Carbon emission measurement and calculation model construction method based on vehicle operation scene difference

Abstract

The invention discloses a vehicle operation scene difference-based carbon emission measurement and calculation model construction method, which comprises the steps of obtaining operation data of a vehicle operation scene based on a target highway, performing localization correction on an MOVES model by using the operation data, outputting carbon emission factors corresponding to a vehicle type and a fuel type according to a localization model, and calculating the carbon emission measurement and calculation model according to the carbon emission factors. Calculating the total carbon emission of the driving scene through an IPCC bottom-to-top method according to the carbon emission factors, obtaining a driving scene model, calculating the additional carbon emission in the slow moving state and the non-moving state in the congestion state based on the driving scene model, obtaining a carbon emission calculation model, and outputting the total carbon emission of the target expressway. According to the method, MOVES model geochemical correction, vehicle specific power VSP distribution and self-adaptive carbon emission factors are used for depicting emission differences of different vehicle types and different scenes, the accuracy and integrity of all-road-domain total carbon emission measurement and calculation are improved, and a scientific and operable measurement and calculation tool is provided for highway carbon emission.

Description

Technical Field

[0001] This invention relates to the field of carbon emission measurement, and in particular to a method for constructing a carbon emission measurement model based on differences in vehicle operating scenarios. Background Technology

[0002] With the rapid development of the transportation industry, vehicle carbon emissions during highway operation have become one of the important sources of greenhouse gas emissions. Accurately measuring carbon emissions in this scenario is of great significance for carbon emission control and environmental policy formulation.

[0003] Currently, the main methods for calculating carbon emissions from vehicles on highways include the life cycle method, the total emission structure method, and the top-down and bottom-up methods proposed by the IPCC (Intergovernmental Panel on Climate Change). The top-down method calculates the total emissions at a macro level using fuel usage data, which is suitable for overall emission estimation, but its granularity is relatively coarse and cannot meet the precise calculation needs of specific road sections and scenarios at the micro level. The bottom-up method directly links parameters such as vehicle travel distance, traffic volume, and emission coefficients, and can realize the calculation of carbon emissions at the end of transportation vehicles, becoming the mainstream method for calculation at the micro level.

[0004] Regarding the determination of emission factors, existing technologies mainly rely on publicly available government data, industry data, LCA databases, or data disclosed in related studies. They generally use a uniform carbon emission factor for calculation, failing to fully consider the impact of vehicle operating scenarios and regional characteristics on emission factors, leading to significant discrepancies between calculated results and actual emissions. Furthermore, among existing emission factor models, the MOBILE and EMFAC models can only be based on average speed or simple parameter corrections, limiting their simulation dimensions. While the MOVES model possesses multi-level simulation capabilities at the macro, meso, and micro levels and incorporates Bin micro-unit emission factors for different vehicle speeds, models, ages, and vehicle power-to-weight ratios (VSPs), it was developed by the U.S. Environmental Protection Agency (EPA) based on local conditions in the United States and has not adapted to the localized needs of China, such as geographical climate, vehicle technical characteristics, and fuel standards.

[0005] Existing localization corrections for the MOVES model mostly focus on adjusting simple input parameters such as temperature, humidity, and vehicle age distribution, failing to comprehensively cover key factors such as road type, vehicle degradation coefficient, fuel type, and emission limits. This results in the corrected emission factors still not accurately matching the actual operating characteristics of Chinese highways. Furthermore, existing calculation models do not clearly define the operating scenarios of vehicles on highways, ignoring the additional carbon emissions generated by vehicle idling, transient acceleration, and transient deceleration in congested scenarios. In these scenarios, vehicle fuel consumption is much higher than at economical speeds, which is a significant contributing factor to increased carbon emissions on highways. The lack of specific calculation logic in existing models further exacerbates the errors in the calculation results. There is an urgent need for a carbon emission calculation model construction method that can take into account both scenario differences and regional adaptability. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios.

[0007] To achieve the above objectives, the present invention is implemented according to the following technical solution: This invention provides a method for constructing a carbon emission measurement model based on differences in vehicle operating scenarios, including: Operational data of vehicle operation scenarios are obtained based on the target highway. The operational data includes vehicle type, fuel type, driving trajectory, geographical climate and vehicle age distribution. The MOVES model is localized using the aforementioned operational data, and carbon emission factors corresponding to vehicle type and fuel type are output based on the localized model. The vehicle operation scenarios are divided into driving scenarios and queuing scenarios. The total carbon emissions of the driving scenario are calculated using the IPCC bottom-up method based on the carbon emission factor to obtain the driving scenario model. Based on the driving scenario model, additional carbon emissions are taken into account for the two states of slow movement and standing still under congestion. A carbon emission calculation model is obtained and the total carbon emissions of the target highway are output.

[0008] Furthermore, the method for obtaining the localized model includes: Based on the operational data, the geographic climate of the target highway is extracted. Based on the geographic climate, similar regions built into the MOVES model are used as matching simulation regions and simulation parameters. The geographic climate includes annual average temperature, altitude, relative humidity, latitude and longitude, season and annual average precipitation. The vehicle age percentage of each model is calculated based on the vehicle statistics yearbook of the city to which the operation data belongs. The vehicle age percentage is used to replace the vehicle age distribution built into the MOVES model. The index categories and data types of the fuel parameters built into the MOVES model are converted into national standard parameters. The built-in simulation year of the MOVES model is converted to the year when the vehicle emission standard was released. The MOVES model maps small passenger cars, medium passenger cars, and medium commercial vehicles to small and medium passenger cars, transport buses, public buses, school buses, and church buses to large passenger cars, single short-haul trucks to small vans, single long-haul trucks to medium vans, combined short-haul trucks to large vans, and combined long-haul trucks to extra-large vans. Based on the mapped vehicle types, vehicle operating condition analysis is performed, and a localized model is obtained based on the operating condition characteristics.

[0009] Furthermore, the method for obtaining the operating condition characteristics includes: Passenger cars, small trucks, and medium-sized trucks are classified as light vehicles, while large passenger cars, large trucks, and extra-large trucks are classified as heavy vehicles. The power-to-weight ratio of each vehicle is calculated using the following formula: ; ; in For light vehicles, the power-to-weight ratio is... For heavy-duty vehicles, the power-to-weight ratio is... For vehicle speed, For acceleration; Based on the driving trajectory obtained from the operational data, different driving cycles and average driving speeds are acquired. The average driving speed is taken as the normal speed, and speed ranges of normal speed, high speed, and low speed are obtained. The weight ratio of different driving cycles is calculated according to the speed range. The formula for calculating the weight ratio is as follows: ; ; in This represents the weighting ratio for the low-speed range. This represents the weighting ratio for the high-speed range. At normal speed, For high speed, Low speed; The overall vehicle power distribution is obtained based on the driving cycle and weight ratio, and the overall vehicle power distribution is used as the operating condition characteristic.

[0010] Furthermore, the method for obtaining the specific power distribution of the vehicle includes: The micro-unit acceleration is calculated second by second based on the vehicle's driving cycle. The vehicle's specific power is then calculated using the micro-unit acceleration. The percentage of driving time for each micro-unit is statistically analyzed. Based on the weighted ratio of the percentage of driving time and the driving cycle, the vehicle's specific power distribution is calculated. The formula for calculating the vehicle's specific power distribution is as follows: ; in For a moment acceleration, For a moment speed, For a moment speed, This represents the total travel time of the vehicle. For driving cycle Distribution time on This represents the distribution percentage of driving cycles. The weighting ratio is the driving cycle. This represents the specific power distribution of the vehicle.

[0011] Furthermore, the method for obtaining the carbon emission factor includes: Based on the localized model, baseline emission rates are output for different vehicle types, fuel types, and vehicle power ratio ranges. The ratio of the standard deviation to the mean is calculated based on vehicle speed, and multiplied by the idling time percentage to obtain the scenario congestion index. The adaptive emission factor is then calculated using the scenario congestion index and the baseline emission rate. The formula for calculating the adaptive emission factor is as follows: ; in Vehicle type ,fuel In the scene Adaptive emission factor For the first Baseline emission rates for each vehicle power-to-weight ratio range The total number of intervals, For the scene lower interval The proportion of time, For the scene average driving speed The free-flow velocity is obtained according to road design specifications. For the scene Traffic flow below, For lane capacity, For the scene Average temperature The average annual temperature of the region. The temperature sensitivity coefficient is obtained by fitting the temperature gradient to the vehicle carbon emission difference. For the scene The congestion correction factor is obtained based on the historical percentage of congested road sections. Vehicle type ,fuel In the scene The scene congestion index; The adaptive emission factor is used as the carbon emission factor corresponding to each vehicle type and fuel type.

[0012] Furthermore, the method for obtaining the driving scenario model includes: Parking queues and queuing while moving are considered queuing scenarios, while scenarios without queuing are considered driving scenarios. A driving scenario model for calculating carbon emissions of all types of vehicles across the entire road segment is constructed using the IPCC bottom-up approach. Based on driving trajectories, the average number of trips, average mileage, and average energy consumption of different vehicle types are used as input data. The total carbon emissions are obtained from the driving scenario model based on the input data. The formulas for calculating the average number of trips and average mileage are as follows: ; ; in For model fuel distribution Total number of trips This represents the total number of vehicle types. This represents the total number of fuel types. For the first Type of vehicle uses fuel Number of trips, For the first The first in the category of car models Vehicles use fuel driving distance, This represents the total number of trips for the corresponding vehicle type. For the first Type of vehicle uses fuel The average driving distance; The formula for calculating the average energy consumption is: ; in For the first Type of vehicle uses fuel Average energy consumption per 100 kilometers For the first The first in the category of car models Vehicles use fuel The theoretical energy consumption per 100 kilometers For the first Type of vehicle uses fuel The energy consumption correction factor per 100 kilometers is obtained by calibrating actual energy consumption monitoring data with theoretical values. For the first The total number of vehicles of each vehicle type; The formula for calculating fuel consumption and carbon emissions is as follows: ; in For the first Type of vehicle uses fuel fuel consumption, Use fuel for all models Total consumption, fuel carbon emission factors, This represents the total carbon emissions during driving scenarios.

[0013] Furthermore, the method for obtaining the carbon emission calculation model includes: Based on the queuing scenario, the number of vehicles and queue length are obtained in two states under congestion: slow movement and standing still. The additional carbon emissions in the slow movement state are included in the driving scenario model. The formula for calculating the additional carbon emissions in the slow movement state is as follows: ; ; ; in Fuel consumption for slow driving. Fuel consumption per 100 kilometers at low speeds. Queue length The total amount of basic carbon emissions that move slowly. Additional carbon emissions due to slow movement. For the number of congested vehicles, Fuel consumption per 100 kilometers in congested traffic; The carbon emission calculation model is obtained by using the number of vehicles and queue length to account for additional carbon emissions in the stationary state through the driving scenario model, and outputs the total carbon emissions of the target highway.

[0014] Furthermore, a method for obtaining additional carbon emissions from the stationary state includes: The additional carbon emissions during the stationary state are calculated based on congestion time and idling fuel consumption. The formula for calculating the additional carbon emissions during the slow-moving state is as follows: ; ; ; ; in For the first Type of vehicle, No. Total energy consumption during acceleration of various fuel vehicles For vehicle quality, For air resistance, For rolling resistance, Gradient resistance is determined by the road slope angle. To increase the driving distance, To assist in understanding the system's energy consumption, historical data corresponding to the vehicle type is used. This represents the total fuel consumption in queuing scenarios. This is the fuel consumption per unit at idle speed, obtained from the vehicle type. For congestion time, Fuel efficiency is determined by vehicle type. The calorific value of a fuel is determined by its type. For fuel density, To stop the base carbon emissions from being stagnant Additional carbon emissions from stopping the process.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: This invention localizes the MOVES model by incorporating geographical climate, vehicle age distribution, fuel standards, and vehicle model mapping, enabling the carbon emission calculation model to adapt to the characteristics of the target region. This improves the regional adaptability of the general model and lays the foundation for accurate calculation. By coupling vehicle specific power (VSP) distribution with multiple factors such as scene weight, temperature, and congestion index, a scene-adaptive carbon emission factor is constructed to characterize the emission differences of different vehicle models and different scenarios. The vehicle operation scenario is divided into driving scenario and queuing scenario. The queuing scenario is further broken down into slow movement and stationary state, respectively. The IPCC bottom-up method and refined additional emission calculation are used to reduce the roughness of carbon emission calculation caused by differences in highway scenarios, improve the accuracy and completeness of the total carbon emission calculation of the entire road area, and provide a scientific and operable calculation tool for the refined management of highway carbon emissions and the implementation of targets. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of a carbon emission calculation model construction method based on differences in vehicle operating scenarios, as described in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] Reference Figure 1 As shown, this invention provides a method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios, including: Operational data of vehicle operation scenarios are obtained based on the target highway. The operational data includes vehicle type, fuel type, driving trajectory, geographical climate and vehicle age distribution. In the actual assessment, a 10km long, six-lane (two-way) and three-lane (one-way) expressway section was selected as the target expressway. Its design speed is 120km / h, primarily serving suburban commuting and freight traffic, with an average daily traffic volume of approximately 52,000 vehicles. Peak hours are 7:00-9:00 and 17:00-19:00, with significant queuing and congestion, mainly concentrated at toll stations and accident-prone sections. July, the summer month, was used as the calculation period. Due to the high temperatures during summer, continuous 24-hour calculations were performed daily to obtain the target expressway's National VI 6b emission standard. The data includes emission standards, HCM (Highway Management Center) capacity standards, and measured vehicle emission data. The target area has an average annual reference temperature of 15.5℃, a daily average temperature of 29℃ in July (driving scenario), and 31℃ in queuing scenarios. The altitude is 50-80m, and the relative humidity is 65%-75%. 72% of the vehicles are gasoline-powered, 45% are 0-3 years old, and 30% are 4-6 years old. The curb weight is 1500kg, and the free-flow speed is 110km / h. Based on HCM standards, the lane capacity is calculated to be 2000 vehicles / hour / lane, which is the baseline capacity at 110km / h. The average speed in driving scenarios is 85km / h, with a flow rate of 3800 vehicles / hour, while the average speed in queuing scenarios is 12km / h, with a flow rate of 5200 vehicles / hour. The driving trajectories of 1000 sample vehicles were collected through 5 GPS monitoring points along the road section, including 700 small passenger cars, 230 large trucks, and 70 medium-sized passenger cars. The gasoline used by the sample vehicles had a calorific value of 32000 kJ / L and a density of 0.74 kg / L. According to the IPCC 2022 guidelines, its standard carbon emission factor is 2.39 kgCO2 / L. Based on the standard coal equivalent, the standard carbon emission factor was directly calculated using a carbon emission factor of 0.6 kgCO2 / kWh for pure electric vehicles, and 1 liter of gasoline resulted in 1.47 kgce. For hybrid vehicles, the carbon emission factor was calculated by converting electricity and fuel consumption separately and then adding them together. 1 liter of diesel fuel resulted in 1.67 kgce. In hydrogen fuel cells, 1 kg of hydrogen resulted in 0.086 kgce. Based on hydrogen production energy consumption, 1 m³ of fuel... 3The energy consumption of natural gas is 1.33 kgce. The theoretical fuel consumption per 100 kilometers for small passenger cars in the sample vehicles is 6.2. The energy consumption correction coefficients in congested conditions are 1.15 for small passenger cars, 1.22 for large trucks, and 1.18 for medium-sized passenger cars. The MOVES model is localized using the aforementioned operational data, and carbon emission factors corresponding to vehicle type and fuel type are output based on the localized model. In practical assessments, based on altitude and humidity data input into the MOVES model, environmental pressure and humidity influencing factors in the model are corrected to adapt emission rate calculations to the current regional climate characteristics. Vehicle age distribution data replaces the default US vehicle age distribution in MOVES. The baseline emission rate is adjusted according to a calibration rule that emissions increase by 8% for every 3 years of vehicle age. Gasoline vehicles equipped with GPF and meeting China VI 6b emission standards are mapped to vehicle categories representing EURO 6d technology levels in the MOVES model. Fuel efficiency coefficients are corrected, mapping Passenger Cars in the MOVES model to small passenger cars, 16-22t Heavy Duty Trucks to large trucks, and Medium Duty Trucks to... The bus is mapped to a medium-sized passenger vehicle. Using a locally modified MOVES model, the baseline emission rates for each vehicle type, fuel, and vehicle power specific (VSP) range are output. Specifically, for VSP 0-5 (low load), the baseline emission rate for gasoline-powered passenger vehicles is 0.62 g / km; for VSP 5-10 (medium load), the baseline emission rate is 1.38 g / km; and for VSP 10-15 (high load), the baseline emission rate is 2.75 g / km. The VSP distribution of the sample vehicles is calculated based on second-by-second trajectory data. The VSP interval time percentages are as follows: in driving scenarios, low load accounts for 25%, medium load for 60%, and high load for 15%, with a corresponding idling time percentage of 3%; in queuing scenarios, low load accounts for 65%, medium load for 30%, and high load for 5%, with a corresponding idling time percentage of 32%. The standard deviation of vehicle speed in driving scenarios is 8.2, the mean is 85, and the congestion index is... The congestion index for queuing scenarios is: The carbon emission factor is calculated based on the scenario, with a temperature sensitivity coefficient of 8 and a congestion index corrected to 0.05 for driving scenario and 0.30 for queuing scenario. Substituting these values ​​into the adaptive emission factor calculation formula, the carbon emission factor for a small passenger car running on gasoline is 0.128 g / km for driving scenario and 0.4388 g / km for queuing scenario. The vehicle operation scenarios are divided into driving scenarios and queuing scenarios. The total carbon emissions of the driving scenario are calculated using the IPCC bottom-up method based on the carbon emission factor to obtain the driving scenario model. In the actual assessment, the driving scenario model for calculating carbon emissions of all types of vehicles on the entire road segment was constructed using the IPCC bottom-up method. The total number of trips for small passenger cars was 37,440 per day, with an average mileage of 85km, fuel consumption of 226,897.44L, and carbon emissions of 542,284.88kgCO2. Based on the driving scenario model, additional carbon emissions are taken into account for the two states of slow movement and standing still under congestion. A carbon emission calculation model is obtained and the total carbon emissions of the target highway are output.

[0019] In the actual assessment, based on the driving scenario model, the queue length during peak hours with slow movement is 3.2km, and the low-speed fuel consumption of a small passenger car is 12.8L / 100km. Therefore, the total fuel consumption in this state is 15256.32L, and the carbon emission is 36462.60kgCO2. The additional emissions from idling and acceleration are as follows: the idling fuel consumption of a small passenger car is 0.85L / h, and the congestion time is 1.8h, with an idling fuel consumption of 15588L and an additional emission of 6821.57kgCO2. The total emission of the queuing scenario is 128.75tCO2 / day. According to the carbon emission calculation model, the total daily carbon emission is: driving scenario 896.32 + queuing scenario 128.75 = 1025.07tCO2 / day.

[0020] In this embodiment, the method for obtaining the localized model includes: Based on the operational data, the geographic climate of the target highway is extracted. Based on the geographic climate, similar regions built into the MOVES model are used as matching simulation regions and simulation parameters. The geographic climate includes annual average temperature, altitude, relative humidity, latitude and longitude, season and annual average precipitation. The vehicle age percentage of each model is calculated based on the vehicle statistics yearbook of the city to which the operation data belongs. The vehicle age percentage is used to replace the vehicle age distribution built into the MOVES model. The index categories and data types of the fuel parameters built into the MOVES model are converted into national standard parameters. The built-in simulation year of the MOVES model is converted to the year when the vehicle emission standard was released. The MOVES model maps small passenger cars, medium passenger cars, and medium commercial vehicles to small and medium passenger cars, transport buses, public buses, school buses, and church buses to large passenger cars, single short-haul trucks to small vans, single long-haul trucks to medium vans, combined short-haul trucks to large vans, and combined long-haul trucks to extra-large vans. Based on the mapped vehicle types, vehicle operating condition analysis is performed, and a localized model is obtained based on the operating condition characteristics.

[0021] In this embodiment, the method for obtaining the operating condition characteristics includes: Passenger cars, small trucks, and medium-sized trucks are classified as light vehicles, while large passenger cars, large trucks, and extra-large trucks are classified as heavy vehicles. The power-to-weight ratio of each vehicle is calculated using the following formula: ; ; in For light vehicles, the power-to-weight ratio is... For heavy-duty vehicles, the power-to-weight ratio is... For vehicle speed, For acceleration; Based on the driving trajectory obtained from the operational data, different driving cycles and average driving speeds are acquired. The average driving speed is taken as the normal speed, and speed ranges of normal speed, high speed, and low speed are obtained. The weight ratio of different driving cycles is calculated according to the speed range. The formula for calculating the weight ratio is as follows: ; ; in This represents the weighting ratio for the low-speed range. This represents the weighting ratio for the high-speed range. At normal speed, For high speed, Low speed; The overall vehicle power distribution is obtained based on the driving cycle and weight ratio, and the overall vehicle power distribution is used as the operating condition characteristic.

[0022] In this embodiment, the method for obtaining the specific power distribution of the vehicle includes: The micro-unit acceleration is calculated second by second based on the vehicle's driving cycle. The vehicle's specific power is then calculated using the micro-unit acceleration. The percentage of driving time for each micro-unit is statistically analyzed. Based on the weighted ratio of the percentage of driving time and the driving cycle, the vehicle's specific power distribution is calculated. The formula for calculating the vehicle's specific power distribution is as follows: ; in For a moment acceleration, For a moment speed, For a moment speed, This represents the total travel time of the vehicle. For driving cycle Distribution time on This represents the distribution percentage of driving cycles. The weighting ratio is the driving cycle. This represents the specific power distribution of the vehicle.

[0023] In this embodiment, the method for obtaining the carbon emission factor includes: Based on the localized model, baseline emission rates are output for different vehicle types, fuel types, and vehicle power ratio ranges. The ratio of the standard deviation to the mean is calculated based on vehicle speed, and multiplied by the idling time percentage to obtain the scenario congestion index. The adaptive emission factor is then calculated using the scenario congestion index and the baseline emission rate. The formula for calculating the adaptive emission factor is as follows: ; in Vehicle type ,fuel In the scene Adaptive emission factor For the first Baseline emission rates for each vehicle power-to-weight ratio range The total number of intervals, For the scene lower interval The proportion of time, For the scene average driving speed The free-flow velocity is obtained according to road design specifications. For the scene Traffic flow below, For lane capacity, For the scene Average temperature The average annual temperature of the region. The temperature sensitivity coefficient is obtained by fitting the temperature gradient to the vehicle carbon emission difference. For the scene The congestion correction factor is obtained based on the historical percentage of congested road sections. Vehicle type ,fuel In the scene The scene congestion index; The adaptive emission factor is used as the carbon emission factor corresponding to each vehicle type and fuel type.

[0024] In this embodiment, the method for obtaining the driving scenario model includes: Parking queues and queuing while moving are considered queuing scenarios, while scenarios without queuing are considered driving scenarios. A driving scenario model for calculating carbon emissions of all types of vehicles across the entire road segment is constructed using the IPCC bottom-up approach. Based on driving trajectories, the average number of trips, average mileage, and average energy consumption of different vehicle types are used as input data. The total carbon emissions are obtained from the driving scenario model based on the input data. The formulas for calculating the average number of trips and average mileage are as follows: ; ; in For model fuel distribution Total number of trips This represents the total number of vehicle types. This represents the total number of fuel types. For the first Type of vehicle uses fuel Number of trips, For the first The first in the category of car models Vehicles use fuel driving distance, This represents the total number of trips for the corresponding vehicle type. For the first Type of vehicle uses fuel The average driving distance; The formula for calculating the average energy consumption is: ; in For the first Type of vehicle uses fuel Average energy consumption per 100 kilometers For the first The first in the category of car models Vehicles use fuel The theoretical energy consumption per 100 kilometers For the first Type of vehicle uses fuel The energy consumption correction factor per 100 kilometers is obtained by calibrating actual energy consumption monitoring data with theoretical values. For the first The total number of vehicles of each vehicle type; The formula for calculating fuel consumption and carbon emissions is as follows: ; in For the first Type of vehicle uses fuel fuel consumption, Use fuel for all models Total consumption, fuel carbon emission factors, This represents the total carbon emissions during driving scenarios.

[0025] In this embodiment, the method for obtaining the carbon emission calculation model includes: Based on the queuing scenario, the number of vehicles and queue length are obtained in two states under congestion: slow movement and standing still. The additional carbon emissions in the slow movement state are included in the driving scenario model. The formula for calculating the additional carbon emissions in the slow movement state is as follows: ; ; ; in Fuel consumption for slow driving. Fuel consumption per 100 kilometers at low speeds. Queue length The total amount of basic carbon emissions that move slowly. Additional carbon emissions due to slow movement. For the number of congested vehicles, Fuel consumption per 100 kilometers in congested traffic; The carbon emission calculation model is obtained by using the number of vehicles and queue length to account for additional carbon emissions in the stationary state through the driving scenario model, and outputs the total carbon emissions of the target highway.

[0026] In this embodiment, the method for obtaining additional carbon emissions from the stationary state includes: The additional carbon emissions during the stationary state are calculated based on congestion time and idling fuel consumption. The formula for calculating the additional carbon emissions during the slow-moving state is as follows: ; ; ; ; in For the first Type of vehicle, No. Total energy consumption during acceleration of various fuel vehicles For vehicle quality, For air resistance, For rolling resistance, Gradient resistance is determined by the road slope angle. To increase the driving distance, To assist in understanding the system's energy consumption, historical data corresponding to the vehicle type is used. This represents the total fuel consumption in queuing scenarios. This is the fuel consumption per unit at idle speed, obtained from the vehicle type. For congestion time, Fuel efficiency is determined by vehicle type. The calorific value of a fuel is determined by its type. For fuel density, To stop the base carbon emissions from being stagnant Additional carbon emissions from stopping the process.

[0027] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

Claims

1. A method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios, characterized in that, Includes the following steps: Operational data of vehicle operation scenarios are obtained based on the target highway. The operational data includes vehicle type, fuel type, driving trajectory, geographical climate and vehicle age distribution. The MOVES model is localized using the aforementioned operational data, and carbon emission factors corresponding to vehicle type and fuel type are output based on the localized model. The vehicle operation scenarios are divided into driving scenarios and queuing scenarios. The total carbon emissions of the driving scenario are calculated using the IPCC bottom-up method based on the carbon emission factor to obtain the driving scenario model. Based on the driving scenario model, additional carbon emissions are taken into account for the two states of slow movement and standing still under congestion. A carbon emission calculation model is obtained and the total carbon emissions of the target highway are output.

2. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 1, characterized in that, The method for obtaining the localized model includes: Based on the operational data, the geographic climate of the target highway is extracted. Based on the geographic climate, similar regions built into the MOVES model are used as matching simulation regions and simulation parameters. The geographic climate includes annual average temperature, altitude, relative humidity, latitude and longitude, season and annual average precipitation. The vehicle age percentage of each model is calculated based on the vehicle statistics yearbook of the city to which the operation data belongs. The vehicle age percentage is used to replace the vehicle age distribution built into the MOVES model. The index categories and data types of the fuel parameters built into the MOVES model are converted into national standard parameters. The built-in simulation year of the MOVES model is converted to the year when the vehicle emission standard was released. The MOVES model maps small passenger cars, medium passenger cars, and medium commercial vehicles to small and medium passenger cars, transport buses, public buses, school buses, and church buses to large passenger cars, single short-haul trucks to small vans, single long-haul trucks to medium vans, combined short-haul trucks to large vans, and combined long-haul trucks to extra-large vans. Based on the mapped vehicle types, vehicle operating condition analysis is performed, and a localized model is obtained based on the operating condition characteristics.

3. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 2, characterized in that, The method for obtaining the operating condition characteristics includes: Passenger cars, small trucks, and medium-sized trucks are classified as light vehicles, while large passenger cars, large trucks, and extra-large trucks are classified as heavy vehicles. The power-to-weight ratio of each vehicle is calculated using the following formula: ； ； in For light vehicles, the power-to-weight ratio For heavy-duty vehicles, the power-to-weight ratio is... For vehicle speed, For acceleration; Based on the driving trajectory obtained from the operational data, different driving cycles and average driving speeds are acquired. The average driving speed is taken as the normal speed, and speed ranges of normal speed, high speed, and low speed are obtained. The weight ratio of different driving cycles is calculated according to the speed range. The formula for calculating the weight ratio is as follows: ； ； in This represents the weighting ratio for the low-speed range. This represents the weighting ratio for the high-speed range. At normal speed, For high speed, Low speed; The overall vehicle power distribution is obtained based on the driving cycle and weight ratio, and the overall vehicle power distribution is used as the operating condition characteristic.

4. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 3, characterized in that, A method for obtaining the specific power distribution of the vehicle includes: The micro-unit acceleration is calculated second by second based on the vehicle's driving cycle. The vehicle's specific power is then calculated using the micro-unit acceleration. The percentage of driving time for each micro-unit is statistically analyzed. Based on the weighted ratio of the percentage of driving time and the driving cycle, the vehicle's specific power distribution is calculated. The formula for calculating the vehicle's specific power distribution is as follows: ； in For a moment acceleration, For a moment speed, For a moment speed, This represents the total travel time of the vehicle. For driving cycle Distribution time on This represents the distribution percentage of driving cycles. The weighting ratio is the driving cycle. This represents the specific power distribution of the vehicle.

5. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 1, characterized in that, A method for obtaining the carbon emission factor includes: Based on the localized model, baseline emission rates are output for different vehicle types, fuel types, and vehicle power ratio ranges. The ratio of the standard deviation to the mean is calculated based on vehicle speed, and multiplied by the idling time percentage to obtain the scenario congestion index. The adaptive emission factor is then calculated using the scenario congestion index and the baseline emission rate. The formula for calculating the adaptive emission factor is as follows: ; in Vehicle type ,fuel In the scene Adaptive emission factor For the first Baseline emission rates for each vehicle power-to-weight ratio range The total number of intervals, For the scene lower interval The proportion of time, For the scene average driving speed The free-flow velocity is obtained according to road design specifications. For the scene Traffic flow below, For lane capacity, For the scene Average temperature The average annual temperature of the region. The temperature sensitivity coefficient is obtained by fitting the temperature gradient to the vehicle carbon emission difference. For the scene The congestion correction factor is obtained based on the historical percentage of congested road sections. Vehicle type ,fuel In the scene The scene congestion index; The adaptive emission factor is used as the carbon emission factor corresponding to each vehicle type and fuel type.

6. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 1, characterized in that, The method for obtaining the driving scenario model includes: Parking queues and queuing while moving are considered queuing scenarios, while scenarios without queuing are considered driving scenarios. A driving scenario model for calculating carbon emissions of all types of vehicles across the entire road segment is constructed using the IPCC bottom-up approach. Based on driving trajectories, the average number of trips, average mileage, and average energy consumption of different vehicle types are used as input data. The total carbon emissions are obtained from the driving scenario model based on the input data. The formulas for calculating the average number of trips and average mileage are as follows: ； ； in For model fuel distribution Total number of trips This represents the total number of vehicle types. This represents the total number of fuel types. For the first Type of vehicle uses fuel Number of trips, For the first The first in the category of car models Vehicles use fuel driving distance, This represents the total number of trips for the corresponding vehicle type. For the first Type of vehicle uses fuel The average driving distance; The formula for calculating the average energy consumption is: ； in For the first Type of vehicle uses fuel Average energy consumption per 100 kilometers For the first The first in the category of car models Vehicles use fuel The theoretical energy consumption per 100 kilometers For the first Type of vehicle uses fuel The energy consumption correction factor per 100 kilometers is obtained by calibrating actual energy consumption monitoring data with theoretical values. For the first The total number of vehicles of each vehicle type; The formula for calculating fuel consumption and carbon emissions is as follows: ； in For the first Type of vehicle uses fuel fuel consumption, Use fuel for all models Total consumption, fuel carbon emission factors, This represents the total carbon emissions during driving scenarios.

7. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 1, characterized in that, The method for obtaining the carbon emission calculation model includes: Based on the queuing scenario, the number of vehicles and queue length are obtained in two states under congestion: slow movement and standing still. The additional carbon emissions in the slow movement state are included in the driving scenario model. The formula for calculating the additional carbon emissions in the slow movement state is as follows: ； ； ； in Fuel consumption for slow driving. Fuel consumption per 100 kilometers at low speeds. Queue length The total amount of basic carbon emissions that move slowly. Additional carbon emissions due to slow movement. For the number of congested vehicles, Fuel consumption per 100 kilometers in congested traffic; The carbon emission calculation model is obtained by using the number of vehicles and queue length to account for additional carbon emissions in the stationary state through the driving scenario model, and outputs the total carbon emissions of the target highway.

8. The method for constructing a carbon emission calculation model based on differences in vehicle operating scenarios according to claim 7, characterized in that, A method for obtaining additional carbon emissions from the stationary state includes: The additional carbon emissions during the stationary state are calculated based on congestion time and idling fuel consumption. The formula for calculating the additional carbon emissions during the slow-moving state is as follows: ； ； ； ； in For the first Type of vehicle, No. Total energy consumption during acceleration of various fuel vehicles For vehicle quality, For air resistance, For rolling resistance, Gradient resistance is determined by the road slope angle. To increase the driving distance, To assist in understanding the system's energy consumption, historical data corresponding to the vehicle type is used. This represents the total fuel consumption in queuing scenarios. This is the fuel consumption per unit at idle speed, obtained from the vehicle type. For congestion time, Fuel efficiency is determined by vehicle type. The calorific value of a fuel is determined by its type. For fuel density, To stop the base carbon emissions from being stagnant To stop the additional carbon emissions.

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