Mobile source carbon metering method based on multi-dimensional coupling optimization

Abstract

The invention discloses a mobile source carbon metering method based on multi-dimensional coupling optimization. The method at least comprises a mobile source carbon metering system. The mobile source carbon metering system comprises a multi-protocol CAN bus analysis module, a dual-mode oil consumption metering module of an OBD and an ultrasonic flowmeter, a three-dimensional coupling optimization model based on space-environment-mechanical three-dimensional parameters and a dynamic speed decision engine containing safety constraint conditions. A multi-protocol compatible terminal of the multi-protocol CAN bus analysis module integrates a CAN FD bus controller and supports on-line identification and conversion of 12 protocols, the dual-mode oil consumption metering module dynamically fuses OBD fuel oil data and an ultrasonic flowmeter, and the environment sensing array is obtained by a nine-axis MEMS sensor and comprises temperature, humidity, air pressure and gradient parameters.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation and green energy intersection technology, and in particular to a mobile source carbon metering method based on multidimensional coupling optimization. Background Technology

[0002] The transportation sector is a major source of carbon emissions, second only to power generation and heating. Promoting carbon reduction in the transportation sector is an effective measure to address the challenges of climate change. For urban areas, due to the relocation of heavily polluting enterprises and the continuous increase in the number of motor vehicles, reducing carbon emissions from urban road traffic has become the main means of urban carbon reduction.

[0003] Accurate calculation of carbon emissions from urban road traffic can provide data support for relevant departments to formulate scientific and reasonable carbon reduction measures, and can also be used to evaluate the effectiveness of carbon reduction measures.

[0004] Due to the complexity of transportation networks and the mobile emission characteristics of motor vehicles, measuring carbon emissions from mobile road sources is more difficult than measuring emissions from stationary sources such as industry. Existing methods for estimating transportation carbon emissions can be divided into two types: top-down and bottom-up. Top-down methods calculate carbon emissions based on energy consumption and energy conversion factors. This method is simple to calculate and applicable to a wide range of spatial measurements, but it may underestimate actual energy consumption and lead to large deviations in measurement results due to inaccuracies in energy carbon emission factors. Bottom-up methods calculate transportation carbon emissions through vehicle type, number of vehicles, mileage, and energy consumption per unit mileage. On the one hand, this method uses the number of vehicles in the network as the actual number of vehicles on the road, ignoring vehicles not on the road. On the other hand, this method does not consider the impact of different driving speeds of vehicles on their carbon emissions, thus leading to inaccurate calculation results.

[0005] In addition, the existing technology also has the following drawbacks:

[0006] 1. Protocol fragmentation: There are 12 mainstream protocols such as SAE J1939 and ISO15765 in the CAN bus of commercial vehicles. Data parsing errors can lead to carbon emission measurement deviations of up to 10-15%.

[0007] 2. Insufficient measurement accuracy: Traditional OBD fuel consumption monitoring is affected by ECU calibration strategies, with a system error of ≥5%, which cannot meet the MRV (monitorable, reportable, verifiable) requirements of the carbon trading market;

[0008] 3. Single optimization dimension: Existing emission reduction systems only consider single factors such as vehicle speed or load, and have not established a collaborative optimization model for space, environment, and mechanical parameters;

[0009] 4. Poor real-time control: The delay from data acquisition to the issuance of optimization instructions is greater than 3 seconds, which cannot adapt to dynamic traffic environments. Summary of the Invention

[0010] This invention proposes a mobile source carbon metering method based on multidimensional coupling optimization, which is applicable to the accurate accounting and proactive emission reduction control of mobile pollution sources such as road transport vehicles and non-road mobile machinery, and is particularly suitable for carbon efficiency optimization under complex road conditions, heavy load conditions and dynamic weather conditions.

[0011] To achieve the above objectives, the technical solution of the present invention is as follows:

[0012] A mobile source carbon metering method based on multidimensional coupling optimization includes at least a mobile source carbon metering system. The mobile source carbon metering system includes a multi-protocol CAN bus parsing module, a dual-mode fuel consumption metering module combining OBD and ultrasonic flow meters, a three-dimensional coupling optimization model based on spatial-environmental-mechanical three-dimensional parameters, and a dynamic speed decision engine with safety constraints. The multi-protocol CAN bus parsing module has a multi-protocol compatible terminal: an integrated CAN FD bus controller supporting online identification and conversion of 12 protocols. The dual-mode fuel consumption metering module dynamically fuses OBD fuel data with ultrasonic flow meter data. The environmental sensing array is acquired by a nine-axis MEMS sensor, including temperature, humidity, air pressure, and slope parameters.

[0013] As an improvement to the above technical solution, the CAN protocol conversion module includes a protocol feature fingerprint database, a reverse parsing fault-tolerant mechanism, and an online protocol library update interface.

[0014] As an improvement to the above technical solution, the three-dimensional coupled optimization model includes a spatial axis, an environmental axis, and a mechanical axis; the spatial axis introduces a real-time traffic flow density factor, the mechanical axis includes an environmental axis to establish a temperature, humidity and wind resistance joint compensation matrix, and the mechanical axis includes a load-level control strategy.

[0015] As an improvement to the above technical solution, the spatial axis is assigned values ​​to the slope θ, altitude h, and traffic flow q, and its formula is: f_s=0.7θ^1.3+0.2h^0.8+0.1q^2.

[0016] As an improvement to the above technical solution, the environmental axis assigns values ​​to temperature T, humidity RH, and air pressure P, and its formula is:

[0017] f_e=e^{-(T-25) / 10}·\frac{P}{101.325}·(1+0.01RH).

[0018] As an improvement to the above technical solution, the mechanical shaft is assigned values ​​for load W, rotational speed n, and exhaust temperature Texh, and its formula is: f_m=\frac{W·n}{1000}·(1-\frac{T_{exh}}{800}).

[0019] As an improvement to the above technical solution, the dynamic speed decision engine adopts a multi-objective optimization algorithm, the objective function of which is min_v[α·E(v)+β·t(v)+γ·(v-v_safe)^2]; where {min}≤n(v)≤n_{max}, T_exh≤650℃, W·θ≤μ·(mg+fracv^2R).

[0020] As an improvement to the above technical solution, the dynamic speed decision engine adopts a multi-objective optimization algorithm, the objective function of which is min_v[0.6E(v)+0.3t(v)+0.1(v-v_flow)^2]; where v_flow is the real-time traffic flow speed.

[0021] Compared with the prior art, the advantages and positive effects of this invention are:

[0022] This invention overcomes the challenges of traditional systems, such as protocol fragmentation, large measurement errors, and limited optimization dimensions, by innovatively integrating core technologies such as multi-protocol compatible terminals, dual-mode fusion metering, and three-dimensional coupling optimization. The system includes intelligent sensing terminals, edge computing modules, and a cloud-based decision-making platform, achieving closed-loop control across the entire chain from accurate data acquisition to real-time carbon efficiency optimization.

[0023] Actual measurements show that the fuel consumption measurement accuracy is within 0.5%, and the carbon emission reduction efficiency is improved by 12-25% under complex operating conditions, providing reliable technical support for scenarios such as logistics fleet management and urban transportation carbon inclusion.

[0024] This invention is applicable to the accurate calculation and proactive emission reduction control of carbon emissions from mobile pollution sources such as road transport vehicles and non-road mobile machinery, and is particularly targeted at carbon efficiency optimization under complex road conditions, heavy load conditions and dynamic weather conditions. Detailed Implementation

[0025] The technical solution of the present invention will be clearly and completely described below with reference to specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art to all other embodiments obtained without creative effort should be included within the protection scope of the present invention.

[0026] The mobile source carbon metering method based on multidimensional coupling optimization of the present invention includes at least a mobile source carbon metering system; the mobile source carbon metering system includes a multi-protocol CAN bus parsing module, a dual-mode fuel consumption metering module of OBD and ultrasonic flow meter, a three-dimensional coupling optimization model based on three-dimensional parameters of space-environment-machinery, and a dynamic speed decision engine with safety constraints; the multi-protocol compatible terminal of the multi-protocol CAN bus parsing module integrates a CAN FD bus controller, supporting online identification and conversion of 12 protocols; the dual-mode fuel consumption metering module dynamically fuses OBD fuel data with ultrasonic flow meter data; the environmental sensing array is acquired by a nine-axis MEMS sensor, including temperature, humidity, air pressure, and slope parameters.

[0027] The CAN protocol conversion module includes a protocol feature fingerprint database, a reverse parsing fault-tolerant mechanism, and an online protocol library update interface. The three-dimensional coupled optimization model includes a spatial axis, an environmental axis, and a mechanical axis; the spatial axis incorporates a real-time traffic flow density factor, the mechanical axis includes a temperature, humidity, and wind resistance joint compensation matrix established by the environmental axis, and the mechanical axis includes a load-level control strategy.

[0028] More specifically:

[0029] The spatial axis assigns values ​​to the slope θ, altitude h, and traffic flow q, and its formula is: f_s=0.7θ^1.3+0.2h^0.8+0.1q^2.

[0030] The environmental axis assigns values ​​to temperature T, humidity RH, and air pressure P, and its formula is: f_e=e^{-(T-25) / 10}·\frac{P}{101.325}·(1+0.01RH).

[0031] Of course, the mechanical shaft is assigned values ​​for load W, rotation speed n, and exhaust temperature Texh, and its formula is: f_m=\frac{W·n}{1000}·(1-\frac{T_{exh}}{800}).

[0032] The dynamic speed decision engine adopts a multi-objective optimization algorithm, and its objective function is min_v[α·E(v)+β·t(v)+γ·(v-v_safe)^2]; where {min}≤n(v)≤n_{max}, T_exh≤650℃, and W·θ≤μ·(mg+fracv^2R).

[0033] The dynamic speed decision engine adopts a multi-objective optimization algorithm, and its objective function is min_v[0.6E(v)+0.3t(v)+0.1(v-v_flow)^2]; where v_flow is the real-time traffic flow speed.

[0034] The effectiveness verification of this invention (actual test data in 2024) is shown in the table below:

[0035] index Traditional system This system Increase CAN data integrity rate 78.2％ 99.1％ +26.7％ Fuel consumption measurement error 5.3％ 0.41％ -92.3％ Carbon emissions per 100 kilometers 32.7kg 28.1kg -14.1％ Optimize response latency 3.2s 0.8s -75

[0036] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A mobile source carbon accounting method based on multi-dimensional coupling optimization, characterized in that: At least comprising a mobile source carbon metering system; the mobile source carbon metering system comprises a multi-protocol CAN bus analysis module, a dual-mode oil consumption metering module of OBD and ultrasonic flowmeter, a three-dimensional coupling optimization model based on space-environment-mechanical three-dimensional parameters and a dynamic speed decision engine with safety constraints; the multi-protocol compatible terminal of the multi-protocol CAN bus analysis module: integrated CAN FD bus controller, supporting online identification and conversion of 12 protocols, the dual-mode oil consumption metering module dynamically fuses OBD fuel data and ultrasonic flowmeter, the perception array of the environment is obtained by a nine-axis MEMS sensor, including temperature, humidity, air pressure, slope parameters.

2. The mobile source carbon measurement method based on multi-dimensional coupling optimization of claim 1, wherein: The CAN protocol conversion module includes a protocol feature fingerprint database, a reverse analysis fault tolerance mechanism and an online protocol library update interface.

3. The mobile source carbon measurement method based on multi-dimensional coupling optimization of claim 1, wherein: The three-dimensional coupling optimization model includes a space axis, an environment axis and a mechanical axis; the space axis introduces a real-time traffic density factor, the mechanical axis includes an environment axis to establish a temperature and humidity-wind resistance joint compensation matrix, and the mechanical axis contains a load grading control strategy.

4. The mobile source carbon measurement method based on multi-dimensional coupling optimization of claim 3, wherein: The space axis assigns values to slope θ, altitude h and traffic flow q, and the formula of the space axis is: f_s=0.7θ^1.3+0.2h^0.8+0.1q^2.

5. The mobile source carbon measurement method based on multi-dimensional coupling optimization of claim 3, wherein: The environment axis assigns values to temperature T, humidity RH and air pressure P, and the formula of the environment axis is: f_e=e^{-(T-25) / 10}·\frac{P}{101.325}·(1+0.01RH).

6. The mobile source carbon measurement method based on multi-dimensional coupling optimization of claim 3, wherein: The mechanical axis assigns values to load W, rotating speed n and exhaust temperature Texh, and the formula of the mechanical axis is:

7. The method of claim 1, wherein: f_m=\frac{W·n}{1000}·(1-\frac{T_{exh}}{800}).

8. The mobile source carbon measurement method based on multi-dimensional coupling optimization of claim 7, wherein: The dynamic speed decision engine adopts a multi-objective optimization algorithm, and the objective function is min_v[α·E(v)+β·t(v)+γ·(v-v_safe)^2]; Where {min}≤n(v)≤n_{max}, T_exh≤650℃, W·θ≤μ·(mg+fracv^2R). The dynamic speed decision engine adopts a multi-objective optimization algorithm, and the objective function is min_v[0.6E(v)+0.3t(v)+0.1(v-v_flow)^2]; Where v_flow is the real-time traffic speed.