Predictive sustainability analysis applied to an automotive design case study.

The paper deals with a predictive sustainability analysis applied to the design of automotive components in lightweight perspective. The analysis is conducted through the integration of the traditional Life Cycle Assessment (LCA) methodology with tailored forecasting algorithms able to provide a predictive evaluation of Climate Change (CC) by elaboration data contained in commercial environmental datasets. The comparison is referred to the entire Life Cycle (LC) of the system (including production, use and End-of-Life) according to a “from cradle to grave approach”. A medium-class car rear crash management system is used as case study, assessing the potential benefits related to the substitution of conventional steel with 6000/7000 series aluminium alloys, along with other minor design changes. Particularly, the study compares the environmental profile of the two solutions based on the CC impact category in application to both an Internal Combustion Engine Vehicle (ICEV) and a Battery Electric Vehicle (BEV). The results show the potentiality of the proposed methodology, highlighting possible improvements/worsenings: new materials and manufacturing technologies adopted in the lightweight rear crash management system entail contrasting environmental effects depending on LC phases, that is, increased CO2eq in production (around 125% - mainly due to the strong energy intensity of aluminium supply chain) and reduced burdens in use and EoL (primarily provided by component mass reduction). That said, the influence of different boundary conditions on the predictive models is significant only for the electricity produced to energize the BEV configuration, leading to an overall variability of comparison results ranging within 3-30% for the BEV case study.


Introduction
The current EU regulations are driving towards the reduction of Greenhouse Gas (GHG) emissions, setting a global reduction target of 60% by 2050 [1].This issue is of great interest in the automotive field, where over 20% of GHG impacts in the European Union are attributed [2], with 50% of them originating from passenger vehicles [3].Urgent measures are therefore needed to reduce the environmental burdens of vehicles.One of the most commonly used methodologies in scientific research to assess such impacts is the Life Cycle Assessment (LCA) methodology [4], which allows for quantitative sustainability evaluation of various subsystems.To date, the adoption of LCA is wellestablished in the automotive sector.Eco-design principles promote the use of recycled materials and the introduction of innovative design solutions to reduce consumption [5].In support of these principles, customized tools can be found in literature that assess the sustainability of solutions selected by the designer [6].More specifical ly, the attention to green solutions is heavily focused on vehicle electrification [7].Numerous articles can be found that highlight the sustainability of electric vehicles compared to internal combustion cars.Helmers et al. [8] presents results for 18 impact categories obtained from the comparison of Battery Electric Vehicles (BEVs) and Internal Combustion Engine Vehicles (ICEVs) using the Recipe method.The results are reported in terms of break -even point and demonstrate a strong dependence on battery production scenarios.Del Pero et al. [9] shows that BEVs result in zero emissions during thee usage phase, but the production of the electric drivetrain poses a barrier to achieving maturity for this technology.In line with this assessment, Anderson et al. [10] demonstrates the potential of using a plug-in hybrid engine powered by renewable fuel to reduce the size of batteries, which are typically large for full electric vehicles.Batteries are also a critical element in the environmental assessment of End-of-Life (EoL) vehicle scenarios, which greatly impact the overall environmental effect [11].The complete transition to electric mobility has also been discussed at the COP 26 1306 (2024) 012041 IOP Publishing doi:10.1088/1757-899X/1306/1/012041 2 [12], but numerous automakers have opposed the complete exclusion of biofuels.However, there is still a need for further increase in green energy sources for electricity generation and strengthening of the infrastructure system [13].Several studies study the environmental impact of using alternative materials (such as polymeric, composites, or aluminium), with a view to reducing component weight or assessing the specific impact of the material itself [14 -15].Akhshik [16] et al. investigates the use of bio-based composites for the production of four components (battery tray, engine beauty shield, cam cover, and oil pan).The results demonstrate a reduction of over 2 times in GHG emissions.The use of such materials brings substantial benefits for car usage exceeding 200,000 km [17], a value that is expected to grow due to the increasing efficiency of propulsion systems over the years.Moreover, the classic approach of LCA does not take into account the influence of time, and the values of environmental impacts during the use and EoL stages are discounted when the analysis is performed.To account for this effect, it is possible to carry out forecasting analyses.Some examples of such applications can be found in literature.Memary et al. [18] discusses the temporal effect (timetrend) on the environmental impact associated with the extraction of metallic materials (copper).The results show that the effect in terms of SO2 and CO2 is expected to increase by up to 300% within the considered time horizon due to the anticipated decline in metal concentration in raw minerals (ore grade).Zhu1 et al. [19] introduces the temporal effect regarding electricity production in the Chinese scenario.The study provides a detailed forecast of the increasing use of renewable sources in the national energy mix throughout the analyzed period.The results predict a reduction of over 50% in greenhouse gas emissions by 2035 compared to 2017.Stranddorf et al. [20] implements forecasts regarding the environmental effect of the European energy mix.The proposed green scenario, which involves a significant increase in renewable energy sources, results in a Climate Change savings of over 80% by 2050.
In this work, the temporal trends' effect on the sustainability of an automotive component is evaluated using insights extracted from the work of Sacchi et al. [21].In particular, a forecast scenario is implemented following the principles dictated by the Shared Socioeconomic Pathways (SSP) [22], which quantify the impacts on Integrated Assessment Models (IAMs) of economic, technological, and urbanization evolution.A predictive LCA is developed to evaluate the time-based effect of an innovative lightweighting solution.The analysis follows a comparative approach between the traditionally used solution and an innovative solution that presents a weight reduction.First, the paper introduces a reengineering activity conducted on a rear Crash Management System (CMS) module.The study begins with the reference steel-based CMS and develops a lightweight version based on ultra-high-strength aluminium extrusions.Such a lightweight variant substantially reduces mass, preserving performance, safety, and functionality standards.Both solutions undergo an evaluation and comparison, examining its lightweight potential, design enhancements, and sustainability improvements, in the predictive LCA context and different propulsion system (BEV or ICEV).Moreover, a detailed sensitivity analysis is presented, based on different powertrain, as well as different vehicle segments and average annual mileage.The results are presented in terms of CO2eq, and they evaluate the dependence of the Break Even Point (BEP) on the aforementioned parameters.The results can be used to inform decision -making regarding the adoption of innovative lightweight solutions and to guide the design of future vehicles.

Module Design
The main components of both the reference (REF) and lightweight (LW) versions of the CMS module are shown in Figure 1 with an exploded view in a CAD environment.The presented assemblies are divided into sub-assemblies, numbered as follows: 1. Backplate; 2. Crash Box; 3. Beam; 4. Towing System.

Reference CMS
Lightweight CMS  The REF design solution is made of cold-stamped steel, weighing approximately 4 kg.The selection of steel grades is guided by attaining optimal performance within the economically favorable range of cold stamping materials.
The LW version of CMS comprises the same components as the baseline version , it does not need for any modification to the surrounding parts, and it is engineered to fulfill the performance criteria of the baseline version concerning two specific factors: 1. maximum deformation and energy absorption in the RCAR low-speed structural crash test protocol for rear impact [23]; 2. bending moment when subjecting the beam to deformation at its center.
An ultra-high-strength aluminum alloy is employed as the base material for the LW design, resulting in a total mass of approximately 2.4 kg.The backplate (1) and crash box (2) are extruded parts made of aluminum alloy 7003, with respective thicknesses of 4.0 mm and 2.1 mm.A highly ductile, high-strength version of the AW7003 in a slightly overaged temper (T7) is employed for the crash boxes, to meet the demanding ductility requirements of these components.Additionally, the chosen alloy provides enhanced performance in terms of crack resistance, deformability, and stability of mechanical properties in large-scale production, thereby combining high strength with good ductility and production efficiency.The novel developed beam (3) is formed using an open beam extruded profile made of the precipitation-hardenable high-strength aluminum alloy 7003, with a thickness ranging from 1.8 to 2.0 mm.This extruded profile design is coupled with a specialized forming concept, offering an optimal balance between mass reduction, cost-effectiveness, and crash resilience.The chosen alloy for the beam profile exhibits slightly higher strength compared to the other two components.The beam, crash box, and backplate components offer favorable characteristics in terms of mass reduction, strength, production efficiency, and design flexibility due to the utilization of a highly ductile aluminum extrusion alloy.Finally, the towing system (4) is crafted from 6082 aluminium.
Overall, the design choices implemented in the LW CMS version, such as material alteration, application of heat treatment, and use of tailored extruded blanks, result in a weight reduction of nearly 40 % compared to the REF CMS version, while simultaneously maintaining the performance, safety, and functionality requirements.Additional requirements apply to both design alternatives, specifying that no breakage should occur at a 105 mm intrusion in the bending load case; furthermore, the CMS must meet the strength and durability standards set by OEM for the towing attachment.

Predictive Sustainability Assessment
This section aims at undertaking a comparative predictive analysis between the CMS reference and lightweight alternatives.To accomplish this, a baseline sustainability assessment is conducted using the LCA methodology [24][25][26].Such an analysis is carried out by integrating the conventional LCA methodology with tailored forecasting algorithms, allowing a predictive assessment of environmental impacts through advanced data processing techniques.
The analysis comprehensively encompasses the entire LC of the CMS module, subdivided into production (i.e., raw material extraction and manufacturing process), use, and End-of-Life (EoL) stages.The environmental profile of both CMS solutions is assessed using the ReCiPe Midpoint (H) 1.13 impact assessment methodology [27] in terms of the Climate Change midpoint (CC) impact category.In this context, the Functional Unit (FU) is defined as the CMS module installed on the reference vehicle, with an average life cycle age of 12 years.[28].It is assumed that both design options maintain unaltered both mechanical and functional performances.The assessment of Fuel Consumption (FC) and exhaust air emissions during operation is based on the Worldwide Harmonized Light-Duty Test Procedure (WLTP) [29].Inventory data is collected for the above three main LC phases, encompassing materials/energy consumption, waste production, and environmental emissions.These factors are modeled using Life Cycle Inventory (LCI) processes and elementary flows sourced from the Ecoinvent 3.9 database.
Regarding production, the inventory is conducted separately for raw material acquisition and manufacturing subphases.Raw material acquisition accounts for the entire production chain, from primary material extraction to the production of semi-finished products.Manufacturing incorporates activities involved in converting semi -finished products into final module components.The inventory of both materials and manufacturing steps is conducted through a break-down approach [30], previously applied in ENLIGHT [31] and e-LCAr [32] projects.This approach assesses each mono-material component of the CMS system, with the overall impact of the production stage determined by aggregating contributions from the individual mono-material parts.
Data collection for raw material acquisition primarily consists of secondary data from scientific literature and the Ecoinvent database, based on composition and manufacturing processes of the CMS materials (see Table 1 and Table 2).The manufacturing considers energy consumption and material losses during production of finished parts.Processes such as joining, assembly, and transportation are excluded from the system boundaries, as a preliminary investigation indicates that their impact is negligible for the CC.Unlike material provision, data collection for the manufacturing phase relies on primary data obtained through direct measurements at supplier and OEM processes sites.Regarding the predictive analysis perspective, the production is modeled by considering the current year of the study (i.e., the year 2023), since the specific assessment is conducted in the present timeframe, thus excluding the use of forecast algorithms.
The use stage involves evaluating the impacts related to the operation of the CMS module, influenced by two factors: the energy/fuel transformation upstream to energy/fuel consumption itself (i.e., Well -to-Tank sub-phase, WTT) and the energy/fuel consumption for car driving, also considering the exhaust air emissions (i.e., Tank -to-Wheel, TTW), modeled through inventory framework reported in Table 3 (see Equations 1-9).

Use Stage Equations
Use ICEVs BEVs ‫ܥܥ‬ ்்ௐ = 0 (8) The assumptions of the predictive evaluation are described in the following bullet po ints, which refer to secondary data from the LCI Ecoinvent 3.9 database: -the Well-To-Tank (WTT) phase is time-variant, as it depends on the environmental impact associated with the production of fuel/energy throughout the entire car operation, which varies over time (i.e., ccfuel and ccmix).-the Tank-To-Wheel (TTW) phase does not depend on time, since vehicle emissions are not time-variant; -according to the framework developed by Del Pero et al. [33][34], the mileage is based on the ACEA report [28], which provides an average lifespan of passenger vehicles of 12 years for the European context.Therefore, an annual mileage of 12000 km/year is assumed, resulting in a total mileage of 144000 km; -the specific impacts of fuel and electricity production (ccfuel and ccmix) are calculated using predictive algorithms over 12 years within the European context (i.e., 2023 -2035) [28], considering four predictive scenarios [21][22]: 1. TRA scenario.It represents the traditional LCA scenario, where the use phase is calculated considering present-day impacts during the vehicle's operational years (TRADITIONAL SCENARIO); 2. SSP2-BASE scenario.It assumes no climate policies, resulting in a temperature increase beyond 3.5°C compared to pre-industrial levels (WORST CASE SCANARIO); 3. SSP2-RCP26 scenario.Aligned with the Paris Agreement, this predictive scenario anticipates a temperature increase between 1.8-2°C compared to pre-industrial levels (MIDDLE CASE SCENARIO); 4. SSP2-RCP19 scenario.Also in line with the Paris Agreement, this predictive scenario projects a temperature increase of 1.5°C compared to pre-industrial levels (BEST CASE SCENARIO).
-the reduction values (i.e., FRV and ERV) are obtained on the basis of modeling framework developed by Del Pero et al. [33][34] The inventory of EoL stage is modelled by according to the Directive 2000/53/EC [35].The scenarios considered for steel and aluminium components are in line with ISO standard 22628:2002 [36], based on the prevailing technological capabilities in Europe.Given that the CMS module is not typically disassembled for reuse or recovery due to its low mass and unfavourable positioning within the vehicle, it is assumed that it is not disassembled at from the vehicle and it undergoes the shredding process.Subsequently, the scenario involves material separation and open -loop recycling.The assessment of recycling takes into account both the environmental impacts associated with material/energy consumption and the impacts caused by recycling activities.The inventory is modeled as the avoided production of primary material, based on the substitution factor approach [37].Concerning the predictive perspective, the EoL phase is modeled by considering the last year of vehicle life-time (i.e., the year 2035), since the above processes occur in the future time-frame, and this is done by using forecast algorithms.
The LCI data for all LC stages are provided in Table 4.  [28] and Modeling Framework

Results and Discussion
LCIA results for REF and LW design alternatives across each LC stage, considering both different propulsion technologies (ICEV, BEV) and predictive scenarios (TRA, SSP2-BASE, SSP2-RCP26, SSP2-RCP19), are reported in Table 5.
Subsequent paragraphs delve into the discussion regarding the impact variations resulting from the adoption of the forecasting scenario in comparison to the baseline approach, as well as the differences between the REF and LW design solutions.These discussions stress the implications that different methodological approaches and design choices have on the environmental performance of the CMS module.

Contribution Analysis of Impacts: Influence of LC Stages on CMS module
Figure 2 presents the contribution analysis of environmental impact, categorized by LC stage, powertrain technology, and predictive scenarios.Regardless of the module design, the most impactful LC phases are production and use, representing approximately 90-95 % of total impact for both BEV and ICEV.Regarding production, it can be observed that CC shows a significant increase, reaching 125 % when passing from the REF to the LW design alternative, mainly due to the higher mass-specific impact associated with aluminium production compared to steel.The mass reduction achieved through aluminium (about 39 %) does not counterbalance the higher mass-specific CC.
IOP Publishing doi:10.1088/1757-899X/1306/1/012041Concerning the REF CMS design, the use stage is the most significant for both powertrain types, except for the predictive scenarios SSP2-RCP26 and SSP2-RCP19 for BEV, for which the production becomes more impactful than the operational phase.This outcome can be attributed to production and procurement of electric energy consumed during operation, whose impact is expected to significantly decrease over time due to increasing use of renewables, as predicted by the employed forecasting algorithms.
Conversely, in the case of the LW CMS design, the operational stage remains the second most significant for all powertrain and predictive scenario configurations since the operational phase directly depends on module mass (see Eq. 1-9).Thus, mass reduction leads to an impact decrease of use stage impact.However, the production shows a higher CC due to the procurement of aluminium, which exhibits higher specific effects than steel.
Explicitly considering the use phase, it remains relatively constant across predictive scenarios for the ICEV configuration, primarily due to the fuel production required for the operational phase, where the impact remains relatively stable or experiences only a slight reduction over time (according to the prediction of the employed forecasting algorithms).On the other hand, for the BEV configuration, a significant reduction is observed , as the predictive scenarios change from the worst (i.e., traditional approach -TRA) to the best (i.e., SSP2-RCP19); such a reduction is mainly due to the decreasing environmental impact of electricity production, as previously specified.
The EoL phase represents the least significant LC step, accounting for approximately 5 % and10% of the total impact respectively for ICEV and BEV.That said, results show that EoL provides an environmental credit due to metallic composition of both design alternatives.However, the percentage shares associated with EoL are minimal: this is primarily due to the low substitution factor of steel and aluminum recycling, which derives from both the high energy demand of re-melting and the need for primary alloy elements to achieve the same quality of primary materials (See Table 4).Interestingly, the environmental credit associated with EoL of LW solution is higher in absolute terms than the one associated with the REF solution, as a result of combination of production and substitution factor.Furthermore, regardless of powertrain configuration and design solution, it is noteworthy to observe a reduction in EoL credit , as the predictive scenarios become more stringent because of stricter environmental policies (from the traditional approach -TRA -to the best approach -SSP2-RCP19).This reduction is not only due to the decreased environmental impact of the recycling process, but also to a reduction in the impact related to specific material procurement.Consequently, this leads to an overall reduction in the total credit.
In the light of previous points and results shown in Table 5, the solutions related to the ICEV configuration exhibit a slightly higher impact as the predictive scenarios become more stringent.These results are due to the reduction in the environmental credit associated with EoL, which is not compensated by use phase, despite this latter provides a decrease at forecast scenarios varying.Instead, the solutions associated with BEV configuration show a lowering impact with at predictive scenarios varying.In this case, the environmental credit associated with EoL is outweighed by a significant decrease in the use phase, since the decrease in electricity production impact is relevant.
Figure 3 illustrates the variation of CC impact for CMS module with respect to mileage over the vehicle lifespan (i.e., 12 years with an annual mileage of 12000 km/year).All the previously defined predictive scenarios are presented in the analysis.Table 6 and Table 7 present the CC percentage variation between the traditional LCA scenario and predictive SSP2 scenarios for both ICEV/BEV and REF/LW combinations.It can be observed that: 1. in the case of ICEV/REF combination, the impacts of the predictive scenarios are slightly higher than the traditional approach, regardless of the year considered (ranging from 0.2% to 7.5%).This outcome can be explained through the reduction in EoL credit, which is not compensated by the impact decrease during use phase (insufficient to compensate for the credit reduction); 2. in the of BEV/REF combination, the impacts of the predictive scenarios are slightly higher than the traditional approach only over the first 3/4 years of the operational phase, after which a significant reduction in environmental impact is observed (ranging from -0.6% to -29% for the SSP2-RCP19 scenario).These results are due to the reduction in the environmental credit associated with EoL, which is counterbalanced by a bigger impact reduction in use stage (concerning the decreasing CC from electricity production in the future years) ; 3. for ICEV/LW configuration, the percentage variations depend on scenario: the more stringent scenarios in line with the Paris Agreement (i.e., SSP2-RCP26 and SSP2-RCP19) exhibit slightly higher impacts than the traditional approach until the last operational year, where there is a reversal of this trend (ranging from -0.014% to -0.027%).On the other hand, the SSP2-BASE scenario shows lower percentage values regardless of the year (ranging from -0.047% to -0.068%).The reduction in EoL credit influences these variations for the first two scenarios.In contrast, the third scenario experiences an increase in credit due to the production and procurement of aluminum required for production and EoL.The impact of aluminum, according to the predictive algorithms, is expected to increase slightly over time for the SSP2 -BASE scenario, thus increasing the credit; 4. in the case of BEV/LW configuration, the percentage variations also differ among scenarios: the SSP2 -RCP26 and SSP2-RCP19 scenarios show slightly higher impacts than the traditional approach only during the first two years of the operational phase, after which there is a notable reduction in environmental impact (ranging from -0.6% to -13.9% for the SSP2-RCP19 scenario).On the other hand, the SSP2-BASE scenario exhibits lower percentage values regardless of the year (ranging from -0.068% to -2.45%).
The environmental credit associated with EoL is counterbalanced by a stronger reduction in use stage, mainly due to a greener electricity production in the future.

Sensitivity and BEP Analysis
A critical aspect that must be considered is that all solutions related to LW CMS design are more impactful if compared to the REF version (see Figure 2).The rationale behind this outcome lies in selecting the average annual mileage (12000 km/year) used for calculating use stage, resulting in a total mileage at the end of the vehicle's operational life that does not reach the break-even point (BEP) between the two CMS design solutions.Against this scenario, a sensitivity analysis is conducted, where the main parameters are powertrain (ICEV, BEV), vehicle segment (A/B, C, D/E), design solution (REF, LW), predictive scenario (TRA, SSP2), and annual mileage; the latter varies from 10000 km/year to 15000 km/year.Interestingly, regardless of the annual mileage, solution, and predictive scenario, the impacts associated with any BEV segment is lower than the ICE configuration (except for the last values associated with the TRA scenario for the LW solution with an avg=15000 km/year).Furthermore, for the BEV configuration, the total impact of the LW solutions fails to reach the CC equivalence with its ICEV counterparts.This is due to the reduction over time of the impact related to the production and sourcing of the electrical energy required for the operational phase, thus leading to a reduction in the overall impact for both REF and LW solutions.Consequently, reaching the BEP does not occur for mileage values which are incompatible with the operational life of passenger vehicles.
Concerning the ICE configuration, it is observed that the CC outcomes of LW alternatives exceed those of the REF solutions for all predictive scenarios (TRA, SSP2) and vehicle segments (A/B, C, D/E) , considering the mileage of 15000 km/year.The mass reduction achieved in the LW solution and the slight impact reduction of fuel production lead to a significant CC decrease associated with use phase.As a result, the BEP is reached before th at vehicle operational life is concluded.Errore.L'origine riferimento non è stata trovata.provides a comprehensive overview of BEP variation based on sensitivity analysis.

Conclusion
This paper focuses on a predictive LCA to assess the time-based effects on a generic automotive component, adopting a comparative approach between the traditionally used design solution and the innovative one, which involves a significant weight reduction.The starting point is the re-engineering of the chosen case study (i.e., a rear CMS module), by replacing conventional steel with ultra-high-strength aluminum alloys, specifically using extruded AW 7003 for beam and crash box components.The innovative design results in a significant mass reduction of nearly 40 %.From a design perspective, adopting the 7000 series aluminum alloy strikes a good balance between strength, production efficiency, and design flexibility.Additionally, using an open beam profile with a customized extrusion design concept yields satisfactory outcomes for both lightweight potential and crashworthiness.
The comparative sustainability assessment, however, yields contrasting results across specific parameters, like powertrain, annual mileage, car segment, predictive scenarios, and design solutions.The environmental assessment outcomes can be summarized as follows: x the contribution analysis of environmental impact, considering LC stages, powertrain technology, and predictive scenarios for the CMS design solutions, reveals that production and use stages are the most impactful, representing approximately 90 -95% of total LC impact for both BEV and ICEV powertrains; x the CC impact category shows a significant increase (125 %) when passing from REF to LW design alternative, mainly due to the higher mass-specific impact associated with aluminum production if compared to steel; x for the REF CMS design, the use stage is the most significant for both powertrain types.However, for specific predictive scenarios of BEV, the production phase becomes more impactful due to the production and procurement of electric energy, which is expected to decrease over time due to the increasing use of renewables.On the other hand, in the case of the LW CMS design, the operational stage remains the second most significant stage for all powertrain and predictive scenario configurations, with weight reduction leading to a reduced impact of.However, the production phase shows a higher impact due to the procurement of aluminium; x the use phase for ICEV remains relatively constant across predictive scenarios.At the same time, there is a significant reduction in BEV as predictive scenarios change from worst to best, mainly due to the decreasing environmental impact of electricity production; x EoL represents the least significant stage, accounting for approximately 5% -and 10 % respectively for ICEV and BEV, resulting in an overall environmental credit; x the percentage variation of CC with mileage demonstrates different behaviors passing from the traditional LCA scenario to the predictive SSP2 ones, considering ICEV/BEV and REF/LW combinations.The predictive results show that the ICEV solutions are almost aligned with the traditional approach, with a percentage reduction ranging from approximately 0 % to 1.2 %.Whereas for BEV solutions, significant impact reductions are obtained, with a percentage reduction ranging from 3 % to 30%; x the LW design solutions are more impactful than the REF alternatives due to the chosen average annual mileage (12000 km/year), leading to a total mileage that does not provide a BEP between the two solutions; x a sensitivity analysis is conducted considering several parameters, such as powertrain, vehicle segment, design solution, predictive scenario, and annual mileage.The analysis shows that the impacts associated with any BEV vehicle segment are consistently lower than their ICEV counterparts, except for one scenario.Furthermore, the LW solutions for BEV fail to reach CC equivalence with ICEV configuration, due to the reduced impact over time of electricity production.Thus, reaching the BEP is only feasible for mileage values which are not compatible with passenger car life-time.For the ICEV configuration, the CC outcomes of REF alternatives become bigger than LW solutions for all scenarios and vehicle segments at a mileage value of 15000 km/year.The mass reduction achieved, coupled with a slight impact reduction in fuel production, leads to a significant CC decrease during use phase, enabling a BEP between the two alternatives.
Overall, the proposed methodology highlights the need for trade-offs between material and manufacturing choices, emphasizing the importance of a holistic approach to predictive sustainability assessment.

Figure 1 -
Figure 1 -CMS CAD -exploded view of reference (a) and lightweight (b) solutions.

Figure 2 .
Figure 2. Contribution analysis of impact by LC stage, powertrain technology, and predictive scenarios for REF and LW CMS design solutions.

Figure 3 .
Figure 3. CC representation of CMS module respect to mileage for ICEV/BEV and REF/LW combinations.

Figure 4 .
Figure 4. CC Heatmap representation of sensitivity analysis results.

Table 1 and
Table 2 present a comprehensive outline of the primary design characteristics of the REF and LW versions of CMS.Such a representation includes relevant information concerning each component, such as composition, thickness, and manufacturing process.The vehicle on which the CMS is installed is a C-class car, both in the ICEV and BEV configurations.

Table 1 -
Overview of reference (REF) design solutions for the CMS assembly.

Table 2 -
Overview of lightweight (LW) design solutions for the CMS assembly.

Table 3 .
Use stage equations for predictive assessment.

Table 4 .
LCI (Life Cycle Inventory) data collection of all LC stages.
* Average value representative of generic C-class gasoline vehicle.** Value calculated according to ACEA Report

Table 5 .
LCIA results for REF and LW design solutions, with different propulsion technologies during use stage (ICEV, BEV).

Table 6 .
CC percentage variation of CMS module between TRA scenario and predictive SSP2 ones, related to ICEV/BEV and REF combinations.

Table 7 .
CC percentage variation of CMS module between TRA scenario and predictive SSP2 ones, related to ICEV/BEV and LW combinations.

Table 8 .
BEP Results obtained from sensitivity analysis.