Heavy-duty diesel vehicle weight estimation method based on remote emission monitoring data

Abstract

The present invention provides a vehicle weight estimation method for a heavy-duty diesel vehicle based on remote emission monitoring data, which relates to the field of vehicle engineering technology, including S1 remote monitoring data preprocessing, S2 dynamic load data grouping, S3 long slope driving data elimination, S4 vehicle speed and resistance relationship fitting, and S5 vehicle weight estimation based on the acceleration process. In terms of vehicle weight estimation, the method only relies on the OBD remote emission monitoring data and wheel radius of the heavy-duty diesel vehicle. These data are easy to obtain without adding additional devices or collecting other data. Therefore, the method has extremely high implementation feasibility and practicality, and makes full use of the huge amount of OBD remote emission monitoring data. By eliminating extreme cases, their adverse effects on vehicle weight estimation results are avoided, and the robustness of the estimation results is ensured. A practical and effective solution is provided for establishing a correlation analysis between vehicle load and NOx emissions, and technical support is provided for the rectification of the problem of overloading of heavy vehicles in reality.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle engineering, and more specifically, particularly relates to a method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data. Background Art

[0002] With the rapid development of my country's economy and the rapid advancement of urbanization, the problem of mobile pollution sources has become increasingly prominent, especially the impact of exhaust gas, noise and other pollutants generated by vehicles during driving on the environment has become increasingly significant. Heavy-duty diesel vehicles play a vital role in the field of passenger and cargo transportation with their excellent power and carrying capacity. Although they have brought great convenience to the development of the national economy, their high emission intensity has also had a significant impact on air quality. During the operation of heavy-duty diesel vehicles, a variety of air pollutants are emitted, mainly including nitrogen oxides (NOx), hydrocarbons (HC), carbon monoxide (CO) and particulate matter, among which NOx emissions are particularly worthy of attention. Changes in vehicle load have a direct impact on NOx emissions, and the relationship between the two has become the focus of industry attention.

[0003] In 2018, the Ministry of Ecology and Environment issued the national standard GB 17691-2018 "Heavy-duty diesel vehicle pollutant emission limits and test methods (China Phase VI)" (hereinafter referred to as the "Standard"), which stipulates the use of OBD remote emission monitoring to monitor the emissions of in-use vehicles. However, in actual operation, since the load of heavy-duty vehicles is in dynamic change, establishing the correlation analysis between vehicle load and NOx emissions has become a major problem facing the industry.

[0004] The existing technologies basically use elevation data, but the vehicle lacks a sensor for measuring elevation, so elevation data is difficult to obtain. The present invention uses the remote emission monitoring data of heavy-duty diesel vehicles to evaluate vehicle weight, and the data is easy to obtain, which has strong promotional potential. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data to solve the above problems.

[0006] The method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data comprises the following steps:

[0007] S1: Remote monitoring data preprocessing;

[0008] S2: dynamic load data grouping;

[0009] S3: Eliminate long slope driving data;

[0010] S4: Fitting the relationship between vehicle speed and resistance;

[0011] S5: Vehicle weight estimation based on acceleration process;

[0012] The preprocessing operations in S1 include processing the continuity, accuracy, and uniqueness of the data;

[0013] In S2, when the parking or shutdown time does not exceed 30 minutes, it is considered that the load state changes slightly. If it exceeds 30 minutes, it is considered that a loading or unloading process may have occurred, causing the vehicle load state to change.

[0014] Preferably, in S3, the vehicle driving data is processed with a sliding window of 3 hours in length and 1 second in sliding step. When the difference between the highest and lowest atmospheric pressures in the window exceeds 3 kPa, it is considered that the vehicle has experienced continuous uphill or downhill and the data in this period is discarded.

[0015] Preferably, the engine output torque TQ in S4 is equivalent to the product of the traction force Ft and the tire radius Rw, which is calculated by the following formula: , where and They represent the force and lever arm of the torque during the i-th transmission process, respectively, and i takes the value of 1, 2,…, n.

[0016] Preferably, the uniform speed driving in S4 is defined as that within a window of a specified time length t, the difference between the maximum and minimum vehicle speeds is less than 5 km / h and the value of t should be greater than 10 seconds.

[0017] Preferably, in S4, the average speed and the average resistance at various vehicle speeds are used to obtain a relationship between the two by fitting, and a linear fitting formula is used to obtain the optimal x and b values ​​by the least squares method. The linear fitting formula is as follows: .

[0018] Preferably, in S5, the vehicle weight is calculated according to Newton's second law using the following formula: ;

[0019] Among them, the traction force Ft is given by The resistance Fr is obtained by The acceleration a is obtained based on the rate of change of the vehicle speed.

[0020] Preferably, in S5, an acceleration process in which the vehicle speed does not exceed 30 km / h is selected for analysis.

[0021] Preferably, in S5, the highest and lowest 20% of the vehicle weight calculation results are removed, and the average value of the middle 60% of the calculation results is used as the final vehicle weight estimation result.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] In the present invention, the vehicle weight estimation only relies on the OBD remote emission monitoring data and wheel radius of the heavy-duty diesel vehicle, and these data are easy to obtain without adding additional devices or collecting other data, so it has extremely high feasibility and practicality.

[0024] In the present invention, the present invention fully utilizes the huge amount of OBD remote emission monitoring data, and effectively avoids their adverse effects on vehicle weight estimation results by eliminating extreme cases, thereby ensuring the robustness and reliability of the estimation results.

[0025] In the present invention, the present invention not only provides a practical and effective solution for establishing the correlation analysis between vehicle load and NOx emission, but also provides important technical support and basis for the rectification of the problem of heavy vehicle overloading in reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the relationship between force and acceleration when the vehicle is traveling on a flat road in the present invention;

[0027] Figure 2 This is a step of estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data of the present invention;

[0028] Figure 3 This is the step of identifying and eliminating long slope driving segments in the present invention;

[0029] Figure 4 It is the fitting step of vehicle speed and resistance in the present invention;

[0030] Figure 5 This is the step of estimating vehicle weight using the vehicle speed acceleration segment in the present invention. DETAILED DESCRIPTION

[0031] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0032] See also Figure 1-Figure 5 The present invention provides a method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data, comprising the following steps:

[0033] S1 remote monitoring data preprocessing:

[0034] According to the Standard, the remote monitoring data of heavy-duty diesel vehicles covers key information such as vehicle speed, atmospheric pressure, engine output torque, friction torque, engine speed, engine fuel flow, SCR upstream and downstream NOx sensor output values, reactant residue, intake volume, SCR inlet and outlet temperature, DPF pressure difference, engine coolant temperature, fuel tank level, geographic location (longitude, latitude) and accumulated mileage. In view of the widespread quality defects of current remote monitoring data, such as discontinuity caused by data packet loss, data errors exceeding regulatory limits, and data duplication, pre-processing operations must be performed before using these data to ensure data continuity, accuracy and uniqueness.

[0035] S2 dynamic load data grouping:

[0036] Considering that the load status of heavy-duty vehicles in actual operation is constantly changing, the data needs to be grouped according to the length of parking or engine shutdown time. Specifically, when the parking or engine shutdown time does not exceed 30 minutes, it is considered that the possibility of load status change is small, and once the time exceeds 30 minutes, loading or unloading may have occurred, resulting in changes in the vehicle load status.

[0037] S3 long slope driving data elimination:

[0038] In view of the significant difference in the engine power demand of the vehicle during uphill and downhill operations, and the current lack of altitude sensors for heavy-duty vehicles, it is difficult to directly obtain slope information, which will interfere with the estimation of vehicle weight. Therefore, it is necessary to use the "atmospheric pressure" information in the remote emission monitoring data to eliminate the segments where the vehicle is driving on long slopes. The specific method is: referring to the requirements of the "Standard" that the altitude change for actual road emission tests for heavy-duty vehicles should not exceed 200m (corresponding to an atmospheric pressure difference of approximately 2kPa), and the fact that the actual road emission test lasts approximately 2 to 3 hours, a sliding window with a duration of 3 hours and a sliding step of 1 second is set for the vehicle's driving data. When the difference between the highest and lowest atmospheric pressures in the window exceeds the set threshold (set to 3kPa considering sensor errors and environmental factors), it is considered that the vehicle has experienced continuous uphill or downhill operations, and the data within this period will be eliminated. For specific steps, see. Figure 3 For gently sloping sections of road, the vehicle consumes more power when going uphill, and consumes less power when going downhill. The two can offset each other, and the engine work is not much different from that on a flat road, so it does not need to be eliminated.

[0039] S4 speed and resistance relationship fitting:

[0040] The dynamic parameters (vehicle speed, acceleration) of a vehicle during driving are the result of the combined effect of traction Ft and resistance Fr, such as Figure 1As shown. When Ft > Fr, the vehicle accelerates, and the acceleration a > 0; when Ft < Fr, the vehicle decelerates, and the acceleration a < 0; when Ft = Fr, the vehicle moves at a constant speed, and the acceleration a = 0. The traction force Ft is the reaction force formed by the torque output by the engine acting on the ground through the transmission system and the wheels. Since the traction force is transmitted in the form of a lever, the torque TQ output by the engine is equivalent to the product of the traction force Ft and the tire radius Rw, as shown in Equation (1). Since TQ and Rw are known terms, Ft can be obtained.

[0041] (1)

[0042] In the formula, and respectively represent the force and the force arm during the i-th transmission process of the torque, and i takes values of 1, 2,..., n.

[0043] The resistance Fr mainly comes from factors such as wind resistance and transmission system friction resistance. After excluding the long-slope driving segments, when the vehicle is in a constant-speed driving state, the work done by the engine is mainly used to overcome the resistance Fr, and the change in gravitational potential energy caused by the elevation change can be ignored. Therefore, when the vehicle is driving at a constant speed at different speeds, the resistance Fr corresponding to the vehicle speed can be measured. Considering that it is difficult to maintain a constant vehicle speed, the present invention defines constant-speed driving as: within a window of a specified time length t, the difference between the maximum and minimum vehicle speeds is less than 5 km / h, and to ensure the stability of the results, the value of t should be greater than 10 seconds.

[0044] Next, calculate the average speed and the average resistance within each window of the time length t, and use the average speeds and the average resistances at multiple vehicle speeds to obtain the relationship between the two through fitting. Considering that the main components of the resistance (such as wind resistance and friction resistance) are basically proportional to the vehicle speed, to simplify the model, the present invention uses the linear fitting formula (2) for fitting and obtains the optimal values of x and b through the least squares method.

[0045] (2)

[0046] S5 Vehicle weight estimation based on the acceleration process:

[0047] During the acceleration process after the vehicle starts, according to Newton's second law, Formula (3) can be obtained. Among them, the traction force Ft is obtained from Formula (1), the resistance Fr is obtained from Formula (2), and the acceleration a is obtained according to the change rate of the vehicle speed. Thus, the vehicle weight m for each acceleration process can be obtained.

[0048] (3)

[0049] In order to estimate the vehicle weight more accurately, the present invention recommends selecting an acceleration process with a speed not exceeding 30 km / h for analysis. Since multiple acceleration processes will result in multiple vehicle weight calculation results, in order to avoid the influence of extreme cases on the estimation results, the present invention proposes to remove the highest and lowest 20% of the vehicle weight calculation results, that is, to use the average of the middle 60% of the calculation results as the final vehicle weight estimation result.

[0050] Firstly, the present invention only relies on the OBD remote emission monitoring data and wheel radius of the heavy-duty diesel vehicle in terms of vehicle weight estimation. These data are easy to obtain and do not require the addition of additional devices or the collection of other data. Therefore, the present invention has extremely high feasibility and practicality.

[0051] Secondly, the present invention makes full use of the huge amount of OBD remote emission monitoring data, and effectively avoids their adverse effects on the vehicle weight estimation results by eliminating extreme cases, thereby ensuring the robustness and reliability of the estimation results.

[0052] In summary, the present invention not only provides a practical and effective solution for establishing the correlation analysis between vehicle load and NOx emissions, but also provides important technical support and basis for the rectification of the problem of heavy-duty vehicle overloading in reality.

[0053] The embodiments of the present invention are given for the purpose of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data, characterized in that: The following steps are involved: S1: Remote monitoring data preprocessing; S2: Dynamic load data grouping for the pre-processed data in S1; S3: Eliminate long slope driving data from the grouped data in S2; S4: Fitting the relationship between vehicle speed and resistance based on the data after removing the long slope driving data in S3; S5: Estimating the vehicle weight based on the acceleration process based on the vehicle speed and resistance relationship obtained by fitting in S4; Among them, in S2, when the parking or shutdown time does not exceed 30 minutes, it is considered that the load state changes slightly, and if it exceeds 30 minutes, it is considered that the loading or unloading process has occurred, causing the vehicle load state to change; In S3, the vehicle driving data is processed with a sliding window of 3 hours and 1 second as the sliding step. When the difference between the highest and lowest atmospheric pressures in the window exceeds 3 kPa, it is considered that the vehicle has experienced continuous uphill or downhill and the data in this period is discarded; The engine output torque TQ in S4 is equivalent to the product of the traction force Ft and the tire radius Rw, which is calculated by the following formula: TQ=F1*L1=F2*L2=...=F n *L n =F t *R w , where and They represent the force and the lever arm of the torque in the i-th transmission process, respectively, and i takes the value of 1, 2, …, n; In S4, uniform speed driving is defined as the difference between the maximum and minimum vehicle speeds within a window of a specified time length t is less than 5 km / h and the value of t should be greater than 10 seconds; In S4, the average speed and average resistance at various vehicle speeds are used to obtain the relationship between the two through fitting, and the optimal x and b values ​​are obtained by using a linear fitting formula and the least squares method. The linear fitting formula is as follows: ； in, is the average resistance at various vehicle speeds, is the average speed under various vehicle speeds, x is the slope of the fitting line, and b is the intercept of the fitting line.

2. The method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data as claimed in claim 1, characterized in that: In S5, vehicle weight is calculated using the following formula based on Newton's second law: F t -F r =on; Among them, the traction force Ft is TQ=F1*L1=F2*L2=...=F n *L n =F t *R w The resistance Fr is obtained by The acceleration a is obtained based on the rate of change of the vehicle speed.

3. The method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data as claimed in claim 2, characterized in that: In S5, the acceleration process with a vehicle speed not exceeding 30 km / h is selected for analysis.

4. The method for estimating the vehicle weight of a heavy-duty diesel vehicle based on remote emission monitoring data as claimed in claim 3, characterized in that: In S5, the highest and lowest 20% of the vehicle weight calculation results are removed, and the average value of the middle 60% of the calculation results is used as the final vehicle weight estimation result.

Images