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The launch of both battery electric vehicles (BEVs) and autonomous vehicles (AVs) on the global market has triggered ongoing radical changes in the automotive sector. On the one hand, the new characteristics of the BEV powertrain compared to the combustion type have resulted in new central parameters, such as vehicle range, which then become an important selling point. On the other hand, electric components are as yet not optimized and the sensors needed for autonomous driving are still expensive, which introduces changes to the vehicle cost structure. This transformation is not limited to the vehicle itself but also extends to its mobility and the necessary infrastructure. The former is shaped by new user behaviors and scenarios. The latter is impacted by the BEV powertrain, which requires a charging and energy supply infrastructure. To enable manufacturers and researchers to develop and optimize BEVs and AVs, it is necessary to first identify the relevant parameters and costs. To this end, we have conducted an extensive literature review. The result is a complete overview of the relevant parameters and costs, divided into the categories of vehicle, infrastructure, mobility, and energy.
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An Overview of Parameter and Cost for Battery Electric Vehicles
Adrian König * , Lorenzo Nicoletti , Daniel Schröder , Sebastian Wolff , Adam Waclaw
and Markus Lienkamp
Citation: König, A.; Nicoletti, L.;
Schröder, D.; Wolff, S.; Waclaw, A.;
Lienkamp, M. An Overview of
Parameter and Cost for Battery
Electric Vehicles. World Electr. Veh. J.
2021,12, 21.
Received: 13 January 2021
Accepted: 30 January 2021
Published: 3 February 2021
Publisher’s Note: MDPI stays neutral
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Institute of Automotive Technology, Technical University of Munich, Boltzmannstraße 15,
85748 Garching, Germany; (L.N.); (D.S.); (S.W.); (A.W.); (M.L.)
The launch of both battery electric vehicles (BEVs) and autonomous vehicles (AVs) on the
global market has triggered ongoing radical changes in the automotive sector. On the one hand, the
new characteristics of the BEV powertrain compared to the combustion type have resulted in new
central parameters, such as vehicle range, which then become an important selling point. On the other
hand, electric components are as yet not optimized and the sensors needed for autonomous driving
are still expensive, which introduces changes to the vehicle cost structure. This transformation is
not limited to the vehicle itself but also extends to its mobility and the necessary infrastructure.
The former is shaped by new user behaviors and scenarios. The latter is impacted by the BEV
powertrain, which requires a charging and energy supply infrastructure. To enable manufacturers
and researchers to develop and optimize BEVs and AVs, it is necessary to first identify the relevant
parameters and costs. To this end, we have conducted an extensive literature review. The result is
a complete overview of the relevant parameters and costs, divided into the categories of vehicle,
infrastructure, mobility, and energy.
Keywords: battery electric vehicles; design parameters; cost assessment
1. Introduction and Background
The megatrends of electrification and automation are posing new challenges for auto-
motive manufacturers [
], giving rise to new requirements for future vehicles and leading
the way to new, as yet unexplored, mobility systems. Powertrain electrification, for exam-
ple, promises a cleaner future, while autonomous driving will improve safety, availability,
and efficiency [
]. However, these trends also set new boundary conditions during vehicle
development and create different cost structures. In the case of BEVs, the traction battery
increases both the vehicle’s weight [
] and purchase price compared to internal combustion
engine vehicles (ICEVs). Moreover, sensors and computers in autonomous vehicles (AVs)
impact auxiliary power consumption and acquisition costs. Detailed knowledge of the new
technologies and their costs is a key requirement of automotive manufacturers’ ability to
plan future vehicle concepts and ensure their success in the market.
To profit from new technologies, concept engineers need to identify optimal techni-
cal solutions for individual components and vehicle packages, which requires detailed
knowledge of the relevant technical parameters. However, the best technical solution is
generally also the most expensive one. Thus, both technical suitability and cost are relevant
optimization objectives that enable an assessment of new vehicle concept’s feasibility.
The aim of this paper is to provide an overview of the cost structure of BEVs and
AVs and to identify the relevant technical parameters. Since BEVs and AVs require new
technologies and components, we first discuss the relevant parameters and assess their
costs (Section 2). Subsequently, since automation and electrification trigger changes in
mobility concepts, we assess the costs associated with various mobility solutions and
identify parameters that describe new mobility behaviors (Section 3). Changes in mobility
World Electr. Veh. J. 2021,12, 21.
World Electr. Veh. J. 2021,12, 21 2 of 29
in turn require a charging infrastructure, and this is described in Section 4. Finally, we
present an overview of energy costs incurred in the operation of the charging infrastructure
(Section 5).
This paper is based on two former publications by our Institute of Automotive Tech-
nology from 2014 and 2017 [
], which provided costs for vehicles, energy, and CO
. We
provide updated values in the following and add important parameters and costs for
mobility and infrastructure.
Cost Assessment Assumptions
We obtained our data from technical experts, scientific papers, and internet sources.
Most sources do not indicate whether the stated prices are for customers or manufacturers,
while the costs given refer to different years and currencies. We therefore make the
following assumptions, following the approach taken in [5]:
The exchange rate used to calculate the price in Euro is fixed and based on [
]. We
apply the average exchange rate for the year 2019, which is $1.13 to 1.
Inflation is taken into account in accordance with [
]. The year to which the price
refers is taken from the publishing date of the source unless it is explicitly given.
Component costs taken from the literature or other sources are assumed to apply to
2. Vehicle Parameters
The cost structure of a BEV is different from that of an ICEV. Battery costs alone can
account for up to one-third of total vehicle costs, as can be seen from Figure 1, which
compares the costs of a compact ICEV with those of a comparable BEV with a 50 kWh
battery. In 2020, an ICEV is still significantly cheaper than a BEV, while, by 2030, falling
battery prices will reduce the price difference to only 9% [8,9].
Figure 1. Cost structure of current and future BEVs compared to ICEVs [8,9].
In the following, we will focus on the BEV powertrain (Section 2.1), as it is the module
whose components have the highest influence on the overall vehicle cost structure. We then
present glider costs, including interior and exterior components (Section 2.2). Since the
automation of the driving function represents a promising development in the automotive
industry, along with electrification, we also consider other necessary components. We
World Electr. Veh. J. 2021,12, 21 3 of 29
therefore estimate the costs of those components needed for driver assistance systems and
autonomous driving (Section 2.3).
2.1. Powertrain Components
In this section, we focus on the main components of the BEV powertrain, identify
their relevant parameters, and assess their typical values. We look in particular at the
following components:
Traction battery
Electric machine
Power electronics
Particular attention is given to the traction battery, as it represents the central compo-
nent of the BEV powertrain.
2.1.1. Traction Battery
The currently established technology for BEVs is the lithium-ion battery [
]. A
lithium-ion battery consists of interconnected cells, with cell dimensions (length, width,
and height) and shape (pouch, prismatic, and cylindrical) varying depending on the
manufacturer. For example, Tesla uses cylindrical cells, BMW has prismatic cells, and
Nissan employs pouch cells [11].
To describe the traction battery, we focus on its central parameters. According to
Matz [12], these are:
Gravimetric energy density (in Wh/kg) at cell and pack level
Volumetric energy density (in Wh/L) at cell and pack level
Battery C-rate
Number of battery cycles
Cost in /kWh
The energy density of lithium-ion cells has been increasing steadily in recent years on
both a gravimetric and a volumetric level.
Figure 2shows the past and future development of gravimetric energy density be-
tween the years 2010 and 2030. It compares the gravimetric cell densities of different BEVs
(from the years 2010 to 2020) taken from [11,1315] with expert projections [12,1621].
Figure 2. Development of gravimetric energy density at cell level between 2010 and 2030.
Back in 2012, the gravimetric energy density of cylindrical cells was almost 100 Wh/kg
higher than pouch and prismatic cells. Nevertheless, the research presented in [
] and
World Electr. Veh. J. 2021,12, 21 4 of 29
the projection shown in [
] suggest that pouch and prismatic cells should be displaying a
similar performance by 2030. The trend in Figure 2suggests that pouch cells will continue
to show more promise than prismatic cells in the long term. Nevertheless, the increase
in energy density has its limits: Fink [
] puts the practical limit at 370 Wh/kg while
Thielmann [
] and Frieske [
] set it at 350 Wh/kg. Since the limit is expected to be
reached by 2030, new cell chemistry and technology will be needed to make further
progress in gravimetric energy density.
Figure 3shows the trends for volumetric energy density between the years 2010 and
2030. It compares several BEVs (from the years 2010 to 2020) from [
] with expert
projections [
]. According to this, pouch cells should attain volumetric energy density
values comparable to cylindrical cells by 2030. All cell types should be able to reach values
of around 1000 Wh/L by 2030.
Figure 3. Development of volumetric energy density at the cell level between 2010 and 2030.
The values showed in Figures 2and 3are set out in Table 1. The projection for the years
2020, 2025, and 2030 is derived from the average energy density values listed in [19,20].
Table 1. Development of volumetric and gravimetric energy densities at the cell level.
Year Cylindrical Cell Prismatic Cell Pouch Cell
Gravimetric Volumetric Gravimetric Volumetric Gravimetric Volumetric
2020 287 Wh/kg 775 Wh/L 187 Wh/kg 425 Wh/L 250 Wh/kg 550 Wh/L
2025 325 Wh/kg 875 Wh/L 230 Wh/kg 550 Wh/L 283 Wh/kg 700 Wh/L
2030 325 Wh/kg 1000 Wh/L 285 Wh/kg 990 Wh/L 323 Wh/kg 1000 Wh/L
Energy densities at the cell level are not directly scalable to pack level, since the battery
also contains other components such as cooling, wires, and module covers [
]. This means
that the energy density at the pack level is lower than at the cell level. It is therefore
necessary to evaluate the densities at the pack level.
During its recent “Tesla Battery days” [
], Tesla announced a new integration prin-
ciple to be applied to upcoming models, which it calls the cell-to-pack strategy [
]. The
aim of this strategy is to supposedly eliminate the cell modules, thus reducing the mass
of the battery by about 10% [
]. Furthermore, elimination of the module casings should
also increase the volumetric energy density at the pack level. However, to date, there is
no vehicle with a cell-to-pack strategy, and an assessment of the energy densities with the
cell-to-pack strategy is currently not possible. For the moment, it is only possible to assess
World Electr. Veh. J. 2021,12, 21 5 of 29
energy densities for existing BEVs. To make this assessment, we therefore distinguish
between gravimetric energy density (Figure 4) and volumetric energy density (Figure 5).
Figure 4. Overview of gravimetric energy density at the pack level for different BEVs.
Figure 5. Overview of the volumetric energy density at the pack level for different BEVs.
Figure 4shows an assessment of gravimetric energy density at the pack level based
on vehicle data taken from [15,2628].
Figure 5shows an assessment of volumetric energy density at the pack level. In this
case, the amount of data available is smaller, and the only possible source are the values
listed in [15].
A forecast of energy densities at the pack level (both gravimetric and volumetric) for
the years 2025 and 2030 can be found in the report by Thielmann et al. [
]. However, this
report does not consider the possibility of a cell-to-pack strategy.
To increase battery lifetime, the installed energy is normally not used up in its en-
tirety [
]. It is therefore necessary to distinguish between installed (gross) energy, and
actually usable (net) energy. Since the values listed in Figures 4and 5refer to gross energy,
we derive a conversion factor to estimate the net energy from the gross value.
World Electr. Veh. J. 2021,12, 21 6 of 29
For this purpose, 26 BEVs from the ADAC database [
] are evaluated; Figure 6
presents the gross energy for each vehicle along with the corresponding net value.
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Figure 6. Comparison between gross and net energy for existing BEVs.
Using the method of the least squares, we derive the regression that best matches the
vehicle data, as shown in Equation (1). The derived regression has an R
of 0.99. According
to the equation, net energy can be approximated as 92% of gross energy. In addition to
the energy densities at the pack and cell levels, other relevant battery parameters are the
C-rate, the number of battery cycles, and battery costs:
Net energy in kWh = 0.9266 gross energy in kWh (1)
The C-rate (in 1/h) describes the maximum charge or discharge current in relation
to the energy of the battery. It is necessary to distinguish between the C-rate for charging
and discharging, whereby the latter is generally higher than the former [
]. A C-rate of
1 means that the battery can be completely discharged in one hour. According to the values
proposed by [
], we assume that, for the maximum C-rate, a range of between
2 and 5 h1is realistic for BEVs.
The number of battery cycles refers to the maximum number of cycles that the battery
can endure before its useable energy drops to 80% of its initial value [
]. A range of
between 1000 and 3000 cycles is considered realistic in the literature [
This range should be sufficient to guarantee a long battery life. Taking the lower value of
the range (1000 cycles), as an example, a BEV with an electrical range of 200 km (which
can nowadays be easily achieved by most car manufacturers) would still be able to cover
a distance of 200,000 km before the battery’s end of life (EOL). The required number of
cycles is chosen according to the vehicle’s range and distance aimed at by the time of the
battery EOL.
There is a high observable variation in battery costs, since economies of scale can be
triggered depending on the total number of vehicles produced [
]. Battery costs can
therefore vary depending on the number of units produced (Table 2).
World Electr. Veh. J. 2021,12, 21 7 of 29
Table 2. Overview of battery costs at pack level for different BEVs, based on [15].
Vehicle Model Year Assumed Units Per Year Pack Costs
BMW i3 2014 15,000 396 /kWh
GM Bolt 2016 20,000 224 /kWh
BMW i3 2017 25,000 254 /kWh
Renault Zoe 2017 40,000 208 /kWh
Tesla Model 3 2018 100,000 164 /kWh
Audi e-tron 2019 100,000 157 /kWh
In addition to state-of-the-art pack costs shown in Table 2, Figure 7shows the derived
range derived from the reviewed literature [
]. As current battery pack costs are
already at the lower end of the reviewed range (indicated as “minimum values” in
Figure 7),
we also estimate the costs by 2030 to be in the lower range. Earlier studies overestimated
the costs in 2020, which explains the increase in battery costs between the years 2019
and 2020, which represents the transition from real prices to forecasts. Furthermore, it is
already possible to estimate the battery costs required to meet certain targets. For example,
91 /kWh is regarded as the economic limit, at which BEVs outperform ICEVs [11].
Figure 7. Development of battery pack costs (no distinction made between cell types).
Two common approaches for forecasting costs are top-down and bottom-up. Top-
down approaches such as those taken by Cano et al. [
] or NPM [
] extrapolate cur-
rent developments in battery prices to estimate future costs. In contrast, bottom-up ap-
proaches calculate the cell and respective pack costs based on material, labor, and overhead
A well-known example of this approach is the battery manufacturing
const estimation model (BatPac model) developed by the Argonne National Laboratory [
While such models offer great flexibility to account for the different battery types, several
assumptions must be made about costs, which both increases uncertainty and decreases
model flexibility.
An additional indicator in cost assessments is the cell-to-pack cost ratio (Table 3).
This combines the additional component costs of a complete automotive battery pack,
such as housing, cooling, and safety structures. By multiplying the cell costs with the
cell-to-pack ratio, it is possible to estimate the corresponding pack costs. The literature
review shows that this ratio is expected to decrease in the future, regardless of the cell type.
This is primarily due to optimized production processes and cell chemistry bringing down
cell costs.
World Electr. Veh. J. 2021,12, 21 8 of 29
Table 3. Development of the cell-to-pack cost ratio based on [5,11,3439].
Year 2020 2025 2030
Minimum value 1.94 1.47 1.17
Maximum value 2.21 1.32 1.24
Mean value 2.07 1.40 1.20
Finally, another significant cost driver of cell production is the energy consumed
during the manufacturing process [
]. This has a significant influence on the carbon
dioxide emissions associated with lithium-ion battery production, which, along with costs,
has been the focus of recent public debate [
]. While Romare and Dahllöf reported
emissions of 150–200 kgCO
/kWh in the year 2015 [
], their update estimates a reduction
in greenhouse gas emissions of between 61 and 106 kgCO
/kWh [
]. Their conclusion
is based on the life-cycle assessment by Dai et al. who estimated that the greenhouse
gas emissions for one automotive battery cell manufacturer are 73 kgCO
/kWh [
]. A
sensitivity analysis of the supply chains performed by Kelly et al. confirms this range
with 65 kgCO
/kWh as the best-case and 100 kgCO
/kWh for state-of-the-art supply
chains [
]. Due to further optimization of cell chemistry, in turn, energy density could
yield emissions below 50 kgCO2e/kWh, as forecasted by Philippot et al. [34].
2.1.2. Electric Machine
In our description of the electric machine, we focus on the following parameters:
Gravimetric power density in kW/kg
Machine overload factor
Maximum rotational speed in min1
Costs in /kW
Two common types are used in current BEV: permanent magnet synchronous ma-
chines (PSM) and induction motors (IM). The assessment by Grunditz [
] proposes a
gravimetric power density range of between 1.7 and 3.7 kW/kg for PSMs. Further im-
provements are expected in the future, since it is the objective of manufacturers to effect
a continuous increase in power density [
]. According to Fireske [
], electric machine
manufacturers aim to reach a value of 5 kW/kg, which is still some way off from the
state-of-the-art range envisaged by Grunditz [26].
As for the machine overload factor, Kampker and Grunditz [
] propose a range
of between 1 and 2. The overload factor may vary according to the machine type or the
manufacturer strategy and does not necessarily increase with the vehicle price bracket. For
example, the Jaguar I-Pace has a ratio between nominal and maximum power of 1.66 [
which is the same as that of the BMW i3 [
]. Nevertheless, an exact evaluation of the
overload factor is difficult, since there is no standard definition of overload time (i.e., the
maximum time during which the machine can remain in the overload area).
Finally, the maximum rotational speed of the electric machine may vary depending on
the maximum speed of the vehicle and the gearbox transmission ratio [
]. At the moment,
the maximum possible rotational speed range is between 9000 and 20,000 min
In the future, the trend could shift towards higher rotational speeds (compensated for by a
higher gearbox ratio), as recent discoveries [
] show that this can reduce the total weight
of the driving unit, i.e., the higher weight of the gearbox (due to greater transmission ratio)
can be compensated for by the reduction in electric machine weight (due to the lower
required maximum torque).
The price of electric machines is depending on the nominal power. We differentiate
between the PSM and an IM (Table 4).
World Electr. Veh. J. 2021,12, 21 9 of 29
Table 4. Costs of electric machines.
Machine Type Price Per kW Source
PSM 10 /kW [5]
IM 8 /kW [5]
2.1.3. Gearbox
The gearbox is coupled with the electric machine and transmits the machine torque
to the wheels. This requires a transmission ratio between torque and machine. In the
case of BEVs, the electric machine is usually equipped with a gearbox with a fixed speed
ratio [
].There are currently only a few exceptions of BEVs with more than one speed
(however these cannot be considered here), such as the Rimac Concept Two [
] and the
Porsche Taycan.
The literature review shows that the gearbox ratio for BEVs with a fixed ratio varies
within a range of 6 and 14 [
]. It is not possible to define an exact value since
the required ratio depends on both the electric machine (torque and maximum rotational
speed) and the desired maximum vehicle speed.
The presence of a gearbox causes further losses and thus increases vehicle consump-
tion. These losses can be further assigned to the main gearbox components. It has to be
further distinguished between losses that depend on the operating point (load depen-
dent) and those that are independent from the operating point (load independent) [
(pp. 69–71):
Gears losses: friction losses (load dependent) and splash losses in the case of splash
lubrication (load independent)
Bearing losses: friction losses (load dependent) and lubrication losses (load independent)
Losses due to sealing component
Losses due to gearbox auxiliaries, such as the oil pumps
Due to the high variety of the gearbox losses, an exact modeling of the gearbox
efficiency is very complicated and depends on the size and type of mounted component,
as well as on the operating points (rotational speed and torque at the gearbox input shaft).
For this reason, most of the reviewed authors usually employ a constant value, which
accounts for the entire losses of the gearbox unit. The literature shows that BEV gearboxes
can achieve high efficiencies, partly due to their small dimensions and the small number of
rotating parts. The identified gearbox efficiency range between 92% and 97% [
2.1.4. Power Electronics
The efficiency of the power electronics influences both vehicle consumption and the
required battery capacity. It is not possible to give an exact value, since the efficiency of the
power electronics varies according to its operation conditions (e.g., input power or battery
voltage [
], or torque and rotation speed [
], see Figure 8). Especially with low torque
and motor speed, the efficiency is reduced [
]. Nevertheless, the range of realistic power
electronics efficiency values is between 85% and 95% [10,12,17,22,59].
2.2. Glider
When the powertrain components are removed from a vehicle, the remainder is
referred to as a glider [
]. The glider comprises the body, chassis, low-voltage electrical
components, exterior, and interior. A gilder price can be calculated either bottom-up or
World Electr. Veh. J. 2021,12, 21 10 of 29
Figure 8. Efficiency map of a SiC MOSFET inverter [57].
To determine the glider price of an existing vehicle using the top-down approach, the
production costs can be derived from the selling price by assuming a surcharge factor. The
production costs (material + labor + depreciation) can be assumed to be around 60% of
the selling price excluding taxes [
], which would correspond to a surcharge factor of 0.6
(Figure 9). After calculating the production costs of the total vehicle, the powertrain costs
are deducted to obtain the glider production costs.
Figure 9. Cost breakdown for the net price of a new car [62].
To estimate glider costs using the bottom-up method, the single component costs are
determined and totaled. Based on expert interviews [
] and data published in [
], we
provide typical costs of a number of glider components (Table 5). We also provide costs
of materials used for the body in white as well as for the interior. The costs can differ
depending on optional preferences (e.g., LED headlights) and size of the car.
Table 5. Costs of standard equipment for a glider and for a medium-size car.
Component Costs Source
Windows 75 [63]
Window lifter 12 [63]
Exterior lights 140 [63]
Low-voltage electronics (excluding wiring harness) 520 [5]
World Electr. Veh. J. 2021,12, 21 11 of 29
Table 5. Cont.
Component Costs Source
Wiring harness 210 [5]
ESP 160 [5]
Airbag 20 [63]
HVAC 80 [63]
Seat warmer 10 [63]
Windshield wiper 30 [63]
Front seat 100 [63]
Body in white and exterior (ICEV) 1700 [9]
Body in white and exterior (BEV) 2100 [9]
Material Specific Costs Source
Aluminum 2,5–4 /kg [63]
High-strength steel 0.6–1 /kg [63]
Plastic PP 1.6–2 /kg [63]
Plastic ABS 2.5–3.5 /kg [63]
2.3. Components for Autonomous Vehicles
Vehicle automation is one of the great challenges that manufacturers are addressing.
In addition to technical hurdles, changes in society and legislation are necessary to enable
the acceptance of higher levels of automation. Although autonomous driving promises
improved comfort, safety, and running costs, the associated acquisition and development
costs are high [
]. The sensors and computers it requires are currently expensive, and it is
still being discussed as to what components should be integrated in the AV and which in
the infrastructure [
]. Furthermore, companies and researchers use different setups with
different sensor types in their vehicles [
]. The selection of sensors and computer unit
thus depends on the envisaged scenario and the vehicle configuration to be specified by
the manufacturer.
For the above reasons, determining the cost of autonomous vehicles is a challenging
task. We therefore decided only to provide the costs of components already in use in mass
production vehicles and prototypes and to refrain from estimating the costs of the complete
sensor setup.
Radar, cameras, and ultrasonic sensors are already used in mass production. Lidar
and computing platforms will continue to adapt in the coming years as they have only just
started to go into mass production but are currently already found primarily in concept
and research cars. In addition to mechanical sensors with several channels, a number
of promising solid-state sensors are being developed for use in future series production.
Current prices given in various press releases, on manufacturer websites, and in internet
articles [
] reveal a high range of variation (of between 88
and 67,250
). Since most
of them have a low production volume and the actual prices will be highly dependent
on technical characteristics and production volumes, the price we assume is that of the
current ScaLa I Lidar by Valeo [
], which, to our knowledge, is the only one used in
mass production.
The costs of the aforementioned lidar sensor together with the radar, camera, and ultra-
sonic sensors are shown in Table 6. These have been collected from expert interviews [
in the context of current series production vehicles. The price of the central computer is an
average value, based on several computers capable of autonomous level 2 and 3 [73,74].
Although computers for higher levels of automation require higher performance and
redundancy levels, their prices will drop due to increased production volume and further
future development. For example, the price of the HW 3 computer used in the Tesla Model
3 is about 20% lower than that of the previous generation, the HW2.5 [74].
World Electr. Veh. J. 2021,12, 21 12 of 29
Table 6. Overview of sensor costs (Level 2–Level 3).
Component Costs Source
Lidar Sensor 540 [72]
Radar Sensor 90 [63]
Camera 25 [63]
Triple-Camera 60 [74]
Ultrasonic Sensor 5 [63]
Computer 680 [73,74]
The costs and parameters presented above refer only to the vehicle itself. However,
the vehicle is only one aspect of an overall mobility system for which multiple costs and
parameters have to be taken into consideration (see Section 3).
3. Mobility
A person’s mobility costs can be divided into those costs relating to a privately-owned
vehicle (Section 3.1) and the costs of the public mobility services used (Section 3.2). For
the former, the approximate costs can be determined by a total cost of ownership (TCO)
calculation. Public service costs, on the other hand, have to be calculated using a price-
based approach based on charges set by the individual service provider. In the following,
the prices of such services are given for the city of Munich. However, the BEV powertrain
will bring about radical changes in mobility behavior, generating new relevant parameters
that need to be assessed (Section 3.3).
3.1. Vehicle Use and Total Cost of Ownership (TCO)
Tables A1 and A2 in Appendix Apresent the parameter values of a TCO calculation
for privately-owned passenger cars, bicycles, pedelecs, scooters, and motorcycles. The
annual costs shown in Tables A1 and A2 are based on annual mileage and the holding
period of the vehicle. We assume an annual mileage of 15,000 km and a holding period of
five years. Explanations of the calculation methods and the sources of the cost factors are
given in Table A3.
Due to the large environmental bonus for plug-in hybrid vehicles (PHEVs) and BEVs
granted by the German government, these vehicles are already competitive in terms of
TCO compared to the conventional powertrains in petrol- or diesel vehicles (Figure 10).
Fuel cell electric vehicles (FCEV) cause about 60% more annual costs in 2020, due to their
higher acquisition price.
Figure 10. TCO per year for small SUVs based on 15,000 km per year and a five-year period of ownership in 2020.
World Electr. Veh. J. 2021,12, 21 13 of 29
For PHEVs, two different cases are defined (Figure 11) whose charging behaviors signifi-
cantly impact the costs and emissions of the vehicle. The PHEV (empty battery) curve reflects
the more realistic scenario of a battery not being fully charged prior to each trip.
Figure 11.
TCO per year relative to time of purchase for small SUVs at 15,000 km per year with a five-year period
of ownership.
The forecast is based on energy cost predictions (cf. Section 5) and the expected
development of component costs for electrified vehicles (cf. Section 2). Expected inflation
leads to slightly increasing curves for all types of powertrains. At the same time, the costs
of BEV and PHEV components like the traction battery, electric machines, and power
electronics are decreasing and thus flattening the inflation gradient. The environment
bonus expires in 2026, by which time BEV will have achieved cost parity with conventional
powertrains without a bonus. According to the forecast shown in Figure 11, PHEV (empty
battery) and FCEVs will be still more expensive than BEVs after 2026.
The TCO does not represent a fixed variable, so Figure 11 shows the expected TCO
development for different powertrain types on the basis of an example small SUV with an
annual mileage of 15,000 km and a five-year period of ownership.
3.2. Mobility Services
Table A4 sets out the prices of various service-based transport modes in Munich,
including conventional public transport services (subway train, streetcar, and bus) provided
by MVV [
]. The MVV (Münchner Verkehrs- und Tarifverbund) coordinates all public
transport providers in the metropolitan region of Munich. All sharing concepts have a price
system that is either time or distance based. In addition, some providers have a minimum
fare that has to be covered, while taxis also charge for time spent waiting in traffic. The
MVV offers different types of tickets for public transport. Table A4 shows the consumer
costs per ride and year for the example scenario of rides within Zone M (2 rides per day,
7 days a week, 50 weeks a year).
Table A4 also includes a further variable: average transport speed. This variable
creates a correlation between price per minute and price per kilometer (see Table A4). For
the purposes of this paper, we use an average speed within Munich and its region that was
derived from tracking devices installed in taxis (Figure 12).
World Electr. Veh. J. 2021,12, 21 14 of 29
Figure 12. Average speed values for different transport modes in Munich [7687].
Future technologies such as AVs will offer new business cases to conventional mobility
services, such as autonomous taxis and buses. Table 7lists cost data for automated driving
services. The table distinguishes between individual and shared mode. The individual
mode of automated driving service is comparable to a conventional taxi without a driver
(Robo-Taxi) while the shared mode is similar to a bus shuttle service excluding the costs of
a driver. The costs per vehicle per km represent the cost per km when there is only one
passenger, while the costs per person per km are based on certain vehicle occupancies
(average numbers of passengers per ride), as defined in the various studies.
Table 7. Summary of forecast values for automated driving services (taxi, e-hailing, bus, etc.).
Vehicle Type Year Costs Per Vehicle Per km Costs Per Person Per km Mode Source
Midsize (regional) 2017 0.33 /km 0.29 /km Individual [76]
Midsize (urban) 2017 0.45 /km 0.37 /km Individual [76]
Car—not specified n/a n/a 0.14 /km Individual [88]
Car—not specified n/a n/a 0.11 /km Individual [88]
Small SUV 2030-2035 0.20 /km n/a Individual [89]
Large SUV 2030-2035 0.22 /km n/a Individual [89]
Microtransit vehicle 2030 n/a 0.07 /km Shared [90]
Car—not specified 2020-2030 0.11 /km n/a Individual [91]
Car—not specified n/a 0.25–0.27 /km n/a Individual [92]
External Costs
A holistic view of mobility costs should not only consider costs to the consumer
but also external costs. According to [
], external costs can be subdivided into the
following subcategories:
Climate costs: Costs attributable to the emission of greenhouse gases and the resulting
climate change (damage cost approach).
Air pollution: Environmental costs resulting from the emission of air pollutants in-
curred in the form of health care costs, crop losses, damage to buildings and materials,
and biodiversity loss.
Up- and downstream processes: Follow-on costs due to the emission of greenhouse
gases and air pollutants from the production, maintenance, and disposal of: energy
sources (fuels, electricity), vehicles, and transport infrastructure.
Accident costs: Traffic accidents (damage cost rate).
Noise costs: Noise-related healthcare costs and costs attributable to noise pollution
(damage costs).
World Electr. Veh. J. 2021,12, 21 15 of 29
Nature and landscape: Habitat losses (through land consumption) and habitat frag-
Table 8shows the sum of external costs for different transport modes.
Table 8. External costs for various transport modes.
Transport Mode External Cost Source
Cars 0.12 /km EU (DG Move) [93] (p. 135)
Motorcycles 0.25 /km EU (DG Move) [93] (p. 135)
Public transport 0.04 /km Bieler and Sutter [94]
Bicycles 0.18 /km Gössling et al. [95]
Walking 0.37 /km Gössling et al. [95]
3.3. Mobility Behavior
BEV manufacturers must ensure that changing over from a conventional vehicle does
not significantly restrict the mobility behavior of the user. For this reason, user movement
data with conventional ICEVs is recorded to test whether a BEV already suits a customer’s
mobility needs. Even if the same distances can be covered with a BEV, some behavioral
changes are required when changing over from an ICEV. The greatest change is the move
from refueling to recharging, which is not considered in the aforementioned mobility data.
The parameters given in Table 9are intended to aid the consideration of new user needs in
the development of a BEV.
Table 9. BEV mobility behavior parameters.
Parameter Description Value Source
Lower SOC-limit Defined by unease due to remaining
range and end of comfort zone. 15–25% or 50–100 km [97102]
Upper SOC-limit Which SOC do users charge up to at
charging stations?
90–100% (private, corporate)
80% [99] (public, charging
speed is reduced from 80%
SOC [103])
Catchment area of
charging station
Maximum distance from charging
station and actual location. Drivers
must travel the distance by car
separately and may have to walk
back to the actual location.
100–500 m [105]
Minimal charging time Parking time, from which a charging
process is considered. 5–15min [101,102]
4. Infrastructure
The electrification of road traffic is significantly dependent on the available charging
infrastructure. Due to the lower ranges and higher refueling times of BEVs, companies and
private individuals need to install a charging infrastructure on their premises. This adds
further costs to the purchase and installation (Table 10).
Along with stationary charging, dynamic charging is another possible solution for
increasing vehicle range. Dynamic charging refers to technologies that use various methods
of energy transfer to charge the vehicle while driving. Current research focuses on conduc-
tive charging using overhead catenary wires, in-road conduction beams, and inductive
charging with wireless power transfer. Since the first solution requires overhead cables
mounted above the legal height limit of 4 m, it is only suitable for heavy-duty vehicles or
buses, as passenger cars would require a high pantograph to bridge the distance between
roof and cables [
]. On the other hand, conductive charging with road conduction
beams and inductive charging could potentially be shared among passenger and commer-
World Electr. Veh. J. 2021,12, 21 16 of 29
cial vehicles. All three technologies share the additional infrastructure and the associated
high costs that are shown in Table 11.
Table 10. Overview of costs of stationary charging infrastructure (business) [106].
Component Cost Category 11 kW AC 22 kW AC 24 kW DC 50 kW DC
Charging point (CP) Acquisition costs per CP 1250 1500 15,000 30,000
Installation costs of base/per CP 1000 + 500 per CP
Charge management Acquisition and installation costs 2500 2500 3500 3500
Installation costs per CP 100 100 100 100
Table 11. Cost of dynamic conductive and inductive charging infrastructures.
Cost Type Investment Costs Operational Costs Additional Vehicle Costs ** Source
Catenary 2–2.5~M/km * 1–2~%capex/year 20,000–47,500~[109]
Conductive 0.5–1.2 M/km * 1–2~%capex/year 10,000 [110]
Inductive 2.6–3.6~M/km * 1–2.5~%capex/year 10,000–10,800 [109,111]
*: per lane; **: refers to commercial vehicles, : own assumption based on inductive system.
5. Energy Costs
Energy costs are a crucial aspect of economic assessment. As Figure 13 shows, a
variety of taxes and levies to energy carriers exist in Germany, potentially resulting in
prices that are 100–270% higher than the respective production costs [
]. This wide range
explains why taxes and subsidies (both highly regional) must be excluded when making
technical comparisons. The costs given in Figure 13 are well-to-pump, which means that
efficiency losses, for example in the powertrain, are not considered.
 15
   PtL, EU   
 
 5
 
Figure 13.
Forecast for Germany. Normalized electricity and energy prices per kilowatt-hour for the
year 2030 under current tax and subsidy policies [
]. Energy prices are also influenced by the
German CO2- tax at a minimum of 25 /tCO2and a maximum of 65 /tCO2[113].
With liquid fuels, for example, only energy taxes are levied, while, for electricity, in
addition to taxes, there are also charges for the expansion of renewable energies under the
German Renewable Energy Sources Act (German: Erneuerbare Energie Gesetz EEG) [
World Electr. Veh. J. 2021,12, 21 17 of 29
Starting from 2021, a CO
tax of a minimum of 25
and a maximum of 65
be introduced for fossil fuels in Germany [113], which will increase the total energy costs.
The ranges for hydrogen are subject to different production paths and production
uncertainties, as these are not available on large scales. Currently, hydrogen is predomi-
nantly produced by a process of natural gas steam methane reforming (SMR) and is thus
regarded as fossil (or grey) hydrogen, despite its relatively low primary energy demand. If
green hydrogen is produced via electrolysis and renewable energy, carbon emissions can
be reduced, although the costs of green hydrogen are higher than those of grey hydrogen
due to electrolysis-losses (30% losses) and the dependency of the process on electricity
costs [114].
The power-to-liquid (PtL) ranges have the same origins as hydrogen fuels. The pro-
duction chain for PtL fuels begins with hydrogen and CO
-extraction to form hydrocarbon
chains. Further possible process steps are methanization or liquification by Fischer–Tropsch
synthesis [
]. However, additional losses (30%) in the liquification processes require
twice as much primary energy as for electrolysis alone (and up to four times as much,
if the well-to-wheel efficiency is considered) [
]. Thus, PtL processes are also strongly
dependent on electricity prices and therefore divided into two regions based on different
price assumptions: (1) domestic EU production (0.09
/kWh) and (2) production in the
Middle East or North Africa (MENA) with lower electricity prices (0.03–0.06
/kWh) [
Since both hydrogen and PtL production costs depend heavily on electricity prices, a
uniform system boundary is essential. The study by the German Energy Agency (German:
Deutsche Energieagentur DENA) lists costs of PtL fuels and hydrogen for different coun-
tries or regions of origin [
]. Purchase costs in Germany are shown in Table 12. As they
have the same fuel supply, these costs are transferable to other European or global countries,
although individual taxation may vary. It also shows the electricity generation costs (i.e.,
the costs of creating a new energy infrastructure) of renewable energy as proposed by Kost
and Schlegl [117].
Table 12.
Current (2020) and projected (2030) production costs of various energy sources normalized to
/kWh. As
there is no current large-scale production of power-to-liquid and hydrogen from electrolysis, no data are available for 2020
[36,115117]. (Note: MENA: Middle East, North Africa).
Year Gasoline
and Diesel
Power to
2020 0.046 0.855 n/a 0.26 n/a n/a 0.105 0.061
2030 0.054 0.307 0.121 0.058 0.121 0.074 0.121 0.061
6. International Carbon-Dioxide Prices
Carbon pricing is one of the currently discussed levers to push the economy towards
less carbon-dioxide emissions. This applies not only for fossil fuels, as discussed above,
but also for the energy production sector and is thus of importance when comparing
different powertrains and electric vehicles in particular. Although several countries in-
troduced carbon pricing, the strategies vary [
]. China, the EU, and parts of the USA
have a certificate-based trading system yielding a dynamic, demand-and-supply oriented
pricing—also referred to as cap-and-trade. In contrast, Switzerland and Sweden are ex-
amples of taxation with defined rates that were increased during the past decades [
Table 13
shows an overview of selected, current carbon-dioxide prices and their respective
introduction years. It must be noted that, despite many countries adopting some measure
of CO2-price, the eventual price and thus effect on greenhouse gas reduction vary.
Figure 14 shows the different introduction years and the two pricing mechanisms.
The data suggests that demand-and-supply did not result in a markable increase of the
-price, while the governmentally controlled Swedish carbon tax increased from 25
in 1991 to 116
today [
]. The excessive disbursement of the European ETS certificates
World Electr. Veh. J. 2021,12, 21 18 of 29
between 2012 and 2017 resulted in a stagnating carbon price at low levels [
]. Shorting
the certificates in 2018 lead to a price increase that stabilized at 2007 levels [
]. The
steadily decreasing emission allowances in California (cap) lead to a steady increase in
-price [
]. Sweden increased taxes with a fixed rate while California, EU ETS, and
China use trading systems [
]. The Chinese trading system is still in use, but
no consecutive data are available.
Figure 14. Historic development of selected international carbon prices.
Table 13.
Overview of international CO
-taxes and certificates in
and the respective introduction
year [119,126128].
Price in /tCO2. Introduction Country
10.5 2008 China (Bejing)
0.99 2015 China (Shenzen)
25–60 2003 Germany
44.6 2014 France
20 2013 Great Britain
2.4 2012 Japan
10.2 2019 Canada
0.07 1990 Poland
112 1991 Sweden
87 2018 Switzerland
7.7 2019 South Africa
20 2015 South Korea
15.5 2013 USA (California)
5 2009 USA (RGGI *)
* RGGI: Regional Greenhouse Gas Initiative includes the states of Connecticut, Delaware, Maine, Maryland,
Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Vermont, and Virginia.
In addition to trading systems, applicable CO
limits and associated penalty payments
can also be understood as CO
prices. The European Union already set the penalty for
exceeding passenger car and light-duty truck (<3.5 t) emission to 95
/km [
Assuming a lifetime of 150,000 to 200,000 km, this leads to 475–633
. In the year
2019, the European union also set CO
-limits for heavy-duty vehicles (>3.5 t), targeting
the road transport sector. The limits of 4250
per gram CO
and vehicle kilometer from
2025 to 2030 and 6300
/tkm onwards [
], take the vehicle payload—an impor-
tant indicator in transportation—into account. Assuming a total mileage of one million
kilometers and an average payload of 19.3 t [
], the limits convert to 220
326 /tCO2, respectively.
World Electr. Veh. J. 2021,12, 21 19 of 29
7. Discussion and Outlook
After discussing the megatrends of electrification and automation, we identified the
changes they cause in the automotive industry, these being the new technical parameters,
which are relevant due to the novel technologies required by BEVs and AVs, and the vehicle
cost structures. The analysis focuses on the vehicle (Section 2), its mobility
(Section 3),
required charging infrastructure (Section 4), and the corresponding energy costs
(Section 5).
In terms of the vehicle, the high costs associated with BEVs compared to ICEVs still
prevent many users from buying electric vehicles today. Another challenge is the vehicle
range of BEVs, which is still not comparable to that available with ICEVs. Regarding
vehicle costs, falling battery prices will lead to almost equal production costs by 2030,
resulting in comparable prices to customers without any subsidies. The range gap between
BEVs and ICEVs is expected to decrease, as the literature review shows that an increase in
gravimetrical and volumetric density can be expected at both cell and pack level. This may
help reduce the fear of low ranges in the future, making BEVs more attractive for buyers.
Regarding mobility, the TCO assessment (Section 3.1) shows that BEVs are currently
more expensive than ICEVs. Nevertheless, the forecast shows that, from 2026, the parity
with ICEV can be reached and BEVs will be cheaper than other electrification solutions
(such as PHEVs and FCEVs).
The charging infrastructure is, besides the vehicle itself, an important component of
mobility. BEVs require a nationwide charging infrastructure to compensate for their current
lower range compared to ICEVs. Dynamic charging could help on long distances without
big batteries, but, in most cases, it is too expensive for passenger cars, especially in view of
the rising energy densities of batteries.
Finally, with regard to energy costs, even if the production costs of fossil fuels are
still lower than those of renewable electric energy, the higher efficiencies in the vehicle
powertrain are an important advantage of BEVs.
In summary, the actual disadvantages of BEVs in comparison to other propulsion
system are expected to decrease in the future thanks to technological developments and
mass production. This could enable BEVs to develop into an optimal mobility solution.
Author Contributions:
As the first authors, A.K. and L.N. defined the structure of the presented
paper and each contributed 30%. D.S. and S.W. contributed knowledge regarding mobility, infras-
tructure, and mobility and each contributed 20%. A.W. provided us with compiled mobility data.
M.L. made an essential contribution to the conception of the research project. He critically revised the
paper for its important intellectual content. M.L. gave final approval of the version to be published
and agrees to all aspects of the work. As a guarantor, he accepts responsibility for the overall integrity
of the paper. All authors have read and agreed to the published version of the manuscript.
The research of A.K. was accomplished within the project “UNICARagil” (FKZ 16EMO0288).
We acknowledge the financial support for the project from the Federal Ministry of Education and
Research of Germany (BMBF). The research of L.N. was funded by the AUDI AG and the Technical
University of Munich. The research of D.S. and S.W. was conducted with basic research funds of the
Technical University of Munich.
The author L.N. would like to thank the colleagues of the AUDI AG in the
persons of Maximilian Heinrich, Martin Abersmeier, and Hendrik Gronau.
Conflicts of Interest:
The authors declare no conflict of interest, and the funders had no role in
the design of the study; in the collection, analyses, or interpretation of data; in the writing of the
manuscript; or in the decision to publish the results.
World Electr. Veh. J. 2021,12, 21 20 of 29
Appendix A
Table A1. Average values for TCO calculation with a holding period of five years and a 15,000 km annual mileage.
Segment 1,2 Acquisition
Bonus 3Depreciation 4Insurance Tax TÜV Maintenance Repair Tires Parking Vehicle Care Consumption
per 100 km
Small Petrol 15,090 01427 437 82 44 180 252 179 805 250 5.9 l
Small BEV 24,771 9570 1437 415 044 108 204 218 805 250 14.3 kWh
Med. Petrol 21,921 02073 492 87 44 180 276 176 805 250 6.5 l
Med. Diesel 22,944 02169 517 214 44 168 288 176 805 250 5.2 l
Med. BEV 36,241 9570 2522 474 044 96 288 259 805 250 15.4 kWh
Large Petrol 33,345 03153 495 178 44 180 288 246 805 250 7.6 l
Large Diesel 34,902 03300 563 263 44 156 300 246 805 250 5.8 l
Large PHEV 38,639 5981 3088 545 044 288 300 252 805 250 3.8 l + 8.3 kWh 5
Large BEV 54,972 7975 4443 895 044 228 252 283 805 250 18.5 kWh
Large FCEV 75,889 7975 6421 895 044 228 252 283 805 250 0.8 kg
SUV Petrol 27,290 02580 390 140 44 180 276 223 805 250 7.1 l
SUV Diesel 28,564 02701 549 240 44 156 288 223 805 250 6.2 l
SUV PHEV 31,546 7178 2304 721 244 216 264 217 805 250 3.8 l + 7.3 kWh 5
SUV BEV 44,949 9570 3345 582 044 120 288 235 805 250 17.2 kWh
SUV FCEV 76,139 7975 6445 582 144 44 120 288 235 805 250 0.9 kg
M-cycle Pet. 16,035 01525 568 88 33 725 004.75 l
M-cycle BEV 22,170 02108 568 033 725 009.2 kWh
Values based on WLTP (ADAC). Exemplary vehicles: Small BEV = VW e-UP, Medium BEV = VW ID3, Large BEV = Tesla Model S, Large FCEV = Toyota Mirai, SUV BEV = VW ID4, SUV FCEV = Hyundai Nexo;
2Only Small SUVs considered under SUV; 3Including 19% Tax; 43.3% per year; 0.82% per 5000 km; 5with charged battery
Table A2. Average values for TCO calculation with a holding period of five years and 2000 km annual mileage.
Bonus 1Depreciation 2Insurance Tax TÜV Maintenance Repair Tires Parking Vehicle Care Consumption
Per 100 km
Bicycle 420 047 00045 18 00n/a
Pedelec 2100 0236 00055 22 000.73 kWh
S-Pedelec 4607 0517 21 0055 22 000.73 kWh
Scooter Pet. 3520 0226 21 00n/a n/a n/a n/a n/a 2.9 l
Scooter BEV 6220 0399 21 00n/a n/a n/a n/a n/a 3.5 kWh
E-Scooter 3799 0145 21 00n/a n/a n/a n/a n/a 1.2 kWh
1Including 19% Tax; 23.3% per year; 0.82% per 5000 km; 3The expected lifetime of an e-scooter is only around 7500 km.
World Electr. Veh. J. 2021,12, 21 21 of 29
Table A3. Explanation and sources for considered cost factors in TCO calculation.
Cost Considered
Description of Approach for Passenger Cars
and Motorized
Main Source (s)
Average sales prices from Statista are utilized as
a baseline for each segment. Average MSRP
listings from ADAC are utilized to calculate
factors for the different propulsion types.
Statista [132], ADAC [133]
Market prices for an example vehicle are
researched for on various vehicle ages between
0 and 10 years and mileages between 0 and
300,000 km.
AutoScout24 [134]
Energy consumption
Average fuel consumption values of vehicles
sold in the respective segment are used for petrol
and diesel ICEVs. Manufacturer specifications of
example vehicles are utilized for PHEV and BEV
fuel consumption.
KBA [135], BMWi [136], MWV,
ADAC [133]
Insurance Insurance costs are researched for each segment
and propulsion type depending on
annual mileage. [137]
Tax contributions are calculated on the basis of
the “Kraftfahrzeugsteuergesetz” regulation in
Germany and based on exemplary vehicle
specifications of frequently-sold vehicles.
KraftStG 2002 [138,139]
TÜV Standard TÜV charge is assumed. TÜV [140]
Inspection and repair Values for inspection and repair are retrieved
from the ADAC online cost calculator for all
segments and propulsion types. ADAC [141]
Tires Summer and winter tire market prices are
retrieved for all segments and propulsion types. [142], DAT [143]
Vehicle care Constant factor independent of assumed annual
mileage assumed. ADAC [144]
Parking Constant factor independent of assumed annual
mileage assumed. INRIX [145]
Cost Considered Description of Approach for Bicycles,
Pedelecs, and e-Scooter Main Source(s)
Bicycle, Pedelecs, E-pedelecs TCO information for bicycles from
different sources [146], VSF [147], ZIV
[148,149], Fahrradblog [150],
Schwalbe [151], [86], GHOST [152]
E-scooter TCO information for bicycles from
different sources BGBI [153,154], IEEE [155], Journals
[156], BCG [157]
Table A4. Price structure for various service-based transport modes in Munich.
Transport Mode Vehicle Type Min. Price Base Price Price per Min Price per Hour Price per km
Car sharing (FF 1))Mini car n/a n/a 0.19 /min n/a n/a
Car sharing (FF 1))Small car n/a n/a 0.28 /min n/a n/a
Car sharing (FF 1))Medium car n/a n/a 0.31 /min n/a n/a
Car sharing (SB 2))Mini car n/a n/a n/a 2.30 /h 0.23 /km
Car sharing (SB 2))Small car n/a n/a n/a 2.65 /h 0.27 /km
Car sharing (SB 2))Medium car n/a n/a n/a 3.00 /h 0.3 /km
Bike sharing Bicycle n/a n/a 0.08 /min n/a n/a
E-Scooter sharing E-Scooter n/a 1.00 0.19 /min n/a n/a
Scooter sharing Motorized Scooter n/a n/a 0.27 /min n/a n/a
World Electr. Veh. J. 2021,12, 21 22 of 29
Table A4. Cont.
Transport Mode Vehicle Type Min. Price Base Price Price per Min Price per Hour Price per km
Ride-hailing Medium/large car 5.00 2.00 0.31 /min n/a 0.90 /km
Ride-hailing Medium/large car 5.00 2.00 0.31 /min n/a 0.90 /km
Ride-hailing Executive car 9.00 6.00 0.50 /min n/a 1.50 /km
Ride-hailing Van 9.00 6.00 0.50 /min n/a 1.50 /km
Taxi 3Executive car n/a 3.70 n/a n/a 1.70–2.00 /km
Transport Mode Ticket Type Price per ride in * Price per year in *
Single Ticket 3.39 2471.20
Daily Ticket 4.01 2920.50
Weekly Ticket 1.31 914.70
Monthly Ticket 0.97 681.40
Annual Ticket 0.77 536.90
Stripe Ticket 1.65 1153.20
Semester Ticket 0.58 402.60
For taxies, a further 0.50
/min must be added to account for waiting time. Sources: Car-sharing [
Bike-sharing [
], E-scooter-sharing [
], Scooter sharing [
], Ride-hailing [
], Taxi [
]. * for exemplary rides within Zone
M; 2 rides per day; 7 days a week, 50 weeks a year using service. Sources: Public Transport [75,170,171].
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Conference on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 10–12 September 2020; pp. 1–5,
ISBN 978-1-7281-5641-5.
Trommer, S.; Kolarova, V.; Fraedrich, E.; Kröger, L.; Kickhöfer, B.; Kuhnimhof, T.; Lenz, B.; Phleps, P. Autonomous Driving.
The Impact of Vehicle Automation on Mobility Behavior; Institute for Mobility Research: Munich, Germany, 2016. Available
on_Mobility_Behaviour (accessed on 20 December 2020).
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... The main disadvantage of LiBs is their price compared to other types of batteries. LiBs are made up of interconnected cells that vary in length, width, and height, as well as shape (pouch, prismatic, and cylindrical, as shown in Figure 1) depending on the manufacturer [30]. To achieve the desired capacity and voltage for automotive applications, LiBs are made up of numerous electrochemical lithium-ion cells that are incorporated into modules before being added to a battery pack [28]. ...
... The main disadvantage of LiBs is their price compared to other types of batteries. LiBs are made up of interconnected cells that vary in length, width, and height, as well as shape (pouch, prismatic, and cylindrical, as shown in Figure 1) depending on the manufacturer [30]. The health of LiBs is directly impacted by the parameters of their operation. ...
... The number of battery cycles considered practical in the literature ranges between 1000 and 3000 cycles, whichshould assure a long battery life. The lower value of the range (1000 cycles) for an EV with an electrical driving range of 200 km is easily achieved by manufacturers nowadays, and the car would still be able to be driven 200,000 km before the battery's life was up [30]. People in Europe expect a driving range of approximate 300 km, which is not easily achieved by using batteries alone [32]. ...
Full-text available
The current worldwide energy directives are oriented toward reducing energy consumption and lowering greenhouse gas emissions. The exponential increase in the production of electrified vehicles in the last decade are an important part of meeting global goals on the climate change. However, while no greenhouse gas emissions directly come from the operations of the electrical vehicles, the electrical vehicle production process results in much higher energy consumption and greenhouse gas emissions than in the case of a classical internal combustion vehicle; thus, to reduce the environment impact of electrified vehicles, they should be used for as long as possible. Using only batteries for electric vehicles can lead to a shorter battery life for certain applications, such as in the case of those with many stops and starts but not only in these cases. To increase the lifespan of the batteries, couplings between the batteries and the supercapacitors for the new electrical vehicles in the form of the hybrid energy storage systems seems to be the most appropriate way. For this, there are four different types of converters, including rectifiers, inverters, AC-AC converters, and DC-DC converters. For a hybrid energy storage system to operate consistently, effectively, and safely, an appropriate realistic controller technique must be used; at the moment, a few techniques are being used on the market.
... The battery costs at pack level for an Audi e-tron is USD 157/kWh in 2019. Therefore, further cost reductions are predicted to be down to USD 76/kWh by 2030 [8,9]. • Power inverter: A power inverter directly connected to the battery system does not have the same performance at all modulation indices (MI), and it is more efficient and produces better waveforms at higher MI values. ...
... Hence, with a dc-dc converter between the battery and the inverter, the dc voltage bus in the inverter input could be optimized based on the motor speed to maximize the electric motor efficiency [12,14]. The costs of electric motors depend on the nominal power; in general, the cost for a PSM is USD 10/kWh and for a IM is USD 8/kWh [9]. Despite all the advantages of incorporating a dc-dc converter in the powertrain and the wide use in the EV market [15], there are still challenges where the improvement of power conversion efficiency stands out. ...
Full-text available
The composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture.
... The cost of increased battery degradation (CBD) depends on the cycle life of a battery (LB) as well as the investment cost for the battery per kWh (PB). It is assumed that the cycle life of an EV battery is 1000-3000 cycles and the investment costs are between 150-250 €/kWh [16]. CBD is calculated according to ...
Conference Paper
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The expansion of photovoltaic systems as well as heat pumps and charging stations for electric vehicles are creating new challenges for low-voltage grids, as peaks in load and generation can lead to grid congestions. A promising approach to these challenges is the use of flexibility from various flexibility options to prevent these grid congestions. In this paper a system for flexibility-based grid congestion management is presented. Furthermore in this paper, different flexibility options are compared in terms of availability and cost to provide flexibility for grid congestion management.
... The total penalty points at any point of starting with the inverter battery are assumed to be around 3000 [14]. The factor of 3000 can be considered as the maximum chargedischarge cycles the battery can go throughout the usable life of the battery. ...
Full-text available
Inverters are one of the most important sources of energy whenever there is a blackout. These help in providing continuous power thus giving an uninterruptible power supply. Equally important is the estimation of the total time taken by the battery of the Inverter to provide the amount of uninterrupted power supply (in terms of time) while the battery is supplying power (during the blackout) and the amount of time taken to charge (while the battery is charging). Another aspect the inverters and battery manufacturers face is the warranty-related issues when the inverter or the battery is not able to provide expected performance while in use. To tackle these issues, this paper provides and discusses a mechanism to conditionally monitor the inverter and its battery while in use. This research introduces certain unique algorithms for the identification of terms related to the battery maintenance like Time Taken to Empty (TTE) the charge of the battery, Time Taken to Fill (TTF) the full charge of the battery, total age remaining of the battery (State of Health or SOH) and total charge expected at a time of a battery (State of Charge or SOC). An algorithm known as Q-point Area (under the curve (AUC-Q) between the area of the actual voltage and the desired voltage curves at a Q-point) is devised for determining the Time of Empty for the Lead-Acid Battery in the Voltage-Power (Charge) usage curve. This solution utilizes the Reward-Penalty rules for optimizing the State of Health of the battery to tackle different user handling conditions of the battery. The error is calculated from the Actual backup time with the predicted backup time of the battery. The devised algorithm flow minimized the error to 10 mins which falls in the range of High load conditions for Normal Battery usage in comparison to 1 hour of error using the Load Curve time assessment. The overall MAPE of the proposed algorithm pipeline is 13% for the different situational conditions taken for the Out of Sample scenarios in the trial field. The proposed conditional health monitoring of the battery helps in tracking the different usage conditions that gives an overall idea about the warranty-related conditions. This solution can also be thought of as a one-of-a-kind solution for providing the health analysis of a lead-acid inverter battery.
... vehicles' cost [7], but this value sometimes exceeds 40%, e.g., 2018 Tesla Model 3 [6]. To make EVs competitive with internal combustion vehicles, the cost-effective battery is seen as an essential [8]. ...
Full-text available
In response to the increasing expansion of the electric vehicles (EVs) market and demand, billions of dollars are invested into the battery industry to increase the number and production volume of battery cell manufacturing plants across the world, evident in Giga-battery factories. On the other side, despite the increase in the battery cell raw material prices, the total production cost of battery cells requires reaching a specific value to grow cost-competitive with internal combustion vehicles. Further, obtaining a high-quality battery at the end of the production line requires integrating numerous complex processes. Thus, developing a cost model that simultaneously includes the physical and chemical characteristics of battery cells, commodities prices, process parameters, and economic aspects of a battery production plant is essential in identifying the cost-intensive areas of battery production. Moreover, such a model is helpful in finding the minimum efficient scale for the battery production plant which complies with the emergence of Giga-battery plants. In this regard, a process-based cost model (PBCM) is developed to investigate the final cost for producing ten state-of-the-art battery cell chemistries on large scales in nine locations. For a case study plant of 5.3 GWh.year −1 that produces prismatic NMC111-G battery cells, location can alter the total cost of battery cell production by approximately 47 US$/kWh, which is dominated by the labor cost. This difference could decrease by approximately 31% at the minimum efficient scale of the battery production plant, which is 7.8 GWh.year −1 for the case study in this work. Finally, a comprehensive sensitivity analysis is conducted to investigate the final prices of battery cell chemistries due to the changes in commodities prices, economic factors of the plant, battery cell production parameters, and production volume. The outcomes of this work can support policy designers and battery industry leaders in managing production technology and location.
... A large portion of RES is represented by DER, especially micro and small (1 kW-5 MW) rooftop PV, wind turbines or battery storages systems, etc. Due to lower prices ale accessibility of this devices they are often located on side of consumers [6]- [8]. Another element contributing to the list of storage/consumption devices are EV. ...
Conference Paper
Distributed energy resources have become a stable and rapidly growing part of power systems. DER character and properties depend significantly on the type used technology and application. Their common feature is the probabilistic nature of their behavior and parameters. Penetration of DER into classical power systems raises the question of power quality and operational safety. This paper presents a general method to implement any DER into a custom power system and calculate the probabilities of possible impacts. The proposed method is based on the combination of deterministic, probabilistic, unknown parameters and information about investigating power system and future scenarios of DER penetration. The Paper also presents a case scenario where penetration of PV system into low voltage network is investigated. The last part shows the complex result of probabilistic power flow calculations. Presented methods have been used for practical applications to determine hosting capacity for PV systems in rural and urban areas of the Slovak republic.
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This article aims to calculate, analyse and compare the optimal powertrain sizing solutions for a long-haul plug-in series hybrid coach running on diesel and hydrotreated vegetable oil (HVO) using a co-design optimisation approach for: (1) lowering lifetime carbon footprint; (2) minimising the total cost of ownership (TCO); (3) finding the right sizing compromise between environmental impact and economic feasibility for the two fuel cases. The current vehicle use case derived from the EU H2020 LONGRUN project features electrical auxiliary loads and a 100 km zero urban emission range requiring a considerable battery size, which makes its low carbon footprint and cost-effective sizing a crucial challenge. Changing the objective between environmental impact and overall cost minimisation or switching the energy source from diesel to renewable HVO could also significantly affect the optimal powertrain dimensions. The approach uses particle swarm optimisation in the outer sizing loop while energy management is implemented using an adaptive equivalent consumption minimisation strategy (A-ECMS). Usage of HVO fuel over diesel offered an approximately 62% reduction in lifetime carbon footprint for around a 12.5% increase in overall costs across all sizing solutions. For such an unconventional powertrain topology, the fuel economy-focused solution neither achieved the lowest carbon footprint nor overall costs. In comparison, CO2−cost balanced sizing resulted in reductions close to the single objective-focused solutions (5.7% against 5.9% for the CO2 solution, 7.7% against 7.9% for the TCO solution on HVO) with lowered compromise on other side targets (CO2 reduction of 5.7% against 4.9% found in the TCO-focused solution, TCO lowering of 7.7% against 4.4% found in the CO2-focused solution).
Technical Report
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4ème rapport des travaux préparatoires du plan ressources minérales de la transition bas-carbone, consacré aux chaînes de valeur des aimants permanents à destination des moteurs électriques et génératrices éoliennes.
The depreciation pattern of passenger vehicles is an important input parameter for economic considerations of buyers and other stakeholders such as financial insti-tutions. Due to their technical specifications and uncertainties in their degradation and life expectancy, it seems plausible that electric vehicles depreciate differently to conventional vehicles. This paper contributes to existing research in the economics of electric vehicles by analyzing the depreciation of electric vehicles along several countries and segments and compares it to gasoline vehicles by empirical analysis of public available data of 24,000 used vehicle sales. The results show that vehicles have a degressive depreciation relationship over the age of the vehicle, but that elec-tric vehicles have a substantially higher depreciation of 1.16% per month (13.9% per annum) compared to gasoline vehicles with 0.87% per month (10.4% per annum). Consequently, research into the economics of vehicles and budgeting considerations should apply a different depreciation rate for electric vehicles than for conventional vehicles. The resulting difference in depreciation to gasoline vehicles imply adapta-tion in economic models of insurance and leasing companies and gives governments the opportunity to adapt subsidies for electric vehicles to become more efficient.
This work presents a mathematical model for the payback time of reusing electric vehicle batteries as residential energy storage systems from the end of life of automotive application. The model was developed using MATLAB software and calculates the payback time of a battery energy storage system (BESS) under different scenarios while considering the daily electricity consumption profile for a UK household. The results show that battery purchase price, BESS capacity, electricity unit rates and electricity demand profile are variables with large effects on the payback time. At the simulated baseline condition with residential households of two people, a BESS using second-life batteries from five different vehicle models showed payback time ranging from 8.3 to 12.8 years. The combination of battery rightsizing to attend peak and standard demand, battery price drop by 46%, reaching the level where EV price becomes competitive with conventional vehicles, and BESS application to three or more households provides the most favourable scenario with the minimum payback time of 4.8 years. Further reduction in the payback time of up to 41% can be achieved with subsidised off-peak electricity unit rate.
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Over recent years, the number of battery electric vehicles (BEVs) has drastically increased due to new European Union (EU) regulations. These regulations force vehicle manufacturers to adjust their product range in order to fulfill the imposed carbon dioxide limits. Such an adjustment enforces the usage of battery electric vehicles. However, research into the optimal BEV architectures and topologies is still in progress. Therefore, the aim of this paper is an analysis of all the current electric vehicle topologies. From this analysis, the authors identify different basic battery shapes. Subsequently, these shapes are used to describe the impact of the battery on the passenger compartment. As an initial result of this analysis, the authors create a new denomination method, via which it is possible to cluster the battery topologies. In a second step, the collected data is clustered using the novel denomination method. Finally, this paper presents the benchmark topologies for the analyzed segments.
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Defining a vehicle concept during the early development phase is a challenging task, since only a limited number of design parameters are known. For battery electric vehicles (BEVs), vehicle weight is a design parameter, which needs to be estimated by using an iterative approach, thus causing weight fluctuations during the early development phase. These weight fluctuations, in turn, require other vehicle components to be redesigned and can lead to a change in their size (secondary volume change) and weight (secondary weight change). Furthermore, a change in component size can impact the available installation space and can lead to collision between components. In this paper, we focus on a component that has a high influence on the available installation space: the wheels. We model the essential components of the wheels and further quantify their secondary volume and weight changes caused by a vehicle weight fluctuation. Subsequently, we model the influence of the secondary volume changes on the available installation space at the front axle. The hereby presented approach enables an estimation of the impact of weight fluctuations on the wheels and on the available installation space, which enables a reduction in time-consuming iterations during the development process.
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In many cities around the world, electric (e-)scooters have emerged as a new means of transportation. They are often advertised as supporting modal shift towards more sustainable transportation and as a tool for enabling more equity in mobility. However, the environmental impact depends on how they are used and what kinds of trips they replace. Integration of e-scooters into urban transport systems also implicates discussions on fair road space allocation. In our study, we assess the socioeconomic profiles and usage patterns of e-scooter users in Vienna, Austria. We differentiate between two basic groups of e-scooter users (renters and owners) and apply two different methods. Firstly, based on an online survey, we examine the age, gender and education of e-scooter users and we look into which kinds of trips (commuting, shopping or leisure) and which other means of transportation are replaced by e-scooter trips. Secondly, we analyse data from field observations at cycle paths in Vienna in order to determine the share of e-scooter riders and their gender distribution. We find that e-scooter users are more likely to be young, male, highly educated and residents of Vienna. According to the survey, there are considerable differences in usage between owners of private scooters and users of sharing schemes. Whereas in both groups, e-scooter trips mostly replace walking and public transport as a mode, e-scooter owners also show a considerable mode-shift from private car trips. These results implicate that e-scooter riders are additional users of cycling infrastructure. This puts further pressure on the current allocation of road space, which provides little space for active modes of transport. We conclude that city policies should address this competitive relationship adequately by allocating more space to safe and convenient cycling infrastructure and traffic-calmed zones. This could not only help ease the current challenges due to e-scooters but also provide better conditions for walking and cycling and thereby at the same time contribute to a more sustainable and equitable urban transport system.
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The derivation of a battery electric vehicle (BEV) architecture represents a challenging task for car manufacturers. For the early development of combustion engine architectures, the required design parameters can be derived from the analysis of previously‑built model series. Regarding BEV architectures, the manufacturers do not yet have a reference series of vehicles on the basis of which they can derive the essential design parameters. Therefore, these parameters are mainly estimated at high cost in the early development phase. To avoid cost-intensive changes in the further course of development it is crucial to choose the right set of design parameters. For this reason, the aim of this paper is the identification of a minimum set of design parameters, derived from the current state-of-the-art of vehicle development by a structured literature comparison. We group the results according to our definition of vehicle architecture and discuss each identified parameter to explain its relevance. The sum of all parameters presented in this paper builds a minimum set of design parameters, which can be employed as a guideline for the definition of BEV architectures in the early development stage.
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In order to achieve the European Commission’s ambitious climate targets by 2030, BEVs (Battery Electric Vehicles) manufacturers are faced with the challenge of producing more efficient and ecological products. The electromechanical powertrain plays a key role in the efficiency of BEVs, which is why the design parameters in the development phase of electromechanical powertrains must be chosen carefully. One of the central design parameters is the maximum speed of the electric machines and the gear ratio of the connected transmissions. Due to the relationship between speed and torque, it is possible to design more compact and lighter electric machines by increasing the speed at constant power. However, with higher speed of the electric machines, a higher gear ratio is required, which results in a larger and heavier transmission. This study therefore examines the influence of maximum speed on the power density of electromechanical powertrains. Electric machines and transmissions with different maximum speeds are designed with the state-of-the-art for a selected reference vehicle. The designs are then examined with regard to the power density of the overall powertrain system. Compared to the reference vehicle, the results of the study show a considerable potential for increasing the power density of electromechanical powertrains by increasing the maximum speed of the electric machines.