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

Contract No. DE-AC36-08GO28308

Variability of Battery Wear in 

Light Duty Plug-In Electric 

Vehicles Subject to Ambient 

Temperature, Battery Size, and 

Consumer Usage

Preprint

Eric Wood, Jeremy Neubauer, Aaron D. Brooker, 

Jeffrey Gonder, and Kandler A. Smith

Presented at the International Battery, Hybrid and Fuel Cell Electric 

Vehicle Symposium 26 (EVS26) 

Los Angeles, California

May 6-9, 2012

Conference Paper

NREL/CP-5400-53953

August 2012
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1

EVS26

Los Angeles, California, May 6-9, 2012

Variability of Battery Wear in Light Duty Plug-In Electric 

Vehicles Subject to Ambient Temperature, Battery Size, 

and Consumer Usage

Eric Wood, Jeremy Neubauer, Aaron D. Brooker, Jeffrey Gonder, and Kandler A. Smith

National Renewable Energy Laboratory, Center for Transportation Technologies and Systems

1617 Cole Blvd., Golden, CO 80401, U.S.A.

eric.wood@nrel.gov

Abstract

Battery wear in plug-in electric vehicles (PEVs) is a complex function of ambient temperature, battery size,

and disparate usage, and is not well understood. Battery life simulation scenarios that capture varying 

ambient temperature profiles, battery sizes, and driving patterns are of great value to battery manufacturers 

and vehicle original equipment manufacturers. This study seeks to improve understanding of battery wear in 

PEVs by implementing a predictive battery wear model, developed by the National Renewable Energy 

Laboratory, that is capable of capturing the effects of multiple cycling and storage conditions in a representative 

lithium chemistry. In particular, this paper explores the sensitivity of battery wear rates to ambient conditions, 

maximum allowable depth of discharge, and vehicle miles traveled. The analysis focuses on two midsize vehicle 

platforms: a battery electric vehicle (BEV) with a nominal range of 75 mi (121 km) and a plug-in hybrid electric 

vehicle (PHEV) with 40 mi (64 km) of nominal charge-depleting range. Current U.S. hybrid electric vehicle 

populations are used to focus analysis on markets where consumers have shown a tendency towards early 

adoption of advanced vehicle technology. Both cross-sectional and longitudinal driving distance distributions are 

implemented to represent the variability of vehicle use, both vehicle-to-vehicle and day-to-day. In the scenarios 

examined, battery wear over an 8-year period was found to be dominated by ambient conditions for the BEV 

with capacity fade ranging from 19% to 32% while the PHEV was most sensitive to maximum allowable depth

of discharge with capacity fade ranging from 16% to 24%. In addition, the BEV and PHEV were found to be 

comparable in terms of petroleum displacement potential after 8 years of service due to the BEV’s limited utility 

for accomplishing long trips. Future work may include incorporating the effects of temperature on pack 

internal resistance/available capacity and analyzing a range of vehicle-to-grid scenarios.

Keywords: lithium battery, battery calendar life, cycle life, BEV, PHEV

1 Introduction

Plug-in electric vehicles (PEVs) are an advanced 

vehicle technology capable of reducing liquid 

petroleum consumption by storing and using 

energy from the electric grid in an on-board 

battery. Widespread adoption of PEVs will be 

impacted by the ability of original equipment 
2

manufacturers to accurately predict battery life to

produce durable vehicles at a reasonable price. 

Unfortunately, battery life in PEVs is inherently 

variable with factors such as ambient temperature, 

vehicle miles traveled (VMT), and charging 

behavior all interacting to produce potentially 

disparate power and energy fade rates. Battery 

wear is also sensitive to maximum allowable depth

of discharge (DoD) and pack thermal management. 

The degree to which these sizing and usage 

conditions impact battery wear rates and the 

variability of wear rates is explored.

2 Project Approach

To explore the sensitivity and variability of battery 

wear rate in PEVs to various parameters, a 

predictive battery wear model developed by the 

National Renewable Energy Laboratory (NREL) 

was implemented [1]. The life model is informed 

by vehicle powertrain and battery pack thermal 

modeling capabilities developed internally at 

NREL. By leveraging these existing capabilities, it 

was possible to capture the effects of drive cyclebased loading and ambient conditions on battery 

wear rates in a predictive and robust method. An 

overview of this integrated approach is provided, 

followed by an explanation of various sizing and 

usage scenarios examined.

2.1 Battery Life Model

Battery aging is caused by multiple phenomena 

related to both cycling and calendar age. Battery 

degradation is accelerated with the DoD of 

cycling, elevated temperature, and elevated voltage 

exposure, among other factors. At the battery 

terminals, the observable effects of degradation are 

an increase in resistance and a reduction in 

capacity. These two effects can be correlated with 

power and energy loss that cause battery end-oflife in an application. Mechanisms for resistance 

growth include loss of electrical conduction paths 

in the electrodes, fracture and isolation of electrode 

sites, growth of film layers at the electrode surface, 

and degradation of the electrolyte. Mechanisms for 

capacity loss include fracture, isolation, and 

chemical degradation of electrode material, as well 

as loss of cyclable lithium (Li) from the system as 

a byproduct of side reactions.

Under storage or calendar-aging conditions, the 

dominant fade mechanism is typically growth of a 

resistive film layer at the electrode surface. As the 

layer grows, cyclable Li is also consumed from the 

system, reducing capacity. In the present model, 

resistance growth and Li-capacity loss are assumed 

to be proportional to the square-root of time, t

1/2, 

typical of diffusion-limited film-growth processes 

[2]. Under cycling-intense conditions, degradation 

is mainly caused by structural degradation of the 

electrode matrix and active sites. Cycling-driven 

degradation is assumed to be proportional to the 

number of cycles, N. Cell resistance growth due to 

calendar- and cycling-driven mechanisms are 

assumed to be additive:

R = ao + a1t

1/2 + a2N (1)

Cell capacity is assumed to be controlled by either 

loss of cyclable Li or loss of electrode sites, 

Q = min(QLi, Qsites) (2)

where

QLi = bo + b1t

1/2 (3)

Qsites = co +c1N (4)

Models (1), (3), and (4) are readily fit to a 

resistance or capacity trajectory measured over 

time for one specific storage or cycling condition. 

Using multiple storage and cycling datasets, 

functional dependence can be built for rate 

constants a1(T, V, DoD), a2(T, V, DoD), b1(T, V, 

DoD), and c1(T, V, DoD). The present battery life 

model was fit to laboratory aging datasets [3]–[6]

for the Li-ion graphite/nickel-cobalt-aluminum 

(NCA) chemistry as described in [6]. The 

graphite/NCA chemistry has generally graceful 

aging characteristics and is expected to achieve 8 

or more years of life when sized appropriately for 

a vehicle application.

The life model employed in this analysis was 

matched to experimental data for a graphite/NCA 

Li-ion cell with up to 25% battery capacity fade. 

Beyond this level of wear, fade rates may 

accelerate, as sometimes evidenced in 

experimental data by a sharp drop in remaining 

capacity with continued cycling. The present life 

model does not capture possible accelerating fade 

mechanisms that could occur beyond 25% capacity 

fade.

In addition, the life model has been shown to have 

weak sensitivity to normal battery temperature 

variation over the course of a single day, 

particularly when the thermal mass of the battery is 

taken into account [7]. As such, this analysis uses 
3

average monthly temperatures as inputs to the life 

model to capture the effect of seasonal variability 

in disparate climate regions of the United States.

While high heat generation rates resulting from 

aggressive driving are correlated to increased 

battery temperature, the impact of fast charging as 

it relates to rate-induced wear is not considered in 

the present model.

2.2 FASTSim Vehicle Model

Vehicle modeling was performed using a highlevel tool developed at NREL known as FASTSim

(Future Automotive Systems Technology 

Simulator). The analysis focuses on two midsize 

vehicle platforms: a battery electric vehicle (BEV)

with a nominal range of 75 mi (121 km) and a 

plug-in hybrid electric vehicle (PHEV) with 40 mi 

(64 km) of nominal charge-depleting (CD) range 

followed by charge-sustaining (CS) operation via a 

gasoline-fueled internal combustion engine. Table 

1 summarizes the platform and component 

parameters selected for the BEV and PHEV 

models, which are roughly similar to the 

configuration of the production Nissan Leaf and 

Chevrolet Volt, respectively [8]–[9].

Table 1. FASTSim vehicle model inputs (baseline 

values).

BEV PHEV

Drag Coefficient (Cd) 0.29 0.28

Frontal Area (m2) 2.27 2.13

Vehicle Mass (kg) 1663 1850

Engine Power (kW) NA 53

Battery Capacity (kWh) 24 16

Maximum SOC 95% 85%

Maximum Allowable DoD 90% 65%

Battery Thermal

Management System

No active 

cooling

Liquid 

cooling

Accessory Load (W) 300 300

Approx UDDS

CD Range (mi) 100 50

Approx Adjusted

CD Range (mi) 75 40

Battery internal heat generation rates were 

correlated with drive cycles through vehicle 

simulations informed with cell-level test data for a 

representative Li chemistry. Nominal heat 

generation rates were determined using the 

California Air Resources Board LA92 drive cycle, 

which was found to produce moderate heat 

generation rates characteristic of real-world drive 

cycles (see Table 2).

Table 2. Cycle attributes determined through simulation 

of BEV and PHEV.

Drive Cycle

Time, s

Distance, km

Avg Speed, 

km*hr-1

BEV Avg 

Heat Gen, W

PHEV Avg 

Heat Gen, W

UDDS 1,369 11.99 31.5 60 103

LA92 1,435 15.80 39.6 152 232

US06 600 12.89 77.8 477 622

2.3 Vehicle Thermal Model

To correlate ambient conditions to battery 

temperature, a detailed thermal vehicle model was 

implemented. Based on previous analysis done by 

NREL on a Toyota Prius [10], the thermal model 

captures heating due to both ambient temperature 

profiles and solar loading (see Figure 1). These 

inputs are merged with battery internal heat 

generation profiles during driving and charging to 

calculate the average battery temperature over the 

course of a 24-hour period. In addition to passive 

heat transfer to ambient, the PHEV battery pack is 

equipped with an active thermal management 

system (TMS) capable of maintaining the battery 

temperature within a desired band when driven or 

plugged in. An active TMS was used to mitigate 

the effects of greater heat generation rates and 

smaller thermal mass in the PHEV pack whereas 

the modeled BEV employed passive thermal

management. This methodology reflects current 

approaches of original equipment manufacturers 

and provides a means for evaluating different TMS 

strategies.

Figure 1. Vehicle thermal model employed to calculate 

battery temperature with respect to ambient temperature, 

solar loading, and thermal insulation.

Battery temperature was correlated to ambient for 

the passively cooled BEV in two steps (see Figure 
4

2). First, an annual average battery temperature 

above ambient temperature in three different 

climates (shown in blue) was calculated according 

to the total solar loading for a given day (shown in 

green). In addition to solar loading, a second 

temperature differential was calculated as a result 

of battery heat generation and passive dissipation 

during driving and charging (shown in red). The 

temperature increase resulting from driving is a 

function of both daily driving distance and average 

ambient temperature.

Figure 2. Average yearly battery temperature 

contributions from ambient, solar loading, and internal 

heat generation for simulated BEV.

Noticeably, contributions to average battery 

temperature from driving are relatively small, 

accounting for an increase of less than one degree 

Celsius in all climates. For the BEV, this can be 

attributed to both the large percentage of drive 

days with zero miles (approximately 16%) and the 

number of trips omitted due to distances greater 

than the range of the vehicle (see section 2.4.3).

Unlike for the BEV, contributions to battery 

temperature for the PHEV cannot be assumed to be 

additive due to the ability of the active TMS to 

heat or cool the battery as necessary. Contributions 

to battery temperature in the PHEV are attributed 

to three sources: (i) ambient temperature, (ii) solar 

loading and (iii) heat generation plus active 

cooling while driving/charging. A baseline battery 

temperature (shown in blue) is calculated as the 

difference above the ambient temperature due to 

solar loading (shown in Figure 3 in green). The red 

bar shows the adjusted temperature due to heat 

generated during driving/charging and the effects 

of the active TMS. The TMS is assumed to only 

operate when the vehicle is being driven or while 

plugged in. All scenarios assume that the PHEV is 

left unplugged and stationary (implying an inactive 

TMS) for approximately 8 hours during the course 

of the day.

While battery temperature calculations are 

performed to account for variations in driver 

aggression, active versus passive TMS, and daily 

distance, the cell-level effects of temperature on 

internal resistance and capacity are not captured in 

the present model. For example, a BEV battery 

pack in Minneapolis may experience significantly 

lower temperatures and subsequently reduced 

vehicle efficiency and range. In addition, auxiliary 

loading placed on the PHEV resulting from 

operation of the active TMS with the potential to 

limit CD range is not considered. Auxiliary climate 

control loading has the potential to significantly 

impact the CD range of PEVs as shown in [11]. 

Future battery wear analysis may address 

temperature effects on cell internal resistance and 

capacity to quantify the impact of active TMS on 

PEV efficiency and utility.

Figure 3. Average yearly battery temperature 

contributions from ambient, solar loading, active 

cooling, and internal heat generation for simulated 

PHEV.

2.4 Design of Experiments

Upon successful integration of the battery life 

model, the vehicle powertrain model, and the 

vehicle thermal model, the BEV and PHEV were 

run through a matrix of location, battery size, and 

usage scenarios with the primary outputs being 

battery resistance growth and capacity fade at 8 

years. An initial sensitivity analysis revealed 

ambient conditions, maximum allowable DoD, and 

VMT to have the greatest influence on battery 

wear. The design of experiments used to study the 

effects of these variables is described in greater 

detail below.

2.4.1 U.S. Ambient Conditions

An expected distribution of wear rates was desired 

for both vehicles subject to U.S. ambient 

conditions. Current hybrid electric vehicle (HEV)

population data were used as an estimate for the 

0

5

10

15

20

25

30

Phoenix, AZ Los Angeles, CA Minneapolis, MN

Temperature (°C)

Average Ambient BEV

w/ solar effects

w/ solar + driving effects

0

5

10

15

20

25

30

Phoenix, AZ Los Angeles, CA Minneapolis, MN

Temperature (°C)

PHEV Average Ambient

w/ solar effects

w/ solar + driving + active TMS
5

future distribution of PEVs. HEV population 

statistics highlight both large markets and regions 

where consumers have shown a tendency towards 

early adoption of advanced vehicle technology. 

The Polk Company’s 2010 light-duty vehicle 

registration dataset [12] was used to determine the 

top 100 U.S. metropolitan areas in terms of 

number of HEVs (see Figure 4). These locations 

account for approximately 75% of the total U.S.

HEV population and represent a plausible estimate 

for the location distribution of PEV early adopters.

Note from Table 3 moderate climates 

representative of the U.S. average.

Figure 4. U.S. metropolitan areas with large HEV 

populations overlaid onto average ambient temperature 

map. (Credit: Evan Burton, NREL)

Table 3. Top five U.S. metropolitan areas in terms of 

HEV population.

Metropolitan Area # of HEVs

Los Angeles, CA 149,042

New York City, NY 86,773

San Francisco, CA 82,756

Washington, DC 66,720

Chicago, IL 52,158

Figure 5. Weighted distribution of average ambient 

temperatures based on existing HEV populations.

Ambient temperature and solar irradiation data 

were assembled from NREL’s Typical 

Meteorological Year Database (TMY3) [13]. The 

national weighted distribution of average yearly 

ambient temperatures can be seen in Figure 5. The 

TMY3 data were aggregated into monthly 

averages because hourly and daily battery 

temperature variations were shown to have a 

negligible effect on wear rates in the battery life 

model.

2.4.2 Depth of Discharge

The effect of maximum allowable DoD on battery 

wear is explored for both the BEV and the PHEV. 

Each vehicle was assigned a nominal value for 

maximum allowable DoD and maximum state of 

charge (SOC). These values are adjusted over a 

feasible range for both the BEV and PHEV to 

explore the effect on wear (80%–94% and 55%–

87% maximum allowed DoD, respectively). The 

SOC window of the pack is adjusted relative to 

total energy to ensure that the available energy in 

the pack remains constant for all maximum 

allowable DoDs. By adjusting the maximum 

allowable DoD and maximum SOC, the life model 

will capture the wear effects of deep cycling and 

operation at high voltages.

Adjusting pack energy has an impact on vehicle 

mass (and cost) and is subsequently related to CD 

range, efficiency, and acceleration. In light of these 

interactions, the maximum allowable DoD was 

restricted to values that produced vehicle range, 

efficiency, and acceleration values within ±1% of 

the nominal design.

2.4.3 Vehicle Miles Traveled

This analysis uses fleet-aggregated driving 

distance statistics in addition to longitudinal 

(multi-day) travel profiles to represent the 

variability of travel behavior, both from vehicle to 

vehicle and from day to day. Fleet-aggregated 

statistics represent a snapshot of the travel patterns 

for a large fleet of vehicles on a given day. In this 

study, fleet-aggregated statistics are taken from the 

2001 National Household Travel Survey (NHTS) 

[14] to reflect the behavior of the U.S. fleet. Using 

the NHTS, a distribution of fleet distances was 

created with a zero mile per day probability 

calculated as approximately 16% (about one day 

per week) such that the nominal VMT of the 

distribution was equal to 12,375 miles per year 

(19,916 km per year). This aggregated distribution 

is used as the default for analysis of ambient 

conditions and maximum allowable DoD.

0%

5%

10%

15%

20%

25%

8 10 12 14 16 18 20 22 24 26

Share of U.S. Distribution

Average Ambient Temperature (C)
6

To investigate the effect of VMT on battery wear, 

a large number of longitudinal distributions were 

simulated to capture the variable behavior of 

consumers. Longitudinal vehicle distributions 

track the driving behavior of individual vehicles 

over time and tend to exhibit a more focused set of 

distances with a small number of probability peaks 

representing routes frequently traveled. Figure 6

shows three examples of longitudinal profiles.

Figure 6. NHTS cross sectional distribution and three 

example longitudinal distributions taken from the

Traffic Choices Study.

Longitudinal distributions used in this analysis are 

derived from the Puget Sound Regional Council’s 

2007 Traffic Choices Study (TCS) [15]. The TCS 

was an investigation of the response of travel 

behavior to variable toll charges in the Seattle 

metropolitan area. The study placed global 

positioning systems in 445 vehicles from 275 

volunteer households that recorded driving 

patterns over an 18-month average per household 

period. The experiment started with a baseline 

period in which no artificial tolls were applied to 

affect behavior. We processed the data for use in 

this study by (i) only considering data collected 

during the approximately 3-month baseline period, 

(ii) eliminating vehicles for which no driving took 

place during the baseline period, (iii) eliminating 

vehicles for which significant errors in data 

recording were identified, and (iv) reducing 

detailed trip data to daily driving distance based 

upon the length of each trip and the date on which 

it was started. The resultant data were then 

converted into 398 longitudinal profiles of daily 

VMT with each profile representing one vehicle 

over multiple days. A distribution of annual VMT 

derived from this set of 398 longitudinal profiles is 

shown in Figure 7.

VMT calculations for the BEV do not include 

driving days where the expected daily distance is 

greater than the nominal vehicle range. This 

assumption represents a conservative, near-term 

outlook. Alternate scenarios considering 

distributed charging, DC fast charging, or battery 

swapping could reflect greater utility for the BEV.

Figure 7. Distribution of annual VMT for 398 TCS 

longitudinal profiles.

3 Results

The BEV and PHEV were simulated to determine 

battery wear rates under the conditions outlined in 

Table 4. 

Table 4. Tested wear conditions for the BEV and 

PHEV.

BEV PHEV

Distribution of U.S.

ambient conditions 

(Portland, ME to

Honolulu, HI)

Distribution of U.S.

ambient conditions 

(Portland, ME to

Honolulu, HI)

Range of max 

allowable DoD

(80%–94%)

Range of max 

allowable DoD

(55%–87%)

Range of VMT

(398 TCS profiles)

Range of CD VMT

(398 TCS profiles)

3.1 Battery Electric Vehicle

3.1.1 U.S. Ambient Conditions

Figure 8 and Figure 9 show resistance growth and 

capacity loss distributions after 8 years of wear for 

the BEV subject to U.S. ambient temperatures and 

U.S. average driving distributions. Resistance 

growth ranges from 12% to 26% while capacity 

loss ranges from 20% to 32% subject to ambient 

conditions.

Wear rate variability is strongly linked to battery 

temperature variability. Figure 10 shows the 

distribution of yearly average battery temperatures 

experienced by the BEV. Pack temperature in the 

BEV was found to be greater than or equal to the 

ambient temperature in the absence of an active 

0%

5%

10%

15%

0 20 40 60 80

Probability

Daily Distance (mi)

Example TCS Profile 1

Example TCS Profile 2

Example TCS Profile 3

2001 NHTS Cross Section

0%

5%

10%

15%

0 3 6 9 12 15 18 21 24 27 30 33 36

Share of TCS Profiles

VMT (1000*mi)

TCS Profiles
7

TMS. The BEV battery pack is heated above 

ambient due to solar loading and internal heat 

generation during driving and charging.

Figure 8. Weighted distribution of 8-year resistance 

growth for BEV exposed to 100 U.S. ambient conditions 

and NHTS national driving distribution.

Figure 9. Weighted distribution of 8-year capacity loss 

for BEV exposed to 100 U.S. ambient conditions and 

NHTS national average driving distribution.

Figure 10. Weighted distribution of average yearly 

battery temperature for BEV exposed to 100 U.S.

ambient conditions and NHTS national average driving 

distribution.

3.1.2 Depth of Discharge

Battery wear rate is sensitive to both maximum 

DoD and maximum SOC allowed by the battery 

management system. This sensitivity was explored 

using the life model by simulating wear rates for a 

number of battery sizes in the BEV architecture. 

All battery sizes allowed the vehicle to discharge 

21.6 kWh of energy from the battery and achieved 

consistent range, acceleration, and efficiency 

values to within ±1% of the nominal vehicle 

design. Figure 11 shows resistance growth and 

capacity loss at 8 years for multiple battery sizes 

subject to ambient conditions in Los Angeles, CA.

Figure 11. Eight-year resistance growth and capacity 

fade as a function of maximum allowable DoD for BEV 

exposed to ambient conditions in Los Angeles, CA and 

NHTS national driving distribution.

As expected, wear can be seen to increase as the 

maximum allowable DoD window is expanded to 

maintain range for smaller battery packs. 

Increasing the maximum allowable DoD of the 

pack from 80% to 94% causes 8-year resistance 

growth and capacity fade values to increase by 6% 

and 8% respectively. Using near-term battery 

prices ($700/kWh production cost [16]) the 94% 

DoD scenario represents a beginning-of-life pack 

cost savings of $700 while the 80% DoD design 

increases cost by $2,100 (both relative to the 90% 

DoD pack).

3.1.3 Vehicle Miles Traveled

Battery wear in the BEV was subjected to 398 

longitudinal distance distributions as interpreted 

from the TCS. Figure 12 shows the results of this 

simulation in terms of resistance growth and 

capacity fade after 8 years subject to ambient 

conditions in Los Angeles, CA.

Increased VMT can be seen to have opposing 

effects on resistance growth and capacity loss in 

the BEV for the simulated longitudinal profiles. 

Eight-year resistance growth increases by 18% 

over the selected range of VMT while capacity 

fade actually decreases by 5% at high VMT.

0%

10%

20%

30%

40%

10%12%14%16%18%20%22%24%26%28%30%32%

Share of U.S. Distribution

8- yr Resistance Growth

BEV

0%

10%

20%

30%

40%

10%12%14%16%18%20%22%24%26%28%30%32%

Share of U.S. Distribution

8 -yr Capacity Loss

BEV

0%

5%

10%

15%

20%

25%

8 10 12 14 16 18 20 22 24 26

Share of U.S. Distribution

Average battery temperature (°C)

BEV

0%

10%

20%

30%

40%

75% 80% 85% 90% 95%

Depth of Discharge

BEV - Los Angeles, CA

8 yr capacity fade

8 yr resistance growth
8

In the life model, capacity fade is dictated by the 

greater of two fade mechanisms, calendar and 

cycling. In this case, calendar fade is the dominant 

mechanism driven by average daily voltage. By 

increasing VMT, the battery is allowed to spend 

greater amounts of time resting at lower voltages 

(before daily recharge), which extends calendar 

life and thus reduces capacity fade.

Figure 12. Eight-year resistance growth and capacity 

fade as a function of VMT for BEV exposed to ambient 

conditions in Los Angeles, CA and 398 longitudinal 

driving distributions.

This analysis is restricted to the single-charge-perday scenario, and as such, driving days with 

distances longer than the nominal range of the 

vehicle are assumed to be accommodated by an 

alternative means of transportation. To represent 

the percentage of annual miles the BEV can 

achieve relative its original distribution, a BEVspecific, multiple day individual utility factor 

(IUFBEV) is implemented according to Equation 5. 

As an example, a longitudinal distribution with an 

IUFBEV equal to 50% would be able to achieve half 

of its annual miles in the BEV with one charge per 

day with the remaining miles accommodated by 

some other means.

(5)

A unique IUFBEV is calculated for each of the 398 

longitudinal profiles. The distribution of IUFBEV 

for the TCS is shown in Figure 13. The minimum 

and maximum of this distribution are 3% and 

100%, respectively, with the mean occurring at an 

IUFBEV of 75%.

Figure 13. Distribution of IUF for BEV exposed to 

ambient conditions in Los Angeles, CA and 398 

longitudinal driving distributions over 8 years.

3.2 Plug-In Hybrid Electric Vehicle

3.2.1 U.S. Ambient Conditions

Resistance growth and capacity fade distributions 

after 8 years of use for the PHEV subject to U.S.

ambient temperatures and average driving 

distributions can be seen in Figure 14 and Figure 

15. Resistance growth ranges from 18%–26%, and 

capacity loss ranges from 14%–20% over 8 years 

subject to variation in ambient temperature.

Figure 16 shows the distribution of battery 

temperatures experienced by the PHEV when 

exposed to U.S. ambient conditions. By reducing 

average battery temperatures and minimizing the 

effect of ambient conditions on the battery, the 

active TMS in the PHEV allows for reduced wear 

rates with relatively low amounts of variability 

with respect to regional climate differences 

experienced in the United States.

Figure 14. Weighted distribution of 8-year resistance 

growth for PHEV exposed to 100 U.S. ambient 

conditions and NHTS national driving distribution.

0%

10%

20%

30%

0 5000 10000 15000

VMT (mi)

BEV - TCS Profiles

Resistance Growth

Capacity Fade

0%

5%

10%

15%

20%

Share of TCS Profiles

Individual Utility Factor

BEV - TCS Profiles

0%

10%

20%

30%

40%

50%

60%

10%12%14%16%18%20%22%24%26%28%30%32%

Share of U.S. Distribution

8 -yr Resistance Growth

PHEV
9

Figure 15. Weighted distribution of 8-year capacity fade 

for PHEV exposed to 100 U.S. ambient conditions and 

NHTS national driving distribution.

Figure 16. Weighted distribution of average yearly 

battery temperature for PHEV exposed to 100 U.S.

ambient conditions and NHTS national driving 

distribution.

3.2.2 Depth of Discharge

Battery wear sensitivity to maximum allowable 

DoD was explored for the PHEV. All battery sizes 

allowed the vehicle to discharge 10.4 kWh of 

energy from the battery and achieved consistent 

CD range, acceleration, and efficiency values to 

within ±1% of the baseline case. Figure 17 shows 

resistance growth and capacity loss at 8 years for a 

range of battery sizes.

Increasing the maximum allowable DoD window 

of the PHEV from 55% to 87% increased 

resistance growth by 18% while capacity loss 

increased by 8% over the same range. As the 

maximum allowable DoD window is expanded, 

increased resistance growth limits the power 

capability of the pack. Loss of pack power would 

be reflected at the vehicle level in an increased 

degree of blended electric/petroleum operation or 

reduced all-electric vehicle power. The 87% DoD 

scenario represents a beginning-of-life pack cost 

savings of $2,800 while the 55% DoD design 

increases cost by $2,100 (both relative to the 65% 

DoD pack).

Figure 17. 8-year resistance growth and capacity fade as 

a function of maximum allowable DoD for PHEV 

exposed to ambient conditions in Los Angeles, CA and 

NHTS national driving distribution.

3.2.3 Vehicle Miles Traveled

The PHEV was subjected to an array of annual 

VMT scenarios according to the 398 longitudinal 

profiles derived from the TCS. Figure 18 shows 

the results of this analysis in terms of resistance 

growth and capacity fade after 8 years subject to 

ambient conditions in Los Angeles, CA.

Figure 18. 8-year resistance growth and capacity loss as 

a function of VMT for PHEV exposed to ambient 

conditions in Los Angeles, CA and 398 longitudinal 

driving distributions.

As with the BEV, increased VMT can be seen to 

have opposing effects on resistance growth and 

capacity loss. Eight-year resistance growth 

increases by 17% over the selected range of VMT 

while capacity fade decreases by 5% at high VMT.

Note that the scatter of resistance growth and 

capacity fade is not as strongly correlated to VMT 

as was the BEV. This is due to the discrepancy in 

wear mechanisms between CD and CS operation 

in the PHEV. Wear induced by the deep cycles of 

CD operation significantly outweigh battery 

0%

10%

20%

30%

40%

50%

60%

10%12%14%16%18%20%22%24%26%28%30%32%

Share of U.S. Distribution

8 -yr Capacity Fade

PHEV

0%

10%

20%

30%

40%

8 10 12 14 16 18 20 22 24 26

Share of US distribution

Average battery temperature (C)

PHEV

0%

10%

20%

30%

40%

50% 60% 70% 80% 90%

Depth of Discharge

PHEV - Los Angeles, CA

8 yr resistance growth

8 yr capacity fade

0%

10%

20%

30%

0 10000 20000 30000 40000

VMT (mi)

PHEV - TCS Profiles Resistance Growth

Capacity Fade
10

degradation associated with the shallow cycling of 

CS operation. Battery wear in the PHEV can be 

seen to be more closely related with CD VMT, as 

shown in Figure 19.

Figure 19. 8-year resistance growth and capacity fade as 

a function of CD VMT for PHEV exposed to ambient 

conditions in Los Angeles, CA and 398 longitudinal 

distributions.

A unique IUF is again calculated for each of the 

398 longitudinal profiles. However, since the

PHEV is assumed to have access to refueling 

stations, allowing it to operate in CS mode for a 

nearly unlimited distance, IUFPHEV is calculated as 

the ratio of annual miles achieved in CD mode to 

the total annual miles (see Equation 6). For 

example, a distribution with an IUFPHEV equal to 

50% would be able to achieve half of its annual 

miles in CD mode (with one charge per day) with 

the remaining miles accomplished with the PHEV 

in CS mode.

(6)

The distribution of IUFPHEV for the TCS is shown 

in Figure 20. The minimum and maximum of this 

distribution are 9% and 100%, respectively, with 

the mean occurring at an IUFPHEV of 78%.

Figure 20. Distribution of IUF for PHEV exposed to 

ambient conditions in Los Angeles, CA and 398 

longitudinal driving distributions over 8 years.

4 Conclusions

The sensitivity of battery wear to ambient 

conditions, battery size, and usage patterns has 

been explored. Major results of this analysis 

include:

• The spectrum of climate and usage conditions 

PEVs are expected to face in the U.S. market 

suggest that the assumption of a single average 

ambient condition for battery wear 

calculations may not be representative of 

observed behavior in the fleet.

• Ambient conditions have a large effect on 

battery wear for all variables considered in this 

study. The effects of ambient conditions on 

battery life can be mitigated by appropriate 

vehicle design. Thermal insulation and TMSs 

can be designed to improve fade rates for each 

vehicle platform.

• TMSs that employ active battery heating/

cooling can reduce the amount of temperature 

variability in the pack. The passively cooled 

BEV experienced yearly average pack 

temperatures from 8°C to 26°C while the 

actively heated/cooled PHEV ranged from 

14°C to 24°C.

• Maximum allowable DoD was found to 

significantly impact battery wear. Resistance 

growth and capacity fade were significantly 

reduced by designing a pack to operate with a 

relatively low maximum allowable DoD. 

However, pack design for low DoD can 

increase up-front vehicle costs by requiring 

additional total energy to achieve a desired CD 

range. For the modeled BEV, the extra battery 

capacity required for an 80% vs. 94% DoD 

window represents a roughly $2,800 increment 

in pack cost. For the modeled PHEV, the extra 

battery capacity required for a 55% vs. 87% 

DoD window represents a roughly $4,900 

increment in pack cost. Increased battery 

energy may also require components such as 

the electric motor to be resized to maintain 

vehicle acceleration.

• The effect of VMT was explored for both the 

simulated BEV and PHEV. Battery wear was 

found to be a strong function of VMT for the 

BEV and of CD VMT for the PHEV. Under 

the single-charge-per-day assumption, 

increasing VMT was observed to decrease 

0%

10%

20%

30%

0 5000 10000 15000

CD VMT (mi)

PHEV - TCS Profiles

Resistance Growth

Capacity Fade

0%

5%

10%

15%

20%

Share of TCS Profiles

Individual Utility Factor

PHEV - TCS Profiles
11

capacity fade as longer driving trips reduced 

the amount of high voltage exposure to which 

the pack was subjected. This effect is believed 

to be pronounced by the unique characteristics 

of the NCA chemistry and would be expected 

to change significantly under alternative 

charge strategies (just-in-time, end-of-day, 

opportunity, 1x/day, 2x/day, etc.).

• The PHEV can achieve a comparable 

distribution of IUF to the BEV over 8 years 

despite the substantially shorter CD range of 

the PHEV. For the 398 longitudinal 

distributions simulated, the BEV achieved an 

average IUF of 75% compared to an average 

of 78% for the PHEV. This result is a product 

of the assumption that driving trips longer than 

the range of the BEV will be accommodated 

by some other means of transportation. The 

effects of this assumption are magnified as the 

BEV experiences reduced range due to 

capacity loss.

Future work may focus on improving the 

comparison of vehicle utility by incorporating 

effects of temperature on pack internal resistance 

and capacity. These effects are expected to reduce 

the utility of both BEVs and PHEVs as vehicle 

range is compromised at low pack temperatures 

and internal resistance increases at high 

temperatures. Additional analysis may also seek to 

develop a range of potential near term vehicle-togrid scenarios to determine the subsequent impact 

on battery wear and achievable VMT.

Acknowledgements

The authors gratefully acknowledge the 

programmatic support of the U.S. Department of 

Energy, Office of Energy Efficiency and 

Renewable Energy, Vehicle Technologies 

Program. The authors particularly wish to thank 

Vehicle Systems activity managers David 

Anderson and Lee Slezak for their guidance and 

support.

References

[1] Smith, K., Markel, T., Kim, G.H., Pesaran, A., 

“Design of Electric Drive Vehicle Batteries for 

Long Life and Low Cost,” National 

Renewable Energy Laboratory, Golden, CO, 

2010, NREL/PR-540-48933.

[2] Idaho National Laboratory, “Technology Life 

Verification Testing,” Idaho Falls, ID, 2010, 

INEEL-EXT-04-01986.

[3] Broussely, M., “Aging of Li-ion batteries and 

life prediction, an update,” 3rd Int. Symposium 

on Large Lithium-Ion Battery Technology and 

Application, Long Beach, California, May 

2007. 

[4] Hall, J., Lin, T., Brown, G., Biensan, P., and 

Bonhomme, F., “Decay processes and life 

predictions for lithium ion satellite cells,” 4th 

Int. Energy Conversion Engineering Conf., 

San Diego, California, June 2006. 

[5] Smart, M., Chin, K., Whitcanack, L., and 

Ratnakumar, B., “Storage characteristics of Liion batteries,” NASA Battery Workshop, 

Huntsville, Alabama, Nov. 2006. 

[6] Broussely, M., Chap. 13 in: Advances in 

Lithium-Ion Batteries, van Schalkwijk, W., 

and Scrosati, B., editors. New York: Kluwer 

Academic / Plenum Publishers, 2002.

[7] Smith, K., Neubauer, J., Earleywine, M., 

Wood, E., and Pesaran, A., “Comparison of 

Plug-In Hybrid Electric Vehicle Battery Life 

across Geographies and Drive-Cycles,” 

Society of Automotive Engineers 2012 World 

Congress, 12PFL-0731.

[8] CarsDirect, Nissan Leaf Specifications, 

http://www.carsdirect.com/nissan/leaf/specs, 

retrieved on 8-25-11.

[9] GM Volt Specifications, http://gmvolt.com/full-specifications/, retrvd. 8-25-11.

[10] National Renewable Energy Laboratory 

Strategic Initiative Working Group Report: 

Thermal Model of Gen 2 Toyota Prius, 

Kandler Smith, Ahnvu Le, Larry Chaney.

[11] Barnitt, R.A., Brooker, A.D., Ramroth, L., 

Rugh, J., Smith, K.A., “Analysis of Off-Board 

Powered Thermal Preconditioning in Electric 

Drive Vehicles,” National Renewable Energy 

Laboratory, Golden, CO, 2010, NREL/CP5400-49252.

[12] R.L. Polk & Co., https://www.polk.com.

[13] National Renewable Energy Laboratory, 

National Solar Radiation Database, Typical 

Meteorological Year Database 3, Golden, CO. 

[14] 2001 National Household Travel Survey, 

http://nhts.ornl.gov/.

[15] National Renewable Energy Laboratory, 

Center for Transportation and Technology 

Systems, Transportation Secure Data Center: 

http://www.nrel.gov/vehiclesandfuels/secure_t

ransportation_data.html. 

[16] U.S. Department of Energy, Vehicle 

Technologies Program, 2011 Annual Merit 

Review and Peer Evaluation Meeting, Energy

Storage R&D, May 2011.
12

Authors

Eric Wood

National Renewable Energy Lab

Golden, Colorado USA

Email: eric.wood@nrel.gov

Mr. Wood is an Engineer at NREL’s 

Center for Transportation 

Technologies and Systems. He holds a 

B.S. in Mechanical Engineering from 

the University of Nebraska (2009) and

a M.S. in Mechanical Engineering 

from Colorado State University

(2011).

Jeremy Neubauer 

National Renewable Energy Lab

Golden, Colorado USA 

Email: jeremy.neubauer@nrel.gov

Dr. Jeremy Neubauer is a Senior 

Engineer with NREL’s Center for 

Transportation Technologies and 

Systems, where his primary 

responsibility lies in researching the 

reuse of retired automotive traction 

batteries and modeling the total cost of 

ownership of advanced vehicle types.

Prior to coming to NREL, Dr. Neubauer was Chief 

Engineer at ABSL Space Products, a leading 

manufacturer of Li-Ion batteries for the space industry.

Dr. Neubauer has a Bachelors, Masters, and Doctorate 

in Mechanical Engineering from Washington University 

in St. Louis.

Aaron D. Brooker 

National Renewable Energy Lab

Golden, Colorado USA 

Email: aaron.brooker@nrel.gov

Mr. Brooker is a Senior Engineer at 

NREL. He holds a B.S. in Mechanical 

Engineering from Michigan 

Technological University (1998) and 

an M.S. in Mechanical Engineering 

from the University of Colorado 

(2000). 

Jeffrey Gonder 

National Renewable Energy Laboratory

Golden, Colorado USA 

Email: jeff.gonder@nrel.gov

Mr. Gonder is a Senior Engineer at 

NREL’s Center for Transportation 

Technologies and Systems. He holds a 

B.S. in Mechanical Engineering from 

the University of Colorado and an 

M.S. in Mechanical Engineering from 

Pennsylvania State University. His 

research activities include both simulation and hardware 

testing of conventional, hybrid, plug-in and fuel cell 

vehicles.

Kandler A. Smith

National Renewable Energy Lab

Golden, Colorado USA

Email: kandler.smith@nrel.gov

Dr. Smith is a Senior Engineer

researching energy storage systems 

for NREL’s Center for Transportation 

Technologies and Systems. He holds a 

Ph.D. degree in Mechanical

Engineering from Pennsylvania State

University. He is an active member of 

the IEEE, the Electrochemical Society, and the Society 

for Automotive Engineers. His research includes 

electric-drive vehicles, battery electrochemical/ thermal 

characterization and modeling, and battery-life 

predictive modeling.

List of Acronyms

BEV battery electric vehicle

CD charge depleting

CS charge sustaining

DoD depth of discharge

FASTSim Future Automotive Systems Technology 

Simulator

HEV hybrid electric vehicle

IUF individual utility factor

LA92 California Air Resources Board Unified 

Driving Schedule

Li lithium

NCA nickel-cobalt-aluminum

NHTS National Household Travel Survey

NREL National Renewable Energy Laboratory

PEV plug-in electric vehicle

PHEV plug-in hybrid electric vehicle

TCS Traffic Choices Study

TMS thermal management system

TMY3 Typical Meteorological Year Database

UDDS Urban Dynamometer Driving Schedule

US06 Supplemental Federal Test Procedure 

Driving Schedule

VMT vehicle miles traveled