Upload
others
View
7
Download
0
Embed Size (px)
Citation preview
Impact Analysis of Mass EV
Adoption and Low Carbon
Intensity Fuels Scenarios
Nikolas Hill
13th Concawe Symposium 2019, Antwerp
18 March 2019
2© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Ricardo is a Global Engineering & Environmental Consultancy with
over 2900 engineers, scientists and consultants in all major regions
Sectors
Automotive
Commercial
Vehicles
Defence
Energy &
Environment
Motorsport
Motorcycle
Off-Highway
Vehicles
Rail
3© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
• Concawe commissioned Ricardo to conduct research and provide analysis of
the wider potential implications of high EV uptake and alternative scenarios
The impact of two scenarios: ‘High EV Adoption’ & ‘Low Carbon Fuels’
on GHG emissions, infrastructure, costs & resources are compared
Note: * The scenarios consider the European light duty vehicle fleet only. L-category vehicles, buses, and medium and heavy duty trucks have not been included in the analysis
# Low Carbon Fuels include biofuels and eFuels generated from renewable energy sources
High EV Scenario Low Carbon Fuels
Scenario
3. What are the cost
implications?
2. What are the implications for
energy supply and electricity
infrastructure?
4. What are the implications on
materials and natural
resources?
1. What are the Greenhouse Gas
(GHG) emissions, including
life cycle emissions?CO2
4© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Quantitative analysis of impacts was conducted using SULTAN
model with defined inputs, and post-processing of the results
Input Data / Pre-processing Output Data / Post-processing
SULTAN
Scenario Database
and Calculation
Engine
Activity by mode
Vehicle Energy Consumption [MJ/km]• By mode, model year, powertrain type *
GHG Emission Factors [CO2e/MJ]• By fuel / energy carrier **
• TTW and WTW
AQP Emission Factors• By mode fuel / powertrain
• Direct emission factors for NOx, SOx, PM
Cost Data• Fuel costs (excl./incl. tax) **
• Capital costs by powertrain
• O&M costs
Vehicle stock• Fleet # projection by mode
• Survival rates
• % share of new vehicles by powertrain *
Results Database and
SULTAN Results Viewer
Notes:
* Key input variable, set by the scenarios
developed for this study
** Input variable to sensitivity scenarios for
this study, e.g. electricity CO2e/kWh, energy
prices, etc.
*** Input data for calculations informed by
Literature Review and Deep Dive analysis
SULTAN Outputs• Fleet numbers / mix by powertrain
• Energy consumption by fuel
• TTW, WTW, AQP emissions
• Energy Security metrics
• Economic outputs (social, end-user)
– TCO: Capital, Fuel and O&M costs
– Net fiscal revenue impact
– External costs of emissions
– Cumulative costs
Additional Post-Processing ***• GHG emissions from vehicle production
and disposal
• Alternative fuel infrastructure requirements
(# by type, share of energy cons., costs)
• Resource requirements
Vehicle numbers by powertrain
Additional Final Results, e.g.• Total life cycle GHG emissions
• Total costs including infrastructure
Scenario Modelling Calculations
Source: Ricardo Energy & Environment. * SULTAN is a Ricardo tool developed for the European Commission as a transport policy modelling tool, with the ability to evaluate the medium- and long-
term (to 2050) impacts of new vehicle technologies
Overview of the SULTAN* modelling analysis
5© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
EU Passenger Car Vehicle Parc
0%
20%
40%
60%
80%
100%
2015 2020 2025 2030 2035 2040 2045 2050
Plug-in Share:
91.3 %
Plug-in Share:
46.5 %
0%
20%
40%
60%
80%
100%
Note: New registrations & vehicle parc profiles calibrated to data and projections from European Commission modelling -Impact Analysis of Mass EV Adoption and Low Carbon Intensity Fuels
Scenarios, Ricardo report for CONCAWE August 2018, https://www.concawe.eu/publications
“H
igh
EV
”
Scen
ari
o
“L
ow
Carb
on
Fu
els
” S
cen
ari
o
68% of energy from
Low Carbon Fuels
in 2050
88% of energy from
Electricity in 2050
• Both scenarios were scoped to deliver Tank-to-Wheel (TTW) GHG savings of 90 percent
compared with 1990 figures
Two contrasting scenario options : What if all cars were electric?
What if the share of Low Carbon Fuels was significantly increased?
6© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
“H
igh
EV
”
Scen
ari
o
“L
ow
Carb
on
Fu
els
” S
cen
ari
o
0%
20%
40%
60%
80%
100%
0%
20%
40%
60%
80%
100%
2015 2020 2025 2030 2035 2040 2045 2050
Fossil fuel
Bio-fuel
E-Fuel
Electricity
% Fuel Share by energy
• In the High EV scenario 88% of
energy is electrical by 2050
• In the Low Carbon fuels scenario 68%
of energy is from bio-fuels and e-fuels
and 22% from electricity
Two contrasting scenario options : What if all cars were electric?
What if the share of Low Carbon Fuels was significantly increased?
7© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
• Net GHG reduction for biofuels is
assumed to reach ~85% by 2050
• After 2020: assumed that the share
of low/no-ILUC biofuel (i.e. from
waste or non-crop feedstocks) will
increase to >95% share by 2050
• For the Low carbon fuels scenario:
– It is assumed that the majority
of biodiesel used post-2025 will
be drop-in fuels (including syn-
diesel, eFuels and HVO) and by
2050 substitution reaches 100%
– Gasoline is also mainly replaced
by advanced biofuels (synthetic
gasoline) and substitution nears
80% by 2050
Both scenarios feature substitution of conventional liquid fuels by
biofuels, with a higher share in the Low Carbon Fuels scenario
European scenarios for biofuel and other low carbon fuel uptake
0%
5%
10%
15%
20%
25%
30%
2015 2020 2025 2030 2035 2040 2045 2050
% s
ha
re
Biodiesel
Total
Biogasoline
Biomethane
BioLPG
Source: Analysis by Ricardo Energy & Environment based on previous work for the EC and other European projects, and the availability (in PJ) of low carbon fuels developed by CONCAWE
Low carbon fuel substitution by energy carrier, High EV scenario
Low carbon fuel substitution by energy carrier, LCFuels scenario
Hig
h E
VL
ow
Ca
rbo
n F
ue
ls
0%
20%
40%
60%
80%
100%
2015 2020 2025 2030 2035 2040 2045 2050
% s
ha
reThe total volume of bio-fuels is within that assumed to be available for LDVs
8© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
0
10
20
30
40
0
200
400
600
800
1000
1200
1400
1600
1800
2020 2025 2030 2035 2040 2045 2050
Mto
e/y
ear
PJ/y
ear
Syndiesel(imported eFuel)
Syndiesel (EUeFuel)
Syndiesel(pyrolysis)
Syndiesel(gasification)
HVO
FAME
The energy available from biofuels and eFuels for European light
duty vehicles has been estimated from other research sources
0
10
20
30
40
0
200
400
600
800
1000
1200
1400
1600
1800
2020 2025 2030 2035 2040 2045 2050
Mto
e/y
ea
r
PJ/y
ear
Biogasoline(imported eFuel)
Biogasoline (EUeFuel)
Biogasoline(gasification)
Ethanol (2Glignocellulosic)
Biogasoline(pyrolysis)
Ethanol (1G)
Bio
eth
an
ol a
nd
bio
ga
so
lin
eF
AM
E a
nd
syn
die
se
l
• Availability of Low Carbon Fuel is
intended to reflect scenario where
the whole biomass supply chain is
optimised to maximise use of
bioenergy
• Quantities available to LDVs allow
for similar substitution levels in
other road transport (e.g. HDVs)
• Use in other transport modes is
not considered explicitly
Source: Directorate-General for Research and Innovation (European Commission), “Research and innovation perspective of the mid-and long-term potential for advanced biofuels in Europe,” 2018;
K. Sub Group on Advanced Biofuels Sustainable Transport Forum, Maniatis, I. Landälv, L. Waldheim, E. Van Den Heuvel, and S. Kalligeros, “Final Report, Building Up the Future,” 2017;
dena (German Energy Agency), “«E-FUELS» STUDY - The potential of electricity-based fuels for low-emission transport in the EU - VDA,” 2017;
H. D. C. Hamje et al., “EU renewable energy targets in 2020: Revised analysis of scenarios for transport fuels.”
9© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
End-of-Life
Adds assessment of environmental
impact of “end of life” scenario (i.e. -
to-Grave). Can include: re-using or
re-purposing components,
recycling materials, energy
recovery, and disposal to landfill
Vehicle Production
Assessment of ‘Cradle-to-Gate’
environmental impact of producing
the vehicle including extract of raw
materials, processing, component
manufacture, logistics, vehicle
assembly and painting
Vehicle cycle “Embedded”
emissions result from from vehicle
production; fluid, filter and component
replacement during life; and end-of-
life activities. A “cradle-to-gate” LCA
study may only consider vehicle or
component production
Well-to-Wheel (WTW) Analysis -
Life Cycle Assessment of the fuel or
electricity used to power the vehicle
Fuel & Electricity
Production
Assessment of (WTT)
environmental impact of producing
the energy vector(s) from primary
energy source to point of
distribution (e.g. refuelling station)
The analysis considered the whole life of the vehicle, Well-to-Wheel
(WTW) fuel production and use and embedded GHG emissions
Vehicle Life Cycle
Use/Operation
• Environmental impact of driving
(TTW emissions)
• Impact from maintenance and
servicing
Study Boundary:
Analysis of the whole vehicle life
lifecycle included embedded
emissions from vehicle production,
maintenance and servicing, and end-
of-life activities, and WTW
(WTT+TTW) emissions from
production and use of the fuel /
energy in operating the vehicle
10© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
BAU Total
0
200
400
600
800
2015 2020 2025 2030 2035 2040 2045 2050
GH
G E
mis
sio
ns
[MtC
O2
e]
EU Light Duty Vehicle Emissions (Well-to-Wheel + Embedded)
Both scenarios result in a similar and significant reduction in GHG
emissions to 2050, with WTW GHG savings reduced 92% vs 1990
Tank-to-Wheels
Well-to-Tank
(Annual) Vehicle
Disposal
(Annual) Vehicle
Production
Embedded emissions from production
and disposal rises to ~25% by 2050
for both the Low Carbon Fuels and the
High-EV scenario
Hig
h E
VL
ow
Ca
rbo
n F
ue
ls
Source: Ricardo Energy & Environment SULTAN modelling and analysis * BAU scenario as used by European Commission as a baseline for quantifying the impact of future policy changes
124
BAU Total
0
200
400
600
800
2015 2020 2025 2030 2035 2040 2045 2050
GH
G E
mis
sio
ns
[MtC
O2
e]
624
624
Both scenarios
result in significant
reduction from
Business As Usual
(BAU) EU reference
Both scenarios
result in significant
reduction from
Business-As-Usual
(BAU) EU reference
135
11© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Sensitivities on electricity GHG intensity affect which scenario
results in lower GHG emissions
Sensitivities on Electricity GHG intensity vs base High EV scenario
Source: Ricardo Energy & Environment SULTAN modelling and analysis
+33 MtCO2e
-8 MtCO2e
“Low” electricity GHG intensity
High EV Scenario produces 8Mt CO2
LESS than the Low Carbon Fuels
Scenario
Electricity Global Warming
Potential (GWP) [kgCO2/kWh]
“High” electricity GHG intensity
High EV Scenario produces 33Mt
CO2 MORE than the Low Carbon
Fuels Scenario
Total GHG emissions
High EV Scenario
Low Carbon Fuels Scenario
→ Sensitivities on low carbon fuel availability show similar
magnitude of variations
12© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Network reinforcement required beyond 15-20% EV penetration to
deliver adequate EV re-charge power will be significant*
Source: The Energy Technologies Institute & *Household Electricity Survey - A study of domestic electrical product usage Intertek Report R66141 ; Impact Analysis of Mass EV Adoption – Ricardo;
Defossilizing the transportation sector - FVV
400 kV / 275 kVTransmission
Network
EHV (132 kV)Extra High Voltage
Network
HV (33 kV – 22 kV)
MV (11 kV – 10
kV)
LV (400 V three phase
- 230 V single phase)
High Voltage (HV)
Network
Grid Supply Point
Bulk Supply Point
Primary Substation
Secondary Substation
Low Voltage (LV)
Distribution Network
Medium Voltage (MV)
Network
Generation
Significant Re-enforcement Required
• Capital costs for re-enforcing EU EV charging
infrastructure & charge facilities for High EV scenario
– €630 billion assuming primarily “home” charging
(€326 billion for Low Carbon Fuels)
– €830 billion assuming “grazing” frequent top-up
• Based on “Smart” network with charge periods
selected to minimise local network loads
Only a small part of total road
transport costs including vehicles
and energy, but who pays for this?
• Electricity for EV charging increases to ~550 TWh
in 2050, ~17.5% EU 2015 electricity generation
13© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
The annual parc total costs to the end user are similar for the High EV
and Low Carbon Fuels scenarios if lost fuel tax revenue is considered
Cost of electricity infrastructure upgrades
Operation and maintenance costs
Fuel / energy costs
Capital (vehicle)
Net Fiscal Revenue (NFR) loss: Reduction
in fuel tax receipts to governments
Total BAU 2,280
2,263
0
500
1,000
1,500
2,000
2,500
2015 2020 2025 2030 2035 2040 2045 2050
An
nu
al C
ost [B
illio
n €
]
Total Parc Annual Costs to End-user for All Light Duty Vehicles
Hig
h E
V
Total BAU 2,280
2,254
0
500
1,000
1,500
2,000
2,500
2015 2020 2025 2030 2035 2040 2045 2050An
nu
al C
ost [B
illio
n €
]
Lo
w C
arb
on
Fu
els
Source: Ricardo Energy & Environment SULTAN modelling and analysis
Note: *Including infrastructure costs but excluding adjustment for Net Fiscal Revenue **BAU scenario as used by European Commission as a baseline for quantifying the impact of future policy changes
• Total annual parc cost to the end
user is similar for both scenarios in
2050, when adjusting to maintain Net
Fiscal Revenue
Taxes are applied for all energy carriers at their current and projected (BAU) levels
Cumulative cost savings to the
end end-user between ~€1,100 &
€1,600bn (1.3% - 1.8%) vs EC
BAU reference to 2050
14© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
• Emission externalities
contribute to significantly
lower social costs for
both scenarios
• Note: Societal costs exclude all taxes
The net societal cumulative costs are lower for High EV scenario
only in later periods
Overall cumulative cost-
effectiveness is best for
the other scenarios up to
2045-2050
Cumulative net societal
costs are significantly
higher for the High EV
scenario in earlier periodsCumulative Net Societal Costs (relative to High EV)
Source: Ricardo Energy & Environment SULTAN modelling and analysis
Cu
mu
lati
ve
So
cie
tal C
os
t
-150
-100
-50
0
50
100
150
2015 2020 2025 2030 2035 2040 2045 2050
Cum
ula
tive C
ost
[Bill
ion €
]
High EV
LCFuels
High EV+externalities
LCFuels+externalities
Total Parc Annual Societal Costs (excl. tax), including Externalities
Hig
h E
V
BAU Total
0
200
400
600
800
1,000
1,200
1,400
1,600
2015 2020 2025 2030 2035 2040 2045 2050
Annual C
ost
[Bill
ion €
]
Externalities
Infrastructure
O&M
Fuel
Capital
External costs (or ‘externalities’) are the monetary value attached to GHG and Air Quality emissions
15© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
• The High EV scenario
requires almost three times
the total battery capacity
compared to the Low Carbon
Fuels scenario
Under the High EV scenario, ~15 Gigafactories would be needed to
supply batteries to the European EV market by 2050
Resources & Materials – Annual Battery Capacity [GWh]
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2015 2020 2025 2030 2035 2040 2045 2050
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2015 2020 2025 2030 2035 2040 2045 2050Source: Ricardo Energy & Environment SULTAN modelling and analysis;
* Tesla (https://www.tesla.com/en_CA/gigafactory)
Hig
h E
VL
ow
Ca
rbo
n F
ue
ls
Note: Tesla Giga Factory
estimates factor in anticipated
battery energy density
improvements per unit from
2025-2050* This output should
be expected to scale with
increased battery kg/Wh
Estimated number of
battery Giga-factories
required for Europe
Tota
l B
atte
ry C
apacity r
equired [
GW
h]
16© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
0
50,000
100,000
150,000
200,000
250,000
300,000
2015 2020 2025 2030 2035 2040 2045 2050
0
50,000
100,000
150,000
200,000
250,000
300,000
2015 2020 2025 2030 2035 2040 2045 2050
The Lithium resource requirements for the Low Carbon Fuels
scenario are less than half of those for the High EV scenario
Resources & Materials – Key Battery Materials [tonnes], annual demand
3
LiLithium
27
CoCobalt
28
NiNickel
Source: U.S Geological Survey (Mineral Commodity Summaries 2017);
Ricardo Energy & Environment Sultan Modelling And Analysis
The use of Cobalt and Nickel in
battery chemistries is expected to be
phased out between 2030 and 2040:
the share after this is uncertain
Hig
h E
VL
ow
Carb
on
Fu
els
• Assuming current chemistry
mixes the resource
requirements for Lithium,
Cobalt and Nickel would
increase very substantially
over the period to 2050,
which would pose a potential
availability risk
• Current global total
production p.a.:
– Li : 35 kt
– Co : 123 kt
Current Co production:
123,000 t
Current Li production:
35,000 t
Current Co production:
123,000 t
Current Li production:
35,000 t
Ma
terial R
equired in E
U [
tonnes]
17© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
• A broad range of
solutions should be
considered,
including
electrification and
liquid or gaseous
fuels, to minimise the
risks associated with
a single scenario
• The efficiency and
emissions of ICEVs
will need to continue
to develop, and to
accept future low
carbon fuels
UncertaintiesPositives
● No behaviour change
in refuelling
● Allows greater use of
our existing
manufacturing skills
and assets
● Low carbon fuel supply chain
and processes scale up
● Development to deliver zero
impact on air quality from
tailpipe
● Most efficient use
renewable electricity
● Free up low carbon fuel
supplies for other
transport applications
● Battery costs and improvements
in energy density
● Investment in charging
infrastructure and electricity
distribution network
● Availability of resources for
batteries (e.g. Lithium & Cobalt)
“H
igh
EV
”
Scen
ari
o
“L
ow
Carb
on
Fu
els
” S
cen
ari
o
Both scenarios have significant challenges. A broad range of
solutions, including liquid low carbon fuels, would minimise the
risks in achieving our future targets
Nikolas Hill
Ricardo Energy & Environment
The Gemini Building
Fermi Avenue
Harwell, Didcot
OX11 0QR
United Kingdom
T: +44 (0)1235 75 3522
Dr Nick Powell
Ricardo Strategic Consulting
Shoreham Technical Centre
Old Shoreham Rd
Shoreham-by-Sea
BN43 5FG
United Kingdom
T: +44 (0)1273 794524
19© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Additional slides
20© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Why are we interested? Combination of changes in the regulatory
environment, as well as the uptake of new fuels and powertrains
32.329.0
0.0
42.039.9
14.6
46.344.2
21.7
34.231.6
10.1
0
5
10
15
20
25
30
35
40
45
50
GasolineICE
G-ICE(+10%bio)
BEV GasolineICE
G-ICE(+10%bio)
BEV GasolineICE
G-ICE(+10%bio)
BEV GasolineICE
G-ICE(+10%bio)
BEV
Tailpipe Only Well-To-Wheel Vehicle Lifecycle Vehicle Lifecycle
2015 2030
GH
G E
mis
sio
ns [tC
O2e
]
Vehicle Use Fuel / Electricity Production Vehicle Embedded Emissions Total
Source: Ricardo life cycle analysis (2018) for average EU passenger car. Assumes lifetime 210,000 km, uplift of NEDC to real-world fuel consumption (~35%/40% for ICE/EV). GHG from fuel/electricity
consumption is based on the 2015 average fuel/grid electricity factor; Literature average 15.3 kgCO2e/kg battery. Avoided burden approach – including credits for recycling.
Historical; CO2
Regulations
-10% -100% -5% -65% -4.5% -53% -8% -70%
Biofuels
deployed; RED
Increase in xEV
Deployment
Future
Developments
Av. Car:
ILLUSTRATIVE
Tailpipe
counted as
zero for bio
component
21© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Primary Energy Air Quality Pollutants
pics • How much / what
types of source?
• What is the most
efficient use of
renewables?
– BEV = 1x
– FCEV = ~3x
– eFuel = ~5x
• Emissions impacts
vary by:
– Powertrain
– Lifecycle stage
– Location
• Can influence
conclusions
Resources [ Lifecycle Costs, i.e. TCO ]
• Availability of key
materials for
batteries, motors
• Biomass supply for
bioenergy and
other uses
• Water consumption
• Total Cost of
Ownership
≈ LCA for money
• Environmental
externalities
(costs) added for
societal analysis
Introduction, background and objectives of the workshop [09:30]
It isn’t all about Greenhouse Gases – other impacts and factors also
influence the overall comparisons of impacts…for example:
3
LiLithium
27
CoCobalt
28
NiNickel
0
50,000
100,000
150,000
200,000
250,000
300,000
2015 2020 2025 2030 2035 2040 2045 2050
Mate
rial
Required [
tonnes]
22© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Key Inputs & Assumptions
The price of liquid fuels and electricity has been based on data from
published studies
0.99
0.87
0.83
0.78
0.55
0.65
0.75
0.85
0.95
1.05
1.15
2025 2030 2035 2040 2045 2050
€/litre
ga
so
line
e
qu
iva
len
t
High price: av. low carbon fuel price
Base case: av. syndiesel price
Diesel price
Gasoline price
Base case: av. low carbon fuel price
Base case: av. biogasoline price
Source: S. Frank et al., “EU Reference Scenario 2016 Energy, transport and GHG emissions Trends to 2050.”, 2016
Directorate-General for Research and Innovation (European Commission), “Research and innovation perspective of the mid-and long-term potential for advanced biofuels in Europe,” 2018;
K. Sub Group on Advanced Biofuels Sustainable Transport Forum, Maniatis, I. Landälv, L. Waldheim, E. Van Den Heuvel, and S. Kalligeros, “Final Report, Building Up the Future,” 2017;
dena (German Energy Agency), “«E-FUELS» STUDY - The potential of electricity-based fuels for low-emission transport in the EU - VDA,” 2017;
H. D. C. Hamje et al., “EU renewable energy targets in 2020: Revised analysis of scenarios for transport fuels.”
0.185
0.165
0.207
0.12
0.14
0.16
0.18
0.20
0.22
2015 2020 2025 2030 2035 2040 2045 2050
Ele
ctr
icity P
rice
[€
/kW
h]
Electricity Price [€/kWh, excl. tax]
Low Carbon Fuels Price [€/litre eq., excl. tax]
European Electricity Scenario
Sensitivity “High GHG” Scenario
Sensitivity “Low GHG” Scenario
Assumed Energy Cost Trends Excluding Taxes
Sensitivity studies were also carried out to understand the affect of electricity and fuel price
• Assumptions about energy costs are
based on the references below
23© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
169
95
60
183
117
72
142
72
42
429
190
130
100
0
50
100
150
200
250
300
350
400
450
2015 2020 2025 2030 2035 2040 2045 2050
Ba
tte
ry P
ack C
ost [$
/kW
h]
Key Inputs & Assumptions
Battery costs are a key component of EV costs. Cost/kWh is
expected to decline by over 70% by 2030 compared to prices in 2015
Assumed Technology Cost Trends – Battery Pack
Source: Ricardo Energy & Environment analysis
Battery Pack Cost [$/kWh]
Typical / Central Case
Sensitivity “High” Scenario
Sensitivity “Low” Scenario
Sensitivity “Very High” Scenario
Sensitivity studies were also carried out to understand the affect of battery cost on overall costs
• Estimates for future battery pack costs
(including assembly) are based on
learning-based cost analysis developed
as part of work for the European
Commission
• Battery costs are used together with
electric range and State of Charge
assumptions to calculate the costs of
baseline xEV powertrain vehicles relative
to conventional equivalents
• Assembly of the battery pack into the
vehicle is considered in vehicle costs
• Sensitivity studies were carried out for
‘Low’, ‘High’ and ‘Very High’ battery cost
trends
• The average battery pack size for an EV
passenger car in 2050 is assumed to be
82kWh (108kWh for an average Light
Commercial Vehicle)
– Battery pack energy density assumed
to increase to 800Wh/kg by 2050
24© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
To investigate the implication for network infrastructure, Ricardo
has considered a series of recharging scenarios for plug-in vehicles
Home charging is where users charge
mainly using off-street home or on-street
residential recharging infrastructure
Same charging type split as “Home
Unmanaged”, but with longer time
periods to simulate managed charging
Recharging Scenarios
Home
Unmanaged
Grazing is where users charge little and
often, mainly using charging points away
from the home
Grazing
Unmanaged
Home
Managed
Same charging type split as “Grazing
Unmanaged”, but with longer time
periods to simulate managed charging
Grazing
Managed
Notes: Current EU housing data shows 28% of households are located in rural environments, and 72% are located in urban and sub-
urban environments. Therefore, Ricardo has assumed an EV electricity demand split of 28% for rural charging and 72% for urban
charging, applied to all four scenarios. Urban includes both urban and sub-urban properties
Source: Eurostat (online data code: ilc_lvho01)
Based on total electrical energy requirements calculated by SULTAN
25© Ricardo-AEA Ltd Unclassified - Public Domain 18 March 2019
Twice the electrical energy is required for EVs in the High EV scenario
compared to the Low Carbon fuels scenario (550 TWh vs 289 TWh)
0
100
200
300
400
500
600
2015 2020 2025 2030 2035 2040 2045 2050
En
erg
y C
on
su
mp
tio
n
[TW
h]
Electricity consumption from recharging by location (Home Charging Scenario)
0
100
200
300
400
500
600
2015 2020 2025 2030 2035 2040 2045 2050
En
erg
y C
on
su
mp
tio
n
[TW
h]
Public rapid
Public
convenience
Commercial
depot
Workplace
On-street
home
Off-street
home
Source: Ricardo Energy & Environment SULTAN modelling and analysis Note: * Including EU electricity used to produce EU eFuels decreases the difference to 39% lower for Low Carbon Fuel vs High EV
Hig
h E
VL
ow
Ca
rbo
n F
ue
ls
Unmanaged charging would require
significantly more upgrades to Low
Voltage (LV) networks to support off-
street and on-street charging (and
therefore much higher cost – more than
double the cost cumulatively to 2050)
• EV charging electricity demand in
2050 in the managed home
charging scenario is ~550 TWh
(1980 PJ)
– ~17.5% of the EU’s 2015
electricity generation
• For ‘Home’ charging scenario, most
of this energy (~60%) is expected
from overnight residential charging
• Requirements are ~47%* lower in
the Low Carbon Fuels scenario,
with a higher share of charging
from residential/home