Heavy-duty vehicle carbon emission and fuel consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test

Abstract

The application discloses a heavy vehicle carbon emission and oil consumption partition reconstruction evaluation method suitable for remote monitoring and actual road test, which is characterized in that: remote monitoring data or actual road test data of a target vehicle model are taken as vehicle real-time data input, vehicle real-time data per second and engine speed and net output torque of the drum data are partitioned and data correlated, average oil consumption level of the target vehicle model is obtained through data clustering of the partitioned data, reconstruction data of carbon emission or oil consumption level of each partition is obtained based on drum data prediction and average oil consumption level of the target vehicle model, different partition reconstruction data are spliced according to engine cycle process of the target vehicle model during oil consumption authentication on a chassis dynamometer, carbon emission or oil consumption level of the target vehicle model during use is obtained, and conformity or compliance of the carbon emission or oil consumption level is compared with an authentication result. The application improves the use efficiency of remote monitoring and actual road test data, and is simple in calculation, high in accuracy and strong in adaptability.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles for remote monitoring and actual road testing. Background Technology

[0002] Heavy-duty vehicle carbon emissions are directly related to fuel consumption. To better control heavy-duty vehicle emissions, the construction of a remote online monitoring system for heavy-duty diesel vehicles is imperative. China's Stage VI emission standard for heavy-duty vehicles, GB17691-2018, added requirements for remote monitoring and real-road testing. After years of development, the remote monitoring system for heavy-duty vehicles has gradually improved its functions, and the number of connected vehicles has also increased. Real-road testing uses a portable vehicle-mounted emission testing system (PEMS) to record actual vehicle operating data and emission results. According to GB / T 27840-2021, heavy-duty vehicle fuel consumption testing must be conducted on a chassis dynamometer according to the Chinese vehicle driving conditions specified in GB / T38146.2-2019, simulating a fully loaded vehicle. During actual vehicle operation, whether through remote monitoring or PEMS testing, differences in load, driver, wind resistance, and road conditions prevent the reproduction of standard fuel consumption test conditions, thus hindering effective monitoring of vehicle fuel consumption (carbon emissions). Summary of the Invention

[0003] The purpose of this invention is to address the technical deficiencies in the existing technology by providing a method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles for remote monitoring and actual road testing. This method applies vehicle operation data obtained from remote monitoring big data or actual road testing to the monitoring of fuel consumption (carbon emissions) of heavy-duty vehicles, thereby extending the regulation of fuel consumption (carbon emissions) of heavy-duty vehicles from the current new vehicle certification stage to the production consistency and in-use compliance inspection stage, improving the utilization rate of remote monitoring and test data, and enhancing the efficiency of regulating fuel consumption (carbon emissions) of heavy-duty vehicles.

[0004] The technical solution adopted to achieve the purpose of this invention is:

[0005] A method for refactoring and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles, including the following steps:

[0006] Data cleaning is performed on the actual operating data of the target vehicle, including remote monitoring data or actual road test data, to obtain valid vehicle data. Preprocessing is then performed on the valid vehicle data and the obtained drum data to obtain the net output torque and power per second of the valid vehicle data and drum data. Based on torque partitioning intervals, the net output torque per second of the valid vehicle data and drum data is partitioned by torque, and based on speed partitioning intervals, the engine speed per second of the valid vehicle data and drum data is partitioned by speed, achieving unified data numbering and ensuring that the valid vehicle data and drum data are categorized by torque and speed partitions. The corresponding connections are then established; the fuel flow rate data corresponding to the valid data for each second is processed through data clustering to obtain the average fuel flow rate; based on the average fuel flow rate, the drum data for each second is processed through cyclic reconstruction to predict and obtain the reconstructed fuel flow rate data for each second and calculate the reconstructed CO2 emission flow rate data for each second; according to the engine cycle process of the target vehicle during fuel consumption certification on the chassis dynamometer, the reconstructed fuel flow rate and CO2 emission flow rate data values ​​calculated in different torque and speed zones are spliced ​​together to calculate the carbon emission and fuel consumption levels of the target vehicle during use, and the compliance with the certification results is compared to see if it is compliant.

[0007] The actual operating data includes coolant temperature, vehicle speed, engine speed, reference torque, actual torque, and fuel flow rate.

[0008] The drum data includes vehicle speed, engine speed, reference torque, actual torque, friction torque, fuel flow rate, and CO2 emission flow rate. The data is obtained by measuring the hot vehicle fuel consumption according to GB / T 27840 standard using a selected chassis dynamometer to test the drum cycle. The vehicle providing the drum data has the same engine model and ECU calibration data as the vehicle providing the actual road test data.

[0009] The actual operating data is cleaned, including removing data where the engine coolant temperature is less than 70°C and the engine speed is less than 300 r / min.

[0010] Among them, the number is The units in the valid data for torque and speed partitions are... L / h The total fuel flow is denoted as set. If set If the number of elements in a set is less than or equal to 5, discard the set. For sets with more than 5 elements Calculate the average value of the dataset. and standard deviation According to the normal distribution In principle, delete smaller than And greater than Given the elements, calculate the average of the remaining elements. The average value calculated for each torque / speed zone is obtained, thereby obtaining the average fuel flow rate.

[0011] The net output torque of the vehicle's effective data and drum data is calculated second by second using the following formula;

[0012] ,

[0013] In the formula, For the first Net output torque per second, For reference torque; and The first Actual torque and friction torque per second;

[0014] Calculate the power of the drum data per second using the following formula;

[0015] ,

[0016] In the formula, For the first Power per second; For the first The engine speed in seconds.

[0017] The torque partition interval is calculated using the following formula;

[0018] , Torque partitioning interval, For reference torque;

[0019] Calculate the rotation speed zone interval using the following formula;

[0020] , For rotational speed partitioning intervals, This represents the maximum engine speed.

[0021] The valid vehicle data and drum data for each second are numbered according to the following formula: ;

[0022] , For the first Net output torque per second;

[0023] The valid vehicle data and drum data for each second are numbered according to the following formula: ;

[0024] , For the first The engine speed in seconds.

[0025] Specifically, when processing the drum data second by second through cyclic reconstruction based on the average fuel flow rate, if the torque / speed partition of the drum data for a certain second has a corresponding... Then the predicted fuel flow rate per second If the torque / speed partition of the drum data for a certain second does not have a corresponding... If the unknown zone fuel flow result prediction method is used, the second-by-second prediction data is recorded as the reconstructed data, and the duration is the same as the drum data.

[0026] Calculate the reconstructed CO2 emission flow rate for each second using the following formula based on the carbon balance principle;

[0027] ,

[0028] In the formula, To reconstruct the data CO2 emission flow rate per second Density of diesel fuel;

[0029] Prediction of fuel flow results for unknown zones: If the torque / speed zone data of the drum is numbered... No corresponding partition If the predicted value is less than 0, it is considered to be 0. The prediction is made by one-dimensional linear interpolation or two-dimensional fitting. The priority order is two-dimensional fitting > one-dimensional linear interpolation of speed partition > one-dimensional linear interpolation of torque partition.

[0030] In calculating the carbon emissions and fuel consumption levels of the target vehicle during use and comparing them with the certification results to determine compliance, the fuel flow rate and CO2 emission ratio of the drum data and the reconstructed data are calculated separately, and the relative errors are calculated separately. Then, it is determined whether the relative error limits of fuel flow rate and CO2 emission ratio are within the set threshold range.

[0031] The formula for calculating the CO2 emissions from the drum data is as follows:

[0032] ,

[0033] The formula for calculating the CO2 emission level from the reconstructed data is as follows:

[0034] ,

[0035] If the starting time of a certain part of the drum test cycle is the... seconds, the end time is the th Calculate the fuel consumption of the reconstructed data per second using the following formula;

[0036] ,

[0037] In the formula, For the first The speed of the car in seconds, For the first Power per second For drum data CO2 emissions per second To reconstruct the data CO2 emissions per second For natural numbers, ， Let the drum data duration be denoted as , and let the data delay time between the instantaneous CO2 test result of the CVS equipment and the instantaneous fuel flow rate of the vehicle data stream be . Seconds, calculate separately =1s to =20s and Pearson correlation coefficient Increase in increments of 1 second. The maximum value corresponding to , recorded as ;

[0038] The relative error is calculated using the following formula:

[0039] ,

[0040] In the formula, This is a relative error. To reconstruct the calculation results of the data, This is the result of the calculation of the drum data.

[0041] This invention utilizes remote network monitoring data or actual road test data of vehicles to reproduce the fuel consumption certification cycle of the target vehicle model on a chassis dynamometer through a partitioned recombination method. This eliminates the influence of factors such as load, driver, wind resistance, and road conditions, improving the efficiency of using remote monitoring and actual road test data. Furthermore, the calculation method is simple, feasible, accurate, adaptable, and widely applicable, solving the problem that existing technologies cannot effectively regulate the in-use compliance of heavy-duty vehicles in terms of carbon emissions and fuel consumption. Attached Figure Description

[0042] Figure 1 This is a flowchart of the heavy-duty vehicle carbon emission and fuel consumption zoning reassessment method of the present invention.

[0043] Figure 2 This is a schematic diagram of the average values ​​calculated for each partition in this invention.

[0044] Figure 3This is a schematic diagram of the Pearson correlation coefficient calculation results of the present invention.

[0045] Figure 4 This is a comparative diagram of the drum data and predicted data (reconstructed data) of the present invention. Detailed Implementation

[0046] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0047] The present invention provides a method for evaluating the carbon emissions and fuel consumption of heavy-duty vehicles by zoning and reconstructing, applicable to remote monitoring and actual road testing. This method uses remote emission monitoring data or actual road test results of the target vehicle model as input. Real-time vehicle data and drum data (per second) are zoned according to engine speed and torque. The average fuel consumption level of each zoning zone of the real-time vehicle data is calculated to obtain the real-time fuel consumption. The data of each zoning zone is correlated with the drum data (per second). Based on the real-time fuel consumption, the drum data is reconstructed to predict the reconstructed fuel flow and CO2 emission flow of the vehicle. According to the engine cycle process during fuel consumption certification on a chassis dynamometer, the reconstructed data (predicted data) from different zones are continuously arranged and spliced ​​second by second to calculate the carbon emissions or fuel consumption level of the vehicle model during use. The results are then compared with the chassis dynamometer certification results for fuel consumption (e.g., according to GB / T 27840 standard) and emissions (e.g., according to GB17691 standard) to verify compliance.

[0048] like Figure 1 As shown in the figure, the heavy-duty vehicle carbon emission and fuel consumption zoning reassessment method applicable to remote monitoring and actual road testing according to an embodiment of the present invention includes the following implementation steps:

[0049] Step 1) Data Input

[0050] 1.1 Actual operating data:

[0051] Select either real-world road test data or remote emissions monitoring data as the actual operating data. The data items are shown in Table 1 below, and the data frequency is 1Hz. The actual operating data should include as many vehicle driving conditions as possible, covering the widest possible engine load range. For real-world road tests, the input data is the OBD data stream recorded by the OBD communication device, and the vehicle load is recommended to be no less than 30%. For remote emissions monitoring data, the input data is the data stream information body. The length of the actual operating data should meet the data cleaning requirements in step 2.

[0052]

[0053] Table 1

[0054] In this embodiment of the invention, actual road test data of the vehicle is selected. The data source is the actual road test results of a certain type of heavy vehicle. The load is 39.1%, and the test duration is 53,230 seconds, as shown in Table 2.

[0055]

[0056] Table 2

[0057] 1.2 Vehicle fuel consumption measurement data (hereinafter referred to as "drum data"):

[0058] The chassis dynamometer test cycle (hereinafter referred to as the "drum cycle") was selected. Hot vehicle fuel consumption was measured according to GB / T 27840 standard. The drum data shown in Table 2 was recorded as input. The data frequency was 1Hz, and the data length was the same as the drum cycle duration (cycle duration denoted as...). J ).

[0059] The vehicles providing the drum data must have the same engine model and ECU calibration data as the vehicles providing the actual road test data.

[0060]

[0061] Table 3

[0062] In this embodiment of the invention, the vehicle was subjected to the C-WTVC test cycle, which lasted 1800 seconds. A 100% load was simulated by setting resistance. The test results are shown in Table 4 below:

[0063]

[0064] Table 4

[0065] Step 2) Data Preprocessing

[0066] 2.1 Data Cleaning:

[0067] Remove data from the actual operating data that meets the following conditions.

[0068] (1) The engine coolant temperature is less than 70℃;

[0069] (2) The engine speed is less than 300 r / min.

[0070] Data that has been cleaned is called "valid data"; the duration of valid data should be greater than 2 hours. If the requirement is not met, return to step 1 to adjust the input data.

[0071] In this embodiment of the invention, after data cleaning, 51673 seconds of valid data remained in the actual running data.

[0072] 2.2 Data Preprocessing:

[0073] 2.2.1 Calculate the net output torque per second of the effective data and drum data using the following formula.

[0074] ,

[0075] In the formula, For the first Net output torque per second, in Nm; Reference torque, a fixed value, in Nm; and The first Actual torque and frictional torque per second, in percent.

[0076] 2.2.2 The first based on drum data Net output torque per second Calculate the power of the drum data per second using the following formula.

[0077] ,

[0078] In the formula, For the first Power per second, measured in kW; For the first Engine speed in seconds, expressed in r / min.

[0079] In this embodiment of the invention, the preprocessing results of the actual operating data are shown in Table 5 below:

[0080]

[0081] Table 5

[0082] Step 3) Data Partitioning

[0083] 3.1 Torque Zones:

[0084] Based on the engine torque range, select the appropriate torque interval according to the parameter torque. The recommended range is 20~200 Nm. The torque interval can be calculated using the following formula. .

[0085] , For reference torque;

[0086] Based on torque partitioning interval The first valid data of the vehicle Net output torque per second The following formula can be used to obtain the valid data for each second. ， Number floor represents the floor operation.

[0087] , For the first Net output torque per second;

[0088] 3.2 Speed ​​Zones:

[0089] 3.2.1 Reasonable selection of speed zone interval The recommended range is 50~200 r / min. Calculate the speed zone interval using the following formula. .

[0090] , This represents the maximum engine speed, expressed in r / min.

[0091] 3.2.2 Based on rotational speed partitioning interval The first valid data of the vehicle engine speed in seconds The valid data for each second is obtained using the following formula, numbered as follows: .

[0092] ,

[0093] The present invention embodiment selects =25 Nm, =100 r / min, and the effective data partitions are shown in Table 6:

[0094]

[0095] Table 6

[0096] Step 4) Data Clustering

[0097] The vehicle's valid data is numbered as follows All fuel flows ( L / h ) is denoted as a set If set If the number of elements in a set is less than or equal to 5, then the set is discarded. This will not be included in subsequent calculations.

[0098] For a set of all elements greater than 5 Calculate the average value of the dataset. and standard deviation Assuming the data distribution follows a normal distribution, according to the normal distribution... Principle, delete set medium to small And greater than Given the elements, calculate the average of the remaining elements. .

[0099] In this embodiment of the invention, valid vehicle data is clustered and then deleted. After taking the discrete data from outside the region, the average fuel flow rate calculated for each region is as follows: Figure 2 As shown in the diagram, each "small square" represents a partition, and each different The different "small squares" corresponding to the numbers.

[0100] Step 5) Refactoring in a loop

[0101] 5.1 Loop Refactoring:

[0102] Following the method in step 3), the torque and speed partition data of the second-by-second drum data are numbered accordingly. And obtain the predicted fuel flow rate for that second. By unifying the data Numbering is used to associate drum data with real-time operating data. If a drum data partition for a certain second does not have a corresponding... Then, predict the results for this area according to Section 5.2; record the fuel flow prediction data for each second as "reconstructed data", with the same duration as "drum data";

[0103] The CO2 emission flow rate was reconstructed by calculating the second-by-second data according to the carbon balance principle using the following formula.

[0104] ,

[0105] In the formula, To reconstruct the data CO2 emission flow rate per second, expressed in g / s. The drum data for that second is labeled as follows: ; This is the density of diesel fuel, expressed in g / L.

[0106] 5.2 Prediction of fuel flow results for unknown zones:

[0107] In this embodiment of the invention, if the drum data is numbered as No corresponding partition The prediction is then performed using either one-dimensional linear interpolation or two-dimensional fitting; the priority order is: two-dimensional fitting > one-dimensional linear interpolation for speed partitions > one-dimensional linear interpolation for torque partitions. If the predicted value is less than 0, it is considered to be 0. The two-dimensional interpolation process is as follows:

[0108] Through all known partition data and To fit a two-dimensional plane equation to the coordinates, find the numbered... The partitioning results.

[0109] The method for processing one-dimensional linear interpolation of rotational speed partitions is as follows:

[0110] If the torque partition is If there are more than 3 valid data partitions, then the rotational speed partitions are predicted using the one-dimensional linear interpolation method. The partitioning results are shown in Table 7 below.

[0111]

[0112] Table 7

[0113] The method for processing one-dimensional linear interpolation of torque partitioning is as follows:

[0114] If the speed partition is If there are more than 3 valid data partitions, then the torque partitions are predicted using one-dimensional linear interpolation. The partitioning results are shown in Table 8 below.

[0115]

[0116] Table 8

[0117] In this embodiment of the invention, the reconstructed data corresponding to the drum data is:

[0118]

[0119] Table 9

[0120] Step 6) Fuel consumption and CO2 prediction

[0121] 6.1 CO2 Data Alignment:

[0122] Because the instantaneous CO2 test results from the CVS (Enterprise Emissions Full Flow Dilution Constant Volume Sampling System) device have a certain delay compared to the instantaneous fuel flow rate in the vehicle data stream, it is necessary to consider the CO2 emission flow rate from the start to the end of the reconstructed data. (The numbers before and after the colon in the subscript indicate the start and end times) and CO2 emission flow rate in the drum data. Perform data alignment.

[0123] Let the data delay time be Seconds, calculate separately =1 to =20 o'clock and The Pearson correlation coefficient is denoted as . The increment is gradually increased in 1-second increments. Search The maximum value corresponding to , recorded as ,but -1 represents the delay time. The correspondence between the reconstructed data and the drum data CO2 emission flow rate after alignment is as follows:

[0124]

[0125] Table 10

[0126] In the embodiments of the present invention The calculation results are as follows Figure 3 As shown, Take the maximum time =10s:

[0127] Calculate the cumulative values ​​of fuel flow and CO2 emissions for drum data and reconstructed data according to 6.2 and 6.3 respectively, and calculate the relative error according to the following formula.

[0128] ,

[0129] In the formula, This is a relative error. To reconstruct the calculation results of the data, This is the result of the calculation of the drum data.

[0130] 6.2 CO2 emissions:

[0131] The CO2 emission ratio of the drum data and the reconstructed data is calculated, and the relative error is calculated. Then, it is determined whether the relative error limit of the CO2 emission ratio is within the set threshold range. The relative error limit can be set according to regulatory requirements.

[0132] The formula for calculating the CO2 emissions from the drum data is as follows:

[0133] ,

[0134] The formula for calculating the CO2 emission level from the reconstructed data is as follows:

[0135] ,

[0136] In the formula, For drum data CO2 emissions per second To reconstruct the data CO2 emissions per second For natural numbers, .

[0137] In this embodiment of the invention, the CO2 emissions and relative errors of the drum data and the reconstructed data are shown in the table below:

[0138]

[0139] Table 11

[0140] 7.2 Fuel Consumption:

[0141] According to the requirements of GB / T 27840 standard, fuel consumption is measured by weighted average of fuel consumption in three parts of the drum test cycle (urban cycle, highway cycle and high-speed cycle for C-WTVC cycle, and only two parts for some CHTC cycles), and the unit is L / 100km.

[0142] Wherein, if the starting time of a certain part of the drum test cycle is the first... seconds, the end time is the th For each second, the fuel consumption of the reconstructed data is calculated using the following formula; for each department, the relative error limit can be set according to regulatory requirements.

[0143] ,

[0144] In the formula, For the first The car travels at a speed of seconds.

[0145] In this embodiment of the invention, the calculation results and relative errors of the drum data and reconstructed data of fuel consumption in urban, highway, and expressway sections of the C-WTVC cycle are shown in the table below:

[0146]

[0147] Table 12

[0148] The relative error between the CO2 emissions and fuel consumption of the vehicle model reconstructed using the data in this embodiment of the invention and the actual measured data from the drum is within ±5%, which shows high conformity.

[0149] Therefore, the partitioned recombination method proposed in this invention is applicable to the testing and evaluation of carbon emissions and fuel consumption of heavy-duty vehicles in remote emission monitoring and actual road testing.

[0150] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention.

[0151] Therefore, the embodiments should be regarded as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the claims be included within the invention.

[0152] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for reconstructing and evaluating carbon emissions and fuel consumption of heavy-duty vehicles, characterized in that... Including the following steps: Data cleaning is performed on the actual operating data of the target vehicle, including remote monitoring data or actual road test data, to obtain valid vehicle data; the valid vehicle data and the obtained vehicle drum data are preprocessed to obtain the net output torque per second of the valid vehicle data and the power per second of the drum data. Based on torque partitioning intervals, the net output torque of the vehicle's effective data and drum data is partitioned second by second. Similarly, based on speed partitioning intervals, the engine speed of the vehicle's effective data and drum data is partitioned second by second. This achieves unified data numbering, establishing a corresponding relationship between the vehicle's effective data and drum data after torque and speed partitioning. Fuel flow data corresponding to the second-by-second effective data is processed through data clustering to obtain the average fuel flow rate. Based on this average fuel flow rate, the second-by-second drum data is processed through cyclic reconstruction to predict and obtain reconstructed fuel flow data, and reconstructed CO2 emission flow data is calculated second by second. According to the engine cycle process during fuel consumption certification of the target vehicle on a chassis dynamometer, the reconstructed fuel flow and CO2 emission flow data values ​​calculated from different torque and speed partitions are concatenated to calculate the carbon emissions and fuel consumption levels of the target vehicle during use. The results are then compared with the certification results to determine compliance.

2. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 1, characterized in that, The actual operating data includes coolant temperature, vehicle speed, engine speed, reference torque, actual torque, and fuel flow rate.

3. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 1, characterized in that, The drum data includes vehicle speed, engine speed, reference torque, actual torque, friction torque, and fuel flow rate. It is obtained by measuring the hot vehicle fuel consumption according to GB / T 27840 standard using a selected chassis dynamometer to test the drum cycle. The vehicle providing the drum data has the same engine model and ECU calibration data as the vehicle providing the actual road test data.

4. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 1, characterized in that, The actual operating data is cleaned, including removing data where the engine coolant temperature is less than 70°C and the engine speed is less than 300 r / min.

5. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 1, characterized in that, Calculate the net output torque per second based on the vehicle's effective data and drum data using the following formula; In the formula, T net，i Let T be the net output torque at second i. ref For reference torque; T act，i and T fri，i These are the actual torque and friction torque at the i-th second, respectively; Calculate the power of the drum data per second using the following formula; In the formula, P i The power at the i-th second; n i Let be the engine speed at the i-th second.

6. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 1, characterized in that, Calculate the torque partition interval using the following formula; T bin =T ref ÷20, T bin For torque partitioning intervals, T ref For reference torque; Calculate the rotation speed zone interval using the following formula; n bin =n max ÷25, n bin For the rotational speed partitioning interval, n max This represents the maximum engine speed. The valid vehicle data and drum data for each second are numbered as a according to the following formula; a = floor(T) net，i ÷T bin ), T net，i Let be the net output torque at the i-th second; The valid vehicle data and drum data for each second are numbered as b according to the following formula; b = floor(n) i ÷n bin ), n i Let be the engine speed at the i-th second.

7. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 6, characterized in that, Let set Q represent all fuel flow rates in L / h from the valid data of torque and speed zones numbered a and b. a，b If set Q a，b If the number of elements in set Q is less than or equal to 5, discard set Q. a，b For a set Q with more than 5 elements a，b Calculate the average value of the dataset. and standard deviation σ a，b According to the 3σ principle of normal distribution, delete values ​​less than σ. And greater than Calculate the average μ of the remaining elements from the given elements. a，b The average value calculated for each torque / speed zone is obtained, thereby obtaining the average fuel flow rate.

8. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 7, characterized in that, Based on the average fuel flow rate, when processing the drum data second by second through cyclic reconstruction, if the torque / speed partition of the drum data for a certain second has a corresponding μ... a，b Then the predicted fuel flow rate per second If the torque / speed partition of the drum data for a certain second does not have a corresponding μ a，b If the unknown zone fuel flow result prediction method is used, the second-by-second prediction data is recorded as the reconstructed data, and the duration is the same as the drum data. Calculate the reconstructed CO2 emission flow rate for each second using the following formula based on the carbon balance principle; In the formula, To reconstruct the CO2 emission flow rate in the i-th second of the data, ρ d Density of diesel fuel; Fuel flow prediction for unknown zones: If the torque / speed zone data of the drum is numbered a and zone b, there is no corresponding μ. a，b If the predicted value is less than 0, it is considered to be 0. The prediction is made by one-dimensional linear interpolation or two-dimensional fitting. The priority order is two-dimensional fitting > one-dimensional linear interpolation of speed partition > one-dimensional linear interpolation of torque partition.

9. The method for reconstructing and evaluating the carbon emissions and fuel consumption of heavy-duty vehicles according to claim 1, characterized in that, When calculating the carbon emissions and fuel consumption levels of the target vehicle during use and comparing them with the certification results to determine compliance, the fuel flow rate and CO2 emission ratio of the drum data and the reconstructed data are calculated separately, and the relative errors are calculated separately. Then, it is determined whether the relative error limits of fuel flow rate and CO2 emission ratio are within the set threshold range. The formula for calculating the CO2 emissions from the drum data is as follows: The formula for calculating the CO2 emission level from the reconstructed data is as follows: If the start time of a certain part of the drum test cycle is second p and the end time is second q, the fuel consumption of the reconstructed data is calculated according to the following formula, and the test data adopts the results of drum bag sampling; In the formula, v i Let P be the vehicle speed at the i-th second. i Let the power be at the i-th second. Let CO2 emissions be the data from the drum at the i-th second. To reconstruct the CO2 emissions at second i, where i and j are natural numbers, 1 ≤ i ≤ j ≤ J, and J is the drum data duration, let Δt seconds be the data delay between the instantaneous CO2 test result of the CVS equipment and the instantaneous fuel flow rate of the vehicle data stream. Calculate the data for Δt = 1s to Δt = 20s. and Pearson correlation coefficient Increment by 1 second. The Δt corresponding to the maximum value is denoted as Δt. max ; The relative error is calculated using the following formula: In the formula, E r M represents the relative error. cal To reconstruct the calculation results of the data, m exp This is the result of the calculation of the drum data.

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