Life cycle assessment of battery cell production in the context of the EU Batteries Regulation: The influence of data aggregation and multifunctionality handling

Life cycle assessment of battery cell production in the context of the EU Batteries Regulation: The influence of data aggregation and multifunctionality handling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Life cycle assessment of battery cell production in the context of the EU Batteries Regulation: The influence of data aggregation and multifunctionality handling Jana Husmann, Johanna Holsten, Gabriela Ventura Silva, Christoph Herrmann This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8285103/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract By enabling carbon footprint reporting and supporting engineering of batteries and their production, life cycle assessment (LCA) is essential for a sustainable energy and mobility transition. This article analyses current gaps and limitations in the carbon footprint calculations as part of the EU Batteries regulation, focusing on data aggregation and multifunctionality handling in the battery cell production. Different approaches to data aggregation and multifunctionality handling are identified. Their implications are identified and discussed based on the application in a case study. Different approaches to handling multifunctionality cause about 6% difference in climate change impacts per cell in the case study. Applying cut-off is recommended as most the transparent and consistent approach. Temporal data aggregation affects the climate change impacts per cell by ± 10%, with higher aggregations being more suitable for reporting and lower aggregations being essential for engineering purposes. Spatial data aggregation does not affect the overall climate change impacts but influences hotspot identification. The study underlines the need for further clarification in the standardised LCA regarding multifunctionality handling and data collection, Further, this article advocates for an extension of the method tailored to different LCA purposes, such as reporting and engineering. allocation battery production data aggregation environmental product declaration life cycle assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The European Union (EU) aims to design a sustainable energy and mobility transition, of which batteries are a key technology. Life cycle assessment (LCA) is a key tool to support these ambitions, on the one hand, to perform mandatory carbon footprint calculations, and on the other hand, to support the design and production of batteries that meet regulatory and company-set targets. In the EU, reporting requirements and regulatory targets stem from the EU Batteries Regulation 2023/1542. From 2025, the carbon footprint of electric vehicle and rechargeable industrial batteries needs to be calculated and declared [ 1 ]. To ensure consistency between manufacturers and batteries, the carbon footprint calculation must follow a standardised method. The carbon footprint calculation standard is still being developed. According to Annex II of the EU Batteries Regulation, the method must align with the Product Environmental Footprint Category Rules (PEFCR) for high-specific rechargeable batteries and reflect scientific and technical progress in LCA, as well as international agreements. The regulation also authorises the European Commission to adopt a Delegated Act to define the official methodology, provided it follows the essential elements in Annex II, which include, for example, the functional unit, the system boundary and the impact assessment method. [ 1 ]. This Delegated Act is currently being developed [ 2 ]. Extensive reviews of current standards and industry practices were performed, including identifying gaps and needs for a future method [ 3 ]–[ 5 ]. Furthermore, various stakeholder groups have developed recommendations for the LCA standard for batteries and electric vehicles [ 3 ], [ 6 ]. This previous research takes a holistic approach to the battery life cycle and focuses on the reporting scope. Therefore, special circumstances of the battery cell production as a single life cycle stage and linked to engineering purposes are not explored in detail. These include data acquisition and aggregation: battery manufacturers typically only have primary data for the battery production and, hence, can decide the spatial and temporal aggregation of their data and their modelling, which they normally cannot influence for up- or downstream processes. Furthermore, handling the multifunctionality in the battery cell production states a new challenge. Multifunctionality means that a process provides more than one function by delivering more than one product output or providing more than one service [ 7 ]. In battery cell production, multifunctionality stems from an increasing interest in recycling production scrap, which could therefore be seen as a co-product [ 8 ], [ 9 ]. This article aims to analyse current gaps and limitations and their practical implications in the carbon footprint calculations as part of the EU Batteries Regulation, focusing on data aggregation and approaches to handle the multifunctionality in a simulation-based case study on a 100 MWh battery cell production in Germany. For that, we introduce in Section 2 the foundations regarding i) the process of battery cell production and the developed simulation model, ii) requirements for data acquisition and aggregation and iii) approaches to solving multifunctionality in current guidelines. Section 3 presents the results for different multifunctionality handling and data aggregation scenarios. Finally, in Section 4, we discuss the implications of the case study results and the derived requirements for the further development of the calculation standard. 2. Materials and Methods 2.1 Battery cell production and simulation The lithium-ion battery (LIB) is currently the main energy storage technology due to its high energy density and long cycle life, being used for traction (e.g., electric vehicles) and stationary applications [ 10 ]. Its cell production comprises anode and cathode production, cell assembly, and cell formation combined in a complex process chain [ 11 ]. The anode and cathode production starts with the mixing of dry powders (e.g., active material, carbon black, binders) and solvent, followed by the coating of the mixture onto a substrate foil. Next, the coated foil is dried and calendered. The electrode production ends with the cutting of the electrode to the desired dimensions. Anodes and cathodes are then assembled into a battery cell, in a process chain that includes packaging, final drying, contacting, electrolyte filling, and tempering. As the materials used in battery cell production are sensitive to humidity, the cell assembly processes take place in a conditioned environment (e.g., dry room) associated with high energy demand [ 11 ], [ 12 ]. Finally, the battery cell production ends with the formation process that includes the charging and discharging of the cells to ensure their electrochemical properties [ 13 ]. The mentioned processes may vary according to the battery chemistry (e.g., NMC, LFP), cell format (e.g., pouch, cylindrical) or used production technology (e.g., traditional or dry coating). Consequently, the battery cell properties, as well as material and energy demand, may also vary between different battery cell producers or production sites [ 14 ]. The complexity of the process chain, summed to the different production possibilities and their influence on the product quality, leads to high scrap rates and challenging upscaling of production [ 15 ]. Recent works describe the influence of production scale and time since the start of production (SoP) on the scrap rates. Further results focused on large-scale production present 28.8% and 8.6% scrap rates, considering values after one year and five years after SoP, respectively [ 16 ]. Primary data for large scale battery productions are often not accessible. Simulation presents an alternative for dealing with the lack of detailed primary data, especially for comparing different scenarios. Different approaches in the literature have been developed for investigating the energy demand at process [ 17 ], [ 18 ] and production level [ 14 ], [ 19 ]. However, these works did not consider the dynamic aspects related to production, such as dependencies between processes, non-productive times, and variations of energy demand and changes in throughput over time. The data investigated in this work originates from a dynamic process chain simulation combining agent-based and discrete event approaches [ 20 ], [ 21 ]. The simulation investigates the material and energy flows as well as the behaviour of products, machines, and processes over time. The process chain and process-specific scrap rates considered in the simulation are presented in Fig. 1 . The proposed modular simulation approach can be parametrised for different scenarios, including process-specific scrap rates and machine configurations. The number of machines at each process step is based on the expected annual production capacity and the throughput of each single machine, as presented by [ 14 ]. The product and machine parameterisation are based on data from the Battery LabFactory Braunschweig (BLB), partially adapted to represent larger production scales. In addition, the dry room simulation proposed by [ 18 ] is integrated into the process chain simulation to support the consideration of variations on the environmental conditions over time and their influence on energy demand. Further information on the modelled scenario and considered parameters is presented in the Supplementary Information (SI 1). 2.2 Data acquisition and aggregation The PEFCR and the draft of the Delegated Act provide different requirements regarding the acquisition and aggregation of data. Both methodologies define company-specific data to be collected. According to the PEFCR, inputs are material inputs that end up in the product as well as energy, auxiliaries and water. Outputs are products and by-products, waste, wastewater, recovered materials, direct process emissions, and combustion emissions. In addition to the mentioned inputs, the draft of the Delegated Act also demands tracking transport as inputs and is more generic in the outputs by demanding the collection of all outputs, including wastewater and any elementary flows [ 2 ], [ 22 ]. The draft of the Delegated Act gives further recommendations and requirements for data collection: the collected data shall be the average of one year or another period if the process has not yet been running for one year. Company-specific data can be collected for each (sub-) process or the entire production. Further rules are provided for (semi-) continuous processes: Measurements shall be done at the point of consumption or emission directly relative to the process. The demand of energy and auxiliaries shall preferably be based on an individual and detailed metering system that enables the attribution of energy and auxiliary consumption to production lines, products, and periods. When the demand of auxiliaries and energy is not directly relatable to the product, it shall be as specific as possible, for example, for electrode manufacturing, cell assembly, cell finishing, and air conditioning of clean or dry rooms. Remaining data gaps can be modelled based on secondary data. Both documents provide data quality requirements and hierarchies on how to choose secondary data [ 2 ], [ 22 ]. The draft of the Delegated Act also demands that suppliers share their entire life cycle inventory or a company-specific dataset with the battery manufacturer [ 2 ]. Both documents also deal with how to allocate energy and auxiliary inputs of production lines if a subdivision is not possible because only one monitoring or energy meter is installed. In that case, allocation can be performed if the production and the product are similar. For batteries with the same geometries, allocation shall be performed based on mass or other physical properties. Otherwise, the allocation shall be based on the installed capacity [ 2 ], [ 22 ]. 2.3 Life cycle assessment and treating multifunctionality LCA is a methodology commonly applied to determine the environmental impacts of products throughout their life cycle. It enables the consistent compilation of emissions generated and resources consumed throughout a product's life cycle [ 23 ]. In the case of multifunctionality in the product system or a process, the input and output flows need to be partitioned between the different functions or product systems [ 24 ]. The PEFCR and the draft of the Delegated Act provide guidance to handle multifunctionality. As a first step, the process shall be split into subprocesses that can be assigned clearly to one product flow. If that is impossible, allocation can be performed based on underlying physical properties such as mass or energy. The allocation factors shall be representative of the drivers of the corresponding input. If an allocation based on physical properties is not feasible, economic allocation is the last step of the hierarchical approach [ 2 ], [ 22 ]. The ISO 14044 and other existing standards to calculate the environmental or carbon footprint of batteries suggest an additional step between subdivision and allocation: system expansion, sometimes also interpreted as substitution [ 6 ], [ 24 ]. System expansion means including all additional functions into the product system and thus expanding the product system and the functional unit [ 24 ]. In the case of substitution, the additional functions are subtracted from the product system [ 25 ], [ 26 ]. The PEFCR also provides a special rule (circular footprint formula, short: CFF) on how to allocate the burdens and benefits from the disposal and recovery of the product assessed and how the use of secondary materials is included in the environmental footprint of the assessed product. The environmental burdens and benefits of each recycling process are allocated between the battery being recycled and the one using secondary material. How the burdens and benefits are allocated between the first and the second product life cycle depends on an allocation factor, which aims to reflect demand and supply on the market. Further major parameters are the recycled content, the recycling rate and the quality of ingoing and outgoing secondary material. Material-specific default values are provided in the guidelines [ 22 ]. The draft of the Delegated Act extends the CFF as presented in the PEFCR keeping the general concept, but providing a more material-specific version and a version for the recycling of production scrap (CFF PS) [ 2 ]. While the PEFCR and the draft of the Delegated Act suggest the CFF, other guidelines for batteries and the battery pass project favour using the cut-off approach [ 6 ], [ 27 ]. In this case, all impacts of the recycling process are allocated to the battery using the secondary material [ 28 ]. 2.4 Case study In this case study, it is investigated how data resolution and multifunctionality handling influence the environmental impacts of producing a single battery cell. An attributional approach is used, as it is the common standard for reporting and in industry [ 3 ]. The product system boundaries are defined as described in Section 2.1 (see Fig. 1 ), with minor adjustments introduced according to the selected approaches for multifunctionality handling. The functional unit is the production of one battery cell with a nominal capacity of 33.3 Wh in Germany during 2023. Impact assessment is conducted using the EF v3.1 (no long-term effects) method, with “climate change” as the sole impact category. Foreground processes are modelled based on the agent-based simulation (see Section 2.1 ), enabling a full trace of the specific conditions under which each cell is produced. Background datasets are predominantly sourced from the Ecoinvent database (v3.9.1 cutoff) [ 29 ], supplemented by GREET 2020 [ 30 ] datasets for LiPF6 electrolyte and PVDF binder and by literature‐derived, remodelled inventories for graphite and cathode active materials [ 31 ], [ 32 ]. Electricity supply datasets are obtained from Electricity Maps [ 33 ]and aggregated to match the selected temporal aggregations. Multifunctionality handling and data aggregation are varied to analyse potential modelling variations. Unlike a comparative LCA, our analysis focuses exclusively on how methodological choices affect the impact results for the same product. All methodological variations in the assessment are implemented in a custom Python tool developed by the authors, which allows flexible aggregation of spatial and temporal data and alternative multifunctionality treatments (see SI.1 for implementation details). 3. Results The following section deals specifically with data aggregation and multifunctionality handling for battery cell production by analysing the modelling implications for data aggregation and multifunctionality handling. The derived modelling options are applied in the described case study to quantify the influence of the modelling options on the climate change impacts per battery cell. Section 3.1 focuses on identifying and applying modelling options regarding the handling of multifunctionality and section 3.2 regarding data aggregation. 3.1 Multifunctionality handling of battery production scrap To assess the influence of different ways to handle the multifunctionality on the carbon footprint calculation, the multifunctionalities in battery cell production need to be identified. Based on Guinée et al., multifunctionality consists of either several functional flows (= flows that constitute (part of) the goal of a unit process) or additional economic flows (= flows with either a positive or zero/negative (waste) economic value) [ 34 ]. For a long time, battery cell production has been seen as a process with one functional flow (the battery cell). With upcoming regulations on mandatory recycled content and increased industry ambitions for a circular economy, some of the former waste flows obtain an economic value: Non-functional cells identified in the final testing may be recycled in the future as they are a short-term available input for recycling facilities and are also necessary to meet the target thresholds of the EU battery regulation [ 8 ], [ 35 ]. Market developments also show an interest in recycling electrodes, as recycling plants are entering the market focused solely on electrode recycling [ 9 ]. Electrodes also have a high potential for direct recycling since the anode and cathode are still separated and the electrolyte has not yet been added [ 36 ]–[ 38 ]. The explanations in the draft of the Delegated Act on recycling manufacturing waste also support this selection of relevant former waste flows to be recycled in future and as a consequence to be considered (to some extent) in the LCA modelling [ 2 ]. The fundamental question is how to consider these recyclable wastes in the LCA modelling as they are not explicitly addressed in most existing guidelines. Following ISO 14044, the typical multifunctionality hierarchy can be applied to solve all multifunctionalities and therefore also when modelling the recyclable waste electrodes and cells [ 24 ]. As described in section 2.3 , the PEFCR and the draft of the Delegated Act only foresee subdivision and allocation to solve the multifunctionality. Subdivision aims to solve the multifunctionality by increasing the modelling resolution. This is not applicable for the battery cell production as the modelling resolution cannot be increased beyond the process steps described in Section 2.1 . As described earlier, the ISO 14044 foresees system expansion or substitution as a step in between. However, system expansion and substitution are not applicable in this case as the former would change the functional unit, and the latter would need a substituted process, which cannot be identified for the production scrap. For allocation, physical properties as allocation factors are the first choice. This is typically mass, which is also applicable in the case of cell production. If mass is not deemed a relevant driver of the processes, it might be chosen to perform an economic allocation. The economic value is typically defined by the market price or production cost [ 39 ]. However, existing guidelines also provide more specific rules for how to handle the multifunctionality at the End-of-Life (EoL), as described in section 2.3 . As the waste electrodes and cells would be recycled in the same way as EoL batteries, the LCA modelling could also be transferable. Following the existing guidelines would mean either applying CFF or cut-off. The cut-off approach with the cut-off point after the production would mean that the waste flows leave the system without any impacts, and the recycled material would take the impacts of the recycling process. The impact of recycling would then be allocated to the cells which are produced with the secondary materials. The application of the CFF is more complex. The draft of the Delegated Act provides the CFF PS specifically for the recycling of production scrap. This version of the CFF only includes downstream recycling and not the secondary materials stemming from recycled waste electrodes and cells. This brings the CFF PS close to the avoided burden method, which is the application of the principle of substitution to recycling processes. The PEFCR does not explicitly address the recycling of production scraps and therefore also does not provide a specific version of the CFF. This might lead the LCA practitioners to applying the classic version of the CFF to the recycling of waste electrodes and cells as well. Therefore, we also apply the classic version in the following case study. In this classic version, the impacts of the cell would include, to some extent, the impacts of the recycled material and the impacts of recycling the flows with credits for the primary materials avoided. Figure 2 summarises the system boundary of the different modelling options to handle the multifunctionality. Figure 3 shows the range of climate change impacts of an average battery cell produced based on the energy and material flows for one month (here March 2023) with the German electricity mix, applying the different approaches to handle the multifunctionality in the battery cell production. The specific parameters used for the allocation are shown in SI 2. The climate change impacts vary by less than 1% between cut-off, CFF and CFF PS, with cut-off having the lowest impacts and the CFF PS having the highest. The contribution analysis reveals the methodological differences between cut-off, CFF and CFF PS: the CFF PS does not consider the recycled production scrap as recycled material input in new cells. Therefore, the cells do not have any recycled materials, and more primary material is needed. Comparing cut-off with the CFF, the difference in impacts stems from the impacts calculated for the recycled materials. The overall climate change impacts are about 6% higher when the multifunctionality is solved with allocation. The contribution analysis highlights the different view taken with this modelling approach: The cells do not carry the impacts of the waste cells and electrodes, which make up about 8% of the impacts for cut-off, CFF and CFF PS. However, the impacts for the supply chain of the recycled material are significantly higher with allocation. This reflects that the impacts of the produced scrap cells and electrodes are allocated to the recycled material instead of the remaining cells. 3.2 Influence of data aggregation on the LCA modelling To reflect the influence of data collection and data aggregation, different temporal and spatial aggregations are modelled. For temporal aggregation, averages of the production data can be calculated and a selection for monthly, daily and hourly aggregations is given for March. Next to the allocation of energy demand and non-recyclable waste flows to the respective battery cells based on the selected temporal aggregation, the carbon footprint of energy usage is calculated, linked with the respective energy mix of that timespan, based on monitored data for Germany in 2023 [ 33 ]. For spatial aggregation, three different ways to collect the data are assumed: i) machine- and productwise, ii) areawise (e.g., electrode production, cell production), iii) factorywise. Figure 4 shows the variation of the climate change impacts per battery cell, depending on the temporal aggregation fro March. Figure 5 provides more detailed insights into selected days and hours in comparison to the monthly average, including the contributions from the different process stages. The daily aggregation shows a variation of the climate change impacts compared to the monthly aggregation of about +/- 10%. The variation mainly stems from the processing energy, the dry room energy and the amount of scrap cells produced in the time period. The hourly aggregation shows that even from one hour to the next, the climate change impacts can vary by about 10%, again variations are driven by the processing energy, the dry room energy and the amount of scraps produced in the time period. The different spatial aggregation at which the data is collected does not influence the overall impact of the cell, but has an influence on the identification of hotspots within the contribution analysis (see Fig. 6 ). With data collection separately for all products and machines, hotspots in single process steps can be identified (e.g., the dry room energy and the dry mixing of the cathode), as shown in Fig. 6 a). The contribution analysis could even be further broken down into material and energy (processing and non-processing) impacts for each process step. With data collection for different areas of the factory (see Fig. 6 b)), the contribution analysis for the energy demand is limited to identifying hotspots in specific parts of the factory, such as the energy demand for the cell production. When the data is only collected for the whole factory, the impacts can only be attributed to the materials, the energy consumed, or the scraps produced (see Fig. 5 c)). With the area- and factorywise aggregation, material streams can still be assigned to specific process steps, as the material inputs are known as production and product parameters. 4. Discussion For the handling of multifunctionality , the calculated impacts per cell vary by less than 10% depending on this modelling perspective. In the case study, allocation was only applied based on mass. Allocation based on production costs would lead to the same results, as the functional and non-functional components (electrodes or cells) share the same production processes and therefore have the same production costs. Both correspond with the scrap rate for the studied case of a battery cell production. For allocation based on the economic value, the values for final electrodes and cells, as well as waste electrodes and cells, are needed, which are currently not transparently available. However, based on the PEFCR and the draft of the Delegated Act, economic allocation shall only be applied if the value of the final cells or electrodes is ten times higher than the value of the waste cells and electrodes, which is not the case for production cost and also rather unlikely for the market price. Applying allocation leads to challenges in the modelling. The waste electrodes and cells takes a share of the upstream impacts of material supply chains and battery production. If the recycled waste is then used in the battery production, it might become recycled again as part of the production scrap. The impacts of the recycled waste and the final electrodes and cells would therefore change every time, depending on how often the recycled material has already been recycled. For robust impact calculations, detailed tracking and tracing of materials would be needed, which is currently not feasible. An alternative modelling option is to perform statistical calculations of how the materials circulate, as shown in [ 8 ]. Production scrap from non-functional electrodes and cells is recycled and used in the same way as EoL batteries. The authors therefore recommend also handling the multifunctionality in the same way. This will also lead to more consistency in the modelling over the battery’s life cycle. The PEFCR and the draft of the Delegated Act recommend the application of the CFF. The implementation of the CFF is very complex, especially because the description of various parameters leaves room for interpretation, and it mixes the product and the material perspective. In general, the parameters lack clear documentation that would allow the use of company-specific data. Furthermore, the default processes of the draft of the Delegated Act are not aligned with the EU Batteries Regulations. The EU Batteries Regulation gives targets for recycled content and recycling rate of lithium, while the process chain described in the draft of the Delegated Act does not recover lithium. Overall, the complexity of the CFF will likely lead to inconsistent applications between different LCA practitioners. In addition, the CFF will cause high modelling efforts for complex products such as batteries. Consistency between production scrap and EoL batteries recycling can also be achieved by applying the cut-off approach. The cut-off approach is less complex and more straightforward to implement. Its implementation also needs less data and parameters. While the regulations developed by or on behalf of the EU Commission favour the CFF, guidelines from other stakeholders for batteries and electric vehicles, as well as for various other industries and products, favour the cut-off approach [ 3 ], [ 4 ], [ 41 ]. Overall, the authors recommend modelling production scrap from non-functional electrodes and cells in the same way as EoL batteries to achieve consistency along the product life cycle, preferably with the cut-off approach. However, this will depend on the final standards set in place for the EU Batteries Regulation. While this publication analyses the handling of the multifunctionality that arises from the increasing interest in recycling production scraps, production scraps should not solely be used to achieve the targets for recycled content from the EU Batteries Regulation. With the highly ambitious targets and the limited availability of EoL batteries in the next years, the EU Batteries Regulation might give the unintended incentive to rely on production scrap generation and recycling to comply with the target values. In the mid-to-long term, more EoL batteries will be available than production scrap [ 8 ], [ 35 ]. This shift from production scrap to EoL batteries recycling also underlines the need for a consistent method to handle the multifunctionality in both cases, as advocated for in this publication. The recommended temporal and spatial aggregation depend on the purpose of the LCA, hence what the LCA results shall be used for (see Fig. 7 ). The different temporal aggregations applied show that higher temporal aggregations (monthly, yearly) lead to less fluctuation in the impact per battery cell produced. Outliers are not visible, such as due to production interruptions. With lower temporal aggregations (daily or hourly), the results react more sensitively to the different energy sources, variations of process parameters, ramp up and idle times and are therefore essential for engineering and development purposes. The insights gained can, for example, be used for process development, production planning, factory planning, and strategic planning of the integration of own renewable energy sources. The data from higher temporal aggregations (monthly, yearly) is more representative of the long-term process conditions, as an adequate time period for the data collection is defined as “a time period which is long enough to account for normal variations in data values” [ 42 ]. This is typically one year or two to six months for emerging technologies [ 42 ]. Therefore, higher temporal aggregations, especially yearly averages, are more appropriate for reporting environmental impacts. As described in section 2.2 , the draft of the Delegated Act also demands data collection over one year. However, it is not clear whether this one year should be calendar year (e.g. data collected in 2025 is the basis for all reported footprints in 2026) or whether it is one year backwards from the point of production (e.g. data collected from 03/2025–02/2026 for production in 03/2026). Taking a calendar year as a basis, reduces the effort in data collection as the collection of yearly aggregated data would be sufficient. Further, it reduces the complexity in reporting and therefore increases consistency. Assessing one year backwards from the point of production would require less aggregated data collection – at least monthly or potentially even less aggregated. While this increases the effort in data collection, it incentivises companies to a more detailed data collection, which is also essential for engineering and development purposes. Additionally, the data collection one year backwards from production would also reflect faster on changes in production and supply chains and especially efforts made to lower environmental impacts, such as changing the supplier, changing product and process parameters or decarbonisation efforts of the electricity mix throughout the year. Regarding spatial aggregation , the aggregated data (e.g., factorywise collection) would be sufficient for reporting, as it is not mandatory to provide insights into the contributions of different process steps to the overall impact of the cell. More detailed data is essential for process development and further planning, as it allows for identifying the high-contributing processes and the effects of process parameters and production planning. For both spatial and temporal aggregation, lower aggregations increase the collection and calculation effort but also the accuracy and information depth. Hence, for process optimisation, lower spatial and temporal aggregations are recommended, while for reporting, higher temporal aggregation is required, and high spatial aggregation would be sufficient. The combination of high spatial and low temporal aggregation can be used to analyse the overall energy demand and to perform load shifting. Overall, several of the analysed methodological aspects of the LCA standard for the EU Batteries Regulation still need further refinement. With regard to the multifunctionality handling, the CFF in the PEFCR needs further instructions on how to apply it for production waste. In terms of the more detailed version in the draft of the Delegated Act, it should further be examined if the deviations of the CFF PS from the CFF are consistent from a product perspective. In general, the CFF would need further work regarding provided default parameters and processes before implementation on the market to match the requirements of the EU Batteries Regulation. In light of the high complexity, it should be evaluated whether it is an appropriate method for large-scale implementation. For the data collection of one year, more clarity is needed how this one-year time span is defined to ensure consistency in reporting and in the declaration in the battery pass. Further, the analysis conducted in this paper, highlighted the strong focus of the LCA methodologies under development on the reporting of carbon footprints. However, the EU Batteries Regulation targets also continuous improvement with the increasingly ambitious targets for the carbon footprint, recycling rates and recycled content. The developed LCA methodology could therefore be refined in a way to support companies in applying LCA for engineering and development purposes. For example, the methodology could set higher standards with regard to temporal and spatial data aggregation but not make the reporting on the detailed level to the public mandatory. In this way, companies are encouraged to establish more detailed data collection in their production facilities. Further, the standardised method could also include an approach for how to use LCA in an engineering process and how to include, for example, also prospective studies in the development. The recently developed TranSensus LCA standard shows first efforts for including these aspects in a standardised method [ 43 ], [ 44 ]. 5. Conclusions In this article, the LCA modelling of the battery cell production in the context of the EU Batteries Regulations was analysed with the focus of identifying gaps and limitations with regard to data aggregation and multifunctionality handling. A case study was performed to discuss the practical implications. The performed analysis highlighted several gaps: the multifunctionality handling of production scrap is not clearly addressed in most existing guidelines and further guidance is needed the CFF in its current form is partly inconsistent with the EU Batteries Regulations (e.g., regarding default processes and parameters) data collection periods need clearer definition for implementation Besides these gaps, also several limitations could be identified: the CFF in its current form is very complex which hinders consistent and comparable implementation and leads to high implementation efforts for complex products the developed LCA standard focuses solely on reporting while the EU Batteries Regulations targets also continuous improvements for which life cycle engineering is needed Based on these gaps and limitations, the authors recommend applying cut-off instead of the CFF in the finalised method and extending the standardised LCA method to incentivise and support industry in the implementation of life cycle engineering. Declarations Funding Sources This publication did not receive any funding. Author Contribution Ja.H.: Conceptualization, methodology, validation, investigation, writing - original draft, writing - review and editing, visualisation.Jo.H.: Conceptualization, methodology, software, validation, investigation, data curation, writing - review and editing, visualisation.G.V.S.: Conceptualization, methodology, software, validation, investigation, data curation, writing - review and editing, visualisation.C.H.: Writing - review and editing, supervision. Data Availability Data will be made available upon reasonable request. References European Parliament and European Council, “Regulation (EU) 2023 of the European Parliament and of the Council concerning batteries and waste batteries,” vol. 2023, no. June. 2023. 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Traverso, “TranSensus LCA - Deliverable 5.2: TranSensus LCA Consolidated Guidelines,” 2025. Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformationforsubmission.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers invited by journal 09 Jan, 2026 Editor assigned by journal 05 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 05 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":104159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBattery cell production process with mass-based Sankey diagram based on an annual production capacity of 100 MWh and a total scrap rate of 10%. The percentages indicate the scrap rate at a process level, which are multiplied throughout the process chain. Further information on the calculation can be found in \u003c/em\u003e[14]\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/a04a16436d134b51e3430830.jpg"},{"id":101204704,"identity":"0e078404-cd4b-4d3e-9739-76544e49242c","added_by":"auto","created_at":"2026-01-27 09:43:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSystem boundaries of the different modelling options to handle the multifunctionality. The CFF system boundary shows the classic interpretation of the CFF. The CFF PS based on the draft of the Delegated Act, would not include secondary material production.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/d636ac7a04d0c1b879774c0d.jpg"},{"id":101020108,"identity":"2617f354-b441-4e97-a838-c70a968e3a9e","added_by":"auto","created_at":"2026-01-24 00:42:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":107983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eClimate change impacts of one battery cell with different approaches to handle the multifunctionality\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/7c0621bb5033bb4f3eab3376.jpg"},{"id":101020110,"identity":"fa96c802-c6ee-40fe-9108-3e79d6bc5e46","added_by":"auto","created_at":"2026-01-24 00:42:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVariation of climate change impacts depending on the temporal aggregation \u003c/em\u003e[40]\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/3e9ab9072071cd77e9f4f016.jpg"},{"id":101204483,"identity":"c242cb1b-9363-47d4-916e-11dc10e70380","added_by":"auto","created_at":"2026-01-27 09:43:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVariation of climate change impacts depending on the temporal aggregation for selected days and hours in comparison to the monthly average, including the contribution analysis\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/2f92c821655233f1a04bd697.jpg"},{"id":101020120,"identity":"3e9e59c1-1638-4fe3-ae22-96f4700f3479","added_by":"auto","created_at":"2026-01-24 00:42:14","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInfluence of different spatial aggregation on the contribution analysis of the climate change impacts\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/00113da1a9d1c93133609502.jpg"},{"id":101020116,"identity":"e42d5a45-5718-41f4-9916-727fd600660f","added_by":"auto","created_at":"2026-01-24 00:42:14","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":80017,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eImplications of different temporal and spatial resolution of modelling data and recommendations for different LCA purposes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/02b24001948ee3246c35d79a.jpg"},{"id":101296687,"identity":"8a77115f-4f0b-4809-9d8d-76972a1e73ec","added_by":"auto","created_at":"2026-01-28 09:18:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/92cbf12e-0ff1-4209-b1f0-04463e229da4.pdf"},{"id":101020106,"identity":"9cbbfa9b-df93-449d-9016-1b8fcbc866a1","added_by":"auto","created_at":"2026-01-24 00:42:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35161,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformationforsubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-8285103/v1/f1bb70e16d2d90629e41f73a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Life cycle assessment of battery cell production in the context of the EU Batteries Regulation: The influence of data aggregation and multifunctionality handling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe European Union (EU) aims to design a sustainable energy and mobility transition, of which batteries are a key technology. Life cycle assessment (LCA) is a key tool to support these ambitions, on the one hand, to perform mandatory carbon footprint calculations, and on the other hand, to support the design and production of batteries that meet regulatory and company-set targets. In the EU, reporting requirements and regulatory targets stem from the EU Batteries Regulation 2023/1542. From 2025, the carbon footprint of electric vehicle and rechargeable industrial batteries needs to be calculated and declared [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo ensure consistency between manufacturers and batteries, the carbon footprint calculation must follow a standardised method. The carbon footprint calculation standard is still being developed. According to Annex II of the EU Batteries Regulation, the method must align with the Product Environmental Footprint Category Rules (PEFCR) for high-specific rechargeable batteries and reflect scientific and technical progress in LCA, as well as international agreements. The regulation also authorises the European Commission to adopt a Delegated Act to define the official methodology, provided it follows the essential elements in Annex II, which include, for example, the functional unit, the system boundary and the impact assessment method. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This Delegated Act is currently being developed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive reviews of current standards and industry practices were performed, including identifying gaps and needs for a future method [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, various stakeholder groups have developed recommendations for the LCA standard for batteries and electric vehicles [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This previous research takes a holistic approach to the battery life cycle and focuses on the reporting scope. Therefore, special circumstances of the battery cell production as a single life cycle stage and linked to engineering purposes are not explored in detail. These include data acquisition and aggregation: battery manufacturers typically only have primary data for the battery production and, hence, can decide the spatial and temporal aggregation of their data and their modelling, which they normally cannot influence for up- or downstream processes. Furthermore, handling the multifunctionality in the battery cell production states a new challenge. Multifunctionality means that a process provides more than one function by delivering more than one product output or providing more than one service [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In battery cell production, multifunctionality stems from an increasing interest in recycling production scrap, which could therefore be seen as a co-product [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis article aims to analyse current gaps and limitations and their practical implications in the carbon footprint calculations as part of the EU Batteries Regulation, focusing on data aggregation and approaches to handle the multifunctionality in a simulation-based case study on a 100 MWh battery cell production in Germany. For that, we introduce in Section 2 the foundations regarding i) the process of battery cell production and the developed simulation model, ii) requirements for data acquisition and aggregation and iii) approaches to solving multifunctionality in current guidelines. Section 3 presents the results for different multifunctionality handling and data aggregation scenarios. Finally, in Section 4, we discuss the implications of the case study results and the derived requirements for the further development of the calculation standard.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Battery cell production and simulation\u003c/h2\u003e \u003cp\u003eThe lithium-ion battery (LIB) is currently the main energy storage technology due to its high energy density and long cycle life, being used for traction (e.g., electric vehicles) and stationary applications [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Its cell production comprises anode and cathode production, cell assembly, and cell formation combined in a complex process chain [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The anode and cathode production starts with the mixing of dry powders (e.g., active material, carbon black, binders) and solvent, followed by the coating of the mixture onto a substrate foil. Next, the coated foil is dried and calendered. The electrode production ends with the cutting of the electrode to the desired dimensions. Anodes and cathodes are then assembled into a battery cell, in a process chain that includes packaging, final drying, contacting, electrolyte filling, and tempering. As the materials used in battery cell production are sensitive to humidity, the cell assembly processes take place in a conditioned environment (e.g., dry room) associated with high energy demand [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Finally, the battery cell production ends with the formation process that includes the charging and discharging of the cells to ensure their electrochemical properties [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mentioned processes may vary according to the battery chemistry (e.g., NMC, LFP), cell format (e.g., pouch, cylindrical) or used production technology (e.g., traditional or dry coating). Consequently, the battery cell properties, as well as material and energy demand, may also vary between different battery cell producers or production sites [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The complexity of the process chain, summed to the different production possibilities and their influence on the product quality, leads to high scrap rates and challenging upscaling of production [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Recent works describe the influence of production scale and time since the start of production (SoP) on the scrap rates. Further results focused on large-scale production present 28.8% and 8.6% scrap rates, considering values after one year and five years after SoP, respectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrimary data for large scale battery productions are often not accessible. Simulation presents an alternative for dealing with the lack of detailed primary data, especially for comparing different scenarios. Different approaches in the literature have been developed for investigating the energy demand at process [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and production level [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, these works did not consider the dynamic aspects related to production, such as dependencies between processes, non-productive times, and variations of energy demand and changes in throughput over time.\u003c/p\u003e \u003cp\u003eThe data investigated in this work originates from a dynamic process chain simulation combining agent-based and discrete event approaches [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The simulation investigates the material and energy flows as well as the behaviour of products, machines, and processes over time. The process chain and process-specific scrap rates considered in the simulation are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe proposed modular simulation approach can be parametrised for different scenarios, including process-specific scrap rates and machine configurations. The number of machines at each process step is based on the expected annual production capacity and the throughput of each single machine, as presented by [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The product and machine parameterisation are based on data from the Battery LabFactory Braunschweig (BLB), partially adapted to represent larger production scales. In addition, the dry room simulation proposed by [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] is integrated into the process chain simulation to support the consideration of variations on the environmental conditions over time and their influence on energy demand. Further information on the modelled scenario and considered parameters is presented in the Supplementary Information (SI 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data acquisition and aggregation\u003c/h2\u003e \u003cp\u003eThe PEFCR and the draft of the Delegated Act provide different requirements regarding the acquisition and aggregation of data. Both methodologies define company-specific data to be collected. According to the PEFCR, inputs are material inputs that end up in the product as well as energy, auxiliaries and water. Outputs are products and by-products, waste, wastewater, recovered materials, direct process emissions, and combustion emissions. In addition to the mentioned inputs, the draft of the Delegated Act also demands tracking transport as inputs and is more generic in the outputs by demanding the collection of all outputs, including wastewater and any elementary flows [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The draft of the Delegated Act gives further recommendations and requirements for data collection: the collected data shall be the average of one year or another period if the process has not yet been running for one year. Company-specific data can be collected for each (sub-) process or the entire production. Further rules are provided for (semi-) continuous processes: Measurements shall be done at the point of consumption or emission directly relative to the process. The demand of energy and auxiliaries shall preferably be based on an individual and detailed metering system that enables the attribution of energy and auxiliary consumption to production lines, products, and periods. When the demand of auxiliaries and energy is not directly relatable to the product, it shall be as specific as possible, for example, for electrode manufacturing, cell assembly, cell finishing, and air conditioning of clean or dry rooms. Remaining data gaps can be modelled based on secondary data. Both documents provide data quality requirements and hierarchies on how to choose secondary data [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The draft of the Delegated Act also demands that suppliers share their entire life cycle inventory or a company-specific dataset with the battery manufacturer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBoth documents also deal with how to allocate energy and auxiliary inputs of production lines if a subdivision is not possible because only one monitoring or energy meter is installed. In that case, allocation can be performed if the production and the product are similar. For batteries with the same geometries, allocation shall be performed based on mass or other physical properties. Otherwise, the allocation shall be based on the installed capacity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Life cycle assessment and treating multifunctionality\u003c/h2\u003e \u003cp\u003eLCA is a methodology commonly applied to determine the environmental impacts of products throughout their life cycle. It enables the consistent compilation of emissions generated and resources consumed throughout a product's life cycle [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the case of multifunctionality in the product system or a process, the input and output flows need to be partitioned between the different functions or product systems [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PEFCR and the draft of the Delegated Act provide guidance to handle multifunctionality. As a first step, the process shall be split into subprocesses that can be assigned clearly to one product flow. If that is impossible, allocation can be performed based on underlying physical properties such as mass or energy. The allocation factors shall be representative of the drivers of the corresponding input. If an allocation based on physical properties is not feasible, economic allocation is the last step of the hierarchical approach [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The ISO 14044 and other existing standards to calculate the environmental or carbon footprint of batteries suggest an additional step between subdivision and allocation: system expansion, sometimes also interpreted as substitution [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. System expansion means including all additional functions into the product system and thus expanding the product system and the functional unit [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In the case of substitution, the additional functions are subtracted from the product system [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PEFCR also provides a special rule (circular footprint formula, short: CFF) on how to allocate the burdens and benefits from the disposal and recovery of the product assessed and how the use of secondary materials is included in the environmental footprint of the assessed product. The environmental burdens and benefits of each recycling process are allocated between the battery being recycled and the one using secondary material. How the burdens and benefits are allocated between the first and the second product life cycle depends on an allocation factor, which aims to reflect demand and supply on the market. Further major parameters are the recycled content, the recycling rate and the quality of ingoing and outgoing secondary material. Material-specific default values are provided in the guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The draft of the Delegated Act extends the CFF as presented in the PEFCR keeping the general concept, but providing a more material-specific version and a version for the recycling of production scrap (CFF PS) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the PEFCR and the draft of the Delegated Act suggest the CFF, other guidelines for batteries and the battery pass project favour using the cut-off approach [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In this case, all impacts of the recycling process are allocated to the battery using the secondary material [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Case study\u003c/h2\u003e \u003cp\u003eIn this case study, it is investigated how data resolution and multifunctionality handling influence the environmental impacts of producing a single battery cell. An attributional approach is used, as it is the common standard for reporting and in industry [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The product system boundaries are defined as described in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with minor adjustments introduced according to the selected approaches for multifunctionality handling. The functional unit is the production of one battery cell with a nominal capacity of 33.3 Wh in Germany during 2023. Impact assessment is conducted using the EF v3.1 (no long-term effects) method, with \u0026ldquo;climate change\u0026rdquo; as the sole impact category. Foreground processes are modelled based on the agent-based simulation (see Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e), enabling a full trace of the specific conditions under which each cell is produced. Background datasets are predominantly sourced from the Ecoinvent database (v3.9.1 cutoff) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], supplemented by GREET 2020 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] datasets for LiPF6 electrolyte and PVDF binder and by literature‐derived, remodelled inventories for graphite and cathode active materials [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Electricity supply datasets are obtained from Electricity Maps [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]and aggregated to match the selected temporal aggregations. Multifunctionality handling and data aggregation are varied to analyse potential modelling variations. Unlike a comparative LCA, our analysis focuses exclusively on how methodological choices affect the impact results for the same product. All methodological variations in the assessment are implemented in a custom Python tool developed by the authors, which allows flexible aggregation of spatial and temporal data and alternative multifunctionality treatments (see SI.1 for implementation details).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe following section deals specifically with data aggregation and multifunctionality handling for battery cell production by analysing the modelling implications for data aggregation and multifunctionality handling. The derived modelling options are applied in the described case study to quantify the influence of the modelling options on the climate change impacts per battery cell. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e focuses on identifying and applying modelling options regarding the handling of multifunctionality and section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e regarding data aggregation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Multifunctionality handling of battery production scrap\u003c/h2\u003e \u003cp\u003eTo assess the influence of different ways to handle the multifunctionality on the carbon footprint calculation, the multifunctionalities in battery cell production need to be identified. Based on Guin\u0026eacute;e et al., multifunctionality consists of either several functional flows (=\u0026thinsp;flows that constitute (part of) the goal of a unit process) or additional economic flows (=\u0026thinsp;flows with either a positive or zero/negative (waste) economic value) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor a long time, battery cell production has been seen as a process with one functional flow (the battery cell). With upcoming regulations on mandatory recycled content and increased industry ambitions for a circular economy, some of the former waste flows obtain an economic value:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eNon-functional cells identified in the final testing may be recycled in the future as they are a short-term available input for recycling facilities and are also necessary to meet the target thresholds of the EU battery regulation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMarket developments also show an interest in recycling electrodes, as recycling plants are entering the market focused solely on electrode recycling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Electrodes also have a high potential for direct recycling since the anode and cathode are still separated and the electrolyte has not yet been added [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe explanations in the draft of the Delegated Act on recycling manufacturing waste also support this selection of relevant former waste flows to be recycled in future and as a consequence to be considered (to some extent) in the LCA modelling [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe fundamental question is how to consider these recyclable wastes in the LCA modelling as they are not explicitly addressed in most existing guidelines. Following ISO 14044, the typical multifunctionality hierarchy can be applied to solve all multifunctionalities and therefore also when modelling the recyclable waste electrodes and cells [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As described in section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e, the PEFCR and the draft of the Delegated Act only foresee subdivision and allocation to solve the multifunctionality. Subdivision aims to solve the multifunctionality by increasing the modelling resolution. This is not applicable for the battery cell production as the modelling resolution cannot be increased beyond the process steps described in Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e. As described earlier, the ISO 14044 foresees system expansion or substitution as a step in between. However, system expansion and substitution are not applicable in this case as the former would change the functional unit, and the latter would need a substituted process, which cannot be identified for the production scrap. For allocation, physical properties as allocation factors are the first choice. This is typically mass, which is also applicable in the case of cell production. If mass is not deemed a relevant driver of the processes, it might be chosen to perform an economic allocation. The economic value is typically defined by the market price or production cost [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, existing guidelines also provide more specific rules for how to handle the multifunctionality at the End-of-Life (EoL), as described in section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e. As the waste electrodes and cells would be recycled in the same way as EoL batteries, the LCA modelling could also be transferable. Following the existing guidelines would mean either applying CFF or cut-off. The cut-off approach with the cut-off point after the production would mean that the waste flows leave the system without any impacts, and the recycled material would take the impacts of the recycling process. The impact of recycling would then be allocated to the cells which are produced with the secondary materials. The application of the CFF is more complex. The draft of the Delegated Act provides the CFF PS specifically for the recycling of production scrap. This version of the CFF only includes downstream recycling and not the secondary materials stemming from recycled waste electrodes and cells. This brings the CFF PS close to the avoided burden method, which is the application of the principle of substitution to recycling processes. The PEFCR does not explicitly address the recycling of production scraps and therefore also does not provide a specific version of the CFF. This might lead the LCA practitioners to applying the classic version of the CFF to the recycling of waste electrodes and cells as well. Therefore, we also apply the classic version in the following case study. In this classic version, the impacts of the cell would include, to some extent, the impacts of the recycled material and the impacts of recycling the flows with credits for the primary materials avoided. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the system boundary of the different modelling options to handle the multifunctionality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the range of climate change impacts of an average battery cell produced based on the energy and material flows for one month (here March 2023) with the German electricity mix, applying the different approaches to handle the multifunctionality in the battery cell production. The specific parameters used for the allocation are shown in SI 2. The climate change impacts vary by less than 1% between cut-off, CFF and CFF PS, with cut-off having the lowest impacts and the CFF PS having the highest. The contribution analysis reveals the methodological differences between cut-off, CFF and CFF PS: the CFF PS does not consider the recycled production scrap as recycled material input in new cells. Therefore, the cells do not have any recycled materials, and more primary material is needed. Comparing cut-off with the CFF, the difference in impacts stems from the impacts calculated for the recycled materials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall climate change impacts are about 6% higher when the multifunctionality is solved with allocation. The contribution analysis highlights the different view taken with this modelling approach: The cells do not carry the impacts of the waste cells and electrodes, which make up about 8% of the impacts for cut-off, CFF and CFF PS. However, the impacts for the supply chain of the recycled material are significantly higher with allocation. This reflects that the impacts of the produced scrap cells and electrodes are allocated to the recycled material instead of the remaining cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Influence of data aggregation on the LCA modelling\u003c/h2\u003e \u003cp\u003eTo reflect the influence of data collection and data aggregation, different temporal and spatial aggregations are modelled. For temporal aggregation, averages of the production data can be calculated and a selection for monthly, daily and hourly aggregations is given for March. Next to the allocation of energy demand and non-recyclable waste flows to the respective battery cells based on the selected temporal aggregation, the carbon footprint of energy usage is calculated, linked with the respective energy mix of that timespan, based on monitored data for Germany in 2023 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For spatial aggregation, three different ways to collect the data are assumed: i) machine- and productwise, ii) areawise (e.g., electrode production, cell production), iii) factorywise.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the variation of the climate change impacts per battery cell, depending on the \u003cem\u003etemporal aggregation\u003c/em\u003e fro March. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides more detailed insights into selected days and hours in comparison to the monthly average, including the contributions from the different process stages. The daily aggregation shows a variation of the climate change impacts compared to the monthly aggregation of about +/- 10%. The variation mainly stems from the processing energy, the dry room energy and the amount of scrap cells produced in the time period. The hourly aggregation shows that even from one hour to the next, the climate change impacts can vary by about 10%, again variations are driven by the processing energy, the dry room energy and the amount of scraps produced in the time period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe different spatial aggregation at which the data is collected does not influence the overall impact of the cell, but has an influence on the identification of hotspots within the contribution analysis (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). With data collection separately for all products and machines, hotspots in single process steps can be identified (e.g., the dry room energy and the dry mixing of the cathode), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The contribution analysis could even be further broken down into material and energy (processing and non-processing) impacts for each process step. With data collection for different areas of the factory (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb)), the contribution analysis for the energy demand is limited to identifying hotspots in specific parts of the factory, such as the energy demand for the cell production. When the data is only collected for the whole factory, the impacts can only be attributed to the materials, the energy consumed, or the scraps produced (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec)). With the area- and factorywise aggregation, material streams can still be assigned to specific process steps, as the material inputs are known as production and product parameters.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFor the \u003cem\u003ehandling of multifunctionality\u003c/em\u003e, the calculated impacts per cell vary by less than 10% depending on this modelling perspective. In the case study, allocation was only applied based on mass. Allocation based on production costs would lead to the same results, as the functional and non-functional components (electrodes or cells) share the same production processes and therefore have the same production costs. Both correspond with the scrap rate for the studied case of a battery cell production. For allocation based on the economic value, the values for final electrodes and cells, as well as waste electrodes and cells, are needed, which are currently not transparently available. However, based on the PEFCR and the draft of the Delegated Act, economic allocation shall only be applied if the value of the final cells or electrodes is ten times higher than the value of the waste cells and electrodes, which is not the case for production cost and also rather unlikely for the market price. Applying allocation leads to challenges in the modelling. The waste electrodes and cells takes a share of the upstream impacts of material supply chains and battery production. If the recycled waste is then used in the battery production, it might become recycled again as part of the production scrap. The impacts of the recycled waste and the final electrodes and cells would therefore change every time, depending on how often the recycled material has already been recycled. For robust impact calculations, detailed tracking and tracing of materials would be needed, which is currently not feasible. An alternative modelling option is to perform statistical calculations of how the materials circulate, as shown in [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProduction scrap from non-functional electrodes and cells is recycled and used in the same way as EoL batteries. The authors therefore recommend also handling the multifunctionality in the same way. This will also lead to more consistency in the modelling over the battery\u0026rsquo;s life cycle. The PEFCR and the draft of the Delegated Act recommend the application of the CFF. The implementation of the CFF is very complex, especially because the description of various parameters leaves room for interpretation, and it mixes the product and the material perspective. In general, the parameters lack clear documentation that would allow the use of company-specific data. Furthermore, the default processes of the draft of the Delegated Act are not aligned with the EU Batteries Regulations. The EU Batteries Regulation gives targets for recycled content and recycling rate of lithium, while the process chain described in the draft of the Delegated Act does not recover lithium. Overall, the complexity of the CFF will likely lead to inconsistent applications between different LCA practitioners. In addition, the CFF will cause high modelling efforts for complex products such as batteries. Consistency between production scrap and EoL batteries recycling can also be achieved by applying the cut-off approach. The cut-off approach is less complex and more straightforward to implement. Its implementation also needs less data and parameters. While the regulations developed by or on behalf of the EU Commission favour the CFF, guidelines from other stakeholders for batteries and electric vehicles, as well as for various other industries and products, favour the cut-off approach [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Overall, the authors recommend modelling production scrap from non-functional electrodes and cells in the same way as EoL batteries to achieve consistency along the product life cycle, preferably with the cut-off approach. However, this will depend on the final standards set in place for the EU Batteries Regulation.\u003c/p\u003e \u003cp\u003eWhile this publication analyses the handling of the multifunctionality that arises from the increasing interest in recycling production scraps, production scraps should not solely be used to achieve the targets for recycled content from the EU Batteries Regulation. With the highly ambitious targets and the limited availability of EoL batteries in the next years, the EU Batteries Regulation might give the unintended incentive to rely on production scrap generation and recycling to comply with the target values. In the mid-to-long term, more EoL batteries will be available than production scrap [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This shift from production scrap to EoL batteries recycling also underlines the need for a consistent method to handle the multifunctionality in both cases, as advocated for in this publication.\u003c/p\u003e \u003cp\u003eThe recommended temporal and spatial aggregation depend on the purpose of the LCA, hence what the LCA results shall be used for (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The different \u003cem\u003etemporal aggregations\u003c/em\u003e applied show that higher temporal aggregations (monthly, yearly) lead to less fluctuation in the impact per battery cell produced. Outliers are not visible, such as due to production interruptions. With lower temporal aggregations (daily or hourly), the results react more sensitively to the different energy sources, variations of process parameters, ramp up and idle times and are therefore essential for engineering and development purposes. The insights gained can, for example, be used for process development, production planning, factory planning, and strategic planning of the integration of own renewable energy sources.\u003c/p\u003e \u003cp\u003eThe data from higher temporal aggregations (monthly, yearly) is more representative of the long-term process conditions, as an adequate time period for the data collection is defined as \u003cem\u003e\u0026ldquo;a time period which is long enough to account for normal variations in data values\u0026rdquo;\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This is typically one year or two to six months for emerging technologies [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, higher temporal aggregations, especially yearly averages, are more appropriate for reporting environmental impacts. As described in section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e, the draft of the Delegated Act also demands data collection over one year. However, it is not clear whether this one year should be calendar year (e.g. data collected in 2025 is the basis for all reported footprints in 2026) or whether it is one year backwards from the point of production (e.g. data collected from 03/2025\u0026ndash;02/2026 for production in 03/2026). Taking a calendar year as a basis, reduces the effort in data collection as the collection of yearly aggregated data would be sufficient. Further, it reduces the complexity in reporting and therefore increases consistency. Assessing one year backwards from the point of production would require less aggregated data collection \u0026ndash; at least monthly or potentially even less aggregated. While this increases the effort in data collection, it incentivises companies to a more detailed data collection, which is also essential for engineering and development purposes. Additionally, the data collection one year backwards from production would also reflect faster on changes in production and supply chains and especially efforts made to lower environmental impacts, such as changing the supplier, changing product and process parameters or decarbonisation efforts of the electricity mix throughout the year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding \u003cem\u003espatial aggregation\u003c/em\u003e, the aggregated data (e.g., factorywise collection) would be sufficient for reporting, as it is not mandatory to provide insights into the contributions of different process steps to the overall impact of the cell. More detailed data is essential for process development and further planning, as it allows for identifying the high-contributing processes and the effects of process parameters and production planning. For both spatial and temporal aggregation, lower aggregations increase the collection and calculation effort but also the accuracy and information depth. Hence, for process optimisation, lower spatial and temporal aggregations are recommended, while for reporting, higher temporal aggregation is required, and high spatial aggregation would be sufficient. The combination of high spatial and low temporal aggregation can be used to analyse the overall energy demand and to perform load shifting.\u003c/p\u003e \u003cp\u003eOverall, several of the analysed methodological aspects of the LCA standard for the EU Batteries Regulation still need further refinement. With regard to the multifunctionality handling, the CFF in the PEFCR needs further instructions on how to apply it for production waste. In terms of the more detailed version in the draft of the Delegated Act, it should further be examined if the deviations of the CFF PS from the CFF are consistent from a product perspective. In general, the CFF would need further work regarding provided default parameters and processes before implementation on the market to match the requirements of the EU Batteries Regulation. In light of the high complexity, it should be evaluated whether it is an appropriate method for large-scale implementation. For the data collection of one year, more clarity is needed how this one-year time span is defined to ensure consistency in reporting and in the declaration in the battery pass.\u003c/p\u003e \u003cp\u003eFurther, the analysis conducted in this paper, highlighted the strong focus of the LCA methodologies under development on the reporting of carbon footprints. However, the EU Batteries Regulation targets also continuous improvement with the increasingly ambitious targets for the carbon footprint, recycling rates and recycled content. The developed LCA methodology could therefore be refined in a way to support companies in applying LCA for engineering and development purposes. For example, the methodology could set higher standards with regard to temporal and spatial data aggregation but not make the reporting on the detailed level to the public mandatory. In this way, companies are encouraged to establish more detailed data collection in their production facilities. Further, the standardised method could also include an approach for how to use LCA in an engineering process and how to include, for example, also prospective studies in the development. The recently developed TranSensus LCA standard shows first efforts for including these aspects in a standardised method [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this article, the LCA modelling of the battery cell production in the context of the EU Batteries Regulations was analysed with the focus of identifying gaps and limitations with regard to data aggregation and multifunctionality handling. A case study was performed to discuss the practical implications. The performed analysis highlighted several gaps:\u0026nbsp;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003ethe multifunctionality handling of production scrap is not clearly addressed in most existing guidelines and further guidance is needed\u003c/li\u003e\n \u003cli\u003ethe CFF in its current form is partly inconsistent with the EU Batteries Regulations (e.g., regarding default processes and parameters)\u003c/li\u003e\n \u003cli\u003edata collection periods need clearer definition for implementation\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBesides these gaps, also several limitations could be identified:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003ethe CFF in its current form is very complex which hinders consistent and comparable implementation and leads to high implementation efforts for complex products\u003c/li\u003e\n \u003cli\u003ethe developed LCA standard focuses solely on reporting while the EU Batteries Regulations targets also continuous improvements for which life cycle engineering is needed\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBased on these gaps and limitations, the authors recommend applying cut-off instead of the CFF in the finalised method and extending the standardised LCA method to incentivise and support industry in the implementation of life cycle engineering.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Sources\u003c/h2\u003e \u003cp\u003eThis publication did not receive any funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJa.H.: Conceptualization, methodology, validation, investigation, writing - original draft, writing - review and editing, visualisation.Jo.H.: Conceptualization, methodology, software, validation, investigation, data curation, writing - review and editing, visualisation.G.V.S.: Conceptualization, methodology, software, validation, investigation, data curation, writing - review and editing, visualisation.C.H.: Writing - review and editing, supervision.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEuropean Parliament and European Council, \u0026ldquo;Regulation (EU) 2023 of the European Parliament and of the Council concerning batteries and waste batteries,\u0026rdquo; vol. 2023, no. 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Traverso, \u0026ldquo;TranSensus LCA - Deliverable 5.2: TranSensus LCA Consolidated Guidelines,\u0026rdquo; 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44498","submissionUrl":"https://submission.springernature.com/new-submission/44498/3","title":"Journal of Industrial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"allocation, battery production, data aggregation, environmental product declaration, life cycle assessment","lastPublishedDoi":"10.21203/rs.3.rs-8285103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8285103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBy enabling carbon footprint reporting and supporting engineering of batteries and their production, life cycle assessment (LCA) is essential for a sustainable energy and mobility transition. This article analyses current gaps and limitations in the carbon footprint calculations as part of the EU Batteries regulation, focusing on data aggregation and multifunctionality handling in the battery cell production. Different approaches to data aggregation and multifunctionality handling are identified. Their implications are identified and discussed based on the application in a case study. Different approaches to handling multifunctionality cause about 6% difference in climate change impacts per cell in the case study. Applying cut-off is recommended as most the transparent and consistent approach. Temporal data aggregation affects the climate change impacts per cell by \u0026plusmn;\u0026thinsp;10%, with higher aggregations being more suitable for reporting and lower aggregations being essential for engineering purposes. Spatial data aggregation does not affect the overall climate change impacts but influences hotspot identification. The study underlines the need for further clarification in the standardised LCA regarding multifunctionality handling and data collection, Further, this article advocates for an extension of the method tailored to different LCA purposes, such as reporting and engineering.\u003c/p\u003e","manuscriptTitle":"Life cycle assessment of battery cell production in the context of the EU Batteries Regulation: The influence of data aggregation and multifunctionality handling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 00:42:09","doi":"10.21203/rs.3.rs-8285103/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"22948384577237446475147166221041557685","date":"2026-01-22T08:48:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52945095546255183996519238040153155419","date":"2026-01-15T14:01:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-09T08:05:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-05T16:03:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T09:47:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Industrial Ecology","date":"2025-12-05T07:33:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-industrial-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"44498","submissionUrl":"https://submission.springernature.com/new-submission/44498/3","title":"Journal of Industrial Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"45e22698-5861-4e99-a0b8-71c2442bf3a4","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-15T14:23:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 00:42:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8285103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8285103","identity":"rs-8285103","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}