CN110827444A - Heavy vehicle emission factor obtaining method suitable for OBD remote emission monitoring data - Google Patents

Heavy vehicle emission factor obtaining method suitable for OBD remote emission monitoring data Download PDF

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CN110827444A
CN110827444A CN201911075589.0A CN201911075589A CN110827444A CN 110827444 A CN110827444 A CN 110827444A CN 201911075589 A CN201911075589 A CN 201911075589A CN 110827444 A CN110827444 A CN 110827444A
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data
obd
pems
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emission
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CN110827444B (en
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吴烨
赵昢
张少君
何立强
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Tsinghua University
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    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0808—Diagnosing performance data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/90335—Query processing
    • G06F16/90348—Query processing by searching ordered data, e.g. alpha-numerically ordered data
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G07C5/0841—Registering performance data

Abstract

The invention provides a heavy vehicle emission factor acquisition method suitable for OBD remote emission monitoring data, which aims to solve the technical problem that an effective method is not available at present, and the OBD remote online monitoring data is used for acquiring a vehicle pollutant emission factor, so that the application of the OBD remote online monitoring data in the supervision field is still in an exploration stage.

Description

Heavy vehicle emission factor obtaining method suitable for OBD remote emission monitoring data
Technical Field
The invention relates to a heavy vehicle emission factor acquisition method suitable for OBD remote emission monitoring data.
Background
In the heavy-duty diesel vehicle pollutant emission limits and measurement methods (sixth stage of china), engine bench testing and PEMS (on-board emission testing) have become regulatory emission testing methods. The engine bench test is easy to control the test working condition and the test condition, the result repeatability is good, but the emission characteristic of the heavy-duty automobile when the heavy-duty automobile runs on the actual road cannot be reflected. The PEMS test can accurately evaluate the actual emission condition of a single road, but the PEMS test has many equipment components, is complex to operate and consumes time and labor. Aiming at the increasingly prominent real road emission supervision requirement of heavy vehicles, OBD (on-board diagnostics) remote online monitoring becomes a research hotspot in the field of domestic and foreign traffic supervision.
Today, the OBD agreement on heavy vehicles is basically agreed internationally, and the related vehicle monitoring sensor (such as NO) is usedXSensor, O2Sensors, temperature sensors, etc.) have matured. The real-time running state and the tail gas emission condition of the vehicle are recorded and stored one by the electric signal returned by the built-in sensor of the vehicle in real time, and the full life cycle emission of the heavy vehicle can be effectively monitored.
The OBD remote on-line monitoring terminal can obtain heavy vehicle data at present and mainly comprises the following fields:
1. time, yyyy-mm-ddhh mm: ss;
2. speed, km/h;
3. mass air intake flow (MAF), kg/h;
4. an engine maximum reference torque (Nm);
5. engine Net output Torque (as a percentage of the engine's maximum reference Torque,%)
6. Engine friction torque (as a percentage of engine maximum reference torque,%);
7. engine speed, rpm;
8. engine fuel flow, L/h;
9. the balance of the reactant (urea)%;
10. a vehicle ID;
11. atmospheric pressure, kPa;
12. NOx concentration upstream of a Selective Catalytic Reduction (SCR) device, ppm;
SCR downstream NOx concentration, ppm;
SCR inlet temperature, deg.c;
SCR outlet temperature, deg.C;
16. diesel particulate trap (DPF) differential pressure, kPa;
17. location state, longitude and latitude;
18. accumulating mileage, km,;
19. engine coolant temperature, deg.C;
20. liquid level of oil tank,%.
The second-by-second emission of the heavy vehicle can be calculated by using the second-by-second OBD remote online monitoring data, the emission condition of the single vehicle is evaluated, and data support is provided for emission characteristics and emission supervision of the heavy vehicle under the actual driving condition. However, due to the reasons that the sensor fails or a fault occurs in the data transmission process, and the like, the OBD online monitoring data transmitted remotely often has a series of data quality problems, such as missing of key data fields (such as mass air intake flow MAF, speed, and the like), reading errors, and a logical relationship among different data fields is not established, so that the OBD online monitoring data cannot be directly used for vehicle pollutant emission test calculation; in addition, no effective method for acquiring the vehicle pollutant emission factor by using the OBD remote online monitoring data exists at present, so that the application of the OBD remote online monitoring data in the supervision field is still in an exploration stage.
Disclosure of Invention
The invention provides a heavy vehicle emission factor acquisition method suitable for OBD remote emission monitoring data, and aims to solve the technical problem that an effective method for acquiring vehicle pollutant emission factors by using OBD remote online monitoring data to cause the application of the OBD remote online monitoring data in the supervision field to be in an exploration stage.
The technical solution of the invention is as follows:
the method for acquiring the emission factor of the heavy vehicle suitable for OBD remote emission monitoring data is characterized by comprising the following steps of:
firstly, judging the quality of OBD remote online monitoring data;
step 1, trip event division:
1.1) data sorting:
sequencing all data according to a time field sequence;
1.2) identifying a parking event:
if the time interval between two adjacent data is greater than 120s and the GPS positioning state changes from the state of GPS positioning, no GPS positioning and GPS positioning, the parking event is considered to occur;
1.3) dividing travel events:
sequentially extracting travel data between two adjacent parking events, and defining the travel data between the two adjacent parking events as a primary travel event;
step 2, time exception handling:
2.1) processing of interval readings:
respectively counting the occurrence proportion of interval readings in each trip event,
if the proportion of the interval readings in a certain trip event exceeds 30% of the total data volume of the trip event, the data is lost too much, the trip event is considered to be invalid, and the data of the trip event is considered to be invalid data;
if the proportion of interval readings in a certain trip event does not exceed 30% of the total data volume of the trip event, single data with the time interval being greater than 1s before and after the trip event is regarded as invalid data;
the interval reading refers to two adjacent data with the time interval being more than 1s in a single trip event;
2.2) processing of time coincidence data:
if the reading time of the front data and the back data is the same, only the data which is most close to the front data is reserved;
step 3, data exception handling:
3.1) judging data abnormity according to the following steps:
3.1.1) if a certain field is empty or exceeds the threshold value, the field is considered as abnormal data; the out-of-limit value refers to a reading exceeding a value range specified in annex Q of emission limits of pollutants for heavy-duty diesel vehicles and a measurement method (sixth stage of China);
3.1.2) if a certain field continuously generates the same internal value in the boundary for a long time, the field is considered as abnormal data;
3.2) processing method of abnormal data:
respectively counting the abnormal proportion of each field data in a single trip event,
if the proportion of the single-field abnormal value in the total data volume of the trip event exceeds 30%, the data of the trip event is considered invalid;
if the abnormal data situation continuously occurs to exceed 5% of the total data volume in a certain trip event, the trip event data is considered invalid and is not included in the emission result evaluation process;
if the proportion of the abnormal value of a certain single word segment in the travel event in the total number of the travel event does not exceed 30%, the abnormal field is considered invalid;
secondly, correcting data;
1) carrying out an OBD-PEMS synchronous experiment:
carrying out a PEMS (routine Per-Performance measurement and monitoring system) experiment on an actual road aiming at a heavy-duty vehicle, measuring and collecting the speed, the engine rotating speed, the net output torque of an engine, the fuel flow of the engine, the concentration of NOx at the downstream of SCR (selective catalytic reduction) and the instantaneous exhaust mass flow in the driving process of the heavy-duty vehicle, and simultaneously collecting OBD (on-board diagnostics) remote online monitoring data of the vehicle in the same time;
2) PEMS data alignment:
aligning PEMS data according to relevant regulations in annex K in pollutant emission Limit and measurement method for heavy-duty diesel vehicles (sixth stage of China);
3) OBD-PEMS data time alignment:
adjusting the front-back alignment mode of the OBD remote online monitoring data and the PEMS data by comparing common data fields of the OBD remote online monitoring data and the PEMS data, so that the correlation degree of the selected alignment variables is maximum;
4) and (3) segmented checking:
4.1) dividing the OBD remote online monitoring data and the PEMS data into a plurality of kilosecond data groups by taking the aligned PEMS time axis as reference and taking 1000s as time step;
4.2) for a single kilosecond data set, taking 1s as a time window, integrally moving the OBD remote online monitoring data set within a range of 10s before and after the corresponding PEMS time, calculating the Pearson correlation coefficient of the PEMS and the moved OBD remote online monitoring data set, and taking the position with the maximum Pearson correlation coefficient as the final alignment position of the OBD remote online monitoring data set and the PEMS data in the kilosecond data set;
4.3) data adjustment:
4.3.1) if some PEMS data does not have corresponding OBD remote online monitoring data, the part of PEMS data is not included in the subsequent data correction process;
4.3.2) if the OBD remote online monitoring data corresponding to a certain PEMS data is not unique, only retaining the data with the earliest time sequence in the OBD remote online monitoring data corresponding to the part of PEMS data;
5) and (3) correcting a NOx volume concentration field in OBD remote online monitoring data:
5.1) data packet:
dividing the OBD remote on-line monitoring data into three groups of low NOx volume concentration, medium NOx volume concentration and high NOx volume concentration;
the low NOx volume concentration refers to a volume concentration at which a NOx volume concentration reading downstream of the SCR is less than or equal to 100 ppm;
the medium NOx volume concentration refers to the volume concentration of NOx in the downstream of SCR, wherein the volume concentration reading of NOx is more than 100ppm and less than or equal to 1000 ppm;
the high NOx volume concentration refers to a volume concentration at which the SCR downstream NOx volume concentration reading is greater than 1000 ppm;
5.2) determining an adjusting coefficient:
respectively carrying out remote online monitoring on each single group of OBD divided in the step 5.1) to obtain the volume concentration of NOx in the PEMS data
Figure BDA0002262331330000051
OBD remote on-line monitoring of NOx volumetric concentration in data for target value adjustmentDetermining linear equations for independent variables using least squaresSingle adjustment in (1)Integer coefficient αxAnd βx；
Wherein:
x represents a group, and x is 1,2,3 represent high, medium, and low concentration groups, respectively;
t represents time;
5.3) calculating the adjusted volume concentration of NOx
Figure BDA0002262331330000064
6) And correcting an engine fuel flow field in the OBD remote emission monitoring data:
6.1) judging idle speed:
if the speed field is 0 and the rotating speed of the engine is less than or equal to the idle rotating speed, the vehicle is considered to be in an idle state at the moment, and the vehicle directly enters the idle group d in 6.3);
6.2) calculating the acceleration of the tested vehicle:
single OBD data calculation acceleration atThe method comprises the following steps:
a. if the time difference between a certain OBD data and the adjacent available previous OBD data is 1s, the time difference is
Figure BDA0002262331330000065
Wherein v ist、vt-1Respectively reading the speed field in the OBD data and the previous OBD data, wherein the unit is km/h; a istHas the unit of m/s2(ii) a b. If the time difference between a certain OBD data and the adjacent previous OBD data is more than 1s, and the time difference between the certain OBD data and the adjacent subsequent OBD data is 1s, the acceleration of the certain OBD data adopts the acceleration in the adjacent subsequent OBD data;
c. if the time difference between a certain piece of OBD data and the adjacent previous piece of OBD data is greater than 1s, and the time difference between the certain piece of OBD data and the adjacent subsequent piece of OBD data is also greater than 1s, the certain piece of OBD data is no longer used for fuel flow calculation in the subsequent step 6.5);
6.3) data packet:
according to the GB17691-2018, the acceleration is taken as a classification variable, and the OBD remote online monitoring data with the calculated acceleration is classified as follows:
a. and (3) an acceleration group: acceleration of 0.1m/s or more2；
b. A deceleration group: acceleration of less than or equal to-0.1 m/s2；
c. Uniform speed group: acceleration is-0.1 m/s2～0.1m/s2To (c) to (d);
d. an idle group: 6.1) the idle part judged in the step (b);
6.4) determining an adjusting coefficient:
for each single set of OBD remote online monitoring data divided in step 6.3), engine fuel flow in PEMS data
Figure BDA0002262331330000071
OBD remote on-line monitoring of engine fuel flow in data as an adjustment target
Figure BDA0002262331330000072
Determining a linear formula by means of least squares as independent variables
Figure BDA0002262331330000073
Single adjustment coefficient gamma inxAnd deltax；
Wherein:
x represents a group, and x represents an acceleration group, a deceleration group, a constant speed group and an idle group, c.d represents a group;
t represents time;
6.5) calculating the adjusted Engine Fuel flow
Figure BDA0002262331330000074
The third step: acquiring an emission factor;
step 1, acquiring instantaneous exhaust mass flow through indirect calculation or data fitting based on OBD remote online monitoring data;
step 2, calculating NO by directly utilizing engine operation parametersXEmission factor, or NO based on specific fuel consumption of the engineXAn emission factor.
Further, in the third step, step 1, the instantaneous exhaust mass flow is calculated according to the following formula:
Figure BDA0002262331330000076
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
Figure BDA0002262331330000077
the mass flow MAF, kg/h of air inlet air read by the OBD is obtained;
engine fuel volumetric flow, L/h, read for OBD;
ρfthe density of the fuel used in the engine, kg/L.
Or, in the third step, the instantaneous exhaust mass flow in the step 1 is calculated according to the following formula:
Figure BDA0002262331330000081
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
Figure BDA0002262331330000082
engine fuel volumetric flow, L/h, read for OBD;
x, y, z are the molar ratio of carbon to carbon in the fuel (C/C), the molar ratio of hydrogen to carbon (H/C), and the molar ratio of oxygen to carbon (O/C), respectively; according to the regulation of GB17691-2018, the diesel oil is CH1.86O0.006LPG is CH2.525Natural gas is CH4；
Figure BDA0002262331330000083
O in exhaust gas for OBD reading2Volume concentration of (d)%;
ρairis the density of ambient air at 0 ℃ and 101.3kPa, and has a value of 1.293kg/m3；
ρexhIn terms of exhaust gas density, in kg/m3According to GB17691-2018, the exhaust density of heavy-duty vehicles burning diesel oil is 1.2943kg/m3The exhaust density of the heavy-duty vehicle burning natural gas is 1.2661kg/m3The density of the heavy vehicle exhaust gas burning LPG is 1.2811kg/m3。
Or, the method for acquiring the instantaneous exhaust mass flow in the third step 1 comprises the following steps:
firstly, carrying out one or more times of fitting by using the instantaneous rotating speed and the instantaneous exhaust flow of the engine obtained by a bench or PEMS test to obtain a fitting coefficient:
Figure BDA0002262331330000084
in the formula:
Q'exhthe instantaneous exhaust flow obtained by bench or PEMS test is kg/h;
EnS' is the instantaneous engine speed, rpm, obtained from bench or PEMS testing;
ai、bifitting coefficients obtained for the ith bench or PEMS test;
then, the fitting coefficient a obtained from the previous stepi、biAnd estimating the instantaneous exhaust mass flow by the engine speed in the OBD remote online monitoring data:
Figure BDA0002262331330000091
in the formula:
Qexhfor instantaneous exhaustMass flow, kg/h;
EnSOBDmonitoring the engine speed, rpm in the data for OBD remote online;
n is the total number of the bench-OBD synchronous tests or the total number of the PEMS-OBD synchronous tests, and n is more than or equal to 1; the synchronous test is to obtain OBD remote online monitoring data while a vehicle is tested by a rack or a PEMS.
Further, in the third step, step 2, NO is calculated directly using the engine operating parametersXThe emission factors are specifically:
Figure BDA0002262331330000092
in the formula:
is NOXEmission factor, g/kWh;
t1、t2respectively the starting time and the ending time of a single trip, s;
Figure BDA0002262331330000094
SCR downstream NO for OBD readingXVolume concentration, ppm;
EnTOBDan instantaneous net engine output torque, Nm, read for or calculated based on OBD read data;
Qexhis the instantaneous exhaust mass flow, kg/h;
EnSOBDthe instantaneous rotating speed of the engine read by the OBD is r/min;
definition of the single trip event:
defining travel data between two adjacent parking events as a single travel event; and if the time interval between two adjacent data is more than 120s and the GPS positioning state has the state change of 'GPS positioning-no GPS positioning-GPS positioning', the parking event is considered to occur.
Alternatively, the NO is calculated in the third step, step 2, on the basis of the fuel consumptionXEmission factorThe specific calculation formula of (A) is as follows:
in the formula:
BSFC is the specific oil consumption of the engine;
Figure BDA0002262331330000102
SCR downstream NO for OBD readingXVolume concentration, ppm;
Figure BDA0002262331330000103
engine fuel volumetric flow, L/h, read for OBD;
t1、t2respectively the starting time and the ending time of a single trip event, s;
definition of the single trip event:
defining travel data between two adjacent parking events as a single travel event; and if the time interval between two adjacent data is more than 120s and the GPS positioning state has the state change of 'GPS positioning-no GPS positioning-GPS positioning', the parking event is considered to occur.
The invention has the advantages that:
1. the invention establishes the calculation of the NO of the heavy vehicle based on the OBD remote online monitoring dataXThe method of the emission result can effectively support the remote supervision of the actual road emission of the heavy-duty vehicle.
2. The invention provides an algorithm for calculating the exhaust flow based on the engine rotating speed, the exhaust flow is obtained without air intake flow and engine fuel flow, and the availability of OBD remote emission monitoring data is greatly improved.
3. Before the emission factor is calculated, OBD remote on-line monitoring data are corrected, and NO of heavy vehicles is improvedXThe accuracy of the calculation of the emission factor.
4. The method summarizes typical problems and an identification method existing in the conventional OBD remote emission monitoring data, can efficiently and quickly identify problematic parts in the original data, is an effective solution for the typical problems, is simple to use, obviously improves the quality of the OBD remote emission monitoring data by further adjusting the data, is beneficial to greatly improving the accuracy and reliability of the method for evaluating the emission of pollutants of a single vehicle based on the OBD remote emission monitoring data, and lays a foundation for the application of the method in traffic supervision.
5. The invention provides an effective means for detecting the quality of OBD remote emission monitoring data, namely an OBD-PEMS synchronization experiment (the OBD remote online monitoring data is obtained while a vehicle is subjected to PEMS test, the same parameters are measured by using PEMS and OBD) in a data correction link, and provides a complete method for summarizing deviation rules (namely single adjustment coefficients) of the OBD remote emission monitoring data.
6. The invention effectively solves NOXThe sensor mass may have an effect on the heavy vehicle emissions assessment.
Detailed Description
The invention provides a heavy vehicle tail gas NOx emission factor obtaining method based on OBD monitoring data, which comprises the following steps:
firstly, determining the quality of OBD remote online monitoring data:
step 1, dividing travel events;
one trip event is defined as the part between two adjacent parking events, and the trip event can be divided according to the following steps:
1.1) data sorting:
sequencing all data according to the sequence of the time fields from early to late;
1.2) identifying a parking event:
if the time interval between two adjacent data is more than 120s and the GPS positioning state has the state change of 'GPS positioning-no GPS positioning-GPS positioning', the parking can be judged to occur;
1.3) dividing travel events:
after all parking events are identified, sequentially extracting travel data between two adjacent parking events, and defining the travel data between every two adjacent parking events as one travel event respectively; performing subsequent quality judgment based on the divided single trip event;
step 2, time exception handling;
under normal conditions, the OBD data records vehicle running condition data one second by one, so that the situation is abnormal when a time field is in a non-second-by-second situation, and the time in a travel event needs to be adjusted; the exception of the time field mainly includes the following cases:
2.1) processing of interval readings;
two adjacent data with the time interval larger than 1s in a single trip event are considered as interval readings;
respectively counting the occurrence proportion of interval readings in each single trip event,
if the proportion of the interval readings in a certain trip event exceeds 30% of the total data volume of the trip event (30% is taken as a judgment threshold value, so that the retained data can ensure the accuracy of the calculation of the subsequent pollutant emission factor), the data is lost too much, the trip event is regarded as invalid, and the data of the trip event is not subsequently used for the pollutant emission test calculation;
if the proportion of interval readings in a certain trip event does not exceed 30% of the total data volume of the trip event, only single data with time intervals greater than 1s before and after the certain trip event is processed, and the single data is regarded as invalid data, namely the invalid data is not used for pollutant emission test calculation in the following process;
2.2) processing time coincidence data;
the time coincidence refers to the situation that the reading times of the two data are completely the same, at the moment, only the most previous data is reserved, and redundant simultaneous data are deleted;
step 3, data exception handling;
the data is judged to be abnormal under the following two conditions:
3.1) the field is empty or exceeds the threshold value;
the out-of-limit value refers to a reading exceeding the value range specified in annex Q of emission limit of pollutants for heavy-duty diesel vehicles and a measurement method (sixth stage of China);
3.2) the field continuously appears the same internal value for a long time (more than or equal to 15 s);
for the speed field, when the long-time reading is 0, firstly judging whether the vehicle is a parking event, if the vehicle is not the parking event, further judging the engine speed,
if the rotating speed of the engine is less than or equal to the idling rotating speed, the motor vehicle is in an idling state at the moment, and the condition is normal;
if the rotating speed of the engine is greater than the idling rotating speed, the speed sensor is considered to be out of order at the moment, and the speed calculated by GPS positioning needs to be used for replacing the ECU speed; if the OBD speed and the GPS positioning state are abnormal at the same time, the vehicle state at the moment cannot be determined, the data is invalid, and the data is not used for pollutant emission test calculation subsequently;
the processing method of the abnormal data comprises the following steps:
respectively counting the abnormal proportions of time, speed, engine speed, SCR downstream NOx and engine fuel flow field data in a single trip event,
if the proportion of the single-field abnormal value in the total data volume of the trip event exceeds 30%, the data of the trip event is considered invalid;
if abnormal conditions of continuous data occur in a certain trip event, whether each section of continuously occurring abnormal data exceeds 5% of the total data volume of the trip event or not is judged, and if at least one section of continuously occurring abnormal data exceeds 5% of the total data volume of the trip event, the trip event data is considered invalid and is not included in the discharge result evaluation process;
and if the proportion of the abnormal value of a certain single-field in the travel event in the total number of the travel event does not exceed 30%, the abnormal field is considered invalid.
Step two, data correction:
step 1, carrying out an OBD-PEMS synchronous experiment:
the method is characterized in that an actual road PEMS experiment (PEMS is a motor vehicle real-time emission testing technology with high internationally recognized accuracy) is carried out on a heavy vehicle and a single vehicle, the speed, the engine rotating speed, the net output torque of an engine, the engine fuel flow vehicle operation parameters, the concentration of NOx at the downstream of SCR and the instantaneous exhaust mass flow in the driving process of the heavy vehicle are measured and collected, and meanwhile OBD remote online monitoring data of the vehicle in the same time period are collected.
Step 2, PEMS data alignment:
aligning PEMS data according to relevant regulations in annex K in pollutant emission Limit and measurement method for heavy-duty diesel vehicles (sixth stage of China);
step 3, time alignment of OBD-PEMS data:
due to different data acquisition instruments, time axes adopted by the OBD remote online monitoring data and the PEMS data recording may not be completely consistent, and there is a possibility of time offset, so that data time alignment needs to be performed according to the following method:
3.1) selecting common fields (such as speed, engine speed, net engine output torque and the like) in the OBD and PEMS data as preliminary alignment variables according to a time axis;
and 3.2) integrally moving the OBD remote online monitoring data within the range of front and back 20s by taking the PEMS time axis aligned in the step 2 as a reference and 1s as a time window, so that the Pearson's R correlation coefficient of the selected alignment variable is maximized, and at the moment, the OBD-PEMS data time alignment is realized.
Step 4, segmented inspection:
since the reading frequency of the on-board OBD sensor is unstable, and there is a possibility of time deviation after accumulation, after the OBD-PEMS data is time-aligned, the following steps are required to be carried out for segment checking:
4.1) dividing OBD remote online monitoring data and PEMS data into a plurality of kilosecond data groups by taking a PEMS time axis after OBD-PEMS data time alignment as reference and taking 1000s as a time step, and sequentially carrying out subsequent alignment check on a single kilosecond data group;
4.2) for a single kilosecond data set, taking 1s as a time window, integrally moving the OBD remote online monitoring data set within a range of 10s before and after the corresponding PEMS time, calculating a Pearson's R correlation coefficient of the PEMS and the moved OBD remote online monitoring data set, and taking the position with the maximum correlation coefficient as the final alignment position of the OBD remote online monitoring data set and the PEMS data in the kilosecond data set;
4.3) after determining the final alignment positions of the OBD remote online monitoring data set and the PEMS data in each kilosecond data set, the following two conditions needing to be adjusted may occur:
4.3.1) some PEMS data do not have corresponding OBD remote on-line monitoring data, and the part of PEMS data is deleted at the moment, namely the part of PEMS data is not included in the subsequent data correction process and the subsequent pollutant emission factor calculation;
4.3.2) some PEMS data corresponding OBD remote on-line monitoring data are not unique, only the data with the earliest time sequence in the corresponding OBD remote on-line monitoring data of the PEMS data are reserved at the moment, and other corresponding data are deleted.
And 5, correcting a NOx volume concentration field in OBD remote online monitoring data:
5.1) data packet:
dividing the OBD remote online monitoring data into low NOx volume concentration (SCR downstream NOx volume concentration reading is less than or equal to 100ppm), medium NOx volume concentration (SCR downstream NOx volume concentration reading is greater than 100ppm and less than or equal to 1000ppm) and high NOx volume concentration (SCR downstream NOx volume concentration reading is greater than 1000ppm) with 100ppm and 1000ppm as boundaries;
5.2) determining an adjusting coefficient:
respectively carrying out remote online monitoring on each single group of OBD divided in the step 5.1) to obtain the volume concentration of NOx in the PEMS dataOBD remote on-line monitoring of NOx volumetric concentration in data for target value adjustment
Figure BDA0002262331330000152
Determining linear equations for independent variables using least squaresSingle adjustment factor αxAnd βxWherein x represents a group (x ═ 1,2,3 represent high, medium, and low rich, respectivelyDegree group), t represents time;
5.3) calculating the adjusted volume concentration of NOx
Figure BDA0002262331330000154
Figure BDA0002262331330000155
And 6, correcting an engine fuel flow field in the OBD remote emission monitoring data:
6.1) judging idle speed:
if the speed field is 0 and the rotating speed of the engine is less than or equal to the idle rotating speed, judging that the vehicle is in an idle state, not performing acceleration calculation in 6.2), and directly classifying the vehicle into an idle group d in 6.3);
6.2) calculating the acceleration of the tested vehicle:
single OBD data calculation acceleration at(unit m/s)2) The method comprises the following steps:
a. if the time difference between a certain OBD data and the adjacent available previous OBD data is 1s, the time difference is
Figure BDA0002262331330000156
Wherein v ist、vt-1The reading units of the speed fields in the OBD data and the previous OBD data are km/h respectively; a istHas the unit of m/s2；
b. If the time difference between a certain OBD data and the adjacent previous OBD data is more than 1s, and the time difference between the certain OBD data and the adjacent subsequent OBD data is 1s, the acceleration of the certain OBD data adopts the acceleration in the adjacent subsequent OBD data;
c. if the time difference between a certain piece of OBD data and the adjacent previous piece of OBD data is greater than 1s, and the time difference between the certain piece of OBD data and the adjacent subsequent piece of OBD data is also greater than 1s, the certain piece of OBD data is not included in the subsequent emission calculation evaluation, namely the certain piece of data is not used for the fuel flow calculation in the subsequent step 6.5), so that the certain piece of OBD data does not need to calculate the acceleration;
6.3) data packet:
according to the GB17691-2018, the acceleration is taken as a classification variable, and the OBD remote online monitoring data with the calculated acceleration is classified as follows:
a. and (3) an acceleration group: acceleration of 0.1m/s or more2；
b. A deceleration group: acceleration of less than or equal to-0.1 m/s2；
c. Uniform speed group: acceleration is-0.1 m/s2～0.1m/s2To (c) to (d);
d. an idle group: 6.1) the idle part judged in the step (b);
subsequent adjustments will be made based on the single group grouped above;
6.4) determining an adjusting coefficient:
for each single set of OBD remote online monitoring data divided in step 6.3), engine fuel flow in PEMS data
Figure BDA0002262331330000161
OBD remote on-line monitoring of engine fuel flow in data as an adjustment target
Figure BDA0002262331330000162
Determining a linear formula by means of least squares as independent variables
Figure BDA0002262331330000163
Single adjustment of coefficient gammaxAnd deltaxWhere x denotes a group (x ═ a, b, c.d denote an acceleration group, a deceleration group, a constant velocity group, and an idle group, respectively), and t denotes time;
6.5) calculating the adjusted Engine Fuel flow
Figure BDA0002262331330000164
Figure BDA0002262331330000165
Thirdly, obtaining emission factors;
step 1, obtaining instantaneous exhaust flow:
because the field available for the OBD remote online monitoring data is limited and direct measurement data of the exhaust flow cannot be provided, the real-time exhaust flow can be obtained only by indirect calculation or data fitting; here, the present invention provides the following three instantaneous exhaust flow rate calculation methods according to the OBD remote online monitoring data situation, and the priority of the following methods is method 1) > method 2) > method 3) in calculating the exhaust flow rate.
Method 1):
calculating to obtain the instantaneous exhaust mass flow according to the mass flow MAF of air intake and the volume flow of the fuel of the engine in the OBD remote online monitoring data, and specifically calculating according to the following formula:
Figure BDA0002262331330000171
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
Figure BDA0002262331330000172
mass air intake flow (MAF), kg/h read for OBD;
Figure BDA0002262331330000173
engine fuel volumetric flow, L/h, read for OBD;
ρfis the density of the fuel (such as diesel oil, natural gas, LPG, etc.) used by the engine, and is kg/L.
Method 2):
engine fuel volume flow and tail gas O in data are remotely monitored on line by utilizing OBD2And (3) estimating to obtain the instantaneous exhaust mass flow by assuming that the fuel is completely combusted, wherein the specific calculation formula is as follows:
in the formula:
Qexhfor instantaneously exhausting gasFlow rate, kg/h;
Figure BDA0002262331330000175
engine fuel volumetric flow, L/h, read for OBD;
x, y, z are the molar ratio of carbon to carbon in the fuel (C/C), the molar ratio of hydrogen to carbon (H/C), and the molar ratio of oxygen to carbon (O/C), respectively; according to the regulation of GB17691-2018, the diesel oil is CH1.86O0.006LPG is CH2.525Natural gas is CH4；
Figure BDA0002262331330000181
O in exhaust gas for OBD reading2Volume concentration,%;
ρairis the density of ambient air at 0 ℃ and 101.3kPa, and has a value of 1.293kg/m3；
ρexhIn terms of exhaust gas density, in kg/m3According to GB17691-2018, the exhaust density of heavy-duty vehicles burning diesel oil is 1.2943kg/m3The exhaust density of the heavy-duty vehicle burning natural gas is 1.2661kg/m3The density of the heavy vehicle exhaust gas burning LPG is 1.2811kg/m3。
Method 3):
estimating instantaneous exhaust mass flow by using the engine speed and a fitting coefficient of the bench/PEMS exhaust flow; because the calibration of engines of different models or families has large difference, the fitting coefficient obtained by the method can only be applied to the engines of the same model or family.
The method comprises the following specific steps:
firstly, carrying out one or more times of fitting by using the instantaneous rotating speed and the instantaneous exhaust flow of the engine obtained by the previous bench or PEMS test to obtain a fitting coefficient:
Figure BDA0002262331330000182
in the formula:
Q'exhfor bench or PEMS testingThe obtained instantaneous exhaust flow rate is kg/h;
EnS' is the instantaneous engine speed, rpm, obtained from bench or PEMS testing;
ai、bifitting coefficients obtained for the ith bench or PEMS test;
then, the fitting coefficient a obtained from the previous stepi、biAnd estimating the instantaneous exhaust mass flow by the engine speed in the OBD remote online monitoring data:
Figure BDA0002262331330000183
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
EnSOBDmonitoring the engine speed, rpm in the data for OBD remote online;
n is the total number of the bench-OBD synchronous tests or the total number of the PEMS-OBD synchronous tests (n is more than or equal to 1); the synchronous test is that the OBD remote online monitoring data are obtained while the vehicle is tested by a rack or a PEMS.
Step 2, calculating NOXEmission factor:
in order to eliminate the influence of noise measured by the sensor, all OBD remote on-line monitoring data are subjected to 60-s sliding average and then emission factor calculation is carried out;
method 1):
calculating by directly using the engine operating parameters:
Figure BDA0002262331330000191
in the formula:
Figure BDA0002262331330000192
is NOXEmission factor, g/kWh;
t1、t2respectively the starting time and the ending time of a single trip, s;
Figure BDA0002262331330000193
SCR downstream NO for OBD readingXVolume concentration, ppm;
EnTOBDan instantaneous net engine output torque, Nm, calculated for or based on OBD readings;
Qexhis the instantaneous exhaust mass flow, kg/h;
EnSOBDengine instantaneous speed, rpm, read for OBD.
Method 2):
calculating by using an emission factor based on oil consumption:
Figure BDA0002262331330000194
in the formula:
BSFC is specific fuel consumption of an engine, can be obtained based on bench or PEMS actual measurement, and can also be determined by referring to the report of the United states environmental protection agency (EPA-420-R-02-005);
Figure BDA0002262331330000201
SCR downstream NO for OBD readingXVolume concentration, ppm;
engine fuel volumetric flow, L/h, read for OBD;
t1、t2respectively the starting time and the ending time of a single trip event, s;
definition of the single trip event:
defining travel data between two adjacent parking events as a single travel event; and if the time interval between two adjacent data is more than 120s and the GPS positioning state has the state change of 'GPS positioning-no GPS positioning-GPS positioning', the parking event is considered to occur.
According to the calculation method, the emission result of a single trip event can be calculated, the emission result of a trip every day or multiple trips can be evaluated according to requirements, and remote emission management of the heavy-duty vehicle is effectively supported.

Claims (6)

1. The method for acquiring the emission factor of the heavy vehicle suitable for OBD remote emission monitoring data is characterized by comprising the following steps of:
firstly, judging the quality of OBD remote online monitoring data;
step 1, trip event division:
1.1) data sorting:
sequencing all data according to a time field sequence;
1.2) identifying a parking event:
if the time interval between two adjacent data is greater than 120s and the GPS positioning state changes from the state of GPS positioning, no GPS positioning and GPS positioning, the parking event is considered to occur;
1.3) dividing travel events:
sequentially extracting travel data between two adjacent parking events, and defining the travel data between the two adjacent parking events as a primary travel event;
step 2, time exception handling:
2.1) processing of interval readings:
respectively counting the occurrence proportion of interval readings in each trip event,
if the proportion of the interval readings in a certain trip event exceeds 30% of the total data volume of the trip event, the data is lost too much, the trip event is considered to be invalid, and the data of the trip event is considered to be invalid data;
if the proportion of interval readings in a certain trip event does not exceed 30% of the total data volume of the trip event, single data with the time interval being greater than 1s before and after the trip event is regarded as invalid data;
the interval reading refers to two adjacent data with the time interval being more than 1s in a single trip event;
2.2) processing of time coincidence data:
if the reading time of the front data and the back data is the same, only the data which is most close to the front data is reserved;
step 3, data exception handling:
3.1) judging data abnormity according to the following steps:
3.1.1) if a certain field is empty or exceeds the threshold value, the field is considered as abnormal data; the out-of-limit value refers to a reading exceeding a value range specified in annex Q of emission limits of pollutants for heavy-duty diesel vehicles and a measurement method (sixth stage of China);
3.1.2) if a certain field continuously generates the same internal value in the boundary for a long time, the field is considered as abnormal data;
3.2) processing method of abnormal data:
respectively counting the abnormal proportion of each field data in a single trip event,
if the proportion of the single-field abnormal value in the total data volume of the trip event exceeds 30%, the data of the trip event is considered invalid;
if the abnormal data situation continuously occurs to exceed 5% of the total data volume in a certain trip event, the trip event data is considered invalid and is not included in the emission result evaluation process;
if the proportion of the abnormal value of a certain single word segment in the travel event in the total number of the travel event does not exceed 30%, the abnormal field is considered invalid;
secondly, correcting data;
1) carrying out an OBD-PEMS synchronous experiment:
carrying out a PEMS (routine Per-Performance measurement and monitoring system) experiment on an actual road aiming at a heavy-duty vehicle, measuring and collecting the speed, the engine rotating speed, the net output torque of an engine, the fuel flow of the engine, the concentration of NOx at the downstream of SCR (selective catalytic reduction) and the instantaneous exhaust mass flow in the driving process of the heavy-duty vehicle, and simultaneously collecting OBD (on-board diagnostics) remote online monitoring data of the vehicle in the same time;
2) PEMS data alignment:
aligning PEMS data according to relevant regulations in annex K in pollutant emission Limit and measurement method for heavy-duty diesel vehicles (sixth stage of China);
3) OBD-PEMS data time alignment:
adjusting the front-back alignment mode of the OBD remote online monitoring data and the PEMS data by comparing common data fields of the OBD remote online monitoring data and the PEMS data, so that the correlation degree of the selected alignment variables is maximum;
4) and (3) segmented checking:
4.1) dividing the OBD remote online monitoring data and the PEMS data into a plurality of kilosecond data groups by taking the aligned PEMS time axis as reference and taking 1000s as time step;
4.2) for a single kilosecond data set, taking 1s as a time window, integrally moving the OBD remote online monitoring data set within a range of 10s before and after the corresponding PEMS time, calculating the Pearson correlation coefficient of the PEMS and the moved OBD remote online monitoring data set, and taking the position with the maximum Pearson correlation coefficient as the final alignment position of the OBD remote online monitoring data set and the PEMS data in the kilosecond data set;
4.3) data adjustment:
4.3.1) if some PEMS data does not have corresponding OBD remote online monitoring data, the part of PEMS data is not included in the subsequent data correction process;
4.3.2) if the OBD remote online monitoring data corresponding to a certain PEMS data is not unique, only retaining the data with the earliest time sequence in the OBD remote online monitoring data corresponding to the part of PEMS data;
5) and (3) correcting a NOx volume concentration field in OBD remote online monitoring data:
5.1) data packet:
dividing the OBD remote on-line monitoring data into three groups of low NOx volume concentration, medium NOx volume concentration and high NOx volume concentration;
the low NOx volume concentration refers to a volume concentration at which a NOx volume concentration reading downstream of the SCR is less than or equal to 100 ppm;
the medium NOx volume concentration refers to the volume concentration of NOx in the downstream of SCR, wherein the volume concentration reading of NOx is more than 100ppm and less than or equal to 1000 ppm;
the high NOx volume concentration refers to a volume concentration at which the SCR downstream NOx volume concentration reading is greater than 1000 ppm;
5.2) determining an adjusting coefficient:
respectively carrying out remote online monitoring on each single group of OBD divided in the step 5.1) to obtain NOx in PEMS dataProduct concentrationOBD remote on-line monitoring of NOx volumetric concentration in data for target value adjustment
Figure FDA0002262331320000041
Determining linear equations for independent variables using least squares
Figure FDA0002262331320000042
Single adjustment factor α inxAnd βx；
Wherein:
x represents a group, and x is 1,2,3 represent high, medium, and low concentration groups, respectively;
t represents time;
5.3) calculating the adjusted volume concentration of NOx
Figure FDA0002262331320000043
Figure FDA0002262331320000044
6) And correcting an engine fuel flow field in the OBD remote emission monitoring data:
6.1) judging idle speed:
if the speed field is 0 and the rotating speed of the engine is less than or equal to the idle rotating speed, the vehicle is considered to be in an idle state at the moment, and the vehicle directly enters the idle group d in 6.3);
6.2) calculating the acceleration of the tested vehicle:
single OBD data calculation acceleration atThe method comprises the following steps:
a. if the time difference between a certain OBD data and the adjacent available previous OBD data is 1s, the time difference isWherein v ist、vt-1Speed in the one piece of OBD data and its predecessor OBD data, respectivelyReading the field in km/h; a istHas the unit of m/s2(ii) a b. If the time difference between a certain OBD data and the adjacent previous OBD data is more than 1s, and the time difference between the certain OBD data and the adjacent subsequent OBD data is 1s, the acceleration of the certain OBD data adopts the acceleration in the adjacent subsequent OBD data;
c. if the time difference between a certain piece of OBD data and the adjacent previous piece of OBD data is greater than 1s, and the time difference between the certain piece of OBD data and the adjacent subsequent piece of OBD data is also greater than 1s, the certain piece of OBD data is no longer used for fuel flow calculation in the subsequent step 6.5);
6.3) data packet:
according to the GB17691-2018, the acceleration is taken as a classification variable, and the OBD remote online monitoring data with the calculated acceleration is classified as follows:
a. and (3) an acceleration group: acceleration of 0.1m/s or more2；
b. A deceleration group: acceleration of less than or equal to-0.1 m/s2；
c. Uniform speed group: acceleration is-0.1 m/s2～0.1m/s2To (c) to (d);
d. an idle group: 6.1) the idle part judged in the step (b);
6.4) determining an adjusting coefficient:
for each single set of OBD remote online monitoring data divided in step 6.3), engine fuel flow in PEMS data
Figure FDA0002262331320000051
OBD remote on-line monitoring of engine fuel flow in data as an adjustment target
Figure FDA0002262331320000052
Determining a linear formula by means of least squares as independent variablesSingle adjustment coefficient gamma inxAnd deltax；
Wherein:
x represents a group, and x represents an acceleration group, a deceleration group, a constant speed group and an idle group, c.d represents a group;
t represents time;
6.5) calculating the adjusted Engine Fuel flow
Figure FDA0002262331320000054
Figure FDA0002262331320000055
The third step: acquiring an emission factor;
step 1, acquiring instantaneous exhaust mass flow through indirect calculation or data fitting based on OBD remote online monitoring data;
step 2, calculating NO by directly utilizing engine operation parametersXEmission factor, or NO based on specific fuel consumption of the engineXAn emission factor.
2. The heavy vehicle exhaust NO of claim 1 based on OBD remote emission monitoring dataXAn emission factor acquisition method, characterized in that:
in the third step, the instantaneous exhaust mass flow in the step 1 is calculated according to the following formula:
Figure FDA0002262331320000056
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
Figure FDA0002262331320000057
the mass flow MAF, kg/h of air inlet air read by the OBD is obtained;
Figure FDA0002262331320000058
engine fuel volumetric flow, L/h, read for OBD;
ρfis an engine stationThe density of the fuel, kg/L, was used.
3. The heavy vehicle exhaust NO of claim 1 based on OBD remote emission monitoring dataXAn emission factor acquisition method, characterized in that:
in the third step, the instantaneous exhaust mass flow in the step 1 is calculated according to the following formula:
Figure FDA0002262331320000061
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
Figure FDA0002262331320000062
engine fuel volumetric flow, L/h, read for OBD;
x, y, z are the molar ratio of carbon to carbon in the fuel (C/C), the molar ratio of hydrogen to carbon (H/C), and the molar ratio of oxygen to carbon (O/C), respectively; according to the regulation of GB17691-2018, the diesel oil is CH1.86O0.006LPG is CH2.525Natural gas is CH4；
O in exhaust gas for OBD reading2Volume concentration of (d)%;
ρairis the density of ambient air at 0 ℃ and 101.3kPa, and has a value of 1.293kg/m3；
ρexhIn terms of exhaust gas density, in kg/m3According to GB17691-2018, the exhaust density of heavy-duty vehicles burning diesel oil is 1.2943kg/m3The exhaust density of the heavy-duty vehicle burning natural gas is 1.2661kg/m3The density of the heavy vehicle exhaust gas burning LPG is 1.2811kg/m3。
4. The heavy vehicle exhaust NO of claim 1 based on OBD remote emission monitoring dataXEmission factorThe acquisition method is characterized by comprising the following steps:
the third step is that the method for acquiring the instantaneous exhaust mass flow in the step 1 comprises the following steps:
firstly, carrying out one or more times of fitting by using the instantaneous rotating speed and the instantaneous exhaust flow of the engine obtained by a bench or PEMS test to obtain a fitting coefficient:
in the formula:
Q'exhthe instantaneous exhaust flow obtained by bench or PEMS test is kg/h;
EnS' is the instantaneous engine speed, rpm, obtained from bench or PEMS testing;
ai、bifitting coefficients obtained for the ith bench or PEMS test;
then, the fitting coefficient a obtained from the previous stepi、biAnd estimating the instantaneous exhaust mass flow by the engine speed in the OBD remote online monitoring data:
Figure FDA0002262331320000071
in the formula:
Qexhis the instantaneous exhaust mass flow, kg/h;
EnSOBDmonitoring the engine speed, rpm in the data for OBD remote online;
n is the total number of the bench-OBD synchronous tests or the total number of the PEMS-OBD synchronous tests, and n is more than or equal to 1; the synchronous test is to obtain OBD remote online monitoring data while a vehicle is tested by a rack or a PEMS.
5. Heavy vehicle exhaust NO based on OBD remote emission monitoring data according to claim 2 or 3 or 4XAn emission factor acquisition method, characterized in that:
third step in step 2 NO is calculated directly using engine operating parametersXThe emission factors are specifically:
Figure FDA0002262331320000072
in the formula:
is NOXEmission factor, g/kWh;
t1、t2respectively the starting time and the ending time of a single trip, s;
Figure FDA0002262331320000074
SCR downstream NO for OBD readingXVolume concentration, ppm;
EnTOBDan instantaneous net engine output torque, Nm, read for or calculated based on OBD read data;
Qexhis the instantaneous exhaust mass flow, kg/h;
EnSOBDthe instantaneous rotating speed of the engine read by the OBD is r/min;
definition of the single trip event:
defining travel data between two adjacent parking events as a single travel event; and if the time interval between two adjacent data is more than 120s and the GPS positioning state has the state change of 'GPS positioning-no GPS positioning-GPS positioning', the parking event is considered to occur.
6. Heavy vehicle exhaust NO based on OBD remote emission monitoring data according to claim 2 or 3 or 4XAn emission factor acquisition method, characterized in that:
third step NO is calculated based on oil consumption in step 2XThe specific calculation formula of the emission factor is as follows:
Figure FDA0002262331320000081
in the formula:
BSFC is the specific oil consumption of the engine;
Figure FDA0002262331320000082
SCR downstream NO for OBD readingXVolume concentration, ppm;
Figure FDA0002262331320000083
engine fuel volumetric flow, L/h, read for OBD;
t1、t2respectively the starting time and the ending time of a single trip event, s;
definition of the single trip event:
defining travel data between two adjacent parking events as a single travel event; and if the time interval between two adjacent data is more than 120s and the GPS positioning state has the state change of 'GPS positioning-no GPS positioning-GPS positioning', the parking event is considered to occur.
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