CN116776229B - Method for dividing typical running conditions of automobile facing carbon emission factors - Google Patents
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Abstract
The invention provides a method for dividing typical working conditions of an automobile running for a carbon emission factor, and belongs to the technical field of division of typical working conditions of the carbon emission factor. The method comprises the following steps: s1, collecting vehicle running state data, including an average vehicle speed v, an acceleration a and an idle time t, forming a data set, removing small sample data of the average vehicle speed v and the acceleration a, and calculating an average mu of the average vehicle speed v and the acceleration a v And standard deviation sigma v The method comprises the steps of carrying out a first treatment on the surface of the S2, carrying out dimensionless treatment on the vehicle running state data; s3, determining the number of grouping under typical working conditions, determining the boundary points of the grouping, and calculating the separation degree of data in each grouping; s4, analyzing carbon emission factor errors corresponding to each typical working condition. The method solves the problem of dividing the running conditions of the automobile which are not calibrated by the carbon emission factors. The invention considers the working condition dividing method of three operation parameters of the vehicle running speed, acceleration and idle time to match the carbon emission factor so as to obtain a more feasible carbon emission factor calibrating method.
Description
Technical Field
The application relates to a typical working condition dividing method, in particular to a carbon emission factor-oriented automobile driving typical working condition dividing method, and belongs to the technical field of carbon emission factor typical working condition dividing.
Background
The vehicle driving condition division can help to know the performance of the vehicle under different roads and driving conditions, and can formulate better traffic policy to provide basis. Carbon emissions are one of the important factors affecting climate change, and automobiles are one of the largest sources of carbon emissions. Knowing the carbon emissions of automobiles on different roads and driving conditions, better traffic policies can be formulated to encourage drivers to use more environmentally friendly vehicles or to take more environmentally friendly driving modes. The running condition of the automobile is also the basis for calibrating the carbon emission factor. One carbon emission factor for each operating condition can result in an excessively large amount of data. At present, the working condition division has individual standards, such as NEDC and WLTP standards, but the standards have some problems. On the one hand, these standards are mostly formulated to meet emission regulations, lacking comprehensive consideration for vehicle performance under different driving conditions. On the other hand, these criteria may not be applicable to certain situations due to differences in road and driving conditions in different regions. Therefore, it is important to develop a classification method for the driving condition of the automobile suitable for specific situations for calculating the carbon emissions.
Disclosure of Invention
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. Its purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
In view of the above, the invention provides a method for dividing typical running conditions of an automobile facing carbon emission factors in order to solve the technical problem of dividing running conditions of the automobile which are not facing carbon emission factor calibration in the prior art. The invention can construct the corresponding carbon emission factors under different running conditions of the automobile; compared with the working conditions of simply relying on the relation between the speed and the carbon emission factors, the method increases the working conditions of two dimensions of acceleration and idle time, divides the typical working conditions by mining the similarity between data sets, and feeds back whether the error of carbon emission corresponding to the typical working conditions is in a reasonable range by using the average absolute percentage error, thereby avoiding each working condition from collecting the carbon emission factors, avoiding the problem of larger error of the carbon emission factors caused by unreasonable interval division, and considering both accuracy and calculation efficiency.
The scheme I is a method for dividing typical running conditions of an automobile facing carbon emission factors, comprising the following steps of:
s1, collecting vehicle running state data comprising vehicle average speed v, acceleration a and idle speed t to form a data set (v) 1 ,a 1 ,t 1 ）,（v 2 ,a 2 ,t 2 ），（v 3 ,a 3 ,t 3 ）…（v n ,a n ,t n ) Collecting carbon emissions corresponding to different driving states in a vehicle-mounted tail gas monitoring device arranged on a test vehicle, wherein each driving state corresponds to a carbon emission factor, removing small sample data of average vehicle speed v and acceleration a, and calculating the average value of the average vehicle speed v and the acceleration aAnd standard deviation->；
S2, carrying out dimensionless treatment on the vehicle running state data;
s3, determining the number of groups under typical working conditions, determining the boundary points of the groups, and calculating the separation degree of data in each group, wherein the method comprises the following steps:
s31, determining the grouping number of typical working conditions, wherein the grouping number is 2-n-1, and the accuracy of the carbon emission corresponding to the grouping is less than 10%; the typical working condition grouping number starts from 2 iterations;
s32, determining boundary points b (Z v,k ,Z a,k ,Z t,k ) Wherein Z is v,k ,Z a,k ,Z t,k Characteristic values of non-dimensionality treatment of average vehicle speed, acceleration and idle time are respectively represented; the boundary points are iterated from the second small characteristic value to the second large characteristic value in sequence, the separation degree of the data in each group is calculated, and the final boundary point is determined according to the separation degree;
s4, analyzing carbon emission factor errors corresponding to each typical working condition.
Preferably, the method for dimensionless processing the vehicle running state data comprises the following steps: data were normalized by dividing the deviation of the data by the standard deviation:
；
；
；
wherein:、/>and->Characteristic values of non-dimensionalized processing of average vehicle speed, acceleration and idle time are respectively represented, and +.>Representing average vehicle speedi=1,2,…n；/>Indicating accelerationi=1,2,…n；/>Indicating idle timei=1,2,…n。
Preferably, the method for calculating the separation degree is as follows:
s321, calculating a central value of each group, wherein the central value is an average value of a maximum value and a minimum value of the feature values after dimensionless processing of the average vehicle speed, the acceleration and the idle time corresponding to the current boundary point, and the calculation formula is as follows:
；
；
；
wherein:represent the firstjCenter value of speed in each packet, +.>、/>Respectively represent the firstjMinimum and maximum speed in each packet; />Represent the firstjThe central value of acceleration in each packet, +.>、/>Respectively represent the firstjMinimum and maximum acceleration rates in the individual packets; />Represent the firstjThe central value of idle time in each packet,、/>respectively represent the firstjMinimum and maximum values of individual packet idle times;
s322, calculating the corresponding separation degree after dividing and grouping each boundary point, wherein the specific formula is as follows:
；
；
；
；
wherein r represents the degree of separation;
，/>and->The separation degree of average vehicle speed, acceleration and idle speed is respectively represented;
，/>and->The center values of the average vehicle speed, acceleration and idle speed are respectively indicated,
，/>and->Average vehicle speed, acceleration, and idle speed are shown, respectively, with k=1, 2 … n.
S323, starting from the second small characteristic value, iterating to the characteristic value n-1 to obtain a degree of separation (r 1 ，r 2 …r n-1 ) Wherein (r) 1 ，r 2 …r n-1 ) And the boundary point corresponding to the minimum value in the model is a final boundary point, so that a typical working condition is obtained.
Preferably, the method for analyzing the carbon emission factor error corresponding to each typical working condition is as follows: the method comprises the steps that under typical working conditions, carbon emission factors corresponding to a plurality of running states are averaged and combined into one carbon emission factor, carbon emission corresponding to different typical working conditions is collected by a vehicle-mounted tail gas monitoring device arranged on a test vehicle, and an actual measurement method is adopted to check the error of the carbon emission factor corresponding to each typical working condition;
and (3) quantitatively calculating the difference between the carbon emission factor and the actual value of the typical working condition by using an average absolute percentage error (MAPE), wherein the calculation formula of the MAPE is as follows:
；
in the method, in the process of the invention,carbon emission factors are typical working conditions; />Is the actual carbon emission factor;nfor the total amount of test samples;
the average absolute percentage error is less than 10%, and the iteration is stopped; if more than 10% is returned to S3.
The second scheme is an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor realizes the step of the typical working condition dividing method for the automobile running facing the carbon emission factor in the first scheme when executing the computer program.
A third aspect is a computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements the method for dividing typical driving conditions of an automobile facing a carbon emission factor according to the first aspect.
The beneficial effects of the invention are as follows:
(1) The invention improves the calculation efficiency, and simultaneously considers the working condition dividing method of three operation parameters of the vehicle running speed, acceleration and idle time to match the carbon emission factor, thereby obtaining a more feasible carbon emission factor calibration method. In addition, in order to avoid excessive carbon emission factors caused by a large number of working conditions and reduce the calculation efficiency, a typical working condition dividing method is introduced, similar working conditions are divided into a category, and feedback is carried out by a checking method, so that the results of reducing the number of the emission factors and keeping higher accuracy are realized.
(2) A new rule is found, and the speed interval of the working condition in the high-speed running state is found to have small influence on the calibration of the carbon emission factor even if the speed interval is enlarged, and particularly the speed is smaller than 80km/h and is suitable for subdivision. The acceleration and the idle speed are considered, and the acceleration starting after long-time idle speed is suitable for subdivision working conditions.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a flow chart of a typical running condition dividing method of an automobile facing carbon emission factors.
Detailed Description
In order to make the technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of exemplary embodiments of the present application is given with reference to the accompanying drawings, and it is apparent that the described embodiments are only some of the embodiments of the present application and not exhaustive of all the embodiments. It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other.
Example 1, referring to fig. 1, the method for dividing typical running conditions of an automobile for carbon emission factors according to the present embodiment includes the following steps:
s1, collecting vehicle running state data comprising vehicle average speed v, acceleration a and idle speed t to form a data set (v) 1 ,a 1 ,t 1 ）,（v 2 ,a 2 ,t 2 ），（v 3 ,a 3 ,t 3 ）…（v n ,a n ,t n ) Collecting carbon emissions corresponding to different driving states in a vehicle-mounted tail gas monitoring device arranged on a test vehicle, wherein each driving state corresponds to a carbon emission factor, removing small sample data of average vehicle speed v and acceleration a, and calculating the average value of the average vehicle speed v and the acceleration aAnd standard deviation->；
Specifically, vehicle running state data is collected within a certain sampling interval;
specifically, the idle time refers to a period of time in which the vehicle is in a completely stopped state and has no acceleration tendency within a sampling interval;
specifically, the average speed and the acceleration keep the last bit of the decimal point, for example, 0.1km/h or 0.1m/s is used as the step length;
specifically, small sample data of the average vehicle speed v is removed, and the average value of the average vehicle speed v is calculatedAnd standard deviation->At 99% confidence interval, the average vehicle speed v is between +.>Is determined to be a reasonable range and is determined to be noise data, thereby avoiding the influence of occasional abnormal data and small sample data.
And removing small sample data of the acceleration a, and removing accidental abnormal data and small sample data by adopting a method for calculating a reasonable range by calculating a mean value and a standard deviation.
S2, carrying out dimensionless treatment on the vehicle running state data; since the units of the average vehicle speed v, the acceleration a and the idle time t are not uniform, the dimension is eliminated by adopting a standardization method, and therefore, the data is standardized by dividing the deviation of the data by the standard deviation:
；
；
；
wherein:、/>and->Characteristic values of non-dimensionalized processing of average vehicle speed, acceleration and idle time are respectively represented, and +.>Representing average vehicle speedi=1,2,…n；/>Indicating accelerationi=1,2,…n；/>Representing a short of lifeTime of speedi=1,2,…n；
S3, determining the grouping number of typical working conditions, determining the boundary points of the grouping, and calculating the separation degree of data in each grouping, so that the number of running working conditions of the automobile is reduced, and the aim of improving the calculation efficiency is fulfilled.
Determining the number of typical working condition groups; when the total number of typical working conditions is n, the number of packets is between 2 and n-1. The number of groupings is reduced as much as possible to improve efficiency, but the accuracy of the corresponding carbon emissions after grouping is required to be less than 10%. Therefore, the number of the groups is set to iterate from 2, at this time, the boundary points of the groups need to be determined, the boundary points sequentially iterate from the dimensionless characteristic values arranged in an ascending order, the separation degree of the data in each group is calculated, and the dimensionless characteristic value corresponding to the minimum separation degree is the final boundary point under the current number of the groups. Sequentially increasing the number of the packets until reaching the requirement that the inspection accuracy is less than 10%;
determining boundary points b (Z v,k ,Z a,k ,Z t,k ) Wherein Z is v,k ,Z a,k ,Z t,k Characteristic values of non-dimensionality treatment of average vehicle speed, acceleration and idle time are respectively represented; the boundary points iterate from the second small characteristic value to the second large characteristic value in turn; calculating the separation degree of the data in each group, calculating the separation degree of each boundary point, and determining a final boundary point according to the separation degree; the method specifically comprises the following steps:
s31, calculating a central value of each group, wherein the central value is an average value of a maximum value and a minimum value of the feature values after dimensionless processing of the average vehicle speed, the acceleration and the idle time corresponding to the current boundary point, and the calculation formula is as follows:
；
；
；
wherein:represent the firstjCenter value of speed in each packet, +.>、/>Respectively represent the firstjMinimum and maximum speed in each packet; />Represent the firstjThe central value of acceleration in each packet, +.>、/>Respectively represent the firstjMinimum and maximum acceleration rates in the individual packets; />Represent the firstjThe central value of idle time in each packet,、/>respectively represent the firstjMinimum and maximum values of individual packet idle times;
s32, calculating the corresponding separation degree after dividing and grouping each boundary point, wherein the specific formula is as follows:
；
；
；
；
wherein r represents the degree of separation;
，/>and->The separation degree of average vehicle speed, acceleration and idle speed is respectively represented;
，/>and->The center values of the average vehicle speed, acceleration and idle speed are respectively indicated,
，/>and->Average vehicle speed, acceleration, and idle speed are shown, respectively, with k=1, 2 … n.
S33, starting from the second small characteristic value to the second large characteristic value, obtaining the separation degree (r 1 ，r 2 …r n-1 ) Wherein (r) 1 ，r 2 …r n-1 ) The boundary point corresponding to the minimum value in the model is the final boundary point, and typical working conditions are obtained;
s4, analyzing the carbon emission factor error corresponding to each typical working condition, averaging and combining the carbon emission factors corresponding to a plurality of running states under the typical working conditions into one carbon emission factor, collecting carbon emission corresponding to different typical working conditions by using a vehicle-mounted tail gas monitoring device placed on a test vehicle, and checking the error of the carbon emission factor corresponding to each typical working condition by adopting an actual measurement method;
and (3) quantitatively calculating the difference between the carbon emission factor and the actual value of the typical working condition by using an average absolute percentage error (MAPE), wherein the calculation formula of the MAPE is as follows:
；
in the method, in the process of the invention,carbon emission factors are typical working conditions; />Is the actual carbon emission factor;nfor the total amount of test samples;
the average absolute percentage error is less than 10%, and the iteration is stopped; if more than 10% is returned to S3. And increasing the grouping number of the typical working conditions to 3, and starting iteration of the boundary points from the second small characteristic value and the third small characteristic value, wherein the minimum characteristic is divided into the typical working conditions 1, the second small characteristic value is divided into the typical working conditions 2, and all other data are divided into the typical working conditions 3. And calculating the central value of each typical working condition, calculating the separation degree of all data in each typical working condition, and selecting a boundary point corresponding to the minimum separation degree as a final dividing boundary. And (5) analyzing the carbon emission factor error corresponding to each typical working condition, and stopping iteration if the average absolute percentage error is smaller than 10% and is within an acceptable range. Otherwise, continuing to execute the steps until the average absolute percentage error is less than 10%.
Abbreviations and key term definitions:
carbon factor emission: the unit emission amount is, for example, carbon emission generated when a certain type of freight vehicle runs for one kilometer under a certain working condition. Therefore, a carbon emission factor needs to be calibrated in advance for each working condition.
Running conditions: according to national standards, a time-speed curve describing the running characteristics of a specific vehicle (passenger car, commercial car, city bus, etc.) is described in a traffic environment.
Idle speed: when the vehicle is in a complete stop state, and the time period without acceleration tendency is an idle working condition;
at a constant speed: the time period of the speed fluctuation not exceeding 1km/h is identified as a uniform speed working condition;
acceleration: the duration of the acceleration or uniform velocity process is not more than 1 second, and the acceleration of the acceleration process cannot be more than 1km/h; otherwise, the working condition identification needs to be carried out again from the point;
in embodiment 2, the computer device of the present invention may be a device including a processor and a memory, for example, a single chip microcomputer including a central processing unit. And the processor is used for realizing the steps of the signal control method for switching the cross-period scheme security when executing the computer program stored in the memory.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Embodiment 3, computer-readable storage Medium embodiment
The computer readable storage medium of the present invention may be any form of storage medium that is readable by a processor of a computer device, including but not limited to, nonvolatile memory, volatile memory, ferroelectric memory, etc., on which a computer program is stored, and when the processor of the computer device reads and executes the computer program stored in the memory, the steps of a signal control method for switching safely across time schemes described above can be implemented.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (3)
1. The method for dividing the typical running conditions of the automobile facing the carbon emission factors is characterized by comprising the following steps of:
s1, collecting vehicle running state data comprising vehicle average speed v, acceleration a and idle speed t to form a data set (v) i ,a i ,t i ) I=1, 2, & n, collecting carbon emission corresponding to different running states in a vehicle-mounted tail gas monitoring device placed on a test vehicle, wherein each running state corresponds to a carbon emission factor, removing small sample data of an average vehicle speed v and an acceleration a, and calculating the average value and standard deviation of the average vehicle speed v and the acceleration a;
s2, carrying out dimensionless processing on the vehicle running state data, and normalizing the data by dividing the deviation of the data by the standard deviation to obtain Z v,i ,Z a,i ,Z t,i ；Z v,i ,Z a,i ,Z t,i Characteristic values of non-dimensionality treatment of average vehicle speed, acceleration and idle time are respectively represented;
s3, determining the grouping number of the typical working conditions, wherein the grouping number is between 2 and n-1 when the total number of the typical working conditions is n; the grouping number is set to iterate from 2, the boundary points sequentially iterate from the dimensionless characteristic values arranged in ascending order, the separation degree of the data in each group is calculated, the dimensionless characteristic value corresponding to the minimum separation degree is the final boundary point under the current grouping number, and the grouping number is sequentially increased until the requirement of the inspection accuracy of less than 10% is met; the method specifically comprises the following steps:
s31, calculating the central value of each group, wherein the central value is、/>And->Respectively representing average values of maximum and minimum values of the feature values after dimensionless treatment of the average vehicle speed, the acceleration and the idle time corresponding to the current boundary point;
s32, calculating the corresponding separation degree after dividing and grouping each boundary point, wherein the specific formula is as follows:
；
；
；
；
wherein r represents the corresponding separation degree after each boundary point is divided into groups;，/>and->The separation degree of the average vehicle speed, the acceleration and the idle time is respectively represented; j represents the number of packets; k=1, 2,..m;
s33, starting from the second small characteristic value to the second large characteristic value, obtaining the separation degree (r 1 ，r 2 …r n-1 ) Wherein (r) 1 ，r 2 …r n-1 ) The boundary point corresponding to the minimum value in the model is the final boundary point, and typical working conditions are obtained;
s4, analyzing the carbon emission factor error corresponding to each typical working condition, averaging and combining the carbon emission factors corresponding to a plurality of running states under the typical working conditions into one carbon emission factor, collecting carbon emission corresponding to different typical working conditions by using a vehicle-mounted tail gas monitoring device placed on a test vehicle, and checking the error of the carbon emission factor corresponding to each typical working condition by adopting an actual measurement method; calculating the difference between the typical working condition carbon emission factor and the true value by utilizing the average absolute percentage error quantization; the average absolute percentage error is less than 10%, and the iteration is stopped; returning to S3 if more than 10%; increasing the grouping number of the typical working conditions to 3, and starting iteration of boundary points from the second small characteristic value and the third small characteristic value, wherein the minimum characteristic is divided into the typical working conditions 1, the second small characteristic value is divided into the typical working conditions 2, and all other data are divided into the typical working conditions 3; calculating the central value of each typical working condition, calculating the separation degree of all data in each typical working condition, and selecting a boundary point corresponding to the minimum value of the separation degree as a final dividing boundary; analyzing the carbon emission factor error corresponding to each typical working condition, and stopping iteration if the average absolute percentage error is less than 10%; otherwise, continuing to execute the steps until the average absolute percentage error is less than 10%.
2. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method for dividing typical vehicle driving conditions for carbon emission factors of claim 1 when executing the computer program.
3. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method for dividing the typical driving conditions of an automobile for carbon emission factors according to claim 1.
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