CN112486962A - Extraction and combination short segment calculation heavy-duty diesel vehicle NOxMethod of discharging - Google Patents

Extraction and combination short segment calculation heavy-duty diesel vehicle NOxMethod of discharging Download PDF

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CN112486962A
CN112486962A CN202011321154.2A CN202011321154A CN112486962A CN 112486962 A CN112486962 A CN 112486962A CN 202011321154 A CN202011321154 A CN 202011321154A CN 112486962 A CN112486962 A CN 112486962A
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vehicle
segments
emission
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vehicles
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甄凯
冯谦
贺可勋
杨邢子
邵文
韩艳艳
杨妍妍
刘保献
秦孔建
郭魁元
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China Automotive Technology and Research Center Co Ltd
CATARC Automotive Test Center Tianjin Co Ltd
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CATARC Automotive Test Center Tianjin Co Ltd
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Abstract

The invention provides a method for extracting combined short segments to calculate NOx emission of a heavy-duty diesel vehicle, which comprises the following steps of (1) acquiring data by using an OBD data acquisition terminal, recording vehicle static information on a platform, and cleaning the data; (2) cutting the fragments of the data set extracted by the platform data cleaning module, and extracting; (3) combining and sequencing the classified fragments in the working condition fragment extraction module; (4) the screening module judges the effectiveness and performs the combination of the working condition segments again; (5) calculating the NOx work base window method by normalizing the working condition segments; (6) and (3) an iteration module in the platform can repeat the calculation modules in the steps (4) and (5), and the vehicle NOx average window ratio emission condition calculated by the calculation module is screened to screen out high-emission vehicles and high-emission vehicle types. The invention discloses a method for extracting and combining short segments to calculate the NOx emission of a heavy-duty diesel vehicle, which aims to solve the problem that a remote monitoring platform lacks a method system for processing data to calculate the NOx emission of the heavy-duty diesel vehicle at present.

Description

Method for extracting combined short segments to calculate NOx emission of heavy-duty diesel vehicle
Technical Field
The invention belongs to the field of remote monitoring of heavy-duty diesel vehicles, and particularly relates to a method for extracting and combining short segments to calculate NOx emission of a heavy-duty diesel vehicle.
Background
At present, mobile interconnection, cloud computing and rapid development of big data urge the emergence of more large-scale data analysis application scenes such as Internet of things and Internet of vehicles. As a vehicle networking technology which is started in recent years, the traditional embedded geographic information system, the mobile internet and the modern network communication technology are fused, and the people-vehicle-road-network technology is organically connected together. The vehicle networking utilizes vehicle-mounted sensor technology (GPS vehicle-mounted terminal and OBD terminal), and location information, vehicle speed, engine operating condition, exhaust system state, fault information and the like in the vehicle driving process are collected in real time.
The environmental protection department in China requires the construction of a heavy vehicle remote emission supervision capacity enhancement area, and the remote emission supervision becomes an effective means and a necessary trend of the heavy vehicle emission supervision area. The remote emission supervision of heavy vehicles by adopting a remote online supervision system is clearly proposed in various policies and standard regulations in China:
(1) in an atmosphere control plan of 2017, an environmental protection management department proposes to strengthen environmental protection supervision on operating vehicles and actively pushes diesel vehicles to be additionally provided with remote supervision terminals with real-time diagnosis functions;
(2) in 12 months in 2017, the department of environmental protection issues technical policies for pollution control of motor vehicles to propose establishing a sound remote supervision system and strengthen the adoption of remote supervision technology for heavy vehicles in operation;
(3) in 12 months in 2017, in order to reduce the emission pollution of motor vehicles and improve the environmental quality, in combination with the actual situation of Beijing, DB11/1476 and 2017, namely emission limit and measurement method (stages IV and V of OBD) of exhaust pollutants of heavy-duty vehicles are formulated, and the heavy-duty vehicles are definitely proposed to be supervised by adopting a remote emission system;
(4) in 8 months in 2018, in order to implement the combined national market supervision and management headquarter of the environmental protection act of the people's republic of China and the atmospheric pollution prevention and control act of the people's republic of China, GB17691-2018 'pollutant emission limit and test method (sixth stage of China)' is published by the State administration of State market supervision and management, and the requirements that a remote emission supervision system must be adopted by six new-country vehicles (stage b) are stipulated.
At present, according to requirements, the national, local and environmental protection departments and heavy vehicle production enterprises need to build a remote emission monitoring platform for heavy vehicles. However, the data volume of the big data of the internet of vehicles is too large, and the data volume processed by a mature internet of vehicles platform only a single day is about 70 GB. However, the platform lacks an analysis system and a processing method for scientifically and efficiently processing the emission data of heavy vehicles, so that a large amount of data is accumulated in a database, and the true value of the data cannot be exerted. From the point of heavy-duty vehicle NOx emission, the aspects of emission, economy, environmental protection, dynamic optimization, engine quality improvement and the like of the whole vehicle product can be optimized through large data mining and analysis. At present, a set of mature emission supervision algorithms is urgently needed to provide necessary management support and finished vehicle quality judgment support for management departments and vehicle enterprises.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for calculating NOx emission of a heavy-duty diesel vehicle by extracting and combining short segments, so as to solve the problem that a remote monitoring platform lacks a method system for calculating NOx emission of a heavy-duty diesel vehicle by processing data at present; the problem that the real value of the big data of the Internet of vehicles cannot be exerted due to data redundancy caused by the fact that a large amount of data are accumulated by a platform is solved; in order to establish a method system for judging the NOx emission level of the heavy-duty diesel vehicle uniformly, fairly and in a normal mode, high-emission vehicles and high-emission vehicle types are screened.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a method for extracting and combining short segments to calculate NOx emission of a heavy-duty diesel vehicle comprises the following steps:
(1) the method comprises the steps that an OBD data acquisition terminal is used for acquiring data, the data are transmitted to a cloud monitoring platform at a transmission frequency of 1Hz and are stored, vehicle static information is put on record on the platform, and the platform stores the data and cleans the data into a data set which can be used for subsequent calculation according to a time sequence;
(2) the working condition segment extraction module in the platform can cut the data set extracted by the platform data cleaning module into segments and extract the segments according to the average speed of the segments and the standard deviation of the speed of the segments;
(3) the working condition fragment combination module in the platform can perform combination sequencing according to the classified fragments in the working condition fragment extraction module;
(4) a combined fragment screening module in the platform judges the effectiveness, abandons the normalized working condition fragments which are judged to be unqualified, and combines the working condition fragments again;
(5) an emission calculation module in the platform calculates the NOx power-based window method on the normalized working condition segments which meet the requirements and are selected by the combined segment screening module;
(6) and (3) the iteration module in the platform can repeat the calculation modules in the steps (4) and (5), error calibration is carried out by iteratively calculating the average specific emission of the vehicle, and the vehicle NOx average window specific emission condition calculated by the emission calculation module is screened to screen out high-emission vehicles and high-emission vehicle types.
The data collected in the step (1) comprise VIN of the diesel locomotive, vehicle speed, net output torque of the engine, friction torque of the engine, rotating speed of the engine, fuel flow of the engine, output value of a NOx sensor at the downstream of SCR, air inflow and temperature of coolant of the engine, and static information needing to be recorded comprises the following steps: VIN, engine type, engine rated power, engine reference torque, engine WHTC cycle power, vehicle quality, emission level, industry type, vehicle manufacturer, engine manufacturer, fuel type, aftertreatment mode, vehicle type, and vehicle type.
The data cleaning method is performed by referring to the calculation formula (1.1), and table 1 shows parameter items and symbol descriptions which need cleaning. And after the data are washed, selecting data with the temperature of the engine coolant being more than 70 ℃, and sorting the data in a positive sequence according to the time sequence of data collection.
itemi＝itemi,o×precisioni-biasi
Figure BDA0002792914030000031
In the formula:
item: cleaning each data value;
i: each data item that needs to be cleaned;
itemi,o: analyzing the original value of the data;
precisioni: the precision of each item of data;
biasi: offset of each item of data;
Figure BDA0002792914030000032
represents an invalid value "OxFF, OxFF" or "OxFF";
rangemin: represents the minimum value of the data range;
rangemax: representing the maximum value of the data range.
TABLE 1 cleaning data item and symbolic description
Figure BDA0002792914030000033
Figure BDA0002792914030000041
Figure BDA0002792914030000051
Carrying out data cleaning on OBD uploaded data according to a formula (1.1), and cleaning the data into an array which is arranged in a positive sequence along with time through a data set calculated by a calculation formula, wherein the array format is as follows:
Figure BDA0002792914030000052
Figure BDA0002792914030000061
the fragments extracted in the step (2) are divided into: high speed driving segments, suburban driving segments, and urban driving segments. After the engine information is cleaned, all data of a single vehicle in a continuous long time are selected to carry out operation of extracting working condition segments. The principle of the working condition segment extraction is to extract the running segment of the vehicle in a running stable state within continuous n seconds as far as possible and calculate the average speed and the standard deviation of the segment.
TABLE 2 Condition fragment Classification and interpretation
Figure BDA0002792914030000062
Figure BDA0002792914030000071
The average speed per hour of N1 and M1 vehicles in the high-speed driving segment is greater than 90km/h, and the average speed per hour is greater than 70km/h within N seconds except for N1 and M1 vehicles.
The judgment formula for whether the vehicles except M1 and N1 are high-speed driving segments is as follows:
Figure BDA0002792914030000072
the judgment formula for whether the M1 and N1 vehicles are high-speed driving segments is as follows:
Figure BDA0002792914030000073
in the formula:
D1: the vehicle types except M1 and N1 are collected, and k is a vehicle index;
D2: the vehicle models M1 and N1 are collected, and k is a vehicle index;
[ i, j ]: the time period from the moment i to the moment j, t is a time index (unit: s);
Figure BDA0002792914030000081
vehicle type k in time period [ i, j]Average speed of (unit: km/h);
Figure BDA0002792914030000082
the instantaneous speed of the vehicle with the vehicle type k at the moment t, (unit: km/h);
σ: standard deviation of speed over a continuous period of time;
s: a maximum value of standard deviation;
n: the length of time for which the vehicle is stably running (unit: s).
In the suburb driving segment, the average speed per hour of N1 and M1 vehicles is more than 60km/h and less than 90km/h, and the average speed per hour within N seconds of the outside of N1 and M1 vehicles is more than 45km/h and less than 70 km/h.
The judgment formula for whether the vehicles except M1 and N1 are high-speed driving segments is as follows:
Figure BDA0002792914030000083
the judgment formula for whether the M1 and N1 vehicles are high-speed driving segments is as follows:
Figure BDA0002792914030000084
the average speed per hour of the vehicles in the urban driving segment is more than 15km/h and less than 30km/h within n seconds,
the formula for judging whether the vehicle is in the high-speed running segment is as follows:
Figure BDA0002792914030000091
the combination mode of extracting the working condition fragments in the step (3) is as follows: the working condition segments of N1 and M1 vehicles (except vehicles executing GB18352.6 standard) sequentially comprise: 34% of urban roads, 33% of suburban roads and 33% of expressway; the working condition segments of N2, M2 and M3 vehicles (except urban vehicles) are sequentially as follows: 45% of urban roads, 25% of suburban roads and 30% of expressway; the working condition segments of the N3 type vehicles (except for urban vehicles) sequentially comprise: 20% of urban roads, 25% of suburban roads and 55% of expressway; the urban vehicle comprises the following working condition segments in sequence: 70% of urban roads and 30% of suburban roads; and arranging and combining the extracted working condition segments according to the sequence of urban areas, suburbs and high speed.
And (4) carrying out effective segmentation according to the normalized working condition segments in the step (4), wherein the calculation formula for calculating the window average power ratio is as follows:
Figure BDA0002792914030000092
in the formula:
Figure BDA0002792914030000093
a window average power ratio;
wref: the cyclic power value of the engine, kwh;
wrate: maximum power of the engine, kwh;
t2,i-t1,i: the difference, s, between the start time and the end time of the cyclic power window i.
Calculating the average power threshold of the window, wherein the calculation formula is as follows:
Figure BDA0002792914030000094
Figure BDA0002792914030000095
k∈[0.2,0.19,0.18,0.17,0.16,0.15,0.14,0.13,0.12,0.11,0.1]
in the formula:
Figure BDA0002792914030000101
the window average power ratio is not less than the number of k times of the maximum power;
k: a maximum power multiple set;
judging whether the effective window ratio meets the requirement, and if the effective window ratio is not less than the specified minimum power threshold and not less than 50%, executing the next step; if the condition is not met, the segment is discarded, and the other segments are selected for segment combination to carry out the normalization working condition.
The result in step (5) comprises: NOx effective point pass rate, NOx window pass rate, NOx average window specific emissions.
Compared with the prior art, the method for extracting and combining the short segments to calculate the NOx emission of the heavy-duty diesel vehicle has the following advantages:
the invention relates to a method for extracting and combining short segments to calculate NOx emission of a heavy-duty diesel vehicle, which efficiently extracts stable working conditions from disordered driving segments of the heavy-duty diesel vehicle and combines the stable working conditions into normalized working condition segments to calculate the emission of the heavy-duty diesel vehicle, and eliminates the influence of external factors (weather, temperature, road surface conditions and the like) on the emission of the heavy-duty diesel vehicle to the maximum extent by a mode of iteratively updating average specific emission. A heavy diesel vehicle emission calculation system with a remote monitoring platform uniformly considering the actual road emission level of the vehicle, such as data cleaning, data processing, working condition extraction, working condition normalization, emission calculation, emission judgment and iterative update, is established. The method has obvious effects and application values for evaluating the quality of the whole vehicle, screening high-emission vehicles and monitoring the emission degradation of the whole vehicle. Meanwhile, effective modes of emission monitoring, quality evaluation, performance evaluation and high-emission vehicle screening of the heavy-duty diesel vehicle are provided for vehicle enterprises and environment monitoring departments. The redundancy existing in the process of collecting a large amount of data of the heavy diesel vehicle is beneficially relieved, and a new thought and direction are provided for deep data mining and data analysis of the heavy diesel vehicle.
Drawings
FIG. 1 is a flow chart illustrating the computing process for a platform according to the present invention;
FIG. 2 is a speed graph of all driving data of a certain N1 vehicle within one month;
FIG. 3 is a combined segment graph formed by extracting all driving data speeds of a certain N1 vehicle within one month through working condition segments and combining;
FIG. 4 is a PEMS test vehicle speed profile;
FIG. 5 is a graph showing the calculation results of the PEMS test.
Detailed Description
Unless defined otherwise, technical terms used in the following examples have the same meanings as commonly understood by one of ordinary skill in the art to which the present invention belongs. The test reagents used in the following examples, unless otherwise specified, are all conventional biochemical reagents; the experimental methods are conventional methods unless otherwise specified.
The present invention will be described in detail with reference to the following examples and accompanying drawings.
1. The data cleaning method is performed by referring to the calculation formula (1), and table 1 shows parameter items and symbol descriptions which need to be cleaned. And after the data are washed, selecting data with the temperature of the engine coolant being more than 70 ℃, and sorting the data in a positive sequence according to the time sequence of data collection.
itemi＝itemi,o×precisioni-biasi
Figure BDA0002792914030000111
In the formula:
item: cleaning each data value;
i: each data item that needs to be cleaned;
itemi,o: analyzing the original value of the data;
precisioni: the precision of each item of data;
biasi: offset of each item of data;
Figure BDA0002792914030000112
represents an invalid value "OxFF, OxFF" or "OxFF";
rangemin: represents the minimum value of the data range;
rangemax: representing the maximum value of the data range.
TABLE 1 cleaning data item and symbolic description
Figure BDA0002792914030000113
Figure BDA0002792914030000121
Figure BDA0002792914030000131
Carrying out data cleaning on OBD uploaded data according to a formula (1.1), and cleaning the data into an array which is arranged in a positive sequence along with time through a data set calculated by a calculation formula, wherein the array format is as follows:
Figure BDA0002792914030000132
Figure BDA0002792914030000141
after the engine information is cleaned, all data of a single vehicle in a continuous long time are selected to carry out operation of extracting working condition segments. The principle of the working condition segment extraction is to extract the running segment of the vehicle in a running stable state within continuous n seconds as far as possible and calculate the average speed and the standard deviation of the segment.
TABLE 2 Condition fragment Classification and interpretation
Figure BDA0002792914030000142
Figure BDA0002792914030000151
Continuous n-second average velocity calculation formula:
Figure BDA0002792914030000152
continuous n-second speed standard deviation calculation formula:
Figure BDA0002792914030000153
for vehicles other than M1 and N1, the following requirements are met:
high speed segment
Figure BDA0002792914030000154
Suburb segment
Figure BDA0002792914030000161
Urban segment
Figure BDA0002792914030000162
The requirements for M1 and N1 vehicles are as follows:
high speed segment
Figure BDA0002792914030000163
Suburb segment
Figure BDA0002792914030000164
Urban segment
Figure BDA0002792914030000165
In the formula:
D1: the vehicle types except M1 and N1 are collected, and k is a vehicle index;
D2: the vehicle models M1 and N1 are collected, and k is a vehicle index;
[ i, j ]: the time period from the moment i to the moment j, t is a time index (unit: s);
Figure BDA0002792914030000171
vehicle type k in time period [ i, j]Average speed of (unit: km/h);
Figure BDA0002792914030000172
the instantaneous speed of the vehicle with the vehicle type k at the moment t, (unit: km/h);
σ: standard deviation of speed over a continuous period of time;
s: the maximum value of the standard deviation.
n: the length of time for which the vehicle is stably running (unit: s).
The working condition segments of N1 and M1 vehicles (except vehicles executing GB18352.6 standard) sequentially comprise: 34% of urban roads, 33% of suburban roads and 33% of expressway; the working condition segments of N2, M2 and M3 vehicles (except urban vehicles) are sequentially as follows: 45% of urban roads, 25% of suburban roads and 30% of expressway; the working condition segments of the N3 type vehicles (except for urban vehicles) sequentially comprise: 20% of urban roads, 25% of suburban roads and 55% of expressway; the urban vehicle comprises the following working condition segments in sequence: 70% of urban roads and 30% of suburban roads; and arranging and combining the extracted working condition segments according to the sequence of urban areas, suburbs and high speed.
The invention proposes that the number of the selected segments which take 300s as a working condition segment is as follows:
the number of the working condition segments of N1 and M1 vehicles (except vehicles executing GB18352.6 standard) is as follows: 6 (high speed): 6 (suburb): 6 (urban area); the number of the working condition segments of N2, M2 and M3 vehicles (except for urban vehicles) is as follows: 7 (urban area): 4 (suburb): 5 (high speed); the number of the working condition segments of N3 vehicles (except for urban vehicles) is as follows: 10 (high speed): 4 (suburb): 4 (urban area); the number of the segments of the urban vehicle under various working conditions is as follows: 14 (urban area): 6 (suburb).
Fig. 2 is a speed curve diagram of all driving data of a certain N1 vehicle within one month, and a combined segment formed by combining through condition segment extraction is shown in fig. 3. By extracting the stable segments, more stable working condition segments can be extracted and used for calculating the emission of the whole vehicle.
And carrying out effective segmentation on the normalized working condition segments, and recording windows of which the average power of the windows of the combined segments i is greater than 10-20% (1% is a step length) of the maximum power of the engine as effective windows by calculating. It is specified that the minimum power threshold cannot be less than 10% of the maximum power and the effective window fraction cannot be less than 50%. Calculating the average power ratio of the window, wherein the calculation formula is as follows:
Figure BDA0002792914030000173
in the formula:
Figure BDA0002792914030000174
window average power ratio
wref: the cyclic power value of the engine, kwh;
wrate: maximum power of the engine, kwh;
t2,i-t1,i: the difference, s, between the start time and the end time of the cyclic power window i.
Calculating the average power threshold of the window, wherein the calculation formula is as follows:
Figure BDA0002792914030000181
Figure BDA0002792914030000182
k∈[0.2,0.19,0.18,0.17,0.16,0.15,0.14,0.13,0.12,0.11,0.1]
in the formula:
Figure BDA0002792914030000183
the window average power ratio is not less than the number of k times of the maximum power;
k: maximum power multiple set
And then, judging whether the effective window occupation ratio meets the requirement or not. If the conditions that the specified minimum power threshold value (10%) is not less than and the effective window proportion is not less than 50% are met, executing the next step; if the condition is not met, the segment is discarded, and the other segments are selected for segment combination to carry out the normalization working condition.
And (3) performing PEMS (positron emission tomography) power basis method calculation on the normalized working condition fragment, wherein the calculation steps are as follows:
(1) peak clipping processing is carried out on NOx emission instantaneous value
Carrying out averaging processing on each extracted working condition segment by adopting a peak clipping mode of a continuous k second average sliding window, and calculating a formula:
Figure BDA0002792914030000184
in the formula:
k: number of consecutive average sliding windows, s;
i: current time, s;
scri: current moment SCR downstream mean, ppm.
(2) Calculating instantaneous mass of nitrogen oxides
Calculating the formula:
Figure BDA0002792914030000185
Figure BDA0002792914030000191
in the formula:
ρdiesel: density of diesel oil, kg/L;
uNOx: a ratio of density of the nitrogen oxide component in the exhaust gas to density of the exhaust gas, 0.001587;
ffre: data transmission frequency, Hz;
noxi: instantaneous emission mass of nitrogen oxides, g.
(3) Calculating instantaneous work of engine
Calculating the formula:
Figure BDA0002792914030000192
Figure BDA0002792914030000193
in the formula:
ffri: engine friction torque,%;
Tref: engine reference torque, N · m;
wi: engine instantaneous work, kwh.
(4) Calculating cyclic work average window specific emissions
Determining the ith averaging window period (t)2,t1) Determined by the following formula:
Figure BDA0002792914030000194
in the formula:
Figure BDA0002792914030000195
from the start to time tj,iInternal engine cycle work, kwh;
wref: circulating work of WHTC, kwh;
t1,i: the starting time of the window i, s;
t2,i: the termination time of window i, s;
t2,ishould be selected by the following formula:
Figure BDA0002792914030000196
in the formula:
Δ t: and the data sampling period is less than or equal to 1 s.
Calculating the cumulative emission of nitrogen oxides in the ith average window period:
Figure BDA0002792914030000201
in the formula:
mnox,i: cumulative nitrogen oxide emissions, g, over the cycle.
And (3) calculating the accumulated work of the engine in the ith average window period:
Figure BDA0002792914030000202
in the formula:
wengine,i: the engine accumulates work, kwh, over the period.
Calculating the specific emission of nitrogen oxides in the ith average window period:
Figure BDA0002792914030000203
in the formula:
enox: specific emission of nitrogen oxides, g/kwh.
(5) Calculating effective data point ratio
Figure BDA0002792914030000204
Figure BDA0002792914030000205
In the formula:
k: the number of SCR downstream NOx instantaneous emission values in the combined segment that are less than the limit j;
j: a set of limits;
n: the total number of the NOx instantaneous emissions at the downstream of the SCR in the combined segment;
i: the vehicle type 6 represents a vehicle of country vi, 5 represents a vehicle of country v, and 4 represents a vehicle of country iv.
(6) Calculating average specific emissions
Calculating the formula:
Figure BDA0002792914030000211
in the formula:
n: and combining the total number of segment average windows.
Since the normalized operating condition segment does not take into account the load of the vehicle and the actual driving condition of the vehicle, the average ratio emission is corrected in an iterative manner. The efficiency of iterative computation decreases with the number of short-stroke cutting segments, and the complexity of computation increases exponentially. Therefore, the calculation time for accurately solving the optimal segment problem is quite long, and a heuristic algorithm can quickly obtain a local optimal solution and has effectiveness for solving a large-scale problem. Therefore, heuristic algorithms such as genetic algorithm, annealing algorithm, etc. can be used to solve such optimal solution problem. The invention takes an annealing algorithm as an example, solves the problem of solving the optimal fragment, and the pseudo code is as follows:
wherein:
j (y): loss function in combining segments
And whtc: value of cycle work
Y (best): current optimal solution
Y (i): representing a current combined segment
Y (i + 1): representing new combined segments
r: for controlling the speed of cooling
T: the temperature of the system should be initially at a high temperature
T _ min: lower limit of temperature, if T reaches T _ min, stopping searching
while(T>T_min)
dE ═ loss _ squr (J (Y (i))// calculate composite fragment loss function
pE (j (y))// calculating the cumulative work of the combined fragments
if (dE <0.1& & pE >4 xwhtc & &7 xwhtc)// expression shift gave better solution, then always accept the shift
Y (best) ═ y (i); // update the current optimal solution
else
Y (i) ═ Y (i +1)// obtaining new combinatorial fragments
T ═ r × T; 0< r < 1. The larger r is, the slower the temperature reduction is; the smaller r is, the faster the temperature reduction is, and if r is too large, the possibility of searching the global optimal solution is higher, but the searching process is longer. If r is too small, the search process will be fast, but eventually a local optimum may be reached.
The calculated result output value is stored in relational database such as MySQL, taking table 3 as an example.
TABLE 3 calculation results and storage types
Figure BDA0002792914030000221
Figure BDA0002792914030000231
According to the static information of the vehicle, high-rank vehicles and high-rank vehicle types are statistically screened according to the vehicle types and the engine types, vehicles with abnormal emission (the abnormality is usually defined as 'arc points' which do not accord with the statistical principle) are selected as the high-rank vehicles, and the vehicle type with higher average specific emission is selected as the high-rank vehicle type. And carrying out visual display.
In order to verify the reliability of the algorithm, a test method of a real vehicle PEMS test is designed for verification. Selecting 3 sample vehicles of different vehicle types to respectively carry out real vehicle PEMS tests, and carrying out vehicle average specific emission measurement and calculation through a PEMS instrument by acquiring vehicle real-time transmission data. Then, the average specific emissions of the same 3 vehicle types (different in use industry) selected from the platform are calculated, and the emission analysis method of the invention is used for error comparison with the PEMS test.
The static information for 3 vehicles is shown in table 4:
TABLE 4 PEMS test vehicle basic information
Figure BDA0002792914030000232
The test route is carried out by three sections of city, suburb and high speed, and the test route and the speed distribution diagram are shown in the following 4-5 diagrams:
the PEMS test results are shown in the following table:
TABLE 5 PEMS test calculation results
Vehicle type number 1 2 6
Average specific emission (g/kwh) 6.74 5.19 0.34
The platform carries out matched vehicle models through a PEMS power-based algorithm, and matched vehicle models of 1 vehicle, 2 vehicles and 6 vehicles are obtained. Matching the error between the vehicle calculation value and the PEMS test calculation, wherein the average absolute error of the No. 1 vehicle is 6.7 percent; the average absolute error of vehicle 2 is 10.9%; the average absolute error of the vehicle No. 6 is 5.1%.
TABLE 6 platform calculation results
Figure BDA0002792914030000241
Through comparison test verification, the method provided by the invention verifies that the errors of the vehicles except the truck No. 1 and the truck No. 2 are larger, the average errors of the other vehicles are about 5%, and the errors are within an allowable range in consideration of the factors of the difference between the actual load of the vehicle actually running and the running road route and the test vehicle.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (9)

1. A method for extracting and combining short segments to calculate the NOx emission of a heavy-duty diesel vehicle is characterized by comprising the following steps: the method comprises the following steps:
(1) the method comprises the steps that an OBD data acquisition terminal is used for acquiring data, the data are transmitted to a cloud monitoring platform and stored, vehicle static information is recorded on the platform, and the platform stores the data and cleans the data into a data set which can be used for subsequent calculation according to a time sequence;
(2) the working condition segment extraction module in the platform can cut the data set extracted by the platform data cleaning module into segments and extract the segments according to the average speed of the segments and the standard deviation of the speed of the segments;
(3) the working condition fragment combination module in the platform can perform combination sequencing according to the classified fragments in the working condition fragment extraction module;
(4) a combined fragment screening module in the platform judges the effectiveness, abandons the normalized working condition fragments which are judged to be unqualified, and combines the working condition fragments again;
(5) an emission calculation module in the platform calculates the NOx power-based window method on the normalized working condition segments which meet the requirements and are selected by the combined segment screening module;
(6) and (3) the iteration module in the platform can repeat the calculation modules in the steps (4) and (5), error calibration is carried out by iteratively calculating the average specific emission of the vehicle, and the vehicle NOx average window specific emission condition calculated by the emission calculation module is screened to screen out high-emission vehicles and high-emission vehicle types.
2. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the claim 1, characterized in that: the data collected in the step (1) comprise VIN of the diesel locomotive, vehicle speed, net output torque of the engine, friction torque of the engine, rotating speed of the engine, fuel flow of the engine, output value of a NOx sensor at the downstream of SCR, air inflow and temperature of coolant of the engine, and static information needing to be recorded comprises the following steps: VIN, engine type, engine rated power, engine reference torque, engine WHTC cycle power, vehicle quality, emission level, industry type, vehicle manufacturer, engine manufacturer, fuel type, aftertreatment mode, vehicle type, and vehicle type.
3. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the claim 1, characterized in that: the fragments extracted in the step (2) are divided into: high speed driving segments, suburban driving segments, and urban driving segments.
4. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the extracted and combined short segment as claimed in claim 3, wherein: the average speed per hour of the vehicles of N1 and M1 in the high-speed driving segment is more than 90km/h, the average speed per hour is more than 70km/h within N seconds except for the vehicles of N1 and M1,
the judgment formula for whether the vehicles except M1 and N1 are high-speed driving segments is as follows:
Figure FDA0002792914020000021
the judgment formula for whether the M1 and N1 vehicles are high-speed driving segments is as follows:
Figure FDA0002792914020000022
in the formula:
D1: the vehicle types except M1 and N1 are collected, and k is a vehicle index;
D2: the vehicle models M1 and N1 are collected, and k is a vehicle index;
[i,j]: time of dayiThe time period to time j, t being the time index, (unit: s);
Figure FDA0002792914020000023
vehicle type k in time period [ i, j]Average speed of (unit: km/h);
Figure FDA0002792914020000024
the instantaneous speed of the vehicle with the vehicle type k at the moment t, (unit: km/h);
σ: standard deviation of speed over a continuous period of time;
s: a maximum value of standard deviation;
n: the length of time for which the vehicle is stably running (unit: s).
5. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the extracted and combined short segment as claimed in claim 3, wherein: in the suburb driving segment, the average speed per hour of N1 and M1 vehicles is more than 60km/h and less than 90km/h, except that the average speed per hour within N seconds of the outside of N1 and M1 vehicles is more than 45km/h and less than 70km/h,
the judgment formula for whether the vehicles except M1 and N1 are high-speed driving segments is as follows:
Figure FDA0002792914020000031
the judgment formula for whether the M1 and N1 vehicles are high-speed driving segments is as follows:
Figure FDA0002792914020000032
in the formula:
D1: the vehicle types except M1 and N1 are collected, and k is a vehicle index;
D2: the vehicle models M1 and N1 are collected, and k is a vehicle index;
[ i, j ]: the time period from the moment i to the moment j, t is a time index (unit: s);
Figure FDA0002792914020000033
vehicle type k in time period [ i, j]Average speed of (unit: km/h);
Figure FDA0002792914020000034
vehicle modelThe instantaneous speed of the vehicle at time t of k, (unit: km/h);
σ: standard deviation of speed over a continuous period of time;
s: a maximum value of standard deviation;
n: the length of time for which the vehicle is stably running (unit: s).
6. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the extracted and combined short segment as claimed in claim 3, wherein: the average speed per hour of the vehicles in the urban driving segment is more than 15km/h and less than 30km/h within n seconds,
the formula for judging whether the vehicle is in the high-speed running segment is as follows:
Figure FDA0002792914020000041
in the formula:
D1: the vehicle types except M1 and N1 are collected, and k is a vehicle index;
D2: the vehicle models M1 and N1 are collected, and k is a vehicle index;
[ i, j ]: the time period from the moment i to the moment j, t is a time index (unit: s);
Figure FDA0002792914020000042
vehicle type k in time period [ i, j]Average speed of (unit: km/h);
Figure FDA0002792914020000043
the instantaneous speed of the vehicle with the vehicle type k at the moment t, (unit: km/h);
σ: standard deviation of speed over a continuous period of time;
s: a maximum value of standard deviation;
n: the length of time for which the vehicle is stably running (unit: s).
7. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the claim 1, characterized in that: the combination mode of extracting the working condition fragments in the step (3) is as follows: the working condition segments of the N1 and M1 vehicles sequentially comprise: 34% of urban roads, 33% of suburban roads and 33% of expressway; the working condition segments of the N2, M2 and M3 vehicles sequentially comprise: 45% of urban roads, 25% of suburban roads and 30% of expressway; the N3 vehicle working condition segments sequentially comprise: 20% of urban roads, 25% of suburban roads and 55% of expressway; the urban vehicle comprises the following working condition segments in sequence: 70% of urban roads and 30% of suburban roads; and arranging and combining the extracted working condition segments according to the sequence of urban areas, suburbs and high speed.
8. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the claim 1, characterized in that: and (4) carrying out effective segmentation according to the normalized working condition segments in the step (4), wherein the calculation formula for calculating the window average power ratio is as follows:
Figure FDA0002792914020000044
in the formula:
Figure FDA0002792914020000051
a window average power ratio;
wref: the cyclic power value of the engine, kwh;
wrate: maximum power of the engine, kwh;
t2,i-t1,i: the difference, s, between the start time and the end time of the cyclic power window i.
Calculating the average power threshold of the window, wherein the calculation formula is as follows:
Figure FDA0002792914020000052
Figure FDA0002792914020000053
k∈[0.2,0.19,0.18,0.17,0.16,0.15,0.14,0.13,0.12,0.11,0.1]
in the formula:
Figure FDA0002792914020000054
the window average power ratio is not less than the number of k times of the maximum power;
k: a maximum power multiple set;
judging whether the effective window ratio meets the requirement, and if the effective window ratio is not less than the specified minimum power threshold and not less than 50%, executing the next step; if the condition is not met, the segment is discarded, and the other segments are selected for segment combination to carry out the normalization working condition.
9. The method for calculating the NOx emission of the heavy-duty diesel vehicle according to the claim 1, characterized in that: the result in step (5) comprises: NOx effective point pass rate, NOx window pass rate, NOx average window specific emissions.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114048639A (en) * 2022-01-13 2022-02-15 天津大学 Monitoring NO of in-use vehicle using network transmitted dataxEmission data processing method
CN114910611A (en) * 2022-05-19 2022-08-16 北京市生态环境监测中心 Detection system and detection method suitable for remote emission monitoring system of motor vehicle
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
CN115796720A (en) * 2023-01-19 2023-03-14 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) Heavy vehicle emission evaluation method and storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109782650A (en) * 2018-12-03 2019-05-21 中国汽车技术研究中心有限公司 A kind of mounted remote monitor terminal based on multiple sensors
CN110827444A (en) * 2019-11-06 2020-02-21 清华大学 Heavy vehicle emission factor obtaining method suitable for OBD remote emission monitoring data
CN111310280A (en) * 2020-02-27 2020-06-19 杭州电子科技大学 Method for extracting automobile kinematic segments and synthesizing working condition diagram
CN111537412A (en) * 2020-05-08 2020-08-14 重庆超力高科技股份有限公司 Emission monitoring device, system and method
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
CN111639683A (en) * 2020-05-14 2020-09-08 上海工程技术大学 Method for evaluating nitrogen oxide emission grade of diesel vehicle based on OBD technology

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109782650A (en) * 2018-12-03 2019-05-21 中国汽车技术研究中心有限公司 A kind of mounted remote monitor terminal based on multiple sensors
CN110827444A (en) * 2019-11-06 2020-02-21 清华大学 Heavy vehicle emission factor obtaining method suitable for OBD remote emission monitoring data
CN111310280A (en) * 2020-02-27 2020-06-19 杭州电子科技大学 Method for extracting automobile kinematic segments and synthesizing working condition diagram
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
CN111537412A (en) * 2020-05-08 2020-08-14 重庆超力高科技股份有限公司 Emission monitoring device, system and method
CN111639683A (en) * 2020-05-14 2020-09-08 上海工程技术大学 Method for evaluating nitrogen oxide emission grade of diesel vehicle based on OBD technology

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114048639A (en) * 2022-01-13 2022-02-15 天津大学 Monitoring NO of in-use vehicle using network transmitted dataxEmission data processing method
CN114048639B (en) * 2022-01-13 2022-04-22 天津大学 Monitoring NO of in-use vehicle using network transmitted dataxEmission data processing method
CN114910611A (en) * 2022-05-19 2022-08-16 北京市生态环境监测中心 Detection system and detection method suitable for remote emission monitoring system of motor vehicle
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
CN114991922B (en) * 2022-05-30 2024-01-23 中国汽车工程研究院股份有限公司 Real-time early warning method for exceeding of NOx emission of vehicle
CN115796720A (en) * 2023-01-19 2023-03-14 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) Heavy vehicle emission evaluation method and storage medium

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