CN115796720A - Heavy vehicle emission evaluation method and storage medium - Google Patents
Heavy vehicle emission evaluation method and storage medium Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 14
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- 239000002826 coolant Substances 0.000 claims description 5
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Abstract
The invention relates to the field of environmental monitoring, and discloses a heavy vehicle emission evaluation method and a storage medium. According to the calculation principle of the power-based window method and the characteristic of large data volume of remote OBD data, constructing a remote OBD emission evaluation model OBD-AWM based on the power-based window method; the vehicle model with high emission can be predicted and evaluated.
Description
Technical Field
The invention relates to the field of environmental monitoring, in particular to a heavy-duty vehicle emission evaluation method and a storage medium.
Background
Source treatment is an important means for improving air quality. With the implementation of the emission standard of the heavy-duty vehicle at the VI stage of China and the upgrading of the fuel quality, the heavy-duty vehicle manufacturers meet the emission standard by adjusting the technical route and improving the existing emission control technology. The method has important significance in analyzing the emission characteristics of the diesel vehicle and can provide a data basis for controlling the pollution of a mobile source.
Most of the prior technical schemes use Portable Emission Measurement System (PEMS) for actual road test to analyze the Emission characteristics of vehicles, but PEMS has high test cost and great operation difficulty and is not suitable for large-scale Emission supervision. The national VI standard applies the requirement of a remote emission management vehicle-mounted terminal (remote OBD) to the national standard for the first time, the newly registered heavy-duty diesel vehicle realizes remote OBD data transmission, and the remote OBD is used as a real-time in-use monitoring system.
However, the current solutions do not fully utilize remote OBD data. On one hand, the technical scheme of analyzing the running state and the emission level of the vehicle by acquiring remote OBD data through the supervision platform is less; on the other hand, the existing evaluation method is based on a bench test and an actual road PEMS test, and an evaluation method based on remote OBD data is not established, so that the analysis of the remote ODB data and the establishment of the emission evaluation method based on the remote OBD are of great significance.
In addition, according to the existing research, the problems of data loss, repetition, abnormality and the like of remote OBD data can be caused by vehicle sensor faults or network faults in the transmission process, the problems are collectively referred to as the measurement failure of the remote OBD data, so that the emission level of the vehicle cannot be accurately judged, and the emission of the vehicle exceeds the standard due to the aging of the vehicle, the untimely addition of urea, the malicious tampering of an owner and the like, so that the vehicle needs to be supervised by an offline testing method, and a supervision closed loop is formed.
According to the remote OBD data characteristics, the emission evaluation model suitable for the remote OBD heavy truck is constructed.
Disclosure of Invention
To solve the above-mentioned problems, the present invention provides a method for evaluating the emission of a heavy vehicle and a storage medium.
In order to solve the technical problems, the invention adopts the following technical scheme:
a heavy vehicle emission evaluation method comprises the following steps:
step S1: the method comprises the steps of obtaining remote OBD data, and conducting data preprocessing on the remote OBD data through travel records in the remote OBD data to obtain effective data;
s2, selecting short-travel segments by using a short-time traffic flow dividing method, and clustering the short-travel segments into high-speed segments, suburban segments and urban segments;
and step S3: combining various short-stroke fragments according to a proportion to obtain combined fragments, evaluating the combined fragments through an OBD-AWM model constructed based on a power-based window method, and judging the emission compliance of the vehicle; the method specifically comprises the following steps:
marking the window with the window average power larger than the engine power threshold value in the combined segment i as an effective window, and marking the window average power of the combined segment i as the window
Comprises the following steps:
；
in order to combine the end times of the segments i,
to combine the start times of the segments i,
is the value of the cyclic work of the engine,
is the maximum power of the engine;
calculating the power-based window specific emission passing rate of the combined segment i
And window effective point passing rate
: power base window specific emission pass rate
；
To combine the number of valid windows in segment i required by NOx emissions,
the total number of the effective windows of the combined segment i;
window effective point passing rate
；
For the number of SCR downstream NOx transient emissions data in combined segment i for which the SCR downstream NOx concentration is less than 600ppm,
the total number of the NOx instantaneous emission data at the downstream of the SCR in the combined segment i; each remote OBD data includes an SCR downstream NOx transient emission data;
when in use
And is
When the NOx emission level of the vehicle reaches the standard, judging the NOx emission level of the vehicle to reach the standard; otherwise, the NOx emission level of the vehicle is judged to be overproof; and screening the vehicle models with the NOx emission level exceeding 50% in a natural month to serve as high-emission vehicle models.
Further, when performing data preprocessing on the remote OBD data in step S1, the method specifically includes:
s1.1: judging whether the vehicle is in a running state or a stopping state according to the engine speed and the coolant temperature in the remote OBD data, wherein the running state to the stopping state of the vehicle at one time is a travel record, and deleting the remote OBD data corresponding to the travel record with the time span of less than thirty minutes;
s1.2, screening effective data: continuous and non-repeated remote OBD data in the primary travel record are normal data, and if the normal data account for more than 80% of the primary travel record data, the primary travel record is an effective travel record; removing remote OBD data with data missing in the primary travel record, wherein if the remaining remote OBD data accounts for more than 80% of the data of the primary travel record, the primary travel record is an effective travel record; the effective travel record data is effective data;
s1.3: and screening remote OBD data with the engine temperature of more than 70 ℃ and the SCR downstream exhaust temperature of more than 150 ℃ in the effective data, and carrying out data cleaning and then arranging the data in a positive sequence according to a time sequence.
A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the heavy vehicle emission evaluation method.
Compared with the prior art, the invention has the beneficial technical effects that:
according to the invention, a remote OBD emission evaluation model (OBD-AWM model) based on a power-based window method is constructed according to the characteristics of remote OBD data, and through verification, the OBD-AWM model can accurately identify high-emission vehicles, misjudgment is not generated on normal vehicles, and prediction evaluation can be carried out on high-emission vehicle types.
Drawings
Fig. 1 is a sample distribution diagram of the OBD-AWM model verification according to the present invention.
Detailed Description
A preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings.
The invention provides a heavy-duty vehicle emission evaluation method based on remote OBD data.
The heavy vehicle emission evaluation method comprises the following steps:
step S1: obtaining remote OBD data and carrying out data preprocessing to obtain effective data;
s2, selecting short-travel segments by using a short-time traffic flow dividing method, and clustering the short-travel segments into high-speed segments, suburban segments and urban segments;
and step S3: and combining the short-stroke fragments according to a proportion to obtain a combined fragment, constructing an OBD-AWM model by a power-based window method (AWM), evaluating the combined fragment, and judging the emission compliance of the vehicle.
The step S1 specifically includes:
s1.1: judging whether the vehicle is in a running state or a stopping state according to the engine speed and the coolant temperature in the remote OBD data, wherein in the embodiment, when the engine speed is greater than 0rmp and the coolant temperature is greater than 70 ℃, the vehicle is judged to be in the running state; and when the engine speed is equal to 0rmp, the temperature of the engine coolant is less than 50 ℃, and the duration time exceeds 2min, determining that the vehicle is in a stop state. The time span from one-time running state to stopping state of the vehicle is one-time travel record, and the time span of one-time travel record is not less than 30min.
S1.2: screening effective data; (1) continuous and non-repeated remote OBD data in the primary travel record are normal data, and if the normal data account for more than 80% of the primary travel record data, the primary travel record is judged to be effective; (2) and removing remote OBD data which are missing data such as instantaneous vehicle speed, engine operating parameters (engine rotating speed and relative torque), oil consumption, post-processing system monitoring state, concentration data, reaction liquid residual amount and the like in the primary stroke record, and judging that the primary stroke record is effective if the residual remote OBD data accounts for more than 80% of the primary stroke record data. The valid trip record data is valid data.
S1.3: and (3) screening remote OBD data with the engine temperature of more than 70 ℃ and the downstream exhaust temperature of SCR (Selective Catalytic Reduction) of more than 150 ℃ in the effective data, and after data cleaning, sequencing the remote OBD data in a time sequence positive sequence.
The step S2 specifically includes:
s2.1: in the process of vehicle running, short-stroke segments are widely used for extracting characteristic values of actual running conditions of the vehicle as a statistical unit for evaluating vehicle running. The method extracts the characteristics of the continuous 300s short-stroke segments, and clusters the short-stroke segments by adopting a mode of combining principal component analysis and cluster analysis. Clustering into three types of working condition segments according to the road traffic law and the PEMS test requirements, as shown in Table 1.
TABLE 1 Condition fragment Classification and interpretation
| Working condition sheet Segment of | Explanation of operating mode segments |
| High speed sheet Segment of | The average speed per hour in the short-stroke fragment 300s is greater than or equal to 70km/h, then the average speed per hour in the continuous fragment 300s is greater than or equal to 70km/h, and the standard deviation of the speed of the continuous fragment 600s is less than 10km/h; |
| suburb sheet Segment of | The average speed per hour in the short-stroke segment is more than or equal to 45km/h and less than 70km/h within 300s, then the average speed per hour in the continuous segment is more than or equal to 45km/h and less than 70km/h within 300s, and the average speed per hour is continuous for 600s The standard deviation of the segment speed is less than 10km/h; |
| city district slice Segment of | The average speed per hour in the short stroke segment is more than or equal to 15km/h and less than 45km/h within 300s, then the average speed per hour in the continuous segment is more than or equal to 15km/h and less than 45km/h within 300s, and the continuous segment is 600s The standard deviation of the segment speed is less than 10km/h. |
Average speed per hour
The calculation formula of (2) is as follows:
standard deviation of
The calculation formula of (2) is as follows:
d1 is a heavy diesel vehicle, D2 is a heavy natural gas vehicle, and k is a vehicle index.
In this embodiment, the speed and the standard deviation of each section of the vehicle should satisfy:
wherein, the first and the second end of the pipe are connected with each other,
and
respectively represent time of day
And time of day
The unit is s;
indicating that the vehicle is in a time period
The average speed in the unit of km/h;
indicating that the vehicle is
The unit of the instantaneous speed of the moment is km/h;
the unit is km/h which is the maximum limit value of the average speed; s =10, in km/h.
S2.2: screening the short stroke fragments before combining the short stroke fragments; the screening comprises primary screening and fine screening. The preliminary screening rule comprises an operation time rule, an acceleration rule, a speed rule and a constant speed proportion rule, the preliminary screening rule is formulated to delete abnormal segments and abnormal kinematic segments (such as GPS following, GPS drifting, data missing points and data mutation), and the movement time rule specifies that the length of each movement segment is greater than or equal to:
wherein, the first and the second end of the pipe are connected with each other,
in order to start and stop the vehicle for a long time(s),
the minimum segment length(s) is generally selected to be 5s, so that the integrity of data characteristics before and after screening can be ensured, and drift points in signals can be deleted.
And (3) obtaining candidate short-stroke fragments after primary screening, formulating a fine screening rule (see table 2) of the candidate short-stroke fragments by statistical analysis and by using WLTP rules, and further screening the short-stroke fragments.
TABLE 2 Fine Screen rules for candidate short run segment
Step S3 specifically includes:
s3.1: according to the specification of the PEMS test standard, working condition segments are combined according to the proportion of different working condition types in the whole test stroke to obtain combined segments; the number of the segments in each working condition in combination is shown in table 3.
TABLE 3 required number of working condition segments for different vehicle types
Wherein N2 represents a cargo vehicle having a maximum design total mass of over 3500kg, but not more than 12000 kg; n3 represents a cargo vehicle with a maximum design total mass exceeding 12000 kg; m2 represents a passenger car which comprises a driver seat, has no more than 9 seats and has the maximum total design mass of no more than 5000 kg; m3 represents a passenger car which comprises a driver seat, has no more than 9 seats and has the maximum total design mass of more than 5000 kg.
S3.2: recording a window with the average power of the window being larger than the power threshold of the engine in the combined segment i as an effective window, wherein the step length of the window is 1%, and the value of the power threshold of the engine meets the following conditions: the engine power threshold is greater than or equal to 10% of the maximum engine power and less than or equal to 20% of the maximum engine power, and the effective window can account for more than 50% of the total window. Window average power of combined segment i
The ratio calculation formula is:
in the formula:
in order to combine the end times of the segments i,
to combine the start times of the segments i,
is the cyclic work value of the engine in units of
；
Is the maximum power of the engine in
。
S3.3, calculating the power base window specific emission passing rate
And window effective point passing rate
: power base window specific emission pass rate
In the formula:
for the number of windows in the combined segment i required to pass NOx emissions,
is the total number of combined segment i windows.
Window effective point passing rate
In the formula:
for the number of SCR downstream NOx transient emissions data in combined segment i for which the SCR downstream NOx concentration is less than 600ppm,
is the total number of NOx transient emission data downstream of the SCR in the combined segment i. The SCR downstream NOx transient emission data is contained in the remote OBD data.
S3.4: vehicle emission compliance determination is performed. The passing rate of the effective points of the windows is judged according to the emission standard of the vehicle
And power-based window specific emission pass rate
At the same time determine when
And is and
when the NOx emission level of the vehicle reaches the standard, judging the NOx emission level of the vehicle to reach the standard; otherwise, the vehicle NOx emission level is determined to be out of compliance. And screening the vehicle models with the NOx emission level exceeding 50% in a natural month to serve as high-emission vehicle models.
And collecting remote OBD data samples to verify the accuracy of the OBD-AWM model.
Firstly, analyzing collected data samples, taking the NOx emission at the downstream of SCR as the real emission of a vehicle, taking the NOx emission at the upstream of SCR as the emission of a failure vehicle, simultaneously calculating emission factors, and calculating 200 data samples for the reason, wherein the number of the real emission samples of the vehicle is 100, and the number of the emission samples of the failure vehicle is 100. The sample distribution is shown in fig. 1.
Using the recognition accuracy rate based on existing sample data
And (4) evaluating the result:
in the formula, M is the number of correct samples identified after the OBD-AWM model is verified, N is the total number of the samples verified by the OBD-AWM model, and the higher the P value is, the better the OBD-AWM model is proved to be.
Since the SCR upstream stroke ratio emission is 8.3 to 8.9g/kWh and the SCR downstream stroke ratio emission is 0.32 to 0.60 g/kWh, the standard limit value is set to 0.69g/kWh, the recognition rate of the real emission of the vehicle is 0 and the recognition rate of the failed vehicle is 100%, so that the OBD-AWM model can recognize the failed vehicle.
The invention provides a heavy-duty vehicle emission evaluation method based on remote OBD data and a storage medium, wherein a remote OBD emission evaluation model (OBD-AWM model) based on a power-based window method is constructed according to the characteristics of the remote OBD data, and the OBD-AWM model can accurately identify high-emission vehicles and does not generate misjudgment on normal vehicles through verification.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein, and any reference signs in the claims are not intended to be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (3)
1. A heavy vehicle emission evaluation method comprises the following steps:
step S1: acquiring remote OBD data, and performing data preprocessing on the remote OBD data through a travel record in the remote OBD data to obtain effective data;
s2, selecting short-travel segments by using a short-time traffic flow dividing method, and clustering the short-travel segments into high-speed segments, suburban segments and urban segments;
and step S3: combining various short-stroke fragments according to a proportion to obtain combined fragments, evaluating the combined fragments through an OBD-AWM model constructed based on a power-based window method, and judging the emission compliance of the vehicle; the method specifically comprises the following steps:
marking the window with the window average power larger than the engine power threshold value in the combined segment i as an effective window, and marking the window average power of the combined segment i as the window
Comprises the following steps:
；
in order to combine the end times of the segments i,
to combine the start times of the segments i,
is the value of the cyclic work of the engine,
is the maximum power of the engine;
calculating the power-based window specific emission passing rate of the combined segment i
And window effective point passing rate
: power base window specific emission pass rate
；
To combine the number of valid windows in segment i required by NOx emissions,
the total number of the effective windows of the combined segment i;
window effective point passing rate
；
For the number of SCR downstream NOx transient emissions data for which the SCR downstream NOx concentration in combined segment i is less than 600ppm,
the total number of the data of the NOx instantaneous emission at the downstream of the SCR in the combined section i; each remote OBD data includes an SCR downstream NOx transient emission data;
when the temperature is higher than the set temperature
And is
Determining that the NOx emission level of the vehicle reaches the standard; otherwise, the NOx emission level of the vehicle is judged to be overproof; and screening the vehicle models with the NOx emission level exceeding 50% in a natural month to serve as high-emission vehicle models.
2. The method for evaluating the emission of heavy-duty vehicles according to claim 1, characterized in that: when the data preprocessing is performed on the remote OBD data in step S1, the method specifically includes:
s1.1: judging whether the vehicle is in a running state or a stopping state according to the engine speed and the coolant temperature in the remote OBD data, wherein the running state to the stopping state of the vehicle at one time is a travel record, and deleting the remote OBD data corresponding to the travel record with the time span of less than thirty minutes;
s1.2, screening effective data: continuous and non-repeated remote OBD data in the primary travel record are normal data, and if the normal data account for more than 80% of the primary travel record data, the primary travel record is an effective travel record; removing remote OBD data with data missing in the primary travel record, wherein if the remaining remote OBD data accounts for more than 80% of the data of the primary travel record, the primary travel record is an effective travel record; the effective travel record data is effective data;
s1.3: and screening remote OBD data with the engine temperature of more than 70 ℃ and the SCR downstream exhaust temperature of more than 150 ℃ in the effective data, and carrying out data cleaning and then arranging the data in a positive sequence according to a time sequence.
3. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, is characterized in that it carries out the method according to any one of claims 1-2 for evaluating the emission of heavy vehicles.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111598424A (en) * | 2020-05-07 | 2020-08-28 | 中汽研汽车检验中心（天津）有限公司 | Emission calculation method based on remote monitoring data of heavy-duty diesel vehicle |
| CN112486962A (en) * | 2020-11-23 | 2021-03-12 | 中汽研汽车检验中心（天津）有限公司 | Extraction and combination short segment calculation heavy-duty diesel vehicle NOxMethod of discharging |
| CN114991922A (en) * | 2022-05-30 | 2022-09-02 | 中国汽车工程研究院股份有限公司 | Real-time early warning method for exceeding NOx emission of vehicle |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111598424A (en) * | 2020-05-07 | 2020-08-28 | 中汽研汽车检验中心（天津）有限公司 | Emission calculation method based on remote monitoring data of heavy-duty diesel vehicle |
| CN112486962A (en) * | 2020-11-23 | 2021-03-12 | 中汽研汽车检验中心（天津）有限公司 | Extraction and combination short segment calculation heavy-duty diesel vehicle NOxMethod of discharging |
| CN114991922A (en) * | 2022-05-30 | 2022-09-02 | 中国汽车工程研究院股份有限公司 | Real-time early warning method for exceeding NOx emission of vehicle |
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