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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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Review
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/
10.3390/wevj12010021
Received: 13 January 2021
Accepted: 30 January 2021
Published: 3 February 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Institute of Automotive Technology, Technical University of Munich, Boltzmannstraße 15,
85748 Garching, Germany; nicoletti@ftm.mw.tum.de (L.N.); schroeder@ftm.mw.tum.de (D.S.);
wolff@ftm.mw.tum.de (S.W.); waclaw@ftm.mw.tum.de (A.W.); lienkamp@ftm.mw.tum.de (M.L.)
*Correspondence: adrian.koenig@ftm.mw.tum.de
Abstract:
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 [
1
], 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 [
2
]. 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 [
3
] 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
(Section 5).
This paper is based on two former publications by our Institute of Automotive Tech-
nology from 2014 and 2017 [
4
,
5
], which provided costs for vehicles, energy, and CO
2
. 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 [
6
]. We
apply the average exchange rate for the year 2019, which is $1.13 to 1.
Inflation is taken into account in accordance with [
7
]. 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
manufacturers.
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
Gearbox
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 [
10
]. 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 [
19
] and
World Electr. Veh. J. 2021,12, 21 4 of 29
the projection shown in [
20
] 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 [
16
] puts the practical limit at 370 Wh/kg while
Thielmann [
19
] and Frieske [
22
] 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 [
11
,
13
15
] with expert
projections [
18
21
]. 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 [
23
]. 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” [
24
], Tesla announced a new integration prin-
ciple to be applied to upcoming models, which it calls the cell-to-pack strategy [
25
]. The
aim of this strategy is to supposedly eliminate the cell modules, thus reducing the mass
of the battery by about 10% [
25
]. 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. [
19
]. 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 [
26
]. 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 [
29
] 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
2
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 [
30
]. A C-rate of
1 means that the battery can be completely discharged in one hour. According to the values
proposed by [
10
,
15
,
26
,
30
,
31
], 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 [
31
]. A range of
between 1000 and 3000 cycles is considered realistic in the literature [
11
,
12
,
15
,
22
,
31
,
32
].
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 [
33
,
34
]. 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 [
5
,
11
,
34
39
]. 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. [
35
] or NPM [
36
] 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
costs
[5,11,34,3739].
A well-known example of this approach is the battery manufacturing
const estimation model (BatPac model) developed by the Argonne National Laboratory [
40
].
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 [
34
]. 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 [
41
]. While Romare and Dahllöf reported
emissions of 150–200 kgCO
2e
/kWh in the year 2015 [
41
], their update estimates a reduction
in greenhouse gas emissions of between 61 and 106 kgCO
2e
/kWh [
42
]. 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
2e
/kWh [
43
]. A
sensitivity analysis of the supply chains performed by Kelly et al. confirms this range
with 65 kgCO
2e
/kWh as the best-case and 100 kgCO
2e
/kWh for state-of-the-art supply
chains [
44
]. 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 [
26
] 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 [
22
]. According to Fireske [
22
], 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 [
10
,
26
] 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 [
27
],
which is the same as that of the BMW i3 [
45
]. 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 [
46
]. At the moment,
the maximum possible rotational speed range is between 9000 and 20,000 min
1[10,26,47,48].
In the future, the trend could shift towards higher rotational speeds (compensated for by a
higher gearbox ratio), as recent discoveries [
45
] 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 [
49
,
50
].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 [
51
,
52
] 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 [
12
,
26
,
45
,
47
,
53
,
54
]. 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) [
53
]
(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% [
47
,
53
,
55
,
56
].
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 [
48
], or torque and rotation speed [
57
], see Figure 8). Especially with low torque
and motor speed, the efficiency is reduced [
58
]. 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 [
60
,
61
]. The glider comprises the body, chassis, low-voltage electrical
components, exterior, and interior. A gilder price can be calculated either bottom-up or
top-down.
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 [
62
], 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 [
63
] and data published in [
5
,
9
], 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 [
64
]. 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 [
2
]. Furthermore, companies and researchers use different setups with
different sensor types in their vehicles [
64
]. 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 [
65
71
] 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 [
72
], 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 [
63
]
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 [
75
]. 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 [
93
96
], 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-
mentation.
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])
[100,102,104]
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 [
107
,
108
]. 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 [
36
]. 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.
5

 15
.s
0.10
5
   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 [
36
,
112
]. 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) [
112
].
World Electr. Veh. J. 2021,12, 21 17 of 29
Starting from 2021, a CO
2
tax of a minimum of 25
/tCO
2
and a maximum of 65
/tCO
2
will
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
2
-extraction to form hydrocarbon
chains. Further possible process steps are methanization or liquification by Fischer–Tropsch
synthesis [
114
]. 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) [
115
]. 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) [
115
].
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 [
116
]. 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
2020
/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
Liquid,
Domestic
Power-to-
Liquid,
MENA
SMR
Hydrogen
Electrolysis
Hydrogen,
Domestic
Electrolysis
Hydrogen,
MENA
Electricity
mix
Renewable
Electricity
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 [
118
]. 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 [
119
].
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
CO
2
-price, while the governmentally controlled Swedish carbon tax increased from 25
in 1991 to 116
today [
120
]. 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 [
121
]. Shorting
the certificates in 2018 lead to a price increase that stabilized at 2007 levels [
122
]. The
steadily decreasing emission allowances in California (cap) lead to a steady increase in
CO
2
-price [
123
]. Sweden increased taxes with a fixed rate while California, EU ETS, and
China use trading systems [
120
,
121
,
124
,
125
]. 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
2
-taxes and certificates in
2020
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
2
limits and associated penalty payments
can also be understood as CO
2
prices. The European Union already set the penalty for
exceeding passenger car and light-duty truck (<3.5 t) emission to 95
gCO
2
/km [
129
].
Assuming a lifetime of 150,000 to 200,000 km, this leads to 475–633
/tCO
2
. In the year
2019, the European union also set CO
2
-limits for heavy-duty vehicles (>3.5 t), targeting
the road transport sector. The limits of 4250
per gram CO
2
and vehicle kilometer from
2025 to 2030 and 6300
gCO
2
/tkm onwards [
130
], 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 [
131
], the limits convert to 220
/tCO
2
and
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),
the
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.
Funding:
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.
Acknowledgments:
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.
Vehicle
Segment 1,2 Acquisition
Bonus
Environment
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
1
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.
Vehicle
Segment
Acquisition
Bonus
Environment
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
Scooters/Motorcycles
Main Source (s)
Acquisition
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]
Depreciation
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. Check24.de [137]
Tax
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. Reifendirekt.de [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
Fahrrad.de [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 *
Subway/Streetcar/Bus
Single Ticket 3.39 2471.20
Subway/Streetcar/Bus
Daily Ticket 4.01 2920.50
Subway/Streetcar/Bus
Weekly Ticket 1.31 914.70
Subway/Streetcar/Bus
Monthly Ticket 0.97 681.40
Subway/Streetcar/Bus
Annual Ticket 0.77 536.90
Subway/Streetcar/Bus
Stripe Ticket 1.65 1153.20
Subway/Streetcar/Bus
Semester Ticket 0.58 402.60
1
Free-floating;
2
Station-based;
3
For taxies, a further 0.50
/min must be added to account for waiting time. Sources: Car-sharing [
158
161
],
Bike-sharing [
162
], E-scooter-sharing [
163
165
], Scooter sharing [
166
], Ride-hailing [
167
,
168
], Taxi [
169
]. * 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].
References
1.
Nicoletti, L.; Bronner, M.; Danquah, B.; Koch, A.; Konig, A.; Krapf, S.; Pathak, A.; Schockenhoff, F.; Sethuraman, G.; Wolff, S.; et al.
Review of trends and potentials in the vehicle concept development process. In Proceedings of the 2020 Fifteenth International
Conference on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 10–12 September 2020; pp. 1–5,
ISBN 978-1-7281-5641-5.
2.
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
online: https://www.researchgate.net/publication/312374304_Autonomous_Driving_-_The_Impact_of_Vehicle_Automation_
on_Mobility_Behaviour (accessed on 20 December 2020).
3.
Nicoletti, L.; Romano, A.; König, A.; Schockenhoff, F.; Lienkamp, M. Parametric Modeling of Mass and Volume Effects for Battery
Electric Vehicles, with Focus on the Wheel Components. WEVJ 2020,11, 63. [CrossRef]
4.
Kochhan, R.; Fuchs, S.; Reuter, B.; Burda, P.; Matz, S.; Lienkamp, M. An Overview of Costs for Vehicle Components, Fuels and
Greenhouse Gas Emissions. Available online: https://www.researchgate.net/publication/260339436_An_Overview_of_Costs_
for_Vehicle_Components_Fuels_and_Greenhouse_Gas_Emissions (accessed on 15 December 2020).
5.
Fries, M.; Kerler, M.; Rohr, S.; Schickram, S. An Overview of Costs for Vehicle Components, Fuels, Greenhouse Gas Emissions and
Total Cost of Ownership: Update 2017. Available online: https://www.researchgate.net/publication/319136996_An_Overview_
of_Costs_for_Vehicle_Components_Fuels_Greenhouse_Gas_Emissions_and_Total_Cost_of_Ownership_-_Update_2017 (accessed
on 28 October 2020).
6.
OFX. Yearly Average Rates. Available online: https://www.ofx.com/en-au/forex-news/historical-exchange-rates/yearly-
average-rates/ (accessed on 28 October 2020).
7.
Triami Media BV. Historic Harmonised Inflation Europe–HICP Inflation. Available online: https://www.inflation.eu/en/
inflation-rates/europe/historic-inflation/hicp-inflation-europe.aspx (accessed on 28 October 2020).
8.
Miller, J. Electric Car Costs to Remain Higher than Traditional Engines. Available online: https://www.ft.com/content/a7e58ce7
-4fab-424a-b1fa-f833ce948cb7 (accessed on 8 December 2020).
9.
Ruffo, G. EVs Are Still 45% More Expensive to Make Than Combustion-Engined Cars. Available online: https://insideevs.com/
news/444542/evs-45-percent-more-expensive-make-ice/ (accessed on 8 December 2020).
10. Kampker, A.; Vallée, D.; Schnettler, A. Elektromobilität; Springer: Berlin/Heidelberg, Germany, 2018; ISBN 978-3-662-53136-5.
11.
Schmuch, R.; Wagner, R.; Hörpel, G.; Placke, T.; Winter, M. Performance and cost of materials for lithium-based rechargeable
automotive batteries. Nat. Energy 2018,3, 267–278. [CrossRef]
12.
Matz, S.; Fuchs, J.; Burda, P.; Horlbeck, L.; Eckl, R.; Lienkamp, M. Beschreibung der Modellierungsart Sowie der Modellierungspa-
rameter von Elektrofahrzeugen in der Konzeptphase. Available online: https://www.researchgate.net/publication/261174335_
Beschreibung_der_Modellierungsart_sowie_der_Modellierungsparameter_von_Elektrofahrzeugen_in_der_Konzeptphase (ac-
cessed on 28 October 2020).
13.
Scrosati, B.; Garche, J.; Tillmetz, V. (Eds.) Advances in Battery Technologies for Electric Vehicles, 1st ed.; Woodhead Publishing:
Cambridge, UK, 2015; ISBN 9781782423980.
14. Blomgren, G.E. The Development and Future of Lithium Ion Batteries. J. Electrochem. Soc. 2017,164, A5019–A5025. [CrossRef]
15.
Anderman, M. Extract from the xEV Insider Report. April 2019 Edition. Available online: https://totalbatteryconsulting.com/
industry-reports/xEV-report/Extract-from-the-xEV-Industry-Report.pdf (accessed on 8 October 2020).
World Electr. Veh. J. 2021,12, 21 23 of 29
16.
Fink, H. Li-ion batteries for automotive applications–Quo vadis. In Internationales Stuttgarter Symposium; Bargende, M., Reuss,
H.-C., Wiedemann, J., Eds.; Springer Fachmedien: Wiesbaden, Germany, 2016; pp. 69–81. ISBN 978-3-658-13254-5.
17.
Fuchs, S. Verfahren zur Parameterbasierten Gewichtsabschätzung neuer Fahrzeugkonzepte. Ph.D. Thesis, Institute of Automotive
Technology, Technical University of Munich, Munich, Germany, 2014.
18.
Placke, T.; Kloepsch, R.; Dühnen, S.; Winter, M. Lithium ion, lithium metal, and alternative rechargeable battery technologies: The
odyssey for high energy density. J. Solid State Electrochem. 2017,21, 1939–1964. [CrossRef]
19.
Thielmann, A.; Neef, C.; Hettesheimer, T.; Döscher, H.; Wietschel, M.; Tübke, J. Energiespeicher-Roadmap (Update 2017).
Hochenergie-Batterien 2030+ und Perspektiven Zukünftiger Batterietechnologien. 2017. Available online: https://www.
isi.fraunhofer.de/content/dam/isi/dokumente/cct/lib/Energiespeicher-Roadmap-Dezember-2017.pdf (accessed on 1 Octo-
ber 2020).
20. Volkswagen AG. Powerful and Scalable: The New ID. Battery System. Available online: https://www.volkswagenag.com/en/
news/stories/2018/10/powerful-and-scalable-the-new-id-battery-system.html (accessed on 29 June 2020).
21.
Hagen, M.; Hanselmann, D.; Ahlbrecht, K.; Maça, R.; Gerber, D.; Tübke, J. Lithium-Sulfur Cells: The Gap between the State-of-
the-Art and the Requirements for High Energy Battery Cells. Adv. Energy Mater. 2015,5, 1–11. [CrossRef]
22.
Frieske, B.; van der Adel, B.; Schwarz-Kocher, M.; Stieler, S.; Schnabel, A.; Tözün, R. Strukturstudie BWe Mobil 2019: Transfor-
mation durch Elektromobilität und Perspektiven der Digitalisierung. Available online: https://www.e-mobilbw.de/service/
meldungen-detail/strukturstudie-bwe-mobil-2019 (accessed on 26 October 2020).
23.
Nicoletti, L.; Mirti, S.; Schockenhoff, F.; König, A.; Lienkamp, M. Derivation of Geometrical Interdependencies between the
Passenger Compartment and the Traction Battery Using Dimensional Chains. WEVJ 2020,11, 39. [CrossRef]
24.
Knecht, J.; Stegmaier, G.; Hebermehl, G. Tesla Battery Day 2020. Available online: https://www.auto-motor-und-sport.de/tech-
zukunft/tesla-battery-day-neue-zellen-kosten-halbiert/ (accessed on 28 October 2020).
25.
Bos, C. Tesla’s New Structural Battery Pack—It’s Not Cell-to-Pack, It’s Cell-to-Body. CleanTechnica [Online]. 11 October 2020.
Available online: https://cleantechnica.com/2020/10/10/teslas-new-structural-battery-pack-its-not-cell-to-pack-its-cell-to-
body/ (accessed on 25 October 2020).
26.
Grunditz, E.A.; Thiringer, T. Performance Analysis of Current BEVs Based on a Comprehensive Review of Specifications. IEEE
Trans. Transp. Electrific. 2016,2, 270–289. [CrossRef]
27.
Fuchss, S.; Michaelides, A.; Stocks, O.; Devenport, R. Das Antriebssystem des neuen Jaguar I-Pace. MTZ Motortech. Z.
2019
,80,
20–27. [CrossRef]
28.
Grebe, U.D.; Nitz, L.T. Voltec–Das Antriebssystem für Chevrolet Volt und Opel Ampera. MTZ Motortech. Z.
2011
,72, 342–351.
[CrossRef]
29.
ADAC. Autodatenbank. Available online: https://www.adac.de/infotestrat/autodatenbank/autokatalog/default.aspx (accessed
on 26 November 2019).
30.
Busche, I. Ein Beitrag zur Optimierten Konzeptauslegung von Fahrzeugen im Bereich der Elektromobilität. Ph.D. Thesis,
University of Magdeburg, Magdeburg, Germany, 2014.
31.
Zubi, G.; Dufo-López, R.; Carvalho, M.; Pasaoglu, G. The lithium-ion battery: State of the art and future perspectives. Renew.
Sustain. Energy Rev. 2018,89, 292–308. [CrossRef]
32.
Hoekstra, A. The Underestimated Potential of Battery Electric Vehicles to Reduce Emissions. Joule
2019
,3, 1412–1414. [CrossRef]
33.
Nykvist, B.; Nilsson, M. Rapidly falling costs of battery packs for electric vehicles. Nat. Clim. Chang.
2015
,5, 329–332. [CrossRef]
34.
Philippot, M.; Alvarez, G.; Ayerbe, E.; van Mierlo, J.; Messagie, M. Eco-Efficiency of a Lithium-Ion Battery for Electric Vehicles:
Influence of Manufacturing Country and Commodity Prices on GHG Emissions and Costs. Batteries 2019,5, 23. [CrossRef]
35.
Cano, Z.P.; Banham, D.; Ye, S.; Hintennach, A.; Lu, J.; Fowler, M.; Chen, Z. Batteries and fuel cells for emerging electric vehicle
markets. Nat. Energy 2018,3, 279–289. [CrossRef]
36.
Nationale Plattform Zukunft der Mobilität. 1. Kurzbericht der AG 2-Elektromobilität. Brennstoffzelle. Alternative Kraftstoffe-
Einsatzmöglichkeiten aus Technologischer Sicht (1st Short Report of AG 2-Electromobility. Fuel Cell. Alternative Fuel
Applications from a Technological Perspective.). 2019. Available online: https://www.plattform-zukunft-mobilitaet.de/wp-
content/uploads/2019/11/NPM-AG-2-Elektromobilit%C3%A4t-Brennstoffzelle-Alternative-Kraftstoffe-Einsatzm%C3%B6
glichkeiten-aus-technologischer-Sicht.pdf (accessed on 8 April 2020).
37. Wentker, M.; Greenwood, M.; Leker, J. A Bottom-Up Approach to Lithium-Ion Battery Cost Modeling with a Focus on Cathode
Active Materials. Energies 2019,12, 504. [CrossRef]
38.
Hettesheimer, T.; Thielmann, A.; Neef, C.; Möller, K.-C.; Wolter, M.; Lorentz, V.; Gepp, M.; Wenger, M.; Prill, T.; Zausch, J.; et al.
Entwicklungsperspektiven für Zellformate von Lithium-Ionen-Batterien in der Elektromobilität (Development Prospects for Cell
Formats of Lithium-ion Batteries in Electromobility). 2017. Available online: https://www.batterien.fraunhofer.de/content/
dam/batterien/de/documents/Allianz_Batterie_Zellformate_Studie.pdf (accessed on 15 December 2020).
39.
Kerler, M. Eine Methode zur Bestimmung der Optimalen Zellgröße für Elektrofahrzeuge. Ph.D. Thesis, Technische Universität
München, München, Germany, 2018.
40.
Nelson, P.A.; Gallagher, K.G.; Bloom, I.; Dees, D.W. Modeling the Performance and Cost of Lithium-Ion Batteries for Electric-Drive
Vehicles. 2011. Available online: https://publications.anl.gov/anlpubs/2011/10/71302.pdf (accessed on 5 November 2020).
World Electr. Veh. J. 2021,12, 21 24 of 29
41.
Romare, M.; Dahllöf, L. The Life Cycle Energy Consumption and Greenhouse Gas Emissions from Lithium-Ion Batteries.
2017. Available online: http://www.energimyndigheten.se/globalassets/forskning--innovation/transporter/c243-the-life-cycle-
energy-consumption-and-co2-emissions-from-lithium-ion-batteries-.pdf (accessed on 15 December 2020).
42.
Emilsson, E.; Dahllöf, L. Lithium-Ion Vehicle Battery Production; IVL Swedish Environmental Research Institute: Stockholm, Sweden,
2019; ISBN 978-91-7883-112-8.
43.
Dai, Q.; Kelly, J.C.; Gaines, L.; Wang, M. Life Cycle Analysis of Lithium-Ion Batteries for Automotive Applications. Batteries
2019
,
5, 48. [CrossRef]
44.
Kelly, J.C.; Dai, Q.; Wang, M. Globally regional life cycle analysis of automotive lithium-ion nickel manganese cobalt batteries.
Mitig. Adapt. Strateg. Glob. Change 2020,25, 371–396. [CrossRef]
45.
Schweigert, D.; Gerlach, M.E.; Hoffmann, A.; Morhard, B.; Tripps, A.; Lohner, T.; Otto, M.; Ponick, B.; Stahl, K. On the Impact of
Maximum Speed on the Power Density of Electromechanical Powertrains. Vehicles 2020,2, 365–397. [CrossRef]
46.
Nicoletti, L.; Mayer, S.; Brönner, M.; Schockenhoff, F.; Lienkamp, M. Design Parameters for the Early Development Phase of
Battery Electric Vehicles. WEVJ 2020,11, 47. [CrossRef]
47.
Angerer, C.R. Antriebskonzept-Optimierung für Batterieelektrische Allradfahrzeuge. Ph.D. Thesis, Institute of Automotive
Technology, Technical University of Munich, Verlag Dr. Hut, Munich, Germany, 2020.
48.
Nemeth, T.; Bubert, A.; Becker, J.N.; de Doncker, R.W.; Sauer, D.U. A Simulation Platform for Optimization of Electric Vehicles
with Modular Drivetrain Topologies. IEEE Trans. Transp. Electrific. 2018,4, 888–900. [CrossRef]
49.
Kasper, R.; Schünemann, M. 5. Elektrische Fahrantriebe Topologien Und Wirkungsgrad. MTZ Motortech. Z.
2012
,73, 802–807.
[CrossRef]
50.
Nicoletti, L.; Ostermann, F.; Heinrich, M.; Stauber, A.; Lin, X.; Lienkamp, M. Topology analysis of electric vehicles, with a focus
on the traction battery. Forsch. Ing. 2020. [CrossRef]
51.
Rimac Automobili C_Two Hypercar. Rimac Automobili C_Two Hypercar—A Car Alive with Technology. Available online:
https://www.rimac-automobili.com/en/hypercars/c_two/ (accessed on 26 October 2020).
52.
Porsche Newsroom. Der Antrieb: Performance pur. Available online: https://newsroom.porsche.com/de/produkte/taycan/
antrieb-18543.html (accessed on 26 October 2020).
53.
Naunheimer, H.; Bertsche, B.; Ryborz, J.; Novak, W.; Fietkau, P. Fahrzeuggetriebe. Grundlagen, Auswahl, Auslegung und Konstruktion,
3rd ed.; Auflage 2019; Springer: Berlin/Heidelberg, Germany, 2019; ISBN 978-3-662-58883-3.
54.
Dominguez Olavarria, G.; Marquez-Fernandez, F.J.; Fyhr, P.; Reinap, A.; Andersson, M.; Alaküla, M. Scalable performance,
efficiency and thermal models for electric drive components used in powertrain simulation and optimization. In Proceedings
of the 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago, IL, USA, 22–24 June 2017; pp. 644–649,
ISBN 978-1-5090-3953-1.
55.
Pesce, T. Ein Werkzeug zur Bestimmung von Individuell Optimalen Antriebssträngen für Elektrofahrzeuge. Ph.D. Thesis,
Technische Universität München, München, Germany, 2014.
56.
Ried, M. Lösungsraumanalyse für Plug-In-Hybridfahrzeuge Hinsichtlich Wirtschaftlichkeit und Bauraumkonzept. Ph.D. Thesis,
Universität Duisburg-Essen, Lehrstuhl für Mechatronik, Duisburg-Essen, Germany, 2016.
57.
Chang, F. Improving the Partial Load Efficiency of Electric Powertrains by Silicon MOSFET Multilevel Inverters. Ph.D. Thesis,
Technical University of Munich, Munich, Germany, 2019.
58.
Wacker, P.; Wheldon, L.; Sperlich, M.; Adermann, J.; Lienkamp, M. Influence of active battery switching on the drivetrain
efficiency of electric vehicles. In Proceedings of the 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago,
IL, USA, 22–24 June 2017; pp. 33–38, ISBN 978-1-5090-3953-1.
59.
Chang, F.; Ilina, O.; Hegazi, O.; Voss, L.; Lienkamp, M. Adopting MOSFET multilevel inverters to improve the partial load
efficiency of electric vehicles. In Proceedings of the 2017 19th European Conference on Power Electronics and Applications
(EPE'17 ECCE Europe), Warsaw, Poland, 11–14 September 2017; pp. 1–13.
60.
Brooker, A.; Gonder, J.; Wang, L.; Wood, E.; Lopp, S.; Ramroth, L. FASTSim: A Model to Estimate Vehicle Efficiency, Cost and
Performance. In Proceedings of the SAE 2015 World Congress & Exhibition, Detroit, MI, USA, 21–23 April 2015.
61.
Del Duce, A.; Gauch, M.; Althaus, H.-J. Electric passenger car transport and passenger car life cycle inventories in ecoinvent
version 3. Int. J. Life Cycle Assess. 2016,21, 1314–1326. [CrossRef]
62.
WirtschaftsWoche. Zusammensetzung des Preises Eines Neuwagens in Deutschland. Available online: https://de.statista.
com/statistik/daten/studie/387632/umfrage/zusammensetzung-des-neuwagenpreises-in-deutschland/ (accessed on 9 Decem-
ber 2020).
63.
Institute of Automotive Technology (FTM). Figures Based on Expert Discussions; Institute Automotive Technology (FTM): Technical
University of Munich: Munich, Germany, 2020.
64.
Mosquet, X.; Dauner, T.; Lang, N.; Russmann, M.; Mei-Pochtler, A.; Agrawal, R.; Schmieg, F. Revolution in the Driver
'
s Seat: The
Road to Autonomous Vehicles; The Boston Consulting Group: Boston, MA, USA, 2015.
65.
Priddle, A.; Woodyard, C. Google Discloses Costs of Ist Driverless Car Tests. Available online: http://content.usatoday.com/
communities/driveon/post/2012/06/google-discloses-costs-of-its-driverless-car-tests/1 (accessed on 27 October 2020).
66.
Krok, A. Velodyne Just Made Self-Driving Cars a Bit Less Expensive. Available online: https://www.cnet.com/roadshow/news/
velodyne-just-made-self-driving-cars-a-bit-less-expensive-hopefully/ (accessed on 27 October 2020).
World Electr. Veh. J. 2021,12, 21 25 of 29
67.
Velodyne Lidar. Velodyne Slashes the Price in Half of Its Most Popular LiDAR Sensor. Available online: https://velodynelidar.
com/press-release/velodyne-slashes-the-price-in-half-of-its-most-popular-lidar-sensor/ (accessed on 27 October 2020).
68.
Ouster, Inc. Ouster OS1 Mid-Range LiDAR Sensor. Available online: https://ouster.com/products/os1-lidar-sensor/ (accessed
on 27 October 2020).
69.
Deyle, T. Velodyne HDL-32E: A New High-End Laser Rangefinder. Available online: http://www.hizook.com/blog/2010/08/24
/velodyne-hdl-32e-new-high-end-laser-rangefinder (accessed on 27 October 2020).
70.
Pacala, A. The ES2: The First True Solid-State High-Performance Digital Lidar. Available online: https://ouster.com/blog/the-
es2-the-first-true-solid-state-high-performance-digital-lidar/ (accessed on 27 October 2020).
71.
General Laser. Mid-Range Lidar Sensors-Ouster OS1. Available online: https://www.general-laser.at/shop-de/lidar-de/ouster-
os1-de (accessed on 27 October 2020).
72.
Woodside Capital Partners WCP. The Automotive LiDAR Market. Available online: http://www.woodsidecap.com/wp-content/
uploads/2018/04/Yole_WCP-LiDAR-Report_April-2018-FINAL.pdf (accessed on 10 November 2020).
73.
Nvidia. Jetson AGX Xavier. Available online: https://www.nvidia.com/de-de/autonomous-machines/embedded-systems/
jetson-agx-xavier/ (accessed on 30 October 2020).
74.
SYSTEMPlus Consulting. A Tesla Model 3 Tear-Down after a Hardware Retrofit. Available online: https://www.eetimes.com/a-
tesla-model-3-tear-down-after-a-hardware-retrofit/4/ (accessed on 30 October 2020).
75.
MVV. Der MVV in Zahlen. Available online: https://www.mvv-muenchen.de/mvv-und-service/der-verbund/mvv-in-zahlen/
index.html (accessed on 15 June 2020).
76.
Bösch, P.M.; Becker, F.; Becker, H.; Axhausen, K.W. Cost-based analysis of autonomous vehicle services. Transp. Policy
2018
.
[CrossRef]
77. Arellano, J.F.; Fang, K. Sunday Drivers, or Too Fast and Too Furious? Transp. Find. 2019. [CrossRef]
78.
BFS; ARE. Swiss Federal Statistical Office (BFS) and Swiss Federal Office for Spatial Development (ARE)-Mobilität in der
Schweiz-Ergebnisse des Mikrozensus Mobilität und Verkehr 2010. Available online: https://www.bfs.admin.ch/bfs/en/home/
statistics/catalogues-databases/publications.assetdetail.348719.html (accessed on 18 December 2020).
79.
Hardt, C.; Bogenberger, K. Usability of escooters in urban environments-a pilot study. In Proceedings of the 2017 IEEE Intelligent
Vehicles Symposium (IV), Los Angeles, CA, USA, 11–17 June 2017. [CrossRef]
80. INRIX. INRIX Global Traffic Scorecard. Available online: https://inrix.com/scorecard/ (accessed on 18 December 2020).
81.
INRIX. Manchester Revealed as UK City with the Most Potential for Shared Bikes and E-Scooters. Available online: https:
//inrix.com/press-releases/micromobility-study-uk-2019/ (accessed on 30 September 2020).
82. Jäger, B.; Wittmann, M.; Lienkamp, M. Analyzing and modeling a City’s spatiotemporal taxi supply and demand: A case study
for Munich. J. Traffic Logist. Eng. 2016,4. [CrossRef]
83. Jiao, J.; Bai, S. Understanding the Shared E-scooter Travels in Austin, TX. ISPRS Int. J. Geo-Inf. 2020,9, 135. [CrossRef]
84.
Knoblauch, R.L.; Pietrucha, M.T.; Nitzburg, M. Field Studies of Pedestrian Walking Speed and Start-Up Time. Transp. Res. Rec.
1996,1538, 27–38. [CrossRef]
85.
Schleinitz, K.; Petzoldt, T.; Franke-Bartholdt, L.; Krems, J.; Gehlert, T. The German Naturalistic Cycling Study–Comparing cycling
speed of riders of different e-bikes and conventional bicycles. Saf. Sci. 2017,92, 290–297. [CrossRef]
86.
Wachotsch, U.; Kolodziej, A.; Specht, B.; Kohlmeyer, R.; Petrikowski, F. E-Rad Macht Mobil: Potenziale von Pedelecs und Deren
Umweltwirkung. Available online: https://www.umweltbundesamt.de/sites/default/files/medien/378/publikationen/hgp_e-
rad_macht_mobil_-_pelelecs_4.pdf (accessed on 18 December 2020).
87.
Zou, Z.; Younes, H.; Erdo˘gan, S.; Wu, J. Exploratory Analysis of Real-Time E-Scooter Trip Data in Washington, DC. Transp. Res.
Rec. 2020, 0361198120919760. [CrossRef]
88.
Sperling, D. Three Revolutions: Steering Automated, Shared, and Electric Vehicles to a Better Future; Island Press: Washington, DC,
USA, 2018; ISBN 9781610919050.
89.
Compostella, J.; Fulton, L.M.; de Kleine, R.; Kim, H.C.; Wallington, T.J. Near-(2020) and long-term (2030–2035) costs of automated,
electrified, and shared mobility in the United States. Transp. Policy 2020,85, 54–66. [CrossRef]
90.
Ongel, A.; Loewer, E.; Roemer, F.; Sethuraman, G.; Chang, F.; Lienkamp, M. Economic assessment of autonomous electric
microtransit vehicles. Sustainability 2019,11, 648. [CrossRef]
91.
Lim, L.; Tawfik, A.M. Estimating future travel costs for autonomous vehicles (AVs) and shared autonomous vehicles (SAVs).
In Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7
November 2018; pp. 1702–1707, ISBN 978-1-7281-0321-1.
92.
Dandl, F.; Bogenberger, K. Comparing Future Autonomous Electric Taxis with an Existing Free-Floating Carsharing System. IEEE
Trans. Intell. Transp. Syst. 2019,20, 2037–2047. [CrossRef]
93.
European Comission. DG MOVE-Handbook on the External Costs of Transport; European Comission: Brussels, Belgium, 2019; ISBN
978-92-79-96917-1.
94.
Bieler, C.; Sutter, D. Externe Kosten des Verkehrs in Deutschland. Straßen-, Schienen-, Luft-und Binnenschiffverkehr. 2017. Available
online: https://www.allianz-pro-schiene.de/wp-content/uploads/2019/08/190826-infras-studie-externe-kosten-verkehr.pdf
(accessed on 20 December 2020).
95.
Gössling, S.; Choi, A.; Dekker, K.; Metzler, D. The Social Cost of Automobility, Cycling and Walking in the European Union. Ecol.
Econ. 2019,158. [CrossRef]
World Electr. Veh. J. 2021,12, 21 26 of 29
96.
Jochem, P.; Doll, C.; Fichtner, W. External costs of electric vehicles. Transp. Res. Part D Transp. Environ.
2016
,42, 60–76. [CrossRef]
97.
Weiss, C.; Mallig, N.; Heilig, M.; Schneidereit, T.; Franke, T.; Vortisch, P. How much range is required? A model based analysis of
potential battery electric vehicle usage. In Proceedings of the Transportation Research Board 95th Annual Meeting, Washington
DC, USA, 10–14 January 2016.
98.
Trantow, M.; Franke, T.; Günther, M.; Krems, J.F.; Rauh, N. Range comfort zone of electric vehicle users–concept and assessment.
IET Intell. Transp. Syst. 2015,9, 740–745. [CrossRef]
99.
Machiels, N.; Leemput, N.; Geth, F.; van Roy, J.; Buscher, J.; Driesen, J. Design Criteria for Electric Vehicle Fast Charge Infrastructure
Based on Flemish Mobility Behavior. IEEE Trans. Smart Grid 2014,5, 320–327. [CrossRef]
100.
Pfriem, M. Analyse der Realnutzung von Elektrofahrzeugen in Kommerziellen Flotten zur Definition Einer Bedarfsgerechten
Fahrzeugauslegung. Ph.D. Thesis, Karlsruher Institut für Technologie, Karlsruhe, Germany, 2015.
101.
Corchero, C.; Gonzalez-Villafranca, S.; Sanmarti, M. European electric vehicle fleet: Driving and charging data analysis. In
Proceedings of the 2014 IEEE International Electric Vehicle Conference (IEVC), Florence, Italy, 17–19 December 2014; pp. 1–6.
[CrossRef]
102.
Krug, S.; Krey, O.; Ohm, B.; Weider, M.; Ziem-Milojevic, S.; Braune, O. Elektromobilität in der Praxis. Zweiter Ergebnisbericht des
Zentralen Datenmonitorings des Förderprogramms Elektromobilität vor Ort. 2020. Available online: https://www.now-gmbh.
de/wp-content/uploads/2020/09/now_elektromobilitaet-in-der-praxis-zdm.pdf (accessed on 15 November 2020).
103.
Schuster, A. Batterie- bzw. Wasserstoffspeicher bei elektrischen Fahrzeugen. Master’s Thesis, Technical University of Wien, Wien,
Austria, 2008.
104.
Fieltsch, P.; Flämig, H.; Rosenberger, K. Analysis of charging behavior when using battery electric vehicles in commercial
transport. Transp. Res. Procedia 2020,46, 181–188. [CrossRef]
105.
Betz, J.; Hann, M.; Jäger, B.; Lienkamp, M. Evaluation of the potential of integrating battery electric vehicles into commercial
companies on the basis of fleet test data. In Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring),
Sydney, NSW, Australia, 4–7 June 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 1–7, ISBN 978-1-5090-5932-4.
106.
Waclaw, A.; Aloise, T.; Lienkamp, M. Charging infrastructure design for commercial company sites with battery electric vehicles:
A case study of a Bavarian Bakery: Article submitted for publication. In Proceedings of the 2020 Fifteenth International Conference
on Ecological Vehicles and Renewable Energies (EVER), Monte-Carlo, Monaco, 28–30 May 2020. [CrossRef]
107.
European Parliament. Directive (EU) 96/53/EC of 25 July 1996 Laying down fo Rcertain Road Vehicles Circulating within the Community
the Maximum Authorized Dimensions in National and International Traffic and the Maximum Authorized Weights Ininternational Traffic;
Directive (EU) 96/53/EC; European Parliament: Brussels, Belgium, 1996. Available online: https://eur-lex.europa.eu/legal-
content/EN/TXT/?uri=CELEX%3A31996L0053 (accessed on 15 December 2020).
108.
Márquez-Fernández, F.J.; Domingues, G.; Lindgren, L.; Alaküla, M. Electric Roads: The importance of sharing the infrastructure
among different vehicle types. In Proceedings of the IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC
Asia-Pacific), Harbin, China, 7–10 August 2017. [CrossRef]
109.
Wolff, S.; Fries, M.; Lienkamp, M. Technoecological analysis of energy carriers for long-haul transportation. J. Ind. Ecol.
2019
,
49, 6402. [CrossRef]
110.
Bateman, D.; Leal, D.; Reeves, S.; Emre, M.; Stark, L.; Ognissanto, F.; Myers, R.; Lamb, M. Electric Road Systems: A Solution for
the Future? 2018. Available online: https://www.piarc.org/en/order-library/29690-en-Electric%20road%20systems:%20a%20
solution%20for%20the%20future (accessed on 15 October 2020).
111.
Moultak, M.; Lutsey, N.; Hall, D. Transitioning to Zero-Emission Heavy-Duty Freight Vehicles. 2017. Available online: https:
//theicct.org/publications/transitioning-zero-emission-heavy-duty-freight-vehicles (accessed on 2 February 2021).
112.
Gesetz für den Ausbau Erneuerbarer Energien (Erneuerbare-Energien-Gesetz-EEG 2017) (Act for the Expansion of Renewable
Energies [Renewable Energies Act-EEG 2017]). IdF d. Art. 1 Nr. 1 G v. 13.10.2016. 2014. Available online: https://www.gesetze-
im-internet.de/eeg_2014/EEG_2017.pdf (accessed on 10 January 2021).
113.
Federal Minister for Environment, Nature Conservation and Nuclear Safety. Klimaschutzprogramm 2030 der Bundesregierung
zur Umsetzung des Klimaschutzplans 2050 (Climate Protection Program 2030 of the Federal Government to implement the
Climate Protection Plan 2050). 2019. Available online: https://www.bundesregierung.de/resource/blob/975226/1679914/e01d6
bd855f09bf05cf7498e06d0a3ff/2019-10-09-klima-massnahmen-data.pdf (accessed on 15 December 2020).
114.
Schmidt, P.R.; Zittel, W.; Weindorf, W.; Raksha, T. Renewables in Transport 2050. Empowering a Sustainable Mobility Future with zero
Emission Fuels from Renewable Electricity; Kraftstoffstudie II Final Report; Springer: Berlin/Heidelberg, Germany, 2016. [CrossRef]
115.
Agora Verkehrswende; Agora Energiewende; Frontier Economics. The Future Cost of Electricity-Based Synthetic Fuels. 2018.
Available online: https://static.agora-verkehrswende.de/fileadmin/Projekte/2017/Die_Kosten_synthetischer_Brenn-_und_
Kraftstoffe_bis_2050/Agora_SynKost_Study_EN_WEB.pdf (accessed on 2 May 2018).
116.
Dena. Dena-Leitstudie Integrierte Energiewende (dena Lead Study on Integrated Energy Transition) Impulse für die Gestaltung
des Energiesystems bis 2050. 2018. Available online: https://www.dena.de/fileadmin/dena/Dokumente/Pdf/9261_dena-
Leitstudie_Integrierte_Energiewende_lang.pdf (accessed on 15 December 2020).
117.
Kost, C.; Schlegl, T. Stromgestehungskosten erneuerbare Energien (Electricity Generation Costs Renewable Energies). 2018.
Available online: https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/DE2018_ISE_Studie_
Stromgestehungskosten_Erneuerbare_Energien.pdf (accessed on 15 December 2020).
World Electr. Veh. J. 2021,12, 21 27 of 29
118.
World Bank. State and Trends of Carbon Pricing 2020. 2020. Available online: https://openknowledge.worldbank.org/bitstream/
handle/10986/33809/9781464815867.pdf?sequence=4&isAllowed=y (accessed on 16 December 2020).
119.
Manager Magazin. CO2-Steuern und Emissionshandel: Diese CO2-Preise Gibt es Schon Heute (CO2 Taxes and Emissions Trading:
These CO2 Prices Already Exist Today). 2019. Available online: https://www.manager-magazin.de/fotostrecke/co2-steuer-
diese-preise-gibt-es-schon-heute-fotostrecke-169692.html (accessed on 30 November 2020).
120.
The Regional Greenhouse Gas Initiative. Elements of RGGI. Available online: https://www.rggi.org/program-overview-and-
design/elements (accessed on 30 November 2020).
121.
C40. C40: Shenzhen Carbon Emission Trading System. Available online: https://www.c40.org/case_studies/shenzhen-carbon-
emission-trading-system (accessed on 30 November 2020).
122.
Center for Climate and Energy Solutions. California Cap and Trade. Available online: https://www.c2es.org/content/california-
cap-and-trade/ (accessed on 30 November 2020).
123.
Åkerfeldt, S.; Waluszewski, D. Carbon Taxation in Sweden. 2020. Available online: https://www.government.se/492a0
1/contentassets/419eb2cafa93423c891c09cb9914801b/200224-carbon-tax-sweden---general-info.pdf (accessed on 16 Decem-
ber 2020).
124.
Quandl. ECX EUA Futures, Continuous Contract #1 (C1) (Front Month). Available online: https://www.quandl.com/data/
CHRIS/ICE_C1-ECX-EUA-Futures-Continuous-Contract-1-C1-Front-Month (accessed on 16 December 2020).
125.
World Bank. State and Trends of Carbon Pricing 2018. 2018. Available online: https://openknowledge.worldbank.org/bitstream/
handle/10986/29687/9781464812927.pdf?sequence=5&isAllowed=y (accessed on 16 December 2020).
126.
California Environmental Protection Agency. Overview of ARB Emissions Trading Program. 2015. Available online: https://ww2
.arb.ca.gov/sites/default/files/classic//cc/capandtrade/guidance/cap_trade_overview.pdf (accessed on 16 December 2020).
127.
European Energy Exchange AG. China Beijing Environment Exchange (CBEEX). Available online: https://www.eex.com/en/
markets/environmental-markets/chinese-carbon-market (accessed on 16 December 2020).
128.
California Air Ressource Board. California and Québec Carbon Allowance Prices. 2020. Available online: https://ww2.arb.ca.
gov/sites/default/files/2020-09/carbonallowanceprices_0.pdf (accessed on 16 December 2020).
129.
Office Publications. Regulation (EC) No 595/2009 of the European Parliament and of the Council of 18 June 2009 on Type-
Approval of Motor Vehicles and Engines with Respect to Emissions from Heavy Duty Vehicles (Euro VI) and on Access to
Vehicle Repair and Maintenance Information and Amending Regulation (EC) No 715/2007 and Directive 2007/46/EC and
Repealing Directives 80/1269/EEC, 2005/55/EC and 2005/78/EC. Regulation (EC) No 595/2009. 2019. Available online:
https://eur-lex.europa.eu/legal-content/DE/TXT/?uri=CELEX%3A32009R0595 (accessed on 15 December 2020).
130.
Office Publications. Regulation (EU) 2019/1242 of the European Parliament and of the Council of of 20 Junel 2019 Setting
CO
2
Emission Performance Standards for New Heavy-Duty Vehicles and Amending Regulations (EC) No 595/2009 and
(EU) 2018/956 of the European Parliament and of the Council and Council Directive 96/53/EC; 2019. Available online:
https://eur-lex.europa.eu/legal-content/EN/LSU/?uri=CELEX%3A32019R1242 (accessed on 15 December 2020).
131.
Rexeis, M.; Quaritsch, M.; Hausberger, S.; Silberholz, G.; Kies, A.; Steven, H.; Goschütz, M.; Vermeulen, R. VECTO Tool
Development: Completion of Methodology to Simulate Heavy Duty Vehicles
'
Fuel Consumption and CO2 Emissions. Upgrades
to the Existing Version of VECTO and Completion of Certification Methodology to be Incorporated into a Commission Legislative
Proposal. 2017. Available online: https://ec.europa.eu/clima/sites/clima/files/transport/vehicles/docs/sr7_lot4_final_report_
en.pdf (accessed on 2 February 2021).
132.
Statista. Mobility Market Outlook. Available online: https://www.statista.com/outlook/mobility-markets (accessed on 23
September 2020).
133.
ADAC. Database for Vehicles. Available online: https://www.adac.de/rund-ums-fahrzeug/autokatalog/marken-modelle/
(accessed on 18 December 2020).
134.
AutoScout24. Fahrzeugbewertung. Available online: https://www.autoscout24.de/fahrzeugbewertung/ (accessed on 15
July 2020).
135.
KBA. Fahrzeugzulassungen (FZ): Neuzulassungen von Kraftfahrzeugen nach Umwelt-Merkmalen. Jahr 2019. Available
online: https://www.kba.de/DE/Statistik/Produktkatalog/produkte/Fahrzeuge/fz14_n_uebersicht.html (accessed on 18
December 2020).
136.
BMWi. Bekanntmachung der Richtlinie zur Förderung des Absatzes von elektrisch betriebenen Fahrzeugen (Umweltbonus). Avail-
able online: https://www.bmwi.de/Redaktion/DE/Downloads/B/bekanntmachung-der-richtlinie-zur-foerderung-absatzes-
von-elektrisch-betriebenen-fahrzeugen-umweltbonus.pdf?__blob=publicationFile&v=4 (accessed on 18 December 2020).
137.
check24.de. Vehicle Insurance Comparison Site. Available online: https://www.check24.de/kfz-versicherung/ (accessed on 25
July 2020).
138.
Bundesregierung. Entwurf eines Siebten Gesetzes zur Änderung des Kraftfahrzeugsteuergesetzes. Available online: https://
www.bundesfinanzministerium.de/Content/DE/Gesetzestexte/Gesetze_Gesetzesvorhaben/Abteilungen/Abteilung_III/19
_Legislaturperiode/2020-06-12-7-Aenderung-Kraftfahrzeugsteuergesetz/1-Regierungsentwurf.pdf?__blob=publicationFile&
v=2 (accessed on 18 December 2020).
139.
Bundesregierung. Kraftfahrzeugsteuergesetz in der Fassung der Bekanntmachung vom 26. September 2002 (BGBl. I S. 3818).
Available online: http://www.gesetze-im-internet.de/kraftstg/KraftStG_2002.pdf (accessed on 17 December 2020).
World Electr. Veh. J. 2021,12, 21 28 of 29
140.
TÜV. Hauptuntersuchung (HU) Gebühren. Available online: https://www.tuvsud.com/de-de/branchen/mobilitaet-und-
automotive/hauptuntersuchung/gebuehren (accessed on 25 September 2020).
141.
ADAC. Autokosten-Rechner. Available online: https://www.adac.de/infotestrat/autodatenbank/autokosten/autokosten-
rechner/default.aspx (accessed on 10 October 2020).
142. Reifendirekt.de. Reifendirekt. Available online: https://www.reifendirekt.de (accessed on 10 October 2020).
143.
DAT. DAT Report 2018. 2018. Available online: https://www.dat.de/news/dat-report-2018-jetzt-erhaeltlich/ (accessed on 18
December 2020).
144.
ADAC. ADAC Autokosten Herbst/Winter 2019/2020-Kostenübersicht für über 1.600 aktuelle Neuwagen-Modelle. Available
online: https://www.adac.de/_mmm/pdf/autokostenuebersicht_47085.pdf (accessed on 18 December 2020).
145.
INRIX. INRIX Studie zu den Kosten des Autofahrens: Parken Verursacht die Höchsten laufenden Ausgaben. Available on-
line: https://inrix.com/press-releases/inrix-studie-zu-den-kosten-des-autofahrens-parken-verursacht-die-hochsten-laufenden-
ausgaben/ (accessed on 11 August 2020).
146.
Fahrrad.de. Fahrrad.de-Studie zu Allgemeinen Fahrradnutzung in Deutschland 2015. Available online: https://www.fahrrad.de/
on/demandware.static/-/Library-Sites-bikester/default/dw7f90cb9f/Blog/studie-fahrradnutzung-deutschland.pdf (accessed
on 17 December 2020).
147.
VSF. VSF-Fachhandelsmitglieder zur Entwicklung des Jahres 2018 (Verbund Service und Fahrrad). Available online: https:
//nationaler-radverkehrsplan.de/de/aktuell/nachrichten/durchschnittspreis-aller-verkauften-fahrraeder-lag (accessed on 15
September 2020).
148.
Zweirad-Industrie-Verband. Zweirad-Industrie-Verband (ZIV)-Wirtschaftspressekonferenz am 11. März 2020 in Berlin: Zahlen–
Daten–Fakten zum Fahrradmarkt in Deutschland 2019. 2019. Available online: https://www.ziv-zweirad.de/fileadmin/
redakteure/Downloads/Marktdaten/PK-2020_11-03-2020_Praesentation.pdf (accessed on 18 December 2020).
149.
Zweirad-Industrie-Verband e.V. (ZIV). Average Cost of Bicycles; Interview with Head of Marketing and Communications: Bad
Soden am Taunus, Germany, 2020.
150.
Fahrradblog.de. Fahrradblog-Fahrradinspektion–Wartung mit Checkliste. Available online: https://www.fahrradblog.de/
sicherheit/fahrradinspektion-wartung-mit-checkliste/#Kosten_fuer_Fahrradinspektion (accessed on 3 October 2020).
151. Schwalbe. Verschleiss Reifen. Available online: https://www.schwalbe.com/de/verschleiss (accessed on 3 August 2020).
152.
GHOST-BIKES. User Manual for Bicycles. Available online: https://www.ghost-bikes.com/fileadmin/user_upload/Downloads/
Bedienungsanleitung/GHOST_bike_user_manual_german.pdf (accessed on 3 October 2020).
153.
Bundesregierung. Elektrokleinstfahrzeuge-Verordnung vom 6. Juni 2019 (BGBl. I S. 756). Available online: https://www.gesetze-
im-internet.de/ekfv/BJNR075610019.html (accessed on 18 December 2020).
154.
Bundesregierung. Straßenverkehrs-Zulassungs-Ordnung vom 26. April 2012 (BGBl. I S. 679). Available online: https://www.
gesetze-im-internet.de/stvzo_2012/BJNR067910012.html (accessed on 18 December 2020).
155.
Severengiz, S.; Finke, S.; Schelte, N.; Wendt, N. Life cycle assessment on the mobility service E-Scooter sharing. In Proceedings of
the 2020 IEEE European Technology and Engineering Management Summit (E-TEMS), Dortmund, Germany, 5–7 March 2020; pp.
1–6, ISBN 978-1-7281-0903-9.
156.
Laa, B.; Leth, U. Survey of E-scooter users in Vienna: Who they are and how they ride. J. Transp. Geogr.
2020
,89, 102874.
[CrossRef]
157.
Schellong, D.; Sadek, P.; Schaetzberger, C.; Barrack, T. The Promise and Pitfalls of E-Scooter Sharing. Available online: https:
//image-src.bcg.com/Images/BCG-The-Promise-and-Pitfalls-of-E-Scooter%20Sharing-May-2019_tcm81-220107.pdf (accessed
on 18 December 2020).
158.
CarSharing.de. CarSharing-Städteranking 2019: Karlsruhe ist weiterhin Spitzenreiter. Available online: https://carsharing.
de/presse/pressemitteilungen/carsharing-staedteranking-2019-karlsruhe-ist-weiterhin-spitzenreiter (accessed on 18 Decem-
ber 2020).
159. Münzel, K.; Boon, W.; Frenken, K.; Vaskelainen, T. Carsharing business models in Germany: Characteristics, success and future
prospects. Inf. Syst. Bus. Manag. 2018,16, 271–291. [CrossRef]
160.
ShareNow. Preisüberblick. Available online: https://www.share-now.com/de/de/pricing/?cid=sn_ppc_de_all_none_
performance_google_prsitelink_none_none_none_none_none_none_none_none&gclid=CjwKCAjww5r8BRB6EiwArcckC5R-
gZb6mzlaAPSRDluq_OWgddb2W8a7nEk2sJR142NgSBfmgb-d3hoCj0MQAvD_BwE (accessed on 10 October 2020).
161. STATTAUTO. Preise. Available online: https://www.stattauto-muenchen.de/standardtarif/ (accessed on 18 December 2020).
162.
MVG. MVG Rad. Available online: https://www.mvg.de/services/mobile-services/mvg-rad.html?pk_campaign=03_Generic_
Standort&pk_kwd=%2Bleihräder%20%2Bmünchen&pk_source=GoogleAds&pk_medium=cpc (accessed on 18 December 2020).
163.
Heineke, K.; Kloss, B.; Scurtu, D.; Weig, F. Micromobility’s 15,000-Mile Checkup. Available online: https://www.mckinsey.com/
industries/automotive-and-assembly/our-insights/micromobilitys-15000-mile-checkup (accessed on 18 December 2020).
164. Tier. Tier App. Available online: https://mytier.app (accessed on 18 December 2020).
165.
Zagorskas, J.; Burinskien
˙
e, M. Challenges Caused by Increased Use of E-Powered Personal Mobility Vehicles in European Cities.
Sustainability 2019,12, 273. [CrossRef]
166. Emmy. Emmy-Sharing: Preise. Available online: https://emmy-sharing.de (accessed on 2 November 2020).
167.
Ennen, D.; Heilker, T. Ride-Hailing Services in Germany: Potential Impacts on Public Transport, Motorized Traffic, and Social
Welfare. Available online: https://ideas.repec.org/p/mut/wpaper/29.html (accessed on 18 December 2020).
World Electr. Veh. J. 2021,12, 21 29 of 29
168. Uber. Uber. Available online: https://www.uber.com/de/en/ (accessed on 18 December 2020).
169.
München, L. TaxitarifO 410-Verordnung der Landeshauptstadt München über Beförderungsentgelte und Beförderungsbedingun-
gen für den Verkehr mit Taxen (Taxitarifordnung). Available online: https://www.muenchen.de/rathaus/Stadtrecht/vorschrift/
410.pdf (accessed on 18 December 2020).
170.
MVV. MVV-Pläne zum Download. Available online: https://www.mvv-muenchen.de/plaene-bahnhoefe/plaene/index.html
(accessed on 31 October 2020).
171.
MVV. Pressemitteilungen. Available online: https://www.mvv-muenchen.de/mvv-und-service/presse/index.html (accessed
on 20 August 2020).
... 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]. ...
Article
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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. ...
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... 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 ...
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... 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. ...
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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]. ...
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