With the number of vehicles on the world's roads expected to grow to 2.9 billion by 2050, steps must be taken to reduce the CO2 emissions from transport. Battery electric vehicles are becoming an increasingly pivotal way in which to curtail future road transport emissions.

This study aims to determine the CO2 emissions stemming from battery electric vehicles and to compare those CO2 emissions to the emissions from similar vehicles based on internal combustion engines. This study determines average passenger vehicle emissions in Melbourne, and models three common battery electric vehicles models. Manufacturer data was collected to determine battery electric vehicles modeled power consumption, as well as any efficiencies lost from charging and battery operation. Their energy consumption is combined with data on the CO2 intensity of the power generation mix in Victoria, modelling four different scenarios reflecting different carbon intensities of electricity generation.

The research aims to answer the questions, if battery electric vehicles models are able to deliver a meaningful decrease in CO2 emissions. For although battery electric vehicles do not produce any tailpipe emissions, they depend strongly on electricity production which is predominantly from non-renewable sources in Melbourne. This infers they are less 'green' than one may think, especially when taking into account life-cycle emissions such as from battery production and disposal.

This study looks at the relationship between energy consumption of battery electric vehicles and the CO2 emissions involved in generating electricity to charge them, and then compares this to the carbon emissions from comparable internal combustion engine passenger vehicles. This study also takes a look at modelling the possible future emissions reduction in the road transport sector by introducing battery electric vehicles into the existing fleet.

Excerpt

emissions from BEVs due to their power source, and to help understand their true emissions abate-

ment potential in the transport sector. A literature review was first conducted in order to establish current

transport emissions and abatement options, detail the electric vehicle option, and how this was related to

the carbon intensity of the power supply. The scope of the modelling undertaken was confined to light

vehicle emissions in metropolitan Melbourne, taking into consideration the most recent estimates of traf-

fic data in Melbourne and annual CO

emissions from passenger vehicles. The study then involved vary-

ing the power generation mix and hence the CO

intensity of electricity production to see the effect on

carbon emissions from charging BEVs. It was hypothesized that EVs may not be as `green' as one might

think, and if their power source is of a very high emissions intensity, they may be in fact worse for the

environment than traditional combustion vehicles from an operational standpoint. Only the technical po-

tential for vehicle emission intensity reductions to mitigate greenhouse gases was considered in this pa-

per, as the economic potential has been found to be much smaller (Michaelis & Davidson, 1996).

LITERATURE REVIEW

2.1

Transport sector emissions and trends

On a global scale, transport was the second highest CO

emitting sector in OECD countries behind energy

and heat in 2015 contributing to 30% of total CO

emissions, up from 25% in 2010 (IEA, 2017). This

growth in vehicle emissions does not seem to be slowing down. China alone has been predicted to in-

crease vehicle ownership to over 600 million cars by 2050, nearly a ten-fold increase from 2009 figures

(Hao, Wang & Ouyang, 2010). India, too, has seen increasing trends in vehicle ownership and emissions.

The number of registered road vehicles has risen from 5.4 million in 1980 to 72.7 million in 2003 (Rama-

chandra & Shwetmala, 2009), with road transport accounting for 80% of all transport sector emissions in

India (Franco, Mandla & Ram Mohan Rao, 2017). Rising vehicle ownership and emissions trends, cou-

pled with increasingly rising global temperatures, give rise to a clear need for emissions reduction in

transport (Michaelis & Davidson, 1996).

On a national scale, Australia's transport sector in 2014 was also the second highest greenhouse gas

emitting sector behind stationary energy, responsible for 18% of the country's total emissions. Road

transport accounted for approximately 85% of emissions within the sector (Commonwealth of Australia,

2016). Figure 1 illustrates Australia's predicted `business as usual' transport sector emissions up to 2050.

This projection shows transport emissions are on an upward trend, with the biggest contribution coming

from the light road vehicles category. This indicates that CO

abatement efforts should bias the light road

sector, further motivating the need to investigate the true CO

reduction potential of BEVs. The problem

this then inspires is determining the optimal strategy or method for achieving this.

2.2 Abatement Options

Various studies have been undertaken in determining different approaches and strategies for emissions

reductions in the road transport sector. Stanley, Hensher and Loader (2011) suggested six different meth-

ods in which emissions might be reduced in the Australian road transport sector. They concluded that sig-

nificant behavioural and technological changes are required to cut substantial emissions from the sector,

with the key area being vehicle emission intensity. Yang, McCollum, McCarthy and Leighty (2008) in-

vestigated the potential for California to reduce its transportation emissions by 80% on 1990 levels by

2050. The paper proposed multiple scenarios focusing on three areas; improving vehicle efficiency, re-

ducing fuel carbon intensity, and reducing travel demand. It was found that if advancements in fuel and

vehicle technologies such as various electric vehicle types became clean enough, "California [could] pre-

serve its current levels of mobility" (Yang et al., 2008, p. 156). Hickman and Banister (2007) discovered

similar results modelling future transport CO

emissions in the UK. The study targeted a 60% reduction in

emissions by 2030. It was found that car traffic could increase by 35% on 2000 levels while still achiev-

ing the 60% CO

reduction in the sector if hybrid vehicle technologies were pushed hard enough. As

such, there is convincing evidence to suggest advancements and adoption of various EV technologies

could greatly mitigate future transport emissions around the globe

Figure 1. Project Greenhouse gas emissions by Australian transport sector. [Source: Cosgrove, Gargett, Evans, Graham & Ritzinger 2012a)]

In 2012, the Australia Low Carbon Transport Forum (ALCTF) was formed by combining industry ex-

perts from various organisations to investigate the options for greenhouse gas abatement in Australia's

transport sector. A summary report was produced in 2012, which primarily identified the following:

There are four general strategies available to reduce greenhouse gas emissions in transport. They are:

· Reduce the emission intensity of transport fuels

· Improve the fuel efficiency of transport to that less fuel is required

· Reduce the amount of transport required to meet society's needs

· Remove and store the emissions after they have been created

(Cosgrove, Gargett, Evans, Graham & Ritzinger., 2012a, p. 8)

A total of 47 abatement options under the preceding strategies were considered. The study initially

considered the individual impact of abatement options in isolation from other options, but then extended

to consider the impact of options when combined in sequence. Cosgrove et al. (2012a) noted that "in real-

ity abatement options will co-exist and these options can substantially reduce the impact of each other

when acting together" (p. 14). In both cases, the option with the highest abatement potential (in Mt CO

e) was electric/plug-in cars. Various other studies have also agreed with this (Perujo & Ciuffo 2010; van

Vliet et al. 2011; Climate Change Authority, 2014).

However, the technical report on which the above summary report was based appeared to include one

key oversight. Cosgrave, Gargett, Evans, Graham and Ritzinger (2012b) calculated their emissions sav-

ings based on the assumption that the carbon intensity of electricity generation in Australia would de-

crease substantially by 2050. More specifically, Cosgrave et. al (2012b) assumed that the tonnes of CO

equivalent per megawatt-hour (tCO

-e/MWh) decreased from a predicted 0.771 in 2020, to 0.209 in 2050.

As the potential for EVs to reduce transport emissions relies heavily on the carbon intensity of electricity

generation (Casals, Martinez-Laserna, García & Nieto, 2016), reducing the future carbon intensity by

such a large amount has the potential to over-estimate possible abatement. A more conservative estimate

could have been used, such as the `medium global action' case of 0.730 tCO

-e/MWh as suggested by the

Commonwealth of Australia's (2011) carbon price modelling report. This is discussed further in section

2.4. The need to study the true abatement potential of BEVs when taking into account their indirect emis-

sions from electrical power generation is clearly evident.

2.2

Electric Vehicle Option

Battery electric vehicles, or BEVs, offer an alternative mode of transport to conventional ICE-based vehi-

cles, and have the potential to decrease transport CO

emissions. BEVs are a category of electric vehicle

that involve an all-electric drivetrain and are powered from energy stored in chemical batteries, most

commonly being nickel-based and lithium-based varieties These differ from other EV types, including

fuel-cell vehicles that directly convert the chemical energy of a fuel into electrical energy, and hybrid

electric vehicles that utilise a number of power sources and power trains in tandem (Ehsani, Gao, Gay &

Emadi 2005). As BEVs do not rely on the combustion of a fuel such as oil, they do not produce any tail-

pipe emissions. An additional benefit of this is helping to reduce localised pollution around dense urban

areas (Casals et al., 2016). When compared to conventional ICE vehices, BEVS also have more efficient

powertrains. This can be attributed to smaller energy losses, such as from regenerative breaking to re-

charge batteries, and fewer moving parts (Campanari, Manzolini & de la Iglesia, 2009; Gantt, Perkins,

Alley & Nelson, 2011). One study found that for a 50km/day journey, an ICE vehicle required

40kWh/day on average, compared to just a 10kWh/day average for an electric vehicle, representing a

75% saving in total energy (Perujo & Ciuffo, 2010). However, these higher operational efficiencies and

reliance on batteries do not come without cost. During the manufacturing phase of a BEV, a higher envi-

ronmental impact is incurred than for a comparable ICE vehicle primarily due to the high GHG intensity

of battery production (Notter et al., 2010). Correspondingly, the electricity consumed by a BEV during

the use phase is primarily sourced from the existing electricity grid, which, as mentioned previously, ac-

counts for largest amount of global CO

emissions as a sector (power generation).

This evidence suggests that although BEVs are directly responsible for less emissions, they may be in-

directly responsible for more. As the scope of this study did not include life-cycle analysis (and hence ig-

nored any manufacturing GHG emissions), it focused on indirect emissions related to the electricity gen-

eration required to charge EV batteries.

2.3

Electricity generation mix

As previously mentioned, BEVs produce no tailpipe emissions, so the emissions intensity of the electrici-

ty generation mix supplying charge to the vehicle is crucial in determining indirect emissions (Nicolay,

2000). Every country or region generates electricity from a different and specific mix of sources, with

each unit of electricity emitting a certain amount of CO

as a product of its generation. This is known as

the CO

intensity of a power source. The CO

intensity of each source is then combined with the overall

amount generated from that source to determine the average CO

intensity of a country or region's power

generation mix. This is usually expressed as a number, in grams of CO

per megajoule (gCO

/MJ) or

grams of CO

per kilowatt hour (CO

/kWh). The electricity generation mix, or electricity mix, is herein

referred to as how the "final energy consumption in a given geographical region is distributed by primary

energy sources" (Casals, Martinez-Laserna & García, 2016, p. 426). The electricity mix composition var-

ies greatly between regions or countries, and depends on the availability of resources, the type of energy

needs to be met, and government policy of that region (Planète Energies, 2015). For example, South Afri-

ca has an electricity mix CO

intensity of 265 gCO

/MJ (955 gCO

/kWh), a very high figure due to ap-

proximately 94% of power coming from fossil fuel combustion (CARMA, 2012). In contrast, the CO

in-

tensity of Norway's electricity mix is only 2gCO

/MJ (7.2 gCO

/kWh), attributable to over 95% of its

electricity originating from hydro-electric power plants (CARMA, 2012). As such, a BEV receiving its

charge from the South African electricity grid would have much higher CO

emissions, per unit of dis-

tance, than an identical vehicle charging in Norway.

METHODOLOGY

The aim of this study was to understand the indirect CO

emissions arising from the operation of BEVs

and their power generation, and to then compare this with equivalent ICE vehicle emissions. As such, da-

ta collection and analysis was implemented to carry out the study. Emission figures relating to ICE vehi-

cles were first estimated using census data and average passenger vehicle emissions, to determine annual

emissions in Melbourne and emissions per unit distance. Next, the CO

intensity of the current elec-

tricity generation mix in Melbourne was calculated and combined to achieve a link between generating

the energy consumed by BEVs and CO

emissions. Finally, the "tank to wheel" energy use of BEVs was

estimated through energy consumption per unit distance travelled. A comparison could then be undertak-

en between BEV CO

emissions and average ICE vehicle CO

emissions, with sensitivity testing involv-

ing the carbon intensity of the power generation mix. Four different energy mix scenarios were modelled.

The following sections detail this further.

3.1

ICE vehicle data and emissions

Data was collected regarding current passenger vehicle use in Melbourne, along with various traffic data

and emissions intensity of ICE vehicles. The Australian Bureau of Statistics' (ABS, 2017) 2016 Survey of

Motor Vehicle Use, Australia, was used as the primary source of traffic data for this study. Average an-

nual distance travelled by passenger vehicles in Melbourne was found to be approximately 11,200km, or

30.68km per day. This was consistent with another paper estimating 29.2km for the average daily com-

mute in Melbourne (Bureau of Infrastructure, Transport and Regional Economics (BITRE), 2015). Total

vehicle kilometres (TVKM) over the year was found to be 30,885 million for passenger vehicles in Mel-

bourne city. Additionally, 3,632,041 passenger vehicles were registered at this time.

Data showed that on average in 2016, passenger vehicles in Victoria were 9.8 years old. As such, the

emissions intensity of the current light vehicle fleet was taken as the average emissions intensity for new

light vehicles sold in Australia in 2006. This was found to be 230 gCO

/km in 2006 (Climate Change Au-

thority (CCA), 2014; National Transport Commission (NTC), 2016). This average considers petrol- and

diesel-based ICE passenger vehicles, with other fuel types such as hybrid/electric vehicles being excluded

for simplicity (as these constituted less than 5% of total passenger vehicles). These statistics were used to

calculate the average CO

emissions for the passenger vehicle cohort in Melbourne.

3.2

Power generation emissions

A vital part of this study was to determine the CO

emissions relating to the power generation mix in

Melbourne. As mentioned earlier, the CO

intensity of the power generation mix is generally expressed in

units of CO

produced per unit of electricity generated (gCO

/kWh). Each power source has a different

carbon intensity, with fossil fuels renewables constituting the different sources in Victoria. It was as-

sumed that the power mix of Victoria was applied equally across the state, and was as an approximate

measure for Melbourne's power mix. Victoria's current power generation mix was assumed to be equiva-

lent to the 2014-15 mix (most recent data), and was found to be sourced primarily from brown coal, with

renewables and then natural gas producing a minority share. The CO

intensity of Victoria's brown coal

and gas fired power plants was calculated using a weighted average based on generation capacity of each

plant, and then combined with the percentage generated from each fuel source. Table 1 summarises this

information. Data was collected from the Department of Industry, Innovation and Science (2016), De-

partment of the Environment (2015) and ACIL Tasman (2009). It should be noted that renewable sources

include biogas, wind, hydro and solar PV, and it was assumed that there were null CO

emissions from

these sources during electricity generation.

Table 1. Electricity generation mix data Victoria 2014-15

Electricity Source

Quantity

(%)

intensity

(gCO

-e/kWh)

Brown coal

85.69

1227

Natural gas

3.884

705.5

Renewable sources

10.43

Total

100.0

1079

A point must be made regarding the marginal emissions factor (MEF) The MEF considers the change

in carbon intensity of electricity generation due to an increase or decrease in produced power (Hawkes,

2010). This is different to the average emissions factor (AEF), which is the grid-average CO

intensity of

electricity generation. For instance, if BEVs were introduced in Melbourne this would increase the load

on the current grid. As such, power generators would need to increase their output or new generators

would need to begin producing power. This would affect the overall CO

intensity of the grid due to a

change in the power generation mix, which is captured by the MEF and hence making it a better measure

of CO

grid intensity. A study of the CO

intensity in the UK's grid showed that from 2002 to 2009, the

MEF measured 192 gCO

/MJ, compared with the AEF measuring 142 gCO

/MJ (Hawkes, 2010). This

can be

explained as an increase in energy demand is usually met by increasing production from plants

burning fossil fuels (Hawkes, 2010). Regrettably, as it is difficult to calculate MEFs due to requiring vast

knowledge of a grid's responses to demand changes, the AEF was used in calculations for this study.

Therefore, it is possible that the CO

emissions from adding BEVs to the system were underestimated, as

the MEF would have likely reflected a higher CO

intensity.

3.3

BEV power consumption

In order to attribute CO

emissions from power generation to BEVs, the rate at which they consume ener-

gy per unit distance was required. This `fuel' economy of BEVs is typically given in watt-hours per kilo-

metre (Wh/km). To calculate this, data regarding the capacity of the battery pack and associated range for

various EVs was collected. For example, a Nissan Leaf has a 30kWh battery pack and an associated

172km range (Nissan, 2017). As such, it is expected to consume energy at the rate of 0.174kWh/km

(174Wh/km). A list of common electric vehicles in Australia was compiled, and an average energy con-

sumption rate for these vehicles was calculated from manufacturer information (BMW, 2017; Mitsubishi,

2017; Nissan, 2017; Renault, 2017; Tesla, 2017).

To more accurately analyse BEV emissions, the charging efficiency was considered. This includes en-

ergy losses due to the EV charger as well as the battery efficiency. First, the efficiency of the instant

charger was found to be generally around 95% (Musavia et al, 2012). Secondly, the efficiency of the bat-

tery was found to be above 95% in standard conditions

but falling below this during fast charges (Kang,

Yan, Zhang & Du, 2014). As such, for this study an average of 90% total efficiency for the battery and

recharge system was applied, effectively increasing the effective energy consumption of BEVs by 10%.

Table 2 summaries the above data.

Table 2. BEV range and power consumption

Model

Range

(km)

Battery capacity

(kWh)

Power consumption

(Wh/km)

Effective consumption

(Wh/km)

Tesla Model S

539

100

185.5

206.1

Tesla Model X

565

100

177.0

196.7

Nissan Leaf

172

174.4

193.8

BMW i3

160

18.8

117.5

130.6

Renault Kangoo Z.E.

125

176.0

195.6

Mitsubishi i-Miev

166.7

185.2

Average

276.2

47.8

166.2

184.6

3.4

Assumptions and Limitations

Assumptions and limitations of this study not made clear in each section are detailed here. Analysis of

emissions for both BEVs and ICE-based vehicles was

limited to `tank-to-wheel' (TTW) analysis, excluding `well-to-tank' (WTT) analysis or a combination of

both (`well-to-wheel' analysis). TTW analysis involves only the contribution to emissions from the opera-

tion of the vehicle, with WTT contribution accounting for the upstream CO

emissions from "fuel extrac-

tion to the refueling station" (Silva, Goncalves, Farias & Mendes-Lopes, 2006, p441). As such, only

emissions relating to fuel consumption from operating an ICE vehicle, or electricity used to recharge a

BEV, were considered.

When data relating specifically to Melbourne was unavailable, this was substituted with relevant Vic-

torian data, and then Australian data (in order of preference). For example, it was assumed that each ICE

vehicle in the current fleet produced on average 230 gCO

/km, based on the Australian average and aver-

age vehicle age (section 3.1). This average takes into account all petrol and diesel light vehicles, taken to

be any road vehicle weighing under 3.5 tonnes, excluding motorcycles and commercial vehicles.

Other emissions attributable to light vehicle travel from ICE vehicles, such as nitric oxides (NOx),

volatile organic compounds (VOCs) and carbon monoxide (CO) were excluded from analysis. This was

due to the fact that although these pollutants have impacts to human health or have secondary greenhouse

effect impacts (that is, they can react with other atmospheric gases to form GHGs), they do not have di-

rect greenhouse effects on the environment. It is commonplace in literature to exclude secondary green-

house gases from emissions analysis.

For battery electric vehicles, it was assumed that the manufacturer quoted range was accurate for the

purposes of this study. It was also assumed that no efficiency losses occurred during the transmission of

generated electricity to charging point, although charging and battery efficiency was considered in section

3.3. However, under real world conditions the true energy consumption of BEVs may vary due to use of

auxiliaries (air-conditioning, radio), weather conditions and differing driving habits or styles (Badin et al.,

2013). Additionally, the feasibility of introducing a large amount of BEVs into the existing light vehicle

fleet was considered outside of the scope of this study.

RESULTS AND DISCUSSION

The following section of the study is split up into two sections. The first section will discuss the results

from modelling BEV CO

intensity based on differing energy mix scenarios, with comparison to ICEVs.

The second section will discuss the results from modelling total passenger vehicle emissions for Mel-

bourne and the potential abatement from introducing BEVs. It must first be noted that when comparing

the relative emissions intensity of BEVs to ICEVs, the average CO

intensity of new passenger vehicles

in Australia was considered in addition to the current fleet average. As such, 175g CO

/km was used as a

more relevant comparison figure, as this was the average emissions intensity for new light vehicles in

2015 (NTC, 2016). It should also be noted that this figure is quite high when compared to other regions,

for example in Europe an average new passenger vehicle in 2015 emitted only 124g CO

/km. Reasons for

this include Australian consumer preferences for heavier and more powerful vehicles, few government

incentives for lower emissions vehicles and lower fuel prices (NTC, 2016).

4.1

BEV CO

intensity

Figure 2 presents the carbon dioxide emissions per unit distance for BEVs, considering four energy

scenarios: charging from the existing Victorian grid mix, a 25% renewables mix, a 50% renewables mix,

and a 75% renewables mix. Two ICEV cases were also plotted; the average CO

intensity for the passen-

ger vehicle fleet in Australia in 2016, and the most recent average for new passenger vehicles sold (2015).

When modelling the three energy scenarios, the ratio of brown coal to natural gas electricity generation

from the 2014-15 case was kept constant. When calculating the operating CO

intensity for the BEVs, an

average effective energy consumption was used as per Table 2, which was then combined with the CO

intensity of the different energy mix scenarios to obtain operating emissions per unit distance. The results

from this modelling were certainly quite remarkable. They show that if a BEV was purchased today in

Melbourne and was charged from the equivalent of Victoria's 2014-15 energy mix, it would in fact be re-

sponsible for more emissions than the current average new passenger vehicle (199.2 gCO

/km and 174

gCO

/km respectively). Even if the energy mix was taken to be the 25% renewables scenario, battery

electric vehicles would only produce marginally less carbon emissions than the new average passenger

2016 fleet

average

new ICEV average

[ZELLBEZ]

25% Renewables

50% Renewables

75% Renewables

100

150

200

250

ity

BEV and ICE Vehicle CO

intensity

ICE vehicles

BEVs

Figure 2. Average carbon dioxide intensity of ICEVs and BEVs

vehicle. The benefit of BEVs is only truly seen when the 50% and 75% renewables cases are considered.

These cases allocated an electric vehicle carbon intensity of 111.2 gCO

/km and 55.59 gCO

/km, yielding

a respective emissions reduction of 36.10% and 68.05% over an average new passenger vehicle.

Figure 3 compares three popular BEVs operating in Melbourne with three top selling ICE vehicles of

the same class (Tesla model S with the Holden Commodore, BMW i3 with the Toyota Corolla, and Nis-

san Leaf with the Mitsubishi Mirage). A benefit of modelling three models was that each had varying

range and battery pack characteristics, and therefore different theoretical power consumption. The first

trend that can be observed was both larger BEVs and ICEVs produced more CO

, which is quite intuitive

due to their increased weight. As shown, The Tesla Model S was less carbon intensive for all modelled

electricity mix scenarios when compared to the comparable large passenger ICEV. However, both smaller

BEVs only produced less CO

emissions per kilometre for the 50% and 25% renewable scenarios. This

can be explained as smaller vehicles are generally marketed as more economic cheaper vehicles to run,

compared to larger vehicles where luxury and space are more important selling points. What is important

take away from this analysis is that in all of the simulated cases, BEVs were far from being `zero-

emission' vehicles when operated in Melbourne. Additionally, in some scenarios even with relatively ag-

gressive assumptions they are no better in terms of operating CO

intensity than standard fossil fuel burn-

ing vehicles. A key reason that may explain this is that Victoria's power generation mix has a very high

carbon intensity. The CO

intensity of the 2014-15 grid in Victoria was calculated to be 1079 gCO

100

150

200

250

Tesla Model S

Nissan leaf

BMW i3

ity

Common BEVs and comparable petrol model

Top selling comparable ICEV

2014-15 Mix

25% Renewables

50% Renewables

75% Renewables

Figure 3. Operating CO

emissions for BEVs under different power mix scenarios, with comparable ICEV

e/kWh, agreeing with literature estimating an intensity of 1130 gCO

-e/kWh for 2013 (Department of the

Environment, 2015). This figure is well above Australia's average and the average for various other coun-

tries (Ang & Su, 2016), as shown in Table 3. The primary reason for such carbon intensive electricity in

Victoria, and hence such high operational CO

emissions for BEVs, is that most of the power is generated

from brown coal-fired plants (ACIL Tasman, 2009), and it's associated high carbon content. Doucette and

McCulloch (2011) showed similar results when modelling BEVs in various countries. The paper found

that for China and India, countries with high CO

intensities, BEVs were not able to achieve a significant

emissions decrease, if at all. However, the paper also found that in regions of less carbon intensive power

generation, such as the United States and especially France, BEVs could provide a much more substantial

reduction in road transport CO

emissions.

4.2

Total vehicle emissions - Melbourne

In order to better gauge the actual potential CO

abatement of BEVs in Melbourne, a number of different

scenarios were modelled. This involved calculating the total annual passenger vehicle CO

emissions for

different cases, and analysing any reductions based on each case.

Table 3. Modelled electricity generation emissions intensities and other region comparisons

Region

intensity

(gCO

-e/kWh)

Victoria 2014-15

1079

25% Renewables (modelled)

903

50% Renewables (modelled)

602

75% Renewables (modelled)

301

Australia

781

China

692

United States

486

France

6,8

6,9

7,1

7,2

7,3

7,4

7,5

7,6

7,7

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

iss

(Mt

)

Base Case

Figure 4. Total passenger vehicle CO

emissions in Melbourne - Base case

Figure 4 represents the base case emissions for Victoria, from 2016 up to 2030. The base case repre-

sents a situation in which no BEVs are introduced into the fleet. It assumed that total vehicle kilometers

(TVKM), which were used to estimate total emissions, were increasing at 3.24% per annum, the average

growth rate over the last 50 years from 2012 (BITRE, 2012). It also assumed that ICE vehicle efficiency

was improving steadily, and hence a 2.59% drop in vehicle CO

intensity per year was applied (NTC,

2016). Therefore, total emissions are seen to peak in 2029 due to the decreasing rate of new vehicles'

emissions. Note that the vertical scale has been adjusted to amplify this trend, and as such does not begin

from zero. Annual emissions were shown to increase from 7.10 MtCO

to 7.57 MtCO

over the 14-year

period.

Figure 5 represents the `50/50' case. This case modelled a steady uptake of both 50% of BEVs into the

existing fleet, and 50% of renewables into the existing grid by 2030. The power generation mix carbon in-

tensity at 2016 was taken to be the same as the 2014-15 mix, and was linearly decreased to the 50% re-

newables intensity by 2030 (values shown in Table 3). This same uptake was emulated with introducing

BEVs into the fleet, increasing from 0% in 2016 to 50% of passenger vehicles in 2030. The blue graphed

area represents the base case emissions, with the orange showing the emissions from the above scenario.

Under the 50/50 case, 2030 annual vehicle emissions were found to be 6.47 MtCO

, yielding a 14.5% re-

duction from 2030 base case, or 8.91% reduction from 2016 base case. The results here show that even if

half of the entire passenger vehicle fleet is replaced by BEVs and half of the grid is replaced with renew-

able sources, both by 2030, there is not a major reduction seen in CO

emissions. Furthermore, the in-

creased amount of electricity that would be required to be generated from the operation of these BEVs

would place excess strain on the grid, but as explained in section 3.4 analysis of this was outside the study

scope.

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

issi

s (

tCO

)

50/50 Case

50% BEVs/50% renewables by 2030

Figure 5. Total passenger vehicle CO

emissions - 50/50 Case

The second scenario was modelled was the `75/75' case. This was similar to the `50/50' case except a

steady uptake of 75% of both BEVS into the passenger vehicle fleet and renewables into the Victorian

grid by 2030 was analysed. The method for modelling this case was repeated from the `50/50' case. Ini-

tially, one can see from the figure that this case does in fact provide a significant potential reduction in fu-

ture carbon emissions for road transport. The results showed that in 2030, the `75/75' case achieved a

48.4% reduction from base case annual CO

emissions, and a 45.0% reduction from today's annual CO

emissions. This is clear evidence to suggest that if it were feasibly possible, an increase of both electric

vehicles on the road and clean power in the grid of this scale would have a meaningful effect on carbon

emissions produced in the road transport sector. It is also interesting to note that an increase of 25% in

both BEV and renewable energy uptake over the `50/50' case demonstrated over a three-fold decrease in

annual emissions in 2030.

The case for large scale adoption of battery electric vehicles in Melbourne based on their CO

reduction

potential and Victoria's current energy mix does not seem overly convincing. As the results have shown,

only a case in which three-quarters of both the grid is renewable and the vehicle fleet is electrified show

any significant emissions reductions results for the effort. To compound this, the economic implications

of such a situation have not been taken into account during this paper. For instance, the situation becomes

even more unviable when considering the capital cost premium that BEVs attract over ICE vehicles.

Studies have shown that electric vehicles are more cost competitive when lower ranges are required and

higher fuel prices are present (Werber, Fischer & Schwartz, 2009). As such, increasing the uptake of

BEVs into the fleet would need to be heavily incentivised through government policy to gain any traction

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

emi

)

75/75 Case

75% BEVs/75% Renewables by 2030

Figure 6. Total passenger vehicle CO

emissions - 75/75 Case

in Melbourne, but efforts would be much better focused on reducing the demand for travel and hence the

need to reduce emissions intensities by lowering the amount of kilometres travelled in total.

4.3

Suggestions for Further Research

As this paper necessitated many assumptions to be made about various future trends and for overall sim-

plicity, there is scope for further research regarding emissions abatement in the road transport sector. One

suggestion for further study would be to investigate the emissions abatement potential of a range of coun-

tries with varying electricity mixes to determine the relative CO

emissions of BEVs in each region. This

would help determine where electric vehicles would be the most viable option for emissions abatement.

In addition, this study has only looked at the potential for BEVs to reduce transport emissions in isolation

from other potential options. In reality, a number of options would likely be implemented in sequence to

achieve optimal results. CCA (2014) suggest that increasing the efficiency of motor vehicles, reducing the

emissions intensity of fuels, and more efficiently managing demand through mode shift and urban plan-

ning are key areas to be considered in conjunction with one another.

Research into the economic costs and benefits, as well as feasible implementation of BEVs also pro-

vides grounds for further study. For example, one could research the relative savings in fuel costs of

BEVs compared to the price premium they attract over petrol or diesel cars, or look into the feasibility of

range due to battery capacity and the limitation of the time and practicality involved with recharging bat-

teries.

Another limitation of this study and hence area for further investigation would be to consider upstream

and downstream emissions of BEVs compared to standard vehicles. This would involve conducting life-

cycle analysis, or well-to-wheel analysis, and would provide a clearer picture as to the total carbon emis-

sions involved in the lifespan of an electric vehicle, not just from operation.

Lastly, in reality it may be unrealistic to assume that Victoria can achieve 50-75% of its energy genera-

tion from renewable sources by 2030, assumed possible for this study. Trainer (2012) argues that it simp-

ly would not be possible to power the currently energy-intensive Australian society on solely renewable

power, as the total investment cost based on intermittency and plant redundancy is unaffordable. The pa-

per concludes by stating that energy and greenhouse problems cannot be solved by supply side actions

such as technical advancements, but demand side actions such as fundamental structural change to the

way society consumes its resources.

CONCLUDING REMARKS

Due to the recent and expected trends of passenger vehicle use and transport emissions globally, battery

electric vehicles are becoming an increasingly pivotal way in which to curtail future road transport emis-

sions. Although BEVs do not produce any tailpipe emissions, they depend strongly on electricity produc-

tion which is predominantly from non-renewable sources in Melbourne. This infers they are less `green'

than one may think, especially when taking into account life-cycle emissions such as from battery produc-

tion and disposal.

This study looked at the relationship between energy consumption of battery electric vehicles and the

CO2 emissions involved in generating electricity to charge them, and then compared this to the carbon

emissions from comparable internal combustion engine passenger vehicles. The results showed that given

the current carbon intensity of electricity generation, BEVs operating in Victoria did not provide any sig-

nificant emissions abatement potential compared to an average new passenger vehicle. In some cases and

for some particular models, they were found to be even more carbon intensive than ICE vehicle compari-

sons. The only modelled scenarios that showed noteworthy potential for BEVs to reduce emissions in the

transport sector was for the 50% renewables case and 75% renewables case, which yielded a respective

emissions reduction of 36.10% and 68.05% for BEVs over an average new passenger vehicle. The rea-

sons behind this revolved around Victoria's highly carbon intensive grid, suggesting that the ability of

BEVs to abate emissions would be more greatly realised in regions with cleaner electricity generation

mixes.

This study also looked at modelling the possible future emissions reduction in the road transport sector

by introducing BEVs into the existing fleet. This was done by varying the percentage uptake of BEVs in-

to the fleet as well as renewable energy sources into the grid. Results showed that due to the trend of

ICEV carbon emissions reducing over time, and again the high carbon intensity of electricity in Victoria,

a significant CO2 reduction was only achieved in the `75/75' case. As such, the ability for BEVs in Mel-

bourne to abate emissions could only occur for high fractional uptake of BEVs into the fleet and high

fractional uptake of renewable energy sources into the grid, although feasibility analysis of this scenario

was not considered.

This paper does not aim to present BEV adoption in a negative light. Instead, it should highlight the in-

centive for Victoria and other regions to reduce the carbon intensity of their power generation mixes, in

order to optimize the potential emissions reduction that the adoption of BEVs can accomplish.

REFERENCES

ACIL Tasman (2009). Fuel resource, new entry and generation costs in the NEM, Melbourne.

Australian Bureau of Statistics (ABS) (2017). Survey of Motor Vehicle Use, Australia, 12 months ended 30 June 2016, data

cube: Excel spreadsheet, cat. no. 9208.0, website accessed

May 2017 from

<http://www.abs.gov.au/ausstats/abs@.nsf/mf/9208.0>

Ang, B. W., Su, B. (2016). Carbon emission intensity in electricity production: A global analysis, Energy Policy 94, 56-63

Badin, F., Le Berr, F., Briki, H., Dabadie, J. C., Petit, M., Magand, S., Condemine, E. (2013). Evaluation of EVs energy con-

sumption influencing factors, driving conditions, auxiliaries use driver's aggressiveness. Electr. Veh. Symp. Exhib.

(EVS27), 2013 World, 1-12

BMW (2017). Technical Data BMW i3. Website accessed May 2017 from <http://www.bmw.com.au/en/all-models/bmw-

i/i3/2013/technical-data.html#tab-0>

Brown, S., Pyke, D., Steenhof, P. (2010). Electric vehicles: the role and importance of standards in an emerging market. En-

ergy Policy 38 (7), 37973806.

Bureau of Infrastructure, Transport and regional Economics (BITRE) (2012). State and Capital City vehicle kilometres trav-

elled 1990-2012, BITRE, Canberra

Bureau of Infrastructure, Transport and Regional Economics (2015). Australia's commuting distance: cities and regions, BI-

TRE, Canberra

Campanari, S., Manzolini, G., de la Iglesia, F. G. (2009). Energy analysis of electric vehicles using batteries or fuel cells

through well-to-wheel driving cycle simulations, Journal of Power Sources 186 (2), 464-477

Casals, L. C., Martinez-Laserna, E., García, B. A., Nieto, N. (2016). Sustainability analysis of the electric vehicle use in Eu-

rope for CO2 emissions reduction, Journal of Cleaner Production 127, 425-437

Carbon Monitoring for Action (CARMA) Database (2012). Website accessed May 2017 from <http://www.carma.org>

Climate Change Authority (CCA) (2014). Light vehicle emissions standards for Australia research report, CCA, Melbourne

Commonwealth of Australia, (2011a). Strong growth, low pollu tion: modelling a carbon price, Treasury, Australian Gov

ernment, Canberra, ACT

Commonwealth of Australia (2016). National Inventory Report 2014 (revised) Volume 1. Department of the Environment

and Energy

Cosgrove, D., Gargett, D., Evans, C., Graham, P., Ritzinger, A. (2012a). Greenhouse gas abatement potential of the Australi-

an transport sector: Summary report from the Australian Low Carbon Transport Forum, CSIRO, Australia.

Cosgrove, D., Gargett, D., Evans, C., Graham, P., Ritzinger, A. (2012b). Greenhouse gas abatement potential of the Australi-

an transport sector: Technical report from the Australian Low Carbon Transport Forum, CSIRO, Australia

Costello, M. (2017). Australia's EV and PHEV sales analysed. Website accessed May 2017 from

<http://www.caradvice.com.au/544843/australias-ev-and-phev-sales-analysed/>

Department of Industry, Innovation and Science (2016). Australia Energy Update 2016, Canberra

Department of the Environment (2015). National Greenhouse Accounts Factors: Australian National Greenhouse Accounts,

Canberra

Doucette, R. T., McCulloch, M. D. (2011). Modeling the CO2 emissions from battery electric vehicles given the power gen-

eration mixes of different countries, Energy Policy 39 (2), 803-811

Ehsani, M., Gao, Y., Gay, S.E., Emadi, A. (2005). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals,

Theory, and Design. CRC Press, New York

Franco, S., Mandla, V. R., Ram Mohan Rao, K. (2017). Urbanization, energy consumption and emissions in the Indian context

A review, Renewable and Sustinable Energy Reviews 71, 1362-0321

Gantt, L. R., Perkins, D. E., Alley, R. J., Nelson, D. J. (2011). Regenerative brake energy analysis for the VTREX plug-in hy-

brid electric vehicle. 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, IL, 1-6

Hao, H., Wang, H., Ouyang, M. (2010). Comparison of policies on vehicle ownership and use between Beijing and Shanghai

and their impacts on fuel consumption by passenger vehicles. Energy Policy 39 (2), 1016-1021.

Hawkes, A. D. (2010). Estimating marginal CO2 emissions rates for national electricity systems, Energy Policy 38 (10), 5977-

5987

Hickman, R., Banister, D. (2007). Looking over the horizon: Transport and reduced CO

emissions in the UK by 2030.

Transport Policy 14 (5), 377-387.

IEA, 2010. Transport Energy Efficiency: Implementation of IEA Recommendations since 2009 and next steps. International

Energy Agency, Paris

IEA, 2017. Energy and CO2 emissions in the OECD. International Energy Agency, Paris

Kang, J., Yan, F., Zhang, P., Du, C., (2014). Comparison of comprehensive properties of Ni-MH (nickel-metal hydride) and

Li-ion (lithium-ion) batteries in terms of energy efficiency. Energy 70, 618-625.

Mitsubishi (2017). Full Specifications. Website accessed May 2017 from <https://www.mitsubishi-

motors.ca/en/vehicle/specifications/i-miev/2017/trims/es/>

Musavi, F., Edington, M., Eberle, W., Dunford, W. G. (2012). Evaluation and efficiency comparison of front end AC-DC

plug--in hybrid charger topologies. Smart grid. IEEE Trans 3, 413-421.

Michaelis, L., Davidson, O. (1996). GHG mitigation in the transport sector. Energy Policy 24 (10-11), 969-984.

Natioanl Transport Commission (NTC) (2016). Carbon Dioxide Emissions Intensity for New Australian Light Vehicles 2015

Information paper, NTC, Melbourne

Nicolay, S. (2000). A Simplified LCA for Automotive Sector Comparison of ICE (Diesel and Petrol), Electric and Hybrid

Vehicles 95

Nissan, 2017, Nissan Motor Company. Website accessed May 2017 from < https://www.nissanusa.com/electric-cars/leaf/>

Notter, D. A., Gauch, M., Widmer, R., Wäger, P., Stamp, A., Zah, R., Althaus, H. J. (2010) Contribution of Li-ion batteries to

the environmental impact of electric vehicles, Environ. Sci. Technol., 44, 65506556

Perujo, A., Ciuffo, B., 2010. The introduction of electric vehicles in the private fleet: Potential impact on the electric supply

system and on the environment. A case study for the Province of Milan, Italy, Energy Policy 38 (8), 4549-4561

Planète Energies (2015). About the Energy Mix. Website accessed May 2017 from <http://www.planete-

energies.com/en/medias/close/about-energy-mix>

Ramachandra, T. V., Shwetmala (2009). Emissions from India's transport sector: Statewise synthesis, Atmospheric Environ-

ment 43 (34), 5510-5517

Renault (2017). Kangoo Z.E. Efficient, smart and versatile. Website accessed May 2017 From

<https://www.renault.co.uk/vehicles/new-vehicles/kangoo-ze.html>

Silva, C. M., Goncalves, G. A., Farias, T. L., Mendes-Lopes. J. M. C. (2006). A tank-to-wheel analysis tool for energy and

emissions studies in road vehicles, Science of the Total Environment 367 (1), 441-447

Stanley, J. K., Hensher, D. A., Loader, C. (2011). Road transport and climate change: Stepping off the greenhouse

gas,

Transportation Research Part A: Policy and Practice 45 (10), 1020-1030

Tesla (2017). Tesla. Website accessed May 2017 from <https://www.tesla.com/en_AU/modelx>

van Vliet, O., Brouwer, A. S., Kuramochi, T., van den Broek, M., Faaij, A. (2011). Energy use, cost and CO2 emissions of

electric cars, Journal of Power Sources 196 (4) 2298-2310.

Werber, M., Fischer, M., Schwartz, P. V. (2009). Batteries: lower cost than gasoline? Energy Policy 37 (7), 2465-2468

Excerpt out of 19 pages - scroll top

Details

Title: Are Battery Electric Vehicles a Possibility to Decrease CO2 Emissions?
College: Monash University Melbourne
Course: Environmental Engineering
Grade: 85
Author: James McCutchan (Author)
Publication Year: 2017
Pages: 19
Catalog Number: V375235
ISBN (eBook): 9783668526785
ISBN (Book): 9783668526792
Language: English
Tags: Electric vehicles EV emissions power generation mix electricity co2 transport abatement Melbourne

Quote paper: James McCutchan (Author), 2017, Are Battery Electric Vehicles a Possibility to Decrease CO2 Emissions?, Munich, GRIN Verlag, https://www.grin.com/document/375235

Are Battery Electric Vehicles a Possibility to Decrease CO2 Emissions?

Excerpt

Details