Electrification is needed to improve the environmental performance of the transportation sector but must be accompanied by technological innovations that eliminate ecological hotspots over the vehicle’s lifetime. Additionally, the lifetime costs of electric vehicles must be reduced. This study uses the battery electric vehicle sustainability impact assessment model (BEVSIM) to analyse the effects of potential changes in recycled plastic content and its waste treatment, electricity grid, and the price of battery electric vehicles (BEVs). Using recycled content in plastic parts improved their environmental performance in all categories except land use; e.g. using 40% recycled plastic reduced 107 kg CO2 eq. per passenger vehicle. Applying a combination of mechanical and pyrolysis technologies for plastic recycling improved several impact categories. However, a trade-off exists for a few impact categories. Finally, using 2030 and 2050 forecasts of electricity grid mixes revealed lifecycle CO2 emissions for BEVs to be 50% and 56% lower than ICE, respectively.
Article
Future options and their impact on the life cycle assessment and life cycle costing of Segment C/D passenger vehicles
https://doi.org/10.21203/rs.3.rs-5029142/v1
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Climate change mitigation requires solid plans specific to different sectors, such as construction, agriculture, aviation, and transportation. Transport remains a key obstacle to achieving the EU’s climate targets1. For example, of Europe's total greenhouse gas emissions in 2018, 15% were produced by passenger cars, whereas 4% were produced from aviation. A shift to battery electric vehicles (BEVs) and a sustainable energy supply has been implemented in several countries and can potentially reduce emissions2. The potential advantages of BEVs are their powertrain efficiency, low maintenance requirements, and zero exhaust emissions. In practice, the environmental impact of a vehicle should be assessed throughout the vehicle’s life cycle and all major impact categories should be considered using a full life cycle assessment (LCA)3. Assuming a 150,000-km vehicle lifetime, EVs powered by the European electricity mix save 26% to 30% GWP relative to gasoline-powered vehicles and 17% to 21% GWP relative to diesel-powered vehicles4. The GWP savings depend heavily on the electricity source used for BEVs. Research has shown that it can be counterproductive to use EVs in regions where electricity is primarily produced from fossil sources, such as lignite, coal, or even heavy oil combustion4. Battery electric vehicles can perform worse than ICEs in terms of human toxicity, freshwater ecotoxicity, freshwater eutrophication, and metal depletion impacts originating from the production phase4,5. The inferior performance of BEVs in terms of greater environmental impact can potentially be managed using “ambitious material efficiency strategies”, such as lightweighting (LW), using recycled materials6 and increasing the recycling rate at the end of life of vehicles7.
The European targets for the reuse and recycling of end-of-life vehicles (ELVs), as well as the reuse and recovery of components, are 85% and 95%, respectively8. The use of recycled plastic in BEVs has been proposed but not implemented. The main reasons for this lack of implementation are cultural, regulatory, economic and technical barriers9. The recycling design is also an important factor in achieving circularity for the sector. Many challenges have been reported for designing recycling in the automotive industry, such as factors related to cost, design, technology, data and information, as well as EoL management and value chain management10. The recycling of end-of-life (EoL) plastics in the automotive industry has only been studied recently7. In this study, a sustainable waste management scheme consisting of novel sorting, mechanical recycling, pyrolysis, and plastic upgrading is proposed. It is projected that using this scheme will result in a reduction in carcinogens, noncarcinogens, global warming impact categories and nonrenewable energy of 138%, 100%, 42%, and 114%, respectively, compared with state-of-the-art technologies.
The environmental impacts of recycling and the use of recycled content in automobiles have still not been thoroughly investigated. To complement environmental and economic impact assessments, i.e., life cycle assessment (LCA) and life cycle costing (LCC), the circularity of materials can be measured via the material circularity index (MCI) developed by the Ellen MacArthur Foundation11. This index was estimated to be 0.23 for tyre production on a 0 to 1 scale. Reducing the energy recovery fraction by 14% and using 10% biobutadiene in the production line was predicted to increase the MCI to 0.49. This value can be considered an achievable goal for the coming years12.
The LCC analysis has been applied to private vehicle ownership to assess the effect of the electricity price on the cost efficiency of electric vehicles compared with that of ICE vehicles13. Over the entire life cycle of a vehicle, the electricity price must be below the so-called critical electricity price for the ICE for the BEV to be more cost-efficient than the ICE. The life cycle costs of a BEV are then below those of an ICE. Among the four BEVs considered in the literature, this critical electricity price has been reached for only two BEVs. In the respective study, the vehicle life cycle costs were expressed as the net present value (NPV) to make a fair comparison, because current and future costs may differ between alternatives. The same conclusion was reached by Ayodele and Mustapa14 in a review of LCC studies from the customer’s perspective. As expected, the two important cost components are the vehicle purchase and operating costs. Khoonsari15 carried out the only LCC study from the producer’s perspective. However, battery costs are important to producers of BEVs, where the battery production cost per kWh capacity will be reduced in the future16. However, lowering the battery price per kWh may have rebound effects by driving a producer to install a larger battery to increase the mileage range of a vehicle, which can a very strong customer preference17.
The aim of this study is to assess the environmental and economic impacts of BEV and ICE vehicles via LCA and LCC. We use the battery electric vehicle sustainability impact assessment model (BEVSIM)18 to carry out a full LCA and LCC and perform an MCI assessment of the two vehicle types. The BEVSIM tool is sector- and application-specific, web-based and has a simple design. The BEVSIM tool contains LCA models for materials production, processing, the use phase, EoL fate and recycling processes. The results of the LCA and MCI are presented for ranges of various parameters, including the 1) recycled content (plastics), 2) electricity grid mix and 3) alternative EoL scenarios for plastics involving pyrolysis and mechanical recycling.
An LCC for a BEV can be carried out from the perspective of the owner (consumer) or producer of the vehicle. In this study, we considered the perspective of the producer, which aligns with the focus on material and vehicle production of the ALMA project (ALMA 2023), of which the BEVSIM is part. Data from the producer's perspective are considerably more scarce than those from the consumer’s perspective. A report by Element Energy Limited (2021)17 can be used to obtain data from the consumer’s perspective.
2.1. LCA
The cradle-to-grave GWP results for BEVs and ICEs show that the use phase constitutes the dominant contribution (at 85%) for ICEs to vehicle lifetime greenhouse emissions, whereas material production, especially battery manufacturing, is the largest contribution (at 38%) for BEVs to the life cycle GWP. Figure 1 shows the cradle-to-grave GWP results for the BEV and ICE vehicles per life cycle stage per functional unit, i.e., one vehicle (BEV or ICE). For this (default) scenario (see the results presented in the first row of Table 1), it is assumed that the plastics used in the vehicle have no recycled content.
Temporal variations in electricity grid mixes are an important consideration for a full LCA of BEVs19. In this analysis, the impact of the future electricity grid mix is studied to assess the use-phase performance of BEVs in 2030 and 2050. The application of forecasts for future (2030 and 2050) electricity grid mixes reveal a considerable reduction in the CO2 emissions of BEVs. The BEV emissions are 28%, 50% and 56% lower for 2021, 2030 and 2050, respectively, than those of the ICE (Figure 2).
Using the ReCiPe 2016 midpoint (H) method shows that the BEV out performs the ICE in terms of the GWP, stratospheric ozone depletion and fossil resource scarcity categories. However, the ICE outperforms the BEV for all other impact categories, in line with past findings5. For all three impact categories in which ICE performs below the BEV, the use phase burdens are the dominant factor (Figure 3). For BEVs, mineral resource scarcity is a crucial issue that can be mitigated through recycling or deploying innovative products with lower contributions to mineral resource scarcity. The major contributors to mineral resource scarcity are cobalt sulfate, lithium hydroxide, and copper, which can be recovered in the forms of Me(Ox), Li3PO4 and Cu3(PO4)220. According to Duarto Castro et al.20, overall recycling credits are obtained for terrestrial toxicity, human noncarcinogenic toxicity, toxicity, and mineral resource scarcity but not for the GWP. However, in the BEVSIM model, the net GWP for battery recycling is negative. This contradiction results from different acids being used in the waste battery treatment process modelled in the BEVSIM and in leaching. Acetic acid is reportedly used for the leaching process, whereas sulfuric acid is applied in BEVSIM, leading to a lower burden in waste treatment in terms of the GWP. According to the Ecoinvent database version 3.6, the GWPs of acetic acid and sulfuric acid are 1.6 and 0.12 kg CO2 eq., respectively, although the use of recycled citric acid lowers emissions by up to four times20. Battery leaching is mostly performed using inorganic acids, typically hydrochloric acid, sulfuric acid, nitric acid, and phosphoric acid, whereas organic acids include citric acid, oxalic acid and tartaric acid21. Thus, the assumed battery waste treatment plays an important role in the analysis and can influence the results considerably. The production of recycled batteries contributes considerably to the impact category of marine ecotoxicity.
The MCI for each material is 0.627. Steel and aluminium have high circularity indices (0.8 and 0.69) because of reuse and recycling. By contrast, plastics in the current EoL scenario (31% mechanical recycling and incineration for the remaining fraction) have an MCI score of only 0.33. This result is alarming, given industry efforts to use less metal and more plastic and composite parts. Replacement of steel with composites must be coupled with a suitable infrastructure for the reuse and recycling of these components.
Plastics in the automotive industry are reported to consist of 0–20% recycled materials6,22. Changing the recycled content of the plastics from zero to 40% improves the GWP of the plastic parts by 28% overall (Table 2).
According to recent studies, increasing the recycling rate from 3% to 50% improves the levels of carcinogens, noncarcinogens, global warming and nonrenewable energy by 138%, 100%, 42% and 114%, respectively7,23. A 33% pyrolysis share was chosen for the EoL treatment because currently, PO can potentially be pyrolyzed, whereas PU and adhesives can only be incinerated, resulting in a maximum 44% share of mechanical recycling of plastics in EoL treatment7. Table 3 shows the results of 18 impact categories determined using ReCiPe Midpoint H for the four studied scenarios. A 57% reduction in the GWP (considering only emissions related to EoL treatment) is achievable by 33% pyrolysis and 44% mechanical recycling compared with state-of-the-art methods (31% mechanical recycling and 69% incineration). This value was reported to be 42% by Cardamone et al.7 The difference between the two values mainly originates from different assumptions being made about the composition of plastics of the vehicles and the inclusion of dissolution-based recycling by Cardamone et al.7 Improvements were not observed in only three categories, namely, freshwater eutrophication, terrestrial ecotoxicity and land use. An overall comparison of all impact categories for all 4 scenarios (3a–d) did not yield conclusive results; instead, a trade-off among the scenarios was obtained.
2.2. LCC
First, it must be stressed that almost no public cost data for the production of passenger cars are available. Thus, the results shown here are only an indication of the real costs. On average, car producers make a profit of approximately 7.5%24. Data from the main car manufacturers in the European market reveal an average margin of 6.8% over the period of 2021–2023 (see Table 4). The relatively high standard deviation indicates considerable variability in the margin. Of course, the margin for the car producer is directly reflected in the price that the car dealer pays, and the consumer price also includes the costs and margin of the car dealer. As we only knew the consumer prices, we subtracted the costs and margins of the car dealer to obtain the revenues for the car producer. As an estimate, we subtracted 20% of the consumer price25. The manufacturer’s suggested retail price was based on that of the Ford Mustang Mach-E (€60,119) for the BEV and on the Ford Active X 1.0 l EcoBoost (€34,800) for the ICE. Our initial results revealed a very large profit, showing that our costs were underestimated. To solve this issue, ‘missing costs’ were added to the LCC. For BEVs, these missing costs were 9–10% for the BEV and approximately 30% for the ICE. These missing costs for the ICE were quite uncertain but were needed to correctly balance the consumer price.
For the BEV, the estimated material cost of €25,930 is the most important part of the manufacturing cost. For the ICE, the estimated process costs of €11,016 (see Figure 4) is the most important part of the manufacturing cost, and the estimated material costs are €5,135. The NMC battery (€21,265) dominates the material costs for the BEV, followed by the electric engine (see Figure 5). As battery costs have been dropping and will continue to drop, the development of the battery price was further analysed. A modified learning rate was derived to estimate the time range over which the battery price would drop. From 2014 onwards, the battery price steeply declines for some years and then decreases more gradually. Mauler et al.26 reported (but did not analyse) this apparent change trend. Battery prices, but not production volumes, are available from 2010 onwards. Thus, the learning curve was modified to use the number of years after 2014 (26,27) instead of the production volumes. The following relation was thereby found:
The battery price predicted using Formula [1] for the year 2030 was 20 €/kg (see Figure 6). Even with the forty percent drop in the NMC battery price from 34 EUR/kg in 2021 to 20 EUR/kg in 2030, the battery remains by far the most important cost item of the BEV in 2030. For the other materials, it was not possible to estimate the 2030 prices, and we had to assume that the other cost items would not change by 2030. In this case, the predicted total cost for BEVs will decrease by 20% in 2030; however, the total costs still exceed those of a comparable petrol ICE vehicle (see Figure 4).
We used the battery electric vehicle sustainability impact assessment model (BEVSIM) to assess the effects of the recycled plastic content and plastic recycling (via pyrolysis and mechanical recycling) on the environmental performance of both BEVs and ICEs, as well as the location of use of these vehicles. Cradle-to-grave GWP results for BEVs and ICEs show that the use phase is the dominant contributor (85%) to vehicle lifetime greenhouse emissions of ICEs and that material production (especially battery manufacturing) is the largest contributor (38%) to the life cycle GWP of BEVs.
Using recycled content and recycling plastics improved the environmental performance of plastic parts in all categories except land use. Using a combination of pyrolysis (33%) and mechanical recycling (44%) for plastic waste treatment improved all impact categories except land use, terrestrial ecotoxicity and freshwater eutrophication.
In addition to evaluating the environmental performance, the economic performance was assessed as a LCC of vehicle production. The NMC battery is and will be (as of 2030) the dominant contribution to the total life cycle costs for the producer. Even with considerably reduced battery costs in the near future, the production costs of a BEV are higher than those of an ICE vehicle with a petrol engine.
The performances of the BEV and ICE are comparable in terms of the circularity index (MCI = 0. 627). Steel and aluminium both have high material circularity indices (0.8 and 0.69) due to reuse and recycling, whereas plastics within the current EoL scenario (31% mechanical recycling and incineration as the remaining share) have an MCI of only 0.33. It is necessary to increase the recycling rates and recycled content of the plastic parts of vehicles. Automotive engineers must emphasize both lightweightness and circularity for batteries. Considering other impact categories, such as ecotoxicity-related categories, results in recycling having the dominant impact on environmental performance. The current automotive recycling infrastructure has limitations. In the recycling of BEVs and ICEs, there are specific infrastructure requirements for recycling vehicle parts and materials because of the complexities of BEV and ICE vehicles and their supply chains. Therefore, the recycling infrastructure must be redesigned and set up according to the vehicle type, i.e., BEV or ICE. In addition to industry efforts to develop recycling technology and establish recycling infrastructure, specific government legislation on automotive recycling for each category may be necessary to increase the circularity performance of the automotive sector.
Another consideration is battery cost. On the basis of results in the literature, we showed that battery costs will substantially decrease at the end of this decade. The overall conclusion of this study is that lightweighting of vehicles (both ICEs and BEVs) can be beneficial for environmental performance in combination with recycling technologies for plastics. The MCI could thus be lowered from its current value of 0.33.
The LCC clearly shows that from the producer’s perspective, the reliability and availability of cost data are strongly limited, probably due to the highly competitive value of this data for producers. The price of BEVs will decrease because the price of the most important cost item, the battery, will continue to decrease in the near future. However, using lighter materials, such as composites and high-strength steel, in the future to increase the range of vehicles may increase material costs. A reduced vehicle mass translates into lower energy costs for the consumer, potentially reducing life cycle costs for the consumer.
4.1. Life cycle assessment
Life cycle assessment (LCA) is a method for quantifying the environmental performance of products or services. The ISO 14040 and 14044 standards are provided by the International Organization for Standardization (ISO) to perform LCAs consistently with high data quality and reproducibility.
4.1.1. Goal, scope and system boundary
The aim of this study is to assess the effects of options, such as recycling, recycled content, and price volatility, on the environmental impacts of BEVs and ICEs. The service life of the studied vehicles is set at 248,500 km18. The default geographical location is Europe; therefore, the average European electricity from the grid is chosen as the energy supply for the use phase of the BEV. The studied system boundary is presented in Fig. 1.
4.1.2. Life cycle inventory
An appropriate comparison of a BEV and an ICE requires the use of a consistent system boundary and lifecycle inventories, including all the relevant differences between the two alternatives, such as the powertrain, batteries, and chassis. The full bill of materials (BoM) for the two vehicle types can be found in the Supporting Information. The BoM is generated from the literature28 and modified using expert opinion. The reader is referred to the literature for more information on the life cycle inventory18.
4.1.3. Life cycle impact assessment
Three environmental impact assessment methods are used in the BEVSIM: ReCiPe 2016 Midpoint (H), cumulative energy demand V1.11, and IPCC 2013 GWP 100a version 1.03 (also part of ReCiPe 2016). The material circularity index (MCI) developed by the Ellen MacArthur Foundation is used for circularity assessment11.
4.1.4. LCA scenarios
To evaluate the impact of future options on the environment, the use of 1) recycled plastic content, 2) alternative electricity grid mixes for different years and 3) different EoL scenarios are studied (Table 1).
4.2. Life cycle costing
4.2.1. LCC methodology
The LCC analysis is a cost-accounting approach that considers the economic costs over the life cycle of a service or a product. We consider the production process of a vehicle to perform a LCC. The LCC includes the costs of designing the vehicle, acquisition of the materials needed, and costs during use of the vehicle for which the producer is responsible, such as those made for take-back actions, and finally, waste-related costs. These costs incurred during different stages of the life cycle are partly based on literautre29 and are balanced by the revenues gained by selling the vehicles to wholesalers or dealers.
The manufacturing stage in the LCC is related to the processing and production stage in the LCA, whereas the distribution stage encompasses transport of the vehicle to the seller. The EoL stage in both the LCC and LCA involves assessing the effects of disposing of production waste on the environment.
4.2.2. LCC cost data
‘Design’ costs are incurred during the process of designing a new car model or redesigning an existing model. However, as no specific open source data on these types of costs are available, a rough estimate of 5% of the sales price was used on the basis of the LCC analysis30,31. Users of BEVSIM or other LCC tools can always use their own internal data to determine these design costs. During the manufacturing stage, costs are incurred associated with materials, energy, labour, processing, and equipment29. Freight-on-board and other market processes were considered to determine the material costs. For a steel BiW, processing costs were estimated to be 5.2 €/kg using 2021 price levels15. Distribution costs are incurred by transporting a stock to car dealers and damage during distribution. A distribution cost of €60 per vehicle was assumed. ‘Use’ costs do not include costs associated with the use of the vehicle by the consumer but include warrants, take-back and service costs incurred by the producer. No proper data are available for these costs as well; thus, the user should estimate these costs according to their specific LCC study. The costs associated with waste generation during the design and production of vehicle are determined during the EoL stage. The balance of costs is closed by the producer’s revenues. Taxes were not included in this LCC.
4.2.3. Future costs
It is important to consider the cost of a lithium-ion battery pack for a BEV. The price of this type of batteries was nearly 1200 $/kWh in 2010 but decreased to 137 $/kWh in 2020 because of technological development and an increase in production 26 . A further price decline is expected on the basis of further improvements in battery technology and a considerable increase in the cumulative production volume. This type of price development can be predicted by so-called learning curves 32 . Learning curves can be used to estimate future cost reduction considering the increased cumulative production capacity for a technology or a process using historical cost reduction data. Good-quality production quantities for the NMC 111 battery were not available, but time‒price series were available 26,27 . Thus, we fit the series data with a power-law to derive a relationship between the time and battery price 33 . As the penetration rate of the batteries changed after 2014, the relationship was derived for 2014 onwards. The conversion from USD to EUR was based on the average yearly exchange rate. Given the inherent uncertainty in these predictions, the time horizon was limited to 2030. The steel cost is also critical for vehicles. Steel prices have fluctuated over the years and are expected to remain important in the future.
The steel and plastic markets are mature markets for which learning curves would not be relevant because we did not consider potential changes in the steel market, such as the development of advanced high-strength steel and oxysteel combined with carbon capture. Changes in market-level supply and demand play a critical role in the economic cycle of these commodities. Therefore, future prices were based on the main cost drivers and the prediction of their future values. The main cost drivers for steel are raw materials (65%), CO2 taxes (28%) and energy prices (17%). Relationships were established among the prices of historic steel, iron ore, coke and hard coal. The steel price from 2010–2020 was found to be positively correlated with the price of coal (correlation factor: 0.43) and slightly negatively correlated with the prices of coke (correlation factor: -0.053) and iron ore (correlation factor: -0.049).
Competing interests
All authors declare no financial or non-financial competing interests.
Author Contribution
Milad Golkaram conducted the LCA Tom Ligthart carried out the LCC Aylin Yildiran and Spela Ferjan supported on the data collection for LCC and LCA, respectively. Rajesh Mehta supported the design of project, and supervised the project Jack J.T.W.E. Vogels and Eugene Someren designed the BEVSIM platform.
Acknowledgement
The authors would like to thank their ALMA partners for valuable contributions, feedback, and collaboration that enabled the ALMA Work Package 1 and LCA and LCC studies using the BEVSIM to be successfully carried out. The ALMA consortium is a diverse group of 9 partners from 4 different EU countries: France, Germany, the Netherlands, and Spain. The 9 ALMA partners are CTAG, Fraunhofer, Ford Werke, ArcelorMittal, BATZ, Rescoll, Innerspec, TNO, and ISWA. Specific and timely insights from experts in the field at Ford, TNO, BATZ, CTAG, Arcelor Mittal, and Fraunhofer contributed to addressing scientific and nonscientific challenges and roadblocks faced by the ALMA Work Package 1 team led by TNO. The authors would also like to thank their colleagues Mark Huijbregts, Henk Bosch and Gerard van der Laan for reviewing the manuscript and providing valuable feedback. The project was funded by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement Number 101006675.
Data availability statement
Data supporting the findings of this study are provided in the Supporting Information.
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Table 1 to 4 are available in the Supplementary Files section.
No competing interests reported.