Essay: Effects of Vehicle Mass Reductions

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  • Subject area(s): Environmental studies essays
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Environmental impacts of battery electric vehicles (BEV) and internal combustion engine vehicles have been broadly studied and compared. However, there is no evidence of studies comparing the potential effect of key factors such as vehicle mass reduction, life cycle inventories regionalization and electricity mix evolution in a Brazilian conditions scenario. The purpose of this study was to evaluate what would be the theoretical environmental impact of manufacturing BEVs and battery electric busses (BEB) in Brazilian south east and to compare those results to what is intended to be a global vehicle in 2015 and 2030. The methodology was based in adapting global life cycle inventories to local Brazilian south east conditions and then to make a comparison. Thus, a representative number of Ecoinvent V.3.02 datasets were adapted to better represent local conditions. The study established a comparison using 1 car and 1 km as functional unit. This research envisioned mass reduction setups for cars and busses in 2030, and additionally two material switching scenarios: plastic based and aluminum based. Bus results suggest that there is a tendency for Brazilian bus to show better results than its global counterpart except for freshwater eutrophication. A large environmental contribution from treatment of mining residues is common for all impact categories where local bus performed worse than global corresponding. This lead us to believe that in order to manufacture an environmentally competitive BEB, reduction of impacts on metal extraction, especially copper, and its residues must be prioritized. Electricity consumption is by far the main contributor for climate change for a functional unit of 1km. Hydroelectricity linked methane emissions from reservoirs and natural gas in the electricity mix are the main sources of GHG emissions. For BEV results in 2030, an unexpected result appears during ozone depletion examination; Brazilian aluminum-based BEV exhibits the largest impact, even though mass reduction was considered. Paradoxically, the large share of aluminum recycling in Brazil acts as a double edge sword since emissions arising from scrap treatment do have a significant impact. Maintenance stage proves itself as the largest contributor for Photochemical oxidant formation due to ethylene use. For human toxicity, metal depletion, freshwater eutrophication and ozone depletion contribution vehicle components count for nearly 80% of total impact per km. It must be considered that this model cannot capture the potential evolution of GLO inventories, especially Chinese manufacturing processes. Any future research must prioritize Brazilian inventories construction.

Vehicle electrification is deemed as a way to mitigate greenhouse gas (GHG) emissions and to improve quality of air on large urban centers around the world. Air quality is a concern for many mega-cities, including São Paulo, the largest metropolitan area in the southern hemisphere. Andrade et al.(2017) conclude that the greatest air quality challenge currently faced by both, São Paulo State Environmental Protection Agency and the local communities is controlling secondary pollutants such as ozone and fine particles. Battery electric vehicles (BEVs) are expected to contribute for GHG mitigation emissions in urban areas since they present zero emissions during their use phase.
BEVs are still far from being a representative share of Brazilian automotive fleet. This is partly due to high ownership costs, driven by tax burdens which create disadvantages for BEVs with regards to conventional cars (AES Brasil, 2017) and lack of charging infrastructure. In an international context, BEVs registrations, including plug-in hybrid vehicles and fuel cell cars, hit a new record in 2016, with over 750 thousand sales worldwide (International Energy Agency, 2017). Arguments supporting BEVs adoption are that electric powertrains are more energy efficient for propelling vehicles than conventional internal combustion ones fueled by petrol, ethanol or diesel, besides electric propulsion barely emits noise (Sadek, 2012). In order to analyze the actual environmental benefits from BEV deployment, numerous studies have paid attention to their performance compared to internal combustion engine vehicles (ICEV) and hybrid electric vehicles throughout their entire life cycle. Research include fuel/electricity generation, use phase, vehicle production and in some cases End-of-Life (EOL) stage (Boureima et al., 2009; Faria et al., 2013; Hawkins, Singh, Majeau-Bettez, & Strømman, 2013; Helms, Pehnt, Lambrecht, & Liebich, 2010; Ma, Balthasar, Tait, Riera-Palou, & Harrison, 2012; Messagie, Boureima, Coosemans, Macharis, & Mierlo, 2014; Rajagopal et al., 2012). Results show lower GHG emissions for BEVs when a complete life cycle is considered. Conventional and hybrid busses have also been a matter of research (Buø, 2015; Olofsson & Romare, 2013) however, few evidence was found on battery electric busses (BEB) comparative LCA (Falco, 2017). Furthermore, there is evidence of BEV/Conventional cars LCA research within the Brazilian framework (Choma & Ugaya, 2013; Velandia Vargas, Seabra, Walter, Cavaliero, & Falco, 2016)
In a literature review containing conclusions from 79 papers, Nordelöf et al.(2014) stressed that vehicle LCA results vary greatly. It is also reported that only a few articles appropriately report the study time scope. Moreover, most of the studies focus on current BEV technology, which is rapidly evolving, meaning that there is a lack of future time perspective, e.g., evolution in materials, mass reduction and variations in electricity production. A conclusion, common to every study is that when the functional unit of comparison is defined as a travelled distance, for instance: 1 Km or 1 mi, electricity generation is the main cause of environmental impact for EVs. Consequently, they can reach their full potential in mitigating global warming only if the charging electricity is not fossil carbon intensive. Surprisingly, very few reports put emphasis in transmitting this conclusion as a core message.
Like all GHG mitigation actions, the implementation of EVs must be evaluated carefully to avoid environmental burden shifting or rebound effects. Skepticism is present, Frischknecht & Flury (2011) even point out that the role and contribution of electric cars to significantly mitigate the environmental impacts of transportation might be substantially overrated and that one core aspect to lower environmental impacts of individual mobility is a considerable evolution in terms of vehicle weight and performance.
Current BEVs manufacture has demonstrated to often increase GHG emissions when compared to ICEVs manufacture (Hao, Mu, Jiang, Liu, & Zhao, 2017; Kim et al., 2016). Battery evolution and mass reduction are promising opportunities to offset BEVs larger environmental burden during production stage.
Location has proven to be an important factor to be considered in both life cycle inventory (LCI) collection and even life cycle inventory analysis (LCIA). Studies have shown that location dependent impact assessment for categories like acidification and eutrophication provide more accurate results than site-generic assessments. LCI uncertainties related to geographical features are a matter of study. Mutel & Hellweg (2009) developed a method to couple existing regionalized characterization factors with large LCI databases which allowed them to obtain different total scores, identify different hotspots and even to vary distributions of the environmental impacts.
Lack of spatially differentiated LCI are specially challenging for large geographies like Brazil, for which most of its inventories are aggregated at country level in the best of cases. Regionalized inventories are essential in order to obtain more reliable results. In the context of the United States, Hao et al (2012) showed that the life cycle GHG and other air pollutants emissions induced by both gasoline and diesel vehicles differ to a varying extent among different regions when considering upstream life cycle stages: crude oil recovery, transportation, refining and distribution. Moreover, Brethauer et al (2015) adjusted both, LCI and characterization factors in order to obtain more spatially differentiated results. Their outcome: It was found that hybrid vehicles known as extended range electric vehicles presented an emission reduction in urban areas when compared to BEVs.
When comparing all LCA stages, the collection of LCI is generally the most effort intensive. This phase includes the quantification of inputs (primary and manufactured), by-products and environmental emissions to air, soil and water. The search for inventories, often results in no available data for a specific region, or in the best of cases LCA practitioners find data adapted to reflect global average values. This lack of geographical detail embodies a great concern for studies which are expected to be more accurate. Country overall data is usually used to represent regions geographically too distant or that present very different environmental conditions e.g. altitude, latitude, weather.
In the same way, LCA primary data is either confidential or it is scattered and difficult to find for researchers who usually do not have access to data for the entire life cycle of the car. Hence, data from diverse stakeholders is required for each stage, adding time and space uncertainties for the study. Furthermore, absence of transparency about the influencing factors of LCA in EVs creates great difficulty for boundary definition and makes the analysis prone to flaws. According to Egede et al. (2015) material composition of the vehicles, electricity mix and use patterns are considered to be the main influencing factors on BEVs environmental assessments.
The National Research Council of the National Academies (2011) points out three main methods to make steel structures lighter. One of them is to substitute lower-strength steel for higher-strength steel. Higher-strength steel can be made thinner thus reducing mass; however, its use can reduce the ability to meet design strength criteria. Furthermore, forming processes might imply an additional environmental burden even greater than that of the avoided mass. Another way to reduce vehicle mass is to substitute conventional steel for sandwich metal material. Sandwich material is light, stiff, and can be formed for many parts. As a downside, joining the parts may be difficult, expensive and it may need additional manufacture processes. Finally, the use of tubes which aim for an optimal use of steel (and aluminum) result in less mass without putting design criteria at risk. Although all of the previously stated methods may increase costs in the present day, this problem is expected to be overcome as mass production is achieved.
Lotus Engineering (2010) reported the technical feasibility for a 2017-2020 mass reduction development program. This model assumed a target total vehicle mass reduction of 40% to be achieved while considering a 50% upper limit constraint on total vehicle piece cost relative to the baseline car: Toyota Venza 2009. This development was intended for a 2020 Model. All technologies used to reduce mass at the vehicle had to be ready to use within the company in 2017 or earlier.
In a comprehensive review of technical literature Lutsey (2010) reports that by means of model redesign, automakers can achieve up to 20% of mass reduction in their vehicles at little or no additional cost and most surprisingly, without deeply shifting their manufacturing technologies. It also reports that a number of technical studies state that vehicle mass reductions from 20-35% in weight could be both feasible and affordable with technology shifts toward mass-reduction techniques. Finally, some automakers roadmaps indicate that mass-reduction technology with minimal additional manufacturing cost could achieve up to a 20% reduction in the mass of new vehicles in the 2015-2020 timeframe.
Reduction material approach is also merged with material switching alternatives. Das (2014) LCA research evaluated alternative lightweight vehicle designs in comparison to a baseline model. A high strength steel and aluminum design (“LWSV”) and an aluminum-intensive design (AIV) were considered. Results show AIV design achieved mass reduction of 25% (compared to baseline) consequently resulting in a decrease in total life cycle primary energy consumption by 20% and CO2 emissions by 17%. In contrast, LWSV have a mass reduction potential of only 15% which leads to higher overall life cycle energy consumption (9%) when compared to AIV design. Overall, the AIV design showed the lowest environmental impact per mile from both; climate change and primary energy consumption point of view.
In another study including mass reduction and materials switching aproach Ricardo AEA (2015b) aimed to understand the potential for automotive mass reduction in the EU market by means of a wide-ranging literature review. Among their conclusions it must be highlight that in spite of the fact that there are examples of vehicles produced almost entirely from high strength steels, aluminum or composite materials, future trends are likely to present a multi-material scenario. Therefore, material use predictions are bound to high levels of uncertainty.
Although there has been a growing interest on electric mobility options in Brazil there is no mass production of electric vehicles in Brazil currently. BEVs future market penetration along with tax regulations for imported goods (AES Brasil, 2017) could encourage automakers to manufacture the cars in the country. However, it remains unclear to what extent a Brazilian car is environmentally advantageous over an imported one. Environmental benefits of BEVs when compared to ICEVs depend greatly on electricity mix but also in the vehicle itself.
The purpose of this study was to evaluate what would be the environmental impact of manufacturing BEVs and BEBs in Brazil by adjusting LCIs to local conditions and then comparing to LCIs which employ data geographically intended to represent a global average. In order to do it, a representative number of Ecoinvent datasets were adapted to better represent Brazilian southeast conditions. Additionally, this research envisioned evolution scenarios for BEVs and BEBs, thus being able to compare Brazilian and global LCIs for a 2030 scenario. Finally, it was our intention to stablish a comparison of environmental impacts per travelled kilometer for each case.
Goal and scope definition

This LCA study was carried out to compare the environmental impact of hypothetically manufactured BEVs and BEBs in Brazil versus their average global counterparts. The Brazilian BEV and BEB life cycle inventories were adapted to best represent a manufacturing process in Brazilian south-east conditions for what is considered to be a 2015 and a 2030 scenario. Then, those results were compared to what is intended to be a global BEV/BEB for 2015 and 2030 as well. For 2030 two lightweight scenarios for the BEV were considered. The functional unit was 1 electric vehicle and 1 electric bus. A cradle to grave product system was considered, thus EOL stage is included. The employed impact assessment method is Recipe Hierarchist midpoint (Goedkoop et al., 2009) while the software employed was SimaPro v8.3.0.(PRé-Consultants, 2014). An attributional approach was adopted based on Ecoinvent v3.02 (Swiss Centre for Life Cycle Inventories, 2015) datasets for the BEV, whereas for BEB material composition we considered information from Garcia Sanchez et al.(2013). Finally, as a way to further contextualize this research in Brazilian conditions we included a use phase for the vehicles, considering a functional unit of 1km.
Boundaries and evolution parameters
The system boundaries for a LCA study determine which processes and activities the overall analysis includes, in this case the boundaries were outlined firstly to evaluate the manufacture stage of both BEVs and BEBs and secondly to analyze the phase use of the vehicles. Ecoinvent LCIs for BEVs can be found following a Unit Process scheme. This scheme creates a hierarchy in which a given process is composed of several inputs which in turn are made of other several inputs. It was our intention to model as much of the vehicle production chain as possible for both BEVs and BEBs by adapting those Unit Process datasets that model the BEVs and BEBs manufacturing stage. Since Ecoinvent V3.02 do not represent the specific Brazilian conditions in most cases the vast majority of the processes must be adapted. We focused on raw materials production, especially steel and aluminum, Brazilian electricity mix, and transportation. A detailed description of all adjusted Ecoinvent processes is shown in the Appendix. General parameters for vehicles evolution are found in Table 1. Electricity mix specifics are presented in electricity generation section.
Table 1. Parameters for 2015 and 2030 scenarios for BEVs and BEBs
Battery Electric Bus Battery Electric Vehicle
2015 scenario
Vehicle w/o battery 11,010 Kg 1,243 Kg
Battery mass 3,289.43 Kg 296 Kg including heaters
Vehicle performance 1.66 Wh Km-1 (1.50 kWh Km-1*90% efficiency) 167 Wh Km-1 (150 Wh Km-1* 90% efficiency)
Energy density 11.40 E-2 kWh Kg-1 10.14 E-2 kWh Kg-1
Electricity mix Year 2014. EPE (2016)
Year 2014. EPE (2016)

Maintenance 17% of materials for assembly stage.
17% of energy required for assembly Ecoinvent dataset: Maintenance, passenger car, electric, without battery, Alloc Def, U. One maintenance for 150,000 km
Life expectancy 220,000 Km Battery
880,000 Km Bus w/o battery 100,000 mi Battery
120,000 mi Car w/o battery
2030 scenario
Bus w/o battery 9.469 Kg Aluminum scenario 1.156,48 Kg
Plastic scenario 1.035,74 Kg
Battery mass 2.857,1 Kg 228.6 Kg
Energy density 16.0 E-2 kWh Kg -1 35.0 E-2 kWh Kg-1
Vehicle performance 1.33 kWh Km-1 (1,20 kWh Km-1*90% efficiency) 133.3 Wh Km-1 (120 Wh Km-1*90% efficiency
Electricity mix Forecast for 2030. EPE(2016)
Forecast for 2030. EPE (2016)

Maintenance 17% of materials for assembly stage.
17% of energy required for assembly Ecoinvent dataset: Maintenance, passenger car, electric, without battery, Alloc Def, U. One maintenance for 150,000 km
Life expectancy 264,000 Km Battery
1’056,000 Km Car w/o battery 120,000 mi Battery
144,000 mi Car w/o battery

Battery Electric Vehicle
BEV Ecoinvent dataset is originally based in Habermacher (2011), whose starting point of analysis was the material content of a Volkswagen Golf A4, as it was modeled by Althaus & Gauch (2010). Habermacher (2011) created a baseline scenario and two lightweight scenarios for car glider, aiming to model future mass reductions based on synthetic and aluminum material substitutions. Glider refers to a vehicle without powertrain and battery. Both, 2015 and 2030 scenarios present the same basic Unit Process structure seen in Figure 1. Both datasets, “Car without battery Alloc Def, U” and the “Li-ion battery, rechargeable, prismatic Alloc Def, U” are adapted to mass data for 30kWh Nissan Leaf 2016 Accenta, Black edition, Tekna (NISSAN, 2016b) as presented in Table 1. Nissan Leaf was chosen for being the second bestselling car in the world, hence we considered it to be representative of BEV market (Cleantechnica, 2017).

Figure 1. Unit process visual scheme for a BEV

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