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Environmental evaluation of future passenger vehicle technologies

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Environmental evaluation of future passenger vehicle technologies

Abstract and Figures

The present report summarizes the work undertaken as part of the ME3 Master’s final internship on the subject of Life Cycle Assessment (LCA) of future electric vehicles at Paul Scherrer Institute. This thesis is part of a larger project called Technology-centered Electric Mobility Assessment (THELMA), aimed at an integrated assessment of a significant penetration of electric vehicles into the Swiss transport sector, and the impacts on both the Swiss electric grid and Switzerland as a whole. The objectives of this thesis are to model the environmental inventories associated with production, use and end-of-life (EOL) of electric vehicles from a life cycle perspective. This will be done for different years (current, 2015, 2030). In addition, the environmental life cycle impacts (climate change, energy demand, air pollution, resource depletion) per km of transport are calculated. Finally, the LCA results of electric cars equipped with batteries are compared with conventional reference technologies (ICE cars fuelled with gasoline, diesel and natural gas). iii
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ENVIRONMENTAL EVALUATION OF FUTURE PASSENGER VEHICLE
TECHNOLOGIES
ashreeta prasanna
A Life Cycle Assessment with focus on electric drivetrains
Ecole des Mines de Nantes
Royal Insitutute of Technology
Universidad Politechnica de Madrid
Paul Scherrer Institute
Photo Credit:Travis Sweet
INDEX NOTE
Report Title: Environmental evaluation of future passenger vehicle
technologies
Placement Title: Industrial project
Year: 2012
Author: Ashreeta Prasanna
Company: Paul Scherrer Institute
Address: 5232 Villigen PSI Switzerland
Company tutor: Christian Bauer
Role: Life cycle analyst research engineer
School tutor: Dr. Valerie Hequet
Key words: Life cycle analysis, electric vehicles, lithium batteries, greenhouse
gas, lightweighting, use phase modelling
Summary: The present report summarizes the work undertaken as part of the
ME3Master’s final internship on the subject of Life Cycle Assessment (LCA)
of future electric vehicles at Paul Scherrer Institute. This thesis is part of a
larger project called Technology-centered Electric Mobility Assessment
(THELMA), aimed at an integrated assessment of a significant penetration of
electric vehicles into the Swiss transport sector, and the impacts on both the
Swiss electric grid and Switzerland as a whole.
The objectives of this thesis are to model the environmental inventories as-
sociated with production, use and end-of-life (EOL) of electric vehicles from a
life cycle perspective. This will be done for different years (current, 2015,2030).
In addition, the environmental life cycle impacts (climate change, energy de-
mand, air pollution, resource depletion) per km of transport are calculated.
Finally, the LCA results of electric cars equipped with batteries are compared
with conventional reference technologies (ICE cars fuelled with gasoline, diesel
and natural gas).
iii
I never see what has been done; I only see what remains to be done.
Buddha
ACKNOWLEDGMENTS
I would like to thank my supervisor Christian Bauer for his encouragement,
guidance and support during this project. I would also like to thank Andrew
Simons and Johannes Hofer for their guidance and help during this project.
Lastly I would like to thank my academic supervisor Valerie Hequet and my
classmates for their continued encouragement during this research work.
iv
CONTENTS
1 introduction 5
1.1Project Context 5
1.2Objectives and Structure 5
2 lca scope and framework 7
2.1Functional unit 7
2.2System boundaries 8
3 vehicle life cycle analysis 11
3.1Electric Vehicle Technologies: Manufacture, Use phase and End
of Life 11
3.2ICEV Vehicle Technologies: Manufacture and End of Life 17
3.3Fuel Consumption Calculations 18
4 results 21
4.1Life Cycle Impact Assessment Methods Used 21
4.2Comparison of Battery Types 21
4.3Life Cycle Emissions Results 23
4.4Vehicle Light-weighting Results 24
4.5Sensitivity Analysis 27
5 conclusions 33
6 limitations and future work 35
i appendix 37
a new data inventories created 39
bibliography 41
v
LIST OF FIGURES
Figure 2.1LCA Boundary 9
Figure 3.1Percentage composition of cell components 12
Figure 3.2Manufacturing a cell 16
Figure 4.1CO2eq emissions of Batteries per kg and per kWh stor-
age capacity 22
Figure 4.2Environmental impacts of Cells, calculated using ReCiPe
World (H,H) 23
Figure 4.3Life Cycle CO2eq emissions for all vehicles 23
Figure 4.4ReCiPe Method results 25
Figure 4.5Effect of light-weighting vehicle glider, EV and Gasoline
vehicle 26
Figure 4.6Comparison of Light-weighting the BIW in vehicle glid-
ers with Aluminum, HHS and CFRP 27
Figure 4.7Effect of light-weighting on EV and Natural gas vehi-
cles 27
Figure 4.8Sensitivity analysis using French and Swiss electricity
mix for EV charging, IPCC GWP 100a Results 29
Figure 4.9Sensitivity analysis using French and Swiss electricity
mix for EV charging, ReCiPe (H,H) method 30
vii
LIST OF TABLES
Table 3.1Vehicle characteristics and source of datasets 12
Table 3.2Lithium Ion Batteries Characteristics 13
Table 3.3Li Air and Li S Battery Characteristics 14
Table 3.4Manufacturing energy demand calculated based on pro-
cessing requirements 15
Table 3.5EV Vehicle characteristics and fuel consumption 16
Table 3.6ICEV Vehicle characteristics and fuel consumption 17
Table 3.7Constants used to calculate energy demand at the wheels 18
ix
EXPLANATION OF VARIOUS TERMS USED
future batteries Batteries expected to enter the market in 2030 to 2050
near future batteries Batteries expected to enter the market in 2015 to 2020
Cradle to Grave Full Life Cycle Assessment from resource extraction
(’cradle’) to use phase and disposal phase (’grave’).
Well-to-Wheel Well-to-wheel is the specific LCA used for transport fuels
and vehicles. It incorporates the feedstock or fuel
production and processing and fuel delivery or energy
transmission as well as vehicle operation.
Cradle to Gate Cradle-to-gate is an assessment of a partial product life
cycle from resource extraction (cradle) to the factory gate
(i.e., before it is transported to the consumer).
glider All vehicle parts excluding the drivetrain. Includes Body
in White
light-weighting Reducing weight of vehicle BIW by substituting Steel with
materials such as Aluminum, HSS and CFRP
embedded emissions Emissions from production of a product. (Cradle to gate)
low carbon Minimal output of greenhouse gas (GHG) emissions into
the environment.
data sets/data inventories The compilation and quantification of the relevant input
and output flows for the the quantification of a product or
process.
Body in White BIW refers to the stage in automotive design or
automobile manufacturing in which a car body’s sheet
metal components have been welded together, but before
moving parts (doors, hoods, and deck lids as well as
fenders) the motor, chassis sub-assemblies, or trim (glass,
seats, upholstery, electronics, etc.) have been added and
before painting.
second life When a battery reaches a point where it can no longer be
used in an electric vehicle, it still retains 75-80% of its
potential capacity for another use. The next use of the
battery is referred to as its second life.
1
LIST OF ABBREVIATIONS
BEV Battery Electric Vehicles
BIW Body-in white
BMS Battery Management System
CFRP Carbon fiber reinforced composite
CH Switzerland
CO2Carbon dioxide
EU European Union
EV Electric Vehicles
FR France
GHG Green House Gases
GWP Global Warming Potential
HHS High Strength Steel
IPCC Intergovernmental Panel on Climate Change
kg CO2-eq kilograms of carbon dioxide equivalent
LCA Life Cycle Assessment
LCI Life Cycle Inventory
LCIA Life Cycle Impact Assessment
LFP Lithium Iron Phosphate
LHV Lower heating value
Li Air Lithium Air
Li S Lithium Sulfur
LW Light weighted
MJ Megajoules
NCA Lithium Nickel Cobalt Aluminum Oxide
NG Natural Gas Vehicle
NOx Nitrogen oxides
PT Powertrain
ReCiPe (H/H) A life cycle impact assessment method which comprises
harmonized category indicators at the midpoint and the
endpoint level
TTW Tank-to-wheel
UCTE Union for the Co-ordination of Transmission of Electricity
vkm vehicle kilometer
WTT Well-to-tank
WTW Well-to-wheels
wt weight
2
ABSTRACT
The transportation sector is projected to account for 82 percent of the total in-
crease in world (energy related) liquid fuel consumption by 2035 and personal
travel will support fast-paced growth in energy use for transportation both in
the short term and over the long term [27]. With growing concern about the
high level of contribution to greenhouse gases from the transportation sector,
governments are responding with policy measures to increase the fuel effi-
ciency of their vehicle fleets. Increasing the percentage of electric vehicles in
the fleet is considered to be one of the solutions towards an environmentally
sustainable transportation system. This research aims to assess the environ-
mental performance of a range of vehicles expected to be introduced into the
market between 2015 to 2030, using a life cycle approach. The vehicle technolo-
gies which have been assessed in this study are battery electric vehicles, natural
gas, diesel and gasoline vehicles. By conducting a cradle-to-grave Life Cycle
Assessment (LCA), a better idea of the impacts of future electric vehicles on
the environment can be obtained, and suitable policy measures implemented
[3].
The environmental performance of passenger transport vehicles in this the-
sis is evaluated by two LCIA methods, the IPCC GWP100a [26] and the ReCiPe
World (H,H) [22] method; and impacts are calculated for one vehicle kilometer
as a functional unit. The IPCC GWP method – providing cumulative Green-
house Gas (GHG) emissions as result – is one of the most commonly used in
LCA and the results can be easily interpreted. However, transport activities
generate several other environmental impacts besides global warming emis-
sions. Thus the ReCiPe method – addressing a comprehensive range of envi-
ronmental burdens – is used to provide an understanding of the vehicle overall
environmental profile in impact categories such as human toxicity, freshwater
eutrophication, resource depletion, etc. As part of this thesis, several new data
inventories have been created for future vehicles, using projections of technol-
ogy improvements and assumptions on future battery technologies [10]. Some
of the important aspects of this research are:
LCA of future electric vehicles with LFP, NCA, Lithium Air and Lithium
Sulfur Batteries
Impacts of light-weighting of the vehicle BIW by using materials such as
high strength steel (HHS), aluminum and carbon fiber reinforced com-
posites (CFRP)
Calculations of use phase fuel demand based on decreased weight and
improved drivetrain efficiencies with a driving cycle that reflects real
world conditions
Recycling of the metallic content in vehicles and disposal of non-recyclable
parts of the vehicle
3
The results of this LCA show that the environmental performance of EV strongly
depends on the electricity mix used for charging. Using the current average Eu-
ropean electricity mix which is carbon intensive results in the life cycle GHG
emissions of EV being similar to those of natural gas vehicles. Gasoline vehi-
cles continue to have the highest greenhouse gas emissions while EV having
batteries with high energy density have lower battery weight and thus lower
manufacturing and use phase emissions per vehicle km.
Results of light-weighting vehicles show that for the boundary conditions
and assumptions used in this study, light-weighting of EV with CFRP does
not decrease life cycle emissions. In the case where the charging electricity mix
is renewable or with low carbon emissions, it is preferable not to lightweight
EV or to lightweight them by using recyclable materials which are less energy
intensive such as HHS or standard steel. This is because when the charging
electricity mix has very low emissions, most of the emissions from EV life
cycle come from the manufacturing stage and further reductions in life cycle
emissions can only come from the manufacturing or end-of-life stages of the
EV life cycle. Decreasing use phase emissions for the EV would have a minimal
impact on life cycle emissions and thus for low carbon electricity mix, life
cycle emissions of EV can be reduced more effectively by improving efficiency
of manufacturing processes, using materials with high recyclability and by
standardizing batteries and their production methods.
4
1
INTRODUCTION
1.1 project context
As part of the Joint European Masters in Management and Engineering of
Environment and Energy, this thesis has been undertaken at the Paul Scherrer
Institute (PSI) in the Technology Assessment Group. PSI is the largest research
centre for natural and engineering sciences within Switzerland and research
work is undertaken in the subject areas of Matter and Material; Energy and
the Environment; and Human Health.
As part of the Laboratory for Energy and Environment Analysis (ENE), the
technology assessment group at PSI assess present and future energy systems.
These include electricity, heating and transportation systems. This LCA has
been undertaken under a broader project called Technology Centered Elec-
tric Mobility Assessment (THELMA), which is aimed at understanding the
multi-criteria sustainability implications of widespread electric vehicle use in
Switzerland. The project is being undertaken by a partnership of 6different re-
search groups within the domain of the Swiss Federal Institutes of Technology,
and funded by a range of stakeholders led by the Competence Center for En-
ergy & Mobility and SwissElectric Research [40]. This thesis is written under
the scope of Work Package 1(WP1) which is part of the THELMA project. The
main goal of WP1is the LCA-based environmental performance evaluation
of vehicles and energy supply chains (electricity and fuels). The focus is set
on technologies and materials related to EV, with competing vehicle technolo-
gies included for comparison. The analysis also addresses future advances in
vehicle technology up to 2030.
1.2 objectives and structure
The objectives of this project are to assess the environmental aspects and po-
tential impacts associated with future electric vehicles by conducting research
divided into the following stages:
Familiarization with LCA methodology and the current work completed
under the THELMA project at PSI.
Familiarization with the LCA software, databases and current datasets
(LCA software SimaPro and the Ecoinvent v2.2database).
Literature review focusing on future development of electric drivetrains.
Interaction with the fuel cell and battery development groups at PSI in
order to get information on material composition of future batteries.
5
Compilation of Life Cycle Inventories (LCI) of production, use phase and
end-of-life of future passenger cars.
Interpretation and discussion of LCA results for these cars.
Writing the thesis report (in parallel to the tasks above)
Some of the main aspects of this thesis are to identify and quantify use of
energy, water and materials usage and environmental releases (e.g., air emis-
sions, solid waste disposal, waste water discharges) for the various data sets
collected. The focus while conducting this thesis research was on the following:
Material composition of future batteries, future battery chemistries and
their characteristics
Performance of these batteries (life time, efficiency, energy density, weight,
etc)
Production of future batteries (process emissions, energy requirements)
Potential recycling of vehicle components
Impacts of vehicle light-weighting
Technology-specific performance of vehicles in the future: exhaust and
non-exhaust emissions, energy demand for driving, etc
Impacts of the charging electricity mix
LCA is an iterative process, and the details of this indicated work program
have been adapted during the project. The results of this work provide a good
estimation of the environmental performance of various car technologies in the
future (until 2030, with an outlook until 2050). The environmental performance
significantly depends, especially for battery vehicles, on the origin of fuel, i.e.
the source of electricity mix which is used to power the EV.
6
2
LCA SCOPE AND FRAMEWORK
This LCA has been carried out according to the procedure described by ISO
14040:2006 and 14044:2006 and the various components in this LCA are de-
scribed as follows:
Goal: To carry out an environmental evaluation of future battery electric
vehicles and in addition, compare their environmental performance to
gasoline, diesel and natural gas vehicles of the future. Future vehicles are
modeled with improved drivetrain efficiencies and light-weighting, but
fuel chains, electricity, emission levels and vehicle weight are modeled
according to current current conditions.
Inventory analysis: In this study new data inventories for battery mate-
rials and various other components in the vehicle life cycle have been
created. The Ecoinvent database v2.2[50] is used as background from
which new datasets have been constructed to conduct the LCA study.
The newly created datasets are listed in Appendix A. In some cases,
where information on air and water emissions is not available, impor-
tance has been placed on accurate material and energy requirements of
the the product or process and emphasis has been placed on CO2equiv-
alent emission results which would give a more accurate picture of the
environmental impacts.
The LCIA has been carried out using the methods GWP100a [26] and
ReCiPe World (H,H) [22]. Evaluation of the results of the inventory analy-
sis and impact assessment has been carried out by selecting the preferred
method to decrease GHG emissions from passenger transport, with a
clear understanding of the uncertainty and the assumptions used to gen-
erate the results. A sensitivity analysis has been carried out to evaluate
the effect of using different charging electricity mix.
2.1 functional unit
The functional unit in this LCA varies according to the product or life cy-
cle stage being evaluated. However, for final results, environmental impact
indicators are expressed on a per vehicle km basis. For the battery LCA, the
functional unit considered is per kilograms. This is because the material com-
position of the battery has been defined in kilograms and battery weight is an
important factor in electric vehicle use phase. In the results section, the envi-
ronmental impacts of the various batteries are also shown on a per kWh basis.
This would be more relevant in the case that batteries are to be compared
independent of their use phase and end of life.
7
The various vehicles modeled have been defined as a combination of vehi-
cle glider, drivetrain and battery. All of these components can be evaluated
individually on a per part basis. In this study an additional analysis has been
conducted with respect to vehicle light-weighting. The effect of light-weighting
in the emissions produced by manufacturing a vehicle are measured on a per
part basis. To evaluate the effects of light-weighting on the use phase, the vehi-
cles are assumed to have a lifetime of 150,000 km and the results are calculated
on a per km basis. In general, for all evaluations which involve a product, the
results are obtained on a per kg basis, while use phase evaluations are done
on per vehicle km basis.
2.2 system boundaries
LCA is conducted for future vehicles, which are categorized as battery electric,
gasoline, diesel and natural gas vehicles. The vehicles are modeled on the Volk-
swagen Golf A4and the data inventories for the vehicle shell and drivetrain
have been derived from Schweimer and Levin [46], Habermacher [24].
The life cycle of a vehicle consists of its manufacture, its use phase, the fuel
chain used in its use phase and finally its end of life or recycling. Descriptions
of these and the corresponding boundaries are shown in Figure 2.1.
8
Primary Energy
Processing
Distribution and
Infrastructure
People
Primary energy of fuel
Primary energy location
Energy Extraction process
Embedded emissions
Method of
distribution
Infrastructure
Number of
Workers
Environmental
legislation
Boundary for Fuel Chain LCA
(a) Elements in Fuel Chain
Design and
Development
Vehicle
Specification
Materials and
Energy
Production
Processes
Logistics
People
R & D
Prototypes
Supplier selection
Testing
Material Selection
Source of Material
Extraction process
Recycled content
Material Availability
Energy Mix
Vehicle size
Vehicle mass
Powertrain
Battery choice
Manufacturing process
Manufacturing efficiency
Location
Waste Produced
Re-use of waste
Supply chain
Types of transport
Transport distance
Number of
Workers
Environmental
legislation
Boundary for vehicle production LCA
(b) Elements in vehicle manufacture
Vehicle
Specification
Fuel
Driver
Geography
Maintenance &
Servicing
Fuel type
Fuel quality
Fuel supplier
Real world
consumption
Vehicle size
Vehicle mass
Powertrain
technology
Battery choice
Tailpipe emissions
Fuel Consumption
Price
Location
Terrain
Climate and
weather
Road congestion
Urban/Motorway
Service interval
Oil and coolant
changes
Replacement
parts
Vehicle lifetime
Boundary for Use Phase LCA
Driving habits
Annual mileage
Care of vehicle
Air conditioning
(c) Elements in vehicle Use Phase
Figure 2.1: LCA Boundary
9
3
VEHICLE LIFE CYCLE ANALYSIS
A detailed description of the various vehicles modeled in this LCA is shown
in Table 3.1.
3.1 electric vehicle technologies:manufacture,use phase and
end of life
Inventory data has been collected for four different battery chemistries used in
EV; two types of Lithium Ion chemistries and Lithium Air and Lithium Sulfur
battery chemistries. Table 3.2and Table 3.3give an overview of the detailed
characteristics of these battery chemistries and Figure 3.1shows the percent-
age composition of various components of the cell for the various batteries
evaluated in this thesis. These particular chemistries have been chosen for eval-
uation since after conducting a literature review they have been identified as
those with the most potential for development and the possibility to offer the
highest specific densities and thus lowest battery weight [5,10,16,35,54].
All modeled batteries are assumed to be made up of cells, a Battery Manage-
ment System (BMS), busbars, cabling, shunts, wiring harnesses, and housing.
The cells are usually packaged in pouch configuration, since pouch cells can
achieve a packaging efficiency of 90 95% and absence of a metal can allows
for lower weight and higher energy density. This cell construction uses a poly-
mer for the electrolyte, and is thus less volatile and less prone to leakage. All
cells consist of the basic components, the anode, cathode and electrolyte. The
anode and cathode are usually positive and negative charged plates in contact
with an electrolyte that produces an electrical charge by means of an electro-
chemical reaction. On discharge, electrolytic cells convert chemical energy to
electrical energy.
Besides the material composition and percentage in mass of the components
in cells, data with respect to manufacturing cells and batteries is another im-
portant aspect for environmental evaluation. Due to reasons of confidentiality
it was not possible to collect detailed information from battery manufacturers
with respect to material composition, emissions, water usage or energy require-
ments. Thus the main sources of information used to model the various cells
and corresponding batteries were from publications and cost estimates. (Table
3.4) [37,18,19,16,53,27,41,54,5]. Some battery manufacturers have offered
feedback on the data collected, and this ensures that the modeled batteries
and cells are based on justified assumptions. The standard energy and heat
required in the manufacture of battery and cells is calculated based on the
energy demand for various processes involved in cell and battery manufac-
ture and assembly. Figure 3.2describes the various required processes based
11
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Figure 3.1: Percentage composition of cell components
12
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References:
a: C. Bauer, 2010. [6]
b: L. Cui et al., 2009. [15]
c: M.-H. Park et al., 2009. [15]
13
!
! "#$%& '()*$%& "#$%& '()*$%&
! +$,$-.!/,(0*1)& 21)*1-3 4-56-, 21)*1-3
'&55!713&#.1$#.8!59:9*!
;<3=
>?(..&!"<)1@&!3()&,1(5!
"#$%&A'()*$%&B
"<)1@&!3()&,1(5 C9DE&# 21)*1-3 4-56-, 21)*1-3
4*$,)!6$,3 ' 21 421
'(0(<1)D!;3"*AE>"<)1@&!
3()&,1(5B=
F!GHII F!JIII F!KIII F!JIII
'-,,&#)!<$55&<)$, '$00&, "5-31#-3 '$00&, "5-31#-3
4&0(,()$,
L5&<),$5D)&
'(0(<1)D;MN*=
?(..!;ME=
+&,<&#)(E&!O)!<&55.
+&,<&#)(E&!O)!*$-.1#E
+&,<&#)(E&!O)!P?4
L#&,ED!7&#.1)D!;N*AME=
QR
QR
QII
KGI
SQI
SGT
UIR
UIR
KUR
KUR
21)*1-3A"1,VP())&,D
21)*1-3A4-56-,VP())&,D
QI
QI
21+WT8!L)*D5&#&!'(,:$#()&
21)*1-3!X,165()&8!L)*D5&#&!
'(,:$#()&!*
+LA++
+LA++
21)*1-3A"1,V'&55!%8&86
21)*1-3A4-56-,V'&55!%8&8E
SG9SG9SYK
SG9SG9SYK
SYKG
SYKG
Table 3.3: Li Air and Li S Battery Characteristics
References:
d: P. G. Bruce et al., Jan. 2012. [11]
e: J. Christensen et al, 2012 [14]
f: Boston Consulting Group,2010. [10]
g: M. Armand et al., Feb. 2008. [5]
h: V. S. Kolosnitsyn et al., Jun. 2008. [29]
14
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!"#"$%&'("$)*+&,&-./"0*&12/*+"234 5657 8.9"$0*$/&0:6:;&<32=/*+&1"#"$%
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HAI,D,JAI,781.2 Q*2/
Table 3.4: Manufacturing energy demand calculated based on processing require-
ments
References: Gaines et al. [18], Ross et al. [44]
on which the manufacturing energy demand has been calculated. Information
about nano structured materials processing has been gathered from reference
materials such as Khanna et al. [28]. While these give a good estimation of
energy requirements, information with respect to particulate emissions is cur-
rently not available. Thus the drawback of this study is that the environmental
impacts calculated have a higher accuracy for results concerning GHG emis-
sions and energy requirements rather than other environmental indicators.
The battery EVs are modeled using a standard vehicle shell, an electric drive-
train and the respective battery with specific chemistry. The drivetrain weight
is 200 kg and the steel vehicle shell weight without light-weighting is 1033
kg. The combined weight of a standard vehicle body and the battery is then
used to conduct the use phase energy demand calculations for the specific ve-
hicles with different battery chemistry and thus different battery weight. The
EV body shell and drivetrain has been modeled by sizing up the existing in-
ventory for the Golf body shell which is in the current Ecoinvent database.
The electric drivetrain has also been sized up to accurately reflect the vehicle
weight of BEV on the current market. Other characteristics of future BEV such
as rolling resistance, charge/discharge efficiency, aerodynamic resistance are
modeled by using average values of vehicles which are currently existing in
the market and from published references. One aspect of future EVs is that
the vehicle shell is light-weighted using high strength steel, aluminum and
carbon fibre reinforced plastics (CFRP). This is a likely development for future
EV’s with newer battery technologies which have been modeled in this study.
EV inventories and assumptions used for the analysis are described in Table
3.5. One of the future developments for EVs is the possibility to use them to
stabilize the grid. The impacts of the smart grid system for an environmental
analysis is as yet uncertain, with the main parameter being affected would be
lower charge efficiency for batteries in EV. This has been modeled by using a
15
Figure 3.2: Manufacturing a cell
Source: http://www.mpoweruk.com/battery_manufacturing.htm
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)*+,-.$/0$8C)$2+,-.E*+,-.*3$E+.-$BWW$56,7 U%P 'O& %(& 'O&P &RS' &RT& 'QR(S 'SR&' U%V 'UR(P
)*+,-.$/0$8C)$2+,-.E*+,-.*3$E+.-$?2HJ+<+HJ$56,7 SOS 'O& %(& '%'S &RSO &RT& 'ORP( 'PR'S U%V 'SRTS
)*+,-.$/0$8C)$2+,-.E*+,-.*3$E+.-$D"G#$56,7 PU' 'O& %(& ''P' &RS( &RT& 'OR'P 'TRT& U%V 'SR&(
!C=BCYZ$WY!#BYG
)*+,-.$/0$>4+,+<92$8C)$56,7 '&OO '%& %&& 'O(O &RS& &RT& '(R&' 'SRQ( U%V %&R&(
)*+,-.$/0$8C)$2+,-.E*+,-.*3$E+.-$BWW$56,7 U%P '(T %&& '%SO &RS' &RT& 'QRQT 'PRSU U%V 'URQ(
)*+,-.$/0$8C)$2+,-.E*+,-.*3$E+.-$?2HJ+<+HJ$56,7 SOS '(T %&& ''UQ &RSO &RT& 'ORTQ 'PR&P U%V 'SR(T
)*+,-.$/0$8C)$2+,-.E*+,-.*3$E+.-$D"G#$56,7 PU' '(T %&& ''QP &RS( &RT& 'OR&T 'TRQU U%V 'PRUO
Table 3.5: EV Vehicle characteristics and fuel consumption
References for efficiencies [13,20,54]
charging efficiency of 92%, which is lower than a projected value of 98% for
future batteries such as lithium air or lithium sulfur.
Recycling has been modeled for the entire metal content of the battery and
vehicle, except lithium. This is because lithium is not recycled currently and it
is uncertain if it will be recycled in the future, since it is present in very small
quantities in the battery. Although current recycling of lithium ion batteries is
not extensive, it would be possible to recycle certain materials such as stainless
steel, copper and aluminum, which are used for cell packaging and electrodes.
Standard recycling rates of 90% for copper and steel and 30% for aluminum
have been applied when modeling batteries. The rest of the components in the
battery are assumed to be treated and disposed in landfill. Although recent
research shows a high potential for a second life for EV batteries in electric grid
stability or grid storage applications, the objective of this study was to capture
16
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)*+,-./01/O&)/2+,-.@*+,-.*3/@+.-/=##/89,: XRU PYH GPHU RHSUT GSUR RWRSHU WYSQP WSXY
)*+,-./01/O&)/2+,-.@*+,-.*3/@+.-/"2CE+7+CE/89,: YPY PYH GRGY RHSUT GSUR RPRSQG WYSQP WSUY
)*+,-./01/O&)/2+,-.@*+,-.*3/@+.-/>LZ?/89,: UXG PYH GGUG RRSHT GSUR RGVSRU WYSQP WSWP
Table 3.6: ICEV Vehicle characteristics and fuel consumption
References: [25,27,48,54,2]
the total environmental impacts of EVs, including battery disposal or recycling
and thus these are included in the boundary of the assessment. The battery re-
use could also apply at cell level and the key battery components, such as cells,
BMS, wiring harnesses, fuses, contactors, etc, could be re-used to manufacture
a battery pack for specific applications. However, the costs incurred in doing
this, combined with uncertain supply of end-of-life batteries, may make re-use
unviable and thus this possibility is not included in the assessment. End of life
of the vehicle shell and drive train has been modeled by a disposal process
which involves separation of all the vehicle components of the vehicle which
cannot be recycled, their treatment or precessing and subsequent disposal in a
landfill.
3.2 icev vehicle technologies:manufacture and end of life
The ICEV vehicles in the study have been modeled as a combination of a
standard vehicle shell and the appropriate ICEV drivetrain depending on the
technology. Future gasoline vehicles are modeled as having a body shell made
of materials such as high strength steel, aluminum and CFRP. In addition,
drivetrain efficiencies are project to increase from 20% to 22 % by year 2030 or
later. Table 3.6shows the various ICEV vehicle technologies (diesel, gasoline
and natural gas) with their characteristics. The vehicle shell dataset is formed
by increasing the weight of the standard body shell in the Ecoinvent database
to match that of the average shell weight of vehicles in the current market.
Scaling of the drivetrain has also been carried out accordingly. Since diesel
drivetrains are slightly larger than gasoline and natural gas drivetrains, this
has been reflected by modeling them with a higher weight.
Recycling has been modeled by applying a recycling rate to all metals present
in the vehicle. Parts that cannot be recycled are separated and disposed in
landfills. All background data with respect to non exhaust emissions, air and
water emissions are from Ecoinvent v2.2database. Manufacturing energy re-
quirements are standard, and the vehicles are assumed to be manufactured in
Europe using a UCTE electricity mix. Although in the real world most vehicles
are manufactured in China or Asia and then transported, since EV are likely
17
to be manufactured or assembled in Europe, modeling in this study has been
done keeping the fixed location of Europe with standard distances used for
logistics.
Road and vehicle maintenance have been included for completeness and
thus all the environmental impacts related to vehicle use are included in the
study. The datasets for road and vehicle maintenance are taken from the Ecoin-
vent v2.2database and are calculated based on available data and standard
assumptions for the conditions in Europe [50].
3.3 fuel consumption calculations
The fuel consumption of the various vehicles which are defined in Tables 3.5
and 3.6are calculated by the basic equations of motion governing longitudinal
vehicle dynamics. On a flat road, the mechanical power at the wheel Pwis
given by
Pw=Pa+Pr+Pm(3.1)
where Pais the power necessary to overcome aerodynamic drag, Prrolling
resistance, and Pmthe power necessary for acceleration or power available
from deceleration [25]. The energy consumption or requirement Econv can be
calculated by integrating the power demand at the wheel divided by the cycle
average vehicle efficiency.
Table 3.7: Constants used to calculate energy demand at the wheels
For electric vehicles, energy can be recuperated during breaking. Thus the
total energy required by the vehicle at the wheels is given by equation 3.2and
table 3.7lists the values of the coefficients which are dependent on the driving
cycle over which the energy demand is calculated. The various parameters
are coefficient of drag cd, coefficient of aerodynamic friction Af, coefficient of
rolling resistance cr, vehicle mass m, powertrain efficiency ηpt, recuperation
efficiency ηrec and Energy demand or fuel consumption, Ewheel.
Ewheel =1
ηpt
·(AcdAf+Bcrm+Cm) + ηrec ·(A0cdAf+B0crm+C0m)(3.2)
18
The driving cycle ARTEMIS 130 has been chosen to calculate the energy
demand since it has been designed as a reference for real-world driving cycles
[4].
19
4
RESULTS
4.1 life cycle impact assessment methods used
Two LCIA methods have been used to measure the environmental impacts
of passenger vehicle transport, the IPCC GWP 100a and ReCiPe World (H,H).
The IPCC GWP is one of the most common assessment methods used for LCIA
and it is calculated by using characterization values for GHG emissions accord-
ing to their global warming potential published by the IPCC [IPCC 26]. The
ReCiPe World (H,H) method [22] provides a global view of environmental im-
pacts by calculating mid-point and end-point indicator scores of the products
and processes assessed. Examples of indicators which are calculated in this
method are particulate matter formation, fossil depletion and human toxicity.
The ReCiPe (H,H) method is one of the most recent and harmonized indicator
approach available in life cycle impact assessment. Using these two methods
the environmental impacts of a cradle to grave analysis on various types of
vehicles can be interpreted with accuracy.
The software used to conduct the LCA is Simapro which has been developed
by Pre Consultants. The main database for most of the materials and processes
used in this study is Ecoinvent database in its current version v2.2[50].
4.2 comparison of battery types
The environmental impacts of four types of batteries modeled in this thesis
in terms of kg CO2eq emissions is presented in Figure 4.1. The emissions are
calculated in terms of the functional unit of per kg of battery and per kWh of
storage capacity. For the results for per kWh, emissions are calculated based
on each battery having a capacity of 30 kWh per charge and energy density
as defined in section 3.1. The results show that future batteries such as Li Air
and Li S batteries produce slightly lower kg CO2eq emissions than NCA and
LFP batteries when compared on a per kWh storage capacity basis but slightly
higher when compared on a per kg of battery basis. The main differences in
environmental impacts of the different batteries would arise from different
active materials, since the materials for cell packaging and battery housing are
the same for all battery chemistries. From the results of cell and battery LCA
it is observed that for the different batteries modeled, the CO2emissions are
not significantly higher or lower for particular chemistries. This is due to the
following reasons:
Active materials which are specific to the battery types do not account
for more than 40% of total battery weight.
21
Figure 4.1: CO2eq emissions of Batteries per kg and per kWh storage capacity
Some of the batteries materials are common for different batteries, eg
packaging and electric tabs.
Since CO2eq emissions for all batteries increases with battery weight, one
way to minimize environmental impacts would be to have lighter/smaller bat-
teries which would require vehicles to have a lower range per charge. The
other possibility would be to continue research and development of batteries
with high energy density since these would have lower weight for the same
range as current batteries.
The environmental impacts calculated based on the ReCiPe World (H,H)
method can be observed in Figure 4.2. As seen from the results, Li Air and Li
Sulfur batteries have the lowest indicator points and have the lowest environ-
mental impacts while NCA cells have the highest impacts. This result is due
to the complex materials used in the data inventories of NCA cells, such as
Cobalt [30,14]. Thus one benefit of developing Li Air and Li Sulfur batteries
is that they do not contain any toxic minerals or compounds in the electrodes.
Since complete data on particulate emissions produced during manufacture of
electrodes using nano materials is not currently available, the results do not
include paticulate emissions produced by future cell chemistries.
In this study, the calculated manufacturing energy demand (electricity and
heat for production) calculated in Table 3.4. It is important to note that in the
real world batteries and cells have a large variety of different configurations
and are usually produced in low quantities. This may lead to variability in
manufacturing energy demand. Implementation of efficiency improvements
and standardization of processing methods for battery production among the
different manufacturers would be an important step in selling batteries on a
22
Figure 4.2: Environmental impacts of Cells, calculated using ReCiPe World (H,H)
Figure 4.3: Life Cycle CO2eq emissions for all vehicles
larger scale as well as lowering manufacturing energy demand and thus CO2
emissions [19].
4.3 life cycle emissions results
After the fuel consumption of the different EV and ICEV vehicles with and
without light-weighting is calculated, the life cycle impact of vehicles is as-
sessed by using a functional unit of one vehicle kilometer.
The data inventory for calculating the CO2eq emissions per vehicle km
includes Well-to-Wheel emissions of supplying the fuel, as well as vehicle and
road manufacture and maintenance. For EV, the results are calculated using the
UCTE mix, which reflects charging of the car with average European electricity
23
production. The contribution of vehicle manufacture to the LCI is calculated
based on the EV and ICEV vehicles having a lifetime mileage of 150,000 km
[17]. All background data is based on the Ecoinvent database v2.2. The results
comparing the life cycle emissions of all vehicles is shown in Figure 4.3. In
this figure the vehicles compared are electric vehicles with various battery
chemistries and gasoline, diesel and natural gas vehicles.
From the results it is seen that when charged with the UCTE electricity mix,
EV have high environmental impacts, and produce almost the same amount of
life cycle GHG emissions as natural gas vehicles. The lowest GHG emissions
are produced by Li Air EV, and this is due to their superior performance in
terms of specific energy density; having batteries with high energy density
means that battery packs can be smaller for the same range, which reduces en-
vironmental impacts from both battery production as well as fuel consumption
since the vehicle weight is decreased with a smaller battery.
Life cycle of an EV includes vehicle manufacture, infrastructure maintenance
as well as recycling and disposal, and the contributions from the different
stages included in the life cycle can be seen in Figure 4.3. The results show that
emissions from vehicle manufacture and disposal contribute a larger percent-
age to the total emissions for EV, than compared to gasoline vehicles. Thus as
well-to-wheel CO2emissions reduce, emissions from production and disposal
of the vehicle become more important. It is important to address the increase
in embedded emissions in vehicle body and manufacture for future vehicle
technologies. From the results of this study, it can be seen that while vehicle
manufacturers focus on the goal of reducing tailpipe emissions, the overall
goal of reducing life cycle CO2equivalent emissions from passenger transport
is also important, especially if energy intensive materials are used for vehicle
and battery manufacture.
The results of LCIA based on the ReCiPe (H,H) method are seen in Figure
4.4. They show that gasoline vehicles have the highest cumulative impacts on
environment. All EV produce a higher amount of human toxicity and this will
be an issue to be addressed as the market share of EV grows and recycling
and safe disposal of batteries becomes necessary. In this research, recycling
of battery materials has been considered to the extent that metals present in
batteries have been recycled (excluding lithium) and all other battery compo-
nents are sent to disposal or incineration. The increased indicator points from
human toxicity and particulate matter formation come from processing of ad-
vanced battery materials and it is important that guidelines and legislation for
recycling are put in place before batteries are produced on a large scale for the
growing EV market [18].
4.4 vehicle light-weighting results
Figure 4.7shows the results of light-weighting the BIW in the glider with
Aluminum, HHS and CFRP. Aluminum and HHS have been modeled with a
recycling rate of 30% and 90% respectively and they have similar processing
24
Figure 4.4: ReCiPe Method results
energy requirements as steel while CFRP is considered to be not recyclable
with higher processing energy requirements.
The impact of material choice on the production stage energy demand is
very important; for example, aluminum can increase the energy consumption
by more than 260% compared to the steel bonnet [31]. It is also important
to consider that energy demand for the production of the materials might
come from different energy generation mixes, and that the fuel electricity mix
varies between countries. As an example, for the French electricity mix 6% of
electricity is carbon-based and 60g of CO2is produced per kWh generated;
while for an average World electricity mix, 50% of electricity is assumed to
be carbon based and 466g of CO2is produced per kWh generated [9]. In this
thesis, the European mix has been used for all production processes and it is
relatively carbon intensive. CFRP light-weighted gliders produce the highest
amount of CO2emissions, and these results can be explained by the fact that:
The vehicle production has been modeled using UCTE mix which is a
carbon intensive electricity supply. Carbon Fiber requires 47kWh per kg
of manufacturing electricity [49] while steel requires 0.021kWh per kg
material manufacturing electricity demand [50] and thus a large percent-
age of emissions are due to the embedded emissions in the electricity
mix.
The percentage of light-weighting is 20%, and light-weighting has been
done only for the BIW in the glider. This results in the consumption
decreasing by 10 to 12 percent for EV which is not sufficient to offest
increase in emissions from manufacturing a more energy intensive mate-
rial.
25
Figure 4.5: Effect of light-weighting vehicle glider, EV and Gasoline vehicle
Steel is recycled while CFRP is disposed without any recycling. This is
an assumption based on the fact that CFRP degrades after recycling and
thus cannot be reused in the vehicle. Currently new uses for recycled
CFRP are still to be found [57].
From the results it is seen that for EV the increase in emissions produced with
a more energy intensive glider material does not offset the decrease in emis-
sions produced due to decreased fuel consumption, while in the case of ICEV,
gasoline vehicles light-weighted with Aluminum and HHS produce slightly
lower GHG emissions than the steel vehicle.
Increase in manufacturing energy requirements has a large impact on CO2
eq emissions. Using a steel BIW results in higher fuel consumption than alu-
minum or plastic composites during the use phase. Increasing the percentage
of light-weighting results in a corresponding decrease in fuel consumption. In
this thesis the total vehicle weight has not been light-weighted by more than
20%, and light-weighting has been done only for the BIW in the glider. This re-
sults in the consumption decreasing by approximately 18 percent for gasoline
vehicles and 10 to 12 percent for EV, as shown in Figure 4.7.
In an LCA it is also important to evaluate other environmental impacts be-
sides GHG emissions and the ReCiPe method results for light-weighting per
v km are showing in Figure 4.6. CFRP light-weighted vehicles have higher
impacts in terms of particulate matter formation, human toxicity and fossil
depletion while steel has better environmental performance. This result is ex-
plained again, by the higher manufacturing energy demand of CFRP material
and the embedded emissions of the electricity mix. Since information with
respect to particulate or toxic emissions produced during Carbon fiber manu-
facture is currently not available, these have not been included in the data sets.
If such information once available is included in the study, the environmental
impacts from CFRP will be higher.
26
Figure 4.6: Comparison of Light-weighting the BIW in vehicle gliders with Aluminum,
HHS and CFRP
Figure 4.7: Effect of light-weighting on EV and Natural gas vehicles
Some important conculsions based on the results:
Emissions from manufacturing EV can contribute up to 40% of total life
cycle emissions.
The life cycle emissions of EV are significantly higher when charged with
a carbon intensive elctricity mix and comparable to life cycle emissions
from natural gas vehicles (Figure 4.7).
Using CFRP to manufacture BIW instead of Steel can cause an increase
in emissions of almost 200%.
4.5 sensitivity analysis
A lot of data inventories modeled in this thesis include products that do not
exist in the current market, and assumptions taken to model future batteries
27
and drivetrains are based on projections and published reports [5,10,16,35,
54].
The total energy demand throughout the life cycle of an automobile is highly
dependent on the boundaries, assumptions, and the scope of the study. Using
different electricity mix for EV charging has a huge impact on emissions pro-
duced [34]. In most of the cases the dominant phase in an LCA is the use phase
with contributions to total LCA emissions ranging between 60 and 80 percent.
The energy required during the end-of-life phase has been considered as negli-
gible in some studies, and production and use phase often dominate life cycle
emissions [35]. The impact of using alternative electricity mixes for EV charg-
ing has thus been chosen as a sensitivity test. In addition, the impact of using a
low carbon electricity mix while evaluating light-weighting of vehicles is also
assessed. The alternate electricity supply mixes chosen were the French low
voltage mix and the Swiss low voltage mix including imports (Ecoinvent v2.2)
which have a high percentage of nuclear and renewable (hydropower) gen-
eration, respectively. As seen from the results in Figure 4.8, having a higher
percentage of renewable energy in the charging electricity mix can decrease
CO2emissions of EV significantly. Thus when introducing or promoting EV
into the passenger transport market, it is important to ensure that the charging
electricity mix has low embedded emissions. As seen in Figure 4.3the results
for EV emissions are higher when using the UCTE mix for charging compared
to the CH and FR mix. Use phase emissions account for the largest percentage
of emissions in the life cycle and reducing the embedded emissions in the elec-
tricity mix results in improved environmental performance. Another result to
note is that for the UCTE mix, EV perform almost the same as NG vehicles.
Thus in markets where the charging electricity has high impact emissions, pro-
moting use of EV over alternative ICEV technologies would not help to reduce
GHG emissions significantly. In these markets, the focus has to first be on re-
ducing electricity generation emissions, before introducing EV. The results for
the ReCiPe method sensitivity analysis are in Figure 4.9. As seen in the results,
the EV have lower environmental impacts than ICEV on the environment, with
highest indicator points coming from fossil depletion and climate change hu-
man health. Light-weighted EV have higher impacts than non light-weighted
EV in terms of climate change human health, climate change ecosystems as
well as fossil depletion.
In the case of light-weighted vehicles and a low carbon charging electricity
mix, interesting results are observed. When light-weighting EV with energy
intensive materials such as CFRP, the total life cycle emissions are not reduced,
but instead increase significantly (Figure 4.9) compared to non light-weighted
vehicles. Due to the renewable electricity mix of the use phase now contribut-
ing a smaller share to the total life cycle emissions, the highest contributions
to life cycle emissions come from the manufacturing phase.
For the boundary conditions and assumptions of this thesis, it is observed
that CFRP has 20 percent higher emissions per kg when compared to steel,
28
(a) Non Light-weighted vehicles
(b) Light-weighted vehicles
Figure 4.8: Sensitivity analysis using French and Swiss electricity mix for EV charging,
IPCC GWP 100a Results
29
(a) Non Light-weighted vehicles
(b) Light-weighted Vehicles
Figure 4.9: Sensitivity analysis using French and Swiss electricity mix for EV charging,
ReCiPe (H,H) method
30
and when used in the glider without recycling, the impacts are compounded
to result in much higher CO2eq emissions. One important conclusion is that if
the charging electricity mix has high percentage of renewables, light-weighting
of EV, especially with high energy intensive materials is not recommended.
With the goal of having lower total life cycle CO2eq emissions in mind, having
a renewable charging electricity mix is more effective than light-weighting EV
with CFRP. Also for countries such as CH and FR, light-weighting of EV with
CFRP would not be necessary, as long as EV weight is not unusually high.
31
5
CONCLUSIONS
In this research work the environmental performance of future vehicles ex-
pected to enter the market between 2015 to 2030 has been evaluated by con-
ducting an LCA. The vehicle technologies which are considered are battery
electric vehicles, natural gas, diesel and gasoline vehicles. The life cycle emis-
sions of future vehicles in their manufacturing, use phase and end-of-life are
obtained and the results have been discussed in the previous section.
Some of the main conclusions of this research work are listed below:
CO2eq emissions for all batteries contribute significantly to the LCA re-
sults. Although active materials are only 40% of the total battery weight,
disposal of these compounds and complexity with recycling will become
an issue when EV have a significant share in the market. Lithium Air and
Lithium Sulfur batteries have better environmental performance because
they offer the possibility for easier recycling, higher energy density (and
thus smaller batteries for the same range) as well as usage of non toxic
electrode materials.
Implementation of efficiency improvements and standardization of pro-
cessing methods for battery production among the different manufac-
turers would be an important step in selling batteries on a larger scale
as well as lowering manufacturing energy demand and thus CO2emis-
sions. Development in terms of connectors, switches, battery packaging,
etc is still necessary as the metals and plastics used in these components
contribute significantly to battery environmental impacts.
Only a "green" electricity mix will significantly reduce the environmental
burdens of BEV compared to ICEV. BEV charged with the current fossil
intensive EU mix have life cycle emissions of about 200 g of CO2per
vehicle km (close to that of natural gas vehicles) are thus not the solution
to sustainable passenger transportation.
As well-to-wheel CO2emissions reduce, emissions from production and
disposal of the vehicle become more important.
Light-weighting is beneficial for ICEV technologies such as gasoline, and
the percentage of GHG emissions reduction is dependent on the amount
of light-weighting as well as the materials used for light-weighting.
EV environmental impacts are much more dependent on emissions from
manufacturing than ICEV technologies and the manufacturing phase can
contribute up to 40% of total life cycle emissions.
Recycling of materials in vehicles plays an important role in reducing life
cycle emissions.
33
CFRP light-weighted vehicles have higher impacts in terms of particulate
matter formation, human toxicity and fossil depletion while steel has
better environmental performance.
If the charging electricity mix has high percentage of renewables, light-
weighting of EV is especially with high energy intensive materials is not
recommended
Other environmental impacts such as air and water pollution are neces-
sary to be taken into account, especially when talking about manufacture
and vehicle end of life when comparing EV and ICEV.
Future policy should include LCA emissions when setting regulations for fu-
ture passenger transport. Focus on recycling, manufacturing as well as the
electricity generation mix (especially when introducing EV or supporting EV
market entry) is necessary to ensure that GHG emissions as well as other pol-
lutant emissions are reduced and the overall goal of a better quality of life is
achieved.
34
6
LIMITATIONS AND FUTURE WORK
Some of the limitations when conducting this LCA are listed below:
No data from manufacturers for manufacturing electricity demand of
future cells and batteries
No data for particulate emissions from manufacture of nano-structured
compounds and carbon fiber
No data for future gasoline, diesel, and natural gas fuel chains
Some aspects of vehicle use are excluded from the LCA boundary and
scope, such as fuel price, tax and regulations
Data for future electricity supply is still under review
Limited data on recycling of batteries and vehicles
Due to time limitations, the the LCA scope had to be bounded. Proposed fu-
ture work includes adding the following aspects to the LCA:
Future European electricity mix for 2030
Future gasoline, diesel and natural gas fuel supply chains for 2030
Including biofuels in comparison
Including updated battery information
Collecting data inventories for particulate emissions from CFRP manu-
facture
Include in data sets the reduction in tailpipe emissions to close to zero
for diesel and gasoline vehicles, based on future projections
Sensitivity analysis for alternate datasets for CFRP
Increase percentage of light-weighting in vehicle body
35
Part I
APPENDIX
A
NEW DATA INVENTORIES CREATED
CELL
Carbon nanowires (per kg)
Si/Carbon nanowire Anode (per kg)
Lithium Triflate (electrolyte for Li S) (per kg)
DME (electrolyte for Li S) (per kg)
BATTERIES
Li Air Battery (per kg)
Li S Battery (per kg)
LFP Battery (per kg)
NCA Battery (per kg)
VEHICLE BODY MATERIALS
Carbon Fiber (per kg)
Carbon Fiber Reinforced Composites (per kg)
HSS (increased processing energy demand) (per kg)
VEHICLE
Upsized Vehicle Glider per part
Upsized Drivetrain per part
Aluminium Lightweighted Glider per part
CFRP Lightweighted Glider per part
HSS Lightweighted Glider per part
EV drivetrains per part
USE PHASE
ICEV Gasoline (vkm)
ICEV Diesel (vkm)
ICEV Natural Gas (vkm)
EV (vkm)
39
All use phases using FR electricity mix, CH electricity mix for charging
DISPOSAL
Recycling and Disposal of EV (per part)
Recycling and Disposal of ICEV gasoline (per part)
Recycling and Disposal of Diesel vehicle (per part)
Recycling and Disposal of Natural Gas Vehicle (per part)
40
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