The transition toward low-carbon mobility has placed electric vehicles at the center of many national decarbonization strategies. In this context, I have focused on the EV battery pack as the most energy-intensive subsystem of an electric vehicle, since its production, operation, and end-of-life management are responsible for a substantial fraction of the total greenhouse gas emissions. My research addresses a key question: how should the carbon footprint of an EV battery pack be evaluated when the influencing factors are not static but evolve continuously with time, policy, and technology? In this article, I present a first-person account of my methodological approach, the quantitative model I built, the case study I performed on a lithium iron phosphate EV battery pack, the dynamic scenario analysis I conducted, and the software platform I subsequently developed to make the evaluation reproducible and accessible.
1. Background and Motivation
Climate change has intensified the global search for technologies that reduce fossil-fuel dependence and greenhouse gas emissions. The transportation sector contributes to a significant share of global carbon emissions, and many countries have introduced policies to accelerate the electrification of road transport. China, for example, declared its intention to peak carbon emissions before 2030 and achieve carbon neutrality before 2060. The automotive sector has responded through a rapid expansion in battery electric vehicle production and sales. National statistical data indicate that new energy vehicle production exceeded 12.8 million units in a single recent year, with a year-on-year growth rate of more than 34%. The growth has in turn driven an extraordinary increase in the manufacturing volume of traction batteries, from roughly 62.2 GWh in 2019 to 548.4 GWh in 2024.
Although the operation of an EV battery pack does not directly produce tailpipe emissions, the emissions embedded in upstream material extraction, battery component manufacturing, and downstream recycling are considerable. Depending on the battery chemistry and the regional electricity mix, the carbon footprint of an EV battery pack can account for up to 40% of the carbon footprint of the complete vehicle. Scientific carbon accounting is therefore essential not only for corporate decarbonization planning but also for compliance with new regulations. A prominent example is the newest European Union battery regulation, which asks manufacturers to disclose the product carbon footprint of an EV battery pack entering the European market. Similar policy developments are emerging in other regions, and Chinese authorities have also begun to develop product-level carbon footprint management systems.
I identified several challenges after reviewing the existing literature. Many prior studies evaluated the carbon footprint of an EV battery pack using a static lifecycle inventory, but the carbon footprint of an EV battery pack is time-dependent. Electricity carbon factors change as renewable energy penetrates the grid; the share of recycled aluminum and other recycled materials changes with circular economy policies; and charging-discharging efficiency is improved by advances in battery and thermal management technologies. These dynamics are not always captured by conventional lifecycle assessments. A second challenge is that data quality often varies across the lifecycle of an EV battery pack, and this uncertainty needs be propagated into the results. A third challenge is the lack of accessible, battery-specific evaluation tools that allow users to perform carbon foot printing under different dynamic scenarios. These gaps motivated me to construct a dynamic carbon footprint evaluation framework for an EV battery pack and to implement that framework in a software tool.
2. Research Framework and Carbon Accounting Model
2.1 Lifecycle Assessment Framework Applied to an EV Battery Pack
My study follows the international lifecycle assessment framework described in ISO standards. I defined the goal of the evaluation as the quantification of greenhouse gas emissions from all relevant stages of the EV battery pack lifecycle, and the scope as a cradle-to-grave analysis covering four stages: raw material preparation, cell and pack manufacturing, vehicle use, and end-of-life recycling. The functional unit I selected is 1 kWh of energy delivered to the electric drivetrain by the EV battery pack during its useful life. This functional unit permits fair comparisons among different battery designs because it normalizes emissions according to the service delivered by the pack rather than by pack mass alone.
The system boundary encloses:
- Raw material preparation: extraction and processing of active materials, binders, conductive agents, foils, casings, electrolytes, separators, and other components.
- Production and manufacturing: electrode manufacturing, cell assembly, module formation, and EV battery pack integration.
- Use phase: charging energy losses and energy consumed to move the additional mass of the pack.
- End-of-life: collection, dismantling, crushing, sorting, material recovery, and final disposal of non-recoverable residues.
I deliberately excluded logistics, packaging, and maintenance in the baseline because the direct field data were difficult to obtain; however, I acknowledge that these exclusions may affect the completeness of the inventory and should be reconsidered in future studies. The inventory data come from a mixture of sources, including company surveys, environmental impact reports, Chinese national databases, and internationally published inventories.
2.2 Carbon Emission Factor Method
The basic accounting formula used in my model is based on the emission-factor method. For any activity \(A_i\) and its associated emission factor \(EF_i\), the carbon emission contribution can be expressed as:
\[
C_i = \sum_{i} AD_i \times EF_i
\]
where \(AD_i\) is the activity data for process \(i\), such as the mass of an input material, the volume of fuel, or the quantity of electricity consumed, and \(EF_i\) is the carbon dioxide equivalent emission factor per unit of that activity. I applied this principle separately to each lifecycle stage of the EV battery pack.
2.3 Raw Material Preparation Model
The raw material stage of an EV battery pack involves both virgin materials and recycled materials. Because the two supply routes have different energy intensities and resulting carbon factors, I represented them explicitly in the accounting model. Let \(m_{ij}\) denote the mass of material \(j\) in component \(i\), and let \(r_j\) denote the fraction of material \(j\) supplied from recycled sources. The carbon footprint of the raw material stage is written as:
\[
C_{\mathrm{material}} = \sum_{i}\sum_{j} m_{ij}\left[(1-r_j)EF_{v,j} \,+\, r_j\bigl((1-A)EF_{v,j} + A\,EF_{r,j}\bigr)\right]
\]
where \(EF_{v,j}\) is the carbon factor of material \(j\) produced entirely from virgin feedstock, \(EF_{r,j}\) is the carbon factor of material \(j\) produced entirely from recycled feedstock, and \(A\) is an allocation factor describing how the environmental burden of the previous lifecycle is shared between the previous product system and the current product system. In the baseline case, I followed a 50/50 allocation rule and used \(A = 0.5\). The model reveals two practical levers for reducing raw material emissions in an EV battery pack: the choice of materials and the proportion of recycled content.
2.4 Manufacturing Stage Model
The manufacturing stage of an EV battery pack includes component manufacturing, electrode processing, cell fabrication, module assembly, and final pack integration. Emissions mainly arise from purchased electricity, natural gas, and direct chemical process emissions. I split the manufacturing stage into component production and battery assembly production. For component production, the carbon footprint is:
\[
C_{\mathrm{part}} = \sum_{u} \sum_{v} e_{u,v} EF_{v} \,+\, \sum_{g} W_g \times GWP_g
\]
where \(e_{u,v}\) is the energy of type \(v\) consumed in component process \(u\), \(EF_v\) is its emission factor, \(W_g\) is the direct emission of greenhouse gas \(g\), and \(GWP_g\) is its global warming potential. Similarly, for cell and EV battery pack assembly, the carbon footprint is:
\[
C_{\mathrm{assembly}} = \sum_{u} \sum_{v} e_{u,v} EF_{v} \,+\, \sum_{g} W_g \times GWP_g
\]
Therefore, the total production stage carbon footprint is:
\[
C_{\mathrm{production}} = C_{\mathrm{part}} + C_{\mathrm{assembly}}
\]
2.5 Use Stage Model
The use stage of an EV battery pack is unique because the pack is recharged many times over a vehicle lifetime. During each charge-discharge cycle, some energy is lost due to internal resistance, heat generation, and auxiliary conversion losses. In addition, the mass of the EV battery pack contributes to the total vehicle mass and increases the energy required to move the vehicle. The total electrical energy that must be supplied by the grid to support the useful battery energy over the vehicle lifetime is decomposed into two terms. The first term is the charging loss caused by the roundtrip efficiency:
\[
E_{\mathrm{charge\,loss}} = E_{\mathrm{service}} \times \frac{1-\eta}{\eta}
\]
where \(E_{\mathrm{service}}\) is the total energy delivered to the drivetrain and \(\eta\) is the roundtrip charging-discharging efficiency. The second term accounts for the mass-induced energy consumption:
\[
E_{\mathrm{mass}} = k \times \frac{m_{\mathrm{pack}}}{m_{\mathrm{vehicle}}} \times E_{\mathrm{service}}
\]
where \(k\) is a mass-energy apportionment coefficient, \(m_{\mathrm{pack}}\) is the mass of the EV battery pack, and \(m_{\mathrm{vehicle}}\) is the curb mass of the electric vehicle. The use stage carbon footprint is then:
\[
C_{\mathrm{use}} = \left(E_{\mathrm{charge\,loss}} + E_{\mathrm{mass}}\right) \times EF_{\mathrm{electricity}}
\]
The use stage model enables me to treat the electricity carbon factor, charging-discharging efficiency, vehicle efficiency, and battery pack-to-vehicle mass ratio as dynamic variables.
2.6 End-of-Life Recycling Stage Model
The end-of-life stage of an EV battery pack is not only an emission source but also a potential source of avoided emissions because recovered materials can substitute for primary materials in the next generation of batteries. My recycling model includes the energy used in dismantling, crushing, and sorting; the auxiliary materials consumed during hydrometallurgical or pyrometallurgical treatment; the direct process emissions; the emissions from final disposal; and the environmental credit for recovered secondary materials. The end-of-life carbon footprint is expressed as:
\[
C_{\mathrm{recycling}} = \alpha \left(C_{\mathrm{auxiliary}} + C_{\mathrm{energy}} + C_{\mathrm{disposal}} – C_{\mathrm{secondary}}\right)
\]
where \(\alpha\) is an allocation coefficient for the recycled material output that burdens or credits part of the recycling phase to the next product system. In my analysis, I set \(\alpha = 0.5\). The term \(C_{\mathrm{secondary}}\) is the avoided carbon emission obtained from recycled metals, recycled graphite, and recycled plastics. This formulation makes it possible to evaluate how alternative recycling processes, energy mixes, and recovery rates change the overall footprint of the EV battery pack.
2.7 Total Carbon Footprint of the EV Battery Pack
The total life cycle carbon footprint is simply the sum of the four stage-level terms:
\[
C_{\mathrm{total}} = C_{\mathrm{material}} + C_{\mathrm{production}} + C_{\mathrm{use}} + C_{\mathrm{recycling}}
\]
A negative value of \(C_{\mathrm{recycling}}\) means that the recovered materials displace more emissions than are generated during the recycling activities. The model is intentionally modular, allowing each stage model to be updated independently when new data become available.
3. Case Study: A Lithium Iron Phosphate EV Battery Pack
3.1 Description of the Selected EV Battery Pack
I selected a commercial lithium iron phosphate EV battery pack manufactured by a Chinese battery enterprise as the reference product. Lithium iron phosphate batteries dominate the Chinese electric vehicle market because of their safety, long cycle life, and relatively low material cost. The EV battery pack selected for the assessment consists of 196 prismatic cells connected in series, giving a nominal bus voltage of 627.2 V. The total pack energy capacity is 57 kWh, and the total pack mass is 485.3 kg. Table 1 summarizes the main parameters used in the assessment.
| Parameter | Value | Unit |
|---|---|---|
| EV battery pack mass | 485.3 | kg |
| EV battery pack capacity | 57 | kWh |
| Pack energy density | 95 | Wh/kg |
| Cell quantity | 196 | cells |
| Warranty life | 8 | years |
| Vehicle curb mass | 1832.6 | kg |
| Vehicle energy consumption | 13 | kWh/100 km |
| Lifetime driving distance | 150,000 | km |
| Total useful energy delivered | 19,500 | kWh |
3.2 Lifecycle Inventory of the EV Battery Pack
I designed a data collection sheet according to the system boundary described above. The data were normalized by the total useful energy delivered by the EV battery pack. For example, the cathode inventory per functional unit includes the lithium iron phosphate active material, the polyvinylidene fluoride binder, N-methyl-2-pyrrolidone solvent, and the aluminum current collector. The negative electrode inventory includes graphite, binder, solvent, and copper foil. The electrolyte, separator, casing, and battery management system inventories are also expressed per functional unit. Table 2 presents the main raw material inventory.
| Component | Material | Mass per 1 kWh delivered (kg) |
|---|---|---|
| Positive electrode | LiFePO4 | 5.44E-03 |
| Positive electrode | NMP | 2.46E-04 |
| Positive electrode | PVDF | 7.44E-04 |
| Positive electrode | Aluminum foil | 1.24E-03 |
| Negative electrode | Graphite | 2.47E-03 |
| Negative electrode | NMP | 4.92E-04 |
| Negative electrode | PVDF | 2.46E-04 |
| Negative electrode | Copper foil | 1.98E-03 |
| Electrolyte | Ethylene carbonate | 9.90E-04 |
| Electrolyte | Dimethyl carbonate | 9.90E-04 |
| Electrolyte | LiPF6 | 4.92E-04 |
| Separator | Polyethylene | 3.69E-04 |
| Separator | Polypropylene | 3.69E-04 |
| Casing | Aluminum | 6.92E-03 |
| Casing | Polypropylene | 9.90E-04 |
| Thermal system | Ethylene glycol | 1.74E-04 |
| BMS | Battery management system | 7.38E-04 |
For the manufacturing stage, I collected electricity and fuel consumption data associated with component production, cell production, module assembly, and pack assembly. The inventory includes electricity consumed by mixing, coating, drying, calendering, slitting, winding, filling, formation, aging, and grading processes. After normalizing by total useful energy, the production stage inventory per functional unit consists of 0.243 kWh of electricity, 0.000444 m³ of natural gas, and a small amount of diesel. Direct emissions during production are mainly carbon dioxide and trace amounts of volatile organic compounds, which are markedly smaller than the upstream emissions but are nevertheless included in the model.
The use stage inventory was derived from the battery and vehicle parameters. In the baseline scenario, the charging-discharging efficiency of the EV battery pack is 90%, the mass-energy apportionment coefficient is 0.56, and the grid electricity carbon factor is 0.59 kgCO2e/kWh. The use stage contributes energy losses from charging and energy losses from carrying the extra pack mass.
The end-of-life stage was modeled using a dismantling process followed by crushing and sorting, then hydrometallurgical refining for the cathode material, thermal purification for graphite, remelting for aluminum, electrolytic refining for copper, and recycling for plastics and the battery management system. The inventory per functional unit includes electricity for dismantling and crushing, auxiliary chemicals such as hydrochloric acid, hydrogen peroxide, sodium hydroxide, and calcium chloride, and the final recovered products. Non-recoverable electrolyte and solid residues are treated by incineration and landfill, respectively.
3.3 Carbon Footprint Results
I combined the carbon accounting models with the inventory data and the emission factor database. Table 3 reports the carbon footprint of the EV battery pack per functional unit. The raw material stage has the largest contribution, followed by the use stage and the manufacturing stage. The end-of-life stage produces a negative carbon footprint because recovered aluminum, copper, steel, plastics, lithium, and graphite provide credits that offset some of the recycling emissions.
| Lifecycle stage | Carbon footprint (kgCO2e/kWh delivered) |
|---|---|
| Raw material preparation | 1.73E-01 |
| Production and manufacturing | 1.44E-01 |
| Use phase | 1.55E-01 |
| End-of-life recycling | -6.68E-03 |
| Whole lifecycle | 4.65E-01 |
The result of 0.465 kgCO2e per kWh delivered implies that an EV battery pack producing 19,500 kWh over its life would be responsible for approximately 9,070 kgCO2e when using the baseline assumptions. More importantly, the result shows that the carbon footprint of an EV battery pack cannot be reduced by focusing on only one stage. Even though the end-of-life carbon credit is relatively small in my allocation scenario, the raw material stage and the use stage dominate the total, and both are highly sensitive to dynamic factors such as material sourcing and electricity carbon intensity.
3.4 Data Quality and Uncertainty
To assess the reliability of the EV battery pack carbon footprint results, I performed a data quality assessment and Monte Carlo uncertainty analysis. I evaluated the inventory data according to time representativeness, technology representativeness, and geographical representativeness. Each flow was assigned a score from 1 to 5, and these scores were converted into a composite data quality indicator DQI. The quality indicator was then used to define beta probability distributions and to propagate data uncertainty into the final estimates. Table 4 lists the DQI and distribution parameters for the activity data and emission factors in each lifecycle stage.
| Lifecycle stage | Data type | DQI | Beta distribution parameters |
|---|---|---|---|
| Raw material preparation | Activity data | 4.5 | ±15%; α=4, β=4 |
| Raw material preparation | Emission factors | 4.0 | ±20%; α=3, β=3 |
| Production and manufacturing | Activity data | 4.5 | ±15%; α=4, β=4 |
| Production and manufacturing | Emission factors | 4.5 | ±15%; α=4, β=4 |
| Use phase | Activity data | 4.5 | ±15%; α=4, β=4 |
| Use phase | Emission factors | 4.5 | ±15%; α=4, β=4 |
| End-of-life recycling | Activity data | 4.5 | ±15%; α=4, β=4 |
| End-of-life recycling | Emission factors | 4.0 | ±20%; α=3, β=3 |
Using the DQI-based beta distributions, I sampled the input data one thousand times in a Monte Carlo simulation. The resulting relative standard deviations are 14.85% for raw materials, 12.92% for manufacturing, 12.75% for the use phase, and 14.24% for recycling. Since all relative standard deviations are below 15%, I concluded that the inventory data supporting the EV battery pack assessment are of good quality. The raw material and recycling stages reveal higher uncertainty than the manufacturing and use stages, which is consistent with the diversity of supplier processes and end-of-life technology routes.
4. Identification and Analysis of Key Dynamic Influencing Factors
4.1 Sensitivity Analysis Approach
The carbon footprint model for an EV battery pack contains many influencing variables. I used single-factor sensitivity analysis to identify which variables have the largest influence on the final results. The sensitivity coefficient is defined as:
\[
S_i = \frac{(A_i – A_0)/A_0}{(F_i – F_0)/F_0}
\]
where \(F_0\) is the baseline value of the influencing factor, \(F_i\) is the changed value, \(A_0\) is the baseline calculated carbon footprint of the EV battery pack, and \(A_i\) is the recalculated carbon footprint after changing the factor. I multiplied these sensitivity coefficients by the carbon footprint weight of the corresponding lifecycle stage in order to identify factors of practical importance to the whole lifecycle of the EV battery pack.
4.2 Key Factors in Each Stage
In the raw material stage, aluminum-containing materials were found to have the largest weighted impact on the carbon footprint of the EV battery pack. This occurs because aluminum is used in both the current collector foil and the pack casing. Changes to the aluminum production route, the aluminum recycling rate, or the aluminum content directly alter the raw material stage emissions.
In the manufacturing stage, purchased electricity is the dominant emission source. Since most production equipment, drying rooms, coating lines, and assembly machines are powered by electricity, the carbon footprint of manufacturing an EV battery pack is closely tied to the local electricity grid. Natural gas and diesel emissions are relatively insignificant in my inventory, although direct process emissions from solvent evaporation may also require attention in plants without effective solvent recovery.
In the use stage, three factors play important roles: charging-discharging efficiency, electricity carbon factor, and mass-energy apportionment. Among these, charging-discharging efficiency has the highest sensitivity because it affects every energy flow passing through the EV battery pack. Mass-energy apportionment also matters because increasing the pack mass increases the rolling resistance, inertial loads, and general energy consumption of the vehicle. The electricity carbon factor is important in absolute terms because it determines how much carbon is associated with each kWh consumed.
These findings guide the rest of my dynamic analysis. I selected four factors for deeper investigation: the electricity carbon factor as a consequence of changing grid generation structure, the recycled aluminum proportion in material supply, the charging-discharging efficiency of the EV battery pack, and the mass-energy apportionment coefficient through lightweight design.
4.3 Electricity Carbon Factor and Grid Structure Evolution
The electricity carbon factor used in the lifecycle inventory of an EV battery pack is not a static quantity. It changes as the generating mix moves toward lower-carbon sources. I collected historical annual power generation data in China from 1993 to 2022, covering hydroelectricity, thermal power, nuclear power, wind power, and solar power. To predict future trends, I normalized the data using z-score normalization and fitted a polynomial regression model for each generation source. The model is:
\[
\hat{y}_i = \beta_0 + \beta_1 x + \beta_2 x^2 + \cdots + \beta_n x^n + \varepsilon
\]
I selected the polynomial degree by minimizing the mean squared error estimated with cross-validation. Table 5 reports the selected degree and the resulting mean squared error for each power source.
| Generation source | Selected polynomial degree | Mean squared error |
|---|---|---|
| Hydroelectricity | 13 | 0.010 |
| Thermal power | 3 | 0.008 |
| Nuclear power | 3 | 0.010 |
| Wind power | 3 | 0.014 |
| Solar power | 10 | 0.016 |
I used the fitted polynomial models to project the generation structure from 2025 to 2032, which corresponds approximately to the warranty life of the selected EV battery pack. The predicted shares show a gradual decline in thermal power generation and a continuous increase in wind and solar generation. The resulting dynamic electricity carbon factors are shown in Table 6 together with two alternative policy-driven scenarios.
| Year | Regression scenario EF (kgCO2e/kWh) | Policy scenario EF (kgCO2e/kWh) | Enhanced policy scenario EF (kgCO2e/kWh) |
|---|---|---|---|
| 2025 | 0.553 | 0.541 | 0.456 |
| 2026 | 0.540 | 0.522 | 0.437 |
| 2027 | 0.524 | 0.503 | 0.418 |
| 2028 | 0.508 | 0.485 | 0.399 |
| 2029 | 0.490 | 0.466 | 0.380 |
| 2030 | 0.470 | 0.447 | 0.361 |
| 2031 | 0.448 | 0.428 | 0.342 |
| 2032 | 0.426 | 0.409 | 0.323 |
I then defined four electricity scenarios for the lifecycle carbon footprint assessment of the EV battery pack. In the baseline scenario, the 2022 static grid emission factor is applied to all stages. In the regression scenario, the EV battery pack is assumed to be manufactured in 2025 and operated until 2032, with the year-specific emission factors from the polynomial regression model. In the policy scenario, the power structure is adjusted to align with Chinese non-fossil energy policy targets. In the enhanced policy scenario, more aggressive renewable energy deployment is assumed. Table 7 shows the cumulative electricity-derived carbon footprint of the EV battery pack under these four scenarios.
| Scenario | Electricity-related carbon footprint (kgCO2e/kWh delivered) | Change vs. baseline |
|---|---|---|
| Static baseline | 0.304 | — |
| Polynomial regression prediction | 0.270 | -11.80% |
| Policy scenario | 0.261 | -14.14% |
| Enhanced policy scenario | 0.217 | -28.62% |
The dynamic assessment shows a meaningful reduction in the carbon footprint of the EV battery pack simply because the future grid is expected to become cleaner. This reduction is invisible in a conventional static LCA. The same result also demonstrates that policy decisions affecting grid decarbonization can make an EV battery pack more or less climate-friendly over its lifetime.
4.4 Effect of Recycled Aluminum Use
Aluminum production is associated with high emissions, primarily due to the electricity demand of electrolysis. Replacing primary aluminum with recycled aluminum could reduce the carbon footprint of the raw material stage of an EV battery pack. In my model, increasing the recycled aluminum fraction impacts the supply chain because the emission factor of alumina refining and electrolysis is replaced by the lower emission factor associated with remelting aluminum scrap.
I quantified the effect by varying the recycled aluminum share from 0% to 100%. The analysis shows that a 10% recycled aluminum fraction reduces the raw material stage carbon footprint by 2.09%. At a 100% recycled aluminum fraction, the raw material stage carbon footprint decreases by 20.91% compared with using only virgin aluminum. This result is significant because the raw material stage is the largest contributor to the lifecycle carbon footprint of the selected EV battery pack. Therefore, source circularity should be regarded as a central strategy for battery aluminum supply.
| Recycled aluminum share | Reduction in raw material stage carbon footprint |
|---|---|
| 0% | 0% |
| 10% | 2.09% |
| 50% | 10.46% |
| 100% | 20.91% |
4.5 Charging-Discharging Efficiency and Mass-Energy Apportionment
The use stage dominates the lifecycle carbon footprint of an EV battery pack because the pack is charged hundreds of times during its service life. Charging-discharging efficiency quantifies how much energy is lost during the conversion from alternating current grid power to chemical energy and later back to useful mechanical energy. In addition, the mass of the EV battery pack affects energy consumption because larger masses require additional traction force. I defined six scenarios to explore the combined influence of charging-discharging efficiency and the mass-energy apportionment coefficient. The results are shown in Table 8.
| Scenario | Charging-discharging efficiency | Mass-energy apportionment coefficient | Use-stage carbon footprint (kgCO2e/kWh delivered) |
|---|---|---|---|
| Baseline | 90% | 0.56 | 0.155 |
| Scenario 1 | 90% | 0.30 | 0.110 |
| Scenario 2 | 95% | 0.56 | 0.121 |
| Scenario 3 | 95% | 0.30 | 0.078 |
| Scenario 4 | 98% | 0.56 | 0.100 |
| Scenario 5 | 98% | 0.30 | 0.059 |
Compared with the baseline, Scenario 2 achieves a reduction of approximately 21.9% in use-stage carbon emissions by improving charging-discharging efficiency from 90% to 95% while keeping the mass-energy apportionment unchanged. Scenario 1, which keeps efficiency constant but reduces the mass-energy apportionment from 0.56 to 0.30, gives a larger reduction because the mass-related component is substantial. The largest reduction occurs in Scenario 5, where the use-stage carbon footprint falls by about 61.9% compared with the baseline. These findings highlight two distinct but complementary pathways for reducing the use stage carbon footprint of an EV battery pack: one is electrochemical and thermal engineering of the pack, and the other is vehicle-level lightweighting and mass optimization.
4.6 Carbon Reduction Recommendations
Based on the dynamic scenario analysis, I propose a set of carbon reduction measures for the EV battery pack life cycle. First, the electricity carbon factor should be reduced by increasing renewable energy penetration in national and regional grids. Since electricity appears in raw material processing, manufacturing, use, and recycling, the benefits of grid decarbonization propagate throughout the entire EV battery pack life cycle. Second, the recycled aluminum content in battery foils and structural casings should be increased. Aluminum recycling avoids the high emissions associated with primary aluminum electrolysis without compromising material quality if appropriate alloy sorting is applied. Third, the charging-discharging efficiency of the EV battery pack should be improved through electrolyte formulation, electrode architecture, and efficient thermal management. Fourth, the mass-energy apportionment coefficient should be reduced by designing lighter battery packs, integrating cells into the vehicle body, and improving vehicle energy efficiency. These measures are not mutually exclusive; when implemented together they have the potential to reduce the total carbon footprint of the EV battery pack substantially.
5. Development of a Carbon Footprint Evaluation System for EV Battery Packs
5.1 Requirements and Feasibility
To make my carbon footprint model useful outside an academic setting, I developed a software system called PB-CF Tool. The tool enables users to construct a lifecycle model of an EV battery pack, enter inventory data for each stage, perform carbon footprint calculations, visualize the stage contributions, and compare dynamic scenarios. I identified both functional and non-functional requirements during the design phase. Functional requirements include user registration and login, creation of a new project, definition of the goal and scope, input of lifecycle inventories, automatic carbon footprint calculation, visualization of results, and scenario analysis. Non-functional requirements include ease of use, reliable behavior, efficient response, and maintainability. I also analyzed technical feasibility, economic feasibility, and operational feasibility. The chosen implementation stack uses JavaScript with the Vue framework for the front end, Node.js for the back end, and MongoDB for the database, which can be deployed on an ordinary laptop without expensive commercial LCA software.
5.2 Architecture and Workflow
The system follows a three-tier architecture comprising the application layer, the logic layer, and the data layer. The application layer provides the user interface for result display and scenario analysis. The logic layer receives user requests and performs carbon accounting operations based on the mathematical models described in Section 2. The data layer stores users, models, lifecycle inventories, emission factor records, and scenario parameters. The workflow allows a user to register, log in, create a new project or open an existing model, define the goal and scope, enter inventory data for raw material preparation, manufacturing, use, and recycling, and then execute the calculation. After the first calculation, the user can return to any earlier stage to improve the data or broaden the system boundary. The dynamic analysis module allows the user to change factors such as the electricity carbon factor, recycled aluminum share, charging-discharging efficiency, and mass-energy apportionment coefficient in order to assess their effects on the carbon footprint of the EV battery pack.
5.3 Database Design
The database schema includes tables for user accounts, model definitions, lifecycle inventories, emission factors, and dynamic analysis settings. Table 9 summarizes the main data tables and their functions in the system.
| Data table | Primary content |
|---|---|
| User information | User ID, account name, password |
| Model definition | Project name, research goal, functional unit, base flow, system boundary |
| Raw material inventory | Material names, quantities, material types, input/output types |
| Manufacturing inventory | Process materials, electricity and fuel consumption, direct emissions |
| Use stage inventory | Driving distance, vehicle efficiency, pack mass, charging efficiency |
| Recycling inventory | Auxiliary materials, energy consumption, recovered products, disposal flows |
| Emission factors | Material, fuel, electricity, and waste treatment emission factor records |
| Dynamic analysis electricity | Scenario type, year, electricity activity, electricity carbon factor |
| Dynamic analysis aluminum | Recycled aluminum share, primary aluminum share, effective carbon factor |
| Dynamic analysis efficiency and mass | Charging-discharging efficiency, mass-energy apportionment coefficient |
The use of dynamic data tables is important because the carbon footprint of an EV battery pack is not a single fixed number. By storing time-dependent emission factors, scenario assumptions, and technology parameters, the database supports temporal updates and more realistic calculations.
5.4 Module Implementation
The user interface consists of three main modules. The login module verifies the identity of a user and provides registration and password-protected access. The front-end operation module includes project creation, inventory data entry, carbon footprint calculation, and visualization of results. The background management module allows an administrator to update the emission factor database, add new materials, and maintain scenario parameters.
In the project creation screen, I ask the user to provide the project title, research objective, functional unit, base flow, and system boundary. In the inventory entry screens, the user enters data separately for the raw material, production, use, and recycling stages. After data submission, the system calculates the carbon footprint of the EV battery pack and displays the results as a bar chart, allowing the user to quickly see the relative contribution of each lifecycle stage. The dynamic analysis screen permits the user to choose one of the identified influencing factors and to compare baseline and modified scenarios. For example, the user can enter electricity emission factors year by year and compare the life cycle carbon footprint of the EV battery pack under a static grid and a cleaner future grid. The system then reports both the absolute emissions and the percentage reduction, which is useful for strategic planning by battery manufacturers, vehicle original equipment manufacturers, and policy analysts.
6. Conclusions and Perspectives
In this work, I developed a dynamic lifecycle carbon footprint evaluation model for an EV battery pack and applied it to a commercial lithium iron phosphate battery system. The carbon footprint of the EV battery pack per functional unit was calculated as 0.465 kgCO2e per kWh delivered, with the raw material stage contributing approximately 0.173 kgCO2e, the manufacturing stage about 0.144 kgCO2e, the use stage about 0.155 kgCO2e, and the recycling stage providing a negative contribution of about -0.00668 kgCO2e. The data quality assessment confirmed that the inventories were suitable for supporting decision-making, although greater attention is needed for raw material and recycling data.
The sensitivity analysis and scenario analysis showed that the carbon footprint of an EV battery pack depends strongly on dynamic factors. Grid decarbonization, with its effect on the electricity carbon factor, can reduce electricity-related emissions by 11.8% under a regression-based prediction, by 14.14% under a policy scenario, and by 28.62% under an enhanced policy scenario. Increasing the recycled aluminum share also reduces the raw material carbon footprint significantly, with a possible 20.91% reduction when recycled aluminum is used exclusively. Improvements in charging-discharging efficiency and reductions in the mass-energy apportionment coefficient can reduce the use-stage carbon footprint of the EV battery pack by up to roughly 62% in the combined scenario. These findings confirm that a single static carbon footprint value for an EV battery pack is insufficient for guiding long-term decarbonization strategy.
The software platform I developed makes the dynamic method operational. It provides a transparent workflow for data collection, calculation, visualization, and scenario comparison. It does not rely on expensive proprietary LCA software, and its modular structure makes it convenient to update both data and models. I hope that the system will help battery engineers, vehicle designers, supply chain managers, and environmental scientists to better understand the carbon hotspot of the EV battery pack and to identify robust pathways toward cleaner battery technology.
Future research can extend the present model by including logistics and use-phase maintenance, which I excluded from the boundary. More detailed experimental measurements of charging-discharging efficiency as a function of battery age would improve the accuracy of the use-stage assessment. In addition, the polynomial regression model for the electricity grid can be updated as new energy policies are announced. Finally, the uncertainty assessment can be refined by replacing generic data quality scores with probability distributions obtained directly from suppliers. The goal of my ongoing work is to continue refining the model and software so that the carbon footprint of every EV battery pack can be assessed in a scientifically robust, increasingly dynamic, and practically useful way.