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In this paper a segmented Total Cost of Ownership (TCO) model is presented for alternative vehicle technologies as well as its extension with external costs related to vehicle ownership and use. Adding external costs to the TCO extends the interpretation of individual ownership to a societal perspective by describing the effect of the technologies on the costs for the society. This extension, called "Total Cost for Society", suggests that battery electric vehicles, plug-in hybrid electric vehicles and hybrid electric vehicles have a lower societal cost than petrol, diesel and compressed natural gas vehicles.
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EVS29 Symposium
eal, Qu´
ebec, Canada, June 19 - 22, 2016
How Total is a Total Cost of Ownership?
Quentin De Clerck1, Tom van Lier1, Philippe Lebeau1, Maarten Messagie1
Lieselot Vanhaverbeke1, Cathy Macharis1, Joeri Van Mierlo1
1Quentin De Clerck (corresponding author), Research Group MOBI, Vrije Universiteit Brussel
Pleinlaan 2, 1050 Brussels, Belgium,
In this paper a segmented Total Cost of Ownership (TCO) model is presented for alternative vehicle
technologies as well as its extension with external costs related to vehicle ownership and use. Adding
external costs to the TCO extends the interpretation of individual ownership to a societal perspective by
describing the effect of the technologies on the costs for the society. This extension, called “Total Cost
for Society”, suggests that battery electric vehicles, plug-in hybrid electric vehicles and hybrid electric
vehicles have a lower societal cost than petrol, diesel and compressed natural gas vehicles.
Keywords: passenger car, electric vehicle, cost.
1 Introduction
One of the most important barriers for battery electric vehicle (BEVs) adoption is the high purchase cost
of the vehicles. Consumer mostly consider the substantial initial cost of BEVs above the many other
costs involved in car ownership [1]. Since some of these other cost, i.e. fuel, can be quite substantial
depending on the car technology, choosing a new car based solely on its purchase price is therefore an
incorrect approach for selecting the less expensive option. This is the m§Total Cost of Ownership (TCO)
methodology [2] is often used to analyze the competitiveness of BEVs [3]. A TCO aims at describing
the full cost of vehicles during the ownership and inform the consumer which vehicle costs less.
[3] review in their article many other articles related to “Vehicle Cost Analysis”. They cite a total of
29 articles, published since 2010, related to this topic. From these 29 articles, 13 articles apply the TCO
methodology for analyzing the difference in cost between different vehicle technologies. This clearly
indicates that the TCO is a suitable method for comparing different vehicle technologies.
[4] identifies two distinct types of TCO studies, namely, consumer-oriented TCO studies and society-
oriented TCO studies. The difference between the two approaches resides in the author’s focus. Consumer-
oriented TCO analyses focus mainly on the difference in cost the consumer should pay depending on the
various vehicle technologies at his disposal. Examples of such studies are [4, 5]. The particularity of
society-oriented TCO studies are the extra relation between the cost of the different vehicle technologies
and their societal impact. [6, 7] are two examples of society-oriented TCO studies.
In this paper, a society-oriented segmented-TCO model, developed in the context of the Brussels Capital
Region, is presented. The model we present is called a Total Cost for Society (TCS), where a TCO anal-
ysis is used as tool for discussing the internalization of external costs. Therefore, the amount of taxation
in the TCO analysis is calculated and used in combination with the external costs. The combined value
enables to extends the interpretation of individual ownership to societal perspectives.
The paper is structured as follows. Firstly, the different methodologies used in this paper are is de-
tailed in section 2. These methodologies comprise a segmented-TCO, an external cost analysis and the
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formulation of a TCO-based tax model. Secondly, the results from the TCO, external costs and Total
Cost for Society are discussed. Finally, this paper ends with the main conclusions.
2 Methodology
Firstly, the assumptions upon which the analysis is based are presented. Secondly, the complete TCO
methodology is detailed. The TCO-methodology is based on the work of [4] . Finally, the details of the
TCO-based tax model are explained.
2.1 General assumptions
A TCO analysis relies heavily on different kind of assumptions. All the assumptions of the TCO pre-
sented in this paper are based on characteristics of the Belgian’s and Brussels’ fleet, since the scope of
this research is the Brussels Capital Region in 2015. The main assumptions of these analyses are sum-
marized in Table 1. The average Belgian owns a car for 8 years and 45 days and drives 15.284 kilometers
a year [8, 9]. This results in a total mileage of 124.156 km during the ownership of the vehicle. A real
discount rate of 0,33% is used for calculating the present value of future costs. This percentage corre-
sponds to the 8-year interest rate for European government bonds [10] . The real discount rate dates from
May 4 2015.
Table 1: General assumption TCO
Assumption Value Reference
Duration ownership vehicle 8 years and 45 days [9]
Yearly mileage 15.284 km / year [8]
Total mileage 124.156 km /
Real discount rate 0.33% [10]
Various different vehicle technologies are assumed in this TCO analysis. Conventional petrol (P) and
diesel (D) vehicles are assumed as well as alternative vehicle technologies such as hybrid electric vehicles
(HEV), plug-in hybrid electric vehicles (PHEV), battery electric vehicles (BEV) and compressed natural
gas vehicle (CNG). In total 45 vehicles are selected: 9 petrol vehicles, 9 diesel vehicles, 6 CNG vehicles,
3 PHEVs, 7 HEVs and 12 BEVs.
These vehicles are divided into three segments, namely, the city car segment, the medium segment and
the premium segment. Those segments are defined based on the vehicle’s size, the boot space and the
power of the motor. The results of the TCO will be presented for each of the three segments. In total
there are 13 vehicles in the city car segment, 22 vehicles in the medium car segment and 10 vehicles in
the premium segment.
When constructing a TCO analysis, different costs at different points in time are assumed. Therefore,
future costs need to be calculated using a discounted formula approximating the value of money in time,
namely, the present value formula [4]. Following formula is applied when facing future one-time costs
P V =At1
(1 + r)t(1)
and in the case of recurring costs, following formula is applied instead:
P V =A0(1 + r)t1
r(1 + r)t(2)
P V : Is the present value
At: Is a one-time cost at time t
A0: Is a recurring cost
r: Is the real discount rate presented in 1
t:Is time expressed in years
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2.2 TCO Calculations
There are three distinct cost categories in a TCO analysis for passenger car, namely, costs at the time of
purchase, operational costs and non-operational costs [4]. These costs will be detailed in next subsec-
2.2.1 Purchase Costs
The initial purchase cost that are considered in this TCO include VAT but exclude any kind of reduction
or promotion and also exclude extra vehicle options. The costs were retrieved on the dealers’ websites.
Table 2: Depreciation rates per vehicles technology
Technology Depreciation rate
P 84,5%
D 82,7%
HEV 83,4%
BEV 78,6%
PHEV 82,4%
CNG 78,1%
Since the vehicles depreciate over time, it is important to consider their depreciation rate in the analysis.
The percentages in Table 2 represent the residual values of the different vehicle technologies after one
year. The percentages of the petrol, diesel and HEV are retrieved from [4] , while the other depreciation
rates are determined using the same methodology (via exponential regression). The data used in this
exponential regression comes from [12]. The values for CNG vehicles and some BEV vehicles are only
available for the first three years, therefore their depreciation rate is less certain. Table 2 summarizes the
depreciation rates per vehicle technology.
The registration tax is a one-time tax due at the time the vehicle is bought. This tax is determined for
vehicles registered in the Brussels Capital Region based on the engine’s power, the fiscal horsepower,
and the engine’s displacement [13].
The charging infrastructure is another one-time cost that can be spent when buying a new BEV or
PHEV. In this analysis we use the prices of Electrabel’s ”CarPlug” charging pole [14]. The pole itself
costs ¤999 and a standard installation costs ¤351 for a total of ¤1.300.
2.2.2 Operational Costs
The fuel costs for petrol (1,5959 ¤/l) and diesel (1,4158 ¤/l) correspond to the average maximum prices
from 2014 according to the Belgian Federation of Petroleum [15]. The electricity costs are calculated
based on trimestrial reports from Brugel in 2014 [16]. The subscription we consider is the standard
subscription for households in the Brussels Capital Region, namely, Electrabel Customer Solutions Easy
e. For an average Brussels household (consumption of 3.500 kWh per year), the cost of electricity
is equal to 0,1809 ¤/kWh. The reference price for CNG that is considered in this paper is equal to 0,87
¤/kg. This was the price for one kilogram CNG on May 25th 2015 [17].
2.3 Non-operational Costs
The road tax is a yearly recurring cost that depends on the vehicle’s fiscal horsepower [18].
In Belgium it is required that every vehicle is at least insured with the civil liability insurance in order to
insure any damage done to another vehicle. For new cars an omnium insurance, which is complementary
to the civil liability insurance, is preferred. This omnium insurance also insures the driver’s vehicle. The
cost of an omnium insurance depends on various characteristics, such as age, type of car and mileage per
year. The insurances were retrieved using the Touring Insurances website [19]. In this analysis an om-
nium insurance is considered for the first three years and afterwards a civil liability insurance is preferred.
Lebeau [4] assume a battery life of 6 years for the battery packs of BEVs. This implies a life-
time equal to 91.704 km given the assumption of 15.284 km driven per year. The battery pack must be
replaced after those 6 years or 91.704 km. If the battery pack is still under the warranty of the manu-
facturer, then no costs are charged. Otherwise the cost of replacing the battery pack is approximated by
charging ¤160 per kWh. This value of ¤160 is based on the article of Nykvist [20] that determines
that market-leading manufacturers sold one kWh of battery for approximately $300 in 2014 and that the
cost of one kWh showed an annual decrease of 8% since 2007. We assume that $300 was still the cost of
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one kWh at the beginning of 2015 and that this cost will evolve to approximately $180 ( ¤160) in 2021
given the annual decrease of 8%. PHEV battery packs are also considered for battery replacement. The
residual value of the vehicle increases linearly when the battery is replaced.
The battery leasing option is offered by some manufacturer. In exchange of a recurring monthly leasing
cost, the manufacturer takes care of the battery replacement for free when the battery’s capacity drops
below a predefined percentage, usually 80%.
Maintenance are crucial costs during the lifetime of the vehicle. They include the replacement of worn
out components such as brakes, filters or oil. Two kind of maintenances are identified depending on
the vehicle’s mileage, namely, small and large maintenances. Small maintenances are performed after
20.000 km and large maintenances after 40.000 km as in [4]. The maintenance prices are specific to each
model and are retrieved online from [21]. [22] suggests that the cost of maintaining an BEV is 35 %
cheaper than conventional vehicles. Therefore, the maintenance costs of BEVs are 35% lower than the
mean of the maintenance costs of conventional cars. The maintenance cost of CNG vehicles and hybrid
vehicles is assumed to be equal to the maintenance costs of conventional vehicles [23].
Another aspect of the maintenance is the replacement of tires. The tires need to be replaced every
40.000 km. The tires and their cost are determined using the websites [24, 25]. A total of ¤56 is added
for the replacement and balancing of the tires [4].
After four years of lifetime, the vehicles must pass an annual technical control. This annual inspection
costs ¤33,80 [26].
Finally, this TCO also comprises less obvious costs, namely, fines and parking fees. According to the
Household Budget Survey from 2014 [27], Belgian households spent on average ¤69 for fines and
¤31 for parking fees. Since 76,78% of all infractions committed by Belgians during 2013 are related
to violations of the traffic code [28], the average amount of fines a Belgian pays per year for traffic
violations are estimated to be ¤52,98 with the assumption the percentage of infractions concerning
traffic are equivalent in 2014.
2.4 External Cost Calculations
In this paper, the impact calculations for external cost comprise a non-exhaustive list of externalities:
climate change, air pollution, noise, accidents, congestion and infrastructure. The total external cost
is thus a summation of these externalities. In order to calculate the impact of each above mentioned
externalities, a scenario representing a typical driving pattern needs to be defined.
2.4.1 External Cost Scenario
The scenario that is defined for the calculation of the external costs is based on the driving motives of
Belgian drivers, since the scope of this study is the Brussels Capital Region. Several travel motives are
identified in the BELDAM study [29]. The average amount of traveled km per trip is given for each
motive. Those mean distances are based on different modes of transport but since the car has the biggest
share for almost every motive, it is assumed that these average travel distances are representative for car
drivers. In this paper the different travel motives are grouped into three main categories: Commuting
and work-related trips, recreative trips and other trips (comprising shopping, services such as the doctor,
picking up people,...). Those three categories represent the three behaviors that define the scenario that
is presented below. The share of the total km driven for each behavior is reported in Table 3 and is
determined based on the average distances per trip for each motive belonging to the specific category.
Table 3: Share of the total km driven per behavior
Behavior Average distance per trip Share of total driven kilometers
Commuting and work-related 24,17 km 54,6%
Recreative 11,61 km 26,2%
Others 8,51 km 19,2%
Total 44,29 km 100%
The scenario upon which the calculations are based is defined is the follows:
Commuting and work-related trips:
During daytime
Share metropolitan motorway and main roads: 80% and 20%
Share peak and off-peak: 20% and 80%
Recreative trips:
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During daytime
Share metropolitan motorway, main roads and small roads: 20%, 30% and 50%
Share peak and off-peak: 5% and 95%
Other trips:
During daytime
Share metropolitan motorway and main roads: 30% and 70%
Share peak and off-peak: 10% and 90%
2.4.2 Climate change
The external climate change costs are determined based on the fuel and electricity consumption using a
well-to-tank approach. Table 4 summarizes the well-to-tank emissions of CO2per unit of fuel/electricity.
A total amount of kilograms CO2emissions are derived using these emissions and the NEDC-based
consumption values of fuel and electricity for each vehicle. Finally the external climate change costs
are determined by multiplying the total emissions with the external climate cost per kg CO2(0,09 ¤/kg
according to [30]).
Table 4: Well-to-tank emissions
Fuel/electricity CO2emissions Reference
Diesel 3,200 kg CO2/liter [31]
Petrol 2,800 kg CO2/liter [31]
CNG 3,070 kg CO2/kg [31]
Electricity (BE mix) 0,187 kg CO2/kWh [32]
2.4.3 Air pollution
As opposed to the external climate change cost, the up- and down-stream costs are not considered for air
pollution, since those are hard to isolate for electric vehicles. The external air pollution costs are thus
calculated based on the total mileage of the vehicles during the ownership period (see Table 1). The
values (in ¤/km) for the marginal external air pollution costs per km are referenced in Table 5. The
marginal external costs depends on vehicle technology, the engine displacement, the EURO-class and
the type of road.
Table 5: Marginal external air pollution costs in ¤/vkm for Belgium. Based on [30]
Vehicle Engine EURO-Class Urban Motorway
Diesel 1,4-2,0l EUR 5 0,0084 0,0041
Diesel >2,0l EUR 6 0,0061 0,0022
Petrol <1,4l EUR 5 0,0034 0,0008
Petrol 1,4-2,0l EUR 5 0,0033 0,0008
Petrol 1,4-2,0l EUR 6 0,0033 0,0008
Petrol >2,0l EUR 6 0,0033 0,0008
2.4.4 Noise
Analogous to external air pollution costs, external noise costs are determined based on the total mileage
of the vehicle and the marginal external noise costs. The marginal external noise costs are determined
by the time of day, the area and traffic density type. In the above described scenario, the time of day
and the area are always defined as daytime and urban. Therefore, the main difference in external noise
cost is determined by the traffic density. This cost is equal to ¤0,0105/vkm for dense traffic and to
¤0,0255/vkm for thin traffic [30]. For BEV and CNG vehicles different values are assumed. BEVs are
assumed to emit no noise and CNG vehicles to emit only 50% of the noise generated by conventional
vehicles because those vehicles have significant lower noise levels.
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2.4.5 Accidents
The marginal external accident costs are defined solely on the type of road. Since the scenario takes place
in an metropolitan setting, only motorways and urban roads are of importance. The marginal external
accident costs for motorways is ¤0,003/vkm and ¤0,004/vkm for urban roads.
2.4.6 Congestion
The marginal external congestion costs depend on the area (metropolitan in the Brussels’ case), the road
type and the type of traffic. Free flow is assumed when the traffic is off-peak. When in rush hours,
the average of near flow capacity and over capacity is assumed. The total external congestion cost is
calculated by multiplying the marginal cost per km with the total mileage of the vehicles.
Table 6: Marginal external congestion costs in ¤/vkm for Belgium. Based on [30]
Road type Free flow Near capacity Over capacity
Motorway 0,0000 0,3192 0,7338
Main roads 0,0111 1,6853 2,1617
Other roads 0,0297 1,9019 2,8930
2.4.7 Infrastructure
The calculation of the external infrastructure costs are similar to the calculation of the other externalities
(except for climate change). The marginal external infrastructure costs depend on the type of road. The
cost per vkm is equal to ¤0,0024 for motorways, ¤0,0033 for other trunk roads and ¤0,0082 for other
roads [30].
2.5 From TCO to Tax Model
By calculating the extend of the vehicle taxes it is possible to combine them with the external costs cal-
culations and thus internalize the external costs given the TCO results. This TCO-based tax model is an
important building stone for extension from TCO to TCS.
There are three kind of taxes that are incorporated in this assessment: the vehicle specific taxes, the
VAT and the excises.
The vehicles specific taxes such as the registration tax, the road tax, the fines and the parking fees are
completely integrated without any reduction.
In Belgium the VAT is usually equal to 21%. Some goods or services have a VAT inferior to 21%, for
example electricity had a VAT of 6% in 2015. The VAT of the installation of the charging pole has also a
VAT of 6% if it is installed in a private house that is older than 5 years [14]. Thus all components of the
TCO are subjected to a VAT of 21% except for electricity and for the charging pole installation.It is also
important to observe that the initial purchase cost is taxed instead and not the depreciation of the vehicle.
The excise, special excise and energy taxes of fuels and electricity in transport applications are summa-
rized in Table 7. Those excises are fully integrated in the tax model derived from the TCO results.
Table 7: Excise, special excise and energy taxes in Belgium in ¤/l [33]
Energy Excise Special excise Energy taxes
Petrol 0,2454146 0,360191 0,0286317
Diesel 0,1983148 0,2183561 0,0148736
Electricity 0 0 0,001914
CNG 0 0 0
3 Results
In this section the results of the TCO, external cost calculations and the internalization of the external
costs to the TCO are presented.
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3.1 Results TCO
Figure 1: TCO results for the city car segment
The TCO results for the city car segment are depicted in Figure 1. The TCO value is denoted by the
left y-axis and the cost per km in ¤is indicated by the right y-axis. Petrol vehicles are clearly the most
competitive vehicles in this segment (0,20 ¤/km), although the TCO of CNG vehicles is rather close
to the TCO for petrol vehicles (0,22 ¤/km). BEVs, both with and without leasing of battery, are not
competitive in this car segment (respectively 0,33 and 0,32 ¤/km). The big difference resides in the
depreciation cost that is much higher for BEVs even if their operational costs are lower.
The error-bars show the deviation in cost per km when driving 10.000 km or 20.000 km per year instead
of 15.284 km. It is clear that the BEV technology shows the most variance in costs depending on the
distance driven. This means that BEVs will become more competitive when used more intensively.
Figure 2: TCO results for the medium car segment
Figure 2 illustrates the results for the medium car segment. In this segment, the difference between BEVs
and conventional vehicle technologies such as petrol and diesel is smaller than for the city car segment.
The difference between these technologies is of 0,09 - 0,10 ¤/km since BEVs have a cost per km equal to
0,37 ¤/km and petrol and diesel vehicles have a cost per km of respectively 0,28 ¤/km and 0,27 ¤/km.
This difference was equal to 0,12 - 0,13 ¤/km in the city car segment. The medium car segment is thus
more interesting for buying a BEV than the city car segment when comparing with the alternatives in
the same segment. CNG vehicles are still competitive (0,31 ¤/km) in this segment but less than in the
city car segment (a difference of 0,03 - 0,04 ¤/km instead of 0,02 ¤/km with respect to conventional
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vehicle technologies). HEV vehicles are an interesting option that is a little bit more expensive than CNG
vehicles, while PHEVs are the most expensive cars with a cost per km equal to 0,41 ¤/km. PHEVs and
BEVs have still too much depreciation costs to compete with the other alternatives.
The conclusion is slightly different in the premium car segment (Figure 3). The HEVs are the most
competitive cars with a cost per km of 0,63 ¤/km. Diesel and petrol vehicles are close with costs per km
of 0,64 ¤/km and 0,67 ¤/km respectively. It is also in this segment that BEVs are the most competitive
with a cost per km equal to 0,73 ¤/km. This is partly due to the fact that BEVs do not have costly road
taxes, that the battery replacement is covered by their warranty and that the depreciation cost is not as
bad as in the other segments. An important observation is that if the vehicles would drive more than
20.000 km, the cost per km would be almost equal for all technologies.
Figure 3: TCO results for the premium car segment
3.2 Result External Costs
Since the conclusions of the results for the different segments are similar, only the medium car segment
will be detailed. The analysis is split in two parts, an analysis including congestion costs and an analysis
excluding congestion costs. It is clear from Figure 4a that the congestion cost are quite high (approxi-
mately ¤19.000). This is typical for the setting of Brussels. Brussels is a city with a huge amount of
congestion, therefore it is not unusual to have such high external congestion costs. It is thus easier to
exclude congestion costs from the analysis in order to generalize the results and notice differences in the
external costs between several vehicle technologies.
The main differences in external costs reside in the climate change, air pollution and noise costs. It is
clear that BEVs have the less significant external costs, approximately ¤1.250 when excluding conges-
tion costs. This is almost five times smaller than the external costs for diesel and petrol vehicles (around
¤6.000 without congestion costs). Notice that air pollution and noise are equal to zero in this analysis.
This is an underestimation since the up- and down-stream costs are not taken into account for air pollu-
tion and that even if electric vehicles have a silent motor, they emit a little bit of noise as well when they
are on the road.
The reasons why PHEVs and CNG vehicles have lower external cost are different, even if both tech-
nologies have lower external costs than petrol, diesel and HEVs. PHEVs have less impact on the climate
change cost due to reduced CO2emissions, but have the same impact as diesel petrol and HEV vehicles
on the external noise cost due to the combustion engine. On the other hand, CNG vehicles have a much
higher climate change cost and a much lower noise cost than PHEV vehicles. Diesel, petrol and HEV
are the vehicle technologies with the highest external costs, namely, between ¤5.000 and ¤6.000 when
excluding congestion costs and almost ¤25.000 when including congestion costs.
3.3 Internalization of External Costs with the TCO-based Tax Model
As in previous subsection, only the medium car segment is considered. Given the TCO-based tax model
depicted in Figure 5 and the previously discussed external costs, a number of observations can be formu-
lated. Combining the tax incomes and the external costs enables to compare the different societal impacts
of the various vehicle technologies. In this analysis the congestion costs are included such that a more
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(a) Including congestion costs (b) Excluding congestion costs
Figure 4: External costs results for the medium car segment
Figure 5: TCO-based tax model results for the medium car segment.
realistic Total Cost for Society (TCS) can be obtained. It is important to mention that it is assumed that
the tax incomes from the TCO reduce directly the external cost generated by the vehicle, even though
normally tax incomes are grouped and redistributed where needed. Nevertheless, this direct comparison
enables to investigate which technology has a bigger impact on the society.
First, the two most taxed vehicles are the HEV and PHEV. Those two vehicle technologies are heav-
ily taxed mainly due to the higher initial purchase price. The taxation of fuel does also play a part in the
high taxation of the HEV technology. When taking Figure 4b into account it is noticeable that PHEV ve-
hicles are also vehicles that do not generate the most external costs. This means that buying a PHEV has
in fact a good impact on the society by on one hand generating a great amount of tax incomes and on the
other hand having a moderate impact on the external costs. The TCS for PHEVs is around ¤7.700. The
HEV vehicles have a smaller societal impact (TCS of ¤8.900) than PHEV vehicles since their external
cost is one of the biggest.
Secondly, BEVs have also a great societal impact with a TCS of ¤8.350. Even though those vehicles are
not heavily taxed, besides the VAT on the higher initial purchase price, their external cost is so low in
comparison with the other vehicle technologies that it has still a big societal impact. CNG vehicles look
like they are in the same situation as BEVs (smaller tax incomes and moderate external costs), but in fact
the TCS is much bigger than expected with a TCS of approximately ¤12.500.
Finally, petrol and diesel vehicles have big TCS values, ¤10.600 and ¤12.600 respectively, which indi-
cates that both technologies have worse societal impacts than in example BEVs or PHEVs .
4 Conclusion
Several analyses have been conducted in this paper. Firstly, a segmented-TCO has been presented. Three
car segments are defined, namely, the city, the medium and the premium car segments. The main con-
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clusion of this segmented-TCO analysis is that BEV and HEV become more competitive in higher cost
segments. The cost per kilometer is reduced from 0,12 ¤/km to 0,07 - 0.09 ¤/km between the city car
segment and the premium car segment for BEVs.
Secondly, an external cost analysis is conducted. A first observation is that the congestion costs are in-
credibly large. This is the result of the scope of this analysis being the Brussels Capital Region, a region
known for its high congestion degree. The results show that the BEV technology generates the lowest
amount of external costs.
Finally the different analyses are combined. A TCO-based tax model is formulated and combined with
the external costs analysis, in one methodology that assesses the societal impact of buying a car depend-
ing on its technology. This methodology, called Total Cost of Society, indicates that the BEV, PHEV and
HEV technologies are more profitable for the society since they generate less costs (¤7.700 - ¤8.900)
than petrol, CNG and diesel vehicles (¤10.600 ¤12.600).
[1] K. Lebeau, J. Van Mierlo, P. Lebeau, O. Mairesse, and C. Macharis, The market potential for plug-in hybrid
and battery electric vehicles in Flanders: A choice-based conjoint analysis, Transportation Research Part D:
Transport and Environment, ISSN 1361-9209, 17(2012), 592–597.
[2] R.J. Brown, A new marketing tool: Lifecycle costing, Industrial Marketing Management, ISSN 0019-8501,
8(1979), 109–113.
[3] J. Roosen, W. Marneffe and L. Vereeck., A review of comparative vehicle cost analysis, Transport Reviews,
ISSN 0144-1647, 35(2015), 720–748.
[4] K. Lebeau, P. Lebeau, C. Macharis and J. Van Mierlo, How expensive are electric vehicles? A
total cost of ownership analysis, 2013 World Electric Vehicle Symposium and Exhibition (EVS27),
ISBN 978-1-4799-3833-9, 4(2013), 2213–2224.
[5] B. Al-Alawi and T. H. Bradley, Total cost of ownership, payback, and consumer preference modeling of
plug-in hybrid electric vehicles, Applied Energy, ISSN 0306-2619, 103(2013), 488–506.
[6] M. Messagie, K. Lebeau, T. Coosemans, C. Macharis and J. Van Mierlo, Environmental and financial
evaluation of passenger vehicle technologies in Belgium, Sustainability, ISSN 2071-1050, 5(2013), 5020–
[7] C. Thiel, A. Perujo, and A. Mercier, Cost and CO2aspects of future vehicle options in Europe under new
energy policy scenarios, Energy Policy, ISSN 0301-4215, 38(2010), 7142–7151.
[8] FOD Mobiliteit en Vervoer, vervoer/1974 kilometres
parcourus par les vehicules belges durant l annee 2013.jsp, accessed on 2016-03-24.
[9] FEBIAC, febiac/2014/rapportannuel2014-FR/, accessed on 2016-03-24.
[10] European Central Bank, KEY¯
165.YC.B.U2.EUR.4F.G N
A.SV C YM.SR 8Y, accessed on 2016-03-24.
[11] T. Mearig, N. Coffee, and M. Morgan, Life Cycle Cost Analysis Handbook, State of Alaska, Department of
Education and Early Development, 1999.
[12] Eurotax,, accessed on 2016-03-24.
[13] FOD Financi¨
en, en belastingen/belasting
op de inverkeerstelling, accessed on 2016-03-24.
[14] Electrabel,, accessed on
[15] Belgian Federation of Petroleum,, accessed on 2016-
[16] Brugel,, accessed on 2016-03-24.
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[17] DATS24,, accessed on 2016-03-24.
[18] FOD Financi¨
en, en belastingen/
verkeersbelasting, accessed on 2016-03-24.
[19] Touring,
touring.htm, accessed on 2016-03-24.
[20] B. Nykvist and M. Nilsson, Rapidly falling costs of battery packs for electric vehicles, Nature Climate
Change, ISSN 1758-678X, 5(2015), 329–332.
[21] Autogids,, accessed on 2016-03-24.
[22] Cars21,, accessed on 2016-03-24.
[23] M. Goedecke, S. Therdthianwonga and S.H. Gheewalab, Life cycle cost analysis of alternative vehicles and
fuels in Thailand, Energy Policy, ISSN 0301-4215, 35(2007), 3236–3246.
[24] Michelin,, accessed on 2016-03-24.
[25] Popgom,, accessed on 2016-03-24.
[26] GOCA,, accessed on 2016-03-24.
[27] Statbel, levensomstandigheden
/huishoudbudgetonderzoek 2014.jsp, accessed on 2016-03-24.
[28] Juridat, dsb/inhoud/nl/nl rapport 00054 main.htm, accessed on 2016-03-
[29] E. Cornelis, M. Hubert, P. Huynen, K. Lebrun, G. Patriarche, A. De Witte, L. Creemers, K. Declerq, D.
Janssens, M. Castaigne, L. Hollaert and F. Walle, Mobiliteit in Belgi¨
e in 2010: resultaten van de BELDAM-
ete, 2012.
[30] G. Gibson, A. Varma, V. Cox (Ricardo-AEA), A. Korzhenevych, N. Dehnen (DIW econ), J. Br¨
ocker, M.
Holtkamp, and H. Meier (CAU), Update of the Handbook on External Costs of Transport, London, DG
Mobility and Transport, 2014.
[31] NEN-EN 16258, Methodology for calculation and declaration of energy consumption and GHG emissions
of transport services (freight and passengers), 2012.
[32] M. Messagie , J. Mertens, L. Oliveira, S. Rangaraju, J. Sanfelix, T. Coosemans, J. Van Mierlo, C.Macharis,
The hourly life cycle carbon footprint of electricity generation in Belgium, bringing a temporal resolution in
life cycle assessment, Applied Energy, ISSN 0306-2619, 134(2014), 469–476.
[33] Belgisch Staatsblad, loi/change
&table name=wet, accessed on 2016-03-24.
Ir. Quentin De Clerck graduated in 2014 of a Master of Science in Applied Sciences and Engineer-
ing: Computer Science at the Vrije Universiteit Brussel and is since 2014 a Research Associate,
under the supervision of Lieselot Vanhaverbeke, for the research group MOBI (Mobility, Logis-
tics and Automotive Technology). His fields of expertise are electric vehicles, location analysis for
charging infrastructure and Total Cost of Ownership.
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Dr. Tom Van Lier is a postdoctoral research associate in the research group MOBI led by Prof. Dr.
Cathy Macharis at the Vrije Universiteit Brussel. His work focuses on evaluating the sustainability
of transport solutions by means of external transport cost calculations. He has been involved in
several research projects dealing with topics such as social cost-benefit analysis of transport options,
external cost savings of freight bundling, carbon footprint calculations and life-cycle assessment of
transport services. Within MOBI, he is also involved in the carbon emission reducing Lean & Green
project of the Flemish Institute for Logistics as neutral assessor.
Dr. Philippe Lebeau graduated in 2011 of a Master in Management Sciences at the Louvain School
of Management and became a Research Associate at the Vrije Universiteit Brussel. He also achieved
a Master in Transport Management in 2013. Since 2011, he belongs to the research group MOBI
(Mobility, Logistics and Automotive Technology), an interdisciplinary group focusing on sustain-
able logistics, electric and hybrid vehicles and travel behaviour. In that context, he is conducting
a Prospective Research for Brussels under the supervision of Prof. Dr. Cathy Macharis and Prof.
Dr. Joeri Van Mierlo. The project is called PULSE and is investigating an innovative distribution
network for the Brussels-Capital Region. His expertise fields are in sustainable logistics, electric
vehicles, urban freight transport in Brussels, Discrete Event Simulation, Total Cost of Ownership,
Conjoint Based Choice and Multi-Criteria Multi-Actor analysis.
Dr. Maarten Messagie is a full-time post-doc researcher at the Vrije Universiteit Brussel, where
he leads the LCA team of MOBI (Mobility, Logistics and Automotive Technology). In 2013 he
received his PhD in engineering from the Vrije Universiteit Brussel with the greatest distinction.
His expertise and research focus is on uncertainty propagation in Life Cycle Assessment, vehicle-
LCA, sustainable energy systems, mineral and metal depletion, electric vehicles, energy storage
technologies, transition pathways, sustainability concepts, consequential LCA, ecodesign, life cycle
management, environmental business model generation, market driven environmental consequences
of consumption and cleantech developments.
Prof. Dr. Lieselot Vanhaverbeke is Assistant Professor at the Vrije Universiteit Brussel, at the de-
partment of Business Technology and Operations (BUTO) and the research group Mobility, Logis-
tics and Automotive Technology (MOBI). She teaches Operations Research and Research Methods.
Her research on location analysis, consumer mobility and economic aspects of electric vehicles is
published in academic ISI journals. She is member of the Belgian Operational Research Society
(ORBEL) and The Institute for Operations Research and the Management Sciences (INFORMS).
Prof. Dr. Cathy Macharis is Professor at the Vrije Universiteit Brussel. She teaches courses in
operations and logistics management, as well as in transport and sustainable mobility. She has been
involved in several national and European research projects dealing with topics such as the location
of intermodal terminals, assessment of policy measures in the field of logistics and sustainable mo-
bility, electric and hybrid vehicles, etc. She is the chairwoman of the Brussels Mobility Commission.
Prof. Dr. Ir. Joeri Van Mierlo is currently a Full-Time Professor at this university, where he leads the
MOBI (Mobility and Automotive Technology Research) Centre. He is expert in the field of Electric
and Hybrid vehicles (batteries, power converters, energy management simulations) as well as to the
environmental and economical comparison of vehicles with different drive trains and fuels (LCA,
TCO). Prof. Van Mierlo was Vice-president of AVERE (2011-2014) the European Electric Vehicle
Association and board member its Belgian section ASBE. He chairs the EPE chapter “Hybrid and
electric vehicles”. He is an active member of EARPA (European Automotive Research Partner
Association) and member of EGVIA (European Green Vehicle Initiative Association). He is member
of the board of Environmental & Energy Technology Innovation Platform (MIP) and chairman of the
steering committee of the sustainable mobility platform of ENERGIK. He is IEEE Senior Member
and member of IEEE Power Electronics Society (PELS), IEEE Vehicular Technology Society (VTS)
and IEEE Transportation Electrification Community.
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... Battery leasing cost refers to the possibility for BEVs' owners to lease battery by paying a recurrent fee (e.g., on a monthly basis) instead of buying it with the car, also having the possibility to increase battery capacity over time (De Clerck et al., 2016, Lebeau et al., 2013. Finally, interest expenses refer to the interests paid on the loan supporting the vehicle purchase, which is addressed in only two contributions (Hagman et al., 2016;Scorrano et al., 2020a). ...
... cost is estimated by considering the unitary fuel price (e.g., €/l or €/kg) and the amount of fuel consumed throughout the OP. Unitary fuel price is typically estimated from reports issued by consulting firms or national associations(De Clerck et al., 2016;Lebeau et al., 2013;Propfe et al., 2012). Electricity cost is estimated by considering the unitary electricity price (€/kWh) and the amount of electricity consumed throughout the OP. ...
... Such possibility is not frequently addressed in the literature, being reported in only four contributions(De Clerck et al., 2016;Desreveaux et al., 2020;Lebeau et al., 2013;Rusich and Danielis, 2015). ...
Electric vehicles have faced a significant diffusion in the last years, however they still represent a minority share over passenger cars registrations worldwide. Economic factors play a crucial role in preventing a higher diffusion of electric vehicles, with particular reference to the higher purchase price compared to internal combustion engine vehicles. Indeed, potential electric vehicles buyers typically tend to underestimate long-term savings that can be achieved due to the lower operating costs characterizing electric vehicles compared to internal combustion engine vehicles (so-called “energy-efficiency paradox”). An emerging literature stream suggests that the availability of comprehensive economic comparisons between electric vehicles and internal combustion engine vehicles, based on the total cost of ownership, would increase the potential customers’ willingness to buy an electric vehicle. However, there is an ongoing debate on the cost competitiveness of electric vehicles considering the whole ownership period, and in particular on the most influencing factors affecting it. The paper aims to fill this literature gap by assessing the cost competitiveness of electric vehicles against a broad set of alternative powertrains, with reference to the Italian market. To this aim, an ad hoc assessment tool based on the total cost of ownership has been developed, leveraging the extant knowledge on the topic and addressing current gaps. Results show that battery electric vehicles are characterized by the lowest total cost of ownership among analysed powertrains in two out of four vehicle segments evaluated, i.e., segments A (mini cars) and C (medium cars). This is mainly due to the presence of purchasing incentives and the lower fuel-related and non fuel-related operating costs characterizing battery electric vehicles compared to other powertrains, which more than offset the higher purchase price. In other cases, the relatively high purchase price characterizing battery electric vehicles (as well as plug-in hybrid electric vehicles) negatively affects their cost competitiveness. The study provides suggestions for managers of companies within the e-mobility value chain and policymakers on the levers to promote the spread of electric vehicles to achieve the decarbonisation targets for the transportation sector.
... Total cost of ownership (TCO) is an appropriate functional unit for cost benchmarking which provides a comprehensive perspective, in terms of both purchasing and operating a vehicle [3]. A general approach is the use simplistic calculation tools -fixed energy mileage [4,5], fixed investment cost irrespective of geographical boundaries [6]. Secondly, most of the cost models focusing on total cost of ownership are limited to fewer transportation technologies and does not reflect how the market conditions and time can influence the cost analysis [2,7,8]. ...
Conference Paper
div class="section abstract"> This study investigates the techno-economic feasibility of India’s evolving transportation technology. The country’s progressive renewable energy targets (energy independent by 2047) and incentivized policies on lower carbon footprint fuels are accelerating the focus on green transport solutions. A bottom-up approach is utilized to demystify the techno-commercial viability of new technologies. The total cost of ownership (TCO) is an important metric for economic analysis. However, generalized data applications and simplified cost assumptions render inapplicability to local markets. In this study, the TCO model compares the vehicle technology’s energy, emissions, and cost, based on scientific co-relations. A 12-meter-bus market is used to compare Battery-powered Electric buses (BEB), Fuel Cell Electric Buses (FCEB), and prevalent Compressed Natural Gas Engine buses (CNGB) for a service life of 12 years. The analysis has two segments: Static analysis depicts the influencing factors (fuel production cost, maintenance, module life) while dynamic simulation shows the effect of technological innovation, carbon incentives, and value of money (employs declining balance method). In the model, TCO for FCEBs ($142/100km) is higher compared to BEBs ($87/100km) and CNG’s ($93/100km) primarily due to energy-infrastructure cost ($5.7/kg) and module maintenance ($0.5/km). However, the life cycle emissions of FCEB (including both fuel and vehicle cycle) are 2.3 times lesser than the second lowest BEB. In the dynamic analysis, the study quantified crucial conditions and innovations (e.g., H<sub>2</sub> production cost drop from $2.7/kg to $1.8/kg, module mileage improvements from 12MJ/km to 10 MJ/km by 2030) for FCEBs commercial acceptability, synchronous with the country’s energy and emission targets. </div
... Members of the MOBI research Group at the Vrije University in Brussels do a series of studies inspecting the role of the TCO in various aspects of logistics and passenger transport. For example, they compare electric and conventional vehicles for logistics [Macharis et al., 2013], conduct a TCO analysis on electrifying light commercial vehicles for city logistics [Lebeau et al., 2015], analyse the light commercial vehicle segment to determine how to improve the TCO [Lebeau et al., 2019], and extend the TCO concept by including the cost for society linked to the purchase of a vehicle [De Clerck et al., 2016]. ...
Groupe Renault is the largest car manufacturer in France. A significant part of its revenue comes from the sale of Light Commercial Vehicles (LCVs). These LCVs with their large carrying capacity for their small size and turning radius are used readily to transport goods inside cities. Indeed, a significant portion of the transport of goods inside cities is accomplished using LCVs. This has been the case for the last few decades. Parallelly, over the last few decades, there have been two changes relevant to the themes of this thesis. First, the size of cities, and thus the demand for goods inside them has grown and keeps on growing. Secondly, a new kind of vehicle technology - autonomous driving - has undergone rapid development and is - according to various industry estimates - at the cusp of adoption. At the time of writing, in some parts of the world, some of these vehicles are currently actively delivering goods. These vehicles may be used for the transport of goods inside cities, for which there is a growing demand. Groupe Renault is faced with the question of whether the advent of autonomous vehicles will impact the sales of LCV’s used for the transport of goods inside cities? This thesis finds its birth in this strategic question Groupe Renault is faced with. The transport of goods inside cities is diverse. There are movements of different kinds with different characteristic. To respond to this question, first a relevant subset of the transport of goods inside cities must be chosen. For this thesis, last mile deliveries are chosen as the domain of interest. Then, to respond to the question, according to the author an intimate cognizance of how LCVs are currently used is necessary. It is also important, to develop a broad yet sufficiently detailed understanding of the operating process inside which these vehicles functions. Once this is accomplished, the question of 8 substitution of these vehicles by a newer technology can be raised. To attain this knowledge, field visits were undertaken: time was spent at various firms engaged in the activity of last mile logistics. The operational process was attentively studied. Interviews were also conducted; professionals and academicians working in the domain of autonomous vehicles and last mile logistics were questioned. This permits the broaching of the question of substitution. This is not sufficient as it is also necessary to have a relevant metric to gauge the substitution with. How to evaluate whether using autonomous vehicles to deliver goods is better than using conventional vehicles (LCVs)? What is better? How is it better? For whom is it better? If it is better, how much better is it and why? This thesis proposes a analytical framework to tackle these questions. The question of substitution is looked at through the angle of cost from a private perspective; for a logistics firm, which vehicle - under a specific set of operating parameters - offers the least cost per delivery? To inspect this question a microeconomic model of costs is developed. Using this model, various vehicles are compared over a set of operating parameters
... The best electric vehicle is that with a single charge, which can travel as far as required without sacrificing the number and comfort of passengers. The goal of all of this is the achievement of suitable costs for the manufacturing and operation of electric vehicles [16]. If not efficient, the design is considered a failure or unworthy of being marketed. ...
Full-text available
This study aimed to determine and analyze the performance of an electric motor installed in a small city car, which was an internal combustion engine (ICE) car with manual transmission and front-wheel drive converted into an electric vehicle. A manual transmission vehicle was used, considering its type is the cheapest. This was to push aside the perception that electric cars are not accessible to the lower classes. Another technical matter was the focus on the power and torque performance of the electric motor and the transmission. A 7.5 KW three-phase induction motor was installed and assembled with 200 AH 76.8 VDC batteries. Electronic power steering (EPS) and the air conditioner (AC) were not operated, while power for the electrical accessories and power analyzer was obtained from a separate 12 VDC battery. Vehicle analysis focused on the power consumption, which was measured and acquired using a power analyzer. The vehicle was driven in real terms with three passengers. GPS was also used to determine the vehicle position and collect elevation data during testing. The derivatives of the GPS data were the speed, acceleration, and distance traveled by the vehicle. The initial hypothesis was that the car could cover a distance of 30 km with regular usage.
... The recent studies which addressed environmental and economic costs in parallel lack this consistency, using disparate models and scenarios for economic and environmental results [3,8,18,19]. Most recent total cost of ownership (TCO) studies showed that current internal combustion vehicles (ICEV) have lowest TCO, while BEV TCO is expected to be lowest in the future [19][20][21][22][23][24]. Battery and fuel price developments have been identified as major drivers for future TCO rankings [8,18,20]. ...
Full-text available
In this analysis, life cycle environmental burdens and total costs of ownership (TCO) of current (2017) and future (2040) passenger cars with different powertrain configurations are compared. For all vehicle configurations, probability distributions are defined for all performance parameters. Using these, a Monte Carlo based global sensitivity analysis is performed to determine the input parameters that contribute most to overall variability of results. To capture the systematic effects of the energy transition, future electricity scenarios are deeply integrated into the ecoinvent life cycle assessment background database. With this integration, not only the way how future electric vehicles are charged is captured, but also how future vehicles and batteries are produced. If electricity has a life cycle carbon content similar to or better than a modern natural gas combined cycle powerplant, full powertrain electrification makes sense from a climate point of view, and in many cases also provides reductions in TCO. In general, vehicles with smaller batteries and longer lifetime distances have the best cost and climate performance. If a very large driving range is required or clean electricity is not available, hybrid powertrain and compressed natural gas vehicles are good options in terms of both costs and climate change impacts. Alternative powertrains containing large batteries or fuel cells are the most sensitive to changes in the future electricity system as their life cycles are more electricity intensive. The benefits of these alternative drivetrains are strongly linked to the success of the energy transition: the more the electricity sector is decarbonized, the greater the benefit of electrifying passenger vehicles.
The paper focuses on measuring and quantification of the negative externality of noise pollution generated by freight transport in the Slovak Republic and the Czech Republic. The paper describes negative impacts and significance of noise externalities, whereas it is established that noise causes psychological and physiological harm to affected persons. A separate part of the paper is dedicated to the current status of the European legislation dealing with the issues of the negative externality of noise pollution, in particular Directive 2002/49/EC of the European Parliament and of the Council and Communication COM(2008) 435. The actual measurement of the total, average and marginal costs of noise pollution is implemented in line with the defined methodology and using expert studies defined in the paper. The measurement results show that the costs of the negative externality of noise pollution are high in both countries. According to authors’ calculations, the total costs of the negative externality of noise pollution amount to EUR 100.8 mil in the Czech Republic and EUR 16.9 mil in the Slovak Republic. The paper contains a proposal of internalisation of these costs in the form of performance charges applied to operation of heavy goods vehicles.
Full-text available
Despite their low environmental impact, electrical vehicles have low penetration in the automotive market. Consumers are reluctant for technical reasons (limited driving range and long charging time) but also for an economic reason (high investment costs). Electric vehicle total cost of ownership (TCO) is often perceived as higher than for a thermal car, especially in Europe where diesel cars have a lower TCO than gasoline cars. Accurate TCO estimations are critical, but most of the techno-economic studies of electrified vehicles are based on very simplified energy models. In this paper, a techno-economic model is developed using an accurate technical model of an electric vehicle and a diesel car of the same segment. These technical models are validated by experimental measurements on real cars using real driving cycles. These models are then coupled to economic models to calculate TCO for a French case study. The total cost of ownership of the studied electric car is lower than for the equivalent diesel car by about 1000€ for a 5-year ownership period. Of particular importance is the finding that using real driving cycles instead of standard driving cycles decreases the TCO of electric cars while simultaneously increasing the TCO of diesel vehicles. This has implications for techno-economic models, suggesting that the typical TCO approach that uses manufacturer-reported standard cycle data may be systemically biased towards thermal vehicles. In order to understand how TCO may change in different locations, a sensitivity analysis varies different technical and economic factors. Government subsidy, ownership duration, and vehicle depreciation are the most important factors for the TCO of electric vehicles. However, TCO of the electric cars can be lower than the TCO of equivalent diesel cars under a wide range of reasonable inputs.
Waste transport plays an important role in the decarbonization of the transport sector. In this article, diesel-powered collections vehicle (dWCV) and electric waste collections vehicle (eWCV) and their operation are analyzed regarding energy demand and total cost of ownership (TCO) integrating well-to-wheel emission costs. Furthermore, an open-source simulation tool with a route synthetization approach is presented using extensive real-life operational data of five different route types. Determined WCV energy demand varies greatly between vehicle topologies and analyzed route types. eWCV shows a mean distance-specific energy demand of 1.85 kWh ·km <sup xmlns:mml="" xmlns:xlink="">-1</sup> , while values for dWCV increase to 5.43 kWh ·km <sup xmlns:mml="" xmlns:xlink="">-1</sup> , respectively. The factors route distance and the number of waste containers collected show the highest influence on results. Therefore, battery capacity should be sized according to specific route types. eWCV shows higher TCO than dWCV under current economic constraints, but fuel price level and annual vehicle mileage show a high influence on economic feasibility. Taking the planned emissions price mechanism of the German Government into account, economic scenarios could be identified, which make eWCV advantageous yet in 2021. In technical terms, there is nothing to stop for the electrification of WCV, and with suitable political instruments, eWCV could become profitable in the short term.
Conference Paper
Full-text available
This paper presents a total cost of ownership (TCO) model for three different car segments. The goal is to investigate the cost efficiency of electric vehicles compared to conventional vehicles. All costs that occur during the expected vehicle's lifespan are included: purchase cost, registration tax, vehicle road tax, maintenance, tires and technical control cost, insurance cost, battery leasing cost, battery replacement cost and fuel or electricity cost. Results are shown per vehicle segment and illustrate the share of all cost components. We find that current electric vehicles are only cost attractive within the premium car segment.
Full-text available
To properly evaluate the prospects for commercially competitive battery electric vehicles (BEV) one must have accurate information on current and predicted cost of battery packs. The literature reveals that costs are coming down, but with large uncertainties on past, current and future costs of the dominating Li-ion technology. This paper presents an original systematic review, analysing over 80 different estimates reported 2007-2014 to systematically trace the costs of Li-ion battery packs for BEV manufacturers. We show that industry-wide cost estimates declined by approximately 14% annually between 2007 and 2014, from above US$1,000 per kWh to around US$410 per kWh, and that the cost of battery packs used by market-leading BEV manufacturers are even lower, at US$300 per kWh, and has declined by 8% annually. Learning rate, the cost reduction following a cumulative doubling of production, is found to be between 6 and 9%, in line with earlier studies on vehicle battery technology. We reveal that the costs of Li-ion battery packs continue to decline and that the costs among market leaders are much lower than previously reported. This has significant implications for the assumptions used when modelling future energy and transport systems and permits an optimistic outlook for BEVs contributing to low-carbon transport.
Full-text available
Vehicles with alternative drive trains are regarded as a promising substitute for conventional cars, considering the growing concern about oil depletion and the environmental impact of our transportation system. However, ―clean‖ technologies will only be viable when they are cost-efficient. In this paper, the environmental impacts and the financial costs of different vehicle technologies are calculated for an average Belgian driver. Environmentally friendly vehicles are compared with conventional petrol and diesel vehicles. The assessments are done from a life cycle perspective. The effect on human health, resources and ecosystems is considered when calculating the environmental impact. The total cost of ownership (TCO) model includes the purchase price, registration and road taxes, insurance, fuel or electricity cost, maintenance, tires replacement, technical control, battery leasing and battery replacement. In the presented analysis different vehicle technologies and fuels are compared (petrol, diesel, hybrid electric vehicles (HEVs), battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs)) on their level of environmental impact and cost per kilometer. The analysis shows a lower environmental impact for electric vehicles. However, electric vehicles have a higher total cost of ownership compared to conventional vehicles, even though the fuel operating costs are significantly lower. The purchase cost of electric vehicles is highly linked to the size of the battery pack, and not to the size of the electric vehicle. This explains the relative high cost for the electric city cars and the comparable cost for the medium and premium cars.
A meta-analysis of 44 studies that conduct a private, external and/or total social cost comparison among conventional and electric vehicles shows that, independent of the studies' goals, the results are often misleading. This distortion occurs because of the omission of one or more relevant cost components and/or the impact of divergent and often unspecified assumptions, which is demonstrated through three detailed examples. Although 30 studies compared private costs, one-third only considered purchase and fuel costs and ignored other costs. Charging infrastructure and residual value were only considered in four and eight studies, respectively. Thirty-five authors performed an external cost evaluation, of which 12 were expressed in monetary terms. The majority of the non-monetary studies only consider one external polluting factor, which is generally CO2/GHG, whereas the monetary studies generally evaluate four or more polluting factors. Furthermore, this article drafts a methodological checklist that (1) defines the preferred evaluation methods according to the study goals, (2) includes all private and external costs in the production, acquisition, usage and disposal stages as well as the existing policy measures and (3) lists the general assumptions that should be specified. This checklist enhances consistent comparability among various social cost studies of different vehicle types, and it supports policy-makers in drafting evidence-based transportation policy conclusions.
Motor vehicles represent one of the widely owned assets in the US. A vehicle’s ownership cost includes fixed expenses to purchase and own the vehicle and variable costs to use and operate the vehicle. Policymakers, analysts and consumers are interested in understanding the total ownership costs of various vehicle types and technologies so as to understand their relative consumer preference and valuation. Plug-in hybrid electric vehicles are an advanced technology vehicle that is presently in limited production, but whose relative cost of ownership is not well-defined. A few studies have attempted to calculate the costs and benefits of PHEVs but none consider the cost and benefits of PHEVs at a level of detail comparable to what has been performed for other vehicle technologies. In order to understand the costs and benefits of PHEVs purchase and use, this study constructs a comprehensive ownership cost model. The model is then used to analyze different PHEV designs within four vehicle classes. This study then performs a sensitivity analysis to understand the sensitivity of total ownership cost and payback period to model parameters and the modeled components of ownership costs. Results show that a more comprehensive PHEV ownership cost model has a lower net cost of ownership than studies to date, resulting in a shorter payback period and higher consumer preference.
Life-cycle costing is a method of calculating the total cost of ownership over the life span of the industrial product. It can be especially useful in the marketing of industrial products that sell for high initial prices, but which provide long-run cost savings. This paper explains the life-cycle concept and its implementation.
High crude oil prices and pollution problems have drawn attention to alternative vehicle technologies and fuels for the transportation sector. The question is: What are the benefits/costs of these technologies for society? To answer this question in a quantitative way, a web-based model ( has been developed to calculate the societal life cycle costs, the consumer life cycle costs and the tax for different vehicle technologies. By comparing these costs it is possible to draw conclusions about the social benefit and the related tax structure. The model should help to guide decisions toward optimality, which refers to maximum social benefit. The model was applied to the case of Thailand. The life cycle cost of 13 different alternative vehicle technologies in Thailand have been calculated and the tax structure analyzed.
New electrified vehicle concepts are about to enter the market in Europe. The expected gains in environmental performance for these new vehicle types are associated with higher technology costs. In parallel, the fuel efficiency of internal combustion engine vehicles and hybrids is continuously improved, which in turn advances their environmental performance but also leads to additional technology costs versus today’s vehicles. The present study compares the well-to-wheel CO2 emissions, costs and CO2 abatement costs of generic European cars, including a gasoline vehicle, diesel vehicle, gasoline hybrid, diesel hybrid, plug in hybrid and battery electric vehicle. The predictive comparison is done for the snapshots 2010, 2020 and 2030 under a new energy policy scenario for Europe. The results of the study show clearly that the electrification of vehicles offer significant possibilities to reduce specific CO2 emissions in road transport, when supported by adequate policies to decarbonise the electricity generation. Additional technology costs for electrified vehicle types are an issue in the beginning, but can go down to enable payback periods of less than 5 years and very competitive CO2 abatement costs, provided that market barriers can be overcome through targeted policy support that mainly addresses their initial cost penalty.