Total Cost of Ownership Comparison for Different Drivetrains of Private Transport Vehicles


Master's Thesis, 2020

88 Pages, Grade: 1,0


Excerpt

Table of Contents

List of Figures

List of Tables

List ofSymbols

List of Abbreviations

1 Introduction

2 Literature Review
2.1 Drivetrains ofPrivate Transport Vehicles
2.2 Total Cost of Ownership
2.3 One-timeCosts
2.3.1 Investment Costs
2.3.2 Battery Replacement Costs
2.3.3 ResaleValue
2.4 Annual Operating Costs
2.4.1 Energy Consumption Costs
2.4.2 Inconvenience Costs
2.4.3 Other Operating costs

3 Design of Total Cost of Ownership Analysis
3.1 Model and Data
3.1.1 TCOModel
3.1.2 VehicleSample
3.1.3 DriverProfile
3.2 One-timeCosts
3.2.1 Investment Costs
3.2.2 BatteryReplacementCosts
3.2.3 ResaleValue
3.3 Annual Operating Costs
3.3.1 Energy Consumption Costs
3.3.2 Inconvenience Costs
3.3.3 Other Operating Costs

4 Total Cost of Ownership Estimates
4.1 BaseCaseScenario
4.2 Sensitivity Analysis
4.2.1 DriverProfile
4.2.2 InvestmentCosts
4.2.3 ResaleValue
4.2.4 Energy Consumption Costs
4.2.5 InconvenienceCosts
4.2.6 DiscountRate

5 Conclusion

List ofReferences

List of Acts, Administrative Orders and Administrative Regulations

Appendix

Declaration of Academic Honesty

List of Figures

Figure 1 - Base Case TCO Estimates

List of Tables

Table 1 - Environmental Bonus for Alternative Drives

Table 2 - Energy Carrier Cost Parameter

Table 3 - TCO Sensitivity per Drivetrain and Vehicle Segment to the Driver Profile.

Table 4 - TCO Sensitivity per Drivetrain and Vehicle Segment to the Discontinuation of Subsidies

Table 5 - TCO Sensitivity per Drivetrain and Vehicle Segment to Resale Value Variations by ±10%

Table 6 - TCO Sensitivity per Drivetrain and Vehicle Segment to the Adjustment of the Resale Value Calculation Basis

Table 7 - TCO Sensitivity per Drivetrain and Vehicle Segment to Energy Carrier Price Variations by ±10%

Table 8 - TCO Sensitivity per Drivetrain and Vehicle Segment to an Increase of the Discount Rate by One Percentage Point

List of Symbols

Abbildung in dieser Leseprobe nicht enthalten

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

1 Introduction

Transportation is the source of 25% of the EU-27 greenhouse gas emissions.1 Specifically, cars are responsible for around 12% of the EU-27 emissions of carbon.2 Additionally, emissions from the transport sector have increased continuously since 2014.3 Therefore, the reduction of emissions from the transport sector is an integral element of the EU climate and energy policy to comply with the Paris Agreement4.5

The electrification of the transport sector offers the opportunity to utilize the increasing share of renewable energy generation.6 Thus, scholars believe that electric vehicles, such as battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and fuel cell electric vehicles (FCEVs), have the potential to lower emissions and contribute to the transformation of the mobility sector.7

However, the breakthrough of electric vehicles still faces several barriers. For instance, BEVs suffer from a short driving range, long recharging processes, and limited charging infrastructure that cause consumers’ hesitation to buy BEVs.8 Similarly, FCEVs need to overcome the lack of hydrogen fuel infrastructure to be functionally competitive with conventional vehicles.9

Perhaps the most significant barrier appears to be the public's general consensus that electric drivetrains are more expensive than conventional drives.10 However, the literature suggests that electric vehicles might already be more cost-efficient than comparable conventional drives.11 Yet, the cost-benefits are mostly non-transparent to consumers as they put too much weight on the high initial investment costs compared to conventional internal combustion engine vehicles (ICEVs) and underestimate the cost savings through lower operating costs.12 In order to overcome this misjudgment and to help consumers make more profound purchasing decisions, the literature suggests the total cost of ownership (TCO) approach, which includes both investment and operating costs over the ownership period.13

This study aims to compare the consumer-oriented TCO for different drivetrains of private transport vehicles in Germany.1415 Although the transportation sector emits 19% of Germany’s greenhouse gases,16 electric vehicles only account for 17% of the newly registered vehicles in the first half of 2020.17 As a result, the German government approved an increase in subsidies for the purchase of electric vehicles in June 2020 to promote the attractiveness of more environmentally friendly vehicles.18 Against that background, this study investigates whether conventional drives still exhibit a financial advantage over electric vehicles. For that purpose, this study compares the TCO of BEVs, PHEVs, HEVs, FCEVs as well as petrol and diesel ICEVs.

This thesis is structured as follows. A literature review in section 2 introduces the different drivetrains, the TCO model, and the TCO cost components.19 Section 3 presents the design of this study’s TCO model and explains how its components are quantified. Section 4 lays out the results of the TCO calculations and critically evaluates the estimates against other studies' findings. Moreover, in order to address uncertainties concerning the quantification of the cost components, this section performs a sensitivity analysis of the results to variations ofkey parameters and assumptions. Section 5 concludes.

2 Literature Review

2.1 Drivetrains ofPrivate Transport Vehicles

This study comprises two conventional and four alternative drivetrains. Petrol and diesel ICEVs are referred to as conventional drivetrains in this study. ICEVs are powered by a heat engine, which generates mechanical energy from the combustion of fuel.20 The internal combustion engine (ICE) can be driven by either petrol or diesel. Therefore, ICEVs are associated with short refueling time.21

Alternative drivetrains included in this study are BEVs, HEVs, PHEVs, and FCEVs. BEVs are propelled by an electric machine powered by electricity stored in an onboard traction battery.22 In contrast to ICEVs, BEVs are characterized by shorter driving ranges and longer refueling time.23

HEVs combine an onboard ICE with an electrical energy storage system and an electric motor.24 HEVs can be subdivided according to their hybrid architecture. In serial configuration, the ICE is used to generate electricity to drive the electric engine25. In parallel configuration, the ICE is mechanically linked to the wheels and allows the parallel operation of both engines.26 The onboard electric motor optimizes the efficiency of the ICE and recovers the kinetic energy during braking or coasting of the vehicle. Therefore, HEVs are more fuel-efficient compared to conventional vehicles.27

Plug-in hybrid electric vehicles are a subcategory of HEVs. While the electric motor of HEVs can only be powered internally, PHEVs can be charged from the electric grid and driven by a larger traction battery for driving ranges of more than 70 km28.29 PHEVs are typically operated in parallel configuration. The serial configuration of a PHEV is known as range-extended electric vehicle (REEV).30 PHEVs usually operate in charge-depleting mode, in which the onboard battery powers the electric machine until the battery reaches a minimum state of charge.31 Once the minimum state of charge is reached, PHEVs change to charge-sustaining mode. That is, PHEVs in parallel configuration operate both drivetrains simultaneously while sustaining the minimum state of charge of the battery.32 In REEVs, the internal combustion engine generates enough energy to sustain the state of charge until the battery is recharged.33

FCEVs are powered by an electric motor using fuel cells to generate electricity from hydrogen. The electricity is either used to drive the vehicle directly or is stored in an energy storage device, such as a battery pack.34 The electric energy is generated through an electrochemical reaction of hydrogen and oxygen, where the reaction byproduct is water. Hence, FCEVs operate locally emission-free.35 For that reason, FCEVs may play a role in mitigating emissions without facing the same limitations in range and refueling time as BEVs.36

2.2 Total Cost of Ownership

The TCO is a purchasing tool and a philosophy aimed at understanding the true cost of buying a particular good or service from a particular supplier.37 Because the TCO is a complex approach, the buyer needs to determine which present and future costs are considered the most important or significant in the acquisition, possession, use, and subsequent disposition of a good or service.38

Scholars propose the TCO as an alternative metric that a rational buyer should take into account when deciding which vehicle to acquire.39 The TCO captures the total discounted costs of the acquisition, operation, and sale of the vehicle.40 The TCO provides a longterm purchasing orientation and deemphasizes the investment costs. Hence, the TCO approach provides the opportunity tojustify higher initial prices based on lower total costs of the vehicle in the long run.41

In theory, the TCO approach is not equivalent to the life cycle costing methodology. Instead, life cycle costing represents a subset of TCO activity. TCO is more comprehensive and includes pre-purchase costs.42 However, most reviewed research designs use the terms interchangeably, as they do not include pre-purchase costs in their TCO calculations.43

As illustrated in Appendix 1, the considered TCO cost parameters are not standardized in the literature. However, the TCO is generally defined as the sum of discounted one-time costs (OTC) and annual operating costs (AOC) as TCO = OTC * PVF + ELi -^t,44 where i is the discount rate, t is the vehicle ownership period, and PVF is the present value factor. The PVF is defined as 1/(1 + i')t. Future expenses are discounted to the present value in order to account for the timing of the costs.45 The equation implies that the impact of the annual operating costs on the TCO increases with the holding period and decreases with the discount rate, which reflects the buyer’s opportunity cost.46 The TCO is often presented as annual costs and divided through the achieved output, the vehicle’s annual driven kilometers, resulting in TCO per kilometer.47

While the TCO creates significant opportunities for cost savings, the widespread adoption of TCO is limited due to its complexity and a lack of readily available costing data.48 Moreover, the estimation of the TCO metric is associated with difficulties regarding the distinction of private and social costs, the uncertainty of future costs, the impact of regulatory and fiscal policies, and the inherently vehicle-, regional- and individual-specific nature of the estimates.49 Consumer-oriented TCO studies comprise the private costs perceived by the consumer. On the other hand, society-oriented TCO studies integrate the external costs associated with externalities, such as carbon dioxide emissions and noise, in addition to the consumer-oriented TCO.50 The inclusion of social costs makes the TCO calculation more challenging and adds uncertainty to the results due to the monetization of nonmarket goods and services.51 The literature proposes probabilistic TCO models in order to incorporate uncertainties with respect to technical, economic, and political aspects.52 Yet, most studies use deterministic parameters to calculate the TCO.53

2.3 One-time Costs

2.3.1 Investment Costs

The investment costs can be determined using either actual vehicle prices or estimates of hypothetical vehicle prices.54 The actual vehicle prices usually base on the manufacturer’s suggested retail price (MSRP). In contrast, the costs for hypothetical vehicles base on top-down experience curves or a bottom-up engineering assessment based on the costs of its components.55

Differences in the investment costs of electric and conventional vehicles are the main driver for the cost differences between the varying technologies.56 The purchase price of an electric vehicle consists of a relatively constant price for the chassis and drivetrain and on the price for the battery system. The electric components such as the electric machine or power electronics are highly developed vehicle parts. Therefore, the costs for these components are not expected to profit from further economies of scale.57 On the other hand, the battery price, which accounts for the majority of BEVs’ investment costs,58 is assumed to decrease in the future.59 Similarly, FCEVs suffer from high investment costs due to the costly fuel cell system, accounting for more than half of the investment costs.60

Using the bottom-up approach to estimate the investment costs requires a set of inputs on a very detailed component level. Based on an extensive review of literature, specific details in terms of the manufacturing process, materials used, and individual components of the product are being taken into account to assess the direct and indirect costs attributed to the process.61 The calculation of the investment costs for an electric vehicle usually starts with estimating the battery cell costs based on the vehicle’s required battery capacity and the battery costs per capacity.62 While some studies state that the lithium-ion battery technology is the dominant automotive battery technology63, other studies consider different battery technologies for different powertrains64. Further, even for the same battery technology, different battery costs per capacity can be applied for different powertrains. Because of their smaller size, the costs per battery capacity are assumed to be higher for HEVs, PHEVs, and FCEVs than for BEVs. This effect results from the costs of pack components that are independent of the number of cells.65 To derive the total costs of the battery implementation, other cost positions, such as pack integration and thermal management costs, are added.66 The costs for remaining powertrain components, such as the fuel cell system and onboard storage tank for FCEVs,67 and the glider68 are incorporated to complete the calculation of the total vehicle costs.69 Lastly, a markup is added to the manufacturer’s production costs to account for overhead costs and a profit margin.70

In order to meet climate targets, fiscal incentives are designed to increase the attractiveness of sustainable vehicles by reducing the vehicle’s TCO. However, the level and design of incentives vary greatly across countries.71 Subsidies reduce the investment costs and, therefore, have to be subtracted from the retail price to calculate the investment costs.72 However, for instance, Bekel and Pauliuk (2019) do not consider government support schemes and price reductions as these measures are limited in time.73 Fiscal incentives in the form of tax exemptions reduce the vehicle’s annual operating costs and will be covered in the other operating costs (section 2.4.3).74

Irrespective of the estimation approach used, investments for the necessary charging infrastructure at home need to be considered for BEVs and PHEVs.75 In addition, some studies assume that the buyer finances the vehicle purchase with a vehicle or private bank loan. Therefore, interests are considered as part of the annual operating costs. The interest payments due are calculated by applying a down payment and an annual interest rate.76

2.3.2 BatteryReplacementCosts

The lifetime of traction batteries is limited in transport applications.77 Once the capacity of the vehicle battery falls below 80%, the literature generally assumes that the end of the battery life is reached.78 Therefore, reviewed research designs include the costs for a battery replacement if the vehicle ownership period exceeds the battery lifetime.79

Although the accurate prediction of the battery life is critical for the development of electric vehicles, the precise prediction is challenging due to diverse aging mechanisms, significant device variability, dynamic operating conditions, and the typically nonlinear degradation process.80 The battery life is generally determined by calendar life and cycle life.81 Both the calendar and cycle life need to be evaluated independently and cannot be simply summed together.82 The calendar life is expected to be longer than the vehicle life and hence, does not trigger battery replacement costs.83

For the batteries of HEVs and FCEVs, which are only charged by recuperation, reviewed studies assume a maximum mileage of 250,000 km.84 Accordingly, none of the reviewed studies includes costs for the battery replacement of HEVs and FCEVs.85 For BEVs and PHEVs, the lifetime can be estimated based on the number of load cycles.86 The cycle life of a battery is expressed by the number of possible load cycles and depends on various factors, such as driving conditions, humidity, operating temperature, the state of charge, and the depth of discharge.87 If the number of necessary load cycles is greater than the number of possible load cycles, the electric battery must be replaced.88 Different studies estimate that several thousand cycles are possible.89 If the battery needs to be replaced, the costs of a new battery are calculated analogously to the estimation of the investment costs of a battery when the vehicle is acquired but depend on the battery price at the time of replacement.90

As shown in Appendix 1, other studies neglect battery replacement costs.91 For instance, Danielis, Giansoldati and Rotaris (2018), claim that no significant battery degradation is expected for a maximum total driving distance of 90,000 km assumed in their model.92 Similarly, Wu, Inderbitzin and Bening (2015) consider no battery replacement costs based on their observation that the battery warranties of OEMs exceed the holding period and the driving range of their model.93 In addition, Martinez-Laserna et al. (2018) question whether the current threshold of 80% of the initial capacity is still valid. They explain that as the driving range of electric vehicles increases, a large share of the daily driving needs would still be met by batteries at 80% and lower remaining capacity values.94

In addition, the lifetime of the fuel cell stacks of FCEVs is limited.95 However, the reviewed studies either do not consider the replacement costs for fuel cells96 or do not explain the derivation of their estimates for future fuel cell cost.97

2.3.3 ResaleValue

If the useful life of the vehicle exceeds the ownership period of the first owner, this constitutes a resale value for the vehicle after the end of the holding period.98 The implied vehicle depreciation is usually the highest cost factor in the TCO model and, consequently, of high importance for precise estimation.99 However, the estimation of the resale value is challenging, as various factors play a role. The annual mileage and holding period are certainly leading parameters.100 In addition, other factors, such as driving habits, color, brand, size, fuel prices, maintenance costs, quality scores as well as specific market demand, influence the resale value.101

The reviewed studies employ different approaches to determine the residual value of the vehicle. Some of them determine the residual value by a price function that takes the characteristics of the vehicle into account.102 For instance, Dexheimer’s hedonic price index incorporates the purchase price, the holding period, and the annual mileage.103 Other studies calculate the resale value as a fixed percentage of the purchase price.104

A crucial question for the comparison of the TCO across different drivetrains is to evaluate whether the depreciation rate differs across different propulsion systems. While the depreciation rate for conventional vehicles is sufficiently known, it is uncertain for alternative drives.105 That is, the empirical findings on the resale value of different drivetrains are ambiguous. For instance, based on the analysis of actual auctions, Gilmore and Lave (2013) find that HEVs retain a higher value of their initial purchase price compared to conventional drives. Moreover, they conclude that the difference in resale value can be explained by the difference in future energy consumption costs.106 In contrast, Lévay, Drossinos and Thiel (2017) analyze different databases107 of second-hand vehicles and find that electric vehicles lose a larger share of their initial value compared to conventional vehicles. However, the results of their study are limited by the absence of sufficient data on the price of electric vehicles that were four years old in 2014 when they conducted their analysis.108 Contrary to both findings, van Velzen et al. (2019) examine a large database of second-hand vehicles and conclude that BEVs are not subject to a significantly different depreciation rate.109

Accordingly, the assumed depreciation rate varies in the literature. Often, studies assume the same depreciation rates across different powertrains due to the lack of unambiguous findings.110 Nevertheless, several studies apply a discount to the resale value of alternative powertrains compared to conventional drives due to the less mature market and the rapid technological depreciation.111 Again, other studies do not consider a resale value at all due to the uncertainty or long holding periods.112

Danielis, Giansoldati and Rotaris (2018) capture the uncertainty with respect to the residual value by treating the resale value as a normally distributed stochastic variable with a mean equal to the baseline residual value.113 Moreover, they assume that the retained value at the end of the holding period is currently lower for BEVs than for ICEVs and HEVs due to the rapid technological development of BEVs. However, they suggest that this trend reverses by 2025 as ICEVs will probably be an old technology subject to many limitations, such as inner-city access restrictions.114

Other studies address the variations between the residual values of different drivetrains by calculating the resale value of the battery separately from the vehicle.115 Battery recycling entails costs, energy, and waste. Instead, the reuse of retired traction batteries as second-life energy storage for non-vehicular applications can generate a revenue stream to help overcome investment costs and create synergic value for energy storage.116 The resale value of the tractions battery depends on the consumer's driver profile, the manufacturer's battery warranty, and the battery value at the end of its second life. The battery value at the end of the second life, in turn, depends on the price of the lithium-ion battery and the recycling costs in the recycling year.117

There is only little experience today from the nascent market for second-life batteries.118 Challenges primarily reside in competition with the decreasing cost of new battery manufacturing and a potentially long and technical refurbishment process. In addition, scholars expect higher failure rates and possibly also a higher fire hazard.119 Moreover, the economic viability and market incentives for recycling are to date limited due to generally low raw material prices and small quantities of batteries for electric vehicles.120 Therefore, scholars suggest that retired traction batteries will only be available for recycling after a progressive market ramp-up and a delay of about 10to 15 years.121

2.4 Annual Operating Costs

2.4.1 EnergyConsumptionCosts

Energy consumption costs are typically the most important component of the operating costs of the TCO.122 Besides the technological configuration of the propulsion technology, the driving pattern of the owner influences energy consumption.123 The energy consumption costs correspond to the product of the driving distance, the consumption efficiency124 of the respective powertrain, and the price of the respective energy carrier.125

The literature takes uncertainties regarding the differences between test and real fuel consumption through different methodologies into account. One approach addresses the uncertainties through the application of a probabilistic model treating the consumption economy126 or energy carrier prices127 as stochastic parameters. A second methodology adds a real-world consumption uplift to the manufacturers’ stated fuel consumption.128

Compared to internal combustion engines, BEVs and PHEVs offer significantly better energy efficiency and cause lower energy consumption costs for the car holder.129 However, the assumptions about the charging behavior influence their electricity costs. That is, the electricity costs depend on whether the vehicle is charged at home, at work, or at public charging stations.130 In addition, the charging frequency influences the share of electric driving ofPHEVs. Usually, studies assume overnight charging.131

In order to calculate the energy consumption costs ofPHEVs, the first step is to estimate the share of the electrically driven kilometers. This share is also denoted as electric driving share or utility factor.132 Generally, a larger battery capacity leads to a higher proportion of electric driving as more trips can be covered within the electric driving range of the vehicle.133 However, due to the high weight of the battery, the relationship between the battery capacity and electric range is non-linear because an ever-greater fraction of the increased battery capacity is used to move the mass of the battery.134 Moreover, as the marginal battery production costs increase with rising capacity, the investment costs of PHEVs increase with the installed battery capacity. Hence, the optimal battery size is a tradeoff between one-time investment costs and operating costs over the holding period.135

Apart from the capacity of the onboard battery and the resulting range of the PHEV, the utility factor depends on the distribution of the daily driving distances of the vehicle owner. That is, the utility factor of the PHEV decreases with increasing daily driving distance as longer distances need to be covered in charge-sustaining mode.136 In order to derive the driving pattern and the distribution of daily driving distances of vehicle owners, studies analyze GPS-137 and survey-based data138. The daily driving distribution can be described by a probability density function with the shape of a right-skewed distribution, such as log-normal, Weibull, or Gamma distribution.139 Plötz, Jakobsson and Sprei (2017) find that none of the aforementioned distributions clearly outperforms the other across different goodness of fit statistics and countries.140 However, the decision between different distributions of daily mileage affects the calculation of the utility factor. The lognormal distribution falls off much slower than the Weibull and Gamma distribution for large distances implying that long daily driving distances are more likely and short distance trips are less likely.141 Furthermore, the analysis of the driving pattern can be used to estimate the share of urban and suburban driving, which affects the vehicle’s consumption.142

2.4.2 Inconvenience Costs

Inconvenience days are defined as days on which the distance traveled exceeds the electric driving range of the BEV.143 Adaptation costs for inconvenience days can comprise costs for alternative transportation options, such as car rental, household second conventional car, rail-based transit, bus, taxi, and car-pooling.144 For the calculation of the inconvenience costs, the reviewed research designs apply the costs for a rental car.145

Similar to the estimation of the utility factor, inconvenience days are derived from the distribution of the daily driving distances.146 As the number of inconvenience days decreases with the electric range of the vehicle, this constitutes a further trade-off between higher investment costs for a larger battery and lower annual operating costs. However, according to the analysis of Jakobsson et al. (2016), the proportion of vehicles that require no adaptation increases linearly for every 10 km of additional range over the entire range span. Thus, in order to minimize adaptation costs, an additional range oflO km isjust as important for short range batteries as for long-range batteries.147

Fast recharging or multiple recharging processes during the day could extend the driving range. However, reviewed studies exclude this option because fast charging is deemed too expensive and the charging infrastructure is not developed enough to allow for large scale charging of electric vehicles.148

Morrison, Stevens and Joseck (2018) also consider inconvenience costs for FCEVs if the daily driving distance exceeds the FCEV’s driving range. As hydrogen fueling stations are spaced further apart than today’s gasoline refueling infrastructure, they consider costs for detour trips based on the traveler's assumed value of time.149

2.4.3 Other Operating costs

Other operating costs include further annual operating expenses, such as insurance, vehicle tax, maintenance and repair, and parking.150 The literature usually considers insurance costs and vehicle tax as fixed operating costs and calculates these expenses as a percentage of the investment costs.151 To increase the attractiveness of environmentally more sustainable vehicles, some countries exempt alternative drives partially or entirely from vehicle taxes.152,153

Vehicle maintenance comprises the maintenance and disposal of vehicle parts.152 153 154 The literature considers maintenance costs as fixed as well as variable costs.155 A function of the annual mileage usually explains the variable maintenance costs.156 157 158 Maintenance costs of BEVs and FCEVs are assumed to be lower than that of other powertrains due to a higher share of low-maintenance components and a reduced number of car fluids. For instance, they do not require a regular machine oil exchange.157,158

3 Design of Total Cost of Ownership Analysis

3.1 Model and Data

3.1.1 TCO Model

The goal of this analysis is to compare the TCO for the first owner of a vehicle in Germany along two dimensions: powertrain technology and vehicle class. This study estimates the TCO from a consumer-oriented perspective and hence does not take social costs into account. In addition, I do not include pre-purchase costs, as I assume that these costs are the same for across all drivetrains and therefore do not affect the comparative cost- efficiency.

Abbildung in dieser Leseprobe nicht enthalten

This study defines the TCO mathematically as where I is the initial investment costs associated with the acquisition of the vehicle, BR is the battery replacement costs, RV is the residual value of the vehicle and the battery, when it is sold at the end of the holding period T. EC is the energy consumption costs, INC is the inconvenience costs and OOC is the other operating costs. PVF is the present value factor, and CRF is the capital recovery factor. The TCO is shown per annual kilometers traveled (AKT). The one-time costs are annualized through multiplication with the capital recovery factor, which determines the annual repayment required for these one-time expenditures. The capital recovery factor is given as

Abbildung in dieser Leseprobe nicht enthalten

The capital recovery formula implies that the annualization factor of one-time costs is positively associated with the discount rate. This study utilizes the interest rate for consumption loans ofhouseholds with a maturity of up to five years as discount rate in all calculations. According to the Deutsche Bundesbank, the average interest rate amounts to 4.26% per July 2020.159 160 The discount rate roughly corresponds to that of other analyzing the German market, as shown in Appendix 1. The following sections of chapter 3 lay out the assumptions of the base case scenario that I deem most likely.

3.1.2 VehicleSample

The analyzed vehicle sample comprises the following drivetrains: petrol and diesel ICEVs, HEVs, PHEVs, BEVs, and FCEVs. For the purpose of the TCO comparison, I select the ten most frequently registered vehicles of each drivetrain in the first six months of 2020 in Germany according to the German Federal Motor Transport Authority (KBA).161162 For simplicity, this analysis does not distinguish between diesel and petrol HEVs. While most carmakers already offer a variety ofBEVs, HEVs, and PHEVs, there are only two FCEVs available to the German mass market.163 The selection of analyzed vehicles can be found in Appendix 2. Since the consumers’ willingness to pay for innovative technology varies across vehicle classes,164 I compare the TCO across different vehicle segments. The German transport vehicle population can be classified into 14 different passenger vehicle categories.165 However, in line with most existing studies166, I aggregate the categories of the vehicle sample into the three main vehicle classes small, medium, and executive in this study, as shown in Appendix 3.

3.1.3 DriverProfile

I assume that the driver profile, that is, holding period, daily driving pattern, and annual kilometers traveled, is equal for all powertrains and vehicle classes. The base case scenario assumes a holding period of six years for the first vehicle owner. This assumption is in line with other studies analyzing the TCO in Germany, as shown in Appendix 1. It is likely that the choice of the vehicle drivetrain affects the travel pattern in order to maximize the utilization of electric driving. For instance, a BEV is better suited as a second household car that has fewer long distance driving days than as a first household car, which is used more frequently for long distance trips.167 Similarly, the owner of a PHEV would operate the vehicle mostly in charge-depleting mode ranges to optimize fuel efficiency168. However, I assume that the annual mileage and the driving pattern are equal across all powertrains for a fair comparison.

The base case scenario derives the driving pattern for the vehicle owner from the German Mobility Panel (MOP). The MOP is an annual household travel survey and comprises information on the everyday mobility of people in a household for one week.169 According to the MOP, the average daily driving distance is equal to 41.3 km per day.170 Assuming 365 driving days, the daily driving distance implies an annual mileage of 15,075 km. The assumed annual driving distance corresponds approximately to the driving distance determined by the KBA, which amounts to 17,566 km annually for vehicles with a maximum age of three years and 15,041 km annually for vehicles between three and five years.171

As previously explained, neither the log-normal nor the Gamma or Weibull distribution clearly outperforms the other across different goodness of fit statistics and countries.

However, the log-normal distribution exhibits the best fit on German driving data for three out of four goodness of fit measures.172 173 174 Besides, the log-normal distribution is likely to overestimate the likelihood of long distance trips, thereby yielding conservative estimates, which partly compensate for overly optimistic manufacturer’s data on the range of the battery and possible capacity degradation. Therefore, this study assumes that the daily driving distance is log-normal distributed as defined by Plötz, Gnann and Wietschel (2012). The probability density function is defined as

This study defines the TCO mathematically as

This study defines the TCO mathematically as

Accordingly, the cumulative distribution function of the daily driving distance is approximated by where DKT is the daily kilometers traveled. Through a regression, Plötz, Gnann and Wietschel (2012) obtain 3.43 for p and 1.20 for a.175

Using the parameters p and o estimated by Plötz, Gnann and Wietschel (2012) results in a mean daily driving distance of 63.1 km. Therefore, I adjust the parameters p and o to match the average daily driving distance of 41.3 km in accordance with the MOP. I do so by iterative approximation using their estimates of p of 3.30 and o of 0.81 for the medium vehicle segment with an average daily driving distance of 38.8 km as the starting point.176 At p of 3.40 and o of 0.77, the daily driving distance corresponds to the target value of 41.3 km. Appendix 4 and 5 illustrate the probability density function and the cumulative distribution function of daily vehicle kilometers traveled applied in this study.

3.2.1 Investment Costs

The initial investment costs with regard to the acquisition of the vehicle are based on the MSRP. For the calculation, this analysis considers the cheapest model of each vehicle in serial equipment. The list price of the selected electric vehicles in this sample includes the costs for the onboard traction battery.177 I derive the MSRPs from the ADAC database178 and randomly validate them with the manufacturers' websites. Furthermore, I include German value added tax (VAT) in the investment costs since the TCO is determined from a consumer perspective. At the time of writing, the German VAT is reduced from 19% to 16% due to a measure taken by the government as a response to the recession triggered by the Corona pandemic.179 However, since the reduction is only temporarily until 31 December 2020,180 I adjust the manufacturer’s MSRP to 19% VAT.

In addition, the base case scenario includes subsidies for alternative drives in Germany. In order to promote the purchase of more sustainable vehicles, the German government grants subsidies in the form of an environmental bonus for the acquisition of BEVs, FCEVs, and PHEV until 31 December 2021. Both the Federal Government, on the one hand, and manufacturers, on the other hand, bear the environmental bonus.181 As summarized in Table 1, the environmental bonus is higher for BEVs and FCEVs than for PHEVs and is lower for more expensive vehicles. Further, the adopted scheme excludes vehicles with a net list price above EUR 60,000 from the environmental bonus. This analysis determines the amount of the subsidy for the vehicle sample based on the list of eligible vehicles of the Federal Office for Economic Affairs and Export Control.182

[...]


1 Cf. Eurostat (2020), p. 3.

2 Cf. European Commission (2020).

3 Cf. European Environment Agency (2019).

4 In the Paris Agreement of 2015, the international community committed to limiting the global temperature increase to less than two degrees Celsius or, if possible, below 1.5 degrees Celsius compared to pre-industrial levels (European Commission (2020a)).

5 Cf. European Commission (2020).

6 Cf. Palmer et al. (2018), p. 108.

7 Cf. Bickert/Kampker/Greger (2015), p. 138; cf. Bekel/Pauliuk (2019), p. 2221; cf. Palmer et al. (2018), p. 108.

8 Cf. Dumortier (2015), p. 82.

9 Cf. Ahmadi/Kjeang (2016), p. 715.

10 Cf. Hagman et al. (2016), p. 11.

11 Cf. Letmathe/Suares (2017), p.315.

12 Cf. Letmathe/Suares (2017), p.315.

13 Cf. Wu/Inderbitzin/Bening (2015), p. 198.

14 The TCO calculation of this study solely focuses on the German market as key parameters, such as driver profile, vehicle purchase prices, energy carrier prices, and fiscal incentives, are country-dependent (Jakob- sson et al. (2015), p. 5; Lévay, Drossinos and Thiel (2017), p. 527).

15 This thesis does not cover the social costs of negative externalities, such as emissions and noise. Moreover, this study considers the costs only from the end-consumer perspective and not from the perspective of a company for fleet vehicles. In addition, this study only comprises passenger cars.

16 Cf. Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (2020), p. 25.

17 Source: GermanFederalMotorTransportAuthority (2020a).

18 Cf. Federal Office for Economic Affairs and Export Control (2020).

19 Against the background of the rapid advancement of the electric vehicles, this study always critically assesses the validity of the assumptions of the reviewed literature and aims to consider the most recent publications.

20 Cf. Wanitschke/Hoffmann (2020), p. 510.

21 Cf. Chan (2007), p. 705.

22 Cf. Chan/Bouscayrol/Chen (2010), p. 589.

23 Cf. Chan/Bouscayrol/Chen (2010), p. 591.

24 Cf. Chan (2007), p. 705; cf. Chan/Bouscayrol/Chen (2010), p. 589.

25 Cf. Chan (2007), p. 705, 708.

26 Cf. Bubeck/Tomaschek/Fahl (2016), p. 67.

27 Cf. Chan (2007), p. 704f.

28 The Mercedes A 250 e 8G-DCT, for instance, can be operated in electric-only mode for a range of 78 km (ADAC (2020)).

29 Cf. Chan (2007), p. 704; cf. Chan/Bouscayrol/Chen (2010), p. 592.

30 Cf.Plötzetal. (2013), p.51.

31 Cf. Bubeck/Tomaschek/Fahl (2016), p. 67; cf. Plötz/Funke/Jochem (2018), p. 332.

32 Cf. Gnann (2015), p. 7.

33 Cf. Bubeck/Tomaschek/Fahl (2016), p. 67.

34 Cf. Chan (2007), p. 704f.

35 Cf. Bubeck/Tomaschek/Fahl (2016), p. 64; Chan (2007), p. 704f.

36 Cf. Miotti/Hofer/Bauer (2017), p. 94.

37 Cf. Ellram (1995), p. 4.

38 Cf. Ellram (1995), p. 4; cf. Scorrano/Danielis/Giansoldati (2020), p. 1.

39 Cf. Scorrano/Danielis/Giansoldati (2020), p. 1.

40 Cf. Comello/Glenk/Reichelstein (2020), p. 5.

41 Cf. Ellram (1995), p. 8.

42 Cf.Ellram(1995), p. 5.

43 E.g., Al-Alawi/Bradley (2013), Bickert/Kampker/Greger (2015), Bubeck/Tomaschek/Fahl (2016), Da- nielis/Giansoldati/Rotaris (2018), Hagman et al. (2016), Letmathe/Suares (2017).

44 Cf. van Velzen et al. (2019), p. 1036.

45 Cf. Lebeau et al. (2013), p. 997.

46 Cf. Palmer et al. (2018), p. 115.

47 Cf. Bickert/Kampker/Greger (2015), p. 142; cf. Wu/Inderbitzin/Bening (2015), p. 199.

48 Cf. Ellram (1995), p. 7f.

49 Cf. Scorrano/Danielis/Giansoldati (2020), p. 1.

50 Cf. Lebeau et al. (2013), p. 997; cf. Letmathe/Suares (2017), p. 315.

51 Cf. Danielis/Giansoldati/Rotaris (2018), p. 269.

52 Cf. Danielis/Giansoldati/Rotaris (2018), p. 268; cf. Hao et al. (2020), p.l; cf. Wu/Inderbitzin/Bening (2015), p. 196.

53 Cf. Danielis/Giansoldati/Rotaris (2018), p. 269.

54 Cf. Lévay/Drossinos/Thiel (2017), p. 525.

55 Cf. van Velzen et al. (2019), p. 1036.

56 Cf. Gnann (2015), p. 60; cf. Lévay/Drossinos/Thiel (2017), p. 529.

57 Cf.Gnann(2015), p. 60.

58 Cf. Lévay/Drossinos/Thiel (2017), p. 529.

59 Cf. Nykvist/Nilsson (2015), p. 329.

60 Cf. International Energy Agency (2015), p. 38.

61 Cf. van Velzen et al. (2019), p. 1036.

62 Cf. Letmathe/Suares (2017), p. 318; cf. Wu/Inderbitzin/Bening (2015), p. 200.

63 Cf. Bubeck/Tomaschek/Fahl (2016), p. 64.

64 Cf. He et al. (2019), p. 11015.

65 Cf. Bubeck/Tomaschek/Fahl (2016), p. 69.

66 Cf. Wu/Inderbitzin/Bening (2015), p. 200.

67 Cf.Ruffini/Wei(2018),p.334.

68 The costs of the glider usually base on a reference vehicle’s net sales price without powertrain-specific components (Bubeck/Tomaschek/Fahl (2016), p. 69).

69 Cf. Wu/Inderbitzin/Bening (2015), p. 200.

70 Cf. Elgowainy et al. (2013), p.628; cf. Wu/Inderbitzin/Bening (2015), p. 200; cf. Morrison/Stevens/Jo- seck (2018), p. 184f.

71 Cf. Lévay/Drossinos/Thiel (2017), p. 524f.

72 Cf. Jakobsson et al. (2016), p. 5; Lévay/Drossinos/Thiel (2017), p. 528.

73 Cf. Bekel/Pauliuk (2019), p. 2225.

74 Cf. Lévay/Drossinos/Thiel (2017), p. 528.

75 Cf. Bickert/Kampker/Greger (2015), p. 142; cf. Gnann (2015), p. 49; cf. Letmathe/Suares (2017), p. 318.

76 Cf. Al-Alawi/Bradley (2013), p.491; cf. Hagman et al. (2016), p. 14.

77 Cf. Lebeau et al. (2015), p. 557.

78 Cf. Han et al. (2014), p. 661; cf. Lebeau et al. (2015), p. 557; cf. Martinez-Lasema et al. (2018), p. 710.

79 Cf. Bubeck/Tomaschek/Fahl (2016), p. 69; cf. Lebeau et al. (2015), p. 557; cf. Letmathe/Suares (2017), p. 318; cf. Miotti/Hofer/Bauer (2017), p. 103; cf. Ruffini/Wei (2018), p. 337.

80 Cf. Severson et al. (2014), p. 383.

81 Cf. Bubeck/Tomaschek/Fahl (2016), p. 69; cf. Davis/Hayes (2019), p. 228.

82 Cf. Hou/Wang/Ouyang (2014), p. 5379.

83 Cf. Bubeck et al. (2016), p. 69; cf. Takami et al. (2013), p. 475.

84 Cf. Bubeck/Tomaschek/Fahl (2016), p. 69; cf. Lave/MacLean (2002), p. 156.

85 E.g., Al-Alawi/Bradley (2013), Bubeck/Tomaschek/Fahl (2016), Elgowainy et al. (2013).

86 Cf. Bubeck/Tomaschek/Fahl (2016), p. 69f.

87 Cf. Han et al. (2014), p. 658f.; cf. Cuma/Koroglu (2015), p. 524.

88 Cf. Bubeck/Tomaschek/Fahl (2016), p. 70f.

89 Cf. Bubeck/Tomaschek/Fahl (2016), p. 70.

90 Cf. Letmathe/Suares (2017), p. 318.

91 Cf. Bickert/Kampker/Greger, p. 141; cf. Zhao/Doering/Tyner (2015), p. 669.

92 Cf. Danielis/Giansoldati/Rotaris (2018), p.271.

93 Cf. Wu/Inderbitzin/Bening (2015), p. 200.

94 Cf. Martinez-Laserna et al. (2018), p. 710.

95 Cf. Davis/Hayes (2019), p. 228; cf. Miotti/Hofer/Bauer (2017), p. 96.

96 E.g., Bekel/Pauliuk (2019), Bubeck/Tomaschek/Fahl (2016).

97 Cf. Miotti/Hofer/Bauer (2017), p. 96; cf. Ruffini/Wei (2018), p. 337.

98 Cf. Breetz/Salon (2018), p. 240.

99 Cf. Hagman et al. (2016), p. 14; cf. Letmathe/Sures (2017), p. 319; cf. Lévay/Drossinos/Thiel (2017), p. 528.

100 Cf. Danielis/Giansoldati/Rotaris (2018), p. 270; cf. Letmathe/Suares (2017), p. 319.

101 Cf. Danielis/Giansoldati/Rotaris (2018), p. 270; cf. Hagman et al. (2016), p. 14.

102 Cf. Gnann et al. (2015), p. 50; cf. Hoekstra/Vijayashankar/Sundrani (2017), p. 5; cf. Letmathe/Sures (2017), p. 319; cf. Moon/Lee (2019), p. 4; cf. Wu/Inderbitzin/Bening (2015), p. 210.

103 Cf. Gnann et al. (2015), p. 50; cf. Wu/Inderbitzin/Bening (2015), p. 210.

104 Cf. Danielis/Giansoldati/Rotaris (2018), p. 271; cf. Elgowainy et al. (2013), p. 629.

105 Cf. Danielis/Giansoldati/Rotaris (2018), p. 270f.; cf. Hao etal. (2020), p. 6.

106 Cf. Gilmore/Lave (2013), p. 207.

107 Lévay, Drossinos and Thiel (2017, p. 528) analyze the vehicle depreciation in different European countries on the websites of the most important online automotive information sources such as Edmunds.com, NADA, KBB, Whatcar.com, and Autoscout24.

108 Cf. Lévay/Drossinos/Thiel (2017), p. 528.

109 Cf. van Velzen et al. (2019), p. 1042.

110 Cf. Elgowainy et al. (2013), p. 629; cf. Hagman et al. (2016), p. 14; cf. Letmathe/Suares (2017), p. 319; cf. Morrison/Stevens/Joseck (2018), p. 185; cf. Palmer et al. (2018), p. 110; cf. Wu/Inderbitzin/Bening (2015), p. 210.

111 Cf. Danielis/Giansoldati/Rotaris (2018), p. 270f.; cf. Hagman et al. (2016), p. 14; cf. Hao et al. (2020), p. 6; cf. Hoekstra/Vijayashankar/Sundrani (2017), p. 5; cf. Lebeau et al. (2015), p. 555; cf. Zhao/Doering/ Tyner (2015), p. 679.

112 Cf. Bekel/Pauliuk (2019), p. 2225; cf. Bickert/Kampker/Greger (2015), p. 142; cf. Bubeck/To- maschek/Fahl (2016), p. 66.

113 Cf. Danielis/Giansoldati/Rotaris (2018), p.271.

114 Cf. Danielis/Giansoldati/Rotaris (2018), p.271.

115 Cf. Hou/Wang/Ouyang (2014), p. 5379f.; cf. Letmathe/Suares (2017), p. 319.

116 Cf. Jiao/Evans (2018), p. 324.

117 Cf. Letmathe/Suares (2017), p. 319; cf. Martinez-Lasema et al. (2018), p. 702.

118 Cf. International Energy Agency (2020), p. 28f.; Jiao/Evans (2018), p. 324.

119 Cf. Thielmann et al. (2020), p.21.

120 Cf. Engel/Hertzke/Siccardo (2019), p. 2f.; cf. International Energy Agency (2020), p. 28f.

121 Cf. Thielmann et al. (2020), p. 21f.

122 Cf. Gnann et al. (2014), p. 10.

123 Cf. Redelbach/Özdemir/Friedrich (2014), p. 159.

124 The consumption efficiency is the quantity of the respective energy carrier consumed to drive 100 km.

125 Cf. Rusich/Danielis (2015), p. 6.

126 Cf. Danielis/Giansoldati/Rotaris (2018), p. 270.

127 Cf. Wu/Inderbitzin/Bening (2015), p. 200f.

128 Cf. Letmathe/Suares (2017), p. 320; cf. Wu/Inderbitzin/Bening (2015), p. 200.

129 Cf. Redelbach/Özdemir/Friedrich (2014), p. 159.

130 Cf. Hoekstra/Vijayashankar/Sundrani (2017), p. 6; cf. Morrison/Stevens/Joseck (2018), p. 189.

131 Cf. He et al. (2019), p. 11014; cf. Plötz/Funke/Jochem (2018), p. 334.

132 Cf. Plötz/Funke/Jochem (2018), p. 332.

133 Cf. Redelbach/Özdemir/Friedrich (2014), p. 159.

134 Cf. Morrison/Stevens/Joseck (2018), p. 183; cf. Zhou et al. (2020), p. 2.

135 Cf. Redelbach/Özdemir/Friedrich (2014), p. 159.

136 Cf. Redelbach/Özdemir/Friedrich (2014), p. 159.

137 Cf. Gnann et al. (2014), p. 6.; cf. Wu/Dong/Lin (2014), p. 207.

138 Cf. Gnann et al. (2014), p. 30; cf. Redelbach/Özdemir/Friedrich (2014), p. 163.

139 Cf. Jakobsson et al. (2016), p. 4; cf. Redelbach/Özdemir/Friedrich (2014), p. 160f.

140 Cf. Plötz/Jakobsson/Sprei (2017), p. 226.

141 Cf. Plötz/Jakobsson/Sprei (2017), p. 226.

142 Cf. Rusich/Danielis (2015), p. 6.

143 Cf. He et al. (2019), p. 11014.

144 Cf. He et al. (2019), p. 11015.

145 Cf. He et al. (2019), p. 11014; cf. Jakobsson et al. (2016), p. 5; cf. Morrison/Stevens/Joseck (2018), p. 186.

146 Cf. He et al. (2019), p. 11014.

147 Cf. Jakobsson et al. (2016), p. 8f.

148 Cf. Jakobsson et al. (2016), p. 13; cf. Plötz/Gnann/Wietschel (2012), p. 13.

149 Cf. Morrison/Stevens/Joseck (2018), p. 186.

150 Cf. Breetz/Salon (2018), p. 240.

151 Cf. Bubeck/Tomaschek/Fahl (2016), p. 70.

152 According to Lévay/Drossinos/Thiel (2017, p. 526), electric vehicles in Norway, Netherlands, Germany, Italy, and Hungary are partially or fully exempt from vehicles taxes.

153 Cf. Lévay/Drossinos/Thiel (2017), p. 526.

154 Cf. Mitropoulos/Prevedouros/Kopelias (2017), p. 271.

155 Cf. Bubeck/Tomaschek/Fahl (2016), p. 65; cf. Wu/Inderbitzin/Bening, p. 201.

156 Cf. Propfe et al. (2012), p. 3.

157 Cf. Bubeck/Tomaschek/Fahl (2016), p. 70; cf. Danielis/Giansoldati/Rotaris (2018), p. 277. iss For instance, Van Velzenet al. (2019, p. 1042) assume that maintenance costs are 50% lower for BEVs thanforICEVs, andDanielis, Giansoldati and Rotaris (2018, p. 277) apply a discount of 30% on the maintenance and repair costs of BEVs compared to that of ICEVs. Similarly, Bubeck, Tomaschek and Fahl (2016, p. 70) consider a discount of 25% for the maintenance costs of BEVs and FCEVs compared to that of ICEVs.

158 cf. Danielis/Giansoldati/Rotaris (2018), p. 277.

159 Cf. Wu/Inderbitzin/Bening (2015), p. 199.

160 Source: Deutsche Bundesbank (2020).

161 Source: German Federal Motor Transport Authority (2020a), German Federal Motor Transport Authority (2020b).

162 As the German Federal Motor Transport Authority (2020c, p. 5) does not provide information on the number of registered PHEVs in the first six months of 2020,1 select the most registered PHEVs in June 2020.

163 At the time of writing, only the Hyundai Nexo and Toyota Mirai are available to the German mass market.

164 Cf. Scorrano/Danielis/Giansoldati (2020), p. 3.

165 Source: German Federal Motor Transport Authority (2020b).

166 ££ Bubeck/Tomaschek/Fahl, p. 67; cf. Plötz et al. (2013), p. 40f.; cf. Wu/Inderbitzin/Bening (2015), p. 199.

167 Cf. Jakobssonet al. (2016), p. 13.

168 Cf. Plötz/Funke/Jochem (2018), p. 335; cf. Wu/Dong/Lin (2014), p. 207.

169 Cf. Ecke et al. (2020), p. 10.

170 Source: Ecke et al. (2020), p. 115.

171 Source: GermanFederalMotorTransportAuthority (2020).

172 Cf. Plötz/Jakobsson/Sprei (2017), p. 217.

173 Cf. Plötz/Gnann/Wietschel (2012), p. 9.

174 Cf. Plötz (2014), p. 2.

175 Cf. Plötz/Gnann/Wietschel (2012), p. 9.

176 Cf. Plötz/Gnann/Wietschel (2012), p. 10.

177 Renault offers the option to lease the battery of the Renault Zoe, which is included in the vehicle sample. However, this study does not consider this option to facilitate the comparability of the TCO results.

178 Source: ADAC (2020).

179 Cf. Press and Information Office of the Federal Government (2020).

180 Cf. Press and Information Office of the Federal Government (2020).

181 Cf. Federal Office for Economic Affairs and Export Control (2020).

182 Source: Federal Office for Economic Affairs and Export Control (2020a).

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Title
Total Cost of Ownership Comparison for Different Drivetrains of Private Transport Vehicles
College
University of Mannheim  (Chair of Business Administration and Accounting)
Grade
1,0
Author
Year
2020
Pages
88
Catalog Number
V993819
ISBN (eBook)
9783346359117
Language
English
Tags
TCO, Total Cost of Ownership, EV, Electric Vehicles, Alternative drives, Cost Comparison, BEV, ICEV, Life cycle costs
Quote paper
Simon Kröger (Author), 2020, Total Cost of Ownership Comparison for Different Drivetrains of Private Transport Vehicles, Munich, GRIN Verlag, https://www.grin.com/document/993819

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