A method, equipment, and medium for constructing a calculation model for vehicle CO2 emission factors in a plateau gradient zone.

Abstract

The present application relates to the field of transportation environment engineering and carbon emission assessment, and particularly relates to a method for constructing a highland gradient zone vehicle CO2 emission factor calculation model, equipment and medium. Through the method, a route-level CO2 emission factor distribution map is obtained, and different quantitative calculation models are formed for different altitude intervals. Specific and quantitative design basis is provided for low-carbon highway design. Through empirical data, it is first revealed that the CO2 emission rate of the highland gradient zone has a significant altitude segmentation effect, and a method of constructing a calculation model by zones is innovatively proposed. The method overcomes the defects of poor adaptability and inaccurate prediction of traditional single models in complex highland environments, and by establishing differentiated quantitative models (such as a low-altitude quadratic function and a high-altitude S-type function) for different altitude intervals, the final zoned model provides a core theoretical tool and decision basis for accurate accounting of highland highway carbon emissions, optimization of low-carbon routes and green transportation construction.

Description

Technical Field

[0001] This invention relates to the fields of transportation environmental engineering and carbon emission assessment technology, and in particular to a method, equipment and medium for constructing a vehicle CO2 emission factor calculation model in a plateau gradient zone. Background Technology

[0002] Carbon dioxide (CO2) emissions from the global transportation industry are a significant contributor to climate change. Accurately obtaining vehicle CO2 emission factors during actual driving is crucial for the precise calculation and effective management of highway carbon emissions. Currently, obtaining vehicle CO2 emission factors primarily relies on emission model calculations, laboratory bench tests, and real-vehicle tests based on onboard emission testing systems.

[0003] However, existing assessment methods face significant challenges in the unique geographical region of the plateau gradient zone. The plateau gradient zone is a transitional area connecting low-lying basins and high-altitude plateaus, characterized by complex topography and road routes with significant elevation differences, long continuous longitudinal slopes, and complex combinations of horizontal and vertical alignments. These factors collectively lead to drastic fluctuations in vehicle operating conditions, profoundly affecting fuel consumption and CO2 emissions.

[0004] The existing technology has the following main limitations:

[0005] First, existing emission models are not sufficiently applicable to high-altitude environments. Currently, widely used international emission models (such as IVE and MOVES) and their built-in emission rate databases are mostly based on test data from standard atmospheric conditions or low- to mid-altitude regions. These models struggle to accurately reflect the systematic impact of the abrupt changes in atmospheric pressure and air density gradients caused by altitude variations on the high-altitude gradient zone on engine intake efficiency, combustion conditions, and vehicle performance. Directly applying such models to high-altitude areas will lead to significant deviations in CO2 emission factor estimations, failing to provide reliable data support for regional carbon inventories.

[0006] Second, the understanding of the coupling effect between road alignment and altitude is unclear. While existing research has focused on the impact of individual alignment parameters such as longitudinal slope and curve radius on vehicle emissions, these studies are mostly conducted in areas with gentle terrain or minimal altitude variation. In high-altitude gradient zones, the same alignment parameters can have drastically different effects on vehicle operation and emissions at different altitudes. For example, vehicles climbing hills in low-oxygen environments experience more severe power loss and harsher operating conditions, potentially leading to a qualitative change in emission patterns. Current technology has failed to reveal this "altitude-alignment" coupling mechanism and segmentation effect, thus failing to provide refined theoretical guidance for the low-carbon route design of highways in high-altitude mountainous areas.

[0007] Third, there is a lack of structured assessment methods tailored to the characteristics of plateau gradient zones. Current real-vehicle emissions testing studies either focus on emissions characteristics at a single altitude point or, while covering different altitudes, only conduct macroscopic statistical analyses. They fail to propose a methodology for scientifically segmenting continuously changing altitudes and establishing differentiated calculation models for different segments. This results in assessments that are either one-size-fits-all averages, masking the significant differences within road segments, or discrete data points, making it difficult to develop generalizable assessment tools and meet the engineering needs for accurate carbon emissions assessment and prediction across long-distance highways.

[0008] Therefore, there is an urgent need for a new method that can fully consider the environment and operational characteristics of the plateau gradient zone and achieve a refined and structured assessment of CO2 emission factors, in order to fill the gap in existing technologies in this special region. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of existing methods for determining vehicle CO2 emissions in high-altitude gradient zone highways, such as poor environmental adaptability, insufficient consideration of the "altitude-linearity" coupling effect, and rough and inaccurate evaluation results. This invention provides a method, equipment, and medium for constructing a calculation model of vehicle CO2 emission factors in high-altitude gradient zones.

[0010] In a first aspect, the present invention provides a method for constructing a calculation model for vehicle CO2 emission factors in plateau gradient zones, specifically including the following steps:

[0011] Step 1: Obtain the alignment and elevation data of the target highway route; conduct a real vehicle emissions test on a highway in the plateau gradient zone to collect vehicle operating conditions, environmental parameters, and CO2 emission data;

[0012] Step 2: Analyze the variation of CO2 emission rate with altitude, and determine the altitude segmentation threshold for dividing altitude ranges based on the inflection point of its distribution characteristics.

[0013] Step 3: Based on the altitude segmentation threshold, divide the real vehicle test data into different datasets;

[0014] Step 4: For datasets corresponding to different altitude ranges, establish quantitative correlation models between road alignment indicators and CO2 emission factors, forming CO2 emission factor calculation models that match each altitude range.

[0015] This invention, through empirical data, reveals for the first time a significant altitude-based segmentation effect in CO2 emission rates across plateau gradient zones, and innovatively proposes a method for constructing a computational model based on these regional variations. This method overcomes the shortcomings of traditional single-model approaches, such as poor adaptability and inaccurate predictions in complex plateau environments. By establishing differentiated quantitative models (e.g., quadratic functions at low altitudes and S-shaped functions at high altitudes) for different altitude ranges, it achieves precise quantification of the coupling relationship between altitude, linearity, and emissions. The resulting regional model provides core theoretical tools and decision-making basis for accurate carbon emission accounting for plateau highways, low-carbon route optimization, and green transportation construction.

[0016] Preferably, the alignment data includes all key geometric parameters that determine the road's geometry and spatial orientation; these data directly determine the vehicle's operating conditions during travel (such as speed, acceleration, and engine load), and operating conditions are the most direct factor affecting vehicle CO2 emissions. In this scheme, the alignment data refers to parameters describing the road's geometry, including at least the radius of circular curves in the horizontal alignment and the longitudinal slope of the road in the vertical alignment.

[0017] Preferably, in step 2, the altitude segmentation threshold is a value determined based on the inflection point of the CO2 emission rate distribution with altitude in the actual vehicle test; the altitude segmentation threshold divides the route into a first altitude interval and a second altitude interval, the first altitude interval being 1400m ~ 2250m and the second altitude interval being 2250m ~ 3240m.

[0018] In this scheme, the altitude segmentation threshold is scientifically determined based on the measured distribution inflection point of CO2 emission rate, a physical quantity that directly reflects vehicle combustion efficiency and workload. This provides an objective and reproducible route zoning standard, avoiding the subjective arbitrariness of zoning. The intervals divided by this threshold ensure that the vehicle emission behavior within each interval is highly homogeneous, while heterogeneity exists between intervals, thus laying the foundation for the subsequent establishment of a high-precision model.

[0019] Preferably, in step 4, the CO2 emission factor calculation model is a quantitative correlation model between the CO2 emission factor and the longitudinal slope of the road; wherein, the correlation model for the first altitude range is a quadratic function model, and the CO2 emission factor first increases and then decreases with the increase of slope; the correlation model for the second altitude range is an S-shaped growth function model, and its CO2 emission factor shows an S-shaped growth trend with the increase of slope.

[0020] The above technical solutions further clarify the differences in the mathematical forms of models in different intervals. The impact pattern of slope on emissions changes fundamentally with altitude. This solution, by accurately quantifying the coupling mechanism of "altitude-slope", enables the model to not only predict the magnitude of emission factors, but also reflect their trend with slope. This provides a profound theoretical tool for understanding the vehicle operation mechanism in plateau environments and makes the prediction results more consistent with physical reality.

[0021] In the technical solution of this application, the different emission characteristics refer to the systematic and essential differences in the overall level, the response law (mathematical model form) of vehicle CO2 emission factors caused by atmospheric environmental differences due to altitude changes, and the sensitivity to various influencing factors. For example, in low-altitude areas, the relationship between emission factors and slope conforms to the characteristics of a quadratic function, while in high-altitude areas, the relationship between emission factors and slope conforms to the characteristics of an S-shaped growth function.

[0022] Preferably, the S-shaped growth function model is the Boltzmann function model. The Boltzmann function is a classic S-shaped curve, whose advantage lies in the clear physical meaning of its parameters (such as the center point and slope). It can well fit saturated growth processes such as chemical reactions and biological physiology, which is highly consistent with the phenomenon of limited engine power output and emissions tending to saturate under high-altitude and low-oxygen environments. Using the Boltzmann function not only ensures fitting accuracy but also gives the model good extrapolation and interpretability. The model's parameters can be correlated with the engine's high-altitude operating characteristics, enhancing the model's robustness and theoretical depth, and avoiding the limitations of a pure black-box model.

[0023] Preferably, in step 3, the operation procedure for the dataset is as follows:

[0024] Unit division: Based on the acquired "linear data", the system will automatically divide the entire target route into hundreds or thousands of continuous linear units according to the above principles;

[0025] Element parameter extraction: For each defined linear element, its feature parameters are extracted. These parameters typically include:

[0026] Planar parameters: radius of the circular curve (if it is a curve), length.

[0027] Longitudinal profile parameters: average slope, slope length.

[0028] Spatial location: The elevation of the starting and ending points of the unit, used to determine the elevation range to which it belongs.

[0029] Model Invocation and Calculation: The system invokes the corresponding CO2 emission factor calculation model based on the altitude range where the unit is located.

[0030] Then, the characteristic parameters of the unit (such as slope and radius) are input into the model. The model outputs the CO2 emission factor for that specific linear unit.

[0031] In the technical solution of this invention, the emission assessment of the route is refined from a route-level average value to a segment-level characteristic value, which can identify specific high-emission segments along the entire route (e.g., a steep slope and sharp bend unit located at high altitude). This ensures that the input parameters (slope, radius) of each unit are relatively homogeneous, making the emission factors calculated by the model highly representative and accurate for that unit. The final output is not a number, but a list or map clearly showing each linear unit (e.g., "K50+100 to K50+500, 3% uphill, radius 1200m") and its corresponding CO2 emission factor. This provides a direct and specific decision-making basis for low-carbon route optimization.

[0032] Preferably, the association model for the first altitude range is:

[0033] ;

[0034] The correlation model for the second altitude range is:

[0035] ;in,

[0036] The average CO2 emission factor distribution across different slope gradients is expressed in g·km². -1 ; This represents the average slope distribution, expressed in % (%). The center of the Boltzmann curve, i.e. The gradient corresponding to the fastest increase in speed, expressed in % , , , , , All are constants. , , , , , All model parameters were determined by fitting real vehicle data.

[0037] Preferably, in step 4, when establishing the quantitative correlation model, for the circular curve road segment, the data is classified or a correction coefficient is introduced according to the radius interval to which the circular curve radius belongs; wherein, the data with the circular curve radius R in the interval [1100m, 1400m) are distinguished from the data with radius R < 1100m.

[0038] This scheme extends from longitudinal slope to horizontal alignment: the radius of circular curves. When calculating emissions from circular curve sections, further differentiation is needed based on the radius. This reflects a more refined consideration of the impact of alignment combinations. The optimal scheme specifies a critical radius value of 1100 meters, achieving refined modeling of the combined effects of longitudinal and horizontal alignments. It clearly indicates that not all large-radius curves are beneficial, but rather there exists a specific optimization threshold (≥1100m). This provides a direct and quantifiable low-carbon design basis for the horizontal curve design of highways, enabling emission reduction measures to be precisely implemented in the selection of alignment indicators.

[0039] Preferably, step 4, before establishing the quantitative correlation model, further includes the following step:

[0040] The weights of each energy consumption component in the vehicle's specific power are analyzed using the CRITIC weighting analysis method.

[0041] The Sobol sensitivity analysis method was used to analyze the sensitivity of speed, acceleration, gradient and air density to the specific power of a vehicle in order to identify the key factors affecting the vehicle's operating status.

[0042] Preferably, the energy consumption components constituting the vehicle specific power (VSP) include four: kinetic energy, gravitational potential energy, rolling resistance, and air resistance. In this scheme, Sobol sensitivity analysis reveals that the greater the disturbance applied to the variables, the higher the sensitivity index of the VSP. Under different disturbance levels, the sensitivity index of each factor to the VSP is ranked as follows: speed > acceleration > gradient > air density, with the VSP being far more sensitive to speed and acceleration than to gradient and air density. This scheme adds a data analysis step before model construction. It utilizes two advanced mathematical tools, CRITIC (objective weighting method) and Sobol (global sensitivity analysis), to deeply mine data patterns from two dimensions: energy consumption and input parameter sensitivity, providing scientific guidance for the subsequent selection of model variables and structure determination. This method further enhances the scientific rigor and reliability of the constructed model. Through these two analyses, it is ensured that the final model focuses on the most critical influencing factors, avoiding biases from human experience-based selection, thus making the constructed partitioned calculation model theoretically sound and predictively robust.

[0043] In the technical solution of this invention, the dataset serves as the training foundation for the model, and its quality, scale, and representativeness directly determine the accuracy and generalization ability of the final model. This dataset is a multidimensional collection containing environmental parameters, vehicle operating conditions, road alignment and geographic information, and direct emission measurements. The core dependent variable in this dataset is the CO2 emission factor, while the independent variables include vehicle operating conditions (instantaneous speed, acceleration), vehicle specific power, road alignment and geographic information (vehicle specific power, road longitudinal slope, horizontal alignment, and altitude), and environmental parameters (atmospheric pressure, ambient temperature, relative humidity, and air density).

[0044] In a second aspect, the present invention discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described method for constructing a CO2 emission factor calculation model.

[0045] Thirdly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the above-described method for constructing a CO2 emission factor calculation model.

[0046] The beneficial effects of this invention are:

[0047] In this invention, CO2 emission factors are determined by zoning, resulting in a route-level CO2 emission factor distribution map. This method no longer yields an average emission factor, but rather differentiated CO2 emission factors across different altitude ranges and linear units along the entire route. This approach outputs scientific altitude segmentation thresholds, based on empirically derived inflection points where CO2 emission rates abruptly change. These thresholds divide the plateau gradient zone into two or more control zones with different emission characteristics. Different altitude ranges employ different quantitative impact models. This provides specific, quantitative design basis for low-carbon highway design. Attached Figure Description

[0048] Figure 1 This is a flowchart of the computational model construction method of the present invention.

[0049] Figure 2 Box plot showing the distribution of CO2 emission rate with altitude.

[0050] Figure 3 This is a map showing the distribution of CO2 emission factors at different altitudes.

[0051] Figure 4 This is a graph showing the variation trend of CO2 emission factors with the radius of the circular curve at different altitudes.

[0052] Figure 5A second-by-second distribution characteristic map of CO2 emission factors for curve units with different radii.

[0053] Figure 6 This is a distribution characteristic map of CO2 emission factors varying with slope at different altitudes. Detailed Implementation

[0054] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0055] Example 1

[0056] This embodiment provides a method for constructing a calculation model for vehicle CO2 emission factors in plateau gradient zones, such as... Figure 1 As shown, the specific steps include:

[0057] Step 101: Obtain the alignment and elevation data of the target highway route; conduct a real vehicle emissions test on the highway in the plateau gradient zone, and collect vehicle operating conditions, environmental parameters and CO2 emission data;

[0058] Specifically, the alignment data includes all key geometric parameters that determine the road's geometry and spatial orientation; these data directly determine the vehicle's operating conditions during travel (such as speed, acceleration, and engine load), and operating conditions are the most direct factor affecting vehicle CO2 emissions. In this scheme, the alignment data refers to parameters describing the road's geometry, including at least the radius of circular curves in the horizontal alignment and the longitudinal slope in the vertical alignment.

[0059] In this plan, the test route selected is the Wenchuan-Ma'anshan-Kangding Expressway as the actual vehicle emission test route. A portable emission testing system (PEMS) will be used to conduct round-trip actual vehicle tests on the Wenchuan-Ma'anshan Expressway (172km long). The route elevation rises from 1320m to 3243m and then descends to 2600m. Its alignment conditions and vehicle operating environment are as follows: Figure 2 As shown, the route direction, radius distribution, slope distribution, and continuous changes in altitude, atmospheric pressure, temperature, and humidity are clearly displayed.

[0060] Data Collection: The parameters of the test vehicle are defined in Table 1. The raw dataset collected includes second-level CO2 emission rates, instantaneous speeds, GPS coordinates, atmospheric pressure, ambient temperature, etc.

[0061] Table 1 Vehicle Parameters

[0062]

[0063] Step 102: Analyze the variation of CO2 emission rate with altitude, and determine the altitude segment threshold for dividing altitude ranges based on the inflection point of its distribution characteristics; In step 2, more specifically, calculate the CO2 emission rate based on the exhaust emission rate, temperature, CO2 wet base concentration, atmospheric pressure, and other data from the round trip of the actual vehicle emission test, where the calculation formula is shown in the following formula.

[0064]

[0065]

[0066] In the above formula: CO2 emission rate, g·s -1 ; For exhaust emission rate, L·s -1 ; CO2 wet basis concentration; CO2 density, g·L -1 ; The pressure is the gas pressure, in Pa. The molar mass is expressed in mol. ρ is the gas constant, 8314 Pa·(mol·K). -1 ; The exhaust gas temperature is K.

[0067] Step 103: Based on the altitude segmentation threshold, divide the real vehicle test data into different datasets;

[0068] In this embodiment, the test route was divided into four altitude ranges: 1400-1750 m, 1750-2250 m, 2250-2750 m, and 2750-3240 m. The variation trend of vehicle CO2 emission rate with altitude on highways in the plateau gradient zone was analyzed, and the box plot of its variation with altitude is shown below. Figure 2 As shown in the box plot, the average CO2 emission rate drops sharply at an altitude of 2250 meters. This is the emission characteristic inflection point identified by this invention, and based on this, the data is divided into two datasets: one for low altitude (1400-2250m) and one for high altitude (2250-3240m).

[0069] More specifically, the CO2 emission factor is used to characterize the mass of CO2 emitted per unit distance traveled by a vehicle, and the calculation formula is shown below:

[0070]

[0071] In the above formula: CO2 emission factor, g·km -1 ; For the first Instantaneous speed of a vehicle per second, m·s -1 ; For the first CO2 emission rate per second, g·s -1 ; The travel time is in seconds.

[0072] The road sections were divided into sections based on altitudes of 1400–2250 m and 2250–3240 m. The distribution of CO2 emission factors in different altitude ranges was analyzed, such as… Figure 3 As shown.

[0073] from Figure 3 The results showed that the CO2 emission factor distribution ranged from 1,400 to 2,250 m altitude to (23 g / km, 185 g / km), and the CO2 emission factor distribution ranged from 2,250 to 3,240 m altitude to (5 g / km, 141 g / km). The KS normality test results for the CO2 emission factor distribution at different altitudes are shown in Table 2.

[0074] Table 2 shows the statistical analysis of CO2 emission factors at different altitudes.

[0075]

[0076] The results showed that the CO2 emission factor followed a normal distribution. The mean CO2 emission factor in the altitude range of 2,250–3,240 m was approximately 61% of that in the altitude range of 1,400–2,250 m.

[0077] Step 104: For datasets corresponding to different altitude ranges, establish quantitative correlation models between road alignment indicators and CO2 emission factors, forming CO2 emission factor calculation models that match each altitude range.

[0078] In step 104, the CO2 emission factor calculation model is a quantitative correlation model between the CO2 emission factor and the longitudinal slope of the road; wherein, the correlation model for the first altitude range is a quadratic function model, and the CO2 emission factor first increases and then decreases with the increase of slope; the correlation model for the second altitude range is an S-shaped growth function model, and its CO2 emission factor shows an S-shaped growth trend with the increase of slope.

[0079] In this embodiment, the vehicle CO2 emission factor generally decreases with increasing curve radius R, and the decreasing trend is more pronounced in the altitude range of 1400~2250 m than in the range of 2250~3240 m. The CO2 emission factor was statistically analyzed second-by-second for each curve unit according to the radius ranges R<700 m, 700m≤R<900 m, 900 m≤R<1100 m, and 1100 m≤R<1400 m. Figures 4-5 As shown.

[0080] The results showed that, within the same altitude range, the mean CO2 emission factor distribution was smaller when 1100 m ≤ R < 1400 m compared to other radius ranges where R < 1100 m. The difference in CO2 emission factor distribution with radius range was greater in the altitude range of 1400–2250 m than in the range of 2250–3240 m. The maximum difference in the mean CO2 emission factor distribution across different radius ranges was 41 g / km and 19 g / km, respectively, representing reductions of 39% and 26%. This is related to the vehicle's operating status within the curve unit. Within the same altitude range, the mean and maximum VSP distribution were lower when 1100 m ≤ R < 1400 m compared to other radius ranges where R < 1100 m, indicating more stable vehicle operation and lower CO2 emission levels within larger radius curves.

[0081] In this embodiment, experimental research revealed that the greater the longitudinal slope of the road, the greater the slope resistance that vehicles need to overcome when climbing, and the higher the proportion of idling speed when descending, the more significant the impact on CO2 emission factors. Statistical analysis of the distribution characteristics of CO2 emission factors with slope variation in altitude ranges of 1400–2250 m and 2250–3240 m was performed, such as… Figure 6 As shown, the mean CO2 emission factor distribution in the altitude range of 1400–2250 m initially increases and then decreases with increasing slope, reaching its maximum value near flat slopes, with a range of 21 g / km. In the altitude range of 2250–3240 m, the mean CO2 emission factor distribution exhibits an S-shaped growth trend with increasing slope, with the fastest growth rate near flat slopes, a range of 53 g / km. In high-altitude areas with steep slopes (t≥3%), the greater the engine load, the greater the impact of the low-oxygen environment, and the greater the CO2 emissions.

[0082] Regression analysis was performed to investigate the correlation between the mean slope distribution and the mean CO2 emission factor distribution in different slope ranges. The results showed that the two had a good correlation, with correlation coefficients R² of 0.84 and 0.97 for the altitude ranges of 1400~2250 m and 2250~3240 m, respectively.

[0083] Low-altitude range model: The low-altitude dataset was analyzed to establish the relationship between CO2 emission factor and slope. After fitting, the CO2 emission factor showed a consistent trend of first increasing and then decreasing in the low-altitude range, so a quadratic function model was adopted.

[0084] High-altitude interval model: Analysis of high-altitude datasets shows a clear S-shaped growth trend in their relationships, with... Figure 6 The distribution characteristics in the mid-to-high altitude range are completely consistent. Therefore, we use the Boltzmann function model for high-precision fitting, as the function shape perfectly matches the critical and saturation phenomena of vehicle dynamic response under high-altitude, low-pressure, and low-oxygen environments.

[0085] Altitude 1400~2250 m:

[0086] Altitude 2250~3240 m:

[0087] The mean CO2 emission factor distribution for different slope ranges, in g·km². -1 ; The slope distribution is the mean, % The center of the Boltzmann curve, i.e. Increase the slope corresponding to the fastest speed, % , , , , , is a constant, and its values ​​are shown in Table 3.

[0088] Table 3 Parameters of the Fitted Curve

[0089]

[0090] In this embodiment, the S-shaped growth function model is the Boltzmann function model.

[0091] In step 104, when establishing the quantitative correlation model, for the circular curve road segment, the data is classified or a correction coefficient is introduced according to the radius interval to which the circular curve radius belongs; specifically, data with a circular curve radius R in the interval [1100m, 1400m) are distinguished from data with a radius R < 1100m. Specifically, in step 4, before establishing the quantitative correlation model, the steps include: using the CRITIC weighting analysis method to analyze the weight of each energy consumption component in the vehicle's specific power; and using the Sobol sensitivity analysis method to analyze the sensitivity of speed, acceleration, gradient, and air density to the vehicle's specific power, in order to determine the key factors affecting the vehicle's operating state.

[0092] This invention, through empirical data, reveals for the first time a significant altitude-based segmentation effect in CO2 emission rates across plateau gradient zones, and innovatively proposes a method for constructing a computational model based on these regional variations. This method overcomes the shortcomings of traditional single-model approaches, such as poor adaptability and inaccurate predictions in complex plateau environments. By establishing differentiated quantitative models (e.g., quadratic functions at low altitudes and S-shaped functions at high altitudes) for different altitude ranges, it achieves precise quantification of the coupling relationship between altitude, linearity, and emissions. The resulting regional model provides core theoretical tools and decision-making basis for accurate carbon emission accounting for plateau highways, low-carbon route optimization, and green transportation construction.

[0093] Example 2

[0094] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the CO2 emission factor calculation model construction method of Embodiment 1.

[0095] Example 3

[0096] Thirdly, the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method for constructing the CO2 emission factor calculation model of Embodiment 1.

[0097] 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 method for constructing a calculation model for vehicle CO2 emission factors in a plateau gradient zone, characterized in that, Includes the following steps: Step 1: Obtain the alignment and elevation data of the target highway route; conduct a real vehicle emissions test on a highway in the plateau gradient zone to collect vehicle operating conditions, environmental parameters, and CO2 emission data; Step 2: Analyze the variation of CO2 emission rate with altitude, and determine the altitude segmentation threshold for dividing altitude intervals based on the inflection point of its distribution characteristics; the altitude segmentation threshold divides the route into a first altitude interval and a second altitude interval. Step 3: Based on the altitude segmentation threshold, divide the real vehicle test data into different datasets; Step 4: For datasets corresponding to different altitude ranges, establish quantitative correlation models between road alignment indicators and CO2 emission factors, forming CO2 emission factor calculation models that match each altitude range. The CO2 emission factor calculation model is a quantitative correlation model between CO2 emission factors and road longitudinal slope. Specifically, the correlation model for the first altitude range is a quadratic function model, where the CO2 emission factor first increases and then decreases with increasing slope. The correlation model for the second altitude range is an S-shaped growth function model, where the CO2 emission factor increases in an S-shape with increasing slope.

2. The method for constructing a vehicle CO2 emission factor calculation model in a plateau gradient zone according to claim 1, characterized in that, The alignment data includes at least the radius of the circular curve in the horizontal alignment and the longitudinal slope of the road in the vertical alignment.

3. The method for constructing a vehicle CO2 emission factor calculation model in a plateau gradient zone according to claim 1, characterized in that, The S-shaped growth function model is the Boltzmann function model.

4. The method for constructing a vehicle CO2 emission factor calculation model in a plateau gradient zone according to claim 1, characterized in that, The correlation model for the first altitude range is: ， The correlation model for the second altitude range is: ,in, The mean distribution of CO2 emission factors across different slope gradients; This represents the average slope distribution. The center of the Boltzmann curve, i.e. The gradient corresponding to the fastest increase in speed; , , , , , All are constants. , , , , , All model parameters were determined by fitting real vehicle data.

5. The method for constructing a vehicle CO2 emission factor calculation model in a plateau gradient zone according to claim 1, characterized in that, In step 4, when establishing the quantitative correlation model, for the circular curve road segment, the data is classified or a correction coefficient is introduced according to the radius interval to which the circular curve radius belongs; among them, the data with circular curve radius R in the interval [1100m, 1400m) are distinguished from the data with radius R < 1100m.

6. The method for constructing a vehicle CO2 emission factor calculation model in a plateau gradient zone according to claim 1, characterized in that, Step 4, before establishing the quantitative correlation model, also includes the following steps: The weights of each energy consumption component in the vehicle's specific power are analyzed using the CRITIC weighting analysis method. The Sobol sensitivity analysis method was used to analyze the sensitivity of speed, acceleration, gradient and air density to the specific power of a vehicle in order to identify the key factors affecting the vehicle's operating status.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for constructing the vehicle CO2 emission factor calculation model in the plateau gradient zone as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method for constructing the vehicle CO2 emission factor calculation model in the plateau gradient zone as described in any one of claims 1-6.

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