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 battery replacement

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 costeffective 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-yet-cost-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

A Techno-Economic Analysis of BEV Service

Providers Offering Battery Swapping Services

2013-01-0500

Published

04/08/2013

Jeremy S. Neubauer and Ahmad Pesaran

National Renewable Energy Laboratory

doi:10.4271/2013-01-0500

NREL/CP-5400-58608. Posted with permission.

Presented at the SAE 2013 World Congress & Exhibition
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.

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 thirdparty 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 realworld 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, 6, 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,

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.

Figure 1. Employed gasoline prices (2012 dollars)

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, 6, 7]. We

assume no tax credits or other purchase incentives are

available.

Figure 2. Employed residential electricity prices (2012

dollars)

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.

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 beginningof-life (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.

Table 1. Vehicle Specifications (all prices in 2012 U.S. dollars)
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 DO-CV. With the medium and high battery

prices, however, no simulated drive patterns see a cost

advantage to driving a DO-BEV, and much larger

percentages of simulated drive patterns incur a TCO premium

of 20% or more when electing the DO-BEV.

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 DOBEV [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.

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 SP-BEV. 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.

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

Figure 4. Annual VMT distribution of selected drive patterns
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 car-share 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

fast-charge 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.

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 carryforwards 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

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).

Table 2. Calculated Battery Swapping Service Plan Usage Statistics
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 mediumand 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 hour

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

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 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, 6, 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.

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.

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.

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

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

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

SP-BEV50 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,

low-service 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 DOFigure 8. Battery swapping service fee breakdown for the low-service, low-cost swap-spot class
CV in significant numbers when both the cost of batteries and

swap-spot infrastructure is low.

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 lowercost, range-unlimited 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

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
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
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