Life Cycle Assessment of Electric Vehicles – A Framework to Consider Influencing Factors

Abstract

The environmental impacts of electric vehicles (EVs) partially depend on the parameters of their site of operation. Variations of average driving patterns in different geographic locations and the use of heating and cooling due to local climate conditions have an impact on the energy consumption of EVs. In combination with the regional electricity mix these factors influence the environmental impact of EVs. Hence, these influencing factors must be included in an ecological assessment. The Life Cycle Assessment (LCA) method is used for the quantitative ecological assessment. An LCA can e.g. serve as a decisions support tool in vehicle engineering. This paper proposes a framework to consider influencing factors for the ecological assessment of EVs. A case study is used to demonstrate the capability of the framework.

Full text

Procedia CIRP 29 ( 2015 ) 233 – 238
Available online at www.sciencedirect.com
2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientifi c committee of The 22nd CIRP conference on Life Cycle Engineering
doi: 10.1016/j.procir.2015.02.185
ScienceDirect
The 22nd CIRP conference on Life Cycle Engineering
Life Cycle Assessment of Electric Vehicles – A Framework to Consider
Influencing Factors
Patricia Egedea,c*, Tina Dettmera,c, Christoph Herrmanna,c, Sami Karab,c
aChair of Sustainable Manufacturing & Life Cycle Engineering, Institute of Machine Tools and Production Technology (IWF), Technische Universität
Braunschweig, Langer Kamp 19b, 38106 Braunschweig, Germany
bSustainable Manufacturing & Life Cycle Engineering Research Group, School of Mechanical & Manufacturing Engineering, The University of New South
Wales, Sydney, NSW 2052 Australia
cJoint German-Australian Research Group on Sustainable Manufacturing and Life Cycle Engineering
* Corresponding author. Tel.: +49-531-391-7145; fax: +49-531-391-5842. E-mail address: P.Egede@tu-braunschweig.de
Abstract
The environmental impacts of electric vehicles (EVs) partially depend on the parameters of their site of operation. Variations of average driving
patterns in different geographic locations and the use of heating and cooling due to local climate conditions have an impact on the energy
consumption of EVs. In combination with the regional electricity mix these factors influence the environmental impact of EVs. Hence, these
influencing factors must be included in an ecological assessment. The Life Cycle Assessment (LCA) method is used for the quantitative
ecological assessment. An LCA can e.g. serve as a decisions support tool in vehicle engineering. This paper proposes a framework to consider
influencing factors for the ecological assessment of EVs. A case study is used to demonstrate the capability of the framework.
© 2015 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the International Scientific Committee of the Conference “22nd CIRP conference on Life Cycle
Engineering.
Keywords: Life Cycle Assessment; Electric Vehicles, Framework, User-specific, Regional
1. Introduction
Motorized vehicles have a significant impact on the global
greenhouse gas emissions. One option to reduce the
environmental impact is electric vehicles (EVs). Like all
reduction measures the implementation of EVs must be
evaluated carefully to avoid problem shifting or rebound
effects. Mostly Life Cycle Assessment (LCA) is used to
quantify the environmental impact along the entire life cycle
from raw material extraction to the end-of-life. However,
calculating the environmental impact with LCAs for EVs is
challenging. Results from different EV studies vary greatly
[1]. With these results it is possible to derive general
conclusion (e.g. “EVs can have lower environmental impacts
than conventional vehicles.”). Yet, more specific questions
are more challenging to answer. It is difficult to determine for
which situations and under which conditions these
conclusions apply. This makes it challenging to base decisions
on these results and to answer specific questions (e.g. “How
long is the ecological amortisation time for lightweight
materials for a specific market?”).
One reason for the difficulty of the calculation and the
variability of results is the lack of transparency of the
influencing factors of the LCA of EVs. The electricity mix,
use patterns and the material composition of the vehicles are
examples for important influencing factors of LCA results of
EVs. Usually the LCA practitioner does not have access to
primary data from the entire life cycle of the EV. Different
stakeholders are involved in the making, the use and the
disposal of the vehicle, which disperse the required
information over the entire supply chain and over time.
Confidentiality issues, complex supply chains, a variety of
possible use patterns and unknown future developments
increase the challenge of gathering the required data and
interdependencies, hence carrying out an LCA of an EV.
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the scientifi c committee of The 22nd CIRP conference on Life Cycle Engineering

234 Patricia Egede et al. / Procedia CIRP 29 ( 2015 ) 233 – 238
Figure 1 shows the array of results qualitatively. The x-axis
specifies the distance travelled; the y-axis marks the resulting
environmental impact. The travel distance underlies a certain
range (see ǻ3). The y-intercepts of the lines represent the
environmental impact of the raw material extraction and the
vehicle manufacturing. Depending on the assumptions made
for these first life cycle phases a range of values is possible
(see ǻ1). In this case two examples for a vehicle with a low
(see lower point of ǻ1) and high (see upper point of ǻ1)
environmental impact of raw material extraction and vehicle
manufacturing are shown. The slope of the line represents the
environmental impact which is caused in the use phase of the
vehicle in relation to the distance travelled. The slope is
defined by the energy consumption of the vehicle and the
environmental impact of the energy mix. Depending on the
scenario the values for the energy consumption and the energy
mix vary which results in a minimum (dotted line) and a
maximum (dashed line) slope. This leads to a range of final
results based on the assumptions of the use phase (see ǻ2).
When combining the ranges of raw material and
manufacturing phase, the use phase and driving distance, a
array is defined with possible outcomes of the LCA (shaded
area). The continuous line shows the ideal case using an
energy mix with (almost) no environmental impact. In this
case the consumption of the vehicle is irrelevant. For reasons
of clarity, the end-of-life phase is not considered in this
qualitative chart. The integration of the end-of-life phase adds
another set of parameters and enlarges the possible corridor of
results. A framework for the LCA of EVs clarifies the
different parameters and describes the system of the EV with
regard to LCA in detail. Depending on the goal and scope of a
study relevant influencing factors can be identified.
A framework clarifies the information that must be
collected and the relevant parameters which must be
considered. Hence, it increases the reproducibility and
transparency of LCAs of EVs.
Figure 1: Range of LCA results for EVs
2. Electric Vehicles and Life Cycle Assessment
2.1. Electric Vehicles
The term EV covers a range of vehicles types, e.g. hybrid
EVs and battery EVs. Hybrids have an electric and a
combustion engine. Depending on the type of hybrid the
vehicle can be charged from the grid such as Plug-In Hybrids.
This paper focuses on battery EVs which have an electric
motor and a battery which is charged externally (besides
recuperation). [1], [2]
EVs are seen as an option to reduce or eliminate the
downsides of using today’s fossil fueled vehicles. Their
characteristics offer advantages which solve problems caused
by conventional vehicles. These are the independence from
fossil fuels, the reduction of noise and the elimination of tail
pipe emissions. [3], [4] Their ability to run on many types of
energy sources via electricity storage in batteries provides the
opportunity for fossil fuel free mobility. The use of EVs
causes hardly any local emissions and causes little noise. In
mega cities which suffer from severe air pollution and high
noise levels these advantages are very valuable. The
disadvantages of EVs are mainly associated with the driving
range and the cost of the vehicles. [3], [4] Another concern is
the change of use of resources in comparison to conventional
vehicles due to use of Lithium-Ion batteries and electric
engines with permanent magnets. This leads to a more intense
use of metals like lithium, manganese or cobalt as well as rare
earth metals like neodymium. [2], [15] Currently, the driving
range of EVs is significantly lower than for conventional
vehicles. Even though this range is sufficient for the majority
of daily travel needs, many customers judge the driving range
of EVs as not sufficient. In addition the use of heating and
cooling devices can reduce the range significantly as these
auxiliaries are very energy intensive. The purchase price of
EVs is higher than for conventional vehicles of comparable
size. However, despite of their disadvantages EVs are
successful even in places with conditions which could be
considered unfavourable. An example is Norway which has a
climate that requires intensive heating which reduces the
range. However, due to incentives offered such as tax
reductions and a municipal charging infrastructure, EVs have
been adopted very well in the Scandinavian countries. [5]
As seen in figure 1 a high share of the environmental
impact of the EV occurs in the use phase and is directly linked
to the energy consumption during usage in combination with
the energy mix. The energy consumption of EVs depends on
different parameters which can be divided into three groups:
driving resistances, the use of auxiliaries and losses. Driving
resistances must be overcome to achieve and maintain a
certain velocity. Examples are the rolling, acceleration and
aerodynamic resistance. Vehicle characteristics like weight
and the frontal area influence the resistances. In addition the
use of auxiliaries such as heating, air conditioning and
ventilation increase the energy consumption. Also losses
occur in the process of converting electric energy into
mechanical energy due to the efficiencies of the different
components. [6], [7]

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Patricia Egede et al. / Procedia CIRP 29 ( 2015 ) 233 – 238
2.2. Life Cycle Assessment
In order to identify the environmental impact of EVs, the
mass and energy fluxes of the respective product systems
need to be compared throughout their life cycles using the
LCA methodology according to ISO 14040 [8]. LCA has
proven to be a useful method for analysing and quantifying
the environmental impacts of products. It became generally
accepted in the last two decades and is internationally
approved and standardised (ISO 14040 [8], ISO 14044 [9]).
The LCA procedure consists of four successive steps: the
goal and scope definition, the inventory analysis, the impact
assessment and the interpretation of the results. The procedure
has to be understood as an iterative process rather than one
exercise. For instance intermediate results obtained from the
inventory analysis, impact assessment and interpretation may
require a modification of the goal and scope definition.
During goal and scope definition, the intended application
(i.e. the questions to be answered by the study), the
motivation for carrying out the study and the intended
audience have to be defined (ISO 14040 [8]). Furthermore,
the product system under study needs to be clearly described
including system boundaries and functional unit (the
quantified performance of the system). Furthermore, a number
of additional methodological details and choices have to be
documented for transparency reasons.
Life Cycle Inventory Analysis aims at understanding and
accounting for input and output flows within the observed
system and its interaction with the environment (elementary
flows). Petri-net based material flow nets can help to finally
calculate all incoming and outgoing flows crossing the system
boundaries. This input-output balance referenced to the
functional unit is called a Life Cycle Inventory.
Based on the Life Cycle Inventory, the potential
environmental impacts resulting from extracted resources and
from emitted pollutants are determined during Life Cycle
Impact Assessment. First, emissions are classified according
to their contribution to the different impact categories.
Second, their potential contribution is expressed in terms of
impact equivalents, e.g. in CO2-equivalents (CO2-eq) for
climate change.
In the interpretation phase, results of the study are plotted
to present the significant issues. Additionally, the reliability of
the study is scrutinized in sensitivity and uncertainty analyses
complemented by consistency and completeness checks.
Finally, recommendations are derived based on the findings of
the study.
2.3. Life Cycle Assessment of Electric Vehicles
As complexity of a product have a significant impact on
the complexity of the respective LCA study, LCA on EVs are
a challenging task. In recent years, a number of LCAs have
been published on EVs (e.g. [2], [10], [11], [12], [13]) or EV
specific components like Li-ion batteries (e.g. [12], [14], [15],
[16], [17]). Hawkins et al. [2] as well as Nordelöf et al. [1]
provided comprehensive reviews on LCAs of EVs. They
identified 55 and 79 relevant studies, respectively, including
full reports, journal papers and conference papers. They both
report widely diverging results which somehow have to be
expected for a complex product in an emerging market. The
divergence can be further explained by differences in
methodological choices and also by unavailability of primary
data. The guidelines of the project E-Mobility Life Cycle
Assessment Recommendations (eLCAr) [18] aim to
harmonise the methodological approach and to enhance
transparency of methodological choices.
As modern EVs have been introduced to the market
recently and there are plenty of ongoing research activities to
further develop the necessary key technologies (e.g. for
energy storage), most LCA studies focus on vehicle
production and related raw material acquisition. Therefore
and due to the lack of long-time measurements/ monitoring/
experiences, only few LCAs address the use phase in
particular (i.e. [19], [20]). Use profiles are mainly derived
from standard driving cycles and the associated energy
consumption is calculated generically as documented, e.g. in
the eLCAr guidelines [18]. Modelling of use phase scenarios
based on real life data and especially site specific
measurements have not been published yet.
3. Framework
The environmental impacts of EVs depend on various
parameters related to the vehicle’s characteristics, their
location of use and user influences. Variations of driving
patterns of different users and the use of heating and cooling
due to local climate conditions have an impact on the energy
consumption of EVs (see section 2.1.). In combination with
the regional electricity mix these parameters influence the
environmental impact of EVs. Therefore, the vehicles must be
seen as a part of the setting with which it interacts to answer
specific LCA questions. When neglecting these
interdependencies, important aspects might be missed and left
out. Connecting external influences with the use phase of the
vehicles assists the LCA practitioner to evaluate the influence
of parameters on the environmental impact. Setting up a
descriptive framework allows the LCA practitioner to
translate external influencing factors into environmental
impacts reducing the uncertainty of LCAs.
Figure 2 shows the proposed framework and illustrates the
EV as an element in a larger system of influencing factors and
highlights the connection of energy consumption and external
factors. The material and energy flows over the entire life
cycle necessary to manufacture and operate the vehicle define
the life cycle of the EV (mid-level). The setting of external
factors in which the EV is deployed (top level) influences the
life cycle and the LCA results. These external factors can be
divided into three groups: the user, the infrastructure and the
surrounding conditions. In this paper we focus on the
influence of these external factors on the use phase, i.e. the
energy consumption. External factors and also internal factors
(characteristics of the vehicle like the weight of the vehicle or
the size of the frontal area influencing the aerodynamic
resistance) affect the energy consumption in the use phase
(bottom level).

236 Patricia Egede et al. / Procedia CIRP 29 ( 2015 ) 233 – 238
Figure 2: LCA framework for electric vehicles; Source of bottom picture see
[21]
3.1. Influencing factor: Vehicle
In the use phase specific characteristics of the vehicle
influence the energy consumption (as described in part 2.1).
These factors are considered internal in this framework as
they are inherent properties of the vehicle.
3.2. Influencing factor: User
The user of the EV influences the environmental impact of
the EV through the driving and charging behaviour as well as
through the intensity of the use of auxiliaries. A more
aggressive driving style leads to a higher energy consumption
whereas a more cautious driving style results in a more
efficient use of energy. Depending on the charging behaviour
and the willingness to install renewable energy specifically
for the EV (e.g. in the form of solar panels), the share of
renewable energy can be increased significantly compared to
the use of grid energy in many countries. Finally, the use of
heating and cooling to achieve the desired temperature has a
significant influence on the energy consumption. The
willingness to accept a warmer vehicle temperature in the
summer and a cooler temperature in the winter is directly
linked to a lower environmental impact.
3.3. Influencing factor: Infrastructure
The electricity mix is one of the most crucial parameters
for the LCA calculation. Using a mix based entirely on
renewable energies delivers a completely different result than
an energy mix based on fossil fuels. Choosing the adequate
mix which reflects the real world situation and leads to fair
and reliable results is challenging. [18] In many LCAs an
energy mix is used which is based entirely on renewable
energy. However, often it is not clear if this represents the
actual grid situation or if it is a case of crediting renewable
energy to the EV rather than a different use. In the latter case
it must be considered if the crediting can be justified. The
charging of EVs can in principle often be carried out at
regular household plugs. Yet, often more sophisticated
solutions are required at workplaces or in public areas to
allow adequate and safe charging. Depending on the
conditions of the site the installation of these charging stations
demands major building activities. These activities can be
significant for specific scenarios in which only one or a few
vehicles use one charging station. The available charging
infrastructure also influences the options of smart charging.
Smart charging applications can increase the share of
renewable energy used to charge the EV.
3.4. Influencing factor: Surrounding conditions
The surrounding conditions influence the environmental
impact of EVs. The climate, the topography and the type of
road are identified as significant factors for the energy
consumption. The climate influences the need for heating and
cooling appliances in the vehicle. The temperature varies both
on a seasonal as well as on a daily level leading to a
fluctuation of the energy consumption. Depending on the
interaction of temperature and humidity the wind shield of the
car can fog up and require ventilation or the use of the air
conditioning and/or heating. Currently, resistance heating is
mostly applied in EVs. Alternative technologies like a heat
pump can reduce the energy consumption for heating. A flat
topography leads to a lower energy demand, than a hilly
landscape. It is important also to consider the breaking
recuperation when calculating the energy consumption. The
type of road such as city streets or highways, define
parameters such as the speed limit and the frequency of stops
e.g. at traffic lights. These parameters influence the
consumption.
3.5. Fields of application of framework
The framework serves as a support for LCA practitioners
by providing the necessary technical background on EVs. It
can be applied to various LCA studies. The influencing
factors have to be discussed in the goal and scope section of
External factors
User:
Comfort requirements
Driving style
Charging behaviour
Internal factors
Vehicl e
Energy consumption
Infrastructure:
Energy mix
Charg ing system
Smart charging
Surrounding
conditions:

Climate
Topography
Type of road

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Patricia Egede et al. / Procedia CIRP 29 ( 2015 ) 233 – 238
the study. Examples for possible areas are comparative
assessments with other vehicle technologies and design
decisions. EVs compete with conventional vehicles as well as
vehicles with other alternative propulsion systems or fuels.
Analyzing the environmental impact in detail helps to identify
use cases and regions for which EVs are particularly useful.
This can allow policy makers or consumers to make robust
choices in increasingly diverse markets. Another decision
context is a design choice for EVs. When evaluating design
alternatives influencing factors can be significant for the
environmental sensitivity. Problem shifting can occur from
one phase to another or from one vehicle component to
another. To evaluate the possible environmental benefit of
design options a detailed analysis of the entire system and its
parameters is necessary to ensure robust results. This is
relevant for automotive manufacturers and their suppliers.
4. Case Study
The following case study shows the relevance of the
framework. The goal of the case study is to determine if and
how influencing factors impact the comparison of different
EV. A lightweight vehicle is evaluated with regard to its
ability to reduce the overall global warming potential (GWP)
in a range of countries with differing electricity mixes. The
results of Germany, Brazil and Spain are discussed in detail.
The purpose of using lightweight materials is to reduce the car
weight and consequently the energy consumption in the use
phase and/or to increase the driving range with the same
battery size. However, the use of lightweight materials usually
comes with higher environmental impacts during the raw
material and manufacturing phase compared to traditional
materials. The end-of-life phase is not considered for reasons
of clarity. Two vehicles are compared, one with a steel and
another with an aluminium chassis. To evaluate the impact of
the influencing factors three scenarios with differing external
factors are defined. The internal influencing factor vehicle
lifetime is analyzed by calculating results for driving
distances of 100,000 km, 150,000 km and 200,000 km.
The case study is performed as a delta analysis. The
relevant figures for the analysis are the weight reduction of
the aluminium vehicle and the CO2-eq of the material supply
of steel and aluminium. Following the LCA of Das [22] the
aluminium chassis achieves a weight reduction of 67%. The
CO2-eq/kg of steel is fixed as 5.7 based on [23] and the
Ecoinvent 3.01 database. [24] Ehrenberger et al. [25] show
the range of CO2-eq for primary aluminium production.
Based on this review and Das [22] the CO2-eq for aluminium
is set as 13 CO2-eq/kg. The CO2-eq of the electricity mixes
of 71 countries (and regions) are extracted from
Ecoinvent 3.01. [24]
4.1. Scenario descriptions of external factors
Three scenarios of external influencing factors are defined
for which the energy consumption is determined. Four
different influencing factors are considered: Driving
behaviour, desired temperature (both influencing factor user),
topography (influencing factor surrounding conditions) and
type of road (influencing factor infrastructure). Table 1 shows
the influencing factors and their specification for each
scenario. Scenario A depicts a rather cautious driver who
moves around in a flat city area. The need for heating and air
conditioning is low. The scenario characteristics result in a
rather low average velocity. Summed up, the average
consumption is therefore small at around 10 kWh/100km.
Scenario B shows a driver with an average driving behaviour
who mostly drives in a hilly city area. The requirement of
heating and cooling is medium. The scenario characteristics
result in a medium average velocity. Altogether, the average
consumption is therefore medium at around 15 kWh/100km.
Scenario C describes a dynamic driver travelling on highways
in a hilly area. The demand for heating and cooling is
medium. The scenario characteristics result in a rather high
average velocity. As a consequence of these characteristics,
the average consumption is therefore high at around
20 kWh/100km.
Table 1: Description of scenarios A, B and C
Influencing factor Scenario A Scenario B Scenario C
Driving behaviour Cautious Average Dynamic
Desired temperature Low Medium Medium
Topography Flat Hilly Hilly
Type of road City City Highway
Energy consumption
[kWh/100km] ~10 ~15 ~20
4.2. Results
The results of the case study are presented in figure 3. The
chart shows for which number of countries the material choice
steel has a lower environmental impact than aluminium (blue
bars) and vice versa (orange bars). The three scenarios A, B
and C are calculated for the three lifetime expectancies of the
vehicles. The comparison of the scenarios reveals that the
lower environmental impact of one material in comparison to
another depends not only on the electricity mix but also on the
energy consumption in the use phase. For the medium life
time expectancy the advantageousness switches in 17
countries from steel to aluminium as the consumption
increases. With a shorter life span of the vehicle the number
increases to 36. In the last example with the longest life span
the result changes for 11 countries. As the life time of the
vehicle increases the differences between the scenarios A, B
and C diminish. Eventually the higher impact of the
production of the aluminium pays off regardless of the energy
consumption per kilometre. The lightweight design becomes
more and more relevant as the vehicle is used longer and
longer.
For the countries Brazil, Germany and Spain the results are
is exemplified. Brazil has an energy mix with rather low CO2
emissions as it is mainly based on hydro power. Germany has
an energy mix with medium CO2 emissions with a rather
divers mix of energy sources. The CO2 emissions of the
Spanish energy mix lie in between the other two countries. In
Brazil the material choice of steel causes lower emissions than
the choice of aluminium. Only in the case of a 200,000 km

238 Patricia Egede et al. / Procedia CIRP 29 ( 2015 ) 233 – 238
driving distance and a high consumption in scenario C the
choice of aluminium pays off. For Germany, the opposite is
the case. Aluminium is the better choice except for the case of
a low consumption (Scenario A) and a short driving distance
(100,000 km). In the case of Spain, the result is not as clear.
For a long driving distance the choice of aluminium pays off.
In the other two cases of medium and low driving distances
(150,000 km and 100,000 km) aluminium only pays off for a
high consumption (Scenario C) or for a medium and high
consumption (Scenarios B and C). It becomes clear that the
external factors can influence whether a material choice pays
off in a specific country or not. Considering average values
for the consumption can lead to misleading results of a study.
Sensitivity analysis can reveal the robustness of results.
However, including external factors systematically can help to
reduce the uncertainty of the use phase by narrowing down
the possible energy consumption and increase the reliability
of LCAs for EVs.
Figure 3: Analysis of lower GWP of steel and aluminium vehicle
Summary and Outlook
This paper presents a framework for the LCA of EVs to
consider influencing factors of the use phase. The vehicle was
identified as an internal factor; the user, infrastructure and
surrounding conditions were defined as external factors. In a
case study the relevance of the identified factors was shown.
The advantageousness of an aluminium lightweight design
changed for a number of countries depending on the
parameter value of the influencing external factors and the
resulting energy consumption per kilometre. Following the
(approach of the) framework, e.g. car manufacturers could
more precisely define design strategies for their different
target markets and governments could include their countries’
characteristic to environmentally meaningful tailor respective
regulation and policies.
The necessity to include or exclude these influencing
factors in an LCA study depends on the defined goal and
scope. Improvements of the framework can be achieved by
determining quantitative relations between the influencing
factors and the energy consumption. Furthermore, the impact
of the external factors on the remaining life cycle phases can
be analyzed.
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