A Dynamic Carbon Emission Calculation Method for Electric Vehicles Considering Production and Service Life

Abstract

The present invention relates to a method for calculating the dynamic carbon emissions of electric vehicles considering production and service life, including: calculating the carbon emission factors of each electric vehicle charging station based on the carbon emission flow theory; calculating the carbon emissions of each stage of electric vehicles based on the production and service life of electric vehicles; performing cluster analysis on electric vehicles to obtain the clustering results of electric vehicles; calculating the charging carbon emissions of electric vehicles every day; updating the carbon emission factors and clustering results once a day, calculating the charging carbon emission situation of electric vehicles every day, and summing up the carbon emissions of each stage to obtain the total carbon emissions of electric vehicles within the production and service life. The present invention aims at the problem of the largest proportion of carbon emissions during the charging process of electric vehicles, uses the carbon emission flow theory to calculate the change of carbon emission factors for different chargings at different times, optimally matches electric vehicles and charging stations, and realizes the minimum carbon emissions during the charging process.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emissions of electric vehicles, and in particular to a method for calculating the dynamic carbon emissions of electric vehicles considering production and service life. Background Art

[0002] In recent years, the problem of global warming caused by greenhouse gases has attracted much attention, and low-carbonization has become a global consensus. At present, the carbon emissions of electric vehicle battery production are relatively high. Ignoring the carbon emissions of electric vehicle production and manufacturing, it is impossible to analyze the emission reduction benefits of future electric vehicles due to the rapid reduction of battery energy consumption. However, the carbon emissions during the driving process of electric vehicles depend on the power grid energy structure. A power grid energy structure dominated by thermal power will limit the emission reduction benefits of electric vehicles. Different power grid energy structures in different regions will also result in significant differences in the carbon emissions of electric vehicles during the driving process in different regions. Therefore, only considering the carbon emissions during the driving process cannot accurately quantify the carbon emissions of electric vehicles. At present, the calculation of carbon emissions of electric vehicles is still in a certain stage of production and service life, and basically does not consider carbon emissions from the perspectives of charging time and location in different regions during production and service life. Therefore, it is urgent to construct a method for calculating the dynamic carbon emissions of electric vehicles based on production and service life in different regions. Summary of the Invention

[0003] To solve the problems that the influence of different regions on the carbon emissions of electric vehicles and the service life of electric vehicles are not fully considered in the calculation of electric vehicle carbon emissions, the purpose of the present invention is to provide a method for calculating the dynamic carbon emissions of electric vehicles considering production and service life, which calculates the change of carbon emission factors for different chargings at different times by using the production and service life of electric vehicles, and realizes the minimum carbon emissions during the charging process.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions: A method for calculating the dynamic carbon emissions of electric vehicles considering production and service life, the method includes the following steps in sequence:

[0005] (1) Based on the carbon emission flow theory, calculate the carbon emission factors of each electric vehicle charging station by using the situation of new energy grid connection in different regions and at different times;

[0006] (2) Calculate the carbon emissions of each stage of the electric vehicle based on the production and service life of the electric vehicle. The stages include the raw material preparation stage, the vehicle production stage, the operation stage, and the recycling and reuse stage. In the operation stage, obtain the average service life T m and the charging duration T c ;

[0007] (3) Cluster the electric vehicles according to the starting charging time, ending charging time, required electricity, and continuous charging time data of the electric vehicles in different regions to obtain the clustering results of the electric vehicles;

[0008] (4) According to the carbon emission factors obtained in step (1) and the clustering results obtained in step (3), through the convolutional neural network based on the deep learning algorithm, and the charging duration T of the electric vehicles obtained in step (2) c Calculate the charging carbon emissions of electric vehicles every day;

[0009] (5) Update the carbon emission factors and clustering results once a day, calculate the charging carbon emission situation of electric vehicles every day, and based on the carbon emission situation of each stage obtained in step (2) and the average service life T of the electric vehicles m Sum up the carbon emissions of each stage to obtain the total carbon emissions of electric vehicles considering production and service life.

[0010] The specific steps of step (1) are as follows:

[0011] (1a) Use power flow calculation to obtain the active power of each part in the distribution network, and based on the carbon emission flow theory, obtain the carbon emission factors of each node. The carbon emission factors of each node are represented by the node carbon potential vector, and the calculation formula of the node carbon potential vector is as follows:

[0012]

[0013] Among them, E N represents the node carbon potential vector; E G represents the carbon emission intensity vector of the generator set; P N represents the node active power flux matrix; P B represents the branch power flow distribution matrix; P G represents the unit injection distribution matrix;

[0014] (1b) By calculating the generator set-node association matrix, obtain the carbon flow contribution rate of each generator set to the node under different new energy grid connection conditions. The calculation formula is as follows:

[0015]

[0016] Among them, R U-n represents the generator set-node association matrix.

[0017] The specific steps of step (2) are as follows:

[0018] (2a) Calculate the carbon emissions of electric vehicle raw material preparation: The raw materials of electric vehicles include steel, iron, aluminum, copper, magnesium, glass, plastics, rubber, and vehicle fluids. The system boundary in the material acquisition stage is from raw material acquisition to the manufacturing of automotive parts. The carbon emissions in this stage come from the carbon emissions generated by energy use in the material acquisition process. Assume that a complete vehicle consists of n parts and uses d materials. The carbon emissions of the d-th material in the n-th part are:

[0019]

[0020] where n represents the part; d represents the material; C MA represents the carbon emissions in the material acquisition stage; m nd represents the mass of material d in the n-th part; k d represents the carbon emission factor corresponding to the d-th material;

[0021] (2b) Calculate the carbon emissions of the electric vehicle whole vehicle production stage: The whole vehicle production consists of multiple independent processes. The carbon emissions in this stage come from the direct emissions of energy use and the indirect emissions of electric energy in the whole vehicle production process. The carbon emissions of the automobile manufacturing of the whole production enterprise come from the carbon emissions of stamping, welding, painting, final assembly, power station buildings, and supporting facilities. Among them, the energy consumed in stamping, final assembly, and power station buildings is only electric energy. The calculation formulas for the carbon emissions of stamping, final assembly, and power station buildings are as follows:

[0022] C PR1-i = E i ×e (4)

[0023] In the formula: i represents the i-th process link that only consumes electric energy; C PR1-i represents the carbon emissions of electricity consumption in the i-th process link; E i represents the electricity consumption in the i-th process link; e represents the carbon emission factor of electricity consumption;

[0024] The carbon emission formula for the welding process is:

[0025] C PR2-hz = E hz ×e + C ys (5)

[0026] In the formula: C PR2-hz represents the carbon emissions during the welding process; E hz represents the electricity consumption during the welding process; C ys represents the carbon emissions escaping into the atmosphere during the welding process;

[0027] The process units of the painting process and supporting facilities consume electric energy, natural gas, and steam. The calculation formulas for the carbon emissions of the painting process and supporting facilities are as follows:

[0028] CPR3-j = E j × e + D j × K j × e2 + S j × β × e3 (6)

[0029] In the formula: C PR3-j represents the carbon emissions of the j-th process step; E j represents the electricity consumption of the j-th step; D j represents the consumption of natural gas; K j represents the average low calorific value of natural gas; e2 represents the carbon emission factor of natural gas; S j represents the consumption of steam; β represents the coefficient of standard coal converted from steam; e3 represents the carbon emission factor of standard coal;

[0030] The carbon emission calculation formula for the vehicle production stage is as follows:

[0031]

[0032] In the formula: C PR represents the carbon emissions in the vehicle production stage; r represents the total number of steps in the power station building, stamping and final assembly processes; t represents the total number of steps in the painting process step and the process units of the supporting facilities;

[0033] (2c) Calculate the carbon emissions during the operation stage of the electric vehicle's service life: During the use of the electric vehicle, the vehicle's driving consumes electric energy, resulting in carbon emissions, and the vehicle's maintenance will generate carbon emissions. The carbon emission calculation formula during the vehicle's use is as follows:

[0034] C use-t = C am + C ct (8)

[0035] Among them, C use-t represents the carbon emissions during the vehicle's use; C am represents the carbon emissions corresponding to maintenance during use, including the replacement of tires and vehicle fluids; C ct represents the carbon emissions during the charging process within the vehicle's service life;

[0036] The carbon emissions C am corresponding to maintenance during use are calculated as follows:

[0037]

[0038] In the formula: m d represents the mass of the material d replaced during vehicle maintenance in the tire and vehicle fluid use stage; k d represents the carbon emission factor of the corresponding material d;

[0039] The carbon emissions generated during the driving process of electric vehicles are related to their service life, charging power, and the carbon emission factor of the power grid. Weibull distribution is used for modeling. The vehicle scrapping rate is defined as the absolute value of the derivative of the vehicle survival rate with respect to the vehicle age. The functions of the vehicle survival rate and the vehicle scrapping rate are described as follows:

[0040]

[0041]

[0042] Among them, SR i，m (t) represents the survival rate of type m vehicles registered in year i at vehicle age t; SP i，m (t) represents the number of non-scrapped vehicles of type m registered in the i-th year at vehicle age t; RP i，m represents the total number of type m vehicles registered in the i-th year; T i，m and k i，m represent characteristic parameters; u i，m (t) represents the scrapping rate of type m vehicles registered in the i-th year at vehicle age t;

[0043] Assume that the parameters of the survival mode model of the sample regression are constant, and simplify T i，m and k i，m to T m and k m , perform regression on the parameters to obtain the average service life T m of the vehicle and the scrapping intensity k m of the vehicle;

[0044] The daily driving mileage of the electric vehicle is obtained to satisfy the following distribution:

[0045]

[0046] In the formula: s is the daily driving mileage; μ D is the mathematical expectation of lns; σ D is the standard deviation of lns;

[0047] The charging duration, charging power, and battery capacity jointly determine the battery power at the starting charging moment. The charging duration is calculated from the daily driving mileage. The formula for the charging duration of the electric vehicle is as follows:

[0048]

[0049] In the formula: T c represents the charging duration; s represents the daily driving mileage; W 100 represents the electric energy consumed by the vehicle per 100 kilometers; η1 represents the charging efficiency; P c represents the charging power;

[0050] According to the properties of the normal distribution, the charging duration is a linear combination of the daily driving mileage s and also conforms to the lognormal distribution, that is:

[0051]

[0052] In the formula: μ tc = ln[W 100 / (100ηP c )] represents the expected value of the charging duration; σ tc = σ D represents the standard deviation of the charging duration; f tc (t) represents the probability distribution function of the charging duration of the electric vehicle at time t;

[0053] Different types of electric vehicles are charged according to specific charging rules, and the Monte Carlo algorithm is used to obtain the charging duration of the corresponding type of electric vehicle; assuming that the charging power of the electric vehicle is constant during the charging process, the carbon emission factor during the charging process of the electric vehicle is obtained from the carbon emission flow theory; the calculation formula for the carbon emissions during the service life of the electric vehicle is as follows:

[0054]

[0055] Among them, C ct represents the total carbon emissions during the charging process considering the service life of the electric vehicle, e c-t represents the carbon emission factor of the electric vehicle at time t;

[0056] (2d) Calculate the carbon emissions during the recycling and reuse stage of the electric vehicle: The scrapped electric vehicle needs to go through the pre-treatment stage, disassembly stage, metal separation stage, and non-metal residue treatment stage, and the separated metals such as copper, aluminum, and steel are smelted and recycled to become new raw materials; the power battery is recycled and reused according to its type using the corresponding recycling process; plastics, glass, and rubber are reused through processes such as melting and reshaping, and the carbon emissions during the recycling and reuse stage of the electric vehicle are calculated. The calculation formula is as follows:

[0057]

[0058] Among them, C cyc represents the carbon emissions during the whole vehicle production and service life; m i represents the mass of the i-th material recycled; e i represents the carbon emission factor of the i-th material recycled; m j represents the recycled mass of the j-th material; η2 represents the recycling efficiency; ed j represents the unit recycling energy consumption of the j-th material; e k represents the carbon emission factor of the k-th energy source.

[0059] Step (3) specifically includes the following steps:

[0060] (3a) Obtain data on the starting charging time, ending charging time, required electricity, and continuous charging time of electric vehicles in different regions;

[0061] (3b) Use the Gaussian mixture model to perform clustering analysis on the obtained data for electric vehicles to obtain the clustering results of electric vehicles.

[0062] Step (4) specifically includes the following steps:

[0063] (4a) Taking 24 hours as a time interval, obtain the real-time change data of the carbon emission factors of different charging stations in this region according to step (1), that is, the real-time carbon emission factors;

[0064] (4b) Combine the clustering results obtained in step (3) with the real-time carbon emission factors, and at the same time use the charging duration T of the electric vehicle c , and finally obtain the charging carbon emissions of electric vehicles every day through a convolutional neural network based on a deep learning algorithm.

[0065] Step (5) specifically includes the following steps:

[0066] (5a) Update the carbon emission factors and clustering results of each charging station once a day, and calculate the carbon emissions during the charging process of electric vehicles every day;

[0067] (5b) According to the carbon emission situation of each stage of the electric vehicle obtained in step (2), sum up the carbon emissions of each stage of the electric vehicle except for the charging carbon emissions during the operation stage to obtain the first carbon emission result;

[0068] (5c) According to the carbon emission situation of each stage obtained in step (2) and the average service life T of the electric vehicle m , the average service life T of the electric vehicle m , sum up the daily charging carbon emissions within the average service life T of the electric vehicle to obtain the second carbon emission result;

[0069] (5d) Add the first carbon emission result and the second carbon emission result to obtain the total carbon emissions of the electric vehicle considering production and service life.

[0070] As can be seen from the above technical solutions, the beneficial effects of the present invention are as follows: First, the present invention calculates the carbon emissions in different stages by using the production and service life of electric vehicles; Second, aiming at the problem of the largest proportion of carbon emissions during the charging process of electric vehicles, the present invention uses the carbon emission flow theory to calculate the change of carbon emission factors for different charging at different times, and optimally matches electric vehicles and charging stations to minimize carbon emissions during the charging process; Third, aiming at the change of seasonal new energy power generation, the present invention monitors the carbon emission factors in the system in real time to further reduce the impact of environmental factors on carbon emissions during the charging process of electric vehicles; Fourth, the present invention uses the Weibull distribution to model the survival mode of vehicles, and uses the method of linear regression to obtain the average service life of electric vehicles and the scrapping intensity of vehicles to estimate the carbon emissions during the service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 is a flowchart of the method of the present invention;

[0072] Figure 2 is a schematic diagram of the carbon emission assessment framework of electric vehicles in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] As Figure 1 shown, a dynamic carbon emission calculation method for electric vehicles considering production and service life, the method includes the following steps in sequence:

[0074] (1) Based on the situation of new energy grid connection in different regions and at different times, calculate the carbon emission factors of each electric vehicle charging station based on the carbon emission flow theory;

[0075] (2) Calculate the carbon emissions in each stage of electric vehicles based on the production and service life of electric vehicles. The stages include raw material preparation stage, vehicle production stage, operation stage and recycling and reuse stage. In the operation stage, obtain the average service life T m of electric vehicles and the charging duration T c of electric vehicles;

[0076] (3) Conduct cluster analysis on electric vehicles according to the starting charging time, ending charging time, required power and continuous charging time data of electric vehicles in different regions to obtain the clustering results of electric vehicles;

[0077] (4) According to the carbon emission factors obtained in step (1) and the clustering results obtained in step (3), through the convolutional neural network based on the deep learning algorithm, and the charging duration T c of electric vehicles obtained in step (2), calculate the daily charging carbon emissions of electric vehicles;

[0078] (5) Update the carbon emission factors and clustering results once a day, calculate the charging carbon emissions of electric vehicles every day, and obtain the total carbon emissions of electric vehicles during production and service life according to the carbon emissions in each stage obtained in step (2) and the average service life T of electric vehicles. m Sum up the carbon emissions in each stage to obtain the total carbon emissions of electric vehicles considering production and service life.

[0079] The specific steps of step (1) are as follows:

[0080] (1a) Use power flow calculation to obtain the active power of each part in the distribution network, and based on the carbon emission flow theory, obtain the carbon emission factors of each node. The carbon emission factors of each node are represented by the node carbon potential vector, and the calculation formula of the node carbon potential vector is as follows:

[0081]

[0082] Among them, E N represents the node carbon potential vector; E G represents the carbon emission intensity vector of the generator set; P N represents the node active power flux matrix; P B represents the branch power flow distribution matrix; P G represents the unit injection distribution matrix;

[0083] (1b) By calculating the generator set-node correlation matrix, obtain the carbon flow contribution rate of each generator set to the node under different new energy grid connection conditions. The calculation formula is as follows:

[0084]

[0085] Among them, R U-n represents the generator set-node correlation matrix.

[0086] The specific steps of step (2) are as follows:

[0087] (2a) Calculate the carbon emissions during the preparation of electric vehicle raw materials: The raw materials of electric vehicles include steel, iron, aluminum, copper, magnesium, glass, plastic, rubber and vehicle fluids. The system boundary of the material acquisition stage is from raw material acquisition to the manufacture of automotive parts. The carbon emissions in this stage come from the carbon emissions generated by energy use during material acquisition; assume that the whole vehicle consists of n parts and uses d materials. The carbon emissions of the dth material of the nth part are:

[0088]

[0089] Among them, n represents the part; d represents the material; C MA represents the carbon emissions during the material acquisition stage; m nd represents the mass of material d in the nth part; k dRepresents the carbon emission factor corresponding to the d-th material;

[0090] (2b) Calculate the carbon emissions during the production stage of the electric vehicle: The vehicle production consists of multiple independent processes. The carbon emissions in this stage come from the direct emissions from energy use and the indirect emissions from electric energy during the vehicle production process; The carbon emissions from the vehicle manufacturing of the production enterprise come from the carbon emissions of stamping, welding, painting, final assembly, power station buildings, and supporting facilities. Among them, the energy consumed by stamping, final assembly, and power station buildings is only electric energy. The calculation formulas for the carbon emissions of stamping, final assembly, and power station buildings are as follows:

[0091] C PR1-i = E i ×e (4)

[0092] In the formula: i represents the i-th process link that only consumes electric energy; C PR1-i represents the carbon emissions from electricity consumption in the i-th process link; E i represents the electricity consumption in the i-th process link; e represents the carbon emission factor for electricity consumption;

[0093] The welding process mainly consumes electric energy, but the direct carbon dioxide emissions into the atmosphere during the welding process cannot be ignored.

[0094] The carbon emission formula for the welding process is:

[0095] C PR2-hz = E hz ×e + C ys (5)

[0096] In the formula: CP R2-hz represents the carbon emissions during the welding process; E hz represents the electricity consumption during the welding process; C ys represents the carbon emissions escaping into the atmosphere during the welding process;

[0097] The painting process link and the process units of the supporting facilities consume electric energy, natural gas, and steam. The calculation formulas for the carbon emissions of the painting process link and the supporting facilities are as follows:

[0098] C PR3-j = E j ×e + D j ×K j ×e2 + S j ×β × e3 (6)

[0099] In the formula: C PR3-j represents the carbon emissions of the j-th process link; E j represents the electricity consumption of the j-th link; D j represents the consumption of natural gas; K jrepresents the average low calorific value of natural gas; e2 represents the carbon emission factor of natural gas; S j represents the consumption of steam; β represents the standard coal conversion coefficient after steam conversion; e3 represents the carbon emission factor of standard coal;

[0100] The carbon emission calculation formula for the vehicle production stage is as follows:

[0101]

[0102] In the formula: C PR represents the carbon emissions in the vehicle production stage; r represents the total number of processes in the power station building, stamping and final assembly processes; t represents the total number of processes in the painting process and the process units of the supporting facilities;

[0103] (2c) Calculate the carbon emissions during the operation stage of the electric vehicle's service life: During the use of the electric vehicle, the vehicle's driving consumes electric energy, resulting in carbon emissions, and the vehicle's maintenance generates carbon emissions. The carbon emission calculation formula during the vehicle's use is as follows:

[0104] C use-t = C am + C ct (8)

[0105] Among them, C use-t represents the carbon emissions during the vehicle's use; C am represents the carbon emissions corresponding to maintenance during use, including the replacement of tires and vehicle fluids; C ct represents the carbon emissions during the charging process within the electric vehicle's service life;

[0106] The carbon emissions corresponding to maintenance during use C am The calculation formula is as follows:

[0107]

[0108] In the formula: m d represents the mass of the material d replaced during vehicle maintenance in the tire and vehicle fluid use stage; k d represents the carbon emission factor of the corresponding material d;

[0109] The carbon emissions generated during the driving of the electric vehicle are related to its service life, charging power, and the carbon emission factor of the power grid. Weibull distribution is used for modeling. The vehicle scrapping rate is defined as the absolute value of the derivative of the vehicle survival rate with respect to the vehicle age. The functions of the vehicle survival rate and the vehicle scrapping rate are described as:

[0110]

[0111]

[0112] Among them, SR i，m (t) represents the survival rate of type m vehicles registered in year i at vehicle age t; SP i，m (t) represents the number of non-scrapped vehicles of model m registered in the i-th year at vehicle age t; RP i，m represents the total number of type m vehicles registered in the i-th year; T i，m and k i，m represent characteristic parameters; u i，m (t) represents the scrapping rate of type m vehicles registered in the i-th year at vehicle age t;

[0113] Assuming that the parameters of the survival mode model of the sample regression are constant, T i，m and k i，m are simplified to T m and k m , and regression is performed on the parameters to obtain the average service life T m of the vehicle and the scrapping intensity k m ;

[0114] The daily driving mileage of the electric vehicle is obtained to satisfy the following distribution:

[0115]

[0116] In the formula: s is the daily driving mileage; μ D is the mathematical expectation of lns; σ D is the standard deviation of lns;

[0117] The charging duration, charging power, and battery capacity jointly determine the battery power at the starting charging moment. The charging duration is calculated from the daily driving mileage, and the formula for the charging duration of the electric vehicle is as follows:

[0118]

[0119] In the formula: T c represents the charging duration; s represents the daily driving mileage; W 100 represents the electric energy consumed by the vehicle per 100 kilometers; η1 represents the charging efficiency; P c represents the charging power;

[0120] According to the properties of the normal distribution, the charging duration is a linear combination of the daily driving mileage s and also conforms to the lognormal distribution, that is:

[0121]

[0122] In the formula: μ tc = ln[W 100 / (100ηP c )] represents the expectation of the charging duration; σ tc= σ D represents the standard deviation of the charging duration; f tc (t) represents the probability distribution function of the charging duration of the electric vehicle at time t;

[0123] Different types of electric vehicles charge according to specific charging rules, and the charging duration of corresponding types of electric vehicles is obtained by using the Monte Carlo algorithm; assuming that the charging power of the electric vehicle is constant during the charging process, the carbon emission factor during the charging process of the electric vehicle is obtained from the carbon emission flow theory; the calculation formula for the carbon emission during the service life of the electric vehicle is as follows:

[0124]

[0125] Among them, C ct represents the total carbon emission during the charging process considering the service life of the electric vehicle, e c-t represents the carbon emission factor of the electric vehicle at time t;

[0126] (2d) Calculate the carbon emission situation in the recycling and reuse stage of the electric vehicle: After the scrapped electric vehicle goes through the pre-treatment stage, disassembly stage, metal separation stage and non-metal residue treatment stage, the separated metals such as copper, aluminum and steel are smelted and recycled to become new raw materials; the power battery is recycled and reused according to its type using corresponding recycling processes; plastics, glass and rubber are put back into use through processes such as melting and reshaping, and the carbon emission in the recycling and reuse stage of the electric vehicle is calculated. The calculation formula is as follows:

[0127]

[0128] Among them, C cyc represents the carbon emission of the whole vehicle production and service life; m i represents the mass of the i-th material recycled; e i represents the carbon emission factor of the i-th material recycled; m j represents the recycled mass of the j-th material; η2 represents the recycling efficiency; ed j represents the unit recycling energy consumption of the j-th material; e k represents the carbon emission factor of the k-th energy source.

[0129] The specific steps of step (3) are as follows:

[0130] (3a) Obtain the data of the starting charging time, ending charging time, required electricity and continuous charging time of electric vehicles in different regions;

[0131] (3b) Use the Gaussian mixture model to perform clustering analysis on the obtained data for the electric vehicles to obtain the clustering results of the electric vehicles.

[0132] The specific steps of step (4) are as follows:

[0133] (4a) Taking 24 hours as a time interval, according to step (1), obtain the real-time change data of the carbon emission factors of different charging stations in this area, that is, the real-time carbon emission factors;

[0134] (4b) Combine the clustering results obtained in step (3) with the real-time carbon emission factors, and at the same time use the charging duration T of the electric vehicle c , and finally obtain the charging carbon emissions of the electric vehicle every day through the convolutional neural network based on the deep learning algorithm.

[0135] The specific steps of step (5) are as follows:

[0136] (5a) Update the carbon emission factors and clustering results of each charging station once a day, and calculate the carbon emissions during the charging process of electric vehicles every day;

[0137] (5b) According to the carbon emissions of each stage of the electric vehicle obtained in step (2), sum up the carbon emissions of each stage of the electric vehicle except for the charging carbon emissions during the operation stage to obtain the first carbon emission result;

[0138] (5c) According to the carbon emissions of each stage obtained in step (2) and the average service life T of the electric vehicle m , for the average service life T of the electric vehicle m , sum up the daily charging carbon emissions within it to obtain the second carbon emission result;

[0139] (5d) Add the first carbon emission result and the second carbon emission result to obtain the total carbon emissions of the electric vehicle considering production and service life.

[0140] Such as Figure 2As shown in the figure, in the carbon emission calculation of the production and service life of electric vehicles, the production and service life of electric vehicles are divided into the material acquisition stage, the vehicle production stage, the use stage, and the recycling and reuse stage. Consumables for electric vehicles are generated in the raw material acquisition stage and the use stage, which will generate corresponding carbon emissions. To calculate the carbon emissions in these two stages, it is necessary to consider the quality of the raw materials used in electric vehicles and the carbon emission factors of the raw materials. Energy consumption occurs in the vehicle production stage, the vehicle use stage, and the recycling and reuse stage. For these processes, it is necessary to consider the quality of the different energies consumed and the carbon emission factors of the energies. The vehicle production stage and the vehicle use stage require the consumption of electric energy. As a secondary energy source, the carbon emission factors of electric energy vary under different power grids. It is necessary to consider the geographical environment, energy structure of each region, and the change in the carbon emission factor of the power grid caused by the increasing proportion of clean energy every year. A large number of recyclable metal and non-metal materials are generated in the recycling and reuse stage. This is one of the few negative carbon emissions in the entire life cycle theory LCA of electric vehicles. At the same time, the recycling of power batteries also generates negative carbon emissions.

[0141] In summary, by taking electric vehicles as the research object, the present invention uses the Weibull distribution to obtain the service life of electric vehicles, improving the accuracy of calculating carbon emissions during the use of electric vehicles. The present invention proposes to use the charging duration probability distribution function and the Monte Carlo algorithm to solve the charging duration of the vehicle, improving the calculation accuracy of the daily charging carbon emissions of the vehicle. The present invention uses the carbon emission flow theory to calculate the carbon emission intensity of each charging station. By calculating the dynamic carbon emission factor on the time scale, the change trend of the carbon emission factor is obtained. At the same time, the intelligent algorithm is used to intelligently match electric vehicles and charging and swapping stations, which can minimize carbon emissions during the charging process and further promote the low-carbon process of electric vehicles.

Claims

1. A dynamic carbon emission calculation method for electric vehicles considering production and service life, characterized in that: The method includes the following steps in sequence: (1) Based on the new energy grid connection situation at different regions and different times, calculate the carbon emission factors of each electric vehicle charging station according to the carbon emission flow theory; (2) Calculate the carbon emissions of electric vehicles at each stage based on the production and service life of electric vehicles. The stages include the raw material preparation stage, the vehicle production stage, the operation stage, and the recycling and reuse stage. During the operation stage, obtain the average service life T of the electric vehicle m and the charging duration T of the electric vehicle c ; (3) According to the starting charging time, ending charging time, required power, and continuous charging time data of electric vehicles in different regions, conduct cluster analysis on electric vehicles to obtain the clustering results of electric vehicles; (4)Based on the carbon emission factors obtained in step (1) and the clustering results obtained in step (3), through the convolutional neural network based on the deep learning algorithm, and the charging duration T of the electric vehicle obtained in step (2) c Calculate the charging carbon emissions of the electric vehicle per day; (5) Update the carbon emission factor and clustering results once a day, calculate the charging carbon emissions of electric vehicles every day, and based on the carbon emissions in each stage obtained in step (2) and the average service life T of electric vehicles m , sum up the carbon emissions in each stage to obtain the total carbon emissions of electric vehicles considering production and service life; The specific steps of step (3) include the following: (3a) Obtain the data of the starting charging time, ending charging time, required power, and continuous charging time of electric vehicles in different regions; (3b) Use the Gaussian mixture model to conduct cluster analysis on electric vehicles for the obtained data to obtain the clustering results of electric vehicles; The specific steps of step (4) include the following: (4a) Taking 24 hours as a time interval, according to step (1), obtain the real-time change data of the carbon emission factors of different charging stations in this region, that is, the real-time carbon emission factors; (4b) Combine the clustering results obtained in step (3) with the real-time carbon emission factors, and at the same time use the charging duration T of the electric vehicle c , and finally obtain the charging carbon emissions of the electric vehicle every day through a convolutional neural network based on a deep learning algorithm.

2. The method for calculating the dynamic carbon emissions of an electric vehicle considering production and service life according to claim 1, characterized in that: The specific steps of step (1) include the following: (1a) Use power flow calculation to obtain the active power of each part in the distribution network, and obtain the carbon emission factors of each node according to the carbon emission flow theory. The carbon emission factors of each node are represented by the node carbon potential vector, and the calculation formula of the node carbon potential vector is as follows: Among them, E N represents the node carbon potential vector; E G represents the carbon emission intensity vector of the generator set; P N represents the node active power flux matrix; P B represents the branch power flow distribution matrix; P G represents the unit injection distribution matrix; (1b) By calculating the generator-node association matrix, obtain the carbon flow contribution rate of each generator set to the node under different new energy grid connection situations. The calculation formula is as follows: Among them, R U-n represents the unit-node incidence matrix.

3. The method for calculating the dynamic carbon emissions of an electric vehicle considering production and service life according to claim 1, characterized in that: The specific steps of step (2) include the following: (2a) Calculate the carbon emissions in the preparation of electric vehicle raw materials: The raw materials of electric vehicles include steel, iron, aluminum, copper, magnesium, glass, plastic, rubber, and vehicle fluids. The system boundary in the material acquisition stage is from raw material acquisition to the manufacture of automotive parts. The carbon emissions in this stage come from the carbon emissions generated by energy use in the material acquisition process. Assume that the whole vehicle consists of n parts and uses d materials. The carbon emissions of the dth material of the nth part are: Among them, n represents a component; d represents a material; C MA represents the carbon emissions in the material acquisition stage; m nd represents the mass of material d in the nth component; k d represents the carbon emission factor corresponding to the dth material; (2b) Calculate the carbon emissions in the whole vehicle production stage of electric vehicles: The whole vehicle production consists of multiple independent processes. The carbon emissions in this stage come from the direct emissions of energy use and the indirect emissions of electric energy in the whole vehicle production process. The carbon emissions of the automobile manufacturing of the whole production enterprise come from the carbon emissions of stamping, welding, painting, general assembly, power station buildings, and supporting facilities. Among them, the energy consumed by stamping, general assembly, and power station buildings is only electric energy. The calculation formulas for the carbon emissions of stamping, general assembly, and power station buildings are as follows: C PR1-i = E i × e(4) Where: i represents the i-th process step that only consumes electric energy; C PR1-i represents the carbon emission of electricity consumption in the i-th process step; E i represents the electricity consumption in the i-th process step; e represents the carbon emission factor of electricity consumption; The carbon emission formula for the welding process is: C PR2-hz = E hz × e + C ys (5) Where: C PR2-hz represents the carbon emissions during the welding process; E hz represents the electricity consumption during the welding process; C ys represents the carbon emissions dissipated into the atmosphere during the welding process. The process units of the painting process and supporting facilities consume electric energy, natural gas, and steam. The calculation formulas for the carbon emissions of the painting process and supporting facilities are as follows: C PR3-j = E j × e + D j × K j × e2 + S j × β × e3 (6) Where: C PR3-j represents the carbon emissions of the j-th process step; E j represents the electricity consumption of the j-th step; D j represents the consumption of natural gas; K j represents the average low calorific value of natural gas; e2 represents the carbon emission factor of natural gas; S j represents the consumption of steam; β represents the coefficient of converting steam into standard coal; e3 represents the carbon emission factor of standard coal; The calculation formula for the carbon emissions in the whole vehicle production stage is as follows: Where: C PR represents the carbon emissions during the vehicle production stage; r represents the total number of processes in the power station, stamping, and final assembly processes; t represents the total number of processes in the painting process and the process units of supporting facilities. (2c) Calculate the carbon emissions in the operation stage during the service life of electric vehicles: During the use of electric vehicles, the driving of the vehicle will consume electric energy, resulting in carbon emissions, and the maintenance of the vehicle will generate carbon emissions. The calculation formula for the carbon emissions during the use of the vehicle is as follows: C use-t = C am + C ct (8) Among them, C use-t represents the carbon emissions during the use of the vehicle; C am represents the carbon emissions corresponding to maintenance during use, including the replacement of tires and vehicle fluids. C ct represents the carbon emissions during the charging process within the service life of the vehicle; The carbon emissions C corresponding to maintenance during use am The calculation formula is as follows: Where: m d represents the mass of material d replaced during vehicle maintenance in the tire and vehicle fluid usage stage; k d represents the carbon emission factor corresponding to material d; The carbon emissions generated during the driving process of electric vehicles are related to their service life, charging power, and the carbon emission factor of the power grid. Weibull distribution is used for modeling. The vehicle scrapping rate is defined as the absolute value of the derivative of the vehicle survival rate with respect to the vehicle age. The functions of the vehicle survival rate and the vehicle scrapping rate are described as follows: Among them, SR i，m (t) represents the survival rate of type m vehicles registered in year i at vehicle age t; SP i，m (t) represents the number of non-scrapped vehicles of type m registered in year i at vehicle age t; RP i，m represents the total number of type m vehicles registered in year i; T i，m and k i，m represent characteristic parameters; u i，m (t) represents the scrapping rate of type m vehicles registered in year i at vehicle age t; Assume that the parameters of the survival mode model for sample regression are constant. Let T i，m and k i，m be simplified to T m and k m . Regress the parameters to obtain the average service life T m of the vehicle and the scrapping intensity k m of the vehicle; The daily driving mileage of electric vehicles satisfies the following distribution: where: s is the daily driving mileage; μ D is the mathematical expectation of lns; σ D is the standard deviation of lns; The charging duration, charging power, and battery capacity jointly determine the battery power at the start of charging. The charging duration is calculated based on the daily driving mileage. The formula for the charging duration of electric vehicles is as follows: Where: T c represents the charging duration; s represents the daily driving mileage; W 100 represents the electric energy consumed by the vehicle per 100 kilometers; η1 represents the charging efficiency; P c represents the charging power; According to the properties of the normal distribution, the charging duration is a linear combination of the daily driving mileage s and also conforms to the log-normal distribution, that is: Where: μ tc = ln[W 100 / (100ηP c )] represents the expected charging duration; σ tc = σ D represents the standard deviation of the charging duration; f tc (t) represents the probability distribution function of the charging duration of the electric vehicle at time t; Different types of electric vehicles are charged according to specific charging rules. The Monte Carlo algorithm is used to obtain the charging duration of the corresponding type of electric vehicle. Assuming that the charging power of the electric vehicle is constant during the charging process, the carbon emission factor during the charging process of the electric vehicle is obtained from the carbon emission flow theory. The calculation formula for the carbon emissions during the service life of the electric vehicle is as follows: Among them, C ct represents the total carbon emissions during the charging process considering the service life of the electric vehicle, and e c-t represents the charging carbon emission factor of the electric vehicle at time t; (2d) Calculate the carbon emissions during the recycling and reuse stage of electric vehicles: After the scrapped electric vehicles go through the pre-treatment stage, disassembly stage, metal separation stage, and non-metal residue treatment stage, the separated copper, aluminum, and steel metals are smelted and recycled to become new raw materials. The power batteries are recycled and reused according to their types using corresponding recycling processes. Plastics, glass, and rubber are reused through the melt reshaping process. Calculate the carbon emissions during the recycling and reuse stage of electric vehicles. The calculation formula is as follows: Among them, C cyc represents the carbon emissions during the whole vehicle production and service life; m i represents the mass of the i-th material recycled; e i represents the carbon emission factor of the i-th material recycled; m j represents the recycled mass of the j-th material; η2 represents the recycling efficiency; ed j represents the energy consumption per unit of recycling of the j-th material; e k represents the carbon emission factor of the k-th energy source.

4. The method for calculating the dynamic carbon emissions of an electric vehicle considering production and service life according to claim 1, characterized in that: The specific steps of step (5) include the following steps: (5a) Update the carbon emission factor and clustering results of each charging station once a day, and calculate the carbon emissions during the charging process of electric vehicles every day; (5b) According to the carbon emissions of each stage of the electric vehicle obtained in step (2), sum up the carbon emissions of each stage of the electric vehicle except for the charging carbon emissions during the operation stage to obtain the first carbon emission result; (5c) Based on the carbon emissions in each stage obtained in step (2) and the average service life T of the electric vehicle m , sum up the charging carbon emissions for each day within the average service life T m of the electric vehicle to obtain the second carbon emission result; (5d) Add the first carbon emission result and the second carbon emission result to obtain the total carbon emissions of the electric vehicle considering production and service life.

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