WO2022036619A1 - 点燃式发动机汽车尾气排放遥感大数据检测方法和*** - Google Patents

点燃式发动机汽车尾气排放遥感大数据检测方法和*** Download PDF

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WO2022036619A1
WO2022036619A1 PCT/CN2020/110173 CN2020110173W WO2022036619A1 WO 2022036619 A1 WO2022036619 A1 WO 2022036619A1 CN 2020110173 W CN2020110173 W CN 2020110173W WO 2022036619 A1 WO2022036619 A1 WO 2022036619A1
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vehicle
emission
remote sensing
exhaust
bin
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French (fr)
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郝利君
葛子豪
尹航
王军方
王小虎
刘嘉
田苗
刘进
王运静
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北京理工大学
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G01M15/04—Testing internal-combustion engines
    • G01M15/10—Testing internal-combustion engines by monitoring exhaust gases or combustion flame
    • G01M15/102—Testing internal-combustion engines by monitoring exhaust gases or combustion flame by monitoring exhaust gases
    • G01M15/108—Testing internal-combustion engines by monitoring exhaust gases or combustion flame by monitoring exhaust gases using optical methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G06V20/625—License plates
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/08—Detecting or categorising vehicles

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  • the invention relates to the technical field of vehicle exhaust gas detection, and more particularly to a method and system for remote sensing big data detection of ignition type engine vehicle exhaust gas emissions.
  • the regular detection method of exhaust emission of ignition engine vehicles in China mainly adopts the steady state method (ASM) or the simple transient operation method (VMAS).
  • ASM steady state method
  • VMAS simple transient operation method
  • the ignition engine vehicle can use the double idle speed method, especially in the process of road inspection, due to the limitation of testing equipment, the ignition engine vehicle generally uses the double idle speed method.
  • the vehicle is stationary and is not in normal driving conditions, so there will be a large deviation from the actual driving emissions.
  • the annual inspection cycle of in-use vehicles is generally once a year, and new vehicles are exempted from inspection for six years. It is impossible to guarantee that the vehicle’s emissions will always meet the standard during the two statutory annual inspections.
  • the vehicle emission remote sensing test method can achieve the purpose of real-time monitoring.
  • the remote sensing detection technology of motor vehicle exhaust pollutants has the characteristics of fast detection speed. It can detect thousands of vehicles in one hour, saving time and effort, and greatly improving the efficiency of vehicle emission detection.
  • the monitoring can be completed during the normal driving process of the vehicle, and the operating conditions of the vehicle engine during monitoring are more representative, and can better reflect the actual situation of vehicle emissions than the traditional contact measurement method.
  • the vehicle emission remote sensing test can complete the test without the driver's knowledge, and avoid the individual driver to take some measures to artificially affect the test results in order to pass the test.
  • Vehicle emission remote sensing testing technology has been practically applied in some countries and regions in North America, Europe, and Asia. Currently, the main applications focus on screening high-emission vehicles, screening clean vehicles, and entry inspection.
  • the remote sensing detection method of ignition engine vehicle exhaust emission is to determine the vehicle emission remote sensing test data within the selected vehicle specific power VSP validity judgment interval, and to screen high-emission vehicles.
  • the U.S. Environmental Protection Agency recommends that the light-duty vehicle VSP be in the range of 0 to 20kW/ton as the judgment interval for the validity of the telemetry result, and the telemetry data with the VSP beyond this range is not used for subsequent evaluation.
  • Relevant studies in my country show that the emission concentrations of CO, HC and NOx in VSP are relatively stable in the range of -5 to 14kW/ton, which is used as a judgment interval for the validity of the telemetry results.
  • this method is equivalent to setting only one VSP Bin interval for the vehicle emission remote sensing data determination interval, and only a limit is given, which does not take into account the variation characteristics of vehicle emissions with VSP, so it is easy to lead to misjudgment. , which is not conducive to scientific and refined management. Since only a limited VSP range is selected for the VSP judgment interval for the validity of vehicle remote sensing data, the telemetry data beyond this range is not used for subsequent evaluation, which makes a large number of remote sensing data invalid. The test found that the emission data in areas outside the selected VSP validity judgment interval were more serious and should be included in the scope of regulation.
  • the present invention provides a remote sensing big data detection method and system for ignition-type engine vehicle exhaust emissions.
  • the content of vehicle exhaust pollutants is collected through the system, and the VSP is divided into a plurality of Bin partitions. Evaluate vehicle emissions and determine high-emission vehicles.
  • a remote sensing big data detection method for ignition type engine vehicle exhaust emissions comprising:
  • the method for remote sensing detection of automobile gaseous exhaust pollutants in the step S1 includes:
  • ⁇ CO , ⁇ HC , and ⁇ NO are the relative volume concentration ratios of CO, HC and NO to CO 2 , respectively, C CO , C HC , C NO and are the concentrations of CO, HC, NOx and CO 2 in the exhaust plume, respectively;
  • P represents CO, HC and NO
  • M P and M fuel are the molecular weights of pollutants P and fuel, respectively, in g/mole
  • ignition-engine vehicles are divided into light-duty vehicles, medium-duty vehicles and heavy-duty vehicles according to the total mass of the vehicle, and further divided into light-duty passenger vehicles, light-duty trucks, medium-duty passenger vehicles, and medium-duty trucks according to the purpose of the vehicle. , heavy passenger vehicles and heavy goods vehicles.
  • the calculation of the vehicle VSP and the Bin partition of the step S2 specifically include:
  • C D is the drag coefficient
  • a f is the windward area of the vehicle
  • ⁇ a is the air density
  • v is the vehicle speed
  • v w is the wind speed
  • g is the acceleration of gravity
  • CR is the rolling resistance coefficient of the tire
  • a is the vehicle acceleration
  • ⁇ i is the mass conversion coefficient of the rotating parts of the powertrain
  • m v is the vehicle mass.
  • each type of vehicle Divides the subdivided driving condition range of each type of vehicle into i+j intervals with VSP as the parameter, and the area with positive VSP is divided into i intervals, which are respectively defined as Bin p1 , Bin p2 , , , Bin pi -1 , Bin pi ; the area with negative VSP is divided into j intervals, which are defined as Bin n1 , Bin n2 , , , Bin ni-1 , and Bin ni respectively, and each Bin interval represents a certain VSP range in which the vehicle travels.
  • the statistical analysis of remote sensing big data specifically includes:
  • the vehicle driving state tester detects the vehicle speed and acceleration, calculates the VSP value under the test condition of the tested vehicle by using the formula (9), and allocates the vehicle emission remote sensing test data to the corresponding Bin interval according to the VSP value;
  • the cumulative distribution function f(x) of the discrete emission data variable x that is, the cumulative distribution probability is
  • the value of the emission data variable x i is greater than or equal to 0, so the cumulative distribution probability curve of the probability distribution function f(x) of the emission data variable x is obtained .
  • the distribution probability function value f(x i ) represents the probability that x falls within the interval (0 ⁇ x i ). If the proportion of high-emission vehicles is defined as y%, the emission measurements with cumulative distribution probability at (100-y)% are intercepted. The value is used as the primary emission limit, as the emission judgment threshold for screening high-emission vehicles.
  • calculating the average vehicle emission level includes:
  • the high-emission vehicle determination method includes:
  • An ignition type engine vehicle exhaust emission remote sensing big data detection system comprising: a vehicle exhaust emission measuring instrument, a main control computer, an information display instrument, a vehicle driving state tester, a weather detector, a license plate camera and a vehicle emission monitoring platform;
  • the automobile exhaust emission measuring instrument, the information display instrument, the automobile driving state tester, the weather monitor and the license plate camera are connected in communication with the main control computer, and the main control computer is connected with the vehicle emission monitoring platform through the Internet;
  • the main control computer (2) is used for processing the data obtained from the vehicle exhaust emission measuring instrument (1), the vehicle driving state tester (4), the weather monitoring instrument (5) and the license plate camera (6).
  • the ignition type engine vehicle exhaust emission measuring instrument adopts a vertical or horizontal optical path, and is arranged in the passing area of the vehicle;
  • the vehicle exhaust emission measuring instrument comprises: a detection light emission and reception device and a detection light reflection device arranged oppositely; the detection light emission and reception device is used for transmitting and receiving detection light passing through the exhaust plume;
  • the vehicle driving state tester is a vehicle speed, an acceleration optical measuring instrument or a radar speed measuring instrument.
  • the information display instrument is a highlight dot matrix screen, which is used to display the relevant information of the inspected vehicle in real time;
  • the relevant information includes: the license plate number, the vehicle speed and the concentration of exhaust pollutants;
  • the weather monitor is a miniature weather station and is arranged in the passing area of the vehicle; it is used to measure environmental parameters.
  • the present invention discloses a method and system for monitoring the exhaust emission of ignition type engine vehicles based on remote sensing big data. To multiple Bin partitions, and according to different vehicle types, to evaluate vehicle emissions and determine high-emission vehicles.
  • FIG. 1 is a schematic diagram of the system structure provided by the present invention.
  • FIG. 2 is a schematic diagram of the classification of vehicle categories and the Bin partitioning method of VSP for each category of vehicles provided by the present invention.
  • FIG. 3 is a schematic diagram of the cumulative distribution density function f(x) of the emission data variable x provided by the present invention.
  • FIG. 4 is a schematic diagram of a method for determining a high emission screening judgment threshold according to the cumulative distribution probability of the emission data variable x provided by the present invention.
  • FIG. 5 is the VSP distribution probability of light passenger vehicles in Beijing provided by the present invention.
  • FIG. 6 is the cumulative distribution probability curve of the remote sensing emission measurement value of the Beijing ASM5024 light-duty passenger vehicle provided by the present invention.
  • the embodiment of the present invention discloses a method and system for detecting remote sensing big data of ignition type engine vehicle exhaust emission; with reference to FIG. : Ignition engine vehicle exhaust emission measuring instrument, main control computer, information display instrument, vehicle driving state tester, weather monitor and license plate camera;
  • Ignition engine vehicle exhaust emission measuring instrument, information display instrument, vehicle driving state tester, weather monitor and license plate camera are connected to the main control computer, and the main control computer is connected to the vehicle emission monitoring platform through the Internet;
  • the vehicle driving state tester is a vehicle speed and acceleration optical measuring instrument or a radar speed measuring instrument, which is arranged beside the road in the vehicle detection area. When the vehicle passes by, it can accurately measure the speed and acceleration of the detected vehicle.
  • the ignition-type engine automobile exhaust emission measuring instrument adopts a vertical or horizontal optical path and is arranged in the passing area of the vehicle; the ignition-type engine automobile exhaust emission measuring instrument includes a detection light emitting device and a detection light receiving device arranged oppositely; The detection light receiving device is used to receive the detection light passing through the exhaust plume, and analyze the pollutant concentration in the exhaust plume of the motor vehicle according to the intensity of the received detection light.
  • the above-mentioned information display device is a high-point array screen, which can display the information of the inspected vehicle in real time; for example, it includes information such as license plate number, vehicle speed and exhaust pollutant concentration.
  • the above weather monitor is a miniature weather station, which is also arranged in the passing area of the vehicle; it can accurately measure environmental parameters, such as wind speed, wind direction, temperature, humidity and other information.
  • the license plate camera is a high-speed camera, which can accurately capture license plate information; other image recognition devices that can obtain license plate information can be used, which is not limited in the present invention.
  • the main control computer is an industrial control computer, which is responsible for the acquisition and processing of all the above input and output signals, as well as system calibration, etc.; completes the calculation of vehicle speed, acceleration, specific power and exhaust emissions; sends data to the vehicle emission monitoring platform through the Internet, and communicates with the vehicle emission monitoring platform. Vehicle Emissions Monitoring Platform Communication.
  • the vehicle emission monitoring platform is responsible for statistical analysis of the big data of vehicle remote sensing detection, and the big data processing system continuously screens out a certain proportion of high-emission vehicles. At the same time, statistical analysis is carried out on the records of emission exceeding standards on a regular basis, and the vehicle models with a high proportion of records exceeding the emission standards are screened out, focusing on the implementation of emission spot checks and emission supervision.
  • the vehicle driving state tester measures the speed and acceleration of the ignition engine vehicle, and the main control computer uses the vehicle speed and acceleration as parameters to calculate the specific power.
  • the ignition engine vehicle exhaust emission measuring instrument detects the emission concentration ratio of CO, HC, NO and CO 2 in the exhaust plume, and calculates the CO, HC and NO emissions per unit mass of fuel consumed according to the carbon balance equation (g/kg fuel); calculate the CO, HC, NO and CO 2 emission concentrations in the exhaust of ignition-type engine vehicles through the inversion calculation method of remote sensing data, so as to realize the real-time measurement of gas emissions in the exhaust-gas of ignition-type engine vehicles.
  • the embodiment of the present invention also provides a remote sensing big data detection method for the exhaust emission of an ignition type engine vehicle.
  • VSP Vehicle Specific Power
  • VSP Vehicle Specific Power
  • the United States conducted the FTP emission test on the vehicle test bench, and found that when 0 ⁇ VSP ⁇ 20kW/ton, the CO emission concentration was relatively stable, and when VSP>20kW/ton, the concentrations of CO and HC were extremely prone to abnormality Therefore, the EPA recommends a VSP range of 0 to 20 kW/ton as a range for judging the validity of vehicle emission telemetry results.
  • Telemetry data with VSP beyond this range is not used for subsequent evaluations.
  • the range of 3 to 22kW/ton of VSP is used as the judgment range for the validity of the telemetry results.
  • this method is equivalent to selecting only one VSP Bin interval for the validity judgment interval of vehicle remote sensing data, and generally only one emission limit is given, without taking into account the variation characteristics of vehicle emissions with VSP.
  • the method is easy to lead to misjudgment, which is not conducive to scientific and refined management.
  • VSP judging interval for the validity of vehicle remote sensing data is only a limited VSP range, the telemetry data beyond this range will not be used for subsequent evaluation, which will cause a large number of remote sensing data to be invalid and reduce the efficiency of vehicle emission remote sensing test equipment. Moreover, we found that the emissions in areas outside the VSP judgment interval for the validity of remote sensing test data are more serious and should be included in the scope of detection and supervision.
  • the invention proposes a remote sensing big data detection method for ignition type engine vehicle exhaust emissions. Taking into account the differences in emission characteristics of ignition-engine vehicles under different VSP operating conditions, a big data processing method for remote sensing emissions based on different categories of ignition-engine vehicles and different VSP intervals is proposed to realize the scientific and refined emission of ignition-engine vehicles. manage.
  • the specific methods and processes include: S101-S111;
  • Ignition engine vehicles can be divided into light-duty vehicles, medium-duty vehicles and heavy-duty vehicles, which can be further divided into light-duty passenger vehicles, light-duty trucks, medium-duty passenger vehicles, medium-duty trucks, heavy-duty passenger vehicles and heavy-duty trucks;
  • VSP is a positive area
  • the subscript p represents) is divided into i intervals, which are defined as Bin p1 , Bin p2 , , , , Bin pi-1 and Bin pi respectively; the area where VSP is negative (Negative, represented by subscript n) is divided into j intervals, They are respectively defined as Bin n1 , Bin n2 , Bin n3 , , , Bin nj-1 , , , Bin nj , and each Bin interval represents the VSP range of the vehicle;
  • the vehicle driving state tester of the vehicle emission remote sensing detection system detects the vehicle speed and acceleration, uses the formula (9) to calculate the VSP value under the test condition of the tested vehicle, and allocates the vehicle emission remote sensing test data to the corresponding Bin according to the VSP value Statistical analysis of vehicle emission remote sensing test results in each Bin interval;
  • the statistical average value of emissions in each Bin interval of the vehicle VSP is used for evaluating the average emission level of the vehicle.
  • the statistical average value in the Bin interval of each VSP is sufficient to represent the real emission value of this type of vehicle in this operating condition area, which can be used for vehicle emission level evaluation and emission estimation;
  • the over-standard emission data of the vehicle in the database shall be deleted, but the over-standard emission information of the vehicle model shall be recorded separately for evaluating the emission levels of various models in the market.
  • the big data processing system continuously and repeatedly screened out a certain proportion of high-emission vehicles and took corresponding rectification measures, the overall average emission of the vehicles in use was reduced. With the passage of time, low-emission vehicles increase and old vehicles are eliminated.
  • the structure of vehicle ownership is constantly changing, due to the real-time update of big data of statistical analysis, the mean and median of the overall remote sensing emission data are also updated synchronously.
  • the changes in the vehicle ownership structure and the overall average emission level of the vehicle are updated simultaneously, and the dynamic adjustment of the remote sensing emission judgment threshold for screening high-emission vehicles is realized, which has the advantage of real-time update compared with the emission inspection standard limit of the annual inspection of the in-use vehicle. ;
  • the vehicle emission monitoring platform regularly conducts statistical analysis on the record information of vehicle models in use, and can evaluate and sort the emission levels and emission control technology levels of all vehicle models on the market, and a relatively high proportion of the emissions records screened out
  • the model focuses on the implementation of emission spot checks and emission supervision.
  • steps S101-S111 including: ignition engine vehicle model classification method, vehicle VSP calculation method, ignition engine vehicle gaseous exhaust pollutant remote sensing detection method, vehicle emission remote sensing test big data statistical analysis method, high emission vehicle determination method , vehicle average emission level evaluation methods and screening and supervision methods for high-emission vehicles.
  • ignition engine vehicle types are divided into different categories, as shown in Figure 2.
  • Ignition engine vehicles can be divided into light, medium and heavy vehicles, further divided into light passenger vehicles, light trucks, medium passenger vehicles, medium trucks, heavy passenger vehicles and heavy goods vehicles.
  • Ignition engines used for heavy-duty vehicles generally have very few gasoline engines, and are usually natural gas engines or alcohol fuel engines.
  • VSP distribution of light-duty passenger cars in ignition-engine vehicles in Beijing is calculated using the vehicle specific power VSP calculation formula:
  • VSP is the specific power of the motor vehicle (kW/ton);
  • C D is the drag coefficient, which is a dimensionless coefficient;
  • a f is the windward area of the vehicle, in m 2 ;
  • ⁇ a is the air density, 1.29kg/m 3 ;
  • v is the vehicle speed, the unit is m/s;
  • vw is the wind speed, the unit is m/s, it is positive in the opposite direction to the vehicle, otherwise it is negative;
  • g is the gravitational acceleration, which is 9.8m/s 2 ;
  • C R is the tire's Rolling resistance coefficient, a dimensionless coefficient;
  • a is the vehicle acceleration, in m/s 2 ;
  • ⁇ i is the mass conversion coefficient of the rotating parts of the powertrain; is the road gradient;
  • m v is the vehicle mass, the unit is ton.
  • VSP values of light-duty ignition engine passenger car models in Beijing obtained by calculation are distributed probability statistics, as shown in Figure 5.
  • the vehicle driving conditions are divided into multiple Bin intervals by VSP parameters. Since the load of the vehicle engine is different in different Bin areas, the CO, HC and NOx emissions of each Bin are obviously different. In addition, due to the different configurations of engines and transmissions of each vehicle, the CO, HC and NOx emission characteristics of each vehicle type are also significantly different.
  • the remote sensing detection system of motor vehicle exhaust pollutants is used to conduct remote sensing detection of exhaust pollutants from ignition engine vehicles in a part of the urban area of Beijing, and the concentration ratio of CO, HC and NO to CO 2 of each vehicle is detected.
  • CO/CO 2 , HC/CO 2 and NO/CO 2 record vehicle license plate information, measure vehicle speed and acceleration, and environmental information;
  • the relative volume concentration ratio of each component of gaseous emissions in the exhaust plume of gasoline vehicles is:
  • ⁇ CO , ⁇ HC , and ⁇ NO are the relative volume concentration ratios of CO, HC and NO to CO 2 , respectively, and C CO , C HC , C NO and are the concentrations of CO, HC, and NOx and CO2 in the exhaust plume, respectively.
  • the emission status of the inspected vehicle can be determined in real time.
  • the CO, HC and NO emission mass per unit mass of fuel for ignition engine vehicles can be calculated:
  • the gasoline molecule is represented by CH 2 and the molar mass is 0.014kg/mol. Therefore, the emission factors EFs for CO, HC and NO based on fuel quality can be calculated by the formula:
  • the emission status of the vehicle under inspection can be determined in real time by the CO, HC and NO emission mass (g/kg fuel) per unit mass of fuel consumed by the ignition engine vehicle.
  • volume percent concentrations of CO, HC, and NO in the exhaust are calculated using the following formulas.
  • the real volume concentration value of gasoline vehicle exhaust emission can be obtained by inversion.
  • the emission status of the vehicle under inspection can also be determined in real time.
  • the analysis is based on a large amount of data obtained from our ASM test conditions for spark-engine light passenger vehicles.
  • the speed of the ASM test condition of the light-duty engine light bus in Beijing is 24km/h, and the VSP value is 0.97kW/ton.
  • Statistical analysis and processing of the measured remote sensing emission data is based on a large amount of data obtained from our ASM test conditions for spark-engine light passenger vehicles.
  • This example takes the statistical analysis of the absolute concentrations of CO, HC and NO in a light-duty light-duty passenger car with an ignition engine in Beijing ASM5024 as an example.
  • CO emission is the control target alone, 5% high-emission vehicles are screened, and the remote sensing emission judgment threshold of high-emission vehicles is 0.82%. If the determined high-emission vehicles meet the emission standards after maintenance, the CO emission after screening out 5% of the high-emission vehicle data will decrease from the maximum value of 12.77% to 0.82%, and the statistical average of CO emission data will decrease from 0.23% to 0.14%.
  • the remote sensing emission judgment threshold of high-emission vehicles is 1425.2ppm. If the determined high-emission vehicles meet the emission standards after maintenance, the NO emission after screening out 5% of the high-emission vehicle data will decrease from the maximum value of 3968ppm to 1425.2ppm, and the statistical average of NO emission data will decrease from 396.62ppm to 302.48ppm.
  • the remote sensing emission judgment threshold of high-emission vehicles is 735ppm. If the determined high-emission vehicles meet the emission standards after maintenance, the HC emission after screening out 5% of the high-emission vehicle data will decrease from the maximum value of 9886ppm to 735ppm, and the statistical average of HC emission data will decrease from 169.26ppm to 47.7ppm.
  • each vehicle emission remote sensing test value is a set of data (CO, HC and NO)
  • the CO, HC and NO emissions of a vehicle may show different emission characteristics
  • a high-emission vehicle is determined to be a high-emission vehicle if one of the exhaust pollutants exceeds the standard, which will actually lead to The proportion of high-emission vehicles screened is far greater than 5%; and if all three pollutants exceed the standard to be determined as high-emission vehicles, the actual proportion of high-emission vehicles screened is less than 5%. Therefore, it is necessary to properly adjust the high-emission vehicle judgment thresholds of the three pollutants through subsequent test tests, so that the proportion of high-emission vehicles actually screened is controlled by 5%.
  • the vehicle under inspection will be judged to be unqualified for emissions
  • the vehicle under test will be determined as a high-emission vehicle
  • the owner will be notified to repair the vehicle, otherwise travel restrictions may be taken.
  • the excess emission data of the vehicle in the database will be deleted, and the excess emission information of the vehicle model will be recorded separately for comprehensive evaluation of the emission levels of various vehicle types.
  • the vehicle emission monitoring platform regularly counts the information records of various vehicle models that exceed the emission standards, and can sort and evaluate the number of excess emissions and emission levels of all models on the market. Focus on the implementation of emission spot checks and emission supervision for models with a high proportion of emissions exceeding the standard information records.
  • the statistical average of the remote sensing monitoring data in the Bin interval of the VSP of each type of vehicle can be calculated by the formula of the statistical average of discrete random variables:
  • the statistical average of remote sensing monitoring data is the weighted average of all data, not only the value of each remote sensing monitoring data, but also the probability corresponding to its value should be considered.
  • each Bin Over time, the amount of remote sensing test data stored in each Bin increases rapidly. Due to the application of probabilistic and statistical analysis methods, the huge number of remote sensing test results no longer become a difficulty or obstacle to solving problems, but instead become a guarantee that the statistical average has practical significance, because at this time, the random emission test results fluctuate around the statistical average. The influence of the statistical average value of each Bin is so small that it can be ignored, then the statistical average value of each Bin is enough to represent the real value of the emission of this type of vehicle in the Bin area under the operating condition, which can be used for vehicle emission level evaluation and emission volume estimate.
  • the statistical analysis of the data is updated in real time. Through this method, a certain proportion of high-emission vehicles can always be screened and corresponding improvement measures can be taken, which can reduce the average emission of the overall vehicle. As time goes by, low-emission vehicles increase and old vehicles are eliminated. Although the structure of vehicle ownership changes, due to the real-time update of statistical analysis of data, the dynamic adjustment of the remote sensing emission judgment threshold for screening high-emission vehicles can be realized, which is consistent with the average vehicle emission.
  • the level and vehicle ownership structure changes are synchronized, which has the advantage of real-time update compared with the limit value of the annual inspection emission inspection standard for in-use vehicles, because the update of the emission standard limit value of the in-use vehicle is restricted by the standard revision cycle and revision process. .

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Abstract

本发明公开了一种点燃式发动机汽车尾气排放遥感大数据检测方法及***，包括：汽车尾气排放测量仪，主控计算机，信息显示仪，车辆行驶状态测试仪，气象检测仪，车牌摄像仪及机动车排放监控平台；所述汽车尾气排放测量仪、信息显示仪、汽车行驶状态测试仪、气象监测仪和车牌摄像仪与所述主控计算机通讯连接，所述主控计算机通过互联网与机动车排放监控平台连接。通过***采集汽车尾气污染物含量，通过将车辆VSP划分到多个Bin分区，并依据不同汽车类型，对汽车排放进行统计分析、评价及对高排放车辆进行判定。

Description

点燃式发动机汽车尾气排放遥感大数据检测方法和*** 技术领域
本发明涉及汽车尾气检测技术领域，更具体的说是涉及一种点燃式发动机汽车尾气排放遥感大数据检测方法和***。
背景技术
目前，国内在用点燃式发动机汽车尾气排放定期检测方法主要采用稳态工况法(ASM)或简易瞬态工况法(VMAS)。当检测条件无法满足时，点燃式发动机汽车可使用双怠速法，特别是路检的过程中，由于测试仪器设备限制，点燃式发动机汽车一般使用双怠速法，尽管设备操作非常简便，但是检测时车辆静止，非车辆正常行驶工况，因此相比实际行驶排放会存在较大的偏差。并且，在用车辆年检周期一般是一年检验一次，新车六年免检，无法保证车辆在法定的两次年检期间排放始终达标，而车辆排放遥感测试手段则能起到实时监控的目的。
机动车排气污染物遥感检测技术，具有检测速度快的特点，1小时可检测上千辆车，省时省力，大大提高了车辆排放检测效率。并且，可以在车辆正常行驶过程中完成监测，监测时汽车发动机的运行工况更具代表性，比传统的接触式测量方法能更好地反映汽车排放的实际情况。车辆排放遥感测试可以在驾驶员不知晓的情况下完成检测，避免了个别驾驶员为通过检测而采取某些手段人为地影响检测结果。
汽车排放遥感测试技术在北美、欧洲、亚洲的一些国家和地区已得到实际应用，目前主要的应用集中在筛查高排放车辆、筛选清洁车辆和入境检查 等方面。
为了改善空气质量，在交通运输领域，加快淘汰黄标车和老旧车辆。2015年我国最新发布的《大气污染防治法》第五十三条明确规定：在不影响正常通行的情况下，可以通过遥感监测等技术手段对在道路上行驶的机动车的大气污染物排放状况进行监督抽测。
截至2016年底，全国约有70余个城市应用尾气遥感监测设备开展道路车辆尾气检测，在全国范围内已建设机动车遥感监测设备400余台(套)，其中固定式遥感监测设备150余台(套)，移动式遥感监测设备250余台(套)。依据全国遥感检测网络规划，全国约有350余个地市，每个地市需要至少10台遥感检测设备，全国范围至少在3500套以上，机动车尾气遥感监测数据将会爆发式增长，基于机动车尾气遥感大数据处理方法，可对机动车实现全国范围联网监测。
截止目前，点燃式发动机汽车尾气排放遥感检测方法是对选定的车辆比功率VSP有效性判定区间内的车辆排放遥感测试数据进行判定，对高排放车辆进行筛选。如美国国家环保局推荐轻型车辆VSP在0～20kW/ton的范围作为遥测结果有效性的判断区间，VSP超出这个范围的遥测数据则不用于后续的评价。我国相关研究表明VSP在-5～14kW/ton范围内CO、HC和NOx的排放浓度相对稳定，而作为遥测结果有效性的判断区间。
但是，实际上这种方法相当于将车辆排放遥感数据判定区间只设定一个VSP的Bin区间，且只给定了一个限值，没有兼顾到机动车排放随VSP变化特性，因此容易导致误判，不利于科学化、精细化管理。由于车辆遥感数据有效性的VSP判定区间只选定了有限的VSP范围，超出这个范围的遥测数据则不用于后续的评价，这样导致大量的遥感数据无效。试验发现排放数据在选定的VSP有效性判定区间以外区域的排放更为严重，应该纳入监管范围。
因此，将排放数据在选定的VSP有效性判定区间以外区域也纳入监管范围，并基于车辆排放遥感测试大数据对点燃式发动机汽车尾气排放检测是本领域技术人员亟需解决的问题。
发明内容
有鉴于此，本发明提供了一种点燃式发动机汽车尾气排放遥感大数据检测方法和***，通过***采集汽车尾气污染物含量，通过将VSP划分到多个Bin分区，并依据不同汽车类型，对汽车排放进行评价及对高排放车辆进行判定。
为了实现上述目的，本发明采用如下技术方案：
一种点燃式发动机汽车尾气排放遥感大数据检测方法，包括：
S1、遥感检测汽车气态排气污染物；
S2、对汽车VSP计算，并进行Bin分区；
S3、根据S1中获取的排气污染物数据及S2中Bin分区信息，对S2中每
个Bin分区的排气污染物数据进行统计分析；
S4、计算车辆平均排放水平；
S5、对高排放车辆进行判定。
优选的，所述步骤S1的遥感检测汽车气态排气污染物方法包括：
1)检测排气烟羽中CO、HC和NO与CO 2的浓度比CO/CO 2、HC/CO 2和NO/CO 2，即φ CO、φ HC和φ NO，各成分相对体积浓度比为：
Figure PCTCN2020110173-appb-000001
Figure PCTCN2020110173-appb-000002
Figure PCTCN2020110173-appb-000003
式中φ CO、φ HC、φ NO分别为CO、HC和NO与CO 2的相对体积浓度比值，C CO、C HC、C NO和
Figure PCTCN2020110173-appb-000004
分别为排气烟羽中CO、HC和NOx与CO 2的浓度；
2)根据1)中计算得到的排气烟羽中各成分相对体积浓度比，依据碳平衡法，计算汽车消耗单位质量燃料的污染物P的排放质量，即质量排放因子：
Figure PCTCN2020110173-appb-000005
其中P表示CO、HC和NO，M P、M fuel分别为污染物P和燃料的分子量，单位为g/mole；
3)根据1)中计算得到的排气烟羽中各成分相对体积浓度比，计算汽车排气中CO 2、CO、HC、NO的体积百分比浓度：
Figure PCTCN2020110173-appb-000006
Figure PCTCN2020110173-appb-000007
Figure PCTCN2020110173-appb-000008
Figure PCTCN2020110173-appb-000009
优选的，依据排放遥感数据量和精细化管理需要将点燃式发动机汽车按照汽车总质量分为轻型车、中型车和重型车，进一步按照汽车用途划分为轻型客车、轻型货车、中型客车、中型货车、重型客车和重型货车。
优选的，所述步骤S2的对汽车VSP计算及Bin分区具体包括：
计算车辆比功率：
Figure PCTCN2020110173-appb-000010
其中，C D为阻力系数，A f为车辆迎风面积，ρ a为空气密度，v为车速，v w为风速，g为重力加速度，C R为轮胎的滚动阻力系数，a是车辆加速度，ε i为动力总成旋转部件的质量转换系数；
Figure PCTCN2020110173-appb-000011
为道路坡度；m v为车辆质量。
将细分后的每一类别汽车的行驶工况范围以VSP为参数划分为i+j个区间，VSP为正的区域划分为i个区间，分别定义为Bin p1、Bin p2、、、Bin pi-1、Bin pi；VSP为负的区域划分为j个区间，分别定义为Bin n1、Bin n2、、、Bin ni-1、Bin ni，每个Bin区间表征了车辆行驶的某一VSP范围。
优选的，遥感大数据统计分析具体包括：
S31、车辆行驶状态测试仪检测车辆车速和加速度，利用公式(9)计算被测车辆测试工况下的VSP值，依据VSP值将车辆排放遥感测试数据分配到对应的Bin区间；
S32、在车辆VSP的每个Bin区间内，采用离散型随机变量的概率分布方法对车辆排放遥感测试数据进行处理：
将检测到的车辆遥感排放数据作为离散型随机变量x，设x 1,x 2,…,x n为遥感排放数据变量x的取值，而p 1,p 2,…,p n为对应上述取值的概率，即概率分布密度，离散型遥感检测数据x i的概率分布可表示为
P(x i)＝p i    (10)
其中，i＝1,2,…,n，
且概率p i满足下列条件
Figure PCTCN2020110173-appb-000012
离散型排放数据变量x的累积分布函数f(x)，即累积分布概率为
Figure PCTCN2020110173-appb-000013
离散型排放数据变量x的值落在[a,b]之内的概率为
P(a＜x≤b)＝f(b)-f(a)    (13)
排放数据变量x i的取值大于等于0，因此得到排放数据变量x的概率分布函数f(x)的累计分布概率曲线，将排放数据变量x i看成是坐标轴上随机点的坐标，累积分布概率函数值f(x i)就表示x落在区间(0～x i)的概率，若高排放车辆 比例划定在y％，则截取累积分布概率在(100-y)％的排放测量值作为初选排放限值，作为筛查高排放车辆的排放判断阈值。
优选的，计算车辆平均排放水平包括：
Figure PCTCN2020110173-appb-000014
优选的，高排放车辆判定方法包括：
如果被测车辆的遥感排放测试结果超过该Bin区间的高排放车辆判断阈值，则记录为车辆排放超标，如果在规定的时间周期内排放超标记录次数达到判定次数，则判定被测车辆为高排放车辆。
一种点燃式发动机汽车尾气排放遥感大数据检测***，包括：汽车尾气排放测量仪，主控计算机，信息显示仪，车辆行驶状态测试仪，气象检测仪，车牌摄像仪及机动车排放监控平台；
所述汽车尾气排放测量仪、信息显示仪、汽车行驶状态测试仪、气象监测仪和车牌摄像仪与所述主控计算机通讯连接，所述主控计算机通过互联网与机动车排放监控平台连接；
所述主控计算机(2)用于对从汽车尾气排放测量仪(1)车行驶状态测试仪(4)、气象监测仪(5)和车牌摄像仪(6)中获取的数据进行处理。
优选的，所述点燃式发动机汽车尾气排放测量仪采用垂直或水平式光路，布置在车辆的经过区域；
所述汽车尾气排放测量仪包括：相对设置的检测光发射接收装置和检测光反射装置；所述检测光发射接收装置用于发射和接收穿过排气烟羽的检测光；
所述车辆行驶状态测试仪为车速、加速度光学测量仪或雷达测速仪。
优选的，所述信息显示仪为高亮点阵屏，用于实时显示被检车辆的相关信息；所述相关信息包括：车牌号、车速和尾气污染物浓度；
所述气象监测仪为微型气象站，布置在车辆的所述经过区域；用于测量环境参数。
经由上述的技术方案可知，与现有技术相比，本发明公开提供了一种基于遥感大数据的点燃式发动机汽车尾气排放监测方法和***，通过***采集汽车尾气污染物含量，通过将VSP划分到多个Bin分区，并依据不同汽车类型，对汽车排放进行评价及对高排放车辆进行判定。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1附图为本发明提供的***结构示意图。
图2附图为本发明提供的汽车类别分类和每一类别车辆的VSP的Bin分区方法示意图。
图3附图为本发明提供的排放数据变量x的累积分布密度函数f(x)示意图。
图4附图为本发明提供的按照排放数据变量x的累积分布概率确定高排放筛查判断阈值方法的示意图。
图5附图为本发明提供的北京市的轻型客车VSP分布概率。
图6附图为本发明提供的北京ASM5024工况轻型客车遥感排放测量值累积分布概率曲线。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明实施例公开了一种点燃式发动机汽车尾气排放遥感大数据检测方法和***；参照图1所示，为本发明实施例提供的一种点燃式发动机汽车尾气排放遥感大数据检测***，包括：点燃式发动机汽车尾气排放测量仪、主控计算机、信息显示仪、车辆行驶状态测试仪、气象监测仪及车牌摄像仪；
点燃式发动机汽车尾气排放测量仪、信息显示仪、车辆行驶状态测试仪、气象监测仪和车牌摄像仪与主控计算机通讯连接，主控计算机通过互联网与机动车排放监控平台连接；
其中，车辆行驶状态测试仪为车速和加速度光学测量仪或雷达测速仪，布置在车辆检测区域道路旁，当车辆经过时，可精确测量被检车辆的速度和加速度。点燃式发动机汽车尾气排放测量仪采用垂直或水平式光路，布置在车辆的经过区域；该点燃式发动机汽车尾气排放测量仪包括相对设置的检测光发射装置和检测光接收装置；检测光发射装置用于发射检测光；检测光接收装置用于接收穿过排气烟羽的检测光，并根据所接收的检测光的强弱分析机动车排气烟羽中污染物浓度。
上述的信息显示仪为高亮点阵屏，可实时显示被检车辆的信息；比如包括：车牌号、车速和尾气污染物浓度等信息。上述气象监测仪为微型气象站，同样布置在车辆的经过区域；可精准测量环境参数，比如风速、风向、温度、湿度等信息。
该车牌摄像仪为高速摄像机，可精准捕获车牌信息；其他可获得车牌信息的图像识别设备均可，本发明对此不作限定。该主控计算机为工业控制计 算机，负责以上所有输入输出信号采集和处理，以及***标定等；完成车速、加速度、比功率和尾气排放等计算；通过互联网向机动车排放监控平台发送数据，以及与机动车排放监控平台通讯。
机动车排放监控平台负责对车辆遥感检测大数据进行统计分析，大数据处理***持续不断周而复始地筛查出一定比例的高排放车辆。同时，定期对排放超标信息记录进行统计分析，筛查出排放超标记录占比较高的车型重点实施排放抽查和排放监管。
本实施例中，车辆行驶状态测试仪测量点燃式发动机汽车的速度和加速度，主控计算机以车辆速度和加速度为参数，计算比功率。同时，点燃式发动机汽车尾气排放测量仪检测排气烟羽中CO、HC、NO与CO 2的排放浓度比值，依据碳平衡方程计算消耗单位质量燃料的CO、HC和NO排放质量(g/kg燃料)；通过遥感数据反演计算方法计算点燃式发动机汽车尾气中CO、HC、NO和CO 2排放浓度，实现对点燃式发动机汽车尾气中气体排放物的实时测量。
本发明实施例还提供了一种点燃式发动机汽车尾气排放遥感大数据检测方法。
车辆比功率(Vehicle Specific Power，简称VSP)是车辆单位质量的瞬时功率，VSP作为表征车辆排放遥测条件的重要参数，应用于点燃式发动机汽车尾气排放稳定区间的判断，对遥测数据的有效性进行筛选。美国通过车辆试验台架进行FTP工况排放测试，发现当0≤VSP≤20kW/ton时，CO的排放浓度相对稳定，而当VSP>20kW/ton时，CO和HC的浓度都极易出现异常高值，所以美国国家环保局推荐VSP范围0～20kW/ton作为车辆排放遥测结果有效性的判断区间，VSP超出这个范围的遥测数据则不用于后续的评价。我国北京市将VSP的3～22kW/ton区间作为遥测结果有效性的判断区间。实际上这种方法相当于将车辆遥感数据有效性判定区间只选定一个VSP的Bin区间，且 一般只给定了一个排放限值，没有兼顾到机动车排放随VSP的变化特性，这种处理方法易导致误判，不利于科学化、精细化管理。由于车辆遥感数据有效性的VSP判定区间只是有限的VSP范围，超出这个范围的遥测数据不用于后续的评价，这样会导致大量的遥感数据无效，降低了机动车排放遥感测试设备的效能。并且，我们发现在遥感测试数据有效性的VSP判定区间以外区域的排放更为严重，应该纳入检测和监管范围。
本发明提出一种点燃式发动机汽车尾气排放遥感大数据检测方法。综合兼顾到点燃式发动机汽车在不同VSP工况的排放特性差异，提出基于点燃式发动机汽车不同类别、不同VSP区间的遥感排放大数据处理方法，实现对点燃式发动机汽车排放的科学化和精细化管理。具体方法和流程包括：S101-S111；
S101、依据点燃式发动机汽车排放遥感数据量和精细化管理需要将车辆类型划分为不同类别，如图2所示。可将点燃式发动机汽车分为轻型车、中型车和重型车，进一步可划分为轻型客车、轻型货车、中型客车、中型货车、重型客车和重型货车；
S102、依据遥感排放数据精细化管理需要，将细分后的每一类别点燃式发动机汽车的行驶工况范围以VSP为参数划分为i+j个区间，VSP为正的区域(Positive，用下标p表示)划分为i个区间，分别定义为Bin p1、Bin p2、、、、Bin pi-1和Bin pi；VSP为负的区域(Negative，用下标n表示)划分为j个区间，分别定义为Bin n1、Bin n2、Bin n3、、、Bin nj-1、、、、Bin nj，每个Bin区间表征了车辆行驶的VSP范围；
S103、车辆排放遥感检测***的车辆行驶状态测试仪检测车辆车速和加速度，利用公式(9)计算被测车辆测试工况下的VSP值，依据VSP值将车辆 排放遥感测试数据分配到对应的Bin区间，在每个Bin区间内对车辆排放遥感测试结果进行统计分析；
S104、通过点燃式发动机汽车尾气排放测量仪检测点燃式发动机汽车排气烟羽中CO、HC、NOx与CO 2的浓度比值φ CO、φ HC和φ NO，利用CO、HC和NO与CO 2的相对体积浓度比φ CO、φ HC和φ NO可用于判定车辆发动机燃烧状况，以及CO、HC和NO排放水平；
S105、依据碳平衡法，利用CO、HC和NO与CO 2的相对体积浓度比值φ CO、φ HC、φ NO，以及物质的分子量，利用公式(4)计算CO、HC和NOx基于单位质量燃料的排放质量(g/kg燃料)，也可实时判定被检车辆的排放状况；
S106、根据所述点燃式发动机汽车排气烟羽中CO、HC、NOx与CO 2浓度比值φ CO、φ HC、φ NO，利用公式(5)、(6)、(7)和(8)计算出点燃式发动机汽车尾气中CO、HC、NOx和CO 2排放浓度，用于实时判定被检车辆排放状况；
S107、在车辆VSP的每个Bin区间内，采用离散型随机变量的概率分布方法对车辆排放遥感测试数据进行处理。按照在用车中高排放车辆筛查比例，截取累积分布概率为排放合格车辆的累计概率值，其对应的排放测量值作为筛查高排放车辆的遥测排放判断阈值；
S108、统计分析车辆遥感排放测量值超标情况，如果被测车辆的遥感排放测试结果超过高排放车辆判断阈值，则记录为车辆排放超标，如果在规定的时间周期内排放超标记录次数达到判定次数，例如，车辆连续两次及以上同种污染物检测结果超过高排放车辆判断阈值，且测量时间间隔在6个自然月内，则判定被测车辆为高排放车辆，通知车主修车，否则可采取限制出行措施；
S109、采用车辆VSP的各个Bin区间内的排放统计平均值用于车辆平均排放水平评价。每个VSP的Bin区间内统计平均值足以代表该类车型在该工况区域的排放真实值，可用于车辆排放水平评价和排放量估算；
S110、超标排放车辆经维修排放检测合格后，则将数据库中该车的超标排放数据删除，但需将该车型排放超标信息另行记录，用于对市场上各类车型排放水平进行评价。由于大数据处理***持续不断、周而复始地筛查出一定比例的高排放车辆并采取相应整改措施，降低了在用车辆整体的平均排放。随着时间推移，低排放车辆增加，老旧车淘汰，尽管车辆保有量结构不断变化，由于统计分析的大数据实时更新，整体遥感排放数据的均值和中值也同步更新，即可实现与在用车辆保有量结构变化和车辆整体平均排放水平同步更新，并实现筛查高排放车辆的遥感排放判断阈值的动态调整，这与在用车年检排放检验标准限值相比具有实时更新的优越性；
S111、机动车排放监控平台定期对在用车型排放超标记录信息进行统计分析，可对市场上所有车型的排放水平及排放控制技术水平进行评价和排序，对筛查出的排放超标记录占比较高的车型重点实施排放抽查和排放监管。
上述步骤S101-S111中，包括：点燃式发动机汽车车型分类方法、车辆VSP计算方法、点燃式发动机汽车气态排气污染物遥感检测方法、车辆排放遥感测试大数据统计分析方法、高排放车辆判定方法、车辆平均排放水平评价方法和高排放车型的筛查和监管方法。
实施例2
1)依据点燃式发动机汽车排放遥感测试数据量和不同车型精细化管理需要将点燃式发动机汽车类型划分为不同类别，如附图2所示。可将点燃式发动机汽车分为轻型车、中型车和重型车，进一步划分为轻型客车、轻型货车、 中型客车、中型货车、重型客车和重型货车。用于重型车的点燃式发动机一般汽油机极少，常见为天然气发动机或醇类燃料发动机。
2)依据北京市部分点燃式发动机汽车中的轻型客车排放遥感测试结果，利用大数据统计分析方法进行处理。
采用车辆比功率VSP计算公式计算北京市点燃式发动机汽车中的轻型客车VSP分布：
Figure PCTCN2020110173-appb-000015
式中，VSP为机动车比功率(kW/ton)；C D为阻力系数，为无量纲系数；A f为车辆迎风面积，单位为m 2；ρ a为空气密度，1.29kg/m 3；v为车速，单位为m/s；v w为风速，单位为m/s，与车辆行进方向相反为正，否则为负；g为重力加速度，为9.8m/s 2；C R为轮胎的滚动阻力系数，为无量纲系数；a是车辆加速度，单位为m/s 2；ε i为动力总成旋转部件的质量转换系数；
Figure PCTCN2020110173-appb-000016
为道路坡度；m v为车辆质量，单位为ton。
将计算获得的北京市的轻型点燃式发动机客车车型的VSP值进行分布概率统计，如图5所示。
3)为了实现精细化管理，我们将VSP划分为24个Bin区间(见表1)，可以看出，轻型客车的VSP频率分布均呈现中间高两边降低的趋势。轻型客车VSP在0～5kW/ton区间内出现的概率密度最高，在此范围以外，随VSP增大或减小，车辆行驶工况出现在该VSP的Bin区间的概率都降低。
表1北京市的轻型点燃式发动机客车VSP的Bin分区
Figure PCTCN2020110173-appb-000017
Figure PCTCN2020110173-appb-000018
表1中以VSP参数将车辆行驶工况分为多个Bin区间，由于在不同的Bin分区中车辆发动机的负荷不同，因而每个Bin的CO、HC和NOx排放明显不同。另外，由于每种车辆的发动机、变速器等配置不同，导致每种车型CO、HC和NOx排放特性也存在显著差异。
4)采用机动车排气污染物遥感检测***对北京市城区的一部分点燃式发动机汽车排气污染物进行遥感检测，检测每辆车的CO、HC和NO与CO 2的浓度比CO/CO 2、HC/CO 2和NO/CO 2，记录车辆车牌信息，测量车辆的速度和加速度，以及环境信息；
汽油车排气烟羽中气态排放物各成分相对体积浓度比为：
Figure PCTCN2020110173-appb-000019
Figure PCTCN2020110173-appb-000020
Figure PCTCN2020110173-appb-000021
式中，φ CO、φ HC、φ NO分别为CO、HC和NO与CO 2的相对体积浓度比值，C CO、C HC、C NO和
Figure PCTCN2020110173-appb-000022
分别为排气烟羽中CO、HC和NOx与CO 2的浓度。
通过点燃式发动机汽车排气烟羽中CO、HC和NOx与CO 2的浓度比值φ CO、φ HC、φ NO，可以实时判定被检车辆的排放状况。
5)计算消耗单位质量燃料的CO、HC和NO排放质量(g/kg燃料)；
依据碳平衡法，可计算点燃式发动机汽车单位质量燃料的CO、HC和NO排放质量：
以汽油为例，汽油分子以CH 2表示，摩尔质量为0.014kg/mol。因此，可以通过公式计算基于燃料质量的CO，HC和NO的排放因子EFs：
Figure PCTCN2020110173-appb-000023
Figure PCTCN2020110173-appb-000024
Figure PCTCN2020110173-appb-000025
可以通过点燃式发动机汽车消耗单位质量燃料的CO、HC和NO排放质量(g/kg燃料)实时判定被检车辆的排放状况。
6)计算点燃式发动机汽车排气中的CO、HC和NO绝对浓度排放，采用下列公式：
Figure PCTCN2020110173-appb-000026
采用下列公式计算得到排气中CO、HC和NO的体积百分比浓度。
Figure PCTCN2020110173-appb-000027
Figure PCTCN2020110173-appb-000028
Figure PCTCN2020110173-appb-000029
通过以上公式，根据测量得到的烟羽中各组分的相对体积比可以反演得到汽油车尾气排放的真实体积浓度值。
因此，通过遥感方法检测点燃式发动机汽车排气中CO、HC和NO浓度，也可实时判定被检车辆排放状况。
6)点燃式发动机轻型客车排放遥感大数据分析
基于我们在点燃式发动机轻型客车ASM测试工况下获得的大量数据进行分析。北京市点燃式发动机轻型客车ASM测试工况的车速为24km/h，计算获得其VSP值为0.97kW/ton，依据VSP将车辆工况参数和遥感排放数据分配在VSP的Bin12区间内，下面对测得的遥感排放数据进行统计分析处理。
本实例以北京ASM5024工况的点燃式发动机轻型客车CO、HC和NO绝对浓度统计分析为例，分别对CO、HC和NO绝对浓度排放遥感测量值的累积分布概率进行分析如图6所示。
7)点燃式发动机轻型客车高排放阈值选择分析
依据北京ASM5024工况点燃式发动机轻型客车遥感排放测量值累积分布概率曲线，对北京点燃式发动机轻型客车ASM工况高排放车辆筛选阈值进行分析，见表3所示。
表3北京点燃式发动机轻型客车ASM工况高排放车辆筛选阈值进行分析
Figure PCTCN2020110173-appb-000030
鉴于目前车辆遥感测试存在较高误判率的问题，通过遥感测试结果只筛查比例占5％的高排放车辆，本例以单独控制一种排放污染物为目标进行分析。即在各污染物累积概率分布曲线上截取累积分布概率在95％的排放测量值作 为初选排放限值，即作为筛查高排放车辆的遥感排放判断阈值。在该Bin区间内遥感测试值超过该高排放车辆筛查阈值的车辆，则记录车辆排放超标。每个Bin区间内遥测排放数据和统计分析结果始终处于动态更新过程，为了分析方便，暂时按静态处理。
如果单独以CO排放为控制目标，筛查5％高排放车辆，高排放车辆的遥感排放判断阈值为0.82％。若判定的高排放车辆维修后排放达标，则筛除5％高排放车辆数据后的CO排放从最大值12.77％降至0.82％,CO排放数据统计平均值从0.23％降至0.14％。
如果单独以NO排放为控制目标，筛查5％高排放车辆，高排放车辆的遥感排放判断阈值为1425.2ppm。若判定的高排放车辆维修后排放达标，则筛除5％高排放车辆数据后的NO排放从最大值3968ppm降至1425.2ppm,NO排放数据统计平均值从396.62ppm降至302.48ppm。
如果单独以HC排放为控制目标，筛查5％高排放车辆，高排放车辆的遥感排放判断阈值为735ppm。若判定的高排放车辆维修后排放达标，则筛除5％高排放车辆数据后的HC排放从最大值9886ppm降至735ppm,HC排放数据统计平均值从169.26ppm降至47.7ppm。
由于每辆车辆排放遥感测试值是一组数据(CO、HC和NO)，且一辆车的CO、HC和NO排放可能呈现不同的排放特性，因此可能存在只有一种污染物超标而其他两种达标的情况，或两种排放物超标而一种达标的情况，也有可能三种都超标。因此，如果车辆的三种排气污染物CO、HC和NO排放均按照5％高排放车辆判断阈值进行筛查，则以一种排气污染物超标就判定为高排放车辆，则实际会导致筛查出的高排放车辆比例远大于5％；而若以三种污染物均超标才判定为高排放车辆，则实际筛查出的高排放车辆比例小于5％。因此， 必须通过后续试验测试适当调整三种污染物的高排放车辆判断阈值，使实际筛查出的高排放车辆比例控制5％。
另外，由表3可以看出，如果按照在用车标准修订中排放限值的确定原则,高排放车辆比例划定在10％～20％之间，以15％为例，截取排放测量值累积分布概率85％对应的排放测量值，作为筛查高排放车辆的遥感排放判断阈值，则排放控制效果则更加显著。
8)点燃式发动机轻型客车高排放超标判定
在车辆VSP的每个Bin区间内，如果车辆排放遥感测试值超过该Bin区间的高排放车辆筛查阈值，则记录车辆排放超标。
如果在规定的时间周期内超标排放记录次数达到判定次数(例如参考HJ845-2017标准，遥感检测结果表明连续两次及以上同种污染物检测结果超过标准规定的排放限值，且测量时间间隔在6个自然月内，则判定受检车辆排放不合格)，则判定被测车辆为高排放车辆，通知车主修车，否则可采取限制出行措施。
该车维修检测合格后，则将数据库中该车的超标排放数据删除，而该车型排放超标信息另行记录，用于对各种车型排放水平进行综合评价。
9)在用车各种车型排放水平综合评价
机动车排放监控平台定期统计各种车型排放超标信息记录，可对市场上所有车型的排放超标次数和排放水平进行排序和评价。对排放超标信息记录占比较高的车型重点实施排放抽查和排放监管。
10)每个Bin区间排放统计平均值
每类车辆VSP的Bin区间内的遥感监测数据的统计平均值，可通过离散型随机变量的统计平均值的公式计算：
Figure PCTCN2020110173-appb-000031
遥感监测数据的统计平均值是所有数据的加权平均，不仅要考虑每个遥感监测数据的取值，还要考虑到它取值所对应的概率。
随着时间累积，每个Bin内储存的遥感测试数据数量快速增加。由于应用概率统计分析方法，数量巨大的遥感测试结果不再成为解决问题的困难或障碍，反而成为统计平均值有实际意义的保证，因为此时随机的排放测试结果围绕统计平均值的涨落对于每个Bin的统计平均值的影响小到可以忽略不计了，那么每个Bin的统计平均值就足以代表该类车型在该工况Bin区域的排放真实值了，可用于车辆排放水平评价和排放量估算。
随着遥感监测数据的增加，数据统计分析实时更新，通过此方法始终能够筛查出一定比例的高排放车辆并采取相应改善措施，可降低整体车辆的平均排放。随着时间推移，低排放车辆增加，老旧车淘汰，尽管车辆保有量结构变化，由于数据统计分析实时更新，即可实现筛查高排放车辆的遥感排放判断阈值的动态调整，与车辆平均排放水平和车辆保有量结构变化同步，这与在用车年检排放检验标准限值相比具有实时更新的优越性，因为在用车排放标准限值的更新受标准制修订周期和制修订流程的制约。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。对于实施例公开的装置而言，由于其与实施例公开的方法相对应，所以描述的比较简单，相关之处参见方法部分说明即可。
对所公开的实施例的上述说明，使本领域专业技术人员能够实现或使用本发明。对这些实施例的多种修改对本领域的专业技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本发明的精神或范围的情况下，在其它实施例中实现。因此，本发明将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，包括：
    S1、遥感检测汽车气态排气污染物；
    S2、对汽车VSP计算，并进行Bin分区；
    S3、根据S1中获取的排气污染物数据及S2中Bin分区信息，对S2中每个Bin分区的排气污染物数据进行统计分析；
    S4、计算车辆平均排放水平；
    S5、对高排放车辆进行判定。
  2. 根据权利要求1所述的一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，所述步骤S1的遥感检测汽车气态排气污染物方法包括：
    1)检测排气烟羽中CO、HC和NO与CO 2的浓度比CO/CO 2、HC/CO 2和NO/CO 2，即φ CO、φ HC和φ NO，各成分相对体积浓度比为：
    Figure PCTCN2020110173-appb-100001
    Figure PCTCN2020110173-appb-100002
    Figure PCTCN2020110173-appb-100003
    式中φ CO、φ HC、φ NO分别为CO、HC和NO与CO 2的相对体积浓度比值，C CO、C HC、C NO和
    Figure PCTCN2020110173-appb-100004
    分别为排气烟羽中CO、HC和NOx与CO 2的浓度；
    2)根据1)中计算得到的排气烟羽中各成分相对体积浓度比，依据碳平衡法，计算汽车消耗单位质量燃料的污染物P的排放质量，即质量排放因子：
    Figure PCTCN2020110173-appb-100005
    其中P表示CO、HC和NO，M P、M fuel分别为污染物P和燃料的分子量，单位为g/mole；
    3)根据1)中计算得到的排气烟羽中各成分相对体积浓度比，计算汽车排气中CO 2、CO、HC、NO的体积百分比浓度：
    Figure PCTCN2020110173-appb-100006
    Figure PCTCN2020110173-appb-100007
    Figure PCTCN2020110173-appb-100008
    Figure PCTCN2020110173-appb-100009
  3. 根据权利要求1所述的一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，依据排放遥测数据量和精细化管理需要将点燃式发动机汽车按照汽车总质量分为轻型车、中型车和重型车，进一步按照汽车用途划分为轻型客车、轻型货车、中型客车、中型货车、重型客车和重型货车。
  4. 根据权利要求3所述的一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，所述步骤S2的对汽车VSP计算及Bin分区具体包括：
    计算车辆比功率：
    Figure PCTCN2020110173-appb-100010
    其中，C D为阻力系数，A f为车辆迎风面积，ρ a为空气密度，v为车速，v w为风速，g为重力加速度，C R为轮胎的滚动阻力系数，a是车辆加速度，ε i为动力总成旋转部件的质量转换系数；
    Figure PCTCN2020110173-appb-100011
    为道路坡度；m v为车辆质量；
    将细分后的每一类别汽车的行驶工况范围以VSP为参数划分为i+j个区间，VSP为正的区域划分为i个区间，分别定义为Bin p1、Bin p2、、、Bin pi-1、Bin pi；VSP为负的区域划分为j个区间，分别定义为Bin n1、Bin n2、、、Bin ni-1、Bin ni，每个Bin区间表征了车辆行驶的某一VSP范围。
  5. 根据权利要求4所述的一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，遥感大数据统计分析具体包括：
    S31、车辆行驶状态测试仪检测车辆车速和加速度，利用公式(9)计算被测车辆测试工况下的VSP值，依据VSP值将车辆排放遥感测试数据分配到对应的Bin区间；
    S32、在车辆VSP的每个Bin区间内，采用离散型随机变量的概率分布方法对车辆排放遥感测试数据进行处理：
    将检测到的车辆遥感排放数据作为离散型随机变量x，设x 1,x 2,...,x n为遥感排放数据变量x的取值，而p 1,p 2,...,p n为对应上述取值的概率，即概率分布密度，离散型遥感检测数据x i的概率分布可表示为
    P(x i)＝p i  (10)
    其中，i＝1,2,...,n，
    且概率p i满足下列条件
    Figure PCTCN2020110173-appb-100012
    离散型排放数据变量x的累积分布函数f(x)，即累积分布概率为
    Figure PCTCN2020110173-appb-100013
    离散型排放数据变量x的值落在[a,b]之内的概率为
    P(a＜x≤b)＝f(b)-f(a)  (13)
    排放数据变量x i的取值大于等于0，因此得到排放数据变量x的概率分布函数f(x)的累计分布概率曲线，将排放数据变量x i看成是坐标轴上随机点的坐标，累积分布概率函数值f(x i)就表示x落在区间(0～x i)的概率，若高排放车辆比例划定在y％，则截取累积分布概率在(100-y)％的排放测量值作为初选排放限值，作为筛查高排放车辆的排放判断阈值。
  6. 根据权利要求5所述的一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，计算车辆平均排放水平包括：
    Figure PCTCN2020110173-appb-100014
  7. 根据权利要求1所述的一种点燃式发动机汽车尾气排放遥感大数据检测方法，其特征在于，高排放车辆判定方法包括：
    若被测车辆的遥感排放测试结果超过该Bin区间的高排放车辆判断阈值，则记录为车辆排放超标，如果在规定的时间周期内排放超标记录次数达到判定次数，则判定被测车辆为高排放车辆。
  8. 一种基于权利要求1-7任意一项权利要求所述的点燃式发动机汽车尾气遥感大数据检测方法的检测***，其特征在于，包括：汽车尾气排放测量仪(1)，主控计算机(2)，信息显示仪(3)，车辆行驶状态测试仪(4)，气象检测仪(5)，车牌摄像仪(6)及机动车排放监控平台(7)；
    所述汽车尾气排放测量仪(1)、信息显示仪(3)、汽车行驶状态测试仪(4)、气象监测仪(5)和车牌摄像仪(6)与所述主控计算机(2)通讯连接，所述主控计算机(2)通过互联网与机动车排放监控平台(7)连接；
    所述主控计算机(2)用于对从汽车尾气排放测量仪(1)、汽车行驶状态测试仪(4)、气象监测仪(5)和车牌摄像仪(6)中获取的数据进行处理。
  9. 根据权利要求8所述的一种点燃式发动机汽车尾气排放遥感大数据检测***，其特征在于，所述汽车尾气排放测量仪(1)采用垂直或水平式光路，布置在车辆的经过区域；
    所述汽车尾气排放测量仪(1)包括：相对设置的检测光发射接收装置和检测光反射装置；所述检测光发射接收装置用于发射和接收穿过排气烟羽的检测光；
    所述车辆行驶状态测试仪(4)为车速、加速度光学测量仪或雷达测速仪。
  10. 根据权利要求8所述的一种点燃式发动机汽车尾气排放遥感大数据检测***，其特征在于，所述信息显示仪为(3)高亮点阵屏，用于实时显示被检车辆的相关信息；所述相关信息包括：车牌号、车速和尾气污染物浓度；
    所述气象监测仪(5)为微型气象站，布置在车辆的所述经过区域，用于测量环境参数。
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Publication number Priority date Publication date Assignee Title
CN112712707A (zh) * 2020-12-26 2021-04-27 清华四川能源互联网研究院 车辆碳排放监测***及方法
CN112858126B (zh) * 2021-01-25 2023-01-31 清华大学 一种高排放车识别***和方法
CN113959964B (zh) * 2021-10-11 2022-05-31 天津同阳科技发展有限公司 基于机动车尾气遥感检测二氧化碳吸收增量的计算方法
CN114019113B (zh) * 2022-01-04 2022-05-17 杭州春来科技有限公司 一种基于多模数据修正的机动车尾气动态监测方法及装置
CN114882614B (zh) * 2022-07-11 2022-10-11 杭州春来科技有限公司 一种机动车碳排放监测方法及***
CN116165350B (zh) * 2023-03-13 2023-09-29 山东交通学院 基于遥感技术的柴油车污染物检测方法、***及设备
CN117093807B (zh) * 2023-10-19 2024-01-02 中汽研汽车检验中心（天津）有限公司 基于多维度Bin区间的车辆尾气排放清单计算方法
CN117949225B (zh) * 2024-03-26 2024-06-11 长春汽车工业高等专科学校 一种汽车排放量检测方法及***

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102445518A (zh) * 2010-10-09 2012-05-09 胡建华 内燃机尾气在线测量诊断方法及设备
CN105181343A (zh) * 2015-05-25 2015-12-23 中国环境科学研究院 适用于装有obd的重型汽车的简易车载排放测试***及方法
CN109443779A (zh) * 2018-11-07 2019-03-08 中国汽车技术研究中心有限公司 一种动态诊断、提取柴油车实际运行nox高排放恶劣工况的方法和装置
CN110967320A (zh) * 2019-11-06 2020-04-07 北京理工大学 一种柴油车气态排气污染物遥感检测***及方法

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103838971A (zh) * 2014-03-12 2014-06-04 中国航天***工程有限公司 一种城市路网动态交通能耗及排放的计算方法
CN104786967A (zh) * 2015-04-22 2015-07-22 北京九五智驾信息技术股份有限公司 驾驶行为分析***
CN106845371B (zh) * 2016-12-31 2019-10-25 中国科学技术大学 一种城市路网机动车尾气排放遥感监控***
CN106650825B (zh) * 2016-12-31 2020-05-12 中国科学技术大学 一种机动车尾气排放数据融合***
CN107356564A (zh) * 2017-07-04 2017-11-17 贵州中航交通科技有限公司 一种汽车尾气遥感检测***及其检测方法
CN107886188B (zh) * 2017-10-18 2021-12-10 东南大学 液化天然气公交尾气排放预测方法
CN111122171B (zh) * 2018-10-30 2021-07-20 中国汽车技术研究中心有限公司 一种基于vsp工况的柴油车与柴油机多种排放检测方法的多源异构数据关联分析方法
CN109613905B (zh) * 2018-11-07 2020-06-30 中国汽车技术研究中心有限公司 一种动态识别重型商用车实际运行高油耗恶劣工况的方法和装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102445518A (zh) * 2010-10-09 2012-05-09 胡建华 内燃机尾气在线测量诊断方法及设备
CN105181343A (zh) * 2015-05-25 2015-12-23 中国环境科学研究院 适用于装有obd的重型汽车的简易车载排放测试***及方法
CN109443779A (zh) * 2018-11-07 2019-03-08 中国汽车技术研究中心有限公司 一种动态诊断、提取柴油车实际运行nox高排放恶劣工况的方法和装置
CN110967320A (zh) * 2019-11-06 2020-04-07 北京理工大学 一种柴油车气态排气污染物遥感检测***及方法

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