CN110162842A - A kind of setting vehicle complete vehicle weight mesh calibration method - Google Patents
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Abstract
The purpose of the present invention is to propose to a kind of setting vehicle complete vehicle weight mesh calibration methods, complete vehicle weight can be estimated out in predevelopment phase, to meet the requirement of technology and market.The existing competition vehicle that setting vehicle complete vehicle weight mesh calibration method of the invention includes the following steps: A, selects target carriage, establish competition vehicle database, with obtain competition vehicle with the highest key parameter of the complete vehicle weight degree of association；B, according to the referential data of key parameter, the weight of target vehicle is calculated.Setting vehicle complete vehicle weight mesh calibration method of the invention is obtained and the highest key parameter of the complete vehicle weight degree of association (the potential logical relation i.e. between each attribute of competition vehicle and complete vehicle weight) by analyzing the data of existing competition vehicle；And the Time To Market of the variation tendency of the key parameter, target carriage is combined to calculate the complete vehicle weight of target carriage, and there is certain reasonability, it can be in reliable reference of the vehicle predevelopment phase as automotive development.
Description
Technical field
The invention belongs to technical field of automobile design, and in particular to arrive a kind of setting vehicle complete vehicle weight mesh calibration method.
Background technique
In automobile R&D process early period, there is no one kind at present can be with the side of the setting complete vehicle weight target of practical operation
Method.
In order to improve research and development speed, meet the increasingly harsh market demand, engineering department and market department are required to whole
It needs to supplement and substitute physical varification by data verification analysis in project early stage in vehicle development process, and the basis verified needs
Complete vehicle weight module group is wanted just to provide the complete vehicle weight of target vehicle in project predevelopment phase.At project initial stage, modules
Group is all carrying out feasibility analysis, can not provide specific weight value, therefore also can not just count vehicle from the bottom up
Weight can estimate out complete vehicle weight in predevelopment phase so needing through a kind of method, to meet wanting for technology and market
It asks.
Summary of the invention
It, can in predevelopment phase the purpose of the present invention is to propose to a kind of setting vehicle complete vehicle weight mesh calibration method
Complete vehicle weight is estimated out, to meet the requirement of technology and market.
Setting vehicle complete vehicle weight mesh calibration method of the invention includes the following steps:
A, select the existing competition vehicle of target carriage, establish competition vehicle database, with obtain competition vehicle with
The highest key parameter of the complete vehicle weight degree of association；
B, according to the referential data of key parameter, the weight of target vehicle is calculated.
Further, the step A is made of following step:
A1, list it is each competition vehicle with the higher parameter of the complete vehicle weight degree of association, composition competition vehicle database；
A2, the correlation of parameters and complete vehicle weight is calculated with the LINEST function of image factoring, is obtained
To with the highest key parameter of the complete vehicle weight degree of association, and obtain the additional regression calculation value of LINEST function；
A3, the linear relationship formula of simple regression method is introduced to confirm the correctness of obtained key parameter, if just
Really, B is thened follow the steps, A2, A3 step are otherwise repeated, until obtaining correct key parameter.
It specifically, include the following ginseng for competing vehicle with the higher parameter of the complete vehicle weight degree of association in above-mentioned A1 step
Number: length, projected area, complete vehicle weight and long * wide ratio, volume, density, oil consumption, braking distance.
The selection principle that vehicle is competed in the step A is as follows: the wheelbase difference of the wheelbase and target carriage that compete vehicle exists
Within ± 50mm；The operation time of vehicle is competed within 5 years；Compete the star of vehicle and target carriage under same crash standards
Grade is identical；It is identical as the discharge capacity of target vehicle to compete vehicle.It, and more can generation to make the selection for competing vehicle closer to target carriage
The status in table market, competition vehicle are identical as the configuration of target vehicle；Independently, Korean and Japanese, the competition vehicle quantity of America and Europe are consistent.
Further, in the step B, the variation tendency of the key parameter in the past few years is analyzed first, and according to
The variation tendency extrapolates key parameter in the referential data of target carriage Time To Market；Then further according to the reference number of key parameter
The Time To Market of value and target carriage, calculates the weight of target vehicle.
Setting vehicle complete vehicle weight mesh calibration method of the invention by analyzing the data of existing competition vehicle, obtain with it is whole
The highest key parameter of the car weight amount degree of association (the potential logical relation i.e. between each attribute of competition vehicle and complete vehicle weight)；
And the Time To Market of the variation tendency of the key parameter, target carriage is combined to calculate the complete vehicle weight of target carriage, have centainly
Reasonability, can be in reliable reference of the vehicle predevelopment phase as automotive development.
Detailed description of the invention
FIG. 1 to FIG. 9 is the correlation curve and relative index figure competed between the parametric variable and complete vehicle weight of vehicle.
Figure 10 is the correlation of 7 parameter of attribute and vehicle listing time in embodiment 1.
Specific embodiment
It is for example related each to a specific embodiment of the invention by the description to embodiment below against attached drawing
The shape of component, construction, the mutual alignment between each section and connection relationship, the effect of each section and working principle etc. are made into one
The detailed description of step.
Embodiment 1:
The present embodiment proposes a kind of setting vehicle complete vehicle weight mesh calibration method, can estimate in predevelopment phase
Complete vehicle weight out, to meet the requirement of technology and market.
The setting vehicle complete vehicle weight mesh calibration method of the present embodiment includes the following steps:
A, select the existing competition vehicle of target carriage, establish competition vehicle database, with obtain competition vehicle with
The highest key parameter of the complete vehicle weight degree of association；
B, according to the referential data of key parameter, the weight of target vehicle is calculated.
It is specifically described the setting vehicle complete vehicle weight mesh calibration method of the present embodiment below.
Complete vehicle weight target sets all based sources in the weight DBMS of competition vehicle, and to competition vehicle
The analysis of relevant parameter.Therefore the selection of competition vehicle is very crucial, needs to meet following condition, as shown in table 1, full
Under the premise of these conditions of foot, selected vehicle quantity is more, and the result of goal-setting is more accurate.
Table 1 competes vehicle selection principle
After choosing competition vehicle according to the principle of table 1, length, the projected area for listing each competition vehicle are (whole
Vehicle top view projects to the area on ground), attribute 7 (ratio of complete vehicle weight and long * wide), volume (long * wide * high), density are (whole
The ratio of car weight amount and volume), oil consumption, braking distance etc. and the higher parameter of the complete vehicle weight degree of association establish Relational database,
As shown in following table 2a, table 2b.
Table 2a
Table 2b
| Vehicle | Attribute 4 | Attribute 5 | Attribute 6 | Attribute 7 | Attribute 8 | Attribute 9 |
| Target vehicle | 0.1552E-03 | 8175600 | 12091712400 | 1.05E-07 | 6.8 | 42.0 |
| BM vehicle 1 | 0.1517E-03 | 8292015 | 12189262050 | 1.03E-07 | 8.5 | 40.4 |
| BM vehicle 2 | 0.1556E-03 | 8265482 | 12257709806 | 1.05E-07 | 7.4 | 40.5 |
| BM vehicle 3 | 0.1588E-03 | 7999200 | 11918808000 | 1.07E-07 | 7.0 | 41.8 |
| BM vehicle 4 | 0.1484E-03 | 8072911 | 12028637390 | 9.96E-08 | 6.6 | 44.3 |
| BM vehicle 5 | 0.155E-03 | 8031744 | 11766504960 | 1.06E-07 | 7.3 | 42.3 |
| BM vehicle 6 | 0.1562E-03 | 8257032 | 12236921424 | 1.05E-07 | 7.6 | 39.4 |
| BM vehicle 7 | 0.1715E-03 | 8016066 | 11919890142 | 1.15E-07 | 5.4 | 41.0 |
| BM vehicle 8 | 0.1516E-03 | 8408400 | 12528516000 | 1.02E-07 | 8.4 | 43.8 |
| BM vehicle 9 | 0.1565E-03 | 8083548 | 14485718016 | 8.73E-08 | 7.3 | 42.7 |
| BM vehicle 10 | 0.1521E-03 | 8253750 | 12009206250 | 1.05E-07 | 7.5 | 40.0 |
| BM vehicle 11 | 0.148E-03 | 8040750 | 11739495000 | 1.01E-07 | 7.4 | 44.9 |
| BM vehicle 12 | 0.1646E-03 | 8262606 | 12203869062 | 1.11E-07 | 8.0 | 43.4 |
| BM vehicle 13 | 0.1487E-03 | 8111750 | 11721478750 | 1.03E-07 | 6.3 | 42.6 |
The correlation of parameters and complete vehicle weight is calculated with the LINEST function of image factoring, obtains shadow
Ring the key parameter of complete vehicle weight.The calculation formula of LINEST function are as follows:
Y=A1*X1+A2*X2+A3*X3+A4*X4+...AN*XN+B；
Wherein, dependent variable Y is the functional value of independent variable X, and A value is coefficient corresponding with each X value, and B is constant.Note
Meaning, Y, X and A can be vector.The array that LINEST function returns is { AN, AN-1..., A1, B }, LINEST function also can return to
Additional regression calculation value.
Table 3 below shows the sequences that the additional regression calculation value of LINEST function returns.
| An | An-1 | ... | A2 | A1 | B |
| sen | sen-1 | ... | se2 | se1 | seb |
| r2 | sey | ||||
| F | df | ||||
| ssreg | ssresid |
Wherein:
①se1, se2..., senIt is coefficient A1, A2..., AnStandard error value, sebThe standard error value of constant B.
②r2It is coefficient of determination；The ratio between estimated value and actual value of Y, range is between 0 to 1.If it is 1, sample has
Good correlation does not have difference between the estimated value and actual value of Y.On the contrary, if it is decided that coefficient 0, then regression formula is not
It can be used to predict Y value.
③seyIt is the standard error of Y estimated value.
4. F is statistics or observed value, it may determine that whether occurred once in a while between dependent variable and independent variable can using F statistics
The relationship observed.
⑤dfIt is freedom degree, for searching F critical value on statistical form.The value checked in from table and LINEST function are returned
The F statistical value returned is compared the confidence interval that can determine model；
⑥ssregIt is regression sum of square；ssresidIt is residual sum of squares (RSS).
It needs to prove above-mentioned nine parameters now and whether the correlation of vehicle variable is reasonable, first, in accordance with image factoring
Establish LINEST function formula:
M=A9*L+A8*W+A7*H+A6*ρS+A5*S+A4*V+A3*ρV+A2*OC
+A1*BL+A10；(formula 1)
Relevant parameter in the data of table 2 is substituted into above-mentioned function formula to calculate, obtains additional regression calculation value, such as
Shown in following table 4.
The additional regression calculation value of certain the target vehicle of table 4
| A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | A9 | A10 |
| -0.087 | 0.342 | 0 | -6.67E-08 | 5.1E-04 | 8093 | 0.538 | -1.156 | -0.458 | 39.89 |
| 0.187 | 0.839 | 0 | 8.64E-08 | 2.5E-04 | 60.7 | 0.698 | 0.979 | 0.38 | 2045 |
| 0.999 | 1.112 | ||||||||
| 5802 | 7 | ||||||||
| 57418 | 8.659 |
From the point of view of according to the result of table 4, coefficient of determination r2 is 0.99, it was demonstrated that parameter selected in the formula is substantially just
It is really and reasonable.In addition statistical value F is 5802, and since there are two freedom degree V1 and V2 by F, constant A10 herein is not 0, therefore is counted
It calculates as follows:
V2=df=7；
V1=N-df- 1=9-7-1=1；
The higher situation of this numeric ratio for statistical value F accidentally occur and being 5802 is calculated with FDIST (F, V1, V2) function
Probability, wherein
FDIST (F, V1, V2)=FDIST (5802,1,7)=1.76858E-11.
From the point of view of calculated result, the probability for high statistical value occur is very small, substantially can not.The LINEST letter established
Number formula can be used to assess complete vehicle weight.9 variables obtained by image factoring, practical operation is relatively difficult, and
It is very weak that relationship is influenced between many variables and weight.The correlation power for carrying out each variable and complete vehicle weight further below is closed
The analysis of system, analysis method introduce the linear relationship formula of simple regression method:
Y=A*X+B；(formula 2)
R2=C；(formula 3)
Wherein A is coefficient of relationship, and B is constant, relative index R2What is reflected is the phase between the variable and complete vehicle weight
The value of Guan Xing, C are bigger, and correlation is stronger.Relevant parameter in table 2 is substituted into above-mentioned linear formula and is calculated, is obtained every
Correlation curve and relative index between a parametric variable and complete vehicle weight, as shown in FIG. 1 to FIG. 9.By above-mentioned calculating
As a result summarize comparative analysis, obtained conclusion is as shown in table 5, and relatively high several parameters are mainly with the correlation of complete vehicle weight
Attribute 7, vehicle width, projected area.
5 simple regression result of table statistical analysis
Find that attribute 7 is strongest to the weight of vehicle by simple regression method analysis.Other attribute factors are opposite
Attribute 7, their influences to complete vehicle weight only have 1/88.25,1/117.67 and 1/16.81 respectively, therefore can be ignored.
Final determine uses key variables parameter of the attribute 7 as complete vehicle weight, function formula are as follows:
M=8091000* ρ S-1.957 (formula 4)；
In the formula 8091000 be simple regression average statistical, vehicle can be used long and wide when calculating specific vehicle
Product replace, therefore above-mentioned formula convert are as follows:
M=L*W* ρ S-1.957 (formula 5).
The correctness of case verification above-mentioned formula 5 further below, as shown in table 6 is the phase of three sections of selected vehicles
Close parameter:
6 analysis of cases of table
For vehicle A, the result being calculated using the formula 1 of image factoring are as follows: M=1268.78kg, using unitary
Result is calculated in the formula 5 of the Return Law are as follows: M=1267.05kg.For vehicle B, calculated using the formula 1 of image factoring
Obtained result are as follows: result is calculated using the formula 5 of simple regression method are as follows: M=1258kg in M=1259kg.For vehicle
C, the result being calculated using the formula 1 of image factoring are as follows: M=1358.4kg is calculated using the formula 5 of simple regression method
Obtain result are as follows: M=1360kg.From the point of view of the calculating of the case of this three sections of vehicles, result and reality that simple regression method is obtained
As a result very little is differed, to the extent permitted by the error, to from which further follow that formula 5 is effective.
According to above-mentioned identified function formula, as long as knowing this parameter value of attribute 7, so that it may calculate target vehicle
Weight, be set as the Weight control that target carries out vehicle, but with the development of Lightweight Technology, the weight that competes in the market
In downward trend year by year, and our target carriage is generally required and could be listed after exploitation verifying in 3~4 years, in order to protect
The weight for hindering target vehicle has advantage, then the complete vehicle weight to look to the future several years is just needed to decline when setting objectives
Trend.Below using the vehicle listing time as variable, this parameter of attribute 7 is still analyzed using simple regression method and vehicle lists
The correlation in time, Figure 10 are the results after correlation analysis.
According to the analysis of correlation as a result, obtaining formula 6 of the attribute 7 with the correlation of model year, X refers in formula 6
It is time variable:
ρ S=-6.19E-07*X+1.4E-03 (formula 6)；
Formula 6 is imported in formula 5, to obtain the calculation formula of complete vehicle weight target:
M=L*W* (- 6.19E-07*X+1.4E-03) -1.957 (formula 7)；
Assuming that the length L of target carriage is 4650mm, width W is 1820mm, plans to list for 2015, then the vehicle of the vehicle
Weight target formula 7 is calculated:
M=4650*1820* (- 6.19E-07*2015+1.4E-03) -1.957=1290Kg.
It should be noted that either using image factoring or simple regression method, finally obtained vehicle target is calculated
The relevance parameter of formula is all to be analyzed to obtain according to the weight DBMS of competition vehicle, so this database is covered
Vehicle it is more, the formula is more accurate.
The present invention is exemplarily described above in conjunction with attached drawing, it is clear that the present invention specifically designs not by aforesaid way
Limitation, if use the improvement for the various unsubstantialities that conception and technical scheme of the invention carry out, or it is not improved will
Conception and technical scheme of the invention directly apply to other occasions, within the scope of the present invention.
Claims (7)
1. a kind of setting vehicle complete vehicle weight mesh calibration method, it is characterised in that include the following steps:
A, it selects the existing competition vehicle of target carriage, establishes competition vehicle database, with obtain competition vehicle and vehicle
The highest key parameter of the weight degree of association；
B, according to the referential data of key parameter, the weight of target vehicle is calculated.
2. setting vehicle complete vehicle weight mesh calibration method according to claim 1, it is characterised in that the step A is by following
Step composition:
A1, list it is each competition vehicle with the higher parameter of the complete vehicle weight degree of association, composition competition vehicle database；
A2, the correlation of parameters and complete vehicle weight is calculated with the LINEST function of image factoring, obtain with
The highest key parameter of the complete vehicle weight degree of association, and obtain the additional regression calculation value of LINEST function；
A3, the linear relationship formula of simple regression method is introduced to confirm the correctness of obtained key parameter, if correctly,
Step B is executed, A2, A3 step are otherwise repeated, until obtaining correct key parameter.
3. setting vehicle complete vehicle weight mesh calibration method according to claim 2, it is characterised in that in the A1 step with
The higher parameter of the complete vehicle weight degree of association include compete vehicle following parameter: length, projected area, complete vehicle weight with
Ratio, volume, density, oil consumption, the braking distance of long * wide.
4. setting vehicle complete vehicle weight mesh calibration method according to claim 2, it is characterised in that competed in the step A
The selection principle of vehicle is as follows: competing the wheelbase of vehicle and the wheelbase difference of target carriage within ± 50mm；Compete the throwing of vehicle
The time is produced within 5 years；It is identical as star of the target carriage under same crash standards to compete vehicle；Compete vehicle and target carriage
The discharge capacity of type is identical.
5. setting vehicle complete vehicle weight mesh calibration method according to claim 4, it is characterised in that competed in the step A
The selection of vehicle further includes following principle: competition vehicle is identical as the configuration of target vehicle.
6. setting vehicle complete vehicle weight mesh calibration method according to claim 4, it is characterised in that competed in the step A
The selection of vehicle further includes following principle: autonomous, Korean and Japanese, the competition vehicle quantity of America and Europe are consistent.
7. setting vehicle complete vehicle weight mesh calibration method according to claim 1 or 2 or 3 or 4 or 5 or 6, it is characterised in that
In the step B, the variation tendency of the key parameter in the past few years is analyzed first, and pass is extrapolated according to the variation tendency
Referential data of the bond parameter in target carriage Time To Market；Then further according to the listing of the referential data of key parameter and target carriage
Time calculates the weight of target vehicle.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110569280A (en) * | 2019-08-29 | 2019-12-13 | 奇瑞商用车（安徽）有限公司 | method for acquiring weight target of whole vehicle |
| CN112036748A (en) * | 2020-08-31 | 2020-12-04 | 东风汽车集团有限公司 | Method for determining servicing quality of newly developed vehicle model and method for decomposing newly developed vehicle model |
| CN113610591A (en) * | 2021-07-09 | 2021-11-05 | 东风汽车集团股份有限公司 | System, method and medium for constructing automobile weight competitive product comparison |
| CN113722820A (en) * | 2021-08-26 | 2021-11-30 | 江铃汽车股份有限公司 | Method for estimating high-order of vehicle weight and setting target of vehicle weight |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110569280A (en) * | 2019-08-29 | 2019-12-13 | 奇瑞商用车（安徽）有限公司 | method for acquiring weight target of whole vehicle |
| CN112036748A (en) * | 2020-08-31 | 2020-12-04 | 东风汽车集团有限公司 | Method for determining servicing quality of newly developed vehicle model and method for decomposing newly developed vehicle model |
| CN113610591A (en) * | 2021-07-09 | 2021-11-05 | 东风汽车集团股份有限公司 | System, method and medium for constructing automobile weight competitive product comparison |
| CN113722820A (en) * | 2021-08-26 | 2021-11-30 | 江铃汽车股份有限公司 | Method for estimating high-order of vehicle weight and setting target of vehicle weight |
| CN113722820B (en) * | 2021-08-26 | 2024-06-07 | 江铃汽车股份有限公司 | Method for high-order estimation of weight of whole vehicle and setting of weight target of whole vehicle |
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Application publication date: 20190823 |