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. https://doi.org/
Received: 13 January 2021
Accepted: 30 January 2021
Published: 3 February 2021
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Institute of Automotive Technology, Technical University of Munich, Boltzmannstraße 15,
85748 Garching, Germany; email@example.com (L.N.); firstname.lastname@example.org (D.S.);
email@example.com (S.W.); firstname.lastname@example.org (A.W.); email@example.com (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. https://doi.org/10.3390/wevj12010021 https://www.mdpi.com/journal/wevj
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
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
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 :
The exchange rate used to calculate the price in Euro is ﬁxed and based on [
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
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 [
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 .
To describe the traction battery, we focus on its central parameters. According to
Matz , these are:
•Gravimetric energy density (in Wh/kg) at cell and pack level
•Volumetric energy density (in Wh/L) at cell and pack level
•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 [11,13–15] with expert projections [12,16–21].
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 [
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
] 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
]. 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 [
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 [15,26–28].
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 .
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-
]. 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.
r ae aa
.¥ \ er
forfourEQA er e
Gross battery energy in kWh
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 h−1is 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
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 .
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
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 .
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
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
World Electr. Veh. J. 2021,12, 21 8 of 29
Table 3. Development of the cell-to-pack cost ratio based on [5,11,34–39].
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
]. 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
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
]. Due to further optimization of cell chemistry, in turn, energy density could
yield emissions below 50 kgCO2e/kWh, as forecasted by Philippot et al. .
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 min−1
•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 .
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 
IM 8 €/kW 
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
].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
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) [
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
], 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].
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 .
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 .
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 [
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 €
Window lifter 12 €
Exterior lights 140 €
Low-voltage electronics (excluding wiring harness) 520 €
World Electr. Veh. J. 2021,12, 21 11 of 29
Table 5. Cont.
Component Costs Source
Wiring harness 210 €
ESP 160 €
Airbag 20 €
HVAC 80 €
Seat warmer 10 €
Windshield wiper 30 €
Front seat 100 €
Body in white and exterior (ICEV) 1700 €
Body in white and exterior (BEV) 2100 €
Material Speciﬁc Costs Source
Aluminum 2,5–4 €/kg 
High-strength steel 0.6–1 €/kg 
Plastic PP 1.6–2 €/kg 
Plastic ABS 2.5–3.5 €/kg 
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
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
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
] reveal a high range of variation (of between 88
). 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
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 .
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 €
Radar Sensor 90 €
Camera 25 €
Triple-Camera 60 €
Ultrasonic Sensor 5 €
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).
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.
TCO per year relative to time of purchase for small SUVs at 15,000 km per year with a ﬁve-year period
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 [76–87].
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 
Midsize (urban) 2017 0.45 €/km 0.37 €/km Individual 
Car—not speciﬁed n/a n/a 0.14 €/km Individual 
Car—not speciﬁed n/a n/a 0.11 €/km Individual 
Small SUV 2030-2035 0.20 €/km n/a Individual 
Large SUV 2030-2035 0.22 €/km n/a Individual 
Microtransit vehicle 2030 n/a 0.07 €/km Shared 
Car—not speciﬁed 2020-2030 0.11 €/km n/a Individual 
Car—not speciﬁed n/a 0.25–0.27 €/km n/a Individual 
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
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
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)  (p. 135)
Motorcycles 0.25 €/km EU (DG Move)  (p. 135)
Public transport 0.04 €/km Bieler and Sutter 
Bicycles −0.18 €/km Gössling et al. 
Walking −0.37 €/km Gössling et al. 
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 [97–102]
Upper SOC-limit Which SOC do users charge up to at
90–100% (private, corporate)
80%  (public, charging
speed is reduced from 80%
Catchment area of
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 
Minimal charging time Parking time, from which a charging
process is considered. 5–15min [101,102]
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) .
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~€
Conductive 0.5–1.2 M€/km * 1–2~%capex/year 10,000 €†
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.
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.
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 , 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
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
]. 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
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 .
Current (2020) and projected (2030) production costs of various energy sources normalized to
there is no current large-scale production of power-to-liquid and hydrogen from electrolysis, no data are available for 2020
[36,115–117]. (Note: MENA: Middle East, North Africa).
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 [
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
]. 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 [
the certiﬁcates in 2018 lead to a price increase that stabilized at 2007 levels [
steadily decreasing emission allowances in California (cap) lead to a steady increase in
]. 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.
Overview of international CO
-taxes and certiﬁcates in
and the respective introduction
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
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
required charging infrastructure (Section 4), and the corresponding energy costs
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.
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
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 €0€1427 €437 €82 €44 €180 €252 €179 €805 €250 €5.9 l
Small BEV 24,771 €9570 €1437 €415 €0€44 €108 €204 €218 €805 €250 €14.3 kWh
Med. Petrol 21,921 €0€2073 €492 €87 €44 €180 €276 €176 €805 €250 €6.5 l
Med. Diesel 22,944 €0€2169 €517 €214 €44 €168 €288 €176 €805 €250 €5.2 l
Med. BEV 36,241 €9570 €2522 €474 €0€44 €96 €288 €259 €805 €250 €15.4 kWh
Large Petrol 33,345 €0€3153 €495 €178 €44 €180 €288 €246 €805 €250 €7.6 l
Large Diesel 34,902 €0€3300 €563 €263 €44 €156 €300 €246 €805 €250 €5.8 l
Large PHEV 38,639 €5981 €3088 €545 €0€44 €288 €300 €252 €805 €250 €3.8 l + 8.3 kWh 5
Large BEV 54,972 €7975 €4443 €895 €0€44 €228 €252 €283 €805 €250 €18.5 kWh
Large FCEV 75,889 €7975 €6421 €895 €0€44 €228 €252 €283 €805 €250 €0.8 kg
SUV Petrol 27,290 €0€2580 €390 €140 €44 €180 €276 €223 €805 €250 €7.1 l
SUV Diesel 28,564 €0€2701 €549 €240 €44 €156 €288 €223 €805 €250 €6.2 l
SUV PHEV 31,546 €7178 €2304 €721 €2€44 €216 €264 €217 €805 €250 €3.8 l + 7.3 kWh 5
SUV BEV 44,949 €9570 €3345 €582 €0€44 €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 €0€1525 €568 €88 €33 €725 €0€0€4.75 l
M-cycle BEV 22,170 €0€2108 €568 €0€33 €725 €0€0€9.2 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 €0€47 €0€0€0€45 €18 €0€0€n/a
Pedelec 2100 €0€236 €0€0€0€55 €22 €0€0€0.73 kWh
S-Pedelec 4607 €0€517 €21 €0€0€55 €22 €0€0€0.73 kWh
Scooter Pet. 3520 €0€226 €21 €0€0€n/a n/a n/a n/a n/a 2.9 l
Scooter BEV 6220 €0€399 €21 €0€0€n/a n/a n/a n/a n/a 3.5 kWh
E-Scooter 3799 €0€145 €21 €0€0€n/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.
Description of Approach for Passenger Cars
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 , ADAC 
Market prices for an example vehicle are
researched for on various vehicle ages between
0 and 10 years and mileages between 0 and
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
KBA , BMWi , MWV,
Insurance Insurance costs are researched for each segment
and propulsion type depending on
annual mileage. Check24.de 
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 
Inspection and repair Values for inspection and repair are retrieved
from the ADAC online cost calculator for all
segments and propulsion types. ADAC 
Tires Summer and winter tire market prices are
retrieved for all segments and propulsion types. Reifendirekt.de , DAT 
Vehicle care Constant factor independent of assumed annual
mileage assumed. ADAC 
Parking Constant factor independent of assumed annual
mileage assumed. INRIX 
Cost Considered Description of Approach for Bicycles,
Pedelecs, and e-Scooter Main Source(s)
Bicycle, Pedelecs, E-pedelecs TCO information for bicycles from
Fahrrad.de , VSF , ZIV
[148,149], Fahrradblog ,
Schwalbe , , GHOST 
E-scooter TCO information for bicycles from
different sources BGBI [153,154], IEEE , Journals
, BCG 
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 [
], 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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