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

Battery Ownership Model:

A Tool for Evaluating the 

Economics of Electrified 

Vehicles and Related 

Infrastructure

Preprint 

Michael O’Keefe, Aaron Brooker,

Caley Johnson, Mike Mendelsohn,

Jeremy Neubauer, and Ahmad Pesaran

Presented at the 25th International Battery, Hybrid and Fuel Cell 

Electric Vehicle Symposium & Exposition

Shenzhen, China

November 5–9, 2010

Conference Paper

NREL/CP-5400-49127

January 2011
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1

Battery Ownership Model: A Tool for Evaluating the Economics

of Electrified Vehicles and Related Infrastructure

Michael O’Keefe, Aaron Brooker, Caley Johnson, Mike Mendelsohn, Jeremy Neubauer, and Ahmad Pesaran

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393, United States of America

Michael.OKeefe@nrel.gov

Abstract—Electric vehicles could significantly reduce greenhouse gas (GHG) emissions and 

dependence on imported petroleum. However, for mass adoption, EV costs have historically been too 

high to be competitive with conventional vehicle options due to the high price of batteries, long refuel 

time, and a lack of charging infrastructure. A number of different technologies and business strategies 

have been proposed to address some of these cost and utility issues: battery leasing, battery fast-charging 

stations, battery swap stations, deployment of charge points for opportunity charging, etc. In order to 

investigate these approaches and compare their merits on a consistent basis, the National Renewable 

Energy Laboratory (NREL) has developed a new techno-economic model. The model includes nine 

modules to examine the levelized cost per mile for various types of powertrain and business strategies. 

The various input parameters such as vehicle type, battery, gasoline, and electricity prices; battery cycle 

life; driving profile; and infrastructure costs can be varied. In this paper, we discuss the capabilities of 

the model; describe key modules; give examples of how various assumptions, powertrain configurations, 

and business strategies impact the cost to the end user; and show the vehicle’s levelized cost per mile

sensitivity to seven major operational parameters.

Keywords: Battery, Economics, Electric Vehicle, Service Provider

1 Introduction

Reducing emissions such as greenhouse gases and 

criteria pollutants from transportation vehicles and

switching to alternative fuels other than petroleum such as 

electricity, bio-fuels, and hydrogen are the goals of many 

countries around the world. The aim is to reduce the 

negative impact on climate change while achieving energy 

security, particularly for transportation. Hybridization and 

electrification of vehicles have the potential to decrease 

GHG emissions and lower and/or displace petroleum with

alternative fuels with fewer environmental and security 

concerns. However, wide-scale consumer acceptance of 

alternatives to conventional gasoline-powered vehicles

(CVs) such as hybrid electric vehicles (HEVs), plug-in 

hybrid electric vehicle (PHEVs), and pure electric vehicles 

(EVs) will depend on their cost-effectiveness and their 

functionality, including driving range and ease of 

refueling.

Each alternative powertrain technology has a unique 

petroleum reduction potential and incremental cost to the 

end user (either positive or negative) depending on the 

driving profile, vehicle performance and range 

requirements, and assumed cost of fuels; each of which 

may have a unique geographic profile.

Incorporation of battery technology into vehicle 

powertrains offers a significant petroleum reduction 

opportunity. The EV powertrain is especially attractive 

among electric traction drive vehicles in that it offers a 

complete transition from petroleum to electricity for 

transportation. However, the current costs of battery 

technology make the economics of pure EVs with range 

and performance equivalent to CVs challenging.

A number of technical and business strategies have 

been proposed and/or deployed to enable the transition to 

these alternative powertrain technologies, including: the 

electric utility utilization of the vehicle batteries as a 

distributed resource; battery leasing by a service provider 
2

who takes on the risk and upfront cost of battery 

ownership; public infrastructure development to recharge

electric vehicles while parked; fast-charge and/or battery 

swap stations that effectively extend EV range; and 

alternative car ownership models that allow users to own 

an EV but rent other vehicles for long-distance excursions. 

Each strategy has unique implications to the vehicle 

design, operating characteristics, and battery life.

Accordingly, it can be challenging to compare different 

system options on a consistent basis.

To address this issue, the U.S. Department of Energy’s 

(DOE’s) NREL has developed a computer tool called the 

Battery Ownership Model (BOM). This paper describes 

the tool and gives an example of its use.

2 Modeling Overview

The purpose of the BOM is to calculate the cost of 

vehicle ownership under various scenarios of vehicle and 

component cost, battery and fuel price forecasts, driving 

characteristics, charging infrastructure cost, financing, and 

other criteria. The vehicle economics that are considered

include vehicle purchase, financing, fuel, non-fuel 

operating and maintenance costs, battery replacement, 

salvage value, and any costs passed on by a third-party 

such as a service provider to account for the installation, 

use, and availability of infrastructure.

There are many reasons why an individual car buyer

chooses one vehicle over another. Economics is an 

important factor for individual consumers, but there are 

many other factors that impact the purchasing decision as 

well. For end-users such as fleet owners, economics is one 

of the top factors for purchasing. In addition, the 

economics of technologies can aid policy makers in 

decision-making. Thus, there is a strong motivation to 

look at the economics of vehicle technologies to see how 

they compare against each other. As such, the primary 

output of the BOM is an economic indicator of end-user 

net present costs called “levelized cost per mile” (LCPM).

The LCPM economic metric is defined as follows:

(1)

The variable c is the cost to the end user during the 

given period, i. The discount factor for the given period is 

d. Finally, the vehicle miles traveled for the given period

is vmt. The total number of periods is represented by N.

The BOM consists of nine modules that are described 

in subsequent sections. The model is currently written in 

Microsoft Excel. A schematic of how the different 

modules (in oval) fit together can be seen in Figure 1.

Figure 1: Overview of the battery ownership model

2.1 Location-Specific Data Module

Four pieces of information are obtained based on 

location: a probability density function for daily driving 

distance, forecasts for annual vehicle miles traveled

(VMT) per person, and location-specific forecasts for cost 

of fuel over time. For this paper, we present data for 

electricity and gasoline, though other fuels such as diesel, 

biodiesel, or ethanol (E85) could be used as well.

The probability density function of daily VMT is 

obtained from the 2001 National Household 

Transportation Survey (NHTS) [1]. The NHTS can be 

used to predict the probability density function for daily 

driving distance. A trip distance distribution for the entire 

United States is shown in Figure 2.

Location-specific forecasts for gasoline and electricity 

costs over time were obtained from the Energy 

Information Administration (EIA) short-term energy 

outlook and annual energy outlook [2, 3]. The EIA 

projections are made for multiple scenarios. Two forecast 

scenarios for gasoline price are presented in Figure 3, 

“reference” and “high oil price” (using terminology from 

[2]). The forecasts for the entire United States are a 

sales-weighted average of prices for all grades of gasoline 

and include federal and state taxes (but not county and 
3

local taxes). A forecast of commercial electricity prices is 

given for several regions in Figure 4: the states of Hawaii, 

California, and Colorado are shown in contrast to the 

national average forecasted electricity price. Note the large 

difference in electricity price by region.

Figure 2: Probability of daily vehicle miles traveled

2.2 Vehicle Performance and Sizing Module

This module simulates the fuel consumption (gasoline, 

electricity, or other fuel) and acceleration performance of 

a given vehicle using attributes such as weight, frontal 

area, drag coefficient, powertrain type (CV, HEV, PHEV, 

or EV), and driving profile. An optimization routine 

included in this module optimizes vehicle component sizes 

(engine power, motor power, battery energy, and battery 

power) to meet minimum performance requirements at the 

lowest cost. NHTS driving distance distributions along 

with location-specific annual VMT are used to predict the 

number and depth of battery discharges. This distribution 

of discharges is used with a battery life model to predict 

battery life. This analysis is repeated for different battery 

power and energy levels until the minimum cost of 

ownership is found for a given scenario. When comparing 

vehicles with different powertrain configurations, we use 

this module to size components so that they have similar 

acceleration performance and meet range requirements to

give a fair comparison based on similar utility to the end 

user.

Figure 3: Gasoline price forecast for the United States

Figure 4: Commercial electricity price forecasts

2.3 Vehicle Component Cost Module

This module predicts the component cost and price of 

various components that make up a vehicle. Vehicle cost 

is the sum of the “glider” cost (the vehicle chassis and 

partial drivetrain without the powertrain), the powertrain

cost, and the battery cost, if applicable. Manufacturing

costs are translated to end user price using a markup 

factor. A markup factor of 1.75 was determined to give a 

good fit when comparing the model-predicted prices with 

actual vehicle manufacturer’s suggested retail price 

(MSRP) as shown in Figure 5. Lower markup factors may 

be possible for wholesale large-quantity purchases such as 

for a service provider who provides batteries and 

infrastructure to an EV end-user.

The price of the CV is calculated by adding the cost of 

the glider to the cost derived using the formula for the 

engine in Table 1. The cost of the HEV is the cost of the 

glider plus engine, battery, motor, and power electronics. 

The cost equation for the PHEV is similar to that of the 

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

0 50 100 150 200

Probability of Occurrence

Daily Vehicle Miles Traveled

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

2010 2015 2020 2025 2030

Price (2007 U.S. Dollars/Gallon)

Year

Reference

High Oil Price

$0.00

$0.05

$0.10

$0.15

$0.20

$0.25

$0.30

$0.35

2010 2015 2020 2025 2030

Price (2007 U.S. Dollars/kWh)

Year

Hawaii

California

United States Average

Colorado
4 

HEV although the component sizes are different and the 

PHEV power electronics must include an on-board 

charger. The EV cost is the cost of the glider, motor, 

power electronics, charger, and batteries; these equations 

are presented in Table 1. An example set of cost 

relationships were obtained from [4] with the exception of 

the battery energy cost coefficient, which was calibrated to 

match the MSRP over a range of advanced vehicles of 

different component sizes (see Figure 5). In Table 1, Pb

represents the battery peak power capability in kW, Eb

represents the battery total energy capacity in kWh, Pm is 

the peak power of the electric motor in kW, and Pe is the 

peak power of the internal combustion engine in kW. 

Table 1: Component cost model 

Battery 

Motor & 

Power 

Electronics 

Engine 

Figure 5: Comparison of vehicle MSRP versus model prediction 

As can be seen from Figure 5, the largest discrepancies 

occur between the MSRP/announced price and predicted 

price of EVs although our model predictions are neither 

consistently high nor consistently low. Because MSRP is a 

combination of manufacturing cost and markup, it is 

difficult to say whether the lack of good fit for EV data 

reflects a choice by the original equipment manufacturer 

to reduce their markup factor [5-9], the achievement of 

lower initial battery costs [10], or some combination of the 

two. 

2.4 Battery Cycle Life Module 

This module estimates the cycle life of a battery based 

on usage and state-of-charge range. In the future, we hope 

to expand this model to include the effects of charge rate 

and temperature. For the EV, trip length is used to 

estimate the level of battery discharge based on the 

vehicle's energy use per mile. Each discharge causes a 

specific level of battery wear, based on data from Johnson 

Controls [11], as seen in Figure 6. Using trip driving 

distance distribution data, battery discharge efficiency, and 

battery cycle life data, the average charge-depleting wear 

per mile can be calculated. The wear per mile due to 

accelerating and braking, which is based on assumed 

speed versus time drive profiles input into vehicle 

simulations, is then added to calculate the total wear per 

mile.

Three curves are presented in Figure 6 to demonstrate 

the battery life model. The curve labeled “Case 1” 

represents data published in [11]. These data were 

obtained at the cell level and do not consider calendar life, 

temperature, or power level effects on life. Case 2 was 

created by adjusting case 1 to match published data for the 

Nissan Leaf [12] and Chevy Volt [13] battery life 

expectations. Case 3 reflects an advanced battery with a 

7,000-cycle life at 100% depth of discharge [14]. For 

reference, Figure 6 also indicates the DOE target of 5,000 

cycles of battery life assumed to be at 80% state-of-charge 

swing [15]. 

Figure 6: Battery cycle life dependence on SOC swing. 

$-

$20,000 

$40,000 

$60,000 

$80,000 

$100,000 

$120,000 

Vehicle Cost Estimate (MSRP)

Actual

Model

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

100 1,000 10,000 100,000 1,000,000

SOC Swing

Cycles

Case 1

Case 2

Case 3

DOE Target

145.71·pow(n, -0.68)

86·pow(n, -0.68)

433·pow(n, -0.68)

note: pow(n, p) = np, n is number of cycles
5

2.5 Electricity Usage Module

This module calculates the total electricity required to 

recharge EVs from the electric utility grid to determine the

electricity cost burden on the end user or on a service 

provider. EVs and PHEVs can be recharged from charge 

points for all daily driving distances below the vehicle’s

range. For daily driving distances above the vehicle’s

electric range, PHEVs must rely on their internal 

combustion engines and EVs must rely on some other 

manner of infrastructure if they are to be used over 

approximately the same driving profiles as conventional 

vehicles. For example, a battery swap station may be used 

to exchange a depleted battery pack for a fully charged 

one. EV fast charging may provide a similar service.

We can also simulate opportunity charging in this 

module, which is defined as the ability to recharge any 

time the vehicle is parked. By using charge points, 

opportunity charging can, among other things, reduce the 

dependence on more expensive infrastructure items such 

as battery swap stations and fast charge stations.

More work is needed to assess the exact charging 

efficiency from grid to battery and impact on the grid by

EV supply equipment, battery swap stations, and battery 

fast charge stations over the EV’s life.

2.6 Infrastructure Requirements Module

This module estimates the amount and cost of the 

needed infrastructures for different EV business 

approaches such as EV direct ownership and EV service 

provision scenarios. In the EV direct ownership, the driver 

purchased the vehicle and its battery and pays for the 

electricity and other cost. An EV service provider is 

assumed to do the following: lease batteries, provide a

charge point network for home and opportunity charging 

when parked, provide battery swap stations, and may even 

provide access to alternate transportation technologies for 

long trips. In this EV service provider scenario, the driver 

purchases the EV without the battery pack, reducing 

upfront cost.

The frequency of use and benefit of various forms of 

EV support technology depend very much on the specifics 

of how the aggregate users of the infrastructure are 

traveling between recharging events. Our model takes

driving distance probability (such as Figure 2) and an 

EV’s range and calculates the “distance driven between 

recharge events.” Distances greater than the EV range are 

transformed into n trips approximately equal to the EV 

range, which implies a stop at a fast charging unit or 

battery swap station plus an additional trip of r miles 

where n is the integer quotient of the intended trip distance 

and the EV range; r is the remainder.

Figure 7: Probability density function: daily driving distance

As an example, consider an EV with a range of 100 

miles (EV100) and the data from Figure 2. If we assume

recharging only occurs once per day, we get the histogram 

labeled “U.S., All Drivers, Recharge Once per Day” in

Figure 7. With once-per-day charging across the U.S. 

driving population (including urban and rural drivers), 

these data indicate approximately 8% of recharging events 

would require range-extending infrastructure such as fast 

charge units or battery swap stations. On the other 

extreme, if we assume the vehicle has access to ubiquitous 

opportunity charging such that the vehicle can recharge 

after every trip and we target drivers living in suburban 

areas (that is, excluding urban and rural residents), we get 

the histogram titled “U.S. Suburban Drivers, Opportunity 

Charge.” In this case, only approximately 0.5% of 

recharging events would require range-extending 

infrastructure. Note that under the ubiquitous opportunity 

charging assumption, recharge events typically occur more 

than one time per day.

2.7 Service Provider Economics Module

As mentioned previously, the service provider can

provide one or more of a range of EV services. If the 

service provider offers recharging infrastructure, to 

various degrees, this can allow a shorter range EV to drive 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0 4 9 13 17 22 26 30 35 39 43 48 52 57 61 65 70 74 78 83 87 91 96 100

Probability of Occurance

Distance Driven Between Recharge Events (miles)

U.S. Suburban Drivers, Opportunity Charge

U.S. All Drivers, Recharge Once per Day
6

similar driving distances as other vehicles due to the 

presence of opportunity charging and battery swap stations 

and/or fast charge stations. The cost of all services offered 

by a service provider must be recouped through some 

manner of service fee. This fee would cover, for example,

battery purchases over the life of the vehicle, the cost to 

install and maintain EV charging infrastructure, and 

sufficient additional funds to cover the operating expenses

(including the cost of electricity) and profit margin of the 

service provider business itself. The purpose of the service 

provider economics module is to compute the fee required 

to cover all costs by the service provider plus profit 

margin which is specified using a return on equity metric 

which is given as an input.

The module uses estimates for the service provider 

customer market growth versus time. The EV service 

provider’s customer growth projections can be taken from 

sources such as [16]. Infrastructure costs are forecast 

based on the ratios of the number of charge points and 

swap stations needed per vehicle in the system. That is, 

the infrastructure investment lags the customer demand, 

although supply and demand in real markets are more 

dynamically linked (particularly for new technology 

adoptions).

2.8 Greenhouse Gas Accounting Module

GHG reduction is a primary goal for many proponents 

of EVs. The Greenhouse Gas Accounting Module tracks 

GHG emissions from vehicle production and disposal as 

well as the fuel cycle, which includes well-to-wheels fuel 

production and use. As shown in Figure 8, EVs can lead to 

substantial GHG reductions if electricity is produced from 

a low-carbon fuel mix. EVs have lower lifecycle GHG 

emissions than CVs in all states examined in Figure 8, and 

they have lower emissions than HEVs or PHEVs when 

charged with average U.S. electricity or electricity from a 

state such as California that generates a substantial portion 

of its electricity using renewable resources and natural 

gas.

GHG emissions from the vehicle production phase are 

from [17] for the CV and the EV and extrapolated for the 

HEV based on battery capacity, power of the electric 

drivetrain, and power of the gasoline drivetrain. Gauch et 

al. [17] assumes that electricity used in vehicle production 

is generated from the European Union fuel mix, which is 

4% less GHG-intense than U.S. electricity and therefore 

slightly under- represents the GHGs coming from vehicle 

production in the U.S. GHG emissions from disposal are

assumed to be 20% of production energy use [18].

Figure 8: Lifecycle GHG emissions for vehicles

GHG emissions from the vehicle fuel include 25 

pounds carbon dioxide equivalent (CO2e) per gallon of 

gasoline drilled, refined, transported, and used [19]. 

Lifecycle emissions for electricity account for producing 

electricity from various primary fuel sources [20], which 

are weighted according to each state’s electricity 

generation primary fuel distribution [21]. Electricity 

transmission losses of 6.5% [22] and battery charger 

losses of 14% [23] are also accounted for.

2.9 Driver Economics Module

The overall cost of each powertrain technology to the 

end user is calculated using LCPM. Costs can be divided 

into two main categories—vehicle financing (including 

tax incentives) and vehicle operating costs (including fuel 

input, insurance, general maintenance, and service 

provider fees if applicable). Maintenance and insurance 

costs are assumed to be the same for all vehicle 

powertrains (CV, HEV, PHEV, and EV). We assume end 

users for all powertrains pay some percentage of the 

upfront costs (nominally 20%) as a down payment with 

the remaining fraction to be financed over some period 

(nominally 5 years). Fuel costs are based on the 

location-specific electricity and gasoline cost projections 

(see section 2.1). Battery replacement costs for HEVs and 

EVs are calculated from section 2.4. The vehicle is 

assumed to be resold at the end of the vehicle ownership 

analysis period. Vehicle resale value is based on data for 

actual vehicles such as the Toyota Corolla [24]. Battery
7

resale (or reuse) values can be specified as well although 

the results are less sensitive the further into the future the 

resale event takes place.

3 Results and Discussion

All currency reported in the results section is in year 

2007 United States dollars. Due to space limitations, in 

this section we provide some example results. Detailed 

scenario and business strategy analysis will be reported in 

other studies.

3.1 Levelized Cost per Mile Validation

As a means of validation, we compared our LCPM

prediction for various CVs with existing data sources. This 

comparison appears in Figure 9. The reference gasoline 

forecast case for U.S. average conditions is assumed along 

with 5 years of ownership. The data labeled as “AAA” is 

referenced from online documentation [25]. The Ward’s 

data [26] were adjusted to the U.S. average annual VMT 

of 12,375 miles/year in 2005 [27]. Data listed as “IRS 

mileage reimbursement” correspond to the federal 

reimbursement rate of 55 cents per mile (in 2008) used to 

calculate the deductible costs of operating an automobile 

for business, charitable, medical or moving purposes when 

filing tax returns [28].

Figure 9. Comparing levelized cost per mile from various 

sources with our model

The NREL-predicted LCPM compares well with the 

data provided by AAA for a small car. Depending on the 

type of vehicle driven and how far it is driven each year, 

LCPM can vary significantly. At the 2005 U.S. average 

VMT of 12,375 miles/year, LCPM varies between $0.49 

per mile and $0.76 per mile. It is noteworthy to compare 

the cost of advanced technology vehicles against the range 

of what people spend for conventional transportation.

3.2 Scenario Analysis

In this section, we present an example demonstrating 

some of the capabilities of our model. An midsize car is 

assumed to be owned by one owner for 15 years. Four 

powertrain options for this vehicle are examined: a CV, 

HEV, a PHEV with 40 miles of electric range (PHEV40), 

and an EV with 100 miles of electric range (EV100). The 

driving distribution from Figure 2 is assumed. The EV is

directly owned by the end user and assumed to be charged 

once per day at home. Due to time and space constraints, 

an EV with a service provider option is not addressed in 

this paper. The components in each vehicle powertrain are 

sized by the program to yield equivalent acceleration 

performance: 0 to 60 mph in ~10 seconds. Note, however, 

that the EV100 does not have the same utility as the other 

vehicles due to the lack of a service provider infrastructure

such as fast charging or battery swap for extended range 

operation. For all powertrain options, we assume the 

vehicle owner makes a down payment of 20% of the 

upfront purchase costs with a total sales tax rate of 7% and 

finances the balance over 5 years at a loan rate of 8%. 

Inflation is assumed at 2.5%. The end user is assumed to 

value money at an annual 8% discount rate. Seven design 

variables are examined as shown in Table 2.

Table 2: Design Variables Examined in this Study

Variable Min Max

D1: GHG Market Cost (2007 

U.S. Dollars/Ton CO2e-Year)

0.00 28.53

D2: Federal Tax Incentive 

(2007 U.S. Dollars)

0 7,500

D3: Gasoline Cost Forecast EIA Reference

EIA 

High-Oil-Price

D4: Annual Distance Driven 

(Miles/Year)

9,059 15,691

D5: Vehicle Auxiliary Load (W) 700 2,200

D6: Battery Energy Cost 

Coefficient (2007 U.S. Dollars /

kWh)

350 700

D7: Battery Life Coefficient

86 (low cycle

life)

433 (high 

cycle life)

30

40

50

60

70

80

90

10,000 12,000 14,000 16,000 18,000 20,000

Levelized Cost per Mile (Cents / Mile)

Annual Vehicle Miles Traveled

Small Car (NREL)

Small Sedan (AAA)

Medium Sedan (AAA)

Large Sedan (AAA)

WARD's 2009 Model Year Average Car

IRS Mileage Reimbursement Rate
8

The design variables include cost for GHG emissions

in dollars per ton of CO2 equivalent emitted per year, the 

amount of federal tax incentive offered to buyers of the

EV100 and PHEV40, the EIA gasoline forecast scenario

used: reference or high-oil price case (see Figure 3), the 

annual VMT per year, the magnitude of accessory loads 

on the vehicle from 0.7 to 2.2 kW, the battery energy cost 

coefficient, and finally, the battery life coefficients 

representing different battery life curves (see Figure 6, 

cases 2 and 3).

Over the range of design variables examined, the 

model predicts fuel economy to be between approximately 

26 and 32 mpg for the CV, 35 and 44 mpg for HEV, and 

between 248 and 353 Wh/mile for the EV100. The 

PHEV40 has aggregate fuel consumption between 54 and 

74 mpg gasoline and 103 to 128 Wh/mile electricity.

Accessory load is a major driving factor behind the change 

in fuel consumption rate of electric traction drive vehicles 

as has been observed elsewhere [29].

The range of variation in vehicle levelized cost ratio 

over the full factorial of all simulated runs is given in 

Figure 10. Vehicle levelized cost ratio is the vehicle’s 

LCPM divided by the CV LCPM for a given scenario. The 

majority of the EV100’s cost is due to the cost of the 

battery pack. Therefore, it is not surprising that the EV100 

shows a large variation in cost ratio over the design 

variables examined. All vehicles, including the EV100, 

achieve a cost ratio below 1.0 over some of the scenarios 

(the minimum EV100 cost ratio is 0.99).

Figure 10: Range of the ratio of vehicle levelized cost ratio

Figure 11 shows the sensitivity of the vehicle levelized 

cost ratio to the design variables listed in Table 2. 

Sensitivity to a given design variable is determined by 

taking the full factorial results for the levelized cost ratio

and taking the absolute value of the difference between the 

average cost ratio at the high setting of the design variable 

and at the low setting of the design variable. All sensitivity 

values were then divided by the largest of all sensitivities 

seen to normalize the maximum sensitivity to a value of 

one. The sensitivity of vehicle levelized cost ratio to 

design variable interactions with each other is not plotted. 

The EV100 vehicle levelized cost ratio is most sensitive to 

battery costs followed by battery cycle life. After that, the 

presence of a federal tax incentive, assumptions on 

gasoline cost, annual distance driven, and magnitude of 

vehicle auxiliary loads all have approximately equal effect 

on the EV100 levelized cost ratio. GHG market cost does 

not seem to be a large economic driver in and of itself 

over the range of assumptions examined.

Figure 11: Sensitivity of vehicle levelized cost ratio to design 

variables

4 Conclusion

Multiple new powertrain configurations, infrastructure 

options, and business strategies are being suggested for 

future electric drive vehicles. To comparatively investigate 

these business approaches, NREL developed a new 

techno-economic model called the Battery Ownership 

Model. The model uses the present value metric of 

levelized cost per mile of owning and operating an EV

under various business strategies and compares it with 

those of CV, HEV, and PHEVs. This paper focused on 

giving an overview of the model and illustrated some of 

the model inputs. We also presented an example analysis 

that investigated the sensitivity of vehicle levelized cost 

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

HEV PHEV40 EV100

Cost Ratio (Vehicle LCPM / CV LCPM)

0.0

0.2

0.4

0.6

0.8

1.0

Cost Ratio Sensitivity

HEV PHEV40 EV100
9

ratio to seven design variables. The vehicle levelized cost 

ratio for an EV with a 100-mile range was found to be 

most sensitive to battery cost and cycle life with accessory 

loads, annual distance traveled, the existence of a tax 

incentive, and gasoline cost assumptions all having a 

secondary though approximately equal effect over the 

range of design variables examined.

In future work, we plan to use our model to further 

explore the techno-economic trade-offs of EV 

technologies, including consideration of service provider 

infrastructure options, markets such as taxis or 

long-distance commuters, alternative vehicle ownership 

scenarios, optimal EV range in the presence of 

infrastructure, optimal battery life and replacement 

schedules, larger vehicle size classes, further depth and 

emphasis on battery costs and associated projections, and 

vehicle usage and recharging strategies (e.g., opportunity 

charging). The present model currently has the capability 

to analyze all of these. In addition, since the battery is 

such a critical element for this model, we would like to 

enhance our battery cycle life model to better predict when 

batteries will fail and what residual value they will have at 

end of life. One area that the BOM will continue to omit is 

non-monetary benefits to the driver such as reduction in 

fossil fuel dependence, reduced GHG emissions, pride of 

driving green technology, reduction in the number of visits 

to gas stations, and the instant torque response of EVs. 

Inclusion of these externalities increases the value 

proposition of EVs to the driver over and above what we 

see from pure economics.

In summary, NREL’s Battery Ownership Model was 

constructed to calculate the present value of costs to the 

end user of advanced electric traction drive vehicles and 

related infrastructure on a consistent basis over multiple 

scenarios. The results of the model show that there are 

scenarios where HEVs, PHEVs, and even EVs can be less 

expensive than CVs, and it also highlights which 

parameters have the largest influence over the vehicle

levelized cost per mile. Furthermore, the BOM is 

equipped to answer many pressing questions that drivers, 

third party service providers, EV marketers, and 

policymakers have as they turn a transportation

electrification system into reality.

5 Acknowledgements

We would like to acknowledge the U.S. Department of 

Energy’s Vehicle Technologies Energy Storage, and 

particularly David Howell, for sponsoring this work. We 

would also like to thank NREL’s Laboratory Program 

Manager for Transportation Research, Terry Penney, and 

the NREL Energy Storage Task Leader, Ahmad Pesaran,

for their support and guidance.

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[Accessed: Sep. 1, 2010].
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Battery Ownership Model: A Tool for Evaluating the Economics of 

Electrified Vehicles and Related Infrastructure: Preprint

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14. ABSTRACT (Maximum 200 Words)

Electric vehicles could significantly reduce greenhouse gas (GHG) emissions and dependence on imported 

petroleum. However, for mass adoption, EV costs have historically been too high to be competitive with conventional 

vehicle options due to the high price of batteries, long refuel time, and a lack of charging infrastructure. A number of 

different technologies and business strategies have been proposed to address some of these cost and utility issues: 

battery leasing, battery fast-charging stations, battery swap stations, deployment of charge points for opportunity 

charging, etc. In order to investigate these approaches and compare their merits on a consistent basis, the National 

Renewable Energy Laboratory (NREL) has developed a new techno-economic model. The model includes nine 

modules to examine the levelized cost per mile for various types of powertrain and business strategies. The various 

input parameters such as vehicle type, battery, gasoline, and electricity prices; battery cycle life; driving profile; and 

infrastructure costs can be varied. In this paper, we discuss the capabilities of the model; describe key modules; give 

examples of how various assumptions, powertrain configurations, and business strategies impact the cost to the end 

user; and show the vehicle’s levelized cost per mile sensitivity to seven major operational parameters.

15. SUBJECT TERMS

Battery; Economics; Electric Vehicle; Service Provider

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