CN108596208B - Vehicle driving cycle construction method for full-working-condition road - Google Patents
Vehicle driving cycle construction method for full-working-condition road Download PDFInfo
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Abstract
The invention relates to a vehicle driving cycle construction method for a full-working-condition road, which comprises the following steps of: analyzing vehicle road operation data, and extracting main information by using a principal component analysis method; classifying the vehicle operation data according to a clustering method; constructing a cycle condition by using a Markov method; checking and checking the circulating working conditions according to the K-S, and combining the working conditions under different categories to form candidate working conditions; classifying vehicle operation data according to different time periods and different roads; constructing a cycle condition by using a short stroke method; and calculating the characteristic values of different candidate working condition segments, combining the similar segments of the characteristic values, and completing the construction of the working conditions. The invention organically combines the clustering method and the classification method, fully considers various vehicle motion characteristics and driver operation characteristics which may occur in the vehicle running process, has wide coverage of the generated working condition and strong applicability, and can meet the requirement of construction of the driving cycle of the road under all working conditions in the target area.
Description
Technical Field
The invention relates to a construction method of a vehicle driving cycle, in particular to a construction method of a vehicle driving cycle for a road under all working conditions.
Background
The vehicle driving cycle condition, also referred to as an automobile operating cycle condition, is a time-speed history relationship that describes vehicle driving characteristics and driver operating characteristics that may occur in all traffic environments within a particular target area. The vehicle driving cycle working condition is a representative vehicle driving working condition which is established on the basis of a multivariate statistical theory by mining mass data generated in the actual road driving process of a vehicle, extracting and analyzing important dynamic characteristics. The vehicle driving cycle working condition is mainly used for carrying out standardized tests on the pollutant emission, the energy consumption level and the like of the vehicle, and is a very important core technology in the automobile industry.
The actual operation conditions of the vehicles are greatly different due to different traffic conditions of different countries, cities and regions, and the difference enables the same vehicle to possibly show different service performances in the actual operation process of different regions, so that the difference between the economical efficiency and the emission performance of the vehicle is obvious. At present, the main method for constructing the driving cycle condition of the vehicle comprises the following steps: short-stroke method, Markov method, clustering method, principal component analysis method. However, the vehicle driving cycle working condition generated based on the single method has the problems of lacking working condition types, limited coverage and low applicability.
Disclosure of Invention
In view of the above problems, the invention aims to provide a vehicle driving cycle construction method for a full-working-condition road, which can obtain the cycle working condition of the actual road under the full working condition, has wide coverage and strong applicability, and better fits the overall characteristics of the actual road.
The invention is realized according to the following technical scheme:
a vehicle driving cycle construction method for a full-working-condition road is characterized by comprising the following steps of:
s1: analyzing vehicle road operation data, and extracting main information by using a principal component analysis method;
s2: classifying the vehicle operation data according to a clustering method;
s3: constructing a cycle condition by using a Markov method;
s4: checking and checking the circulating working conditions according to the K-S, and combining the working conditions under different categories to form candidate working conditions;
s5: classifying vehicle operation data according to different time periods and different roads;
s6: constructing a cycle condition by using a short stroke method;
s7: and calculating the characteristic values of different candidate working condition segments, combining the similar segments of the characteristic values, and completing the construction of the working conditions.
In the above technical solution, the step S1 specifically includes the following steps:
s101: analyzing GPS vehicle data and deleting error data;
s102: dividing short stroke segments, and defining the motion of the vehicle from one parking to the next parking as a short stroke;
s103: calculating average speed, running speed, speed variance, maximum acceleration, minimum deceleration, idle time, deceleration time, uniform speed time, acceleration time, average acceleration and average deceleration as segment characteristic values;
s104: and extracting principal components by using principal component analysis.
In the above technical solution, the step S2 specifically includes the following steps:
s201: selecting main principal components obtained by a principal component analysis method, and carrying out clustering analysis;
s202: and dividing the database into four types by using a K-means clustering method to obtain four different types of vehicle data.
In the above technical solution, the step S3 specifically includes the following steps:
s301: dividing the vehicle road operation data into four segments of acceleration, deceleration, uniform speed and idling according to the motion state;
s302: dividing the segments into different speed states according to the average speed of each segment;
s303: calculating the transition probability among the states by using a maximum likelihood method;
s304: selecting a starting segment with a certain length as an initial stage of a cycle condition;
s305: and continuously selecting the next state according to the transition matrix until the candidate working condition reaches the ideal length.
In the above technical solution, the step S4 specifically includes the following steps:
s401: performing Kolmogorov-Smirov (K-S) test on the transition probabilities of the candidate working conditions and the experimental data, and returning to the step S3 if the transition probabilities are different greatly;
s402: and combining the candidate working conditions under the four categories to obtain the candidate working conditions based on the clustering method and the Markov method.
In the above technical solution, the step S5 specifically includes the following steps:
s501: according to different time periods, the short stroke segments are divided into three types: morning and evening peak hours, daytime off-peak hours, nighttime off-peak hours;
s502: according to different road types, each time period is subdivided into three types: ordinary roads, elevated roads and highways.
In the above technical solution, the step S6 specifically includes the following steps:
s601: randomly combining short strokes to reach a certain length to form candidate working conditions;
s602: respectively calculating absolute values of average relative errors of the characteristic parameters of all candidate working conditions and experimental data;
s603: and taking the candidate working condition with the minimum average relative error as the representative running working condition under the category.
In the above technical solution, the step S7 specifically includes the following steps:
s701: respectively calculating characteristic parameters of the candidate working conditions in the step S4 and the step S6;
s702: calculating the average relative error of the characteristic parameters among the candidate working conditions;
s703: combining two sections of candidate working conditions with small average relative error into one section;
s704: and combining all the candidate working conditions to obtain a representative running working condition.
Compared with the prior art, the invention has the following beneficial effects:
the invention organically combines the clustering method and the classification method, fully considers various vehicle motion characteristics and driver operation characteristics which may occur in the vehicle running process, has wide coverage of the generated working condition and strong applicability, and can meet the requirement of construction of the driving cycle of the road under all working conditions in the target area.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a schematic flow chart of a driving cycle construction method suitable for a full-condition road according to the present invention;
FIG. 2 is a schematic diagram of representative operating conditions of the combination according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1, the present invention comprises the steps of:
step 1: analyzing vehicle road operation data, and extracting main information by using a principal component analysis method, wherein the specific process comprises the following steps:
1) analyzing GPS vehicle data and deleting error data;
2) dividing short stroke segments, and defining the motion of the vehicle from one parking to the next parking as a short stroke;
3) calculating average speed, running speed, speed variance, maximum acceleration, minimum deceleration, idle time, deceleration time, uniform speed time, acceleration time, average acceleration and average deceleration as segment characteristic values;
4) and extracting the principal component by using a principal component analysis method.
Step 2: in view of the fact that the clustering method can collect data with similar physical characteristics in big data information to measure the similarity among different data sources, the clustering method is adopted to classify the vehicle operation data;
classifying the vehicle operation data according to a clustering method, which comprises the following specific processes:
1) selecting the first two principal components obtained by a principal component analysis method, and carrying out cluster analysis;
2) dividing the database into four categories by using a K-means clustering method to obtain four different categories of vehicle data;
3) setting the total working condition duration to be 1400s, and dividing the time occupied by four different types of working conditions according to the proportion.
And step 3: a Markov method is utilized to construct a cycle working condition, and the specific process is as follows:
1) dividing the vehicle road operation data into four segments of acceleration, deceleration, uniform speed and idling according to the motion state;
2) dividing the segments into different speed states according to the average speed of each segment;
3) calculating the transition probability among the states by using a maximum likelihood method;
4) selecting a 60-90 second starting segment as the starting stage of the cycle condition;
5) and continuously selecting the next state according to the transfer matrix until the candidate working condition duration reaches the working condition duration of the category, and completing the construction of the candidate working condition of the category.
And 4, step 4: checking and checking the circulating working conditions according to K-S, combining the working conditions under different types to form candidate working conditions, and the specific process is as follows:
1) performing Kolmogorov-Smirov (K-S) test on two independent samples of the transition probabilities of the candidate working conditions and the experimental data, and returning to the step (3) if the average double-tail similarity level is less than 0.9;
2) and combining the candidate working conditions under the four categories to obtain the candidate working conditions based on the clustering method and the Markov method.
And 5: according to different time periods and different roads, vehicle operation data are classified, and the specific process is as follows:
1) according to different time periods, the short stroke segments are divided into three types: morning and evening peak hours (7: 00-10: 00, 15: 00-20: 00), daytime off-peak hours (10: 00-15: 00), nighttime off-peak hours (20: 00-7: 00 the next day);
2) according to different road types, each time period is subdivided into three types: ordinary roads, elevated roads and highways;
3) setting the total working condition duration as 1400s, and dividing the time occupied by nine different working conditions according to the proportion.
Step 6: a short stroke method is utilized to construct a cycle working condition, and the specific process is as follows:
1) randomly combining short strokes and working condition duration in the category to form candidate working conditions;
2) respectively calculating absolute values of average relative errors of the characteristic parameters of all candidate working conditions and experimental data;
3) and taking the candidate working condition with the minimum average relative error as the representative running working condition under the category.
And 7: calculating the eigenvalue of different candidate working condition segments, combining the eigenvalue similar segments to complete the construction of the working conditions,
the specific process is as follows:
1) respectively calculating characteristic parameters of the candidate working conditions in the step (4) and the step (6);
2) calculating the average relative error of the characteristic parameters among the candidate working conditions;
3) combining two sections of candidate working conditions with the average relative error smaller than 1% into one section;
4) all the candidate conditions are combined to obtain a representative running condition, as shown in fig. 2.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (7)
1. A vehicle driving cycle construction method for a full-working-condition road is characterized by comprising the following steps of:
s1: analyzing vehicle road operation data, and extracting main information by using a principal component analysis method;
s2: classifying the vehicle operation data according to a clustering method;
s3: constructing a cycle condition by using a Markov method;
s4: checking and checking the circulating working conditions according to the K-S, and combining the working conditions under different categories to form candidate working conditions;
s5: classifying vehicle operation data according to different time periods and different roads;
s6: constructing a cycle condition by using a short stroke method;
s7: calculating the characteristic values of different candidate working condition segments, combining the similar segments of the characteristic values, and completing the construction of the working conditions;
the step S7 specifically includes the following steps:
s701: respectively calculating characteristic parameters of the candidate working conditions in the step S4 and the step S6;
s702: calculating the average relative error of the characteristic parameters among the candidate working conditions;
s703: combining two sections of candidate working conditions with small average relative error into one section;
s704: and combining all the candidate working conditions to obtain a representative running working condition.
2. The vehicle driving cycle construction method for the full-condition road according to claim 1, characterized in that: the step S1 specifically includes the following steps:
s101: analyzing GPS vehicle data and deleting error data;
s102: dividing short stroke segments, and defining the motion of the vehicle from one parking to the next parking as a short stroke;
s103: calculating average speed, running speed, speed variance, maximum acceleration, minimum deceleration, idle time, deceleration time, uniform speed time, acceleration time, average acceleration and average deceleration as segment characteristic values;
s104: and extracting principal components by using principal component analysis.
3. The vehicle driving cycle construction method for the full-condition road according to claim 1, characterized in that: the step S2 specifically includes the following steps:
s201: selecting main principal components obtained by a principal component analysis method, and carrying out clustering analysis;
s202: and dividing the database into four types by using a K-means clustering method to obtain four different types of vehicle data.
4. The vehicle driving cycle construction method for the full-condition road according to claim 1, characterized in that: the step S3 specifically includes the following steps:
s301: dividing the vehicle road operation data into four segments of acceleration, deceleration, uniform speed and idling according to the motion state;
s302: dividing the segments into different speed states according to the average speed of each segment;
s303: calculating the transition probability among the states by using a maximum likelihood method;
s304: selecting a starting segment with a certain length as an initial stage of a cycle condition;
s305: and continuously selecting the next state according to the transition matrix until the candidate working condition reaches the ideal length.
5. The vehicle driving cycle construction method for the full-condition road according to claim 4, characterized in that: the step S4 specifically includes the following steps:
s401: performing Kolmogorov-Smirov test on two independent samples according to the transition probability of the candidate working conditions and the experimental data, and returning to the step S3 if the average two-tail similarity level is less than 0.9;
s402: and combining the candidate working conditions under the four categories to obtain the candidate working conditions based on the clustering method and the Markov method.
6. The vehicle driving cycle construction method for the full-condition road according to claim 1, characterized in that: the step S5 specifically includes the following steps:
s501: according to different time periods, the short stroke segments are divided into three types: morning and evening peak hours, daytime off-peak hours, nighttime off-peak hours;
s502: according to different road types, each time period is subdivided into three types: ordinary roads, elevated roads and highways.
7. The vehicle driving cycle construction method for the full-condition road according to claim 1, characterized in that: the step S6 specifically includes the following steps:
s601: randomly combining short strokes to reach a certain length to form candidate working conditions;
s602: respectively calculating absolute values of average relative errors of the characteristic parameters of all candidate working conditions and experimental data;
s603: and taking the candidate working condition with the minimum average relative error as the representative running working condition under the category.
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