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Available online at w

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Impact of energy supply infrastructure in life cycle analysisof hydrogen and electric systems applied to the Portuguesetransportation sector

Alexandre Lucas a,*, Rui Costa Neto b, Carla Alexandra Silva b

aMassachusetts Institute of Technology Portugal Program, Avenida Prof. Cavaco Silva, Campus IST e TagusPark, Room e 16.6,

2780-990 Porto Salvo, PortugalbDepartment of Mechanical Engineering, IST-Technical University of Lisbon, Av. Rovisco Pais 1, Pav. Mec. I, 2� andar,

1049-001 Lisboa, Portugal

a r t i c l e i n f o

Article history:

Received 8 November 2011

Received in revised form

25 April 2012

Accepted 26 April 2012

Available online 7 June 2012

Keywords:

Hydrogen

Electric vehicle

Life cycle analysis

Infrastructure

Uncertainty

* Corresponding author. Tel.: þ35 1961741327E-mail addresses: alexandre.lucas@ist.utl

utl.pt (C.A. Silva).0360-3199/$ e see front matter Copyright ªdoi:10.1016/j.ijhydene.2012.04.127

a b s t r a c t

Hydrogen and electric vehicle technologies are being considered as possible solutions to

mitigate environmental burdens and fossil fuel dependency. Life cycle analysis (LCA) of

energy use and emissions has been used with alternative vehicle technologies to assess the

Well-to-Wheel (WTW) fuel cycle or the Cradle-to-Grave (CTG) cycle of a vehicle’s materials.

Fuel infrastructures, however, have thus far been neglected. This study presents an

approach to evaluate energy use and CO2 emissions associated with the construction,

maintenance and decommissioning of energy supply infrastructures using the Portuguese

transportation system as a case study. Five light-duty vehicle technologies are considered:

conventional gasoline and diesel (ICE), pure electric (EV), fuel cell hybrid (FCHEV) and fuel

cell plug-in hybrid (FC-PHEV). With regard to hydrogen supply, two pathways are analysed:

centralised steam methane reforming (SMR) and on-site electrolysis conversion. Fast,

normal and home options are considered for electric chargers. We conclude that energy

supply infrastructures for FC vehicles are the most intensive with 0.03e0.53 MJeq/MJ

emitting 0.7e27.3 g CO2eq/MJ of final fuel. While fossil fuel infrastructures may be

considered negligible (presenting values below 2.5%), alternative technologies are not

negligible when their overall LCA contribution is considered. EV and FCHEV using elec-

trolysis report the highest infrastructure impact from emissions with approximately 8.4%

and 8.3%, respectively. Overall contributions including uncertainty do not go beyond 12%.

Copyright ª 2012, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights

reserved.

1. Introduction growth in Europe and in the USA has been 8% and 20%,

In the last two decades, world energy use has risen 45%,

drivenmostly by the development in countries such as China,

India and the middle east [1,2]. From 1990 to 2008, the same

..pt, Alexandre.Lucas@efa

2012, Hydrogen Energy P

respectively [1,3]. In Portugal, this increase has been approx-

imately 57%. In 2009, primary energy imports represented

81.2%, making Portugal one of the most exterior energy-

dependent countries in Europe. In the same year, the

cec.com (A. Lucas), costaneto@ist.utl.pt (R.C. Neto), carla.silva@ist.

ublications, LLC. Published by Elsevier Ltd. All rights reserved.

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 510974

transportation sector in Portugal accounted for 38.2% of final

energy use. From this percentage, 86% was due to road

transportation [4]. Without substantial changes in policy and

practice, this unsustainable tendency will evidently continue.

In 2009, both the European Union (EU) and G8 leaders

agreed on the long-term goal that CO2 emissions should be cut

by 80% by 2050. However, achievement of this goal means that

95% decarbonisation must be achieved in the road trans-

portation sector [5]. The European 20-20-20 agreement set

ambitious goals for 2020, cutting greenhouse gases by 20%

(compared with 1990 levels), reducing energy use by 20% and

meeting 20% of European energy needs from renewable

energy sources (RES) [3]. Portugal has set its goals even higher,

with 20% efficiency, 60% electricity production from RES and

31% of final energy from RES, with 10% energy use reduction

within the transportation sector [6]. Hydrogen and electricity

have been regarded as possible solutions tomitigate fossil fuel

scarcity and environmental impact. However, hydrogen and

electricity alone cannot directly and unconditionally solve the

problem. Hydrogen in particular, when compared to elec-

tricity, has good filling time and provides a good range of

autonomy, despite the considerable amount of energy asso-

ciated with its compression.

Previous life cycle analysis (LCA) has focused on the fuel

cycle divided into its upstream impact (Well-to-Tank, WTT)

and downstream impact (Tank-to-Wheel, TTW) stages and

has included only fewmaterials typically related to the vehicle

itself (manufacturing, maintenance, end-of-life). The final

report of the Hysociety Project [7] shows an extensive amount

of research work related to LCA studies of hydrogen applied to

the transportation sector. No work refers to the energy supply

infrastructure, including final refuelling stations. Expected

energy use and emissions from passenger car transportation

with different engine technologies in 2020 have been analysed

by [8]. The study covered pure combustion engines (gasoline,

diesel), hybrids combining combustion engines with electrical

engines and fuel cells and battery-powered electric cars. The

study concluded that hybrids with a diesel combustion engine

combinedwith an electrical engine had the lowest energy use,

followed by a hybrid combining a CNG combustion engine

with an electrical engine and a similar hybrid using a gasoline

combustion engine. Reference studies from [9] present

a comprehensive report of WTW, clearly defining the term

WTT as the direct energy use for producing and distributing

the fuel required for propulsion of the vehicle, while TTW

corresponds to the term for direct energy use or propulsion

energy. Estimates are given for several hydrogen pathways,

electricity, and conventional fuels, as well as local or distrib-

uted philosophies. Regarding plug-in hybrids, possible

scenarios of infrastructure implementation and associated

costs are presented by [10]. A similar study for hydrogen

fuelling infrastructure assessment was performed by [11],

elaborating future costs of hydrogen fuel to develop large-

scale facilities. They demonstrate that a hydrogen infra-

structure that could support volume deployment of fuel cell-

electric vehicles can be commercially viable. Another study

by [12] compares and assesses the WTW impact of plug-in

technology with hybrid and conventional technologies

considering different AER (All Electric Range), with pure CD

(charge depletion), blended and CS (charge sustaining) mode.

They conclude that the PHEVs offer reductions in the use of

petroleum energy as compared with regular hybrid electric

vehicles (HEVs). More petroleum energy savings and Green-

house Gas (GHG) emissions reductions were realised as the

AER increased except when the marginal grid mix was

dominated by oil-fired and coal power generation.

The driving cycle considered in the different studies has

also been an issue for review, as the driving cycle is crucial for

a proper comparison between vehicles and estimations. A

consensus remains to be reached among academia, industry

and policy makers because driving behaviours differ from

region to region. Because public policies or decisions are also

often at a regional level, such behaviours have been inter-

esting to assess. Differences should, nevertheless, be well

understood and described. Using a Portuguese driving cycle

[13], estimate WTW and Cradle-to-Grave (CTG) for vehicle

materials impacting a large spectrum of vehicle technologies

in the same study, enhancing the importance of plug-in

vehicle penetration to mitigate both energy and emissions

problems. In another WTW study [14], an energy analysis of

electric vehicles using batteries or fuel cells using the ECE-

EUDC driving cycle was developed. The investigators

conclude that the best results are achieved by pure EVs for

only very limited driving range requirements, while the fuel

cell solutions achieve best performance for more extended

driving ranges where the battery weight becomes too high.

The energy use, emissions and cost of plug-in hybrid vehicles

were evaluated comparing different driving cycles (CAFE,

FTP75, NEDC and JC08), different driving distances and

different user behaviours regarding battery recharging, and

the main characteristics between them were described [15].

In terms of infrastructures, several passenger transport

systems, such as cars, buses, trains and air travel, were ana-

lysed [16]. Energy use and emissions arising from construc-

tion, operation and maintenance of the transport system

infrastructures, in addition to the WTW and CTG stages, were

included in the authors’ analysis. For road transportation,

these infrastructures dealtmostly with roadway construction,

operation and maintenance, which can also be found in [17]

LCA and parking lots. For on-road transportation, the

authors found that the life cycle component contributions are

roughly the same as the GHG contributions and produce

1.4e1.6 times larger life cycle factors than the operational

components. Infrastructures alone ranged from approxi-

mately 7 to 18% in MJ/PKT and CO2eq/PKT in the total LCA for

a conventional gasoline-powered sedan.

Regarding the actual fuel infrastructure, few studies

address direct and indirect energy and emissions contribu-

tions in vehicle LCA [18e20]. A study has identified the envi-

ronmental and economic aspects of hydrogen production

through water electrolysis, using wind power and the Korean

electricity mix, applying wind power and the Korean elec-

tricity mix to FCHEV and has compared them to conventional

fuels (gasoline and diesel) [18]. This study makes a scale-up

inventory from a small H2 station used in California and also

accounts for the wind structure. The authors conclude that

although the wind station may be economically viable, it is

extremely dependent on wind availability.

The first study to address energy supply infrastructures

applied to vehicles, comparing conventional fossil fuels and

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 5 10975

electricity supply chain facilities, was performed by [19]. The

study focuses on EV technology using estimations for other

LCA stages to assess the weight of infrastructure. Using the

2006 WTT average value from the EU electricity mix, the

impact of the EV energy supply infrastructure did not go

beyond 8%, while conventional fuel infrastructure remained

below 2% impact. The study uses estimated service ratios

based on the foreseen use of public charging facilities and

identifies these variables as sensitive to the results. Never-

theless, this study develops inventories for petrol stations and

charging points and presents a methodology to estimate

energy supply infrastructure impacts, contemplating other

scenarios of WTW and CTG stages.

A brief literature summary was performed and is pre-

sented in Table 1, identifying values for the most highly

influential parameters and differences between the studies.

Due to the use of different vehicle references within a given

technology, one may argue that it would be reasonable to

obtain different results of TTW, WTT or CTG. However, other

factors influence the results as well. One is the lifetime

parameter of kilometres driven, a parameter which can vary

from 150,000 km [13] to 300,000 km [8]. This parameter directly

affects the CTG stage because the absolute values of the LCA

are divided by the lifetime driving distance of the vehicle to

obtain a value per kilometre. Another important parameter is

the Driving Cycle considered in the study, fundamental to

determine the TTW impact. Europe has been adopting the

New European Driving Cycle (NEDC), while many car manu-

facturers still have their own driving cycles. In the USA, the

most common Driving Cycle used is the Federal Test Proce-

dure (FTP). The main differences between cycles are acceler-

ation, velocity and mobility dynamics. Other local or specific

cycles found in the literature can also be used to assess real

scenarios [13]. Within the scope and aim of LCA, although all

studies tend to address energy and emission impacts, not all

studies include the CTG stage [9].

Given the identified lack of assessments of the whole

energy supply infrastructure impacts in the literature, the aim

of this study is to perform a life cycle analysis to estimate

energy use and greenhouse gas emissions associated with the

construction, maintenance and decommissioning of fuel

support facilities for ICE vehicles, EV, FCHEV, FC-PHEV and

their impact in overall vehicle LCA. This LCA is particularly

important when considering alternative technologies with

different pathways to provide policy and decision makers the

most complete set of information about each choice. Despite

being applied to the specific case of Portugal, themethodology

could be extended to other countries, similar to the case of

fuel WTW studies.

2. Methodology

Life cycle analysis, particularly in the case of infrastructure,

raises two major methodological concerns. The first concern

is about system boundaries, where one should, on the one

hand, be cautious not to double-count efforts or activities

already assessed in other stages, and, on the other hand, not

disregard a relevant activity. Another concern deals with how

to allocate burdens to different products with different

energy contents that may share the same facilities. This

study addresses energy infrastructure according to each

technology and pathway. For that reason, roads, traffic signs,

parking lots, repair shops, and car factories were not

considered in this study as they are not a distinctive char-

acteristic between technologies. The principles of ISO 14040

[22] were followed. The methods used were Global Warming

Potential for 100 years (GWP100) and Cumulative Energy

Demand (CED). GWP is based on the heat-absorbing ability of

each gas relative to that of CO2, as well as the decay rate of

each gas, i.e., the amount removed from the atmosphere over

a given number of years [23]. Because infrastructures are

being considered in this study, and given the contribution of

different gases and their ability to linger in the atmosphere

for different time ranges, a 100 year time horizon was

considered. CED methodology was used to investigate the

energy use throughout the life cycle of a good, in this case

infrastructure. The CED methodology is especially suited to

determine and compare the energy intensity of processes,

including the direct as well as the indirect uses of energy.

Indirect energy inputs consider all inputs that are used for

purposes other than manufacturing products, such as infra-

structure and equipment. The Simapro tool [24] allows the

comparison between contributions of different sources.

However, because the goal of the study is to obtain compa-

rable values, the output unit of MJeq was used, assuming

Simapro primary energy conversion factors.

Supply chain scope and boundaries were outlined for the

technologies considered until the vehicle was reached. Three

major groups could be identified: primary fuel handling

infrastructure, end fuel transport system and distribution

facilities.

2.1. Scope

Fig. 1 systematises the boundaries for the base case supply

chain. For the extraction of raw material, an oil well and an

offshore platform were used. For the exploration well, the oil

hydrocarbon probability of concentration in depth formation

varies according to a normal distribution, so the mean value

was considered to be approximately 2,600 m [25], to represent

the depth and the corresponding drilling efforts to explore

a North Sea well. Despite the existence of two refineries in

Portugal, only Sines refinery was used, as this refinery repre-

sents almost the entire share of refining products [26].

Refinery efficiency was considered to be 85% [9]. As Simapro

does not account for construction of office buildings in the

refinery, 50% of the area was taken as ameasure for inventory

of buildings. Roads inside the refinerywere also accounted for.

Storage facilities were omitted. For fuel transportation, the

main existing infrastructure is a multiuse pipeline, so alloca-

tions were made to account only for the gasoline and diesel

share. There were 2.52 thousand refuelling stations in 2010,

and these stations were assumed to satisfy the demand of all

the fleet perfectly. The fleet was composed of 5.63 million

vehicles, of which approximately 3.00 million were gasoline-

powered and 2.63 million were diesel-powered [27]. Mainte-

nance was considered only for the refinery. The electric

infrastructure and H2 production supply chain using the

electrolysis pathway are represented in Fig. 2.

Table 1 e Brief LCA Literature Overview.

Study [9] [8] [13] [21]

Scope and

Aim

CO2eq (100y);

Energy Use;

WTW e 2010þ

CO2eq (100y); Energy Use;

WTW þ CTG for 2020

CO2; Energy Use;

WTW þ CTG

CO2eq (100y); Energy Use;

Criteria pollutants;

WTW þ CTG þ end of life

Software/

database

E3 database

by LBST

ADVISOR; TEDB; EPA;

NHTSA; FHWA 2005; ETH

Zurich Matlab Simulink

CTG e GREET

Copert

Advisor

Ecoscore

Ecoinvent

Technologies ICE; FCHEV ICE; FCHEV; EV ICE; FCHEV; FCPHEV; EV ICE; FCHEV; EV

Pathways Petrol and diesel

EU Mix

SMR:yes

Electrolysis:yes

Others

Petrol and diesel US Mix

SMR decentralized for H2

conversion; US average

mix for electricity

EU fossil fuel Mix

EU Electric Mix

SMR centralized

(a) Electrolysis

decentralized (b); Others

SMR:yes

Electric Mix BE and EU Mix

Power/Weight

t ratio (W/kg)

PISI- 65

DISI- 54

DICI-54

FC:54

Base SI:75

Adva. SI ICE:75

Adva. CI ICE:75

EV:75

FC:75

Petrol:75

diesel:55

EV:54

FCHEV:54

FCPHEV:57

Base SI:75

Adva. SI ICE:75

Adva. CI ICE 75

EV:75

FCHEV:59

Driving Cycle

Uncertainty

NEDC; Yes FTP; Yes Lisbon to Cascais; No NEDC; Yes

TTW PISI:1.90 Base SI:1.75 Petrol:1.96

DISI:1.88 Adva. SI ICE:1.54 diesel:1.67 Petrol: 1.98

DICI:1.61 Adva. CI ICE:1.35 FCHEV:1.08 diesel: 1.76

- MJeq/km FCEV:0.94 EV:0.51 FCPHEV:0.55 FCHEV: 1.25

FCHEV :0.81 EV: 0.57 EV: 0.54

- gCO2eq/km PISI:140.3 Base SI:126.0 Petrol:143.0 Petrol: 144.0

DISI: 138.8 Adva. SI ICE:111.0 diesel:124.4 diesel: 128.0

DICI: 119.7 Adva. CI ICE:103.0 FCHEV, FCPHEV and FCHEV and EV:0.0

FCEV: 0 FCHEV and EV:0.0 EV:0.0

CTG e Base SI:0.22 Petrol:0.48 CTG-End of life treatment

Adva. SI ICE:0.22 diesel:0.50 Petrol:0.28

Adva. CI ICE:0.23 FCHEV:0.73 diesel:0.28

- MJeq/km EV:0.28 FCPHEV:0.77 FCHEV: No ref.

FCHEV:0.27 EV: 0.77 EV:0.56

Base SI: 15.8 Petrol:30.7 CTG-End of life treatment

Adva. SI ICE (Petrol):15.4 diesel:32 Petrol:11.2

- gCO2eq/km e Adva. CI ICE (diesel):16.1 FCHEV:48.4 diesel:12.0

EV:19.1 FCPHEV:49.5 FCHEV: No ref.

FCHEV :18 EV: 47.8 EV:16.1

WTT

- MJeq/km

Crude Oil to Petrol:

0.32 (PISI); 0.32 (DISI)

Crude Oil to Diesel: 0.31

SMR with 4000 km

pipeline: 0.677

EU-mix electricity,

on-site electrolysis:

3.62 (MJx/MJf)

Petrol: 0.37 (Base SI);

0.32 (SI ICE);

diesel: 0.19

Electricity Mix:1.10

On site SMR:0.62

Petrol and diesel:0.27

FCHEV(a):0.62

FCHEV(b):3.89

FCPHEV(a):0.31

FCPHEV(b):1.97

EV: 1.06

Petrol: 0.76

diesel: 0.53

FCHEV: No ref.

EV: 1.18

- gCO2eq/km Petrol: 26.98 (PISI);

26.68 (DISI)

Diesel: 25.61

SMR with 4000 km

pipeline: 92.87;

EU-mix electricity,

on-site electrolysis:

209.1(gCO2/MJf)

Petrol: 31.5 (Base SI);

27.7 (SI ICE);

diesel:16.5

Electricity Mix:101.1

On site SMR:107.0

Petrol:24.5

diesel:23.7

FCHEV(a):95.4

FCHEV(b):223.3

FCPHEV(a):56.7

FCPHEV(b):96.4

EV:72.9

Petrol: 37.0

diesel: 26.0

FCHEV:110.1

EV: 56.5

Lifetime (km) 200,000 300,000 150,000 230,500

Note: (a) centralized Steam Methane Reforming (SMR) pathway; (b) On-site Electrolysis pathway. Port injection spark ignition (PISI), direct

injection spark ignition (DISI), and direct injection compression ignition (DICI). Net energy expended (MJx) (excluding the energy transferred to

the final fuel. Energy of final fuel (MJf).

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 510976

Each of the power plants composing the electric mix of

2010 in Portugal [28] was assessed. Infrastructure for extrac-

tion of raw materials such as coal, oil and gas was ignored.

Due to the high use of natural gas as a feedstock for power

plants, only the pipeline from Algeria crossing Spain and

Portugal was considered. For allocation purposes, only the

portion sold to Portugal and used by the power plants was

considered [29]. For the power plants, with the identification

Fig. 1 e System boundaries for conventional fossil fuel supply chain.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 5 10977

of the feedstock types, the installed capacity of the power

plants and their sold energy, their capacity factor and mix

were determined. For the electric grid stage, 8% losses were

assumed. All 50.6 TWh of 2010 energy [28], without imports

and pumping, transited through the electric grid, so all high,

medium and low voltage lines were considered. For FC-PHEV

and EVs, a charging infrastructure had to be considered, so

they were divided into three categories according to charging

options in Portugal: normal charging, fast charging and home

charging, according to Table 4 of supporting information (SI).

While addressing EV technology [19] only considered public

charging service rates for diurnal and nocturnal areas.

Furthermore it assumes that the total charging needs would

be fulfilled the same way by normal and fast chargers.

However, private infrastructure must also to be considered,

and a better understanding of the use of charging options

must also be investigated further. According to comprehen-

sive studies for France performed by [30,31], public and private

service rates are suggested to be 1.1 (chargers or plugs/car) at

a stable penetration scenario. This ratio can be even higher if

installation of chargers at working places is also considered.

Regarding maintenance, electricity charging points have low

routine maintenance, requiring only periodic inspections,

cleaning, repairs and communication systems and lighting

testing, so only power plant maintenance activities were

considered [32]. Also represented in Fig. 2 is the centralised

SMR pathway, which includes the natural gas (NG) pipeline

Fig. 2 e System boundaries for electricity and Hydroge

and a hydrogen plant based on [33]. A compressor exiting the

plant was also considered to normalise the pressure of

hydrogen to be transported. For the H2 pipeline estimations,

the Simapro database was considered, assuming similarity

with the transport of CH4 in terms of technical specifications

and construction characteristics even though compression is

different. For this reason, the compressors used in the study

are, in fact, for H2 compression and not CH4. Compression

stations and required buildings were also included in the

pipeline analysis.

In the Inventory stage, Simapro 7.1 was considered as the

reference source and used in triangulationwith the GEMIS [34]

database and existing literature. Scaling techniqueswere used

to adjust raw data from the databases to the Portuguese

installation characteristics (for example, installed power,

capacity factor or mix).

An inventory for H2 refuelling stations had to be reformu-

lated based on [35,36], to contemplate an average-size station

comparable to a conventional Portuguese fuel station and also

to make the distinction between an incoming pipeline H2

station and an onsite electrolysis production station.

Charging points were also missing from the literature or

databases with few exceptions [32,37] where, although small

inventories were conducted, these inventories were somehow

scarce in information and quantities regarding the three

chargingmethods: normal, fast and home charging developed

in Portugal. Based on earlierwork [19] andwith further contact

n (Electrolysis and SMR pathways) supply chains.

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 510978

with the leading charging stations manufacturer in Portugal

(EFACEC), a more detailed inventory was developed.

In addition, petrol stations also had to be inventoried

because values from Simapro, although existing, did not

correspond to the size of an average fuel station in Portugal.

Furthermore, because fuel stations offer services other than

just fuel distribution, only the facilities attributable to that

activity had to be considered; thus, an adjusted inventory was

required. The same was conducted for H2 stations with the

necessary scale and technical adjustments based on [33],

whether SMR or electrolysis pathways were being considered.

The H2 plant from the SMR inventory was followed according

to [33].

Regarding the fleet, the 2010 Portuguese numbers were

considered as a reference for light-duty diesel and gasoline

vehicles only. Energy allocations were made according to the

2005 refining mix [26] for conventional fuels and the 2010

electric mix [28] for the EV, FCHEV(b) and FC-PHEV(b). Main-

tenance was considered only for power plants, the refinery

and the H2 station. Gross values were calculated, taking into

account [38], and then adjusted according to the installed

capacity and capacitor factor for each plant. Energy use in

power plant maintenance was considered to be mainly from

heavy duty diesel trucks or machinery operation.

Estimations were made using two functional units. First,

the energy and carbon intensity of construction, maintenance

and decommissioning of all facilities was calculated and

divided by the total output energy during its lifetime, so units

of MJeq/MJ were obtained. Second, those values were multi-

plied by the TTW stage of each technology, adjusted according

to each facility’s efficiency along the supply chain. This

procedure allows other investigators to use these energy-

based estimations with different TTW values or technologies.

A choice of similar characteristics between vehicle technolo-

gies should be taken into account when evaluating options.

The power/weight ratio is a determinant characteristic to

be described when comparing technologies. Vehicles used

should belong to the same performance class, otherwise an

error of judgment could occur due to performance issues. For

the purpose of this study, values from [21] were used for the

TTW stage and WTT estimations were calculated based on [9]

adapted to the 2010 Portuguese emissions and efficiency. For

Table 2 e Inventory for construction and installation of Chargi

Material Home Charger

Copper (kg) e

Aluminium (kg) e

Iron (kg) e

Concrete (kg) e

Stainless Steel (kg) 3.5

PVC - Polyvinyl chloride (kg) 7.5

ABS, Glass Fibre, PVC (kg) e

Characteristics

Lifetime (years) 15

Service rate (socket) 0.941

Impact kgCO2eq MJeqValues per charger 3.2 Eþ01 6.17 Eþ02

Source: Own inventory based on EFACEC product line. *Average values p

non-existent values of TTW for FC-PHEV technologies, esti-

mations were made based on the [13] study, using as a refer-

ence gasoline vehicles from both studies and applying the

same difference ratio between technologies. All used values

can be observed in Table 6 of (SI). For FC-PHEV technology,

a 0.21 MJ/km electricity use and 0.34 MJ/km of hydrogen were

considered. For the CTG values [21], adjustments were also

made keeping in mind the difference of kilometres driven

between the studies. A lifetime range of 150,000 km, corre-

sponding to an average of 12,800 km per year in Portugal, was

used. Values from EURO 5 legislationwere considered, and the

VW Golf was used as a reference vehicle by [21]. Another

factor that influenced the choice of the study was the use of

NEDC. On the one hand, use of NEDC allows a generalisation

for future studies, and on the other hand, NEDC is closer to

a Portuguese driving cycle than FTP. Energy use and emissions

LCA estimations are given by Eqs. (1) and (2) in terms of their

final units:

The infrastructure value is directly proportional to the

TTW stage given by MJ/km:

LCAMJeq ¼�MJWTT

MJþMJInfra:

MJ

��MJTTW

kmþMJTTW

kmþ MJCTGkmtotal

; (1)

LCACO2eq¼�gCO2eq:WTT

MJþgCO2eq:Infra

MJ

��MJTTW

kmþgCO2eq TTW

km

þgCO2eq:CTG

kmtotal(2)

Relevant losses for all facilities, presented in Table 5 of the

SI were added to the corresponding load (TTW) to be deman-

ded from each facility along the chain.

2.2. Inventory and data assembly

2.2.1. Construction and decommissioningDetails of inventory and complementary considerations

regarding the overall lack of data are described in Table 2 of

the SI section. Lifetimes considered can be found in Table 1

and Table 2 of the SI based on Simapro and the literature

[39e41]. Values simulated by Simapro for the transport and

distribution grid were validated by the literature [42,43]. The

power plant characterisation mix can be found in Table 1 of

ng Points.

Normal Charger* Quick Charger

2 206

2 34

e 228

960 2400

2.5 90

e 12

81 380

6 12

0.164 0.003

kgCO2eq MJeq kgCO2eq MJeq2.9 Eþ02 6.20 Eþ03 2.9 Eþ03 5.96 Eþ04

er satellite, considering 1 central column per 10 satellites.

Table 3 e Inventory for construction and installation ofa conventional refuelling station.

Total Gravel ETH U 31.6 ED04

Area covered with gravel equivalent 31.6 Eþ04

Total Concrete, sole plate and foundation 1.31 ED03

Foundation for 4 dispensers m3 4.80 Eþ02

Foundations for building support m3 3.50 Eþ02

Foundations for metallic structure m3 4.80 Eþ02

Total Building, hall/CH/I U 2.00 ED02

Payment building with storage room m2 2.00 Eþ02

Total Steel I 8.00 ED04

Support Gantries 3.00 Eþ04

Covering metallic structure 500m2 2.50 Eþ04

Interior metallic supports 2.50 Eþ04

Total Bitumen, at refinery/RER U 2.19 ED05

Interior accesses 2.19 Eþ05

Mechanic Componentsa MJeq kgCO2eq

Pipelines, tubes and accessories 1.05 Eþ05 3.0 Eþ03

Storage Tanks and reservoirs 8.90 Eþ05 4.5 Eþ04

Other components 1.32 Eþ04 6.0 Eþ02

Storage

Diesel Storage Tanks 4.81 Eþ06 4.4 Eþ04

Gasoline Storage Tanks 6.58 Eþ06 1.7 Eþ04

Total 2.70 Eþ07 5.5 Eþ05

a Data from contractor company (Petroassist). Values adjusted for

4 dispensers. Electrical components were not considered.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 5 10979

the SI, excluding values of import/export or pumping.

Upstream from the power plants, only the natural gas pipeline

was considered to be representative of the main source of

natural gas importation in Portugal. All of the 1638 km [29]

length of the pipeline was taken into consideration.

However, because only 8.2% was found to be used for elec-

tricity conversion at Portuguese plants, only this portion was

added to natural gas power plants. Regarding hydro plants,

taking into account that Simapro data are based on five Swiss

and Austrian plants and that no installed power data are

provided, according to the UDI World Electric Power Plants

Database, a mean value of 544.77 MWwas used as a reference

for scaling up the Portuguese installed capacity. For the

charging point inventory, Table 2 presents a brief list of the

main materials and quantities considering installation

requirements. Normal charger average values consider

a central column and 10 satellites with 2 sockets each.

Using a total service ratio of 1.1 [30,31], partial ratios by

charging option were estimated. Although [44] present some

estimation to what these may be, the study never refers an

aggregate service ratio, thus estimations were followed

according to [30,31] expectations. They define that from the

1.1 service ratio, 0.105 are secondary public chargers, 1

primary chargers and 0.003 fast chargers. According to those

studies, 90% of the charging is expected to be 3 kVA primary

charging. Because not all users will be able to charge in private

garages, we consider that 6% of level 1 chargers will be on the

streets, which added to the 0.105 given by the study means

a total of 0.164 of normal chargers and 0.941 home chargers.

We also assume that on average 20.5% of all charging (15%

normal chargers and 5.5% fast chargers) will be performed

using public chargers. This means that from the 90% of

primary charging, 79.5% will be on home chargers, and 10.5%

on normal chargers. The other 4.5% of normal charging and

5.5% using fast chargers will be in other public spaces. Table 2

of SI presents a summary of these characteristics.

According to the same studies 3 kVA chargers are physi-

cally identical to level 2 normal chargers, only with minor

differences in connections and cabling. Every two normal

charger connections correspond to one satellite. Lifetimes in

years were estimated according to the manufacturer’s

expectations, taking into account technological transition,

usage degradation and the payback period. For FC-PHEV

technology, the EV MJ-based value of emissions and energy

use of all charging points was considered before calculating

the final km-based functional unit.

Table 3 presents the inventory for a conventional refuelling

station based on [19], making the necessary adjustments to

contemplate only the services attributable to fuel distribution.

The H2 station inventory shown in Table 4 was based on

the Grjothals station, situated on Reiquejavique Island. This

stationwas built by HydroStatoil, and its life cycle was studied

by [36]. Valueswere inventoried by quantity to be simulated in

Simapro and separated by modules, to distinguish the two

pathways analysed with or without electrolyser.

To obtain a relationship between distributed energy and

the LCA estimations for the H2 station, a service ratio

(stations/car) had to be estimated according to each pathway.

Regarding SMR, and from the demand side, considering that

the FCHEV has been increasing its autonomy range and even

though it is below conventional fuels range, it can be consid-

ered somewhat similar in refuelling time and frequency of

refills. As for the supply side, because production is central-

ised and the distribution is made by pipeline, no limitations

exist, so the same service rate as a conventional station was

considered. As for the on-site electrolysis scenario, there is

a supply limitation, and that limitation is the hydrogen

production capacity of the electrolyser. Because the installed

capacity of the station is 47,250 kg H2/year [36] and its capacity

factor is 92.88%, due to maintenance activities and general

idle hours, taking into account the TTW [21] energy use and

the vehicle lifetime driving range of 12,800 km per year,

a value of approximately 329 cars per station can be reached.

2.2.2. MaintenanceThemaintenance stage was considered for the refinery, for all

power plants and for the H2 refuelling station. Concerning

power plants, maintenance data from [38] were used. Because

operation values are part of WTW assessments, only data for

expendables and maintenance were considered. Data repor-

ted were given in g CO2/kWh units according to the specific

lifetime, capacitor factor and installed power. So that it could

be adapted to Portuguese reality, the gross value of CO2 was

calculated applying the values presented in the 2010 electric

mix characterisation shown in Table 1 of the SI section, and

new values of g CO2/kWh were found and are presented in

Table 5 of the SI. Because maintenance activities involve

mostly transportation and electricity use, and given that no

specific data were found, the CO2 released was considered to

be due to burned diesel fuel in the operation of trucks and

machinery. Regarding refinery data, no literature data were

found regarding quantities of materials used in passive or

active maintenance. However, the main activities include

tube restoration, painting and galvanisation, scaffolding,

Table 4 e Inventory for construction and installation of a H2 refuelling station.

Materials/Energy Quantity (kg) Compressor (cont.) kg

Buildings or foundations Stainless steel 18/8, installation 58.81

Steel for concrete, installation 278.68 Casting iron, installation 18.56

Smooth coated Glass, installation 100.5 Etilenoglicol, installation 0.22

Plaster fibre plate, installation 3.09 Lubricant oil, installation 0.56

Silica sand, installation 1781.77 Aluminium, production mix, installation 1.86

Concrete, installation m3 0.31 Insulation tube, installation 0.47

Resistant concrete, installation m3 4.02 Cooper, regional storage 1.39

Gravel, not specified, installation 55735.22 Electricity (kWh) 30.94

Lubricant oil, installation 0.62 Heat, NG, ind. furnace > 100 kW (MJ) 111.47

Electricity (kWh) 15.49 Transport lorry 32t (tkm) 15.89

Diesel (MJ) 1325.36 Additional Electrolysis module

Transport lorry 32t (tkm) 6846.22 Electrolyser

Storage module Chromium steel 18/8 at plant 262.88

Stainless Steel 18/8, installation 2602.44 Nickel 99,5% at plant 30.94

Electricity 29.71 Synthetic rubber, at plant 1.55

Diesel 26.51 Reinforcing steel at plant 82.07

Transport lorry 32t (tkm) 260.24 Cooper at regional storage 23.7

Other components Tube insulation, at plant 10.53

Steel for concrete, installation 50.91 Aluminium, production mix, at plant 6.8

Nitrogen, liquid, installation 4.43 Acrylonitrile-butadiene-styrene copolymer, ABS, at plant 2.48

Stainless Steel 18/8, installation 12.51 Polyethylene, LDPE, granulate, at plant 6.19

Polypropylene, granulated, installation 0.31 Glass fibre, at plant 6.19

Transport, lorry 32t (tkm) 3028.13 Cast iron, at plant 2.11

Compressor Nylon 66, glass-filled, at plant 0.77

Steel for concrete, installation 76.8 Transport lorry 32t (tkm) 43.49

Total kgCO2eq MJeqWithout Electrolyser 1.8 Eþ04 3.03 Eþ05

With Electrolyser 1.9 Eþ04 3.38 Eþ05

Source: Based on [36].

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 510980

insulation works, welding of materials and equipment lubri-

cation. Themainmaterials used and the expendables are rock

and glass wool, aluminium, copper, stainless steel, paint, steel

SAE 1020, zinc bath in the galvanisation process, diesel and

electricity. With the cost of construction of a similar refinery,

a direct relationship was established between the cost and

CO2eq emissions and energy use assessed by Simapro, and the

same relationship was attributed to the annual cost of

Table 5 e Complete LCA values of fossil fuels and EV technolo

WTT TTW VehicleMaterial

Gasoline MJeq/km 0.34 1.98 0.43

gCO2eq/km 28.1 144.0 17.7

Diesel MJeq/km 0.33 1.76 0.43

gCO2eq/km 28.0 128.0 18.5

FCHEV (a) MJeq/km 0.90 1.25 0.65

gCO2eq/km 123.5 0.0 28.0

FCHEV (b) MJeq/km 4.09 1.25 0.65

gCO2eq/km 141.2 0.0 28.0

FCPHEV (a) MJeq/km 0.57 0.56 0.69

gCO2eq/km 48.4 0.0 28.6

FCPHEV (b) MJeq/km 1.45 0.56 0.69

gCO2eq/km 53.3 0.0 28.6

EV MJeq/km 0.83 0.54 0.86

gCO2eq/km 36.60 0.00 24.80

a Difference in percentage when compared to gasoline base case.

maintenance. Knowing that 40% of the cost is spent in

material acquisition and the rest in hand labour [41], values

for maintenance were obtained. As for the H2 refuelling

station, compressor equipment has to be subjected to

a significant overhaul every few years (typically assumed to be

at 5-year intervals) [33]. Because the lifetime of the station is

15 years [36], calculations were made for two total

replacements.

gies.

Infrastructuremin-max (best est.)

Infrastructure Impactmin-max (best est.)

% of basecasea

0.03e0.06 (0.05) 0.9%e2.3% (1.8%) 100%

0.5e1.2 (1.0) 0.3%e0.6% (0.5%) 100%

0.02e0.06 (0.05) 0.9%e2.2% (1.8%) 89%

0.6e1.3 (1.2) 0.3%e0.7% (0.7%) 112%

0.06e0.17 (0.07) 2.0%e6.2% (2.4%) 127%

1.5e3.5 (2.0) 1.1%e2.5% (1.4%) 194%

0.20e0.41 (0.23) 3.2%e6.5% (3.7%) 452%

13.0e22.0 (15.4) 7.0%e11.9% (8.3%) 1489%

0.05e0.09 (0.06) 2.9%e5.0% (3.0%) 111%

2.4e3.4 (2.7) 3.1%e4.5% (3.5%) 262%

0.10e0.17(0.10) 3.4%e6.0% (3.7%) 203%

5.6e8.7 (6.5) 6.4%e10.0% (7.3%) 627%

0.09e0.14 (0.10) 3.8%e6.2% (4.3%) 197%

4.9e7.0 (5.6) 7.3%e10.3% (8.4%) 544%

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 5 10981

2.3. Uncertainty

A total of 166 variables were submitted to uncertainty anal-

ysis, according to the fit test distribution, observable in Table 3

of the SI. Uncertainty Simapro data are based on the Ecoin-

vent’s pedigree matrix, which refers to basic uncertainty and

under-specification uncertainty characterised by 6 indicators,

each with 5 levels. These values were subjected to 1000 iter-

ations, and variations were matched with a curve fit tool into

Crystal Ball.

Regarding inputs for own inventories such as chargers and

values used for H2 stations, because a case study is being

considered, only basic uncertainty, which relates to

measurements of quantities, was taken into consideration

and assumed to vary according to normal distributions (Table

3 of the SI). Attempting to generalise the study will certainly

increase uncertainty, especially for the inventories developed.

Issues such as the sample size, reliability, geographic corre-

lation, underspecification and impact uncertaintywill become

relevant issues to deal with [45].

A 10,000-trial simulation was then run in Crystal Ball with

all 166 variables. Maximum and minimum values were

obtained for each technology assessed. WTW and CTG

uncertainty stages were not considered. Calculations were

based on their best estimated values.

Fig. 3 e Energy Use (a) and emissions (b) uncertainty

analysis.

3. Results and discussion

All infrastructure values simulated in Simapro were corrobo-

rated by GEMIS or the literature. Regarding distribution facil-

ities, inventories were conducted for all technologies

according to the lifetime driving range characteristics and use

of energy. When comparing distribution infrastructures of

conventional with hydrogen, electric or plug-in vehicles,

caution should be exercised regarding refuelling or charging

times. In typical petrol or H2 stations, the time required for

filling a tank may vary between 4 and 7 min, whereas an

electric charge varies, according to Table 4 of the SI, from 20 to

480min. A questionmay be raised regarding levelling the time

of refuelling and only then comparing technologies and

facilities. However, because refuelling times are different

between technologies, one may assume that the service rate

will change accordingly. That is, if one facility is occupied for

more than a certain period, another installation should be

provided to the market. However, if charging/refuelling times

are sufficiently low to accommodatemore vehicles per time or

kilometres driven, fewer facilities are required, compensating

in terms of materials and corresponding construction, main-

tenance and decommissioning efforts. When comparing the

amount of energy supplied by distribution facilities, a refuel-

ling station provides more MJ than a charging facility.

However, electric vehicles are more efficient than an ICE,

resulting in a benefit to the system. This adjustment is

assumed to be linear in this study, even though we recognise

that further work should be performed.

Total results for all facilities are presented by technology in

Table 5 of the SI, in final energy use units, and for each

infrastructure group analysed, including construction,

decommissioning and maintenance. Energy use and emis-

sions uncertainty are represented in Fig. 3 for each of the

technologies studied. In total, five parameters are repre-

sented: median, 1st percentile, minimum, maximum and 3rd

percentile. The median value was preferred to the mean, so

that it could represent the central trend.

The uncertainty value range of infrastructures is higher in

FCHEV(b) when compared to other technologies in several

aspects. Higher amplitude of deviation from the median

value, both in minimum andmaximum extremes, is reported.

Furthermore, the interquartile range is higher in both FCHEV

than other technologies, reducing the probability of the best

estimate value falling far from the median. Overall, lower

amplitude of uncertainty in both emissions and energy use of

ICE vehicles can be explained by a higher accuracy in

measurements due to standardised practices and processes of

construction, appropriate for a mature technology. Regarding

alternative vehicle support infrastructures, construction

techniques, scale, and geographic positioning, raw materials

processing and technology used can differ widely, meaning

higher uncertainty. Regarding median positioning between

extremes, EV presents a midpoint location, expected when

most probability functions, or those that represent the most

weight, are normal distributions. Other technologies present

median values closer to the minimum extreme, which is

influenced by the use of lognormal distributions. To under-

stand the nature of this positioning, although the uncertainty

behaviour in terms of ranges and probabilities was identified,

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 510982

it is still crucial to knowwhat variables contribute the most to

such uncertainty. Contribution of the main variables to

emission and energy use variance is shown in Fig. 4. Only

variables with contribution to variance higher than 4% were

distinguished. Variables lower than 4%were added and placed

under the category “others”.

In Fig. 4, variables that contribute the most for EV, emis-

sions and energy use uncertainty are the normal charger

lifetime with 57.9% influence and overall power plant

construction, especially hydro plants. FC-PHEV(a) emissions

uncertainty also depends on 42.7% of the normal charger

lifetime and the H2 pipeline’s energy use with 52.4% influence,

as is FCHEV(a) with 75.7% and 37.3% for emissions. Both FC

technologies using the electrolysis pathway depend on

approximately 50% of power plants’ construction activities,

mostly hydro plants (30%) and H2 stations. Regarding both ICE

vehicle technologies, two variables report higher impact both

Fig. 4 e Contribution to energy use (a) and emissions (b)

variance.

in emissions and energy use. The first variable is the lifetime

of refuelling stations with an influence of approximately 60%

in the case of emissions and 83% in energy use for both

technologies. The second variable is civil works activities

during refinery construction, with an influence, for both

emissions and energy use, of approximately 25% and 7%,

respectively.

Fig. 5 presents the absolute values of infrastructure per

each technology, showing values per unit of final energy.

Absolute values report higher energy use and CO2 emissions

from construction, maintenance and decommissioning for

alternative vehicle technologies. Higher uncertainty in alter-

native fuels can be explained by a longer supply chain and

a variety of efficiencies, as well as the reason for higher energy

and carbon intensity for these technologies, as hybrid solu-

tions need complementary infrastructures for both fuels used.

The energy per kilometre travelled, relative to the infrastruc-

ture, is necessarily higher, as this energy will depend on its

TTW stage, as illustrated in Figs. 1 and 2 of the SI. Even though

some technology infrastructure best estimate values are

inferior to others, they can have a higher impact on their LCA

when presented in their final functional units.

Table 5 shows the results for all stages and the corre-

sponding impact of infrastructure usingminimum,maximum

and best estimated values. While the impact of conventional

Fig. 5 e Energy use (a) and emissions (b) of energy supply

infrastructures per technology.

i n t e r n a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 5 10983

fuel can be neglected under the assessed conditions because

its contribution is below 2.5%, this is not the case in alterna-

tive vehicle technologies.

The difference between the contribution of fossil fuel

infrastructures and alternative technologies is illustrated

mostly by end distribution facilities, H2 pipelines and power

plants (see Table 5 of the SI). Charging point facilities are about

ten times higher in energy use and five times higher in

emissions than a conventional refuelling station. In the H2

supply chain, H2 refuelling stations, when using the SMR

pathway, are very similar to conventional refuelling stations.

However, when considering the on-site electrolysis system,

the H2 station emits about 6 times more CO2eq per unit of

distributed MJ than conventional stations. To compare the

contribution of fuel infrastructures in WTW and CTG LCA, all

stages are presented in Fig. 6. Uncertainty corresponds only to

infrastructure.

The impacts of infrastructures relate only to each tech-

nology LCA. Values are presented in this way to highlight the

weight of this stage on the total LCA of each technology.

Percentage weights among technologies should not be

compared. The best estimated impact of infrastructure on

total EV LCA was 4.3% for energy use and 8.4% for emissions.

The impact of diesel infrastructure in its total LCA is 1.8% of

Fig. 6 e LCA energy use (a) and emissions (b) per stage and

technology.

energy use and 0.7% of emissions. Gasoline energy supply

facilities represent 1.8% of energy use and 0.5% of emissions in

total LCA. H2 energy supply infrastructure represents 2.4% of

energy use and 1.4% of emissions using the SMR pathway and

3.7% and 8.3% using electrolysis. FC-PHEV infrastructures

account for 3.0% of energy use and 3.5% of emissions using the

SMR pathway and 3.7% and 7.3% using electrolysis.

Pipelines and end distribution facilities are extremely

significant for their overall contribution; thus, definition of

their service ratios is extremely important. In this study, we

have broadened the technologies assessed in terms of their

energy supply infrastructure and used service ratio estimates

of 0.941 for home chargers, 0.164 for normal chargers and

0.003 for fast chargers. A higher level of attention was given to

the input materials for all chargers. Being two times higher

than fast chargers, normal chargers report the highest values

of energy use and emissions per kilometre among all chargers,

being very sensitive to their lifetime usage. If the effective use

increases (i.e., if the energy flow increases in the normal

charger use), its impact would decrease. In addition, if the

overall service ratio of 1.1 estimated by [30] should decrease,

then the absolute value per kilometre of normal chargers

would be reduced the most. If a decrease should occur in the

CTG and WTT stages, both TTW and infrastructures will have

a higher impact on the total LCA. If the TTW stage decreases,

the contribution of infrastructures per kilometre will also

decrease due to the direct proportional relationship between

the absolute value of infrastructures and the TTW energy use

value. The more efficient vehicles become, the less impact

TTW and infrastructure per kilometre will have (see Eqs. (1)

and (2)).

4. Conclusions

The study goal was to provide energy use and CO2eq estima-

tions related to energy supply infrastructure for several

vehicles and to estimate the impact on their overall LCA. A

methodology [19] was used with Portugal as a case study and

applied to conventional diesel and petrol vehicles as well as

alternative technologies such as EV, FCHEV and FC-PHEV.

With the case study being analysed, alternative vehicle tech-

nologies energy supply infrastructures are more carbon- and

energy-intensive per unit of supplied fuel than conventional

energy supply infrastructures. For the current scenario of 2234

vehicles per conventional and H2 (SMR) refuelling station and

the foreseen service rate scenarios of 333.3 vehicles per fast

charger, 6.1 vehicles per normal charger (socket), 1.1 per home

charger and 329 vehicles per H2 (electrolysis) station, the LCA

of conventional fuel infrastructure potentially represents an

energy use of 0.01e0.04MJeq/MJfuel for both gasoline and diesel

fuels. Emissions represent 0.3e0.9 g CO2eq/MJfuel for gasoline

and 0.3e1.0 g CO2eq/MJfuel for diesel. For electricity, these

values are 0.15e0.30 MJeq/MJfuel with emissions of 8.5e14.7 g

CO2eq/MJfuel. For hybrid vehicles, FCHEV(a) shows 0.03e0.12

MJeq/MJfuel and 0.7e2.9 g CO2eq/MJfuel, the FCHEV(b) shows

0.08e0.23 MJeq/MJfuel and 5.5e12.0 g CO2eq/MJfuel. For plug-in

technologies, FC-PHEV(a) shows 0.18e0.36 MJeq/MJfuel and

8.7e16.6 g CO2eq/MJfuel, and FC-PHEV(b) shows 0.23e0.53 MJeq/

MJfuel and 13.6e26.7 g CO2eq/MJfuel.

i n t e rn a t i o n a l j o u r n a l o f h y d r o g e n en e r g y 3 7 ( 2 0 1 2 ) 1 0 9 7 3e1 0 9 8 510984

EV reports the highest infrastructure LCA contribution per

kilometre with 8.4%, followed by the FCHEV (b) with 8.3% in

emissions. Conventional fuel facilities report the lowest

values, contributing impacts below 2.5%. EV is mostly affected

by the charging infrastructure with approximately 60%weight

followed by power plants andmaintenancewith about a 33.2%

contribution in terms of energy use and 42.6% in emissions,

while FCHEV (b) is mostly affected by power plant construc-

tion andmaintenancewith approximately 68%,with 30% from

H2 stations. End distribution facilities tend to be the higher

contributors among energy supply infrastructures. Overall

contributions with uncertainty do not go beyond 12%with the

assessed conditions.

A more friendly choice of materials or higher efficiency in

H2 stations, H2 pipelines and electric chargers is advisable.

Total and partial service ratios as well as usage from each type

of chargers are crucial issues and should be further investi-

gated. Increasing the energy flow homogeneously in normal

chargers by promoting the vehicle to grid (V2G) concept would

decrease the impact of chargers. The set environmental goals

within the electricity generation mix and an increase in the

lifetime driven distance will lower the WTT and CTG stages.

These facts make the contribution of energy supply infra-

structure pertinent to analysis in future studies, particularly

in the alternative vehicle technologies studied. A generalisa-

tion of the study is, however, advisable. Due to a large range of

uncertainty, closer attention should be paid to how the

impacts vary according to different scenarios of behaviours,

geographies or material inputs.

Support information

Supplementary data attached in Appendix A.[46e48]

Acknowledgments

The authors would like to thank theMIT-PP and the support of

all companies that made this study possible, in particular

EFACEC for the financial support. Special thanks to Elsevier

Language Editing Services.

Appendix A. Supplementary material

Supplementary data associated with this article can be found,

in the online version, at doi:10.1016/j.ijhydene.2012.04.127.

Nomenclature

AER All Electric Range;

CAFE Corporate Average Fuel Economy;

CD Charge Depleting;

CED Cumulative Energy Demand;

CS Charge sustaining;

GHG Green House Gas;

CTG Cradle-to-Grave of vehicle materials;

DICI Direct Injection Compression Ignition;

DISI Direct Injection Spark Ignition;

ECE-EUDC European Driving Cycle Extra-Urban Driving Cycle;

EV Full Electric Vehicle;

FCHEV Fuel Cell Hybrid Electric Vehicle;

FC-PHEV Fuel Cell Plug-in Hybrid Electric Vehicle;

FTP Federal Test Procedure;

GWP Global Warming Potential;

ICE Internal Combustion Engine;

LCA Life Cycle Analysis;

NEDC New European Driving Cycle;

PISI Port Injection Spark Ignition;

PKT Passenger Kilometre travelled;

RES Renewable Energy Sources;

SI Supporting Information;

SM Steam Methane Reforming;

TTW Tank to Wheel;

WTT Well to Tank;

WTW Well to Wheel.

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