Carbon emission calculation method for automatic driving electric vehicle under different traffic conditions

Abstract

The invention provides a carbon emission calculation method for an automatic driving electric vehicle under different traffic conditions. The carbon emission calculation method comprises the following steps that a carbon emission calculation model considering the full life cycle of power production is built; constructing car following models of different levels of AD, simulating vehicles from L0 to L5, and adjusting model parameters; by simulating and researching a specific urban highway system, the differences and interrelationships between CAVs of different levels are obtained. According to the method, a carbon emission calculation model considering the full life cycle of power production is established, and the carbon emission of the electric vehicle under different automatic driving levels can be accurately calculated.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic flow simulation, and in particular relates to a method for calculating carbon emissions of self-driving electric vehicles under different traffic conditions. Background Art

[0002] The rapid growth of car ownership has led to a large amount of carbon emissions and has had a profound impact on the environment. In 2022, the number of cars in the world reached about 1.446 billion. According to the International Energy Agency (IEA), the transportation sector accounts for 25% of global carbon emissions, and this proportion is increasing rapidly. The widespread adoption of electric vehicles is a key strategy for the transportation sector to meet the environmental challenges of carbon emissions. By 2050, all-electric vehicles are expected to reduce carbon emissions by more than 60% compared with traditional models. The development of electric vehicles is closely related to AD (autonomous driving) technology. The intelligent on-board system in CAV (Connected and Autonomous Vehicle) requires a lot of electrical energy. Therefore, the progress of electric vehicle batteries contributes to the progress of AD technology. Conversely, the improvement of AD technology helps to reduce the electrical energy consumption of electric vehicles and improve the comfort of passengers. The integration of electric vehicles and AD technology marks a new stage in the development of transportation systems and environmental protection. However, due to the limited commercial deployment of L4 and L5 vehicles, the current level of AD for carbon emission calculation is relatively limited. Summary of the invention

[0003] Aiming at the problem of calculating the carbon emissions of electric vehicles under different traffic conditions and different driving levels, the present invention provides a carbon emissions calculation method for self-driving electric vehicles under different traffic conditions. Combining intelligent agent simulation with object-oriented design ideas, a comprehensive calculation method combining Krauss, intelligent driver model and collaborative adaptive cruise control module is constructed to simulate L0-L5 vehicles.

[0004] The present invention specifically provides a method for calculating carbon emissions of autonomous electric vehicles under different traffic conditions. The power system state assessment method comprises the following steps:

[0005] Step 1: Build a carbon emission calculation model considering the entire life cycle of power production;

[0006] Step 2: Car-following models for different levels of AD. Construct car-following models for different levels of AD, simulate vehicles from L0 to L5, and adjust model parameters.

[0007] Step 3: Study a specific urban highway system through simulation to obtain the differences and relationships between different levels of CAVs.

[0008] Step 1: Building a carbon emission calculation model, including the following steps:

[0009] Step 1.1: Use SUMO (Simulation of Urban Mobility) to integrate the electric vehicle energy consumption model into a complex vehicle model by integrating mechanical and electric vehicle parameters;

[0010] Step 1.2: Calculate the various parameters required by the model during the operation of the vehicle, including kinetic energy, potential energy, increment of rotational energy, vehicle mass, vehicle speed, etc. The energy change of the time step can be calculated using formula (1) (2):

[0011] The energy change of the time step can be calculated using equations (1) and (2):

[0012] E veh [k] = E kin [k]+E pot [k]+E rot,int [k] (1)

[0013]

[0014] In formula (1) and (2), E veh [k] represents the energy change of the vehicle at time step k during operation, E kin [k]E pot [k]E rot,int [k] represents the kinetic energy, potential energy and rotational energy of the vehicle at the time step during operation, m represents the mass of the vehicle, v[k] represents the speed of the vehicle at time k, g represents the acceleration of gravity, h[k] represents the height of the vehicle at that time, and J int Represents the vehicle's moment of inertia.

[0015] The vehicle energy and loss formula described in Equation (3) calculates the energy gained by the vehicle at each time increment.

[0016] ΔE gain [k] = E veh [k+1]-E veh [k]-ΔE loss [k] (3)

[0017] In formula (3), ΔE loss [k] represents the loss of electrical energy during the operation of the vehicle.

[0018] Step 1.3: Calculate the change in vehicle battery energy by introducing two constant efficiency factors;

[0019]

[0020] E Bat [k+1]=E Bat [k]+ΔEgain [k]·η recup (5)

[0021] In formula (4) and (5), (η prop ) represents the power transfer efficiency during battery discharge, (η recup ) represents the efficiency during brake regeneration, E Bat [k] represents the vehicle battery energy at step k.

[0022] Step 1.4: Integrating these parameters allows for a more accurate assessment of battery energy dynamics, including energy consumption and recovery;

[0023] Step 1.5: Consider the carbon emission characteristics of the entire life cycle. The integrated data is used to accurately calculate the carbon emission coefficient per unit of electricity according to the World Energy Statistical Yearbook published by the Energy Institute.

[0024] The formula for calculating the carbon emission coefficient per unit of electricity is as follows:

[0025] c e =∑α i ·c i (6)

[0026] In formula (6), c e represents the carbon emission factor of electricity, α i It indicates the proportion of electricity generated by the power generation method to the total power generation of the power plant, c i Indicates the carbon emission coefficient of the power generation method.

[0027] Step 1.6: Determine the total carbon emissions produced by the vehicle during a specific period by summing up the carbon emissions at each time step.

[0028]

[0029] In formula (7), C output Represents the total carbon emissions produced during a specific period.

[0030] Step 2: Construct vehicle following models for different levels of AD to simulate vehicles from L0 to L5. This includes the following steps:

[0031] Step 2.1: Classify autonomous vehicles from L0 to L5 according to the SAE J3016 standard released by SAE in 2014;

[0032] Step 2.2: Comparative analysis of Krauss, IDM and CACC models was performed;

[0033] Step 2.2: Adjust settings such as minimum headway, acceleration rate, reaction time, and other parameters to reflect the different characteristics of the CAV at each automation level;

[0034] Step 2.3: Comparative analysis of Krauss, IDM and CACC models was performed;

[0035] The specific processing process of step 2.3 includes the following steps:

[0036] Step 2.3.1: Analyze the Krauss model and adjust its parameters. The formula for calculating the vehicle safety speed is as follows:

[0037]

[0038] In formula (8), v l (t) represents the speed of the leading vehicle at that time, v f (t) represents the speed of the following vehicle at that time, t r represents the driver's reaction time, v l represents the average speed of the following vehicle from the current time to full braking, b represents the optimal deceleration for vehicle comfort, and g(t) is the distance between the leading vehicle and the following vehicle. In order to comply with the safety consideration of collision-free, the expected speed of the vehicle in the subsequent time step is selected as the minimum of the safe speed, lane speed limit and maximum uniform speed of the vehicle. The formula is as follows:

[0039] v des (t) = min[v max ,v(t)+a·Δt,v safe (t)] (9)

[0040] In formula (9), v(t) represents the vehicle speed at time t, v max represents the maximum speed of the vehicle, and a represents the vehicle speed variation coefficient.

[0041] Finally, the vehicle's position and velocity at that time are calculated as follows:

[0042] v(t+Δt)=max[0,v des (t)-η] (10)

[0043] x f (t+Δt)=x f (t)+v f (t+Δt)·Δt′ (11)

[0044] In equations (10) and (11), η represents the random deviation of the driver’s behavior, Δt represents each simulation step in SUMO, and x f (t) represents the vehicle position at time t.

[0045] Step 2.3.2: Analyze the IDM model in detail and adjust its parameters.

[0046] In the IDM model, the expression of vehicle acceleration is as follows:

[0047]

[0048] In formula (12) represents the acceleration of the αth vehicle, v α represents the speed of the αth vehicle, δ represents the power coefficient of speed, Δv α represents the speed difference between the αth vehicle and the vehicle ahead, s α It represents the distance between the αth vehicle and the vehicle in front, and s′ is the expected following distance.

[0049] The expression for the minimum required spacing is as follows:

[0050]

[0051] In formula (13), s0 represents the safety distance, s1 represents the congestion distance, v0 represents the expected speed of the vehicle, T represents the safety factor of the headway (a coefficient used to adjust the safety distance, which is related to the vehicle speed and time), a represents the maximum acceleration, and b represents the required deceleration.

[0052] Step 2.3.3: Analyze the CACC model in detail and adjust its parameters.

[0053] The vehicle clearance formula is as follows:

[0054] e i,k =x i-1 -x i -Tv i (14)

[0055] In formula (14), x i-1 represents the position of the vehicle in front, x i and v i It represents the current position and speed of the vehicle, and T represents the safety factor of the headway.

[0056] When the vehicle ahead appears within the detection range of the autonomous vehicle, in order to maintain a constant time interval with the vehicle ahead, the formula for the autonomous vehicle's speed is as follows:

[0057]

[0058] In formula (15), v i,k represents the vehicle speed at time k, e i,k and represents the gap error between the front and rear vehicles and its derivative, k p and k d is the gain parameter of the control.

[0059] Step 2.4: Modify the following model parameters of vehicles with different AD levels to improve the accuracy of L0-L5 AD scenario modeling;

[0060] Step 2.5: Use the IDM model to simulate L1 to L3 AD vehicles;

[0061] Step 2.6: Use the CACC model to simulate L4 and L5 AD vehicles;

[0062] In step 3, the differences and relationships between different levels of CAV are captured, including the following steps:

[0063] Step 3.1: Select a specific urban highway system for simulation study;

[0064] Step 3.2: Select a specific model of electric vehicle as the simulation experiment object;

[0065] Step 3.3: Along the simulation path, experiments were conducted to simulate vehicles with a single-level AD under different traffic volume conditions. Three different traffic scenarios were explored: 500, 1000, and 1500 vehicles per hour to simulate the impact of different traffic densities on vehicle energy consumption. The total carbon emissions generated by a specific level of vehicles under each traffic volume scenario were calculated and divided by the total number of vehicles to determine the average carbon emissions of that level under current traffic conditions. ;

[0066] Step 3.4: Compare the average carbon emissions of vehicles with different traffic volumes to gain a deeper understanding of the energy consumption characteristics of this AD level.

[0067] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the carbon emission calculation method for autonomous driving electric vehicles under different traffic conditions of the present invention are implemented.

[0068] According to another aspect of the present invention, there is provided a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the carbon emission calculation method for autonomous electric vehicles under different traffic conditions of the present invention are implemented.

[0069] Compared with the prior art, the beneficial effects are:

[0070] 1. This invention is aimed at urban road scenes with continuous intersections, and studies the carbon emissions of vehicles with different AD levels under different scenarios;

[0071] 2. This paper proposes a model that counts all resources involved in the power generation process and calculates the carbon factor of each resource throughout its life cycle. Then, by considering the proportion of energy from various sources in power generation, the model accurately calculates the carbon emissions of electric vehicles. In order to explore the carbon emissions associated with different AD levels, electric vehicles from L0 to L5 were simulated using SUMO. A series of comparative experiments were conducted to validate our model, covering different levels of AD, different penetration rates, and spatiotemporal distributions. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A structural schematic diagram of a method for calculating carbon emissions of an autonomous electric vehicle under different traffic conditions according to the present invention;

[0073] Figure 2 A schematic diagram of a simulated road network according to a preferred embodiment of the present invention;

[0074] Figure 3 This is a diagram of total carbon emissions and average carbon emissions divided by vehicle level in traffic volume in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0075] The following is a detailed description of a specific implementation method of a carbon emission calculation method for an autonomous electric vehicle under different traffic conditions according to the present invention in conjunction with the accompanying drawings.

[0076] Embodiment 1:

[0077] like Figure 1 As shown, the carbon emission calculation method of the present invention for an autonomous driving electric vehicle under different traffic conditions includes the following steps:

[0078] Step 1: Build a carbon emission calculation model considering the entire life cycle of power production;

[0079] Step 2: Car-following models for different levels of AD. Construct car-following models for different levels of AD, simulate vehicles from L0 to L5, and adjust model parameters.

[0080] Step 3: Study a specific urban highway system through simulation to obtain the differences and relationships between different levels of CAVs.

[0081] Step 1: Building a carbon emission calculation model, including the following steps:

[0082] Take the example of the urban highway system in a certain province and region. Figure 2As shown, (a) is a high-resolution satellite image of the simulated road. (b) is a simulated road network. The highway system includes a 4 km x 5 km area covering the inner ring. These highways have low speed limits, many intersections, and frequent traffic signals. SUMO simulation improves accuracy by utilizing GIS data and road network files adjusted with high-resolution satellite images. This approach ensures accurate alignment with latitude, longitude, and lane count, unlike traditional OpenStreetMap (OSM) data.

[0083] Step 1.1: Use SUMO (Simulation of Urban Mobility) to integrate the electric vehicle energy consumption model into a complex vehicle model by integrating mechanical and electric vehicle parameters;

[0084] Step 1.2: Calculate the various parameters required by the model during the vehicle operation, including the increment of kinetic energy, potential energy, rotational energy, vehicle mass, vehicle speed, etc. The energy change of the time step can be calculated using formula (1) (2):

[0085] The energy change of the time step can be calculated using equations (1) and (2):

[0086] E veh [k] = E kin [k]+E pot [k]+E rot,int [k] (1)

[0087]

[0088] In formula (1) and (2), E veh [k] represents the energy change of the vehicle at time step k during operation, E kin [k]E pot [k]E rot,int [k] represents the kinetic energy, potential energy and rotational energy of the vehicle at time step k during operation, m represents the mass of the vehicle, v[k] represents the speed of the vehicle at time k, g represents the acceleration of gravity, h[k] represents the height of the vehicle at that time, and J int Represents the vehicle's moment of inertia.

[0089] The vehicle energy and loss formula described in Equation (3) calculates the energy gained by the vehicle at each time increment.

[0090] ΔE gain [k] = E veh [k+1]-E veh [k]-ΔE loss [k] (3)

[0091] In formula (3), ΔE loss[k] represents the loss of electrical energy during the operation of the vehicle.

[0092] Step 1.3: Calculate the change in vehicle battery energy by introducing two constant efficiency factors;

[0093]

[0094] E Bat [k+1]=E Bat [k]+ΔE gain [k]·η recup (5)

[0095] In formula (4) and (5), (η prop ) represents the power transfer efficiency during battery discharge, (η recup ) represents the efficiency during brake regeneration, E Bat [k] represents the vehicle battery energy at step k.

[0096] Step 1.4: Integrating these parameters allows for a more accurate assessment of battery energy dynamics, including energy consumption and recovery;

[0097] Step 1.5: Consider the carbon emission characteristics of the entire life cycle. The integrated data is used to accurately calculate the carbon emission coefficient per unit of electricity according to the World Energy Statistical Yearbook published by the Energy Institute.

[0098] The formula for calculating the carbon emission coefficient per unit of electricity is as follows:

[0099] c e =∑α i ·c i (6)

[0100] In formula (6), c e represents the carbon emission factor of electricity, α i It indicates the proportion of electricity generated by the power generation method to the total power generation of the power plant, c i Indicates the carbon emission coefficient of the power generation method.

[0101] Step 1.6: Determine the total carbon emissions produced by the vehicle during a specific period by summing up the carbon emissions at each time step.

[0102]

[0103] In formula (7), C output Represents the total carbon emissions produced during a specific period.

[0104] Table 1 shows the power generation, carbon emission factors and proportions of various resources in 2023.

[0105] Table 1

[0106]

[0107]

[0108] Step 2: Construct vehicle following models for different levels of AD to simulate vehicles from L0 to L5. This includes the following steps:

[0109] The model construction is divided into three steps: Data collection: Collect actual traffic data, vehicle driving data and emission test data of the urban highway system in the sample area. Model verification: Compare the model prediction results with the actual data to verify the accuracy and reliability of the model. Model calibration: According to the verification results, make necessary adjustments and optimizations to the model to improve the prediction accuracy.

[0110] Step 2.1: Classify autonomous vehicles from L0 to L5 according to the SAE J3016 standard released by SAE in 2014;

[0111] Step 2.2: Comparative analysis of Krauss, IDM and CACC models was performed;

[0112] Step 2.2: Adjust settings such as minimum headway, acceleration rate, reaction time, and other parameters to reflect the different characteristics of the CAV at each automation level;

[0113] Step 2.3: Comparative analysis of Krauss, IDM and CACC models was performed;

[0114] The specific processing process of step 2.3 includes the following steps:

[0115] Step 2.3.1: Analyze the Krauss model and adjust its parameters. The formula for calculating the vehicle safety speed is as follows:

[0116]

[0117] In formula (8), v l (t) represents the speed of the leading vehicle at that time, v f (t) represents the speed of the following vehicle at that time, t r represents the driver's reaction time, v l represents the average speed of the following vehicle from the current time to full braking, b represents the optimal deceleration for vehicle comfort, and g(t) is the distance between the leading vehicle and the following vehicle. In order to comply with the safety consideration of collision-free, the expected speed of the vehicle in the subsequent time step is selected as the minimum of the safe speed, lane speed limit and maximum uniform speed of the vehicle. The formula is as follows:

[0118] v des (t) = min[v max,v(t)+a·Δt,v safe (t)] (9)

[0119] In formula (9), v(t) represents the vehicle speed at time t, v max represents the maximum speed of the vehicle, and a represents the vehicle speed variation coefficient.

[0120] Finally, the vehicle's position and velocity at that time are calculated as follows:

[0121] v(t+Δt)=max[0,v des (t)-η] (10)

[0122] x f (t+Δt)=x f (t)+v f (t+Δt)·Δt′ (11)

[0123] In equations (10) and (11), η represents the random deviation of the driver’s behavior, Δt represents each simulation step in SUMO, and x f (t) represents the vehicle position at time t.

[0124] Step 2.3.2: Analyze the IDM model in detail and adjust its parameters.

[0125] In the IDM model, the expression of vehicle acceleration is as follows:

[0126]

[0127] In formula (12) represents the acceleration of the αth vehicle, v α represents the speed of the αth vehicle, δ represents the power coefficient of speed, Δv α represents the speed difference between the αth vehicle and the vehicle ahead, s α It represents the distance between the αth vehicle and the vehicle in front, and s′ is the expected following distance.

[0128] The expression for the minimum required spacing is as follows:

[0129]

[0130] In formula (13), s0 represents the safety distance, s1 represents the congestion distance, v0 represents the expected speed of the vehicle, T represents the safety factor of the headway (a coefficient used to adjust the safety distance, which is related to the vehicle speed and time), a represents the maximum acceleration, and b represents the required deceleration.

[0131] Step 2.3.3: Analyze the CACC model in detail and adjust its parameters.

[0132] The vehicle clearance formula is as follows:

[0133] e i,k =x i-1 -x i -Tv i (14)

[0134] In formula (14), x i-1 represents the position of the vehicle in front, x i and v i It represents the current position and speed of the vehicle, and T represents the safety factor of the headway.

[0135] When the vehicle ahead appears within the detection range of the autonomous vehicle, in order to maintain a constant time interval with the vehicle ahead, the formula for the autonomous vehicle's speed is as follows:

[0136]

[0137] In formula (15), v i,k represents the vehicle speed at time k, e i,k and represents the gap error between the front and rear vehicles and its derivative, k p and k d is the gain parameter of the control.

[0138] Step 2.4: Modify the following model parameters of vehicles with different AD levels to improve the accuracy of L0-L5 AD scenario modeling;

[0139] Step 2.5: Use the IDM model to simulate L1 to L3 AD vehicles;

[0140] Step 2.6: Use the CACC model to simulate L4 and L5 AD vehicles;

[0141] Table 2 shows the parameters of the following car model at different levels.

[0142] Table 2

[0143]

[0144] In step 3, the differences and relationships between different levels of CAV are captured, including the following steps:

[0145] Step 3.1: Select a specific urban highway system for simulation study;

[0146] Step 3.2: Select a specific model of electric vehicle as the simulation experiment object;

[0147] Step 3.3: Along the simulation path, experiments were conducted to simulate vehicles with a single-level AD under different traffic volume conditions. Three different traffic scenarios were explored: 500, 1000, and 1500 vehicles per hour to simulate the impact of different traffic densities on vehicle energy consumption. The total carbon emissions generated by a specific level of vehicles under each traffic volume scenario were calculated and divided by the total number of vehicles to determine the average carbon emissions of that level under current traffic conditions;

[0148] Step 3.4: Compare the average carbon emissions of vehicles with different traffic volumes to gain a deeper understanding of the energy consumption characteristics of this AD level.

[0149] To effectively test and analyze the differences and relationships between different levels of CAV, the electric vehicle model selected is the 2020 version of the Kia Soul electric vehicle. The parameters of this model are consistent across the six levels of autonomous driving, and the difference in energy consumption is attributed to driving behaviors such as acceleration and minimum safe following distance settings. The simulated traffic flow is set along the path from the Jia Road subway station to the Yi Bridge, driving from south to north along Jia Road, about 4.6 kilometers. The route passes through 14 intersections.

[0150] Along the simulation path, experiments were conducted to simulate vehicles with a single-stage AD under different traffic volume conditions. Three different traffic scenarios were explored: 500, 1000, and 1500 vehicles per hour to simulate the impact of different traffic densities on vehicle energy consumption. The total carbon emissions generated by a specific level of vehicles under each traffic volume scenario were calculated and then divided by the total number of vehicles to determine the average carbon emissions of that level under current traffic conditions.

[0151] Comparing the average carbon emissions of vehicles with different traffic volumes can provide insight into the energy consumption characteristics of that AD level. Figure 3 As shown, at similar traffic volumes, higher carbon emissions correspond to lower carbon emissions. At a traffic volume of 500 vehicles / hour, the transition from L0 to L5 shows a significant decline, with the most significant decline from L4 to L5, a reduction of 13.8%. Similarly, at traffic volumes of 1000 and 1500 vehicles / hour, the trend from L0 to L5 continues, with large declines of more than 8.6% observed between L3 to L4 and L4 to L5.

[0152] Meanwhile, L0-L3 vehicles showed a positive correlation between average carbon emissions and increased traffic volume, while L4 and L5 vehicles showed a negative correlation, indicating that emissions gradually decreased with increasing traffic volume. The above results indicate that in congested conditions, low-level AD systems are less efficient than high-level systems.

[0153] Embodiment 2:

[0154] The computer-readable storage medium of this embodiment stores a computer program thereon, which, when executed by a processor, implements the steps in the method for calculating carbon emissions of an autonomous electric vehicle under different traffic conditions of Example 1.

[0155] The computer-readable storage medium of this embodiment may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal; the computer-readable storage medium of this embodiment may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. equipped on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.

[0156] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.

[0157] Embodiment 3:

[0158] The computer device of this embodiment includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for calculating carbon emissions of autonomous electric vehicles under different traffic conditions of Embodiment 1 are implemented.

[0159] In this embodiment, the processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, readily available programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0160] Those skilled in the art will appreciate that the disclosed content of the embodiments may be provided as methods, systems, or computer program products. Therefore, the present solution may adopt the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Moreover, the present solution may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program codes.

[0161] The present solution is described with reference to the flowchart and / or block diagram of the method and computer program product according to the embodiment of the present solution. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions; these computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0165] The examples described in the present invention are merely descriptions of the preferred implementation modes of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made to the technical solutions of the present invention by engineers and technicians in this field should all fall within the protection scope of the present invention.

Claims

1. A method for calculating carbon emissions of autonomous electric vehicles under different traffic conditions, characterized in that: The steps include: Step 1: Building a carbon emission calculation model; Step 2: Construct vehicle-following models for different levels of AD, simulating vehicles from L0 to L5; Step 3: Obtain the differences and correlations between different levels of CAV.

2. The carbon emission calculation method for autonomous electric vehicles under different traffic conditions according to claim 1 is characterized in that: The carbon emission calculation model described in step 1 is constructed, including the following steps: Step 1.1: Electric vehicle energy consumption model using SUMO to integrate mechanical and electric vehicle parameters into one vehicle model; Step 1.2: Calculate the various parameters required by the model during vehicle operation; Step 1.3: Calculate the change in vehicle battery energy by introducing two constant efficiency factors; Step 1.4: Integrate parameters to evaluate battery energy dynamics; Step 1.5: Calculate the carbon emission coefficient per unit of electricity; Step 1.6: Determine the total carbon emissions produced by the vehicle during a specific period by summing up the carbon emissions at each time step.

3. The carbon emission calculation method for autonomous electric vehicles under different traffic conditions according to claim 2 is characterized in that: The following vehicle models of different levels of AD are constructed to simulate vehicles from L0 to L5, including the following steps: Step 2.1: Classify autonomous vehicles from L0 to L5 according to the SAE J3016 standard released by SAE in 2014; Step 2.2: Comparative analysis of Krauss, IDM and CACC models; Step 2.2: Adjust parameter settings to reflect the different characteristics of the CAV at each automation level; Step 2.3: Comparative analysis of Krauss, IDM and CACC models; Step 2.4: Modify the following model parameters of vehicles with different AD levels to improve the accuracy of L0-L5 AD scenario modeling; Step 2.5: Use the IDM model to simulate L1 to L3 AD vehicles; Step 2.6: Use the CACC model to simulate L4 and L5 AD vehicles.

4. The carbon emission calculation method for autonomous electric vehicles under different traffic conditions according to claim 3 is characterized in that: The specific processing process of step 2.3 includes the following steps: Step 2.3.1: Analyze the Krauss model in detail and adjust the parameters of the Krauss model; Step 2.3.2: Analyze the IDM model in detail and adjust the IDM model parameters; Step 2.3.3: Analyze the CACC model in detail and adjust the CACC model parameters.

5. The carbon emission calculation method for autonomous electric vehicles under different traffic conditions according to claim 4 is characterized in that: Capturing the differences and relationships between different levels of CAVs includes the following steps: Step 3.1: Select the urban highway system to be tested for simulation study; Step 3.2: Select a specific model of electric vehicle as the simulation experiment object; Step 3.3: Experiment with a single-stage AD simulation vehicle under different traffic volume conditions along the simulation path; explore different traffic scenarios to simulate the impact of different traffic densities on vehicle energy consumption; Calculate the total carbon emissions produced by vehicles under each traffic volume scenario and divide by the total number of vehicles to determine the average carbon emissions at that level under current traffic conditions; Step 3.4: Compare the average carbon emissions of vehicles with different traffic volumes to gain a deeper understanding of the energy consumption characteristics of the AD level.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the carbon emission calculation method for an autonomous electric vehicle under different traffic conditions as described in any one of claims 1 to 5.

7. A computer device, characterized in that: The computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the carbon emission calculation method for an autonomous electric vehicle under different traffic conditions as described in any one of claims 1 to 5 are implemented.

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