the data normalization of each dimension of each vehicle；
Factorial analysis module does factorial analysis for the data after standardization, obtains Factor load-matrix and choose energy conservation
The factor；
Power saving factor analysis module is linearly calculated using power saving factor score coefficient, obtain each vehicle it is energy saving because
Sub- score.
Further, data decimation module includes selection unit, for choosing complete vehicle curb weight, length, space
Volume, comprehensive operating condition oil consumption, urban conditions oil consumption, suburbs operating condition oil consumption, max. speed, torque, power, specific power；Wherein institute
Stating specific power is power/kerb weight.
Further, standardized module includes: Standardisation Cell, the Standardisation Cell to data be standardized including
But it is not limited to: normal standardized, normative standard.
Further, factorial analysis module includes:
Factor load-matrix unit is sought, for acquiring Factor load-matrix according to standardized data；Acquire factor loading
The method of matrix is including but not limited to principal component analysis, principal factor method, maximum-likelihood method；
Orthogonal rotary unit passes through orthogonal rotation for Factor load-matrix, obtains variance maximum Factor load-matrix；
Sub-unit, for arriving each factor score by variance maximum Factor load-matrix and linearly related matrix.
Further, power saving factor analysis module includes power saving factor score coefficient calculation unit, and the power saving factor obtains
Point coefficient calculation unit carries out the calculating of power saving factor score coefficient: weighted least-squares method, the Return Law.
Compared with prior art, the present invention have it is following the utility model has the advantages that
The present invention establishes the passenger car product energy conservation Competition Ability Evaluation System of complete set, from fuel economy, dynamic property
Can, space attribute etc., by the method for factorial analysis, find each technical parameter of passenger car behind implicit power saving factor,
Practical sex factor, power sex factor.Power saving factor is exactly the key index of passenger car energy conservation competitiveness evalua- tion, represents passenger car
Energy consumption level.This index can effectively evade the limitation of single operating condition the criterion of the fuel consumption evaluation, will be in without reorganizing and outfit matter
Amount, different capacity, various sizes of passenger car, which are put together, is comprehensively compared vehicle energy consumption level.
Detailed description of the invention
Fig. 1 is the factorial analysis flow diagram of the embodiment of the present invention.
Specific embodiment
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.
Factorial analysis is a kind of very extensive Data Dimensionality Reduction analysis method of current application range.Factorial analysis is intended to excavate
The factor information that surface data implies behind, and evaluation ranking is carried out by these implicit corresponding research objects of factor pair.Therefore
Factorial analysis introducing passenger car energy conservation competitiveness evalua- tion is great operability by the present invention.
The present invention is using conventional gasoline passenger car as research object, from the overall performance angle of vehicle, from space reality
12 item data indexs, i.e. complete vehicle curb weight, length, sky are chosen with three property, fuel economy, power performance aspects
Between volume (using " length × width × height " as approximate substitution), comprehensive operating condition oil consumption, urban conditions oil consumption, suburbs operating condition oil consumption, most
High speed, torque, power, specific power etc..
The fuel consumption data management system data reported according to enterprise in 2017, summarize production as unit of vehicle model
Data are measured, according to occupation rate of market, retains car vehicle model of the annual output greater than 1000 and annual output is greater than 500
SUV and MPV vehicle model, and point vehicle carries out factorial analysis respectively and obtains power saving factor ranking.Chiasma type passenger car is due to year
Yield is smaller and apparent falling tendency is presented, therefore not within this evaluation limit of consideration.
Tri- car, SUV and MPV vehicles is divided to do factorial analysis home-produced fuel passenger car in vehicle model level.
As shown in Figure 1, the Factor Analysis of data normalization is described as follows:
(1) Factor Analysis Model structure
Count X=(X1,X2,…,X12)TIt is 12 dimension model datas of selected each vehicle, and
E (X)=μ
Var (X)=∑
Wherein, μ is the expectation of each dimension data of vehicle, and ∑ is the covariance matrix of each dimension data of vehicle.Then construct because
The citation form of sub- analysis model is
X=μ+AF+ ε
Wherein F=(f1,f2,f3)TFor common factor, ε=(ε1,ε2,…,ε12)TFor specific factor, A=(aij)12×3For because
Sub- loading matrix, that is to say, that cov (X, F)=A, i.e. factor loading aijIt is the phase relation of i-th of variable and j-th of common factor
Number.
It usually assumes that
E (F)=0
Var (F)=Im
E (ε)=0
Cov (F, ε)=0
(2) factorial analysis process
1. couple sample data X=(X1,X2,…,X12)TIt is standardized, obtains standardized data Y=(Y1,Y2,…,Y12)T。
Standardized method is including but not limited to normal standardized, normative standard.
It is normal standardized:
Have at this time
E (Y)=0
Var (Y)=R=(σij)12×12
Normative standard:
In above formula, XiFor i-th of dimension data of each vehicle, YiFor number of i-th of the dimension data of each vehicle after standardization
According to.For the mean value of i-th of dimension data of each vehicle, var (Xi) be i-th of dimension data of each vehicle variance, max (Xi) be
The maximum value of i-th of dimension data of each vehicle, min (Xi) be i-th of dimension data of each vehicle minimum value.
2. seeking Factor load-matrix A.The method of Factor load-matrix A is sought including but not limited to principal component analysis, main cause
Sub- method, maximum-likelihood method.
Principal component analysis:
The characteristic value of journalists' association variance matrix R is λ=(λ1,λ2,…λ12), wherein λ1≥λ2≥…≥λ12≥0.Each characteristic value
Corresponding unit orthogonal eigenvectors are respectively l1,l2,…l12, then variance matrix ∑ can be analyzed to
Usual preceding 3 characteristic values are larger, and rear 9 characteristic values are smaller, then
Wherein
In D
As a result, i.e. according to principal component analysis, Factor load-matrix A is acquired.
Principal factor method:
Principal factor method is that loop iteration is carried out on the basis of principal component analysis, is the amendment to principal component analysis.
It can be obtained by principal component analysis
It is rightThe solution procedure for repeating principal component analysis, takes preceding 3 characteristic valuesIndividual features vector isThus it can acquire
Wherein
Then matrixIt is exactly revised Factor load-matrix, repeats this process, until Factor load-matrix is restrained.
Maximum-likelihood method:
Assuming that common factor F~N3(0,I3), specific factor ε~N12(0,I12), and it is mutually indepedent.If observing data X(1)
X(2)…X(n)For from X~N12The n vehicle sample of (μ, ∑), the log-likelihood function L (A, D) of sample, then (A, D's) is very big
Likelihood estimatorMeet condition
Following equation group should be met
Wherein
For the solution of equation group, the method for generalling use iterative solution.First choose initial matrix
Preceding 3 characteristic value θ1≥θ2≥θ3> 0, corresponding feature vector are l1,l2, l3, enable Θ=
diag(θ1, θ2, θ3), L=(l1, l2, l3), it enables
And then it can acquire
A can be acquired by repeating the above process1, this process is repeated, untilSatisfaction makes the maximized equation of likelihood function
Group.
3. the Factor load-matrix A that pair step 2 acquires carries out orthogonal rotation, the maximum Factor load-matrix of variance is obtained
A′。
The first row and secondary series of Factor load-matrix are chosen first
Take orthogonal matrixThen
It is the Factor load-matrix of Φ F, hereinIt should meet
Wherein
And
Then B is the variance maximum Factor load-matrix that A is obtained according to the first and second column by orthogonal rotation.In this Factor minute
During analysis, Factor load-matrix B successively carries out orthogonal rotation according to second and third column and the first and third column, as completes a wheel just
Friendship chooses to install, and the maximum Factor load-matrix A ' of variance can be obtained when population variance change is little by multiple orthogonal rotation.
4. asking the factor score of each vehicle according to variance maximum Factor load-matrix A '.The method for asking factor score includes
But it is not limited to: weighted least-squares method, the Return Law.
Weighted least-squares method:
Least square function is
Seek the estimated value of FSo thatIt can obtain
A, D estimated value that abovementioned steps are obtainedBring the factor score that can acquire each vehicle into.
The Return Law:
Point of the factor of each vehicle is calculated with the linear combination of variable, then factor score has following form
F=BY
According to correlation analysis, it is clear that have
A=RBT
Wherein R is the related correlation matrix of Y, then
B=ATR-1
Thus dividing for the factor of each vehicle is represented by
(3) evaluation points meaning
On the basis of being built upon correlation due to Factor Analysis Model, difference is obvious between car, SUV, MPV vehicle,
Therefore it is divided into 3 independent system construction Factor Analysis Models, introduces the meaning of evaluation points by taking car as an example below.
By certain 557 sedans, 12 dimension reference data, after normal standardized, Maximum-likelihood estimation, orthogonal rotation
(as described in preceding step), the variance maximum Factor load-matrix for obtaining car are as follows:
| The factor 1 | The factor 2 | The factor 3 | |
| Quality | 0.817 | 0.444 | 0.363 |
| It is long | 0.681 | 0.389 | 0.273 |
| It is wide | 0.773 | 0.313 | 0.262 |
| It is high | -0.124 | 0.096 | -0.123 |
| Space | 0.734 | 0.421 | 0.259 |
| It is comprehensive | 0.298 | 0.938 | 0.163 |
| Urban district | 0.226 | 0.928 | 0.136 |
| Suburbs | 0.385 | 0.83 | 0.204 |
| Speed | 0.636 | 0.146 | 0.636 |
| Torque | 0.612 | 0.249 | 0.701 |
| Power | 0.519 | 0.363 | 0.771 |
| Specific power | 0.159 | 0.195 | 0.96 |
The factor 2 and comprehensive operating condition oil consumption, urban conditions oil consumption, suburbs operating condition oil consumption correlation are larger, be defined as it is energy saving because
Son.Using the Return Law (as described in preceding step four), the factor score coefficient for obtaining car power saving factor is as follows:
| Power saving factor coefficient | |
| Quality | 0.343 |
| It is long | 0.004 |
| It is wide | 0.011 |
| It is high | -0.002 |
| Space | 0.006 |
| It is comprehensive | -1.15 |
| Urban district | -0.088 |
| Suburbs | -0.036 |
| Speed | 0.018 |
| Torque | 0.028 |
| Power | 0.098 |
| Specific power | -0.031 |
Data after 12 dimensions are standardized are calculated by power saving factor score coefficient, and the energy conservation of each vehicle can be obtained
Factor score.According to the power saving factor score of each vehicle, evaluation ranking is carried out to the energy saving competitiveness of passenger car.
Ranking was carried out to volume production car model in 2017 according to energy saving competitiveness factor score, amounts to 173 adopted names
In 557 vehicle models incorporate car energy conservation competitiveness evalua- tion range.Before car ranking in ten vehicles, there are 9 sections to apply turbine
Supercharging technology, 6 sections are applied 3 Cylinder engines.
The above is only a preferred embodiment of the present invention, it for those skilled in the art, is not taking off
Under the premise of from present inventive concept, any improvements and modifications made are also belonged in the scope of the present invention.
Claims (10)
1. a kind of passenger car energy conservation competitiveness evaluation method characterized by comprising
S1, the space practicability from vehicle overall performance, fuel economy, several dimensions are chosen in terms of power performance three
Data；
S2, vehicle is divided, by the data normalization of each dimension of each vehicle；
S3, factorial analysis is done for the data after standardization, obtains Factor load-matrix, and choose power saving factor；
S4, it is linearly calculated using power saving factor score coefficient, obtains the power saving factor score of each vehicle.
2. a kind of passenger car energy conservation competitiveness evaluation method according to claim 1, which is characterized in that number described in step S1
According to include: complete vehicle curb weight, length, spatial volume, comprehensive operating condition oil consumption, urban conditions oil consumption, suburbs operating condition oil consumption,
Max. speed, torque, power, specific power；Wherein the specific power is power/kerb weight.
3. a kind of passenger car energy conservation competitiveness evaluation method according to claim 1, which is characterized in that number described in step S2
It include: normal standardized, normative standard according to standardized method.
4. a kind of passenger car energy conservation competitiveness evaluation method according to claim 1, which is characterized in that described in step S3 because
Son analysis method include:
S3.1, according to standardized data, acquire Factor load-matrix, the method for acquiring Factor load-matrix includes: principal component analysis,
Principal factor method, maximum-likelihood method；
S3.2, Factor load-matrix pass through orthogonal rotation, obtain variance maximum Factor load-matrix；
S3.3, power saving factor is chosen according to variance maximum Factor load-matrix.
5. a kind of passenger car energy conservation competitiveness evaluation method according to claim 1, which is characterized in that saved described in step S4
The method that energy factor score coefficient calculates includes: weighted least-squares method, the Return Law.
6. a kind of passenger car energy conservation competitiveness evaluation system characterized by comprising
Data decimation module is selected in terms of the space practicability in vehicle overall performance, fuel economy, power performance three
Take the data of several dimensions；
Standardized module, by the data normalization of each dimension of all kinds of vehicles；
Factorial analysis module does factorial analysis for the data after standardization, obtains Factor load-matrix and choose power saving factor；
Power saving factor analysis module is linearly calculated using power saving factor score coefficient, and the power saving factor for obtaining each vehicle obtains
Point.
7. a kind of passenger car energy conservation competitiveness evaluation system according to claim 6, which is characterized in that data decimation module
Including selection unit, for choosing complete vehicle curb weight, length, spatial volume, comprehensive operating condition oil consumption, urban conditions oil
Consumption, suburbs operating condition oil consumption, max. speed, torque, power, specific power；Wherein the specific power is power/kerb weight.
8. a kind of passenger car energy conservation competitiveness evaluation system according to claim 6, which is characterized in that standardized module packet
Include: Standardisation Cell, it includes: normal standardized, normative standard that the Standardisation Cell, which is standardized data,.
9. a kind of passenger car energy conservation competitiveness evaluation system according to claim 6, which is characterized in that factorial analysis module
Include:
Factor load-matrix unit is sought, for acquiring Factor load-matrix according to standardized data；Acquire Factor load-matrix
Method include: principal component analysis, principal factor method, maximum-likelihood method；
Orthogonal rotary unit passes through orthogonal rotation for Factor load-matrix, obtains variance maximum Factor load-matrix；
Sub-unit, for arriving each factor score by variance maximum Factor load-matrix and linearly related matrix.
10. a kind of passenger car energy conservation competitiveness evaluation system according to claim 6, which is characterized in that power saving factor point
Analysing module includes power saving factor score coefficient calculation unit, and the power saving factor score coefficient calculation unit carries out power saving factor and obtains
Dividing coefficient calculating includes: weighted least-squares method, the Return Law.
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