A method for assessing the carbon footprint of electric vehicles throughout their entire life cycle, taking into account the gradient recycling of power batteries.

Abstract

This invention provides a method for assessing the carbon footprint of electric vehicles (EVs) throughout their entire lifecycle, taking into account the gradient recycling of power batteries. The method includes constructing a lifecycle carbon footprint assessment model encompassing the raw material acquisition, manufacturing, operation, and retirement / recycling stages of both EVs and internal combustion engine vehicles. A gradient recycling strategy for power batteries is employed during the retirement / recycling stage of EVs. Model parameters are configured, inputting material usage, energy consumption data, grid carbon intensity, carbon price, and social carbon cost for each stage of the EV and internal combustion engine vehicle process. The model is then run to calculate the total lifecycle carbon footprint of both EVs and internal combustion engine vehicles in each stage. Economic carbon benefits are calculated based on carbon emission reductions and carbon prices, and social carbon benefits are calculated based on social carbon costs. The final output is the total lifecycle carbon footprint, economic carbon benefits, and social carbon benefits for both EVs and internal combustion engine vehicles. This invention accurately quantifies the emission reduction effect of gradient recycling, achieving a dual-dimensional assessment of carbon benefits from both economic and social perspectives.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a method for assessing the carbon footprint of electric vehicles throughout their entire life cycle, taking into account the gradient recycling of power batteries. Background Technology

[0002] Current electric vehicle carbon footprint assessments are mostly based on the life cycle assessment (LCA) method, mainly covering the stages of raw material acquisition, production and manufacturing, operation and use, and decommissioning and recycling. However, they have obvious limitations: First, current electric vehicle carbon footprint assessments usually only simply include carbon emissions in the recycling and treatment stage, failing to quantify the substantial emission reduction potential of secondary utilization (such as energy storage) in extending battery life and reducing reliance on virgin materials. Second, current electric vehicle carbon footprint assessments mostly focus on economic benefits (such as carbon trading), failing to integrate the multi-dimensional value of environmental health and ecosystem protection covered by social carbon costs. Finally, existing models lack a dynamic correlation mechanism between gradient recycling and carbon footprint, and cannot accurately reflect the emission reduction differences under variables such as different grid carbon intensity and recycling scale.

[0003] In summary, the existing technology has the following main drawbacks: (1) Incomplete assessment: The secondary use stage of power battery gradient recycling is ignored, resulting in a low carbon emission accounting for the whole life cycle, which cannot reflect the actual environmental benefits.

[0004] (2) The carbon benefit dimension is singular: the economic benefits are calculated only through carbon trading prices, without considering the social value of carbon emission reduction to public health and climate change mitigation, resulting in insufficient support for decision-making.

[0005] (3) Insufficient dynamism: It does not take into account variables such as grid carbon intensity and recycling technology level, and cannot adapt to the assessment needs of different regions. Summary of the Invention

[0006] This invention provides a method for assessing the carbon footprint of electric vehicles throughout their entire lifecycle, taking into account the gradient recycling of power batteries, to solve the aforementioned technical problems. Specifically, it includes: constructing a full lifecycle carbon footprint assessment model, which encompasses the entire lifecycle of electric vehicles and internal combustion engine vehicles, including the raw material acquisition stage, manufacturing stage, operation and use stage, and retirement and recycling stage, wherein the retirement and recycling stage of electric vehicles adopts a gradient recycling strategy for power batteries; configuring model parameters, including inputting material usage, energy consumption data, grid carbon intensity, carbon price, and social carbon cost for the electric vehicles and internal combustion engine vehicles at each stage; running the full lifecycle carbon footprint assessment model to calculate the total carbon footprint of the electric vehicles and internal combustion engine vehicles in stages; calculating the difference between the total carbon footprint of the electric vehicles and the total carbon footprint of the internal combustion engine vehicles to obtain carbon emission reductions; calculating economic carbon benefits based on the carbon emission reductions and the carbon price, and calculating social carbon benefits based on the social carbon cost; and outputting the total carbon footprint of the electric vehicles and internal combustion engine vehicles throughout their entire lifecycle, the economic carbon benefits, and the social carbon benefits.

[0007] Compared with the prior art, the present invention has the following technical effects: (1) More comprehensive assessment: This invention incorporates the gradient recycling of power batteries into the entire life cycle, which reduces the error of carbon footprint accounting by 10%-15% and more accurately reflects environmental benefits.

[0008] (2) Multidimensional carbon benefits: This invention simultaneously quantifies economic benefits (such as carbon trading) and social value (such as health cost savings).

[0009] (3) Strong dynamic adaptability: The present invention can adjust parameters according to the power grid structure and recycling scale to adapt to the evaluation needs of different regions and scenarios and improve the practicality of the model. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a method for assessing the carbon footprint of an electric vehicle throughout its entire lifecycle, taking into account the gradient recycling of power batteries, according to the present invention.

[0011] Figure 2 This is the overall technology roadmap.

[0012] Figure 3 This is a schematic diagram of the system boundary.

[0013] Figure 4 This is a schematic diagram for analyzing the power battery inventory.

[0014] Figure 5 A flowchart for the graded recycling process of retired power batteries.

[0015] Figure 6This is a diagram illustrating the fossil fuel consumption over the life cycle of a vehicle.

[0016] Figure 7 This is a schematic diagram comparing the actual and predicted carbon intensity of two days' samples in January and July.

[0017] Figure 8 A time-series diagram illustrating the carbon footprint per charge during the use of an electric vehicle.

[0018] Figure 9 This is a diagram illustrating the comparison of carbon footprints throughout the entire life cycle.

[0019] Figure 10 This is a diagram showing the comparison of total carbon footprint over the entire journey.

[0020] Figure 11 This diagram illustrates the impact of carbon price changes on the carbon benefits of electric vehicles.

[0021] Figure 12 This diagram illustrates the aging trend of batteries under different load levels.

[0022] Figure 13 This is a schematic diagram of the carbon footprint measurement model for each link on the power grid side.

[0023] Figure 14 This diagram illustrates the comparison of carbon emissions throughout the lifecycle of power batteries under different scenarios.

[0024] Figure 15 This diagram illustrates the impact of carbon price changes on carbon benefits under different scenarios. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] See Figure 1 The present invention provides a method for assessing the carbon footprint of electric vehicles throughout their entire life cycle, taking into account the gradient recycling of power batteries, comprising the following steps: Step S1: Construct a full life cycle carbon footprint assessment model. The full life cycle carbon footprint assessment model includes the full life cycle of electric vehicles and internal combustion engine vehicles. The full life cycle includes the raw material acquisition stage, the production and manufacturing stage, the operation and use stage, and the retirement and recycling stage. The retirement and recycling stage of electric vehicles adopts a power battery gradient recycling strategy. Step S2: Configure model parameters, including inputting the material usage, energy consumption data, grid carbon intensity, carbon price, and social carbon cost of the electric vehicle and the internal combustion engine vehicle at each stage; Step S3: Run the full life cycle carbon footprint assessment model to calculate the total full life cycle carbon footprint of the electric vehicle and the internal combustion engine vehicle in stages; Step S4: Calculate the difference between the total life cycle carbon footprint of the electric vehicle and the total life cycle carbon footprint of the internal combustion engine vehicle to obtain the carbon emission reduction. Step S5: Calculate the economic carbon benefits based on the carbon emission reduction and the carbon price, and calculate the social carbon benefits based on the social carbon cost; Step S6: Output the total carbon footprint of the electric vehicle and the internal combustion engine vehicle throughout their entire life cycle, the economic carbon benefits, and the social carbon benefits. Optionally, the power battery gradient recycling strategy is a step-by-step recycling mode, including: classifying retired power batteries into reusable batteries and recyclable batteries based on their remaining capacity and performance; disassembling, repairing and reassembling reusable batteries for use in energy storage scenarios; and disassembling and extracting metal materials from recyclable batteries.

[0027] Optionally, the carbon emission reduction is calculated using the following formula: CR = CE ICEV CE EV in, CE ICEV This refers to the total lifecycle carbon footprint of an internal combustion engine vehicle obtained through the aforementioned phased accounting. CE EV This refers to the total carbon footprint of an electric vehicle throughout its entire lifecycle, calculated through the aforementioned phased accounting.

[0028] Optionally, the economic carbon benefit is calculated using the following formula: ECB = CR × P carbon / 1000 in, ECB For economic carbon benefits, CR For carbon emission reductions, Pcarbon This refers to the carbon price.

[0029] Optionally, the social carbon benefit is calculated using the following formula: SCB =(Δ CE Battery +Δ CE Grid Δ CE EV prod )× SCC in, SCB For social carbon benefits, Δ CE Battery The carbon emission reduction Δ is due to the gradient recycling of power batteries. CE Grid The carbon emission reductions resulting from the clean power system, Δ CE EV prod The increase in carbon emissions during the production and manufacturing phase of electric vehicles. SCC For the social carbon cost.

[0030] Optionally, the carbon intensity of the power grid is calculated using the following formula: C ( t )=∑ i=1 N c i ( t )× p i ( t ) / P total ( t ) in, c i ( t ) is the first i Carbon intensity of this energy source p i ( t ) is the first i Energy in time t Electricity generation, P total ( t (This is time) t Total electricity generation.

[0031] Optionally, in the phased accounting, carbon emissions during the raw material acquisition phase... E raw Calculated using the following formula: E raw =∑ i M i × EF raw,i in, Eraw This refers to the total carbon emissions during the raw material acquisition phase. M i Let represent the amount of the i-th material used.

[0032] Optionally, in the phased accounting, carbon emissions during the production and manufacturing phase can be included. E prod Calculated using the following formula: E prod = E ep × EF gp + E fp × EF f in, E prod This refers to the total carbon emissions during the manufacturing phase. E ep This refers to the total amount of electricity consumed during the manufacturing process. EF gp The carbon emission factor of the power grid where the production and manufacturing stage is located. E fp Energy generated from fossil fuels consumed in the production and manufacturing process.

[0033] Optionally, in the phased accounting, carbon emissions during the operation and use phase are calculated separately for each vehicle type: Carbon emissions from electric vehicle operation E use , EV for: E use , EV = D × e c × EF gu in, E use , EV This refers to the total carbon emissions of electric vehicles during their operation and use phase. D The total mileage driven over the entire lifespan of the vehicle. e c This refers to the average energy consumption per kilometer for electric vehicles. EF gu The carbon emission factor of the local power grid during the operation and use phase; Carbon emissions from internal combustion locomotive operation E use,ICEV for: E use,ICEV= E burn + E fuel prod = D × f cons ×( EF burn + EF fuel prod ) in, E use,ICEV This refers to the total carbon emissions of diesel locomotives during their operation and use. D The total mileage over the entire lifespan. f cons This represents the average fuel consumption per unit distance. EF burn As fuel combustion emission factors, EF fuel prod These are the emission factors from the extraction to refining of fuel oil.

[0034] Optionally, in the phased accounting, carbon emissions during the decommissioning and recycling phase... E EOL Calculated using the following formula: E EOL =∑ i [ r i M i × EF recycle,i +(1 r i ) M i × EF disposal,i r i M i × EF raw,i ] in, E EOL This refers to the total carbon emissions during the decommissioning and recycling phase. r i Let i be the recovery rate of the i-th material. M i Let i be the amount of the i-th material. EF recycle,iTo recycle and process the carbon emissions generated per kg of material i, EF disposal,i To address carbon emission factors, EF raw,i is the carbon emission factor generated by the primary production of the i-th material.

[0035] Specifically, the solution of the present invention is further described with reference to the following examples: To address the shortcomings of existing assessment methods in considering the tiered recycling of power batteries and their limited carbon benefit dimensions, this invention aims to construct a carbon footprint assessment model that covers the entire chain from "raw material acquisition to production and manufacturing, operation and use, tiered recycling, and final disposal." This model accurately quantifies the emission reduction effect of tiered recycling and assesses carbon benefits from both economic and social dimensions, providing a scientific basis for the green development of the electric vehicle industry.

[0036] See Figure 2The study presents a closed-loop logic from theoretical construction to practical application of the research on "LCA electric vehicle carbon footprint assessment taking into account the gradient recycling of power batteries". The overall progress is based on the framework of "problem definition - theoretical support - factor analysis - model construction - empirical verification - conclusion output". First, the core objective is clearly defined: to construct a full life-cycle carbon footprint and carbon benefit assessment system encompassing the gradient recycling of power batteries, providing decision support for the "dual carbon" goals in the transportation sector. Based on this, a methodological and theoretical framework is established for the research, including the assessment process of Life Cycle Assessment (LCA), defining core concepts and quantification methods related to carbon emissions and carbon benefits, and explaining the value of graded utilization of retired batteries in conjunction with reverse logistics and the circular economy through the gradient recycling theory of power batteries. Furthermore, the key factors influencing the full life-cycle carbon footprint of electric vehicles are analyzed from three dimensions: vehicles (involving energy efficiency and weight), batteries (involving type and recycling efficiency), and the power grid (involving carbon intensity and charging scheduling). The reduction of carbon footprint through gradient recycling of power batteries by extending lifespan and reducing reliance on virgin materials is also analyzed separately. The carbon benefit analysis is based on the triple bottom line theory, considering the technological dimensions of gradient recycling and grid cleanliness, the economic dimensions of carbon pricing and carbon taxes, the social dimensions of public health and ecological protection, and the environmental dimensions of reducing total carbon emissions. This study analyzes the impact mechanism of carbon benefits from multiple dimensions. Based on this, it constructs a full life-cycle carbon footprint model to calculate carbon emissions in each stage of raw materials, production, use, and recycling. Subsequently, the case study and simulation section verifies the model's effectiveness using multi-source data, such as the Ecoinvent database and enterprise survey data. It compares the carbon footprint differences between electric vehicles and internal combustion engine vehicles, quantifies the economic and social carbon benefits under different carbon price scenarios, and reveals the impact of recycling scale on carbon footprint and carbon benefits through multi-scenario simulations of full / partial / no gradient recycling. For example, gradient recycling can reduce the full life-cycle carbon footprint by 9%-15%. Finally, the study summarizes the research results, clarifies the key role of gradient recycling in improving carbon benefits, summarizes the innovative points, and proposes future research directions, such as regional heterogeneity analysis and dynamic model optimization, forming a complete research path of "theory-model-empirical-application". The core is to provide a scientific and quantitative assessment tool and decision-making reference for the green development of the electric vehicle industry by systematically integrating the gradient recycling mechanism of power batteries.

[0037] See Figure 3The vehicle system boundary diagram clearly defines the system boundaries and input-output relationships of electric vehicles and internal combustion engine vehicles throughout their entire lifecycle. It covers the complete chain from raw material acquisition, manufacturing and assembly, operation and use to scrapping and recycling, intuitively demonstrating the differences in material and energy flows between the two types of vehicles throughout their lifecycle. In the raw material acquisition stage, the system boundary includes the mining and processing of various basic materials: electric vehicles, due to the demand for power batteries, focus on lightweight materials such as aluminum, lithium, and cobalt, as well as rare metals, with outputs of battery raw materials and lightweight body components; internal combustion engine vehicles primarily use traditional materials such as steel, cast iron, and copper, with outputs of engine castings and fuel system components. Both types of vehicles involve common inputs such as mining equipment and chemical reagents, and common outputs such as mining waste, processing carbon emissions, and water pollution. In the manufacturing and assembly stage, the system boundary of electric vehicles focuses on battery assembly and electric drive system production, with inputs of core components such as lithium-ion battery cells and electric motors, and outputs of a complete vehicle including battery packs and electronic control systems; internal combustion engine vehicles revolve around components such as the internal combustion engine block and fuel injection system, with outputs of a complete vehicle including the engine and transmission. Both electric vehicles and internal combustion engine vehicles consume electricity and auxiliary materials, and their common outputs include industrial wastewater, volatile organic compounds (VOCs), and production waste. However, electric vehicles have higher carbon emissions due to battery production, while internal combustion engine vehicles generate more industrial waste. During the end-of-life recycling phase, the electric vehicle system boundary primarily covers: 1) the tiered recycling of retired battery packs, mainly referring to cascade utilization or material regeneration; 2) aluminum alloy body treatment, outputting recycled lithium, cobalt, and other metals, and remanufactured battery modules. Internal combustion engine vehicles focus on the recycling of engine debris, waste oil, and catalytic converters, outputting recycled steel, cast iron, and waste oil incineration ash. Both require dismantling equipment and chemical treatment agents for recycling, and their common outputs are air pollutants (such as battery electrolyte volatilization and incineration exhaust gas) and solid waste pollution. Electric vehicles, due to the higher difficulty in battery electrolyte treatment, involve more complex environmental treatment processes at their system boundary. By clearly defining the material inputs, energy consumption, and environmental emissions at each stage, the system boundaries of the two types of vehicles throughout their entire life cycle are clearly delineated, highlighting the special impact of power batteries on the electric vehicle system boundary, and laying a clear scope foundation for subsequent carbon footprint accounting and comparative analysis.

[0038] See Figure 4By constructing a full-chain data collection and quantification system encompassing "raw materials - processing - assembly - secondary utilization - recycling," precise traceability and assessment of carbon emissions throughout the entire lifecycle of power batteries can be achieved. This includes the following core components: 1. Closed-loop data collection for raw material preparation and processing: For cathode materials such as lithium, nickel, and cobalt, and graphite anode materials, IoT sensors record in real-time energy consumption and chemical reagent consumption during mining and hydrometallurgical processes, linking this data to the corresponding carbon emission factors in the Ecoinvent database to form a dynamic inventory of raw materials. Simultaneously, for auxiliary materials such as separators (polypropylene) and electrolytes (lithium hexafluorophosphate), blockchain technology stores the energy consumption data of their production batches, ensuring data immutability. 2. Quantification of energy consumption during component processing and assembly: In the electrode preparation stage, smart meters and thermal energy meters are integrated to collect real-time energy consumption during coating and rolling. In the battery assembly stage, the MES system records cumulative data on cell stacking, electrolyte injection, and formation, allocating this data according to the capacity of each battery cell to generate a unit energy consumption inventory. 3. Data Linkage Between Second-hand Utilization and Recycling Stages: For retired batteries, the health status is read through the Battery Management System (BMS), recording the number of charge-discharge cycles in energy storage scenarios. For batteries entering material recycling, sensor data from the crushing and acid leaching processes, combined with metal recovery rates, is used to calculate carbon emissions during the recycling stage and compare them with emissions from virgin material production to quantify emission reduction benefits. 4. Inventory Data Integration and Carbon Footprint Calculation Engine: Data from each stage is integrated into a distributed database, and a Python script automatically performs Life Cycle Assessment (LCA) calculations: For the raw material stage, the calculation is based on "material usage × emission factor"; for the processing and assembly stage, it's based on cumulative energy consumption × grid emission factor; for the second-hand utilization stage, it's based on actual charge-discharge energy consumption × real-time grid carbon intensity; and for the recycling stage, it's based on "processing energy consumption and emissions - emission reduction from replacing virgin materials," ultimately outputting the carbon footprint of a single power battery throughout its entire life cycle. This solution, through full-process data collection and quantitative modeling, overcomes the traditional inventory's neglect of the tiered recycling stage, reducing the error in power battery carbon footprint assessment. It provides accurate underlying data support for the full life cycle carbon footprint calculation of electric vehicles and provides a quantitative basis for optimizing battery recycling processes.

[0039] See Figure 5The recycling model of this invention is primarily a tiered recycling model. This model involves classifying batteries into different levels for recycling and processing based on their performance and condition at the time of retirement. This typically involves categorizing batteries according to factors such as performance, health status, and remaining capacity, and then applying different processing methods to batteries of different levels. Batteries with poor performance and low remaining capacity may require material recycling or resource recovery. When the remaining capacity of a power battery decays to 20-60%, it should be disassembled and recycled, and metal extraction should be performed on the disassembled battery components. Generally, batteries with better performance and higher remaining capacity can undergo gradient recycling. When the remaining capacity of a power battery decays to 60-80%, it should be recycled in a gradient manner. Appropriate adjustments and processing of retired power batteries can be applied to energy storage, communication base stations, power supply regulation, low-speed electric vehicles, and other fields. Implementing a battery gradient recycling strategy requires more refined management of battery material recycling. This includes assessing the battery's condition at the end of its use and selecting appropriate recycling and reuse pathways. Specifically, the technical solution of this invention addresses the tiered recycling of retired power batteries: First, retired power batteries are recycled, then evaluated and screened. Based on battery capacity and performance, they are categorized into high-capacity batteries, low-capacity batteries (both falling under the category of reusable batteries), and scrapped retired batteries (used only for material recycling). For high- and low-capacity batteries, reusable batteries are first disassembled, separating reusable and non-reusable components. Reusable components are modified and improved, such as through cell repair and circuit adaptation, before entering the new battery assembly and remanufacturing stage, ultimately adapting to high-energy storage scenarios, such as grid peak shaving, and low-energy storage scenarios, such as home energy storage. Non-reusable components are combined with scrapped battery components obtained from the disassembly of scrapped retired batteries and enter a unified processing flow for unusable battery components. By separating waste materials, such as separators and electrolyte residues, and using compliant methods such as solidification, usable materials are screened, such as extracting metals like lithium and cobalt, achieving environmentally friendly waste treatment and material recycling.

[0040] (1) Model running process Input basic data: material usage, energy consumption, grid carbon intensity, carbon price, SCC, and other parameters; Carbon emissions are calculated in stages, with a focus on integrating the emission reduction benefits of the gradient recycling process; Calculating economic and social carbon benefits based on carbon emission reductions Output the total carbon footprint and carbon benefit assessment results for the entire life cycle to support industry decision-making.

[0041] (2) Phased accounting of vehicle carbon footprint throughout its entire life cycle The four stages of the entire life cycle of electric vehicles and internal combustion engine vehicles include raw material acquisition, manufacturing, operation and use, and retirement and recycling.

[0042] Phase 1: Raw Material Acquisition Phase. See Table 1: Table 1. Raw Material Acquisition Stage List Analysis

[0043] Carbon emissions primarily originate from mineral extraction and materials production, including the mining, refining, and processing of metals such as lithium, nickel, cobalt, and manganese. The formula can be expressed as the sum of the amounts of various materials used multiplied by their respective carbon emission factors: E raw =∑ i M i × EF raw,i in, E raw This represents the total carbon emissions (kg CO2) during the raw material acquisition stage. M i Let i represent the amount (kg) of the i-th material, including the mass of steel, aluminum alloy, plastics, and battery positive and negative electrode materials (lithium, nickel, cobalt, etc.) required for the entire vehicle. The material composition and mass vary between different vehicle models, and i can be determined based on the specific data of the vehicle model. EF raw,i The carbon emission factor (kg CO2 / kg) for a unit mass of raw materials of material i represents the average carbon emissions generated from the resource extraction, smelting, and preparation processes of producing 1 kg of this material. Each material has a different emission factor; for example, the emission intensity of steel and aluminum production is usually higher than that of plastics, and battery materials (such as lithium compounds) also have their specific emission factors.

[0044] Phase 2: Manufacturing Phase. See Table 2: Table 2. Manufacturing Stage Inventory Analysis

[0045] Carbon emissions primarily originate from energy consumption (such as electricity and fuel) in automobile and battery manufacturing plants, as well as direct emissions generated during the manufacturing process. The formula represents the sum of all energy consumption during production multiplied by their corresponding emission factors: E prod = E ep × EF gp + E fp × EF f in, E prodThis represents the total carbon emissions (kg CO2) during the manufacturing phase. E ep This refers to the total amount of electricity consumed during the manufacturing process (kWh). EF gp The carbon emission factor (kg CO2 / kWh) of the power grid at the production site. This factor reflects the carbon intensity of electricity used during the production phase and depends on the power source structure of the factory's location (such as the proportion of thermal power and renewable energy). E fp Energy generated from fossil fuels consumed during the manufacturing process (calorific value, MJ). This includes heating or process energy consumption from natural gas, oil, etc., that may be used in the factory. If the manufacturing process primarily uses electricity and does not directly burn fossil fuels, this item can be 0.

[0046] Phase 3: Operation and Usage Phase. See Table 3: Table 3. Operation and Usage Phase Checklist Analysis

[0047] The operational phase is the core difference between the carbon footprints of electric vehicles and internal combustion engine vehicles: 1) The carbon emissions of electric vehicles during operation mainly come from the indirect emissions generated by the electricity consumed while the vehicle is in motion (electric vehicles themselves emit zero exhaust fumes during operation, but carbon emissions occur during the power generation process). The formula can be derived based on driving mileage, electricity consumption, and grid emission factors, and is as follows: E use , EV = D × e c × EF gu in, E use , EV This represents the total carbon emissions (kg CO2) during the operation and use phase. D This represents the total mileage (km) traveled over the entire lifespan of the vehicle. For example, suppose a vehicle travels D kilometers over its lifespan (this depends on assumptions about the vehicle's lifespan and average annual mileage). e c This refers to the average energy consumption per kilometer (kWh / km) of an electric vehicle. Energy consumption varies depending on the vehicle model, its weight, energy efficiency, and driving conditions. For example, a compact electric vehicle might consume 0.15 kWh / km, while an SUV might consume 0.20–0.25 kWh / km. EF guThis represents the carbon emission factor (kg CO2 / kWh) of the local power grid during the usage phase. This indicates the carbon intensity of electricity used for vehicle charging, which varies by region. For example, charging using a grid primarily powered by coal... EF gu Higher; if a grid with a high proportion of renewable energy is used or green electricity is specifically procured, EF gu The answer is lower.

[0048] 2) The operation and use of internal combustion locomotives includes two parts. Based on the principle of carbon emissions throughout the fuel life cycle, the model formula is as follows: ① Fuel combustion emissions (exhaust gas emissions) CO2 produced by the direct combustion of gasoline or diesel fuel while the vehicle is in motion: E burn = D × f cons × EF burn ② Fuel production emissions (emissions at the beginning of the oil product life cycle) Carbon emissions from crude oil extraction, transportation, and refining: E fuel prod = D × f cons × EF fuel prod The total emissions formula for the merged operation phase is: E use,ICEV = E burn + E fuel prod = D × f cons ×( EF burn + EF fuel prod ) in, E use,ICEV Total carbon emissions (kg CO2) during the operation and use phase. D Total mileage (km) over the vehicle's lifecycle. f consThis represents the average fuel consumption per unit distance (L / km). EF burn Emission factor for fuel combustion (kg CO2 / L). EF fuel prod The emission factor (kg CO2 / L) of fuel oil from extraction to refining is typically 0.4 to 0.6 kg CO2 / L, depending on the type of oil and the region.

[0049] Phase 4: Retirement and Recycling Phase. The retirement of electric vehicles focuses on battery recycling (secondary use or material regeneration), while internal combustion engine vehicles require the disposal of engine metal debris and waste oil. See Table 4: Table 4. Inventory Analysis of the Decommissioning and Recovery Phase

[0050] The formula for quantifying the emission reduction benefits of battery reuse is as follows: E EOL =∑ i [ r i M i × EF recycle,i +(1 r i ) M i × EF disposal,i r i M i × EF raw,i ] in, E EOL E_EOL represents the total carbon emissions (kg CO2) during the decommissioning and recycling phase. Note that E_EOL can be positive, zero, or even negative; if the recycling benefits (emissions avoided from the production of virgin materials) exceed the emissions from the recycling process, the net value will be negative, indicating that carbon reduction benefits have been achieved in this phase. r i Let be the recovery rate (% or decimal) of the i-th material. represents the proportion of that material that is recycled and reused after the vehicle is scrapped. M i The total mass (kg) of the i-th material is the same as that in the raw material stage mentioned above. M i This indicates the quantity of the material that needs to be disposed of when the vehicle is scrapped (equivalent to the total amount of the material in the vehicle). EFrecycle,i Carbon emissions (kg CO2 / kg) generated from recycling each kg of material i. This includes energy consumption and process emissions from recycling processes (such as dismantling, smelting, refining battery materials, etc.). Generally, emissions from recycling the same amount of material are lower than those from virgin production. EF disposal,i Carbon emission factor (kgCO2 / kg) is used for disposal. This refers to the carbon emissions (kgCO2 / kg) generated per kg of the i-th material's waste disposal. If the material is not recycled, it will be disposed of through landfill, incineration, etc., which may generate some carbon emissions (e.g., burning plastics produces CO2). The direct carbon emissions from landfill disposal of many metal materials are very low, approximating to zero.

[0051] EF raw,i The carbon emission factor (kg CO2 / kg) of the primary production of the i-th material, compared with that used in the raw material stage. EF raw,i same.

[0052] In summary, by adding up the carbon footprints of the four stages, we can obtain the full life-cycle carbon footprint models for electric vehicles and internal combustion engine vehicles, respectively.

[0053] E Total = E raw + E prod + E use + E EOL (3) Phased accounting of the carbon footprint of electric vehicle power batteries throughout their entire life cycle In the lifecycle management of electric vehicles, the manufacturing and recycling of power batteries are key factors determining their overall environmental impact, and therefore will be discussed in detail. This invention studies a tiered recycling model, primarily a step-by-step recycling model. This step-by-step recycling model refers to recycling and processing power batteries at different levels based on their performance and condition at the time of retirement. The input list mainly involves four basic parts: data on raw material acquisition, manufacturing process, usage stage, and end-of-life recycling. The output list focuses on all possible outputs throughout the power battery's lifecycle, including but not limited to pollutants emitted into the air, water, and soil. The resource consumption and environmental impact of the raw material acquisition and manufacturing stages share significant commonalities with the vehicle manufacturing process and will not be discussed again; instead, the focus is on the core roles of the power battery's unique usage and recycling stages.

[0054] Phase 3: Operation and Usage Phase The formula for the operation and use stages of a power battery is: E use =∑ j ( Q e,j × EF e ) in, Q e,j The amount of electricity (kWh) charged per charge. EF e The carbon emission factor of the power grid (kg CO2 / kWh) is usually a fixed value or varies by region.

[0055] Emissions per unit mileage were derived: EF km = E use / M in, EF km Carbon emissions per kilometer a vehicle travels (kg CO2 / km). M This represents the total mileage traveled by the vehicle. The carbon emissions during the vehicle's usage phase are calculated based on a fixed mileage, and the derived formula is as follows: E use,m = m × EFkm = m ×∑ j ( Q e,j × EF e ) / M M The model assumes a fixed mileage, a constant. The vehicle lifespan and annual mileage in this invention are fixed at 12 years and 15,000 km / year, respectively.

[0056] Phase 4: Gradient Recycling Phase Cascade utilization refers to the conversion of retired power batteries into stationary energy storage systems (such as V2B and V2G). This extends the economic lifespan of the batteries, reducing the demand for new materials and thus lowering carbon emissions and energy consumption in raw material mining and processing. Power batteries, as "hidden carbon carriers," can have their carbon emissions partially or entirely offset by grid-connected renewable energy sources during their operation. The cascade utilization model formula is as follows: E re = Q SLB × EF re +∑ tt=1 ( E l,t × C g,t ) Q a × EF bat Q SLB For gradient recovery of battery capacity (kWh), EF re Carbon emission factor per unit of battery reprocessing (kg CO2-eq / kWh) E l,t For additional energy loss (kWh), Q a To avoid producing new battery capacity (kWh) due to gradient recycling, EF bat To generate carbon emission factor (kg CO2-eq / kWh) for new batteries.

[0057] (4) Two-dimensional carbon benefit assessment of electric vehicles throughout their entire life cycle Carbon benefits primarily encompass two dimensions: Economic Carbon Benefit (ECB) and Social Carbon Benefit (SCB). The former focuses on the direct economic gains from carbon emission reduction, while the latter covers the comprehensive impact on environmental and social systems. Specifically: Economic carbon benefits refer to the economic gains obtained by electric vehicles over their life cycle compared to internal combustion engine vehicles, valued at current carbon prices. These benefits are primarily reflected in carbon asset returns from the carbon trading market, carbon tax reductions, and related incentive subsidies. The realization of the economic value of carbon emission reductions depends on the operating mechanism of the carbon market, and its quantification can provide a basis for enterprises' low-carbon investment decisions.

[0058] Social carbon benefits refer to the comprehensive value generated by electric vehicles through carbon reduction in order to improve the environment, optimize the energy structure, and mitigate climate change. While these benefits are not directly reflected in tradable economic gains, they can be converted into monetary units through the Social Cost of Carbon (SCC). Social carbon benefits reflect the potential contribution of carbon reduction to human health, ecosystems, and the avoidance of infrastructure damage, and are a key indicator for measuring sustainable transportation development.

[0059] Dimension 1: Economic Carbon Benefits (ECB) Based on carbon trading prices, the formula is as follows: ECB = CR × P carbon / 1000 in, ECB Economic benefits of carbon emission reduction (yuan / 100 km). Pcarbon The value is the carbon price (yuan / ton CO2), with 1000 as the unit conversion factor, converting kg CO2 to ton CO2. CR This refers to carbon emission reduction (kgCO2). Carbon reduction (CR) is defined as the difference in lifecycle carbon emissions between internal combustion engine vehicles and electric vehicles per unit driving distance, reflecting the carbon emission advantage of electric vehicles during use. The specific formula is: CR = CE ICEV CE EV in, CE ICEV The carbon emissions per unit driving distance of an internal combustion engine vehicle (kg CO2 / 100 km) is the total carbon footprint of an internal combustion engine vehicle throughout its entire life cycle, calculated through the aforementioned phased accounting. CE EV The carbon emissions per unit driving distance of an electric vehicle (kg CO2 / 100 km) is the total carbon footprint of an electric vehicle throughout its entire life cycle, calculated through the aforementioned phased accounting.

[0060] It should be understood that the total carbon footprint of electric vehicles and the total carbon footprint of internal combustion engine vehicles throughout their respective lifecycles are the sum of carbon emissions from their respective raw material acquisition, manufacturing, operation and use, and retirement and recycling stages.

[0061] Dimension 2: Social Carbon Benefits (SCB) Based on social carbon cost (SCC) accounting, the formula is: SCB =(Δ CE Battery +Δ CE Grid Δ CE EV prod )× SCC in, SCB For social carbon benefits (yuan / 100 km). Δ CE Battery The carbon emission reduction resulting from the tiered recycling of power batteries. Δ CEGrid The carbon emission reductions resulting from cleaner power systems. Δ CE EV prod The increase in carbon emissions during the electric vehicle manufacturing process; SCC The social carbon cost is (yuan / kgCO2).

[0062] (5) Results of carbon footprint throughout life cycle In the full lifecycle carbon footprint calculation section, a multi-source database integration method was adopted, dividing the research object into two independent database modules. The basic lifecycle data for electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) originated from the internationally recognized Ecoinvent 3.7 lifecycle database, covering stages such as material production, vehicle manufacturing, and end-of-life disposal. Specifically for dynamic data during the EV operation phase, this study constructed a refined database including charging modes, energy consumption characteristics, and driving behavior by acquiring real-time monitoring data of EVs deployed within the Lawrence Berkeley National Laboratory campus in California, USA. The power battery gradient recycling dataset comes from PulseBat, an open dataset for the diagnostics of power battery cascade utilization, jointly developed by Tsinghua University Shenzhen International Graduate School and Xiamen Lijing New Energy Technology Co., Ltd., aiming to support the state assessment, performance prediction, and safety analysis of retired lithium-ion batteries. This collaborative application of multi-source heterogeneous data ensures both the standardization and comparability of the basic data and enhances the spatial resolution and timeliness of the empirical data during the operation phase.

[0063] We adopt a technical approach that combines the Brightway2 life cycle assessment framework with the LSTM deep learning algorithm. By integrating the product life cycle inventory database and the environmental impact database, we can achieve accurate carbon footprint calculation.

[0064] 1) Raw material acquisition stage See Figure 6The radar charts show a comparison of the lifecycle carbon emissions of battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) under different energy paths and vehicle types. The charts include four typical vehicle types: buses, heavy-duty trucks, logistics vehicles, and passenger cars. The charts also show the lifecycle carbon emissions of electric vehicles under different power sources. It is evident that the overall carbon emission level of electric vehicles is significantly lower than that of internal combustion engine vehicles, especially when powered by renewable energy sources such as photovoltaics (PV), wind electricity, and biomass, where carbon emissions are lowest and significantly better than grid electricity or a coal-based power structure. Among different vehicle types, heavy-duty trucks and buses have relatively higher carbon emissions due to their higher energy consumption, but these are still significantly lower than their corresponding internal combustion engine models, demonstrating that the electrification transition has the most significant carbon reduction benefits for high-energy-consuming vehicles. The comparison between the two charts shows that electric vehicles have a significant advantage in controlling their lifecycle carbon footprint, especially under the scenario of cleaner power sources, where the emission reduction effect is most prominent. In contrast, internal combustion engine vehicles are unlikely to achieve effective carbon emission reduction under the existing fossil energy structure.

[0065] 2) Manufacturing and assembly stage / Production and manufacturing stage Research data shows that the emissions per unit mile (35.07 gCO2eq / km) and total emissions (6,313 kg CO2eq) during the EV manufacturing stage are 5.05 times higher than the corresponding values ​​for ICEV (6.95 gCO2eq / km, 1,251 kg CO2eq), exhibiting an order-of-magnitude difference. This difference mainly stems from the high-carbon characteristics of the EV power battery manufacturing process, as shown in Table 5.

[0066] Table 5 Comparison of carbon footprint in the manufacturing and assembly stages

[0067] Note: Total carbon emissions are calculated based on a total lifecycle mileage of 180,000 km, using the formula: unit carbon emissions × 180,000 ÷ 1,000.

[0068] Although manufacturing and assembly constitute the main contributor to the early-stage carbon footprint of EVs, empirical studies show that strategies such as clean energy-driven smart manufacturing technology innovation (e.g., green-electricity-powered battery factories), the construction of closed-loop material recycling systems, and the cascade utilization of retired batteries can effectively reduce carbon emission intensity in this stage. Therefore, the low-carbon transformation of manufacturing processes is a key point in improving the environmental performance of electric vehicles throughout their entire life cycle.

[0069] 3) Operation and Use Phase As shown in Table 6, the data indicates that the emission intensity per unit mileage for EVs is 69.90 gCO2eq / km, which is only 46.6% of that for ICEVs (150.00 gCO2eq / km), demonstrating that EVs reduce carbon emissions by 53.4% ​​compared to ICEVs during the usage phase. Based on the total vehicle lifecycle mileage (180,000 km), the total carbon emissions for EVs and ICEVs are 12,582 kgCO2eq and 27,000 kgCO2eq, respectively. EVs achieve a cumulative emission reduction of 14,418 kgCO2eq, demonstrating a significant emission reduction advantage.

[0070] This difference stems from the fundamental differences in the energy drive systems of the two types of vehicles: ①The carbon emission lock-in effect of ICEVs: Based on the energy supply mechanism of fossil fuel combustion, its carbon emission intensity is limited by the thermal efficiency of internal combustion engines (usually <35%), and direct emissions (exhaust CO2) account for more than 70% of the total carbon footprint over the life cycle, exhibiting high rigidity characteristics. ② Dynamic correlation of carbon intensity of EVs: Their carbon emissions are closely coupled with the energy structure of the power grid. Under the current level of power grid carbon intensity, the emission intensity of EVs has been reduced by more than 50% compared with ICEVs; if the penetration rate of renewable energy in the power grid increases to 80%, its emissions per unit mileage can be further reduced to 20-30 gCO2eq / km (sensitivity analysis results), showing significant low-carbon elasticity.

[0071] Table 6 Comparison of carbon footprint during operation and use phases

[0072] Note: Total carbon emissions are calculated based on a total lifecycle mileage of 180,000 km, using the formula: unit carbon emissions × 180,000 ÷ 1,000.

[0073] Based on actual grid carbon intensity data from California, a systematic assessment of the carbon emissions of electric vehicles (EVs) during charging was conducted. The analysis revealed a close correlation between the carbon emission levels associated with EV charging and the temporal variation of grid carbon intensity, particularly during peak electricity consumption periods when the proportion of fossil fuels increases significantly, leading to a substantial rise in carbon emissions per unit of electricity. Case study results indicate that, considering the average grid carbon intensity, EV charging generates 20,808 kgCO2 annually. By optimizing charging methods to reduce unnecessary electricity input, annual carbon emissions can be reduced to 15,665 kgCO2, achieving an average annual carbon reduction of approximately 5,143 kgCO2. This reduction is equivalent to approximately 30% of the annual carbon emissions of a traditional gasoline-powered vehicle, demonstrating significant emission reduction potential.

[0074] In addition, see Figure 7 Comparing ground-level realities (blue) and predicted carbon intensity (red) for two-day samples in January and July, carbon emissions exhibit significant fluctuations across seasons, with lower carbon intensity in spring and higher intensity during peak summer and winter periods. The annual peak-valley differences have a substantial impact on the carbon footprint of electric vehicles. This result suggests that carbon emission accounting needs to comprehensively consider regional power grid characteristics and temporal variations to reflect a more accurate carbon footprint level.

[0075] Figure 8 It shows the temporal variation characteristics of carbon emissions generated by electric vehicles during actual charging. Figure 8Data shows that electric vehicles (EVs) have the lowest carbon emissions per unit of electricity when charged during the daytime in spring (especially from 8:00 to 16:00), at approximately 0.13 mTCO2 / MWh. At this time, due to the higher proportion of clean energy, the carbon footprint of EVs decreases significantly, resulting in more low-carbon operation. In contrast, carbon intensity remains high throughout the day in winter, especially during morning and evening peak hours, exceeding 0.35 mTCO2 / MWh. The carbon footprint of EVs during their use also increases accordingly, exhibiting clear temporal and seasonal differences. Regarding the changes in the cleanliness of grid power supply, the data in the graph shows that carbon intensity is lowest overall in spring, especially between 8:00 AM and 4:00 PM, reaching its lowest level of the year, close to 0.13 mTCO2 / MWh. This is mainly attributed to abundant sunshine and a higher proportion of renewable energy, particularly solar power, in spring, thus reducing the carbon emissions per unit of electricity generated by the grid. In contrast, carbon intensity is slightly higher in summer and autumn during the same period, while winter carbon intensity remains at its highest level throughout the year. Although the daily carbon intensity fluctuates less, it remains above 0.30 mTCO2 / MWh, indicating a significant increase in the proportion of fossil fuel power generation and insufficient output of clean energy in winter. Furthermore, carbon intensity in each season shows a rapid decline between 6:00 AM and 8:00 AM, followed by a rapid rebound after 4:00 PM. This "V"-shaped curve reflects the intraday fluctuation pattern of grid carbon intensity under the interaction of photovoltaic power generation and load demand. Simultaneously, the figure clearly reveals that the choice of electric vehicle charging time has a significant impact on carbon emissions, especially charging during the low-carbon-intensity daytime hours in spring, which can achieve significant carbon reduction effects.

[0076] 4) Decommissioning and Recovery Phase Electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) exhibit significant differences in carbon emissions at this stage: EVs have a carbon intensity of 11.50 gCO2eq / km and total emissions of 2,070 kgCO2eq; while ICEVs have a carbon intensity of only 0.22 gCO2eq / km and total emissions of 40 kgCO2eq. The carbon intensity of EVs is 51.75 times that of ICEVs, highlighting the carbon-intensive nature of battery recycling systems. See Table 7: Table 7 Comparison of carbon footprint during the decommissioning and recycling phases

[0077] Note: Total carbon emissions are calculated based on a total lifecycle mileage of 180,000 km, using the formula: unit carbon emissions × 180,000 ÷ 1,000.

[0078] See Figure 9In the carbon footprint comparison analysis section, this invention comprehensively evaluates the carbon emission performance of electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) throughout their entire life cycle, from four stages: raw material acquisition, manufacturing and assembly, operation and use, and retirement and recycling. The ratio of EVs to ICEVs reflects the differences in each stage: EVs have higher emissions in the raw material, manufacturing, and recycling stages, but exhibit significant emission reduction advantages during operation, ultimately resulting in slightly lower total life-cycle carbon emissions compared to ICEVs. The carbon emission per unit mile for EVs is 171.70 gCO2eq / km, with a total life-cycle carbon emission of 30,906 kg CO2eq; while the carbon emission per unit mile for ICEVs is 185.24 gCO2eq / km, with a total life-cycle carbon emission of 33,344 kg CO2eq. Overall, EVs achieve approximately 7.3% carbon emission reduction over their entire life cycle, demonstrating certain environmental advantages. However, considering the entire life cycle, the carbon emission per unit mile for electric vehicles is 199.16 gCO2eq / km, and the total life cycle carbon emission is approximately 35.85 tons of CO2eq. Compared to internal combustion engine vehicles' 210.03 gCO2eq / km and 37.81 tons of CO2eq, electric vehicles achieve a carbon emission reduction of approximately 5.2%, and the overall emission reduction effect is not yet significant. (See Table 8 for details.) Table 8. Overall Comparison of Carbon Emissions Throughout the Life Cycle of Electric Vehicles (EVs) and Internal Combustion Vehicles (ICEVs)

[0079] Note: 1. Total carbon emissions are calculated based on a total lifecycle mileage of 180,000 km. Emissions per mile are multiplied by 180,000 and then divided by 1,000 to convert to kg CO2eq. 2. The total lifecycle data is the sum of all stages and may have decimal differences, which have been rounded appropriately.

[0080] See Figure 10 A thorough comparison can be made both horizontally and vertically: (1) Horizontal comparison 1) In the raw material acquisition stage, the carbon footprint per unit mileage of electric vehicles is 55.23 gCO2eq / km, which is significantly higher than that of internal combustion engine vehicles (28.07 gCO2eq / km). This difference is mainly attributed to the high-energy-consuming mining and smelting processes of key materials such as lithium, nickel, and cobalt in the manufacturing process of electric vehicle power batteries.

[0081] 2) During the manufacturing and assembly stage, the carbon footprint per unit mileage of electric vehicles reaches 35.07 gCO2eq / km, which is 5.05 times that of internal combustion engine vehicles (6.95 gCO2eq / km). This is mainly due to the complexity of the power battery manufacturing process, including high-energy-consuming processes such as electrode material synthesis, electrolyte preparation and battery pack packaging.

[0082] 3) During the operation and use phase, the carbon footprint of electric vehicles per unit mileage is only 69.90 gCO2eq / km, which is significantly lower than that of internal combustion engine vehicles (150.00 gCO2eq / km). This advantage is mainly due to the high efficiency of electric drive, especially in the context of a power grid environment where clean energy accounts for a high proportion, the carbon footprint advantage of electric vehicles is more significant.

[0083] 4) During the retirement and recycling phase, the carbon footprint per unit mileage of electric vehicles is 11.50 gCO2eq / km, which is much higher than that of internal combustion engine vehicles (0.22 gCO2eq / km). This is mainly due to the complexity of high-energy-consuming processes such as hydrometallurgy during the recycling of power batteries.

[0084] (2) Vertical comparison A longitudinal comparison reveals significant differences in the carbon footprint of electric vehicles across their various lifecycle stages. Firstly, the carbon footprint is highest during the raw material acquisition and manufacturing stages, reaching 55.23 gCO2eq / km, primarily influenced by material type, production processes, and energy structure. Particularly in the battery manufacturing process, high-energy-consuming processes such as hydrometallurgy and high-temperature sintering significantly increase the carbon footprint at this stage. Furthermore, different materials, such as ternary lithium batteries and lithium iron phosphate batteries, exhibit substantial differences in carbon footprint due to variations in metal resources and processing techniques.

[0085] Secondly, the carbon footprint of electric vehicles during operation is only 69.90 gCO2eq / km. However, this carbon footprint is highly dependent on the energy structure and cleanliness of the power grid. As the proportion of renewable energy increases, the carbon footprint of electric vehicles decreases significantly during operation, thus achieving a significant emission reduction advantage over traditional gasoline vehicles. Conversely, in power grids dominated by coal, the indirect carbon footprint is high, weakening the environmental benefits of electric vehicles.

[0086] Finally, the carbon footprint per unit mile of electric vehicles in the retirement and recycling phase is 11.50 gCO2eq / km, which is constrained by the maturity and efficiency of battery recycling technology. Gradual recycling technology, by extending the actual lifespan of batteries, effectively reduces the demand for new battery production, lowers resource consumption and environmental burden, and thus significantly reduces the carbon footprint at this stage. Based on relevant data and model calculations, this study shows that implementing gradual recycling can reduce the carbon footprint in the retirement phase by up to approximately 14%, making a significant contribution to reducing the total carbon footprint over the entire lifecycle. This finding emphasizes the crucial role of promoting the upgrading of power battery recycling technology and popularizing the tiered utilization strategy in achieving carbon emission reduction throughout the entire lifecycle of electric vehicles.

[0087] In the future, with the improvement of the cleanliness of the power grid structure, the development of new low-carbon materials, and the optimization of battery manufacturing and recycling processes, the carbon emission reduction potential of electric vehicles will be further released. It is estimated that under a scenario where the penetration rate of renewable energy in the power grid reaches 80%, the carbon emissions of electric vehicles throughout their entire life cycle will be reduced by more than 30% compared to internal combustion engine vehicles, resulting in more significant environmental benefits. Implementing tiered recycling of power batteries can reduce the carbon footprint at the retirement stage by up to approximately 14%, making a significant contribution to the reduction of the total carbon footprint throughout the entire life cycle. This finding emphasizes the crucial role of promoting the upgrading of power battery recycling technology and the widespread adoption of tiered utilization strategies in achieving carbon emission reduction throughout the entire life cycle of electric vehicles. Based on a comprehensive life cycle analysis, the total carbon emissions of electric vehicles are approximately 30.91 tons of CO2eq, compared to 33.34 tons of CO2eq for internal combustion engine vehicles, achieving a reduction of approximately 7.3%. Although the overall emission reduction effect has not yet reached a significant level, with the optimization of the power grid structure and the in-depth development of tiered utilization and recycling technologies for power batteries, the carbon emission reduction potential of electric vehicles is enormous.

[0088] (6) Results of life cycle carbon benefit assessment of electric vehicles This invention constructs a carbon emission reduction economic value assessment system based on the triple bottom line theory to scientifically evaluate the carbon benefits of electric vehicles (EVs) throughout their entire life cycle. The calculation of carbon benefits is based on the difference in carbon emissions between electric vehicles (EVs) and internal combustion engine vehicles (ICEVs). To ensure the applicability of the calculation, this invention selects the closing price of the Chinese carbon market in 2021 and the closing price of the California-Quebec carbon market in 2021. When calculating the social carbon benefits, this invention selects the US carbon emission social cost index in 2021. After adjusting for inflation with a 3% discount rate, the carbon market price in China is significantly lower than that in the United States.

[0089] 1) Analysis of economic carbon benefits See Figure 11 To ensure the applicability of the calculations, this invention selected the closing prices of the Chinese carbon market and the California-Quebec carbon market in 2021. Electric vehicles, when using clean electricity, offer significantly higher carbon emission reduction benefits compared to internal combustion engine vehicles, with varying benefits across different vehicle types. Among them, fuel-electric logistics vehicles (FCLVs) have the highest carbon benefits. The generation of economic carbon benefits is highly dependent on the carbon market mechanism and the decarbonization process of the power grid. Research shows that fluctuations in carbon prices have a significant impact on the economic viability of electric vehicles. Furthermore, optimizing the power grid's energy structure can further unleash economic potential. For example, in a power grid scenario with 80% renewable energy penetration, carbon emissions during the operation of electric vehicles are reduced by 65% ​​compared to current levels, with a corresponding increase in economic carbon benefits of 40%. This data reflects that while reducing direct carbon emissions, electric vehicles possess certain monetization potential for their carbon value, providing data support for future participation in the carbon market and carbon asset management.

[0090] This invention conducted a multidimensional sensitivity analysis, selecting carbon price as the core variable. The figure illustrates the changing trend of the unit carbon benefit of electric vehicles under different carbon price conditions. The horizontal axis represents the carbon price in the carbon trading market (USD / ton CO2), and the vertical axis represents the carbon benefit of electric vehicles (USD / kg-H2). As the carbon price increases, the carbon benefit of FCEVs shows a linear increase, indicating that the carbon price is significantly sensitive to the economics of FCEVs, reflecting the positive incentive effect of the carbon market on the promotion of FCEVs. The results show that carbon price is the most significant factor affecting the carbon benefit of FCEVs. Furthermore, the development of clean electricity is also a key driving factor for the growth of carbon benefit. With the future improvement of the greening level of the power grid, the carbon emission reduction potential of EVs will continue to expand.

[0091] 2) Analysis of social carbon benefits Based on the obtained carbon emission reductions, this invention selected the US carbon emission social cost index for 2021. After adjusting for inflation with a 3% discount rate, the carbon market price in China was significantly lower than that in the US. The analysis also shows that the assessment of social carbon benefits needs to fully consider the differences in regional power grid carbon intensity. In California, USA, relying on the coordinated dispatch of photovoltaic power generation and energy storage systems, the carbon emissions from daytime charging of electric vehicles are almost zero, and its social carbon benefits are about 2.3 times higher than those in China, which is mainly based on coal combustion. This spatial heterogeneity fully highlights the key role of the power system decarbonization process in realizing the environmental value of electric vehicles.

[0092] Innovation in power battery technology and optimization of the recycling system are core pathways to improving the carbon efficiency of electric vehicles. The application of solid-state battery technology is expected to increase battery energy density to 400 Wh / kg, approximately 40% higher than current mainstream technologies, while extending cycle life to 2000 cycles, thereby achieving a reduction of approximately 12% in carbon emissions over the entire lifecycle. The promotion of cascade utilization technology reduces the demand for new batteries by approximately 40% by extending the economic lifespan of batteries (secondary utilization cycle of approximately 4 to 8 years), resulting in a reduction of approximately 30% in carbon emissions during the recycling phase of retired batteries. In an optimistic scenario, assuming a clean power grid rate of 80% and a battery recycling rate of 95%, the carbon emissions over the entire lifecycle of electric vehicles are reduced by 44.5% compared to traditional internal combustion engine vehicles, representing a 300% improvement in carbon efficiency. These results demonstrate that the environmental advantages of electric vehicles are not static but continuously enhanced with technological progress, and their contribution to the carbon neutrality process will continue to expand during the energy system transition.

[0093] (7) Simulation results of gradient recovery of power battery The system evaluates the impact of power batteries on the carbon footprint and carbon benefits of electric vehicles throughout their entire life cycle under the scenario of cascaded utilization.

[0094] Based on the following settings and assumptions: First, regarding lifecycle assumptions, the service life of electric vehicles is 12 years, with the initial lifespan of the power battery being 8 years, followed by tiered utilization in energy storage scenarios, with a tiered utilization cycle of 4 years. It is assumed that the battery performance remains stable during tiered utilization, with no significant capacity decay, and the utilization efficiency remains above 80%. The battery capacity is standardized at 60 kWh, and all batteries can be fully incorporated into the tiered utilization process after recycling, without considering recycling losses or technical obstacles.

[0095] Second, regarding the substitution benefit setting, it is assumed that the effective power generated by the secondary use can completely replace the market demand for newly produced batteries of the same capacity, thereby avoiding carbon emissions during the manufacturing process of new batteries. The carbon emission factor per unit of new battery production is set at 150 kg CO2 / kWh, derived from the average data of mainstream literature. The secondary use process requires the consumption of some electricity, and the carbon intensity of the electricity is set at the US average of 0.45 kg CO2 / kWh.

[0096] Third, regarding economic parameters, two carbon price scenarios are set: one is $30 / ton CO2, representing the current average in mainstream carbon markets; the other is $23.34 / ton CO2, representing the future trend of carbon price changes. This is used to assess the impact of changes in the monetary value of carbon emission reductions on the economic benefits of tiered utilization. Furthermore, the carbon emissions from transportation in the manufacturing and recycling stages of power batteries are uniformly converted to 5% of total carbon emissions to simplify the accounting model.

[0097] The experimental design includes three scenarios: Scenario A: No secondary use is performed; the batteries are directly recycled. Scenario B: All batteries are reused in a tiered manner to replace the production of new batteries, thus eliminating unnecessary power consumption; Scenario C: Partial reuse is carried out, and considering energy consumption and some losses, the replacement ratio is reduced to 70%.

[0098] Compare carbon footprint and carbon benefit indicators under three scenarios.

[0099] The battery gradient recycling data were all collected by a battery life testing platform independently developed by the Probabilistic Mechanics Laboratory at the University of Central Florida. This platform can simultaneously conduct continuous cycle tests on multiple battery groups and continuously record key data such as current, voltage, and battery temperature throughout the entire charge-discharge cycle. These experiments were conducted thanks to close collaboration between the University of Central Florida and the Diagnostics and Prediction Group of the Intelligent Systems Division at NASA Ames Research Center, aiming to provide high-quality measured data support for the development and validation of battery life prediction models.

[0100] This dataset provides data on the life cycle testing of lithium-ion batteries, with a particular focus on battery performance and aging characteristics under a wide range of load levels. The battery packs used consist of two 18650 lithium-ion batteries connected in series, with voltage ranging from 8.4 volts at full charge to a cutoff voltage of 5.0 volts. The entire life cycle testing involved 26 battery packs, which were divided into several groups and operated under both constant load conditions and randomly varied load conditions. Load levels and their variation ranges were systematically set to investigate the impact of different current levels on the battery aging process. In addition, the data includes load cycling experiments for "rechargeable batteries," where cells that remained active in previous aging tests were reassembled into new battery packs for subsequent testing. The dataset also includes multi-dimensional and multi-scenario assessments of carbon emissions, cost, and resource requirements based on a functional unit of 1 kWh battery capacity or 1 kg of material. The table displays information on component configurations (including photovoltaic capacity, energy storage capacity, inverter capacity, etc.), economic indicators (levelized cost of electricity, LCOE, annualized cost), and life-cycle greenhouse gas emissions (GWP, in kg CO2eq. / kWh) for different scenarios (such as natural gas, grid, and combinations of photovoltaics and energy storage). The data in this literature covers dimensions such as electricity demand, energy allocation, life-cycle carbon emissions, and economic evaluation, providing a comprehensive reference for conducting research on carbon footprint and carbon benefits.

[0101] See Figure 12 The study demonstrates the trends of lithium-ion battery capacity and maximum available ion count (qmax) under different constant current load conditions, revealing the aging behavior of the battery during long-term cycle use. Figure 12 In the diagram, solid dots represent changes in battery capacity, and hollow dots represent changes in the maximum number of usable ions. Different colors correspond to different load levels, and two sets of batteries were tested under each load condition. With increasing cumulative energy, both battery capacity and the maximum number of usable ions show a significant degradation trend. Under high load conditions (e.g., 19.0A), capacity decay is more pronounced, and the battery reaches its end-of-life capacity earlier, indicating a faster aging rate. In contrast, under low load conditions (e.g., 9.3A), the rate of decay in battery capacity and the maximum number of usable ions is slower, exhibiting a longer cycle life. Furthermore, Figure 12A highly consistent relationship between capacity and maximum available ion count can be observed, indicating that the maximum available ion count can serve as an effective indicator of battery capacity changes. This figure visually reflects the significant impact of load level on battery aging rate, providing important experimental evidence and data support for analyzing the evolution of battery state of health (SOH) and establishing an aging prediction model.

[0102] See Figure 13 In the simulation, the carbon footprint model of the power battery lifecycle includes four stages: raw material acquisition and manufacturing, initial service period, cascade utilization period, and final recycling and disposal. Carbon emissions for each stage are calculated by multiplying the unit carbon factor by the energy consumption, and are adjusted for emissions from transportation and processing. The formula for calculating the total carbon footprint is derived as follows: CF total = CF prod + CF first-use + CF reuse + CF recycle CF substitute in, CF substitute The carbon emission reduction resulting from the replacement of new battery production with secondary battery use is the core indicator for measuring the emission reduction benefits of secondary battery use. Based on the life cycle theory and factor analysis, the calculation formula is derived as follows: CF substitute = Q sub × EF battery-prod in, Q sub For replacement capacity. EF battery-prod Carbon factor production per unit capacity of new batteries. Calculate the emission reduction potential of tiered utilization by combining replacement rates under different scenarios.

[0103] A carbon footprint encompasses the carbon dioxide (CO2) emissions generated during the electricity production process when electricity is drawn from the grid to charge an electric vehicle. Because the energy mix of the power grid changes over time (e.g., more solar power is generated during the day and fossil fuels are relied upon at night), the carbon footprint of electric vehicle charging also varies depending on the charging time.

[0104] Carbon intensity in the power grid refers to the amount of carbon dioxide emissions generated during the production of each unit of electricity (kWh), expressed as kgCO2 / kWh or mTCO2 / MWh. Based on the principles of carbon intensity in the power grid, the calculation formula is as follows: C ( t )=∑ i=1 N c i ( t )× p i ( t ) / P total ( t ) in, c i ( t ) is the first i The carbon intensity of a type of energy. p i ( t ) is the first i Energy in time t The amount of electricity generated. P total ( t (This is time) t Total electricity generation.

[0105] Simulation results show that in scenario A, without secondary use, the total carbon emissions over the lifecycle of a single standard power battery (60 kWh) are 12,000 kg CO2, with the manufacturing stage accounting for as much as 75%. In scenario B, which introduces secondary use, the total carbon emissions are reduced to 10,200 kg CO2 due to the effective replacement of new battery production, resulting in a reduction of 1,800 kg CO2, equivalent to a 15% reduction in lifecycle carbon emissions. In scenario C, considering the energy consumption and reduced replacement rate during secondary use, the carbon emissions are still 10,800 kg CO2, achieving a 10% carbon reduction compared to scenario A.

[0106] See Figure 14 The study compared and presented the carbon emissions over the life cycle of power batteries under three different scenarios of tiered utilization. The results showed that scenario A (no tiered utilization) had the highest total carbon emissions, reaching 12,000 kg CO2; scenario B (complete replacement of new battery production) reduced carbon emissions to 10,200 kg CO2, showing the most significant emission reduction effect; and scenario C (partial replacement) had carbon emissions of 10,800 kg CO2. Figure 14 The data clearly reflect the positive role of tiered utilization in mitigating carbon emissions, demonstrating significant emission reduction potential.

[0107] Further analysis shows that carbon emission reduction is directly proportional to substitution capacity, while energy consumption during the tiered utilization process has a certain offsetting effect on emission reduction benefits. In the model validation phase, the rationality of the carbon emission calculation results was verified by comparing with existing literature data. The error range was controlled within 5%, indicating that the established model has good reliability and applicability. Furthermore, sensitivity analysis was used to verify the impact of key parameters (such as unit carbon emission factor, substitution rate, and energy efficiency loss rate) on the model output. The results show that the model has strong robustness and can adapt to the evaluation needs of different tiered utilization paths and technical routes.

[0108] See Figure 15 This study analyzes the monetization of the carbon benefits of cascade utilization. In Scenario B, the average annual carbon emission reduction per unit of power battery is 450 kg CO2 / year. Based on the current carbon price of $30 / ton CO2, the annual carbon benefit is $13.5, and the cumulative carbon benefit over the entire cascade utilization period (4 years) is $54. In the scenario where the carbon price increases to $23.34 / ton CO2, the annual carbon benefit reaches $36, and the cumulative carbon benefit increases to $144, demonstrating that the carbon market pricing mechanism has a significant amplifying effect on the economic potential of cascade utilization. The horizontal axis represents the carbon price (USD / ton CO2), and the vertical axis represents the cumulative carbon benefit per unit of power battery (USD). The carbon benefit in Scenario B increases from $54 to $144, while in Scenario C it increases from $48 to $128. The curves in the graph show linear growth, indicating that rising carbon prices significantly amplify the monetary value of carbon emission reductions, thereby enhancing the economic attractiveness of cascade utilization. Meanwhile, considering the scenario of large-scale cascade utilization (taking the recycling of batteries from 10,000 electric vehicles as an example), the cumulative carbon emission reduction under scenario B is 18,000 tons of CO2, corresponding to a carbon benefit of US$540,000 to US$1.44 million, highlighting the emission reduction potential and economic value of cascade utilization of power batteries under large-scale implementation. In addition, compared with the carbon benefit under scenario C (cumulative carbon benefit of approximately US$120 per battery), although it is weakened due to the decrease in the replacement rate, it still has a significant economic advantage overall.

[0109] Compared with the prior art, the present invention has the following technical effects: (1) More comprehensive assessment: This invention incorporates the gradient recycling of power batteries into the entire life cycle, which reduces the error of carbon footprint accounting by 10%-15% and more accurately reflects environmental benefits.

[0110] (2) Multidimensional carbon benefits: This invention simultaneously quantifies economic benefits (such as carbon trading) and social value (such as health cost savings).

[0111] (3) Strong dynamic adaptability: The present invention can adjust parameters according to the power grid structure and recycling scale to adapt to the evaluation needs of different regions and scenarios and improve the practicality of the model.

[0112] Specific embodiments of the invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing can be advantageous.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the embodiments of the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims.

Claims

1. A method for assessing the carbon footprint of electric vehicles throughout their entire lifecycle, taking into account the gradient recycling of power batteries, characterized in that, include: A full life cycle carbon footprint assessment model is constructed, which includes the entire life cycle of electric vehicles and internal combustion engine vehicles. The full life cycle includes the raw material acquisition stage, the production and manufacturing stage, the operation and use stage, and the retirement and recycling stage. The retirement and recycling stage of electric vehicles adopts a power battery gradient recycling strategy. Configure model parameters, including inputting the material usage, energy consumption data, grid carbon intensity, carbon price, and social carbon cost of the electric vehicle and the internal combustion engine vehicle at each stage; The total life-cycle carbon footprint of the electric vehicle and the internal combustion engine vehicle is calculated in stages by running the full life-cycle carbon footprint assessment model. The carbon emission reduction is obtained by calculating the difference between the total life cycle carbon footprint of the electric vehicle and the total life cycle carbon footprint of the internal combustion engine vehicle. Calculate the economic carbon benefits based on the carbon emission reduction and the carbon price, and calculate the social carbon benefits based on the social carbon cost; Output the total carbon footprint of the electric vehicle and the internal combustion engine vehicle throughout their entire life cycle, the economic carbon benefits, and the social carbon benefits.

2. The method according to claim 1, characterized in that, The power battery gradient recycling strategy is a step-by-step recycling mode, including: Based on the remaining capacity and performance of retired power batteries, retired power batteries are divided into batteries that can be reused in a tiered manner and batteries that require material recycling. After being disassembled, repaired, and reassembled, reusable batteries can be applied to energy storage scenarios. Disassemble batteries that require material recycling and extract metal materials.

3. The method according to claim 1, characterized in that, The carbon emission reduction is calculated using the following formula: CR = CE ICEV CE EV in, CE ICEV This refers to the total lifecycle carbon footprint of an internal combustion engine vehicle obtained through the aforementioned phased accounting. CE EV This refers to the total carbon footprint of an electric vehicle throughout its entire lifecycle, calculated through the aforementioned phased accounting.

4. The method according to claim 1, characterized in that, The economic carbon benefits are calculated using the following formula: ECB = CR × P carbon / 1000 in, ECB For economic carbon benefits, CR For carbon emission reductions, Pcarbon This refers to the carbon price.

5. The method according to claim 1, characterized in that, The social carbon benefits are calculated using the following formula: SCB =(D CE Battery +D CE Grid D CE EV prod )× SCC in, SCB For social carbon benefits, Δ CE Battery The carbon emission reduction Δ is due to the gradient recycling of power batteries. CE Grid The carbon emission reductions resulting from the clean power system, Δ CE EV prod The increase in carbon emissions during the production and manufacturing phase of electric vehicles. SCC For the social carbon cost.

6. The method according to claim 1, characterized in that, The carbon intensity of the power grid is calculated using the following formula: C ( t )=∑ i=1 N c i ( t )× p i ( t ) / P total ( t ) in, c i ( t ) is the first i Carbon intensity of this energy source p i ( t ) is the first i Energy in time t Electricity generation, P total ( t (This is time) t Total electricity generation.

7. The method according to claim 1, characterized in that, In the phased accounting, carbon emissions during the raw material acquisition phase E raw Calculated using the following formula: E raw =∑ i M i × EF raw,i in, E raw This refers to the total carbon emissions during the raw material acquisition phase. M i Let represent the amount of the i-th material used.

8. The method according to claim 1, characterized in that, In the phased accounting, carbon emissions during the production and manufacturing phase... E prod Calculated using the following formula: E prod = E ep × EF gp + E fp × EF f in, E prod This refers to the total carbon emissions during the manufacturing phase. E ep This refers to the total amount of electricity consumed during the manufacturing process. EF gp The carbon emission factor of the power grid where the production and manufacturing stage is located. E fp Energy generated from fossil fuels consumed in the production and manufacturing process.

9. The method according to claim 1, characterized in that, In the phased accounting, carbon emissions during the operation and use phase are calculated separately for each vehicle type: Carbon emissions from electric vehicle operation E use , EV for: E use , EV = D × e c × EF gu in, E use , EV This refers to the total carbon emissions of electric vehicles during their operation and use phase. D The total mileage driven over the entire lifespan of the vehicle. e c This refers to the average energy consumption per kilometer for electric vehicles. EF gu The carbon emission factor of the local power grid during the operation and use phase; Carbon emissions from internal combustion locomotive operation E use,ICEV for: E use,ICEV = E burn + E fuel prod = D × f cons ×( EF burn + EF fuel prod ) in, E use,ICEV This refers to the total carbon emissions of diesel locomotives during their operation and use. D The total mileage over the entire lifespan. f cons This represents the average fuel consumption per unit mileage. EF burn As fuel combustion emission factors, EF fuel prod These are the emission factors from the extraction to refining of fuel oil.

10. The method according to claim 1, characterized in that, In the phased accounting, carbon emissions during the decommissioning and recycling phase... E EOL Calculated using the following formula: E EOL =∑ i [ r i M i × EF recycle,i +(1 r i ) M i × EF disposal,i r i M i × EF raw,i ] in, E EOL This refers to the total carbon emissions during the decommissioning and recycling phase. r i Let i be the recovery rate of the i-th material. M i Let i be the amount of the i-th material. EF recycle,i To recycle and process the carbon emissions generated per kg of material i, EF disposal,i To address carbon emission factors, EF raw,i is the carbon emission factor generated by the primary production of the i-th material.

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