CN113032898A - Construction method of semi-trailer tractor working condition - Google Patents
Construction method of semi-trailer tractor working condition Download PDFInfo
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Abstract
The invention provides a construction method of a semi-trailer tractor working condition, which comprises the following steps: dividing market fields by utilizing driving characteristics of various semi-tractors, collecting driving data of the semi-tractors in a target field, and establishing a kinematic fragment database in the field; calculating characteristic values of all the kinematic segments, and obtaining a dimensionless index after standardization processing; performing decorrelation and dimension reduction processing on the dimensionless indexes by using a principal component analysis method to obtain principal component values of all the kinematic segments; clustering all the kinematic segments according to the principal component values, and segmenting the time of the representative working condition according to the proportion of all the kinematic segments occupied by each cluster; selecting kinematic segments meeting the segment time of the representative working condition from each cluster to carry out random combination, and screening candidate working conditions from all combinations according to the speed-acceleration joint probability distribution and the characteristic value of all combinations; and selecting one candidate working condition from the two candidate working conditions as a representative working condition of the target field through simulation.
Description
Technical Field
The invention belongs to the field of vehicle working condition construction, and particularly relates to a construction method of a semi-trailer tractor under different market typical working conditions.
Background
The most important input condition for evaluating the fuel economy and the emission performance of the vehicle is the running condition of the vehicle, and whether the running condition can accurately restore the running characteristics of the real road has important guiding significance for the configuration and the model selection of the power assembly at the initial stage of vehicle research and development and the later-stage direction optimization, and is a key ring for vehicle research and development.
Because the running characteristics of the semi-trailer tractor such as use purpose, load and transportation route are fixed, the different power assembly configuration and model selection of the tractor aiming at areas with different use purposes and different running characteristics has important significance for reducing vehicle oil consumption, reducing emission, improving driving feeling and other performances, although the train running condition (CHTC-TT) of the Chinese semi-trailer tractor suitable for domestic road conditions is constructed in the industry at present, the model selection of the power assembly configuration cannot completely represent the running characteristics of tractors in different market fields, and certain difficulty is brought to the model selection of the power assembly configuration, and the popularization of the semi-trailer tractor products is not facilitated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a construction method of a semi-trailer tractor working condition, which aims to solve the technical problems.
The invention provides a construction method of a semi-trailer tractor working condition, which comprises the following steps:
step 2, calculating characteristic values of all the kinematic segments, and carrying out standardization processing on the characteristic values to obtain dimensionless indexes;
step 3, performing decorrelation and dimension reduction processing on the dimensionless indexes by using a principal component analysis method to obtain principal component values of all the kinematic segments;
and 6, selecting one candidate working condition from the two candidate working conditions as a representative working condition of the target field through simulation.
Further, the step 1 comprises:
dividing different market fields according to different semi-trailer types, different road section driving occupation ratios, driving forms, engine operating condition point distribution, vehicle single-trip operating mileage and annual operating mileage data;
the method comprises the steps of collecting driving data of a semi-trailer tractor in any market field, wherein the driving data comprises time, vehicle speed, engine rotating speed and torque, the sampling frequency is 1Hz, and effective data is not less than 10 thousands;
removing repeated data and abnormal data;
and setting the data of each time the vehicle is started to the next stop as a kinematic segment, and constructing a kinematic segment database in the field.
Further, the step 2 comprises:
respectively calculating a characteristic value of each kinematic segment, wherein the characteristic value comprises: average speed, speed standard deviation, maximum acceleration, acceleration section average acceleration, maximum deceleration, deceleration section average deceleration, maximum speed, acceleration standard deviation, deceleration standard deviation, acceleration proportion, deceleration proportion, uniform proportion and running time;
the characteristic values of all the kinematic segments are standardized by using the following formula, and all the characteristic values are converted into dimensionless indexes:
Zi＝(Xi-mean(X))./std(X)
wherein Z isiFor the normalization of the dimensionless indices, X represents the characteristic values of all kinematic segments, XiRepresents the characteristic value of any kinematic fragment, mean represents the mean calculation of X, and std represents the standard deviation calculation of X.
Further, the step 3 comprises:
and performing decorrelation and dimensionality reduction on the dimensionless indexes subjected to the standardization processing by using a principal component analysis method to obtain mutually irrelevant variables, and screening out the variables of which the numerical value is greater than 1 and the cumulative contribution rate is greater than or equal to 85% from the variables as principal component values of the clustering analysis.
Further, the step 4 comprises:
calculating the optimal clustering number of the kinematics segment clustering by using a distance evaluation function of the Mahalanobis distance, and carrying out K-means clustering calculation on all kinematics segments based on the optimal clustering number;
calculating the clustering number and the clustering center by using a fuzzy subtraction clustering method, and then carrying out fuzzy C-means clustering calculation on all the kinematic segments;
and comparing and analyzing the K-mean clustering result and the fuzzy C-mean clustering result, and selecting clusters with densely distributed clustering centers and uniformly distributed overall clustering as the optimal clustering result.
Further, the step 4 comprises:
aiming at the kinematics segment exceeding more than twice of the representative working condition time, searching each speed inflection point or peak in the kinematics segment, and performing disassembly from each speed inflection point or peak;
defining data before the first speed inflection point or wave crest as a starting section, defining data after the last speed inflection point or wave crest as a stopping section, and defining all data between the starting section and the stopping section as a running section;
the operation section is divided into a plurality of operation section segments, each operation section segment is recombined with the starting section and the parking section respectively, the speed difference of the selected data connection position is less than 0.5km/h, and the acceleration difference is less than 0.1m/s2As a new kinematic fragment;
and carrying out smoothing filtering processing on the new kinematic segment, wherein the smoothing filtering processing mode comprises the following steps: gaussian filtering, median filtering.
Further, the step 4 further includes:
and if the duration of the recombined new kinematic segment is still greater than the expected working condition duration, compressing the kinematic segment in a manner of resampling by a polyphase filter, and performing smooth filtering processing on the compressed kinematic segment.
Further, the step 5 comprises:
and respectively calculating the speed-acceleration joint probability distribution and the characteristic value of all the kinematic fragment combinations, selecting the kinematic fragment combination with the minimum deviation from the overall speed-acceleration joint probability distribution of each cluster in the step 4 as a first candidate working condition by using a chi-square test mode, and selecting the kinematic fragment combination closest to the overall characteristic value of each cluster in the step 4 as a second candidate working condition by using the Euclidean distance shortest principle.
Further, the method further comprises:
and respectively importing the candidate working conditions into simulation software, combining the simulation calculation results and comparing the working conditions of the engine operated by the actual vehicle, and selecting the candidate working condition with the lowest oil consumption and the lowest exhaust emission from the candidate working conditions as the representative working condition of the target field.
Further, the method further comprises:
and verifying representative working conditions by using a real vehicle rotary drum and a road test, and recommending the optimal power assembly configuration of the vehicle type in the field.
The invention has the advantages that the construction method of the working condition of the semi-trailer tractor carries out market field division according to the driving characteristics of the semi-trailer tractor, obtains different field kinematics segment databases by collecting driving data, then establishes a distance evaluation function based on the Mahalanobis distance to carry out K mean value clustering validity analysis, utilizes the fuzzy subtraction idea to carry out fuzzy C mean value clustering number and clustering center analysis, avoids the defect of clear traditional clustering analysis boundary because the K mean value clustering processing speed is high, the occupied memory is small, the processing precision is high, the fuzzy C mean value clustering carries out fuzzy division on the object to be identified, each sample belongs to a certain class with a certain membership degree, extracts a better one from two classes of results as a clustering result, can ensure the credibility and the higher precision of the clustering result, and carries out the disassembling and recombining processing aiming at the data segment of which the length of a single kinematics segment exceeds the time length of the expected working condition, the method has the advantages that consistency of characteristic values of data before and after processing is guaranteed to the maximum extent, the sample size is increased, then the data is resampled and compressed by the multi-phase filter, trend and characteristics of the data before and after processing are guaranteed to be basically unchanged, construction of subsequent working conditions is facilitated, finally, candidate working conditions are respectively screened out by combining speed-acceleration joint probability distribution chi-square test and characteristic value Euclidean distance, representative working conditions are extracted by means of simulation and real vehicle verification, construction of the working conditions is completed, and optimal power assembly configuration of vehicle types in the field is recommended according to the working conditions. The method has the characteristics of perfect theory, high precision of constructed working conditions and the like, and is suitable for developing the working conditions of the semi-trailer traction vehicle.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method of one embodiment of the present application.
Fig. 2 is a K-means cluster validity analysis cluster number optimization process in an embodiment of the present application.
FIG. 3 is a graphical representation of the before and after disassembly in one embodiment of the present application.
FIG. 4 is a graphical representation of curves before and after compression in one embodiment of the present application.
FIG. 5 illustrates candidate operating conditions based on a velocity-acceleration joint probability distribution according to an embodiment of the present application.
FIG. 6 shows candidate operating conditions constructed according to feature values in an embodiment of the present application.
FIG. 7 is a plot of actual and simulated engine operating points in one embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example 1
Referring to fig. 1, the present embodiment provides a method for constructing a working condition of a semi-trailer tractor.
Taking a 6 x 4 semi-trailer tractor as an example, the running condition of a short-distance transportation running section is constructed.
Market domain division and data acquisition and preprocessing: according to market research and partial heavy tractor driving data tracking analysis results, according to the single-trip running mileage and the annual running mileage of the vehicle, the heavy tractor market in the east region of China is divided into long-distance transportation, medium-distance transportation and short-distance transportation, and vehicles with different distances can be further divided according to use scenes, transportation loads and vehicle types: for example, a 6 x 4 vehicle model of a certain logistics company mainly transports bulk cement, gravel and the like, the operation route is zibo-dongyng, the transport distance is about 112km, the standard load is 49t for transportation, the national road proportion of the transportation route is 16.61%, the provincial road proportion is 25.93%, the time for waiting for traffic lights during parking and idling is longer, the vehicle is further divided according to the characteristics, and part of the fields are divided as shown in the following table 1.
TABLE 1 heavy tractor segment market segment plus division
Typical running vehicles are selected and driving data are collected in the target field, and partial data distortion is caused by data collection equipment, external uncertain interference factors and the like, so that abnormal speed points in original data are removed, the whole data are subjected to smoothing processing, and 139 thousands of effective data are obtained.
Dividing and processing a kinematic fragment: defining data from each starting to the next stopping of the vehicle as a kinematic segment, dividing the effective data into 2941 kinematic segments, and respectively calculating 13 characteristic values for each kinematic segment: average speed, speed standard deviation, maximum acceleration, acceleration section average acceleration, maximum deceleration, deceleration section average deceleration, maximum speed, acceleration standard deviation, deceleration standard deviation, acceleration proportion, deceleration proportion, uniform speed proportion and running time, and part of data is shown in the following table 2.
TABLE 2 Zibo-Dongying eigenvalue matrix for short haul transport
All the characteristic values of the kinematic segments are subjected to standardization processing and are converted into dimensionless indexes through the following formula:
Zi＝(Xi-mean(X))./std(X)
wherein Z isiFor the dimensionless index after standardization, X represents any kind of characteristic value of all kinematic segments, XiRepresenting any type of characteristic value of any kinematic segment, mean representing the mean calculation of X, and std representing the standard deviation calculation of X.
And performing dimensionality reduction on all dimensionless indexes by using a principal component analysis method to obtain a plurality of irrelevant variables, screening the variables of which the numerical values are more than 1 and the corresponding cumulative contribution rate is more than or equal to 85%, and selecting the first four variables as principal component values of cluster analysis.
Clustering analysis: the distance evaluation function based on the Mahalanobis distance is used as a new clustering effectiveness index of the clustering division of the kinematic segments, and the distance evaluation function is defined as follows:
wherein D isoutTo cluster-to-cluster distance, DinAs intra-cluster distance, CiFor a certain kinematic segment clustering, m is the sample mean, miClustering C for kinematic fragmentsiMean of all samples in the spectrum, τ is the sample covariance matrix, τ-1Is an inverse matrix of τ, P is CiAnd finally obtaining the minimum value of F of any space object in the space object group, namely the optimal clustering number.
As shown in fig. 2, the minimum value corresponding to the ordinate F is the optimal cluster number, and the optimal cluster number obtained in this embodiment is 5.
And based on the optimal clustering number, clustering calculation is carried out on the kinematic segments by utilizing K-means clustering.
Then, clustering calculation is carried out by using the fuzzy C-means poly-pair kinematic segments, firstly, the fuzzy subtraction clustering method is used for calculating the clustering number and the clustering center, and the clustering center searched by using the programming language is as follows:
applying the clustering centers to fuzzy C-means clustering, first defining initial clustering centers viHill climbing function of (1):
where a is a normal number, taking the maximum value of the hill-climbing function
V of (a)iAs a cluster center
Continue to calculate other cluster centers:
wherein M isj(mi) As the centre of clustering viHill climbing function of (M)j-1(mi) As a function of the hill climbing in the previous step,
is Mj-1(mi) The maximum value of (a) is,
and (4) the clustering center is a new clustering center, beta is a normal number, and the obtained clustering center and the obtained clustering number are substituted into a fuzzy C mean value clustering algorithm to perform secondary clustering on all the kinematic segments.
And comparing and analyzing the K-means clustering result and the fuzzy C-means clustering result, selecting a result with densely distributed clustering centers and uniformly distributed overall clustering as an optimal clustering result, and selecting the K-means clustering result as the optimal clustering result if the clustering results of the two algorithms have small difference and high result reliability.
Calculating the length of the segment under the expected working condition and disassembling and recombining the ultralong kinematic segment: calculating the integral characteristic value, the speed-acceleration joint probability distribution and the proportion of each kinematics fragment cluster in the integral kinematics fragment according to the 5 clusters, setting the upper limit and the lower limit of the time length of the expected working condition to be 1000s-1200s, respectively calculating the upper limit and the lower limit of the specific time length of 5 segmental working conditions of the expected representative working condition according to the proportion, and obtaining the calculation result shown in the following table 3.
| Segmental operating mode(s) | First stage | Second section | Third stage | All four sections | Fifth stage |
| Lower limit of expected operating duration | 133.80 | 22.30 | 21.10 | 26.50 | 557.90 |
| Upper limit of expected operating duration | 219.32 | 31.22 | 29.54 | 33.10 | 753.06 |
TABLE 3 segmented Condition Upper and lower limits of expected duration
Performing disassembly and recombination processing on a single kinematics segment with the time length exceeding more than two times of the expected working condition time length, firstly searching all speed inflection points or wave crests in the kinematics segment, disassembling the kinematics segment from each speed inflection point or wave crest, defining data before the first speed inflection point or wave crest as a starting section, defining data after the last speed inflection point or wave crest as a stopping section, defining data between the first speed inflection point or wave crest and the last speed inflection point or wave crest as a running section, disassembling the running section by a plurality of speed inflection points or wave crests to obtain a plurality of running section segments, finally respectively combining each running section segment with the starting section and the stopping section, selecting a speed difference value of a data connection position not more than 0.5km/h and an acceleration difference value not more than 0.1m/s2As a new kinematic fragment. As shown in fig. 3, a segment of very long kinematic segment is decomposed and then recombined into 8 new kinematic segments, thereby ensuring the integrity of the overall kinematic segment data, increasing the number of kinematic segments, and improving the accuracy of the later analysis.
If the time length of the decomposed and recombined kinematic segment is still larger than the expected working condition time length, the polyphase filter is used for resampling and compressing the kinematic segment, so that the change range of the acceleration and deceleration of the kinematic segment before and after compression does not have mutation and does not influence subsequent analysis and calculation, the length of a compressed object is not more than three times of the upper limit of the expected working condition time length, as shown in fig. 4, a certain section of kinematic segment is compressed from 2100s to 700s after being subjected to resampling and compression by the polyphase filter.
And carrying out smooth filtering treatment on the new kinematic segment subjected to the disassembly, recombination and compression treatment, wherein the smooth filtering treatment mode can adopt linear Gaussian filtering and nonlinear median filtering, and 195804 sections of kinematic segments are obtained after the disassembly and recombination treatment, so that the amount of samples to be treated is effectively increased.
Constructing candidate working conditions: setting up the upper and lower limit intervals of each segment time of representative working conditions, selecting the kinematic segments meeting the upper and lower limit intervals of the segment time of the representative working conditions in each cluster to carry out random combination, respectively calculating the velocity-acceleration joint probability distribution of all the kinematic segment combinations, respectively comparing with the integral velocity-acceleration joint probability distribution of each cluster before disassembly and recombination by using a chi-square test method, as shown in fig. 5, a group with the minimum deviation of velocity-acceleration joint probability distribution in the kinematic segment combinations is selected as a first candidate condition, then the overall characteristic values of all the kinematic segment combinations are respectively calculated, as shown in fig. 6, the euclidean distance shortest rule is used to compare with the global eigenvalues of the clusters before the disassembly and reassembly, and a group with the closest global eigenvalue in the combination of the kinematic segments is selected as the second candidate condition.
And (3) importing the two candidate working conditions into Cruise vehicle fuel simulation software for economic simulation, comparing a simulation result with the working condition points of the running engine of the real vehicle as shown in FIG. 7, selecting the candidate working condition with the lowest oil consumption and tail gas emission from the first candidate working condition and the second candidate working condition as a representative working condition of the market field, wherein part of characteristic values are shown in Table 4.
Table 4 construction of comparison results of characteristic values of part of working conditions
Selecting a typical vehicle in the field to carry out drum and real vehicle road verification, knowing that the representative working condition can meet the requirement of vehicle early-stage research and development precision according to the oil consumption result in a table 4, inputting the parameters of the power assembly such as an engine, a gearbox, a drive axle and the like which are currently or planned to be developed into Cruise vehicle fuel simulation software, and on the premise of ensuring the requirement of the power property, selecting the optimal power assembly configuration of the vehicle type in the field by using the road spectrum of the representative working condition, thereby providing guiding basis for the research and development of the vehicle type.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A construction method for working conditions of a semi-trailer tractor is characterized by comprising the following steps:
step 1, dividing a market field by utilizing driving characteristics of various semi-tractors, acquiring semi-tractor driving data of a target field, and establishing a kinematics fragment database of the field;
step 2, calculating characteristic values of all the kinematic segments, and carrying out standardization processing on the characteristic values to obtain dimensionless indexes;
step 3, performing decorrelation and dimension reduction processing on the dimensionless indexes by using a principal component analysis method to obtain principal component values of all the kinematic segments;
step 4, clustering all the kinematic segments according to the principal component values, respectively calculating the overall speed-acceleration joint probability distribution and the overall characteristic value of each cluster, and segmenting and dividing the time of a preset representative working condition according to the proportion of each type of kinematic segment in all the kinematic segments;
step 5, selecting the kinematic segments meeting the segment time of the representative working condition from each cluster to carry out random combination to obtain a plurality of kinematic segment combinations, and screening two kinematic segment combinations from the plurality of kinematic segment combinations as candidate working conditions according to the speed-acceleration joint probability distribution and the characteristic value of each kinematic segment combination;
and 6, selecting one candidate working condition from the two candidate working conditions as a representative working condition of the target field through simulation.
2. The method of claim 1, wherein step 1 comprises:
dividing different market fields according to different semi-trailer types, different road section driving occupation ratios, driving forms, engine operating condition point distribution, vehicle single-trip operating mileage and annual operating mileage data;
the method comprises the steps of collecting driving data of a semi-trailer tractor in any market field, wherein the driving data comprises time, vehicle speed, engine rotating speed and torque, the sampling frequency is 1Hz, and effective data is not less than 10 thousands;
removing repeated data and abnormal data;
and setting the data of each time the vehicle is started to the next stop as a kinematic segment, and constructing a kinematic segment database in the field.
3. The method of claim 2, wherein step 2 comprises:
respectively calculating a characteristic value of each kinematic segment, wherein the characteristic value comprises: average speed, speed standard deviation, maximum acceleration, acceleration section average acceleration, maximum deceleration, deceleration section average deceleration, maximum speed, acceleration standard deviation, deceleration standard deviation, acceleration proportion, deceleration proportion, uniform proportion and running time;
the characteristic values of all the kinematic segments are standardized by using the following formula, and all the characteristic values are converted into dimensionless indexes:
Zi＝(Xi-mean(X))./std(X)
wherein Z isiFor the normalization of the dimensionless indices, X represents the characteristic values of all kinematic segments, XiRepresents the characteristic value of any kinematic fragment, mean represents the mean calculation of X, and std represents the standard deviation calculation of X.
4. The method of claim 3, wherein step 3 comprises:
and performing decorrelation and dimensionality reduction on the dimensionless indexes subjected to the standardization processing by using a principal component analysis method to obtain mutually irrelevant variables, and screening out the variables of which the numerical value is greater than 1 and the cumulative contribution rate is greater than or equal to 85% from the variables as principal component values of the clustering analysis.
5. The method of claim 4, wherein the step 4 comprises:
calculating the optimal clustering number of the kinematics segment clustering by using a distance evaluation function of the Mahalanobis distance, and carrying out K-means clustering calculation on all kinematics segments based on the optimal clustering number;
calculating the clustering number and the clustering center by using a fuzzy subtraction clustering method, and then carrying out fuzzy C-means clustering calculation on all the kinematic segments;
and comparing and analyzing the K-mean clustering result and the fuzzy C-mean clustering result, and selecting clusters with densely distributed clustering centers and uniformly distributed overall clustering as the optimal clustering result.
6. The method of claim 5, wherein the step 4 comprises:
aiming at the kinematics segment exceeding more than twice of the representative working condition time, searching each speed inflection point or peak in the kinematics segment, and performing disassembly from each speed inflection point or peak;
defining data before the first speed inflection point or wave crest as a starting section, defining data after the last speed inflection point or wave crest as a stopping section, and defining all data between the starting section and the stopping section as a running section;
the operation section is divided into a plurality of operation section segments, each operation section segment is recombined with the starting section and the parking section respectively, the speed difference of the selected data connection position is less than 0.5km/h, and the acceleration difference is less than 0.1m/s2As a new kinematic fragment;
and carrying out smoothing filtering processing on the new kinematic segment, wherein the smoothing filtering processing mode comprises the following steps: gaussian filtering, median filtering.
7. The method of claim 6, wherein the step 4 further comprises:
and if the duration of the recombined new kinematic segment is still greater than the expected working condition duration, compressing the kinematic segment in a manner of resampling by a polyphase filter, and performing smooth filtering processing on the compressed kinematic segment.
8. The method of claim 7, wherein the step 5 comprises:
and respectively calculating the speed-acceleration joint probability distribution and the characteristic value of all the kinematic fragment combinations, selecting the kinematic fragment combination with the minimum deviation from the overall speed-acceleration joint probability distribution of each cluster in the step 4 as a first candidate working condition by using a chi-square test mode, and selecting the kinematic fragment combination closest to the overall characteristic value of each cluster in the step 4 as a second candidate working condition by using the Euclidean distance shortest principle.
9. The method of claim 8, further comprising:
and respectively importing the candidate working conditions into simulation software, combining the simulation calculation results and comparing the working conditions of the engine operated by the actual vehicle, and selecting the candidate working condition with the lowest oil consumption and the lowest exhaust emission from the candidate working conditions as the representative working condition of the target field.
10. The method of claim 9, further comprising:
and verifying representative working conditions by using a real vehicle rotary drum and a road test, and recommending the optimal power assembly configuration of the vehicle type in the field.
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| CN115601855A (en) * | 2022-11-29 | 2023-01-13 | 天津所托瑞安汽车科技有限公司（Cn） | Vehicle driving condition construction method, electronic device and storage medium |
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