NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

A Techno-Economic Analysis of 

BEV Service Providers Offering 

Battery Swapping Services 

Preprint

J. Neubauer and A. Pesaran

National Renewable Energy Laboratory

To be presented at the SAE World Congress and Exhibition

Detroit, Michigan

April 16-18, 2013

Conference Paper

NREL/CP-5400-57502

March 2013
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1 

A Techno-Economic Analysis of BEV Service Providers Offering 

Battery Swapping Services 

Jeremy Neubauer and Ahmad Pesaran

National Renewable Energy Laboratory

ABSTRACT

Battery electric vehicles (BEVs) offer the potential to reduce 

both oil imports and greenhouse gas emissions, but high 

upfront costs, battery-limited vehicle range, and concern over 

high battery replacement costs may discourage potential 

buyers. A subscription model in which a service provider 

owns the battery and supplies access to battery swapping 

infrastructure could reduce upfront and batteryreplacement 

costs with a predictable monthly fee, while expanding BEV

range. Assessing the costs and benefits of such a proposal are 

complicated by many factors, including customer drive 

patterns, the amount of required infrastructure, battery life, 

etc. The National Renewable Energy Laboratory has applied 

its Battery Ownership Model to compare the economics and 

utility of BEV battery swapping service plan options to more 

traditional direct ownership options. Our evaluation process 

followed four steps: (1) identifying drive patterns best suited 

to battery swapping service plans, (2) modeling service usage 

statistics for the selected drive patterns, (3) calculating the 

cost-of-service plan options, and (4) evaluating the economics 

of individual drivers under realistically priced service plans. 

A service plan option can be more cost-effective than direct 

ownership for drivers who wish to operate a BEV as their 

primary vehicle where alternative options for travel beyond 

the single-charge range are expensive, and a full-coverage-yetcost-effective regional infrastructure network can be deployed. 

However, under our assumed cost of gasoline, tax structure, 

and absence of purchase incentives, our calculations show the 

service plan BEV is rarely more cost-effective than direct 

ownership of a conventional vehicle.

1. INTRODUCTION

Plug-in electric vehicles, which include both plug-in hybrid 

electric vehicles and battery electric vehicles (BEVs), offer the 

potential to reduce both oil imports and greenhouse gases. 

BEVs are particularly attractive for their potential to fully 

eliminate tailpipe emissions and the direct use of gasoline. 

However, BEV batteries’ high upfront and replacement costs, 

and battery-limited vehicle range discourage many potential 

purchasers.

A subscription model offering access to range extension 

infrastructure could reduce upfront and replacement costs for 

BEV batteries with a predictable monthly fee, while

expanding range. In such a scenario, drivers would purchase a 

BEV without a battery–the most expensive element of a BEV–

while a service provider would supply batteries in exchange 

for a monthly subscription fee. Subscribers would also gain 

access to a network of range extending infrastructure–such as 

public chargers or battery swapping stations, the latter 

functioning much like today’s gas stations from the driver’s 

point of view. For example, when a driver’s battery was 

running low on energy mid-trip, the subscriber would pull into 

a battery swapping facility to quickly replace the nearly-empty 

in-vehicle battery with a fully-charged one. This would allow 

the vehicle to continue on to its final destination, with the 

range of BEV service limited by the scale and convenience of 

the battery swapping infrastructure, rather than by the size of 

the vehicle battery. 

While there are many questions regarding consumer 

acceptance of such electrification strategies, economic issues 

are frequently raised. Economic comparison of this service 

plan option to traditional direct-ownership (DO), though, is 

not straightforward. Computing the total cost of ownership 

(TCO) of BEVs with a direct ownership (DO) scenario is 

challenging [1]. Adding a service provider and range 

extension technology to the equation makes this task even 

more complex. The principle additional difficulty is 

quantifying the economics of a service provider in order to 

estimate the fee that must be charged to each subscriber. This 

is complicated by the amount and cost of batteries and 

infrastructure that must be procured, which are, in turn, 

functions of how subscribers will utilize the services―which 

is, itself, dependent on subscriber driving patterns and vehicle 

capabilities. Battery lifespan is also crucial to consider, but 

difficult to quantify. Batteries operated within a swapping 

scenario would be subject to diverse and complex operational 

duty cycles specific to the interaction of vehicle platforms, 

driving habits, and availability of swapping infrastructure.
2 

With support from the Vehicle Technologies Program in the 

U.S. Department of Energy, the National Renewable Energy 

Laboratory (NREL) has developed a vehicle TCO calculator 

known as the Battery Ownership Model (BOM) to evaluate 

and analyze these and other challenges associated with electric 

vehicles’ lifecycle economics and advanced business 

strategies. We applied the BOM to compare the economics 

and utility of BEV battery swapping service plan (SP) options 

to more traditional DO options. We evaluated BEV battery 

swapping SPs via the following four steps: (1) identification of 

drive patterns best suited to a range extension SP, (2) 

modeling service usage statistics for the selected drive 

patterns, (3) calculation of the cost of SP options, and (4) 

evaluation of the economics of individuals driving realistically 

priced SPs.

2. TOTAL COST OF OWNERSHIP 

CALCULATION APPROACH

In this study we use the BOM to calculate the present value of 

the TCO for various vehicles and business strategies, applying 

hundreds of real world drive patterns. The BOM is an 

advanced TCO calculator that takes into account various 

vehicle and component cost scenarios, battery and fuel price 

forecasts, drive patterns, annual vehicle miles traveled (VMT), 

battery wear, purchase incentives, financing, ownership, and 

other criteria. Of particular relevance to this study is its ability 

to account for costs associated with third-party installation and 

operation of infrastructure, as well as the impact of complex 

battery duty cycles on battery wear [1-2].

Our methods and assumptions are generally consistent with 

those in our past studies [1-2], including use of 398 real-world 

longitudinal drive patterns from the Puget Sound Regional 

Council’s Travel Choice Study (TCS) [3], except when 

explicitly noted otherwise. The vehicles employed for this 

study, summarized in Table 1, are the same as those in the 

Journal of Power Sources [1], where a variable drivetrain is 

adapted to a standard platform similar to that of a Chevy 

Cruze to yield a 9 second 0-60 mph acceleration time and, in 

the case of the BEVs, a specified range. The glider is assumed 

to cost $14,715.50 per [1], while power electronics are priced 

at the DOE’s 2015 target of $12/kW per [4] and multiplied by 

a 1.5 manufacturing to retail markup [5-7]. Note that the 

vehicle retail price listed in Table 1 is the combination of the 

glider price plus the marked-up power electronics cost, and 

does not include the price of the battery (assumed to be 

covered by the service provider’s monthly fee). Batteries are 

sized to provide the designated range while operating between 

a minimum SOC of 0% and the maximum SOC listed in Table 

1. Both fuel and electricity consumption are calculated via

simulation of the highway and urban driving dynamometer 

schedule, weighted and combined to be representative of the 

U.S. Environmental Protection Agency window sticker rating 

[8] assuming a constant 300 W auxiliary load [9]. 

3. DIRECT OWNERSHIP ANALYSIS

Prior to addressing the range extension service provider cases, 

we analyze two traditional competing alternatives consumers 

are likely to consider: (1) the DO of a CV, and (2) the DO of a 

BEV. The TCO for each vehicle is computed over all 398 TCS 

drive patterns assuming a 2015 vehicle purchase year, 15-year 

analysis period, and 8% driver discount rate. Average taxes 

and insurance rates are included, as are tire and maintenance 

costs set at $0.0533 per mile for both CVs and BEVs, per the 

AAA’s 2010 estimate of typical mid-size car costs [10]. 

While there is motivation to believe that the maintenance cost 

of BEVs is less than that of CVs, we exclude that factor due to 

the lack of necessary data to quantify the difference.

For the CV we employ the vehicle specifications of Table 1 to 

yield a 32 mpg vehicle with an upfront price of $17,687 (in 

2012 dollars) [1]. National average gasoline price forecasts, 

Table 1. Vehicle Specifications (all prices in 2012 U.S. dollars)

Vehicle Electric Range 

(mi)

Maximum 

SOC

Engine or Motor 

Power (kW)

Battery Energy 

(kWh)

Vehicle Efficiency 

(kWh/mi)

2015 Vehicle 

Retail Price (w/o 

Battery)

CV 0 n/a 100 0 32 mi/gal $17,687

BEV50 50

100% 79.7 16.6 0.332 $16,150

95% 80.3 17.5 0.333 $16,161

90% 80.8 18.6 0.334 $16,170

BEV75 75

100% 85.3 25.7 0.343 $16,252

95% 86.3 27.2 0.345 $16,268

90% 87.2 28.8 0.347 $16,285

BEV100 100

100% 91.1 35.4 0.355 $16,356

95% 93.0 37.6 0.358 $16,390

90% 94.4 40.0 0.361 $16,415
3 

as reported in the Energy Information Administration’s 

(EIA’s) 2011 high oil price scenario [10], are employed to 

calculate recurring energy costs (Figure 1). This scenario is 

selected as it best agrees with EIA’s reported actual 2011 

gasoline costs [11]. Accrued VMT follows the TCS drive 

patterns directly, as there are no daily range restrictions placed 

upon this vehicle. No independent infrastructure costs are 

associated with this scenario.

For the BEV we employ the Table 1 vehicle specifications for 

the 75-mile-range BEV utilizing a 100% maximum SOC, as 

this was identified as the most cost-effective BEV solutions 

when the cost of unachievable VMT is high (e.g., single 

vehicle households) [1] and is a reasonable representation of 

currently available BEV options. Three different battery 

costs ($125/kWh, $300/kWh, and $475/kWh) that span the 

DOE’s advanced battery cost targets [12] and several industry 

battery cost forecasts [13, 28, 29] are applied and assumed 

constant through our period of analysis. Note that these values 

are assumed to be applicable to usable battery energy, 

enabling our use of a 0% minimum SOC. For our low, 

medium, and high battery costs, the 2015 purchase price for 

this BEV comes to $26,653; $33,400;and $40,146, 

respectively, after adding the battery to the values of Table 1 

and applying a manufacturer-to-retail markup factor of 1.5 [5-

7]. We assume no tax credits or other purchase incentives are 

available. 

Residential customer electricity price projections from the 

EIA’s 2011 baseline scenario [10] reproduced in Figure 2 are 

used to calculate energy costs, as its 2011 values agree well 

with actual prices [11]. The amount of energy consumed is 

calculated based upon an 85% charging efficiency and the 

achieved annual VMT, which changes annually in response to 

battery degradation.

Figure 1. Employed gasoline prices (2012 dollars)

Figure 2. Employed residential electricity prices (2012 

dollars)

Battery degradation is calculated using a high-fidelity 

degradation model [14] that projects capacity loss and 

resistance growth at the end of each service year based on the 

selected drive pattern, a just-in-time charge strategy, and 

national average environmental conditions. Minimum SOC is 

adjusted each year such that no less than 80% of beginning-oflife (BOL) power can be delivered at the end of charge 

depleting operation without violating battery minimum 

voltage requirements, translating the effect of power fade to a 

reduction in available energy and vehicle range. In addition to 

calculating achieved VMT, we also leverage this capability to 

employ bounded, cost-optimal battery replacement 

schedules[1]. 

A cost is applied to unachievable VMT per the high-cost 

approach [1], which assumes that a CV is rented via a carshare program on days where the daily vehicle miles traveled 

exceeds the range capability of the car. This is selected over 

the low-cost approach (representative of a multi-car household 

with an additional CV available for long trips) to better 

represent likely candidates for a battery swapping SP or other 

range extension options – those without an additional means 

of convenient, long-range transportation available. 

The only infrastructure cost associated with this scenario is a 

32 amp, 240 V charger priced at $1,200 installed at the 

owner’s most convenient location. 

$4.00 

$4.50 

$5.00 

$5.50 

$6.00 

2015 2020 2025 2030

Gasoline Prices 

($/gal)

Year

$0.112 

$0.115 

$0.118 

$0.121 

2015 2020 2025 2030

Residential 

Electricity Price 

($/kWh)

Year
4 

Figure 3. DO-BEV to DO-CV cost ratio distributions

We compare the TCO of the DO-BEV to that of the DO-CV 

with the aforementioned conditions for all 398 drive patterns 

in Figure 3. We see that for the low cost of batteries scenario, 

9% of the simulated drive patterns receive a cost benefit to 

electing a DO-BEV over a DO-CV. Forty-five percent of the 

simulated drive patterns achieve a DO-BEV TCO greater than, 

but within 20% of that of a DO-CV, while the remaining 46% 

of simulated drive patterns incur a TCO premium of 20% or 

greater when electing to drive a DO-BEV rather than a DOCV. With the medium and high battery prices, however, no 

simulated drive patterns see a cost advantage to driving a DOBEV, and much larger percentages of simulated drive patterns 

incur a TCO premium of 20% or more when electing the DOBEV.

A significant factor in calculating these economic trends is the 

assumption of the high cost of unachievable VMT. In contrast 

to these results, similar comparisons with the low cost of 

unachievable VMT assumption show that a larger fraction of 

drive patterns can benefit financially from a DO-BEV [1]. 

However, the low cost of unachievable VMT assumption is 

generally not as applicable to single-vehicle households as the 

alternative high-cost assumption is, and certainly some of 

these households will have a desire to purchase a BEV. This 

provides the motive for our investigation of a BEV operated 

with a SP providing battery swapping for these households, 

reducing the amount and cost of unachievable VMT.

4. BATTERY SWAPPING SERVICE 

PROVIDER ANALYSIS

In this section we assess the TCO to the consumer of BEVs 

operated with battery swapping SPs. SP fees are calculated 

using a bottom-up approach that accounts for all of the service 

provider’s battery, infrastructure, electricity, and other costs, 

as well as the cost of financing such an operation. There are 

four phases to this analysis:

(1) Analyzing all 398 drive patterns to down-select a 

subset of drive patterns suitable for more detailed battery 

swapping analysis

(2) Identifying average service usage statistics for this 

subset of drive patterns, including battery life, electricity 

usage, and battery swapping frequency

(3) Calculating SP fees for multiple scenarios based on 

the identified service usage statistics and a rigorous economic 

model of the service provider’s business

(4) Investigating individual driver economics with the 

calculated SP fees

4.1. Identifying Drive Patterns Suitable to 

Service Plan BEVs with Range Extension 

To identify drive patterns most suitable to a SP-BEV with 

range extension for further study, we assess the economics of 

SP-BEVs when the range extension features are perfect and 

without limit. We select the drive patterns that show the most 

potential for economic savings over either a DO-CV or a DOBEV on the basis that both are important criteria for a 

consumer to consider when electing to subscribe to such an 

SP. TCO is computed as it is for the DO-BEV, but (1) all 

battery, infrastructure, and electricity costs are replaced by a 

flat-rate monthly service fee, and (2) we assume all VMT in 

the drive pattern is achievable via implementation of a perfect 

range extension technology. 

0

0.1

0.2

0.3

0.4

0.5

0.8 to 11 to 1.2

1.2 to 1.41.4 to 1.61.6 to 1.8

1.8 to 22 to 2.2

2.2 to 2.42.4 to 2.62.6 to 2.8

Direct Ownership BEV to Direct Ownership CV 

Cost Ratio

$125/kWh Batteries

0

0.1

0.2

0.3

0.4

0.5

0.8 to 11 to 1.2

1.2 to 1.41.4 to 1.61.6 to 1.8

1.8 to 22 to 2.2

2.2 to 2.42.4 to 2.62.6 to 2.8

Direct Ownership BEV to Direct Ownership CV 

Cost Ratio

$300/kWh Batteries

0

0.1

0.2

0.3

0.4

0.5

0.8 to 11 to 1.2

1.2 to 1.41.4 to 1.61.6 to 1.8

1.8 to 22 to 2.2

2.2 to 2.42.4 to 2.62.6 to 2.8

DO-BEV to DO-CV Cost Ratio

$475/kWh Batteries

Probability Distribution Function
5 

We calculate the SP TCO for a monthly fee ranging from $100 

to $600 per month to the TCO of each direct ownership 

scenario for all of the 398 TCS drive patterns. Note that these 

are assumed monthly SP fees, not calculated. Although this 

range of SP fees are shown later to largely encompass our 

bottom-up calculated SP fees, they are selected at this point in 

the analysis only to support the identification of relevant drive 

patterns. We compare the resultant SP-BEV TCO to that of 

the DO-CV and three DO-BEV cases, each with different 

battery cost coefficients ($125/kWh, $300/kWh, and 

$475/kWh). 

We find that the SP economics viewed by the driver are 

generally indifferent to vehicle range, swapping infrastructure, 

electricity cost, battery degradation, etc., due to the 

assumption of perfect range extension technology and the flat 

monthly SP fee. However, the specific drive patterns best 

suited to the SP option vary slightly as a function of both the 

monthly service provider fee and the basis of comparison (CV, 

BEV with $125/kWh batteries, BEV with $300/kWh batteries, 

or BEV with $475/kWh batteries). This poses a minor 

challenge to down-selecting the most relevant drive patterns 

for further study, since the actual service provider fee is not 

yet known, battery cost is a variable we would like to continue 

to include, and comparisons to both DO-CV and DO-BEV 

options are relevant to a consumer’s decision to elect a SPBEV. The cost of a SP-BEV must be competitive against both 

a DO-CV and a DO-BEV to be viable on a mass market scale.

To solve this problem, we implement a scoring system to 

grade each drive pattern based upon its calculated cost-ratio 

relative to that of all other drive patterns with each service 

provider fee and basis for comparison. We then average the 

grade of each drive pattern across all service provider fees 

and, in the case of the BEV basis, all battery costs, as well. 

This creates two sets of drive pattern rankings–one sorting 

drive patterns on their suitability for a SP-BEV in comparison 

to a DO-CV, the other on their suitability for a SP-BEV in 

comparison to a DO-BEV. From each set we isolate the top 

125 drive patterns, then take the intersection of each reduced 

set to yield a 100 drive pattern subset for continued study.

Perhaps unsurprisingly, the defining characteristic of drive 

patterns in this subset is simply annual mileage. The 

minimum annual VMT of the selected subset is 11,471 miles, 

and every drive pattern with an annual VMT greater than 

16,446 miles from the full TCS data set has been included 

(10% of the entire TCS data set, but 40% of the 100 selected 

drive patterns). This is logical given that our 100 selected 

drive patterns are parsed on an economic basis where the 

primary means of financial benefit is reducing the cost of gas 

expenditures (which rises with increased VMT), and the 

limitations of achievable mileage have been eliminated by the 

assumption of a perfect range extension technology. The 

distribution of annual VMTs of this selection is shown in 

Figure 4. 

As designed, the results of this section are applicable to the 

theoretical limits of any range extension technology, inclusive 

of not only battery swapping, but also opportunity charging, 

fast charging, electric roadways, etc.

4.2. Service Usage Statistics

Now we analyze the service usage patterns of the selected 100 

drive patterns with the application of more realistic 

assumptions around the VMT achievable via battery 

swapping. Relative to both the perfect range extension and 

direct ownership cases, our improved set of assumptions for 

BEVs operated with a battery swapping SP affects three 

factors that must be accounted for to model service usage 

statistics: achievable VMT, battery degradation, and battery 

replacement criteria. 

Figure 4. Annual VMT distribution of selected drive patterns

0%

5%

10%

15%

20%

25%

30%

35%

Fraction of Selected 

Drive Patterns
6 

Achievable VMT is affected primarily by vehicle range and 

range extension infrastructure. We consider three vehicle 

ranges (50, 75, and 100 miles) and three maximum SOCs 

(90%, 95%, and 100%) with the vehicle properties as defined 

in Table 1. The combination of vehicle range with time 

constraints to complete a day’s driving, range extensions, and 

home charging, as well as with the perceived inconvenience of 

range extension stops, will limit daily VMT. We account for 

the impact of time limitations by allocating a maximum of 24 

hours to a day’s driving, range extension, and home charging 

activities (allowing successive back-to-back occurrences of 

the most demanding drive days). We assume a range 

extension event occurs only when the battery is completely 

empty and that a battery swap spot is immediately available at 

this point in time and space. In doing so, we do not factor in 

the effects of imperfect battery state of health identification 

and vehicle range prediction, driver psychology around range 

anxiety, and temporal and spatial availability of battery 

swapping infrastructure. While these are important factors, we 

must exclude them from this study due to the lack of necessary 

supporting data and analysis resources. However, we do 

restrict the number of range extensions to a maximum of four 

per day as a proxy for these effects and the willingness of 

drivers to change behavior. Where more than four range 

extensions would be required by a day’s driving, or when the 

time required to complete the driving, charging, and range 

extensions exceeds 24 hours, we assume that the driver instead 

acquires a vehicle from a car-share program. Note that carshare cost accounting is performed as detailed in the Journal 

of Power Sources [1] and is not a cost covered by the SP per 

our assumptions, although in practice this could be something 

a SP may offer with its BEV services.

Battery degradation is affected by the different cycle and SOC 

history induced by the utilization of range extension 

infrastructure, thus we must modify our input to the battery 

degradation algorithm to accurately represent the battery 

swapping SPs. Most apparently, we must account for the 

increased cycling induced by range extension. These extra 

cycles include a relatively fast 30-minute charge to minimize 

the service provider’s investment in costly batteries at swap 

stations. Unfortunately, the battery degradation model we 

employ does not account for additional wear mechanisms 

induced by high-rate charging. Although an argument might 

be made that the incremental wear of such high-rate charging 

is minimal on the grounds that the time at peak charge rate of 

approximately 2.5C is limited to much less than 30 minutes 

due to voltage related limitations [15], we instead apply an 

artificial fast-charge wear factor to these cycles to increase 

their effect on capacity and resistance degradation by a userdefined value. For this study we shall employ fast-charge

wear factors of 1 and 10 to mimic the possible effect of fast 

charging on battery wear and to investigate the economic 

sensitivity thereof. The fast-charge wear factor of 1 implies 

that there is no added effect of the increased charge rates – all 

fast charges are accounted for within the battery wear model 

as normal cycles. The fast-charge wear factor of 10, on the 

other hand, logs 10 normal cycles in place of every single fastcharge cycle, thus, the effect on capacity loss and resistance 

gain of fast-charge cycles is increased by a factor of 10 

relative to a slow-charge alternative.

We must also account for the slight surplus of batteries greater 

than that of vehicles due to the presence of additional batteries 

charging at swap stations, which effectively decreases the 

amount of cycling for each battery. Assuming for now that 7 

batteries are being charged or are resting at a swap spot for 

every 100 vehicles on the road, we simulate 7 days of rest for 

every 100 days dictated by the drive pattern. Though the ratio 

of extra batteries to vehicles is selected arbitrarily at this point 

in the analysis, subsequent results shall prove them to be 

relevant estimates. Finally, in both cases, home charging is 

largely unaffected; we continue to assume that home charging 

is performed on a just-in-time schedule, such that the 

maximum SOC is reached just prior to the vehicle’s first 

departure of the day. 

It is further necessary to adjust the battery replacement 

criteria. In the direct-ownership case, we applied a costoptimal replacement schedule. As discovered in the Journal of 

Power Sources [1], such an approach to battery replacement 

typically results in long battery lifetimes and significantly 

reduced vehicle range near end of life (EOL). Although this 

may be financially optimal for individual drivers, it is unlikely 

that a SP subscriber would tolerate the high degree of 

uncertainty and variability of range following a battery swap 

that may result. Accordingly, we enforce a battery EOL 

condition of 80% BOL range at 80% BOL power to increase 

the predictability of vehicle range and performance following 

a battery swap event, thus improving consumer satisfaction.

The resultant average battery life, number of annual battery 

swaps, and annual electricity consumption are reported in 

Table 2 for every combination of vehicle range, maximum

SOC, and fast-charge wear factor. As might be expected, we 

find that the number of annual battery swaps is reduced by 

increased vehicle range, average battery life is extended when 

vehicle range is increased from 50 to 75 miles due to 

decreased cycling. It is worth noting that increasing the 

vehicle range from 75 to 100 miles decreases battery life 

slightly, presumably due to higher average SOCs through life. 

In addition, a strong sensitivity of battery life to the maximum

SOC is clearly observed. Fast-charge wear factor is shown to 

reduce battery lifetime noticeably for all BEV50 scenarios, 

presumably due to the much increased range extension 

frequency, but is seen to have little to no effect on the BEV75 

and BEV100 cases.
7 

Table 2 also shows the utility factor for each scenario, defined 

as the ratio of miles traveled electrically in a BEV to the total 

VMT defined by the original drive pattern. Recall that we 

impose a maximum of four battery swaps per day, which 

reduces the frequency at which the SP-BEV is utilized. The 

time required for driving, charging, and battery swapping also 

affects SP-BEV utilization. For the cases studied, we find that 

vehicle range is the primary determinant of the utility factor, 

yielding 85%, 89%, and 94% utility factors for the 50-, 75-, 

and 100-mile SP-BEVs, respectively. Clearly all cases are 

capable of completing a large fraction of driver miles 

electrically. The fact that these values are not closer to 100%, 

though, implies that an alternative mode of transportation will 

still need to be procured occasionally for many drivers.

4.3. Service Provider Fee

Now we apply the usage statistics for likely subscribers 

acquired in Section 4.2 to calculate actual monthly service 

provider fees with various scenarios. To do so, we construct a 

financial model of a service provider that includes all capital 

and recurring costs, return on equity (ROE), cost of debt, etc., 

and then calculate the monthly fee charged to customers that 

enables the business to meet its ROE requirements after 15 

years of operation. 

4.3.1. Financial Accounting

The business of our service provider is very capital intensive 

due to the cost of batteries, range extension infrastructure, and 

charging infrastructure. Thus, the means of financing the 

business is expected to have a considerable impact on the 

required service provider fee. In this analysis, we assume that 

the service provider acquires the necessary capital to finance 

the battery and infrastructure purchases of the first year of 

operation equally from debt and equity investments at the 

beginning of that year. The cost of these expenditures is 

determined as discussed below, assuming an initial customer 

base of 10,000 subscribers. The cost of debt is varied from 4% 

to 12%, with annual debt payments calculated to pay off the 

full balance in 15 years where interest accrues annually. Our 

assumed ROE is coupled to the cost of debt, ranging from 5% 

to 15%.

Annual revenue is calculated from the monthly service 

provider fee and the number of customers. From this the gross 

taxable income is computed after deducting annual operating 

expenses (described below), interest payments on debt, 

depreciation of assets, and any applicable loss carry-forwards 

from previous years. Asset depreciation includes batteries 

(annualized per the calculated average battery life of Table 2), 

battery swapping infrastructure (annualized at 5%/yr), and 

charging infrastructure (annualized at 5%/yr). Taxes are then 

computed against the gross taxable income assuming an 

average 39.3% corporate tax rate [17].

The remaining working capital at the end of each year is 

calculated by subtracting the annual debt payment, operating 

expenses, and taxes from the annual revenue. We assume this 

remaining capital is spent in the subsequent year to buy 

batteries and build infrastructure to support additional 

customers. As such, the profit from year one determines the 

increase of customers in year two, and so on.

The monthly service fee charged is determined by an iterative 

process to ensure that the company net worth at year 15 is 

equal to the value of the initial equity investment after 15 

Table 2. Calculated Battery Swapping Service Plan Usage Statistics

Range Max SOC Fast-charge

Wear Factor

Battery Life

(yrs)

Annual Electricity 

(kWh)

Swaps per Year

(No.) Utility Factor

50 mi

100% 1 8.4 5684 158.4 86%

10 6.6 5673 157.9 85%

95% 1 11.1 5681 152.7 85%

10 8.6 5675 150.8 85%

90% 1 14.2 5670 150.1 85%

10 11.6 5672 147.3 85%

75 mi

100% 1 9.0 6059 71.8 89%

10 8.9 6055 71.8 89%

95% 1 12.3 6059 70.5 89%

10 12.1 6066 70.4 89%

90% 1 14.7 6066 66.3 89%

10 14.6 6072 66.5 89%

100 mi

100% 1 8.4 6572 43.2 94%

10 8.4 6572 43.2 94%

95% 1 11.5 6596 41.9 94%

10 11.5 6597 41.9 94%

90% 1 14.4 6600 39.8 94%

10 14.4 6600 39.8 94%
8 

years of growth at the prescribed ROE. For example, if the 

required battery and infrastructure expenses for year one totals 

$2 million and a 15% ROE is specified, our requirement 

would demand that the monthly service fee is set to result in a 

business with a net worth of $8.14 million at the end of year 

15–equivalent to the value of the initial equity investment ($1

million) growing at 15% per year for 15 years (the remaining 

$1 million of initial costs are covered by loans). The net 

worth of the company at year 15 is defined simply as the sum 

of all past capital expenditures, minus the sum of all past 

depreciation taken, plus the profit made in the final year (note 

that the debt term aligns with the analysis term, and the 

company has no debt remaining at year 15 to consider).

4.3.2. Charging and Range Extension 

Infrastructure

The total cost of home charging infrastructure is computed

from the number of new subscribers each year and a flat fee of 

$1,200 per charge point installed. 

To compute the cost of deployed range extension 

infrastructure, we must first calculate the amount of deployed 

infrastructure. While we recognize that the amount of 

deployed infrastructure will be dependent on traffic patterns, 

geography, and acceptable customer wait-times for battery 

swaps in practice, addressing this level of detail is beyond the 

scope of this study. Instead, we develop a proxy approach that 

attempts to calculate the amount of range extension 

infrastructure that would offer a level of battery swap-spot 

convenience and availability equivalent to today’s network of 

gasoline pumps.

We begin this proxy approach by defining the battery swapspot utilization rate as the average hours per day that the 

battery swap spot is occupied by a customer. To provide 

similar service quality to the BEV driver as gas stations 

provide today’s CV drivers, we set our baseline swap spot 

utilization rate equivalent to that of the average gas pump. 

Our calculations indicate that the average gas pump is 

occupied approximately 1.2 hrs per day, based upon the 

amount of fuel consumed between light-, medium-, and 

heavy-duty vehicles [18]. We assume an average fuel tank size 

of 18 gallons for light-duty and 200 gallons for medium- and 

heavy-duty vehicles, and a maximum pump flow rate of 10 

gallons per minute [19]. We calculate an average of 3 extra 

minutes per stop for payment processing and other activities, 

and from the number of fueling stations [20] and average 

pumps per station [26] in the United States. We also employ a 

three times larger utilization rate of 3.7 hours per day for swap 

spots as an upper bound, allowing a battery swap spot to be 

occupied three times as much as an average gas pump. 

Increasing the swap-spot utilization rate in this way decreases 

the total required cost of infrastructure a service provider 

deploys on a per-customer basis, but may also decrease the 

level of service to the customer, and thereby customer 

satisfaction. 

From here we can compute the ratio of customers per battery 

swap spot by dividing the utilization rate by the average time 

per day each customer spends at a battery swap spot. The 

average battery swap-spot time per day per customer is 

calculated by multiplying the average number of annual range 

extensions per subscriber (Table 2) by the time required per 

swap {1 minute for a battery swap [27], and 2 minutes for 

margin and related activities; note that waiting time is not 

included}and dividing by 365 days per year. For the battery 

swapping scenarios we find this ratio to vary from a low of 61 

customers per swap spot for the BEV50 with a 1.2 hours per 

day utilization rate, to a high of 680 vehicles per swap spot for

the BEV100 with a 3.7 hours per day utilization rate. Finally, 

dividing the number of subscribers by the ratio of customers 

per battery swap spot yields the total number of spots required. 

The cost of each range extension position is computed based 

on the battery charging equipment, which varies with vehicle 

battery size, plus a flat fee to cover other costs. Charger 

power is calculated via Equation 1 (see below) to ensure 

batteries are recharged to 80% of their available energy in a 

30-minute period. The number of chargers installed per 

position is calculated to ensure a maximum time between 

consecutive vehicles of 3, 5, or 7 minutes, targeting 

approximately 30 minutes per battery charge while accounting 

for the 1-minute swap time. This corresponds to 11, 7, and 5 

chargers per swap spot, respectively, with rated charger 

powers ranging from 44 kW to 101 kW depending on the time 

between swaps and vehicle configuration. Total charging 

equipment cost per swap spot is determined by multiplying the 

power of each individual charger, the number of chargers per 

swap spot, and a $200/kW cost coefficient taken from recent 

announcements of fast charger technology [21]. 

Equation 1.

Charge Power (kW) = 1.5 * Battery Available Energy (kWh) / 

0.5 hr

Note that in addition to its effect on infrastructure cost, the 

maximum time between swaps could significantly affect 

subscriber satisfaction during high traffic times. Arriving at a 

swap spot with a line of waiting customers and a maximum

time between swaps of 7 minutes could result in a wait of 

more than 20 minutes, frustrating current subscribers and 

discouraging the enrollment of future ones. Thus service 

providers will need to carefully balance the cost benefits of 

increasing maximum time between swaps with its potentially 

negative impact on the subscriber experience.

The flat fee is varied from $100,000 to $400,000, and assumes 

to cover the cost of land, the building, the battery swapping 

mechanism, thermal management systems, computers, 

installation, commissioning, and other related elements. 

Combined with the variable cost of charging equipment 

described above, this results in total swap-spot costs that vary 

from a low of $143,928 for the BEV50 charged to 100% 

maximum SOC with a $100,000 flat fee and 7 minutes 

maximum between swaps, to a high of $622,786 for the 
9 

BEV100 charged to 90% maximum SOC with a $400,000 flat 

fee and 3 minutes maximum between swaps. The total annual 

service provider expenditure on battery swap spots then 

becomes the incremental number of positions required in a 

given year multiplied by the individual position cost.

4.3.3. Battery Expenditures 

The number of batteries purchased in a given year is equal to 

the number of new customers, plus the number of additional 

batteries required by new battery swap-spot infrastructure, 

plus the number of batteries required to replace those removed 

from service due to wear. The number of batteries purchased 

for each new swap spot is equivalent to the number of 

chargers installed at said swap spot. The number of 

replacement batteries is calculated based on the average 

battery lifetime per Table 2 and the history of new customer 

and swap-spot battery purchases.

The manufacturing cost of each battery is computed using the 

size of the battery for the specific range and maximum SOC 

combination as reported in Table 1 and one of the three 

aforementioned battery costs. A manufacturing-to-retail 

markup factor of 1.5 [5-7] and a sales tax rate of 6.2% are 

included to calculate the cost of each battery to the service 

provider. We then compute the total annual battery 

expenditure from the number of batteries purchased and the 

cost of each battery.

We include the option of crediting the battery’s salvage value 

at the end of its automotive life assuming it sees service in a 

second-use application. Second-use salvage value is 

computed per noted methods [22, 23], assuming $18/kWh for 

repurposing, a 0.75 used product discount factor, and health 

factor of 0.67. The value of 0.67 is based on our calculations 

of second-use battery life for the 80% range at EOL 

requirement per documented methods and assumed duty 

cycles [23]. When second use is dismissed, we set the battery 

salvage value to zero, effectively assuming that the cost or 

value of EOL handling (i.e., disposal or recycling) is 

negligible.

4.3.4. Operating Expenses 

The main operating expense for the service provider is 

electricity. The price of commercial electricity per kWh 

shown in Figure 5 is taken from the Annual Energy Outlook 

2011 with Projections to 2035 [10] and multiplied by the 

amount of consumed electricity per subscriber (Table 2) and 

the amount of subscribers per year to yield the total cost of 

electricity to the service provider. Annual electricity numbers 

include an assumed efficiency of 85% for at-home charge 

points, and 75% for the batteries charged on the higher-rate 

swap-spot charging equipment. A fee of $20/kW/month is 

applied to the peak battery charging load at each range 

extension position to represent demand charges similar to 

those of Southern California Edison’s TOU-GS-3-SOP [24] 

and San Diego Gas & Electric’s AL-TOU [25] rate schedules. 

Total demand charges vary from $797 to $19,800 per month 

per position.

We also include annual operating costs of $25,000 for each 

battery swapping position to cover maintenance of onsite 

equipment, a fraction of an attendant’s salary (noting that 

multiple swap spots are likely to be installed at the same 

location), and other miscellaneous recurring costs. Finally, 

we include the cost of general and administrative activities at 

the cost of $100 per subscriber per year.

Figure 5. Employed commercial electricity prices (2012 

dollars)

4.3.5. Service Provider Fee Results: Sensitivity to 

Variables 

We calculate the monthly service provider fee for an 

expansive set of ROE, cost of debt, maximum SOC, vehicle 

range, battery manufacturing cost, fast-charge wear factors, 

second-use health factors, battery swap-spot utilization rate, 

maximum time between swaps, and swap-spot flat fee values 

as detailed in the previous sections. All told, this results in 

thousands of unique combinations of these variables.

$0.094 

$0.096 

$0.098 

$0.100 

2015 2020 2025 2030

Commercial 

Electricity Price 

($/kWh)

Year
10

To assess the impact of an individual variable, we perform a 

sensitivity analysis of the monthly service fee for each 

variable. For each variable, we calculate the median service 

fee of all cases where the variable of interest is at best-case 

(encouraging lower service provider fees) and worst-case 

(encouraging higher service provider fees) values, then rank 

the importance of that variable based on the difference in the 

two calculated median service fees (see Figure 6).

For example, to study the effect of battery cost on the service 

provider fee, we first calculate the median of all service fees 

where the battery cost is set to $125/kWh (yielding a median 

service fee of $257/month). We repeat this process when 

battery cost is set to $475/kWh (yielding a median service fee 

of $472/month). The large difference in median service fees 

($215/month) indicates the high level of impact that battery 

cost has on service fee; indeed, in Figure 6 we see that battery 

cost is the most sensitive variable in this analysis.

It is clear that across the range of variables considered in this 

analysis, the cost of batteries, cost of financing, and the battery 

swap-spot utilization rate are the most predominant factors 

driving the service fee value. At the opposite end of the 

spectrum, we find that the maximum SOC, swap-spot flat fee, 

and the fast-charge wear factor have negligible impact on the 

service fee.

4.3.6. Service Provider Fee Results: Cost 

Breakdown 

Before digging deeper into the service provider cost results, 

we limit our investigation to a subset of variables. First, we 

eliminate from consideration multiple maximum SOCs and 

fast-charge wear factors due to their small impact, proceeding 

only with a 90% maximum SOC and the 10x wear factor to 

compose a conservative fast charging scenario. Second, we 

eliminate consideration of battery second use, as it also has a 

small impact and comes with considerable uncertainty. Third, 

we group the swap-spot infrastructure variables into two 

classes:

(1) A high-service, high-cost class where the utilization 

rate and the maximum time between swaps are set to the 

minimum values, and the swap-spot flat fee is set to the 

maximum value. 

(2) A low-service, low-cost class where the swap-spot

utilization rate and the maximum time between swaps are set 

to the maximum values, and the swap-spot flat fee is set to the

minimum value. 

Finally, we restrict the cost of financing to the medium case 

(8% cost of debt and 10% return on equity) on the basis that 

the low values are unlikely without government participation, 

and the high values are unlikely to support a compelling 

business case relevant to mass markets due to the resultant 

high SP fees.

Figure 6. Sensitivity of battery swapping service plan fee to variables

$-

$100 

$200 

$300 

$400 

$500 

$600 

Battery Cost

($125/kWh to $475/kWh)

Cost of Financing

(4% debt, 5% ROE to 12% 

debt, 15% ROE)

Swap Spot Utilization Rate

(1.2 to 3.7 hrs/day)

Max Time Between Swaps

(3 to 7 minutes)

Battery Second-Use

(included or not included)

Vehicle Range

(50 to 100 miles)

Max SOC

(90% to 100%)

Swap Spot Flat Fee

($100k to $400k)

Fast Charge Wear Factor

(1 to 10)

Median Monthly 

Service Provider Fee

Worst-Case Input Best-Case Input
11

The cost of batteries, vehicle range, and the range extension 

class become our remaining variables of study. The 

breakdown of the monthly service fee for these remaining 

cases is shown in Figures 7 and 8. 

The most clearly evident point that arises from these results is 

that there is a strong difference in service fees between the two 

range extension classes. Clearly, the quantity and cost of 

infrastructure deployed–and thereby the level of service to the 

subscriber–is an important driver of service fee. With the 

high-cost, high-service class we see that the cost of swap spots 

is always a significant component of the total cost breakdown, 

and often the largest single cost element. This incentivizes a 

careful trade-off of vehicle range and infrastructure 

deployment to minimize total cost. Our results suggest that 

the optimum vehicle range is between 50 and 100 miles when 

battery prices are at or above $300/kWh.

Alternatively, the low-cost, low-service swap-spot class 

encourages less vehicle range to minimize cost. However, it 

must be noted that at such short vehicle ranges, subscribers 

will visit swap spots much more frequently, and the effect of 

the level of service on subscriber satisfaction will be 

compounded.

4.4. Individual Driver Economics

In this section we investigate the individual driver economics 

of likely subscribers (identified in Section 4.1) using realistic 

assumptions on swap-spot utilization (Section 4.2) and 

bottom-up calculated service fees (Section 4.3). Relative to 

the individual driver simulations performed in Section 4.2 to 

compute SP usage statistics, the only parameter changes are 

the SP fee and the ratio of total batteries in circulation to the 

number of customer vehicles. As we compute no financial 

metrics in Section 4.2, the change in the SP fee has no impact 

requiring further consideration; however, the change in the 

battery ratio affects battery lifetime, which could affect the 

other usage statistics, and in turn necessitate revisiting our SP 

fee calculations. Accordingly, before discussing individual 

Figure 7. Battery swapping service fee breakdown for the high-service, high-cost range extension class

Figure 8. Battery swapping service fee breakdown for the low-service, low-cost swap-spot class

$-

$100 

$200 

$300 

$400 

$500 

$600 

$700 

BEV50 BEV75 BEV100 BEV50 BEV75 BEV100 BEV50 BEV75 BEV100

Monthly Service Fee

G&A

Private Chargers

Electricity

Batteries

Swap Spots

$125/kWh Batteries $300/kWh Batteries $475/kWh Batteries

$-

$100 

$200 

$300 

$400 

$500 

$600 

$700 

BEV50 BEV75 BEV100 BEV50 BEV75 BEV100 BEV50 BEV75 BEV100

Monthly Service Fee

G&A

Private Chargers

Electricity

Batteries

Swap Spots

$125/kWh Batteries $300/kWh Batteries $475/kWh Batteries
12

driver economics, we take a moment to compare the usage 

statistics calculated in Section 4.2 with the observed usage 

statistics calculated with updated in-vehicle to at-swap-spot 

battery ratios.

Previously, we assumed that 7 batteries were being charged or 

resting at a swap spot for every 100 vehicles on the road. 

However, here we have applied values calculated in Section 

4.3, ranging from 0.7 (SP-BEV100; low-cost, low-service 

swap-spot class) to 18.0 (SP-BEV50; high-cost, high-service 

swap-spot class) batteries being charged or resting at a swap 

spot for every 100 vehicles on the road. The implication of 

reducing the number of extra batteries in rotation is to increase 

the number of cycles for each battery, and vice versa. We find 

that this change increases the average battery life of the 

BEV50 from 11.6 years to 14.3 years, due to the significantly 

reduced cycling per battery that results from updating our 

original assumption. The increase in cycling applied to the 

BEV100, on the other hand, has a negligible impact on its 

battery life. The battery life of the BEV75 is largely 

unaffected as the number of extra batteries only changes 

slightly from our initial assumption. The remaining usage 

statistics – electricity use, utility factor, and annual battery

swap frequency – are not noticeably affected. As the only 

impact of the adjusted battery ratio is a 30% increase in SPBEV50 battery life, the financial impact of which will be 

marginalized by the time-value of money and the service 

provider’s ability to depreciate battery wear, we continue our 

analysis without an update to our calculated SP fees. 

Moving now to driver economics, we present the results of our 

final TCO calculations in Figure 9. Subplot (a) of this figure 

shows the frequency at which electing the SP-BEV is more 

cost-effective than electing a DO-BEV75. It includes all three 

vehicle ranges and battery prices and both the low-cost, lowservice and high-cost, high-service range extension classes, 

illustrating that the SP-BEV can be more economical than a 

DO-BEV for many of the down-selected high-mileage drive 

patterns with a broad spectrum of conditions. This is due 

primarily to the fact that available battery swapping increases 

the utility of the BEV and greatly reduces these drivers’ 

unachievable VMT, which can be a major cost when a driver 

must rent a separate vehicle to complete this travel. However, 

the subplot (b) shows that electing a SP-BEV is only more 

cost-effective than both the DO-BEV75 and DO-CV in 

significant numbers when both the cost of batteries and swapspot infrastructure is low. 

Figure 9. Frequency at which a SP-BEV is (a) more cost-effective than a 

DO-BEV75, and (b) more cost-effective than both a DO-BEV75 and a DO-CV

0.0

0.2

0.4

0.6

0.8

1.0

BEV50BEV75

BEV100

BEV50BEV75

BEV100

BEV50BEV75

BEV100

$125/kWh 

Batteries

$300/kWh 

Batteries

$475/kWh 

Batteries

0.0

0.2

0.4

0.6

0.8

1.0

BEV50BEV75

BEV100

BEV50BEV75

BEV100

BEV50BEV75

BEV100

Low Cost, Low Service Swap Spots

High Cost, High Service Swap Spots

$125/kWh 

Batteries

$300/kWh 

Batteries

$475/kWh 

Batteries

Fraction of Down-SelectedDrive Patterns 

Where A Battery Swapping Service Plan Is 

More Cost Effective Than Direct Ownership

(a)

(b)
13

5. CONCLUSIONS

In this study we have analyzed the economics of BEVs 

operated with a service plan where battery swapping is 

available to extend vehicle range. Our evaluation process 

followed four steps: (1) identifying drive patterns best suited 

to a battery swapping service plan, (2) modeling service usage 

statistics for the selected drive patterns, (3) calculating the cost 

of service plan options given these statistics, and (4) 

evaluating the economics of individual drivers with

realistically priced service plans. For comparison, we have 

also calculated the TCO of both a BEV75 and a CV operated 

with a conventional DO scenario. A high-fidelity battery 

degradation model has been employed throughout to forecast 

battery wear, its effect on vehicle range, and required battery 

replacements. Real-world drive patterns from the TCS project 

have been utilized to support the calculation of realistic battery 

usage, the frequency of battery swapping events, and the 

fraction of achievable VMT. The cost of unachievable VMT 

has been accounted for based on the cost of popular car-share 

programs, making our results most generally applicable to 

drivers without access to an alternative lower-cost, rangeunlimited mode of transportation (e.g., a second non-BEV car 

owned by or freely available to said driver). Further, a 

detailed accounting of the economics of a battery swapping 

service provider, including consideration of the amount of 

required infrastructure, financing of capital expenditures, 

taxes, depreciation, etc., has been applied to calculate the fee 

charged to the consumer for battery swapping service plans.

As should be expected, we find that drive patterns with high 

annual VMT are generally best suited to range extension (e.g., 

battery swapping) SPs. The range extension infrastructure 

utilization to be expected from such drive patterns varies 

considerably–primarily as a function of the range of the BEV, 

where shorter BEV ranges encourage a higher frequency of 

range extension events. For all vehicle ranges, though, the 

achievable utility factor is high when range extension 

infrastructure is adequately placed and up to four range 

extensions per day are allowed, spanning from a low of 85% 

at 50 miles to a high of 94% at 100 miles.

In calculating the monthly service fee of a battery swapping 

SP, we find that the high level of capital expenditures involved 

in the service provider’s business model makes the cost of 

financing very powerful. The large capital costs are generally 

best attributed to the cost of batteries (as opposed to 

infrastructure, electricity, charge points, and other general and 

administrative costs). However, if the cost of individual 

battery swap-spots approaches or exceeds the high end of our 

assumptions, and geographic and/or customer service 

requirements demand a high number of swap-spot

deployments per customer, infrastructure costs can compete 

with and even exceed those of batteries.

In applying the calculated service fee to individual driver 

economics where the cost of unachievable VMT is high, our 

simulations show that the economic efficiency of a battery 

swapping SP-BEV is often challenged by the cost of batteries, 

infrastructure, and financing. With our assumed cost of 

gasoline, tax structure, and absence of purchase incentives, we 

find that these factors generally make the TCO of the SP-BEV 

more expensive than that of a DO-CV. Only when battery 

costs reach the DOE’s most aggressive target ($125/kWh), and 

infrastructure costs achieve our lowest assumed values, do we 

see significant numbers of drive patterns benefiting 

economically over the CV option.

However, where the cost of unachievable VMT is high, we do 

find the SP-BEV to be a more cost-effective approach to 

electrifying travel for a significant number of drive patterns 

than a DO-BEV75 with a broad range of scenarios for the cost 

of batteries and infrastructure. There is also additional 

potential for service providers to improve the relative value of 

BEVs via multi-tiered fee and service strategies, optimal 

allocation and down-cycling of aged batteries, and revenue 

generation via vehicle-to-grid services (which a service 

provider is well positioned to optimize).This, along with 

several assumptions and simplifications made in this analysis, 

encourage further study of battery swapping service plans. 

Along with these aforementioned avenues for value 

improvement, such efforts must also address several aspects of 

battery swapping SP business models that could detract value, 

including accounting for business start-up costs (which could 

be significant in the years before the venture generates any 

revenue); marketing, engineering, and management staff 

expenses; increased cost to the vehicle glider for a swappable 

battery; and increased infrastructure utilization due to the 

geographic and temporal distribution of subscriber battery 

swapping needs and other unaccounted for driver behavior 

(e.g., range anxiety). Further, the concurrence of both a high 

cost of unachievable VMT and high annual VMT, which may 

decrease the potential customer base, should be considered. 

Finally, other elements that are more difficult to quantify 

economically must be addressed as well – such as customer 

willingness to subscribe to a service contract for battery 

service while owning a vehicle without a battery, to increase 

stopping frequency on long travel days. 

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ACKNOWLEDGEMENT

This study was supported by the Energy Storage, Vehicle 

Technologies Program, Office of Energy Efficiency and 

Renewable Energy, U.S. Department of Energy. The authors 

would specifically like to thank Dave Howell and Brian 

Cunningham of the U.S. Department of Energy for their 

continued guidance and support. The use of the battery 

degradation and FASTSim vehicle simulation tools, both 

developed at the National Renewable Energy Laboratory with

funding from the U.S. Department of Energy’s Vehicle 

Technologies Program, were critical to the completion of this

study. Special thanks to Aaron Brooker for development of 

FASTSim and support of the Battery Ownership Model; 

Kandler Smith for developing and supporting the integration 

of the battery degradation model; and Terry Penney and 

Ahmad Pesaran, for their continual guidance.

DEFINITIONS/ABBREVIATIONS

BEV Battery electric vehicle

BOL Beginning of life

BOM Battery Ownership Model

CV Conventional vehicle

DO Direct ownership

DOE Department of Energy

DVMT Daily vehicle miles traveled

EIA Energy Information Administration

EOL End of life

PDF Probability density function

ROE Return on equity

SOC State of charge

SP Service plan

TCO Total cost of ownership

TCS Traffic Choices Study

VMT Vehicle miles traveled