CN117893381A - Method for reducing uncertainty of traffic carbon emission measurement - Google Patents
Method for reducing uncertainty of traffic carbon emission measurement Download PDFInfo
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- CN117893381A CN117893381A CN202410079050.7A CN202410079050A CN117893381A CN 117893381 A CN117893381 A CN 117893381A CN 202410079050 A CN202410079050 A CN 202410079050A CN 117893381 A CN117893381 A CN 117893381A
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- 238000005259 measurement Methods 0.000 title claims abstract description 64
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 53
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004364 calculation method Methods 0.000 claims abstract description 48
- 238000004458 analytical method Methods 0.000 claims abstract description 13
- 238000012546 transfer Methods 0.000 claims abstract description 13
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 238000013076 uncertainty analysis Methods 0.000 claims abstract description 10
- 238000010206 sensitivity analysis Methods 0.000 claims abstract description 7
- 239000000446 fuel Substances 0.000 claims description 21
- 230000007613 environmental effect Effects 0.000 claims description 19
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 claims description 13
- 238000012937 correction Methods 0.000 claims description 10
- 230000035945 sensitivity Effects 0.000 claims description 9
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000013139 quantization Methods 0.000 claims description 6
- 239000007789 gas Substances 0.000 claims description 5
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 3
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 3
- 239000001569 carbon dioxide Substances 0.000 claims description 3
- 238000002485 combustion reaction Methods 0.000 claims description 3
- 230000002596 correlated effect Effects 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims 1
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- 238000011156 evaluation Methods 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 239000013618 particulate matter Substances 0.000 description 2
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- 238000005070 sampling Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
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Abstract
The invention discloses a method for reducing uncertainty of measurement and calculation of traffic carbon emission. Firstly, equipment selection is carried out, and accurate data related to vehicle carbon emission are acquired. And secondly, selecting and testing samples, selecting a representative sample vehicle, and repeatedly measuring for a plurality of times to reduce random errors of measuring and calculating results. And then, selecting influence factors, selecting factors considered in the measuring and calculating process, and correcting according to the factors. Then, selecting a model and a calculation method, and selecting a proper model and a calculation method to accurately calculate the carbon emission of the traffic. And finally, uncertainty analysis is carried out, wherein the uncertainty analysis comprises sensitivity analysis and error transfer analysis, so as to evaluate the reliability and uncertainty range of the measuring and calculating result. The invention can solve the technical problems of how to comprehensively and accurately analyze the carbon emission measurement result and how to systematically reduce the uncertainty result.
Description
Technical Field
The invention belongs to the technical field of traffic environment, and particularly relates to a method for reducing uncertainty of traffic carbon emission measurement.
Background
At present, the measurement and calculation of the carbon emission of the traffic have been advanced to a certain extent, and a vehicle-mounted emission test method is mainly adopted, and various parameters of the vehicle are recorded and the carbon emission of the vehicle is calculated by installing sensors and data acquisition equipment on the vehicle.
However, the measurement and calculation of the carbon emission of the traffic still have some problems, and the most important problem is that the accuracy and reliability of the existing measurement and calculation method still need to be improved. The emission test is carried out in the actual running process, and due to the influence of the running environment of the vehicle and the complexity of the sampling process, certain errors exist in the measurement result, such as the accuracy of a sensor, the acquisition and processing of data and the like. In addition, the traffic carbon emission measurement and calculation also has the problems of data acquisition and sharing. The quality and quantity of existing data are limited due to the source and sharing difficulty of the data. This is also one of the difficulties in developing effective carbon emission management policies and measures for highway vehicles.
Problems and defects existing in the prior art are as follows: the analysis of the carbon emission measurement results cannot be performed comprehensively and accurately, and there is no method how to systematically reduce the uncertainty results.
Disclosure of Invention
The invention aims to provide a method for reducing uncertainty of traffic carbon emission measurement and calculation, which aims to solve the technical problems of how to comprehensively and accurately analyze carbon emission measurement and calculation results and how to systematically reduce the uncertainty results.
The invention relates to a method for reducing uncertainty of measurement and calculation of traffic carbon emission, which can reduce the uncertainty and comprises the following steps:
step one, selecting equipment: data concerning the carbon emissions of the vehicle is collected, including the use of sensors, gauges, data logging devices, and the like.
Step two, sample selection and testing: a representative sample vehicle is selected and repeated measurements are made multiple times to reduce random errors in the measurement results.
Step three, influence factor selection: and selecting factors considered in the measuring and calculating process, such as the service condition of the vehicle, the driving mode, the environmental factors and the like, and correcting according to the factors.
Step four, selecting a model and a calculation method: and selecting a proper model and a proper calculation method to accurately calculate the carbon emission of the traffic.
Step five, uncertainty analysis: uncertainty analysis, including sensitivity analysis and error propagation analysis, is performed to evaluate the reliability and uncertainty range of the measurement.
Further, in the first step, the device selecting process reduces uncertainty, and the device capable of reducing uncertainty includes:
(1) Emission sensor: the carbon emissions in the vehicle exhaust can be measured directly using a dedicated emissions sensor, such as an exhaust gas analyzer. These sensors typically measure carbon dioxide (CO 2 ) Concentration of emissions such as nitrogen oxides (NOx) and particulate matter.
(2) Vehicle-mounted data recorder: the vehicle-mounted data recorder may record travel data of the vehicle, including position, vehicle speed, acceleration, travel distance, and the like. These data may be correlated with carbon emission data to obtain more comprehensive vehicle carbon emission information. The on-board data logger may be a hardware device or may be a software application based on a vehicle diagnostic interface (e.g., an OBD-II interface).
(3) Vehicle diagnostic tool: some vehicle diagnostic tools may obtain vehicle related data, including engine load, fuel consumption rate, etc., through a diagnostic interface (e.g., OBD-II interface) connected to the vehicle.
Further, in the second step, the sample selection and testing process reduces uncertainty, and the required factors include:
(1) Sample selection: a representative sample vehicle is selected that is capable of representing the characteristics and variability of the entire vehicle population. The selection may be made taking into account the following factors: vehicle type, age, displacement, fuel type, mileage, etc.
(2) Sample number: a statistical sample size calculation is required to determine the appropriate sample size to ensure a representative of the entire vehicle population;
(3) Repeated measurement: multiple repeated measurements (greater than 10) of a selected sample vehicle are critical to reducing random errors. Each sample vehicle should make multiple independent measurements to obtain a series of measurements. The diversity of measurements can be increased by making multiple measurements at different times and under different environmental conditions.
Further, in the third step, the influence factor selection process reduces uncertainty, and the influence factors include:
(1) Vehicle use conditions: the vehicle use condition refers to a specific running mode and working condition of the vehicle, such as city driving, highway driving, parking, etc.
(2) Running mode: the running mode refers to parameters such as speed, acceleration, deceleration, etc. of the vehicle during running. The change in the running mode may have an influence on the energy efficiency and carbon emission of the vehicle. The driving cycle data or model may be used to estimate the actual carbon emissions of the vehicle according to different driving modes.
(3) Environmental factors: environmental factors include temperature, humidity, altitude, etc. These factors affect the fuel combustion efficiency, air density, etc. of the vehicle, and thus affect the carbon emission. The measurement may be corrected using an environmental correction factor (temperature correction factor, humidity correction factor, etc.) based on the change in environmental factors.
Further, in the fourth step, the uncertainty is reduced in the selection process of the model and the calculation method, and the requirements to be considered include:
(1) Availability of data: the available data and information is evaluated. This includes vehicle data (e.g., vehicle type, vehicle speed, travel distance), fuel data (e.g., fuel type, fuel consumption), and travel environment data (e.g., road type, traffic conditions). Ensuring that the selected model and calculation method can utilize the available data.
(2) Accuracy requirements: and determining the accuracy requirement for the measurement and calculation of the carbon emission of the traffic. Different application scenarios may have different accuracy requirements. For example, policy formulation may require higher accuracy, while preliminary evaluation may require less accuracy. The appropriate model and calculation method are selected according to the accuracy requirements.
(3) Data sources: the source of the data is determined. The data may be from vehicle tests, references, official statistics, etc. And selecting a proper model and a proper calculation method according to the reliability and the applicability of the data.
The above model and calculation method are shown in table 1.
Further, in the fifth step, the uncertainty analysis process reduces uncertainty, and the specific analysis includes:
(1) Sensitivity analysis:
determining key input parameters: first, key input parameters that affect the measurement result are determined. These parameters may include vehicle data, fuel data, travel patterns, and the like.
Change parameter values: for each key input parameter, an attempt is made to change its value, for example by increasing or decreasing a fixed ratio, or by randomly varying over a range.
Analysis result change: and analyzing the measuring and calculating result under each parameter change condition. The degree of change in the measured results is observed to determine which parameters have a greater impact on the results.
Sensitivity index: depending on the degree of variation of the result, a sensitivity index (e.g., spearman correlation coefficient, impact metric, etc.) may be used to quantify the sensitivity of the parameter to the result.
2) Error transfer analysis:
determining the error source: the sources of errors that may be introduced during the measurement process are identified, such as measurement errors of the input data, uncertainty of model parameters, and the like.
And (3) error quantization: each error source is quantified, e.g., standard deviation of measured errors, confidence interval of model parameters, etc.
Error transfer calculation: and transmitting the error into the measuring result through a proper calculation method (such as Monte Carlo simulation, transfer function and the like) according to the quantization result of the error source.
Uncertainty range assessment: based on the result of the error transfer calculation, the uncertainty range of the measurement result may be evaluated, for example, a confidence interval or standard deviation may be calculated.
The invention has the advantages and positive effects that:
the invention can comprehensively and accurately analyze the carbon emission measurement result, and has the advantage of systematically reducing the uncertainty result.
Drawings
Fig. 1 is a schematic overall diagram of a method for reducing uncertainty in measurement of carbon emission in traffic according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly explain the drawings needed in the embodiments of the present application, and it is obvious that the drawings described below are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Aiming at the problems in the prior art, the invention provides a method for reducing the uncertainty of measurement and calculation of traffic carbon emission, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for reducing uncertainty of measurement and calculation of carbon emission in traffic provided by the embodiment of the invention comprises the following steps:
s1, selecting equipment: data concerning the carbon emissions of the vehicle are collected using sensors, gauges, data recording devices, and the like.
S1.1, an emission sensor: the carbon emissions in the vehicle exhaust can be measured directly using a dedicated emissions sensor, such as an exhaust gas analyzer. These sensors typically measure carbon dioxide (CO 2 ) Concentration of emissions such as nitrogen oxides (NOx) and particulate matter.
S1.2, an on-vehicle data recorder: the vehicle-mounted data recorder may record travel data of the vehicle, including position, vehicle speed, acceleration, travel distance, and the like. These data may be correlated with carbon emission data to obtain more comprehensive vehicle carbon emission information. The on-board data logger may be a hardware device or may be a software application based on a vehicle diagnostic interface (e.g., an OBD-II interface).
S1.3, vehicle diagnostic tool: some vehicle diagnostic tools may obtain vehicle related data, including engine load, fuel consumption rate, etc., through a diagnostic interface (e.g., OBD-II interface) connected to the vehicle.
S2, sample selection and testing: a representative sample vehicle is selected and repeated measurements are made multiple times to reduce random errors in the measurement results.
S2.1, sample selection: a representative sample vehicle is selected that is capable of representing the characteristics and variability of the entire vehicle population. The selection may be made taking into account the following factors: vehicle type, age, displacement, fuel type, mileage, etc.
S2.2, number of samples: the number of samples should be large enough to ensure a representative of the entire population of vehicles. A statistical sample size calculation may be performed to determine the appropriate sample size.
S2.3, repeat measurement: multiple repeated measurements of a selected sample vehicle are critical to reducing random errors. Each sample vehicle should make multiple independent measurements to obtain a series of measurements. The diversity of measurements can be increased by making multiple measurements at different times and under different environmental conditions.
S3, influence factor selection: and selecting factors considered in the measuring and calculating process, such as the service condition of the vehicle, the driving mode, the environmental factors and the like, and correcting according to the factors.
S3.1, vehicle service conditions: the vehicle use condition refers to a specific running mode and working condition of the vehicle, such as city driving, highway driving, parking, etc. According to different vehicle use conditions, different correction factors or coefficients can be used for adjusting the measuring and calculating results so as to reflect actual conditions.
S3.2, driving mode: the running mode refers to parameters such as speed, acceleration, deceleration, etc. of the vehicle during running. The change in the running mode may have an influence on the energy efficiency and carbon emission of the vehicle. The driving cycle data or model may be used to estimate the actual carbon emissions of the vehicle according to different driving modes.
S3.3, environmental factors: environmental factors include temperature, humidity, altitude, etc. These factors affect the fuel combustion efficiency, air density, etc. of the vehicle, and thus affect the carbon emission. The environmental correction factor may be used to correct the measurement results based on changes in environmental factors.
S4, selecting a model and a calculation method: and selecting a proper model and a proper calculation method to accurately calculate the carbon emission of the traffic.
S4.1, availability of data: the available data and information is evaluated. This includes vehicle data (e.g., vehicle type, vehicle speed, travel distance), fuel data (e.g., fuel type, fuel consumption), and travel environment data (e.g., road type, traffic conditions). Ensuring that the selected model and calculation method can utilize the available data.
S4.2, accuracy requirement: and determining the accuracy requirement for the measurement and calculation of the carbon emission of the traffic. Different application scenarios may have different accuracy requirements. For example, policy formulation may require higher accuracy, while preliminary evaluation may require less accuracy. The appropriate model and calculation method are selected according to the accuracy requirements.
S4.3, data source: the source of the data is determined. The data may be from vehicle tests, references, official statistics, etc. And selecting a proper model and a proper calculation method according to the reliability and the applicability of the data.
S4.4, model complexity: consider the complexity and operability of the model. More complex models may require more input data and computing resources and may require higher technical requirements. Appropriate models and calculation methods are selected based on available resources and technical capabilities.
S4.5, practical application cases: consider existing practical application cases and research results. Knowing the application and effect of existing models and computing methods in similar scenarios can provide a reference for selection.
S5, uncertainty analysis: uncertainty analysis, including sensitivity analysis and error propagation analysis, is performed to evaluate the reliability and uncertainty range of the measurement.
S5.1, sensitivity analysis:
s5.1.1, determining key input parameters: first, key input parameters that affect the measurement result are determined. These parameters may include vehicle data, fuel data, travel patterns, and the like.
S5.1.2, variation parameter values: for each key input parameter, an attempt is made to change its value, for example by increasing or decreasing a fixed ratio, or by randomly varying over a range.
S5.1.3, analysis results change: and analyzing the measuring and calculating result under each parameter change condition. The degree of change in the measured results is observed to determine which parameters have a greater impact on the results.
S5.1.4, sensitivity index: depending on the degree of variation of the result, a sensitivity index (e.g., spearman correlation coefficient, impact metric, etc.) may be used to quantify the sensitivity of the parameter to the result.
S5.2, error transfer analysis:
s5.2.1, determining the error source: the sources of errors that may be introduced during the measurement process are identified, such as measurement errors of the input data, uncertainty of model parameters, and the like.
S5.2.2, error quantization: each error source is quantified, e.g., standard deviation of measured errors, confidence interval of model parameters, etc.
S5.2.3, error transfer calculation: and transmitting the error into the measuring result through a proper calculation method (such as Monte Carlo simulation, transfer function and the like) according to the quantization result of the error source.
S5.2.4 uncertainty Range assessment: based on the result of the error transfer calculation, the uncertainty range of the measurement result may be evaluated, for example, a confidence interval or standard deviation may be calculated.
Table 1:
Claims (6)
1. a method for reducing uncertainty in measurement of carbon emissions in traffic, the method comprising:
(1) And (3) selecting equipment: collecting data related to carbon emissions of a vehicle by using a sensor, a measuring instrument and a data recording device; (2) sample selection and testing: selecting a representative sample vehicle, and repeatedly measuring for a plurality of times to reduce random errors of the measuring and calculating result; (3) influence factor selection: selecting factors considered in the measuring and calculating process, including vehicle service conditions, running modes and environmental factors, and correcting according to the factors; (4) model and calculation method selection: selecting a proper model and a proper calculation method to accurately calculate the carbon emission of the traffic; (5) uncertainty analysis: uncertainty analysis, including sensitivity analysis and error propagation analysis, is performed to evaluate the reliability and uncertainty range of the measurement.
2. The method for reducing uncertainty in carbon emissions measurement in traffic of claim 1, wherein the device selection process reduces uncertainty, and wherein the device operable to reduce uncertainty comprises:
(1) Emission sensor: the device comprises an exhaust gas analyzer, wherein the exhaust gas analyzer can be used for directly measuring carbon emission substances in the exhaust gas of a vehicle, and comprises the steps of measuring the concentration of carbon dioxide, nitrogen oxides and particulate matters;
(2) Vehicle-mounted data recorder: the vehicle-mounted data recorder can record the running data of the vehicle, including position, vehicle speed, acceleration and running distance; these data are correlated with carbon emission data to obtain more comprehensive vehicle carbon emission information; the vehicle-mounted data recorder can be a hardware device or a software application based on a vehicle diagnosis interface;
(3) Vehicle diagnostic tool: the vehicle diagnostic tool may obtain vehicle related data including engine load, fuel consumption rate through a diagnostic interface connected to the vehicle.
3. The method of reducing uncertainty in measurement of carbon emissions in traffic of claim 1, wherein the sample selection and testing process reduces uncertainty, and wherein the specific considerations include:
(1) Sample selection: selecting a representative sample vehicle capable of representing characteristics and variability of the entire population of vehicles; the selection is made taking into account the following factors: vehicle type, age, displacement, fuel type, mileage;
(2) Sample number: a statistical sample size calculation is required to determine the appropriate sample size to ensure a representative of the entire vehicle population;
(3) Repeated measurement: multiple repeated measurements of a selected sample vehicle are critical to reducing random errors; each sample vehicle should make multiple independent measurements to obtain a series of measurements; the diversity of measurements is increased by making multiple measurements at different times and under different environmental conditions.
4. The method for reducing uncertainty in measurement of carbon emissions in traffic of claim 1, wherein the influencing factor selection process reduces uncertainty, and wherein the influencing factors include:
(1) Vehicle use conditions: the vehicle use condition refers to a specific running mode and working conditions of the vehicle, including urban driving, highway driving and parking;
(2) Running mode: the driving mode refers to the speed, acceleration and deceleration parameters of the vehicle during driving; the change of the driving mode can affect the energy efficiency and carbon emission of the vehicle; estimating an actual carbon emission of the vehicle using the driving cycle data or model according to the different driving modes;
(3) Environmental factors: environmental factors include temperature, humidity, altitude; these factors affect the fuel combustion efficiency, air density, etc. of the vehicle, and thus affect the carbon emission; according to the change of the environmental factors, the environmental correction factors can be used for correcting the measuring and calculating results; the environmental correction factors include a temperature correction factor, a humidity correction factor, and a height correction factor.
5. The method for reducing uncertainty in carbon emission measurements in traffic of claim 1, wherein the model and calculation method selection process reduces uncertainty, and wherein the specific consideration requirements include:
(1) Availability of data: evaluating available data and information; this includes vehicle data including vehicle type, vehicle speed, travel distance, fuel data including fuel type, fuel consumption, and travel environment data including road type, traffic condition; ensuring that the selected model and calculation method can utilize the available data;
(2) Accuracy requirements: determining the accuracy requirement for the measurement and calculation of the carbon emission of the traffic; different application scenarios may have different accuracy requirements; selecting a proper model and a proper calculation method according to the accuracy requirement;
(3) Data sources: determining the source of data from vehicle tests, references, official statistics; and selecting a proper model and a proper calculation method according to the reliability and the applicability of the data.
6. The method for reducing uncertainty in measurement of carbon emissions in traffic of claim 1, wherein the uncertainty analysis process reduces uncertainty, and wherein the specific analysis comprises:
(1) Sensitivity analysis:
determining key input parameters: firstly, determining key input parameters affecting a measuring and calculating result; these parameters may include vehicle data, fuel data, travel mode;
change parameter values: for each key input parameter, attempting to change its value, increasing or decreasing a fixed ratio, or randomly changing within a certain range;
analysis result change: analyzing the measuring and calculating result under the condition of each parameter change; observing the degree of change of the measurement result to determine which parameters have a larger influence on the result;
sensitivity index: depending on the degree of variation of the result, the sensitivity of the parameter to the result may be quantified using metrics including a spearman correlation coefficient and an impact metric sensitivity index.
(2) Error transfer analysis:
determining the error source: identifying sources of errors possibly introduced in the measuring and calculating process, wherein the sources comprise measurement errors of input data and uncertainty of model parameters;
and (3) error quantization: quantifying each error source, including standard deviation of measurement errors and confidence interval of model parameters;
error transfer calculation: transmitting the error to the measuring and calculating result by a Monte Carlo simulation and transfer function calculation method according to the quantization result of the error source;
uncertainty range assessment: and evaluating the uncertainty range of the measuring and calculating result according to the result obtained by error transfer calculation, wherein the uncertainty range comprises a calculation confidence interval or standard deviation.
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