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

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Measuring the Benefits of Public 

Chargers and Improving 

Infrastructure Deployments 

Using Advanced Simulation 

Tools

Preprint

Eric Wood, Jeremy Neubauer, and Evan Burton

National Renewable Energy Laboratory

To be presented at the SAE 2015 World Congress and Exhibition

Detroit, Michigan

April 21–23, 2015

Conference Paper

NREL/CP-5400-63422

February 2015
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Available electronically at http://www.osti.gov/scitech

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

NREL prints on paper that contains recycled content.
1

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Measuring the Benefits of Public Chargers and Improving Infrastructure 

Deployments Using Advanced Simulation Tools

Eric Wood, Jeremy Neubauer, and Evan Burton

National Renewable Energy Laboratory

Abstract

With support from the U.S. Department of Energy’s Vehicle 

Technologies Office, the National Renewable Energy Laboratory 

developed BLAST-V—the Battery Lifetime Analysis and Simulation 

Tool for Vehicles. The addition of high-resolution spatial-temporal 

travel histories enables BLAST-V to investigate user-defined 

infrastructure rollouts of publically accessible charging infrastructure, 

as well as quantify impacts on vehicle and station owners in terms of 

improved vehicle utility and station throughput. This paper presents

simulation outputs from BLAST-V that quantify the utility 

improvements of multiple distinct rollouts of publically available 

Level 2 electric vehicle supply equipment (EVSE) in the Seattle, 

Washington, metropolitan area. Publically available data on existing 

Level 2 EVSE are also used as an input to BLAST-V. The resulting 

vehicle utility is compared to a number of mock rollout scenarios. 

Discussion focuses on the estimated number of Level 2 stations 

necessary to substantially increase vehicle utility and how stations 

can be strategically sited to maximize their potential benefit to 

prospective electric vehicle owners.

Introduction

Battery electric vehicles (BEVs) are seen as an advanced vehicle 

technology with the potential to reduce petroleum consumption, 

decrease greenhouse gases, and improve air quality (the latter two

depending upon the generation mix of the electric power grid). The 

market potential of BEVs is likely constrained by current battery 

technology, which limits single-charge driving range and requires

relatively long recharge times. Advocates argue that increased access 

to public charging stations or electric vehicle supply equipment 

(EVSE) would spur increased rates of consumer adoption by 

increasing vehicle range between charges at the vehicle’s home

location and by psychologically minimizing the effect of range 

anxiety.

Unfortunately, the relationship between BEV utility and access to 

public EVSE is difficult to quantify (utility is used in this paper to 

express the percent of travel accomplished with a BEV relative to a 

conventional vehicle or CV). Recent efforts by Idaho National 

Laboratory as part of the EV Project [1] have provided real-world 

public charging data from early BEV adopters (primarily of Nissan 

Leafs). While reporting that approximately 15% of sampled charging 

occurs away from the vehicle’s home location [2], it is impossible to 

calculate how much additional utility the owner would have achieved 

if his/her BEV had range and recharge/refill characteristics similar to 

a standard gasoline vehicle (e.g., how many times was the BEV 

forgone in favor of an alternate form of transportation on account of 

range limitation?).

Quantification of BEV utility relative to travel accommodated by a 

CV is a problem that lends itself nicely to a modeling and simulation 

approach. Researchers at the University of California, Davis have 

taken such an approach by linking spatial travel data with a 

simplified vehicle model in a geographic information system 

environment [3]. This geographic information system tool has been 

used primarily to evaluate and optimally locate public EVSE in 

California. Lawrence Berkeley National Laboratory has taken a 

similar approach in developing the V2G-Sim tool that, in addition to 

spatial travel data, leverages detailed powertrain simulation and 

battery life modeling to estimate impacts of various vehicle-to-grid 

communication and power flow scenarios [4]. Oak Ridge National 

Laboratory also has ongoing modeling and simulation activities 

related to assessing impacts of charging availability on electric 

vehicle utility and energy outcomes [5].

With support from the U.S. Department of Energy’s Vehicle 

Technologies Office, the National Renewable Energy Laboratory 

developed BLAST-V—the Battery Lifetime Analysis and Simulation 

Tool for Vehicles. BLAST-V has been previously used in parallel 

with travel data from the Seattle, Washington, metropolitan area to 

quantify vehicle utility and battery life outcomes resulting from 

various levels of charging availability [6]. However, that analysis

featured limited spatial resolution and thus evaluated public charging 

at various power levels assuming ubiquitous availability (essentially

placing EVSE at every trip destination in the dataset).

This paper discusses updated spatial capabilities within BLAST-V for 

evaluating utilization of and incremental utility afforded by various 

public EVSE scenarios. The analysis focuses on quantifying impacts 

of multiple distinct rollouts of publically available Level 2 EVSE in 

the Seattle metropolitan area. Publically available data on existing 

Level 2 EVSE are also used as an input to BLAST-V with resulting 

vehicle utility compared to a number of mock rollout scenarios. The 

discussion focuses on the estimated number of Level 2 stations 

necessary to substantially increase vehicle utility and how stations 

can be strategically sited to maximize their potential benefit to 

prospective electric vehicle owners.
2

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

BLAST-V for BEV Utility Estimation

Nominal Capabilities

BLAST-V is an electric vehicle simulator that focuses on computing 

long-term effects of complex operational scenarios on vehicle utility 

and battery performance. It considers the vehicle powertrain, battery 

control strategy, driving and charging patterns, local climate, the 

vehicle-battery-environment thermal system, battery chemistry, and 

other factors in computing short-term vehicle and battery 

performance (e.g., vehicle range, battery voltage, state of charge 

(SOC), and temperature) and long-term vehicle utility and battery 

degradation. An approximate graphical representation of the key 

elements and flow of data within BLAST-V is illustrated in Figure 1.

Further detail on the methods employed in this simulation can be 

found in [7].

Figure 1. Graphical illustration of BLAST-V simulation algorithms.

A determination of which trips to take with a BEV and which to 

forgo is a key element of BLAST-V. As the driving patterns input are 

generally sourced from real-world operation of CVs, certain trips 

(and sequences of trips) will exceed the driving range of the

simulated BEV and result in full battery depletion. Given the cost and 

inconvenience associated with stranded vehicles, BLAST-V assumes 

BEV drivers will rely on conservative estimates of vehicle range and

a detailed knowledge of travel itineraries to avoid running out of 

charge mid-trip.

BLAST-V structures travel data as a sequence of tours. Each tour 

consists of consecutive trips with the first trip beginning and the last 

trip ending at the vehicle’s home location (with assumed access to 

charging). Prior to the start of each tour, BLAST-V considers the 

battery’s current SOC, distance and expected duration of pending 

trips in the tour, historical depletion rates from similar trips, and the 

availability of work/public EVSE to estimate battery SOC throughout 

the potential tour. This estimated SOC informs a go/no-go decision at 

the beginning of each tour. If the estimated SOC is maintained above 

a specified threshold for the entire tour, the simulated driver selects 

the BEV for travel and the tour is simulated in greater detail,

considering electrical, thermal, and life models of the battery pack. 

However, if battery SOC is estimated to be depleted below the 

specified threshold, the driver forgoes use of the BEV and electrical, 

thermal, and life models of the battery pack are simulated with the 

vehicle in its parked mode for the duration of the tour. While 

BLAST-V is not primarily concerned with alternate travel modes in 

situations where BEV travel is forgone, it is reasonable to assume 

that real-world drivers would coordinate use of a secondary 

household vehicle (likely a CV), arrange for a short-term rental 

vehicle, utilize some form of public transportation, plan a carpool, or 

potentially omit the tour entirely.

BLAST-V’s go/no-go decision for determining BEV travel is 

believed to mirror the way that real-world drivers make personal 

travel decisions. By implementing a low-order planning model prior 

to tour evaluation, BLAST-V simulates the hundreds of tour 

decisions a driver makes every year when determining whether 

his/her BEV is suitable for a particular tour.

Spatial Enhancements

Recent upgrades to BLAST-V include the addition of detailed spatial 

travel data and charging logic that enables vehicles to charge away 

from home when in the presence of public EVSE. In addition to 

travel information, including trip start/end time, distance, and 

destination code (home, work, public), BLAST-V now accepts 

latitude/longitude coordinates defining trip destination locations. 

These spatially resolved destination locations are used by BLAST-V 

in parallel with user-defined public EVSE rollouts, where each
3

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

charging station is defined by a latitude/longitude position and 

maximum charging power. During the tour planning and evaluation 

phases, BLAST-V calculates the distance between the vehicle’s 

current parked location and the nearest public charging station. If the 

distance is within a predefined threshold, the vehicle is simulated as 

plugged in to the charging station and accepting electricity as

necessary.

Analysis

Simulation Parameters

Having established a methodology for estimating BEV utility that is 

sensitive to user-defined rollouts of public EVSE, the next step is to 

investigate various deployments of public charging infrastructure. In 

doing so, a number of simulation parameters must be defined,

including travel profiles, driver behavior, vehicle performance, 

battery attributes, environmental conditions, and charging 

infrastructure.

Travel Profiles

Herein we employ historical travel data from the Puget Sound

Regional Council’s (PSRC’s) Traffic Choices Study [8], processed 

per [7] to yield 317 real-world travel histories, each consisting of 365 

continuous days of uninterrupted data. The resulting histories provide

trip distance, trip and park durations, and destination data for each 

trip event. The data include codes such as home, work, or public in 

addition to precise latitude/longitude coordinates. Relevant statistics 

for the 317 vehicle samples are shown in Figure 2.

Figure 2. Trip distance, daily distance, annual distance, and trip average speed 

distributions for all 317 PSRC vehicle histories from the PSRC study.

We then filter these histories to those that accrued 8,000 miles or 

more over this one-year period to focus the simulation on higher 

mileage drivers. In Figure 3 we plot all 317 histories to show the 

utility factor (percent of CV miles achieved during BEV simulation)

and the annual mileage they would achieve driving a 75-mile BEV 

without public charging. The black points to the upper left of the 

diagonal line represent the 137 drivers that completed fewer than 

8,000 miles in a CV. These profiles are of lesser interest to this study 

as the low annual mileage implies they are unlikely to (1) benefit 

significantly from public EVSE, or (2) accumulate sufficient fuel 

savings to justify the upfront price premium of a BEV. The 91 drivers 

in the upper right corner of the plot (shown in blue) represent those 

that both completed more than 8,000 miles and achieved a utility 

factor greater than 80% in the 75-mile-range BEV (referred to as 

Profile Set A later in this study). Arguably, these drivers are well

suited to driving such a BEV without public charging, but they are 

still included. The remaining 89 drivers (shown in red) are highmileage drivers that achieve low utility factors with a 75-mile BEV, 

and thus drivers that could benefit significantly from range extension 

methods like public charging (referred to as Profile Set B later in this 

study).

Figure 3. Simulated utility and achieved vehicle miles traveled (VMT) for 

PSRC travel histories in a 75-mile BEV.

A sample of the detailed spatial data utilized in this study is shown in 

Figure 4. This aerial view of a shopping center in urban Seattle 

includes markers indicating parked locations from all 365 days and 

317 vehicles overlaid on satellite imagery. This map is an example of 

the relative accuracy of the spatial data contained in the PSRC dataset 

with parked locations from global positioning system devices 

clustered around and coinciding with painted parking stalls from 

satellite imagery.
4

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 4. Aerial view of a shopping center in urban Seattle with markers indicating parked locations from all PSRC travel data overlaid on satellite imagery. (Google 

Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas)

Driver Behavior

It is assumed that all drivers in this study operate BEVs with 

“normal” levels of driver aggression (25th to 75th percentile) as 

described in previous BLAST-V studies [7, 9].

For the purposes of making a go/no-go decision prior to the start of 

each tour, it is assumed that all drivers impose a minimum allowable 

SOC tolerance of 15% (approximately 11 miles for a 75-mile BEV)

per the discussion above of BLAST-V’s “Nominal Capabilities,”

which is to say that drivers will only elect to drive their BEV on tours 

where the estimated battery SOC is greater than 15% for the entire 

tour. This SOC tolerance provides a reasonable buffer in situations 

where simulated driving range turns out to be less than the pre-tour 

estimate.

Vehicle Performance

We employed a mid-size sedan with technology and performance 

levels anticipated for a 2020 model year vehicle. We utilized

FASTSim (Future Automotive Systems Technology Simulator) [10]

to simulate the vehicle response to the Urban Dynamometer Driving 

and Highway Fuel Economy Driving schedules, the results of which 

are weighted and combined per [11] to approximate the U.S. 

Environmental Protection Agency-rated range. We further employed

FASTSim to simulate the vehicle’s response to the National 

Renewable Energy Laboratory’s DRIVE cycle to calculate the 

vehicle’s real-world efficiency [9]. Note that within BLAST-V 

simulations, auxiliary loads for the vehicle’s cabin heating, 

ventilation and air conditioning and battery thermal management 

system are added separately, and the efficiency computed from the 

DRIVE cycle is adjusted for the speed and distance of each trip.

Vehicle parameters are given in Table 1.

Table 1. Parameters for simulated 75-mile BEV.

0-60 mph Acceleration 9 sec

Approximated U.S. 

Environmental Protection 

Agency-rated Range

75 miles

Battery Energy 22.1 kWh (100% usable)

Motor Power 106 kW

Vehicle Curb Weight 1,576 kg

Vehicle Efficiency

220 Wh/mi on DRIVE cycle

(excludes auxiliary loads 

accounted for during BLAST-V 

simulations)

Battery Attributes

All battery electrical, thermal, and life calculations in the study 

employ a single-node battery model that assumes uniform response 

between all cells in the pack. Electrical modeling is done using a

zero-order equivalent circuit approach with open circuit voltage and 

internal resistance parameters based on a lithium-ion cell with a 
5

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

nickel-cobalt-aluminum cathode and graphite anode. Thermal 

modeling considers battery response to ambient and cabin 

temperatures in the presence of an active battery cooling system. Life 

modeling is implemented via a physically justified and empirically fit 

system of equations for describing calendar- and cycling-induced 

resistance growth and capacity fade based on a thru-life nickelcobalt-aluminum dataset. While battery degradation calculations are 

inclusive in this analysis, the duration of simulations (all one year 

long) and moderate climate (Seattle) resulted in a negligible impact 

on the results. For more extensive documentation on BLAST-V pack

modeling approaches, please refer to [7].

Environmental Conditions

Seattle was selected for ambient temperature and solar irradiation 

input data as it is coincident with the PSRC travel data and represents 

a relatively moderate climate. Typical meteorological year data for 

Seattle is taken from [12] and illustrated in Figure 5.

Figure 5. Ambient temperature data from Seattle, Washington.

Charging Infrastructure

For vehicle charging, we assumed a Level 2 charger (6.6 kW AC) is 

installed at each driver’s home and used in an “opportunity” mode 

(i.e., whenever the driver is at home, the vehicle is plugged in and 

charging). Several public networks of Level 2 chargers are placed in 

the Seattle metropolitan area for simulation. The first deployment 

mimics existing Level 2 charger locations in the Seattle area, per the 

U.S. Department of Energy’s Alternative Fuels Data Center [13] 

(sourced Jan 2014). Figure 6 shows the locations used to represent 

existing infrastructure.

Figure 7 shows maps of the two base layers used for locating 

synthetic rollouts of publically available EVSE. The map on the left 

consists of 33,477 unique locations based on all trip destinations from 

the PSRC dataset (referred to as Infr302). Infr302 provides an upper 

bound for the incremental BEV utility afforded by public charging as 

it makes Level 2 charging available everywhere a vehicle parks in the 

BLAST-V simulations. However, when considering that Seattle had 

fewer than 300 public charging stations as of January 2014, a 

deployment on the order of Infr302 is unlikely to be available any

time soon.

Figure 6. Existing Level 2 charger locations in the Seattle area (U.S. 

Department of Energy’s Alternative Fuels Data Center, sourced Jan 2014). 

(Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas)

This reality prompted the need to prioritize siting EVSE such that the 

most critical stations (in terms of incremental BEV utility) are located

first. The most obvious prioritization method was to sort each unique 

location by the sum of vehicle dwell time (referred to as Infr302a). 

Unfortunately, this prioritization was clearly suboptimal as it 

potentially gave weight to stations with high amounts of dwell time 

on tours that were already within the single-charge range of the 

simulated BEV.

To address this bias, Infr302a was down selected to Infr402a (shown 

in the right half of Figure 7) using a baseline BLAST-V simulation 

with no public charging infrastructure to eliminate trip destinations 

from tours within the single-charge vehicle range. Infr402a was likely 

an improvement over Infr302a, but because it was still sorting based 

on dwell time, it was possibly skewing towards locations with 

relatively low amounts of electrical throughput. This prompted the 

creation of Infr402b, which sorted based on simulated electrical 

throughput from a BLAST-V simulation with all 11,578 stations from 

Infr402 available.

The final iteration resulted in Infr402c, which addressed the potential 

misalignment between all charging throughput and charging

throughput that enables completion of long-distance tours. Infr402c 

was prioritized using an additional BLAST-V simulation in which all 

11,578 stations from Infr402 were made available (as in Infr402b) 

with the condition that drivers only utilize public chargers as 

necessary to complete tours. Throughput from this simulation was 

summed by location and used to generate prioritization for Infr402c.
6

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 7. Unique public parked locations from all 317 PSRC vehicles (at left) and unique public parked locations from all 317 PSRC vehicles on tours not taken (TNT)

in baseline simulation (at right). Exact trip destinations in map have been modified to mask precise locations as a privacy precaution. (Google Maps credit: © 2014 

Google, Map Data © 2014 Tele Atlas)

In summary, the following Level 2 public charging station 

deployment/prioritization combinations were constructed for 

BLAST-V evaluation:

• Infr302a: All trip destinations ranked based on the sum of 

vehicle dwell time at each location.

• Infr402a: TNT (tours not taken) trip destinations ranked based 

on the sum of vehicle dwell time at each location.

• Infr402b: TNT trip destinations ranked based on the sum of 

station electrical throughput from BLAST-V simulation with all 

11,578 stations available.

• Infr402c: TNT trip destinations ranked based on the sum of 

station electrical throughput from BLAST-V simulation with all 

11,578 stations available, but where drivers only charge at 

public stations when necessary to complete tour.

Simulation Results

Although some parameter uncertainty exists in the underlying 

historical drive and climate data employed in this study, the main 

source of uncertainty is structural. The principal structural 

uncertainties include the approach to modeling human tour decisions, 

the method of computing vehicle energy consumption, and the

battery performance and life models employed. Quantifying the level 

of uncertainty present in our modeling of human tour decisions is 

challenged by the need for large amounts of data on the real-world 

tour decisions of BEV drivers. The additional aspect of drivers 

changing tours in response to infrastructure is addressed in a parallel 

study [14].

The second factor, computation of vehicle energy consumption, is 

applied consistently across all scenarios herein. Thus, while it is 

expected to affect the absolute vehicle utilities calculated, it should 

not significantly affect the relative impacts of different public 

charging scenarios. Improving the accuracy of battery performance 

and life models to account for cell-to-cell variation within a pack and 

better ascertain the impacts of fast charging on battery wear is a 

major focus of a parallel study [15]. Despite these uncertainties, 

however, the following findings are telling as to the relative impact of 

public charging impact on overall BEV utility.

Vehicle to Station Proximity Tolerance

As discussed above, at the end of every non-home trip BLAST-V 

calculates the distance from the current parked location to the nearest 

public EVSE. It assumes the vehicle is plugged in if the calculated 

vehicle-to-station proximity is within some predefined tolerance. This 

logic assumes that drivers would be willing to adjust their parking 

behavior by up to some distance in order to utilize public EVSE. To 

better understand the impact the value of proximity tolerance has on 

simulated BEV utility, two infrastructure scenarios (existing and 

Infr402c), each with 281 public EVSE stations, were run in BLASTV using a range of proximity tolerances. The results of this sensitivity 

analysis are presented in Figure 8. As expected, BEV utility shows a 

positive correlation with the proximity tolerance variable (drivers can 

better utilize public charging given a high level of flexibility with 
7

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

regard to where they park their vehicle). It is interesting to note the 

high level of sensitivity observed in scenario Infr402c when the 

proximity of the vehicle to a station is less than 500 feet.

Figure 8. Averaged achieved BEV VMT for 281 stations cited using 1) 

existing locations and 2) citing algorithm 402c. Sensitivity to station 

proximity tolerance is explored (distance between original parked location and 

nearest EVSE to assume vehicle is plugged in during simulation).

Unfortunately, selecting a representative vehicle-to-station proximity 

tolerance for BLAST-V simulations is constrained by a lack of data 

in this area. Additionally, it is likely that this tolerance is nonuniform and variable with respect to destination. For example, travel 

to a single-family residential home would likely present a low 

tolerance for modifying the original parked location (parking at a 

public EVSE station and then walking to the original destination). 

However, public venues with large parking lots and several entry 

points would likely offer a great deal of flexibility (especially in 

cases where EVSE is located near the venue and reserved for plug-in 

vehicles). In light of these factors, a proximity tolerance of 528 feet 

(0.1 mile) is selected for the remainder of this analysis.

Existing Public Level 2 EVSE Scenario

Evaluation of the utility benefit afforded by existing Level 2 public 

charging requires the simulation of a baseline scenario with no public 

charging. The 317 simulated BEVs achieved an average of 7,236 

miles in the baseline scenario out of an original average of 9,153 

miles achieved in a CV (79.1% utility). Enabling 281 Level 2

charging stations in the Seattle area increased utility to 7,355 miles 

(80.4% utility). At first glance, a BEV utility increase of a little over 

1% seems rather insignificant. What this number does not capture is 

the amount of electrical throughput provided (11.4% of all simulated

electricity was sourced from public stations, a value that agrees well 

with the EV Project calculation that 15% of all charge events occur

away from vehicle home locations [2]), potential psychological 

benefit of knowing that public charging is available if necessary, or 

benefits to plug-in hybrid electric vehicles with nominally shorter allelectric ranges. It also does not convey the fact that individual drivers 

and particular subsets of drivers accrued significantly greater benefit. 

Ultimately, the effectiveness of any infrastructure scenario is difficult 

to assess independent of competing scenarios. To that end, four 

synthetic public Level 2 EVSE scenarios are presented to provide 

context and understand alternative planning approaches.

Synthetic Public Level 2 EVSE Scenarios

Figure 9 provides BEV utility values for each of the previously 

identified station prioritization methods. Simulation results are 

presented for each method in increments of 10 stations with 

prioritization determined by previously identified methods. In 

addition to the four utility curves resultant from synthetic public 

EVSE rollouts, a number of reference points are provided. Average 

utility from the original CV travel profiles is shown at 9,153 miles 

(100% utility). Average BEV utility, assuming no public charging, is 

shown at 7,236 miles (79.1% utility). Average BEV utility assuming 

ubiquitous public Level 2 charging (all 33,477 stations from Infr302) 

is shown at 8,069 miles (88.2% utility). Average BEV utility 

assuming existing public Level 2 charging (281 Seattle area stations) 

is shown at 7,355 miles (80.4% utility).

Figure 9 shows how station prioritization method Infr402c provides 

superior BEV utility across all station counts. By ranking potential 

charging stations using a combination of spatially resolved travel data 

and high-fidelity vehicle simulation, Infr402c provides the most 

incremental BEV utility on a per-station basis relative to alternative 

scenarios (including existing infrastructure deployment).

Figure 9. Average BEV VMT achieved relative to various public EVSE 

availability scenarios.

However, even method Infr402c requires a large volume of EVSE 

stations to approach the maximum potential of public charging. At 

2,000 stations, under Infr402c the average simulated BEV utility is 

7,998 miles (87.4% utility). To put this number of stations into 

perspective, consider that the Seattle metropolitan area (defined here 

as Snohomish, King, and Pierce counties) had 896 gasoline refueling 

stations as of 2011 [16].

Even in simulations where public Level 2 charging is made 

universally available (all 33,477 stations from Infr302), there remains 

an 11.8% shortfall in achieved BEV mileage. At first this may seem 

to be a puzzling conclusion until recalling the rigid implementation of 

travel data in BLAST-V. As driver travel histories are directly 

applied from real-world CVs to our simulated BEVs, there exists no 

margin for altering travel behavior to allow for extended charging 

events, mid-trip stops for charging, or a priori travel planning with 

vehicle range limitation in mind. While neglecting these very real 

human behavior considerations may result in underestimation of BEV 

utility relative to public charging availability, it is also very possible 

that many consumers will not be willing to adapt their travel behavior 

to accommodate the range and recharging limitation of a BEV. 
8

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

BLAST-V is used to explore the tradeoff between travel behavior 

modification and BEV utility in a parallel study [14].

Thus far, we have explored all simulation results in aggregate across 

the 317 driving profiles. However, it is interesting to note relative 

differences in incremental utility afforded by public charging to 

different groups of driving profiles. Table 2 shows achieved utility 

for the simulated 75-mile BEV from three groups of driving profiles:

• Full Set: All 317 profiles

• Profile Set A: Drivers with over 8,000 miles of original travel 

and achieving at least 80% of those miles in the simulated 75-

mile BEV

• Profile Set B: Drivers with over 8,000 miles of original travel 

and achieving less than 80% of those miles in the simulated 75-

mile BEV.

Simulated utility results are shown in Table 2 with and without public 

charging available. Results with public charging are extracted from 

Infr402c (the most effective scenario at improving aggregate BEV 

utility) at the 1,000-station level.

Table 2. Average achieved BEV VMT with and without top 1,000 stations 

from Infr402c broken down by groups of driving profiles.

Full Set Profile Set A Profile Set B

Profile count 317 91 89

Avg original VMT 9,153 11,474 12,201

Avg VMT achieved

(no public stations) 7,236 10,085 8,071

Avg VMT achieved

(1,000 public stations) 7,859 10,644 9,352

Avg utility factor

(no public stations) 79% 88% 66%

Avg utility factor

(1,000 public stations) 86% 93% 77%

Avg miles enabled by 

public stations 623 559 1,281

The group of drivers befitting most from public charging is Profile 

Set B, which experiences an average incremental utility of nearly 

1,300 miles. However, even with 1,000 public charging stations 

available, Profile Set B is only able to achieve a utility factor of 77%. 

Alternatively, Profile Set A experiences an average incremental 

utility of less than 600 miles, but is able to achieve a utility factor of 

93%. This finding raises an interesting question as to whom public 

charging is most valuable to: drivers who see the largest incremental 

utility as a result of public infrastructure, or drivers who come close 

to 100% with public infrastructure.

While the spatial upgrades to BLAST-V documented in this paper 

have primarily focused on quantifying BEV utility, there is also value 

to the infrastructure stakeholders in understanding how station 

placement and utilization are related. Figure 10 describes such a 

relationship by plotting the percent of total electrical throughput from 

public stations (averaged across all 317 driving profiles) against 

station availability. For example, for prioritization method 302a, at a 

200-station deployment, 20% of all charging is done via public Level 

2 stations. These statistics can be translated into economic indicators 

that could be used to estimate metrics such as payback period given a 

set of financial conditions (down payment, interest rate, etc.).

Figure 10. Percent of total simulated BEV energy originating from public 

Level 2 EVSE as a function of prioritization method and station count.

Examination of vehicle utility and station throughput curves 

highlights the conflicting objectives of consumers and EVSE 

operators in locating charging stations. For instance, Infr402c was 

shown to provide superior incremental BEV utility across all station 

counts; however, Infr402b can be seen to offer the highest throughput 

levels. Therefore, drivers would benefit more from chargers deployed 

per Infr402c, while EVSE operators that profit from increased 

throughput would benefit more from the same number of chargers 

deployed per Infr402b.

It should be noted the values in Figure 10 assume drivers plug in and 

utilize public EVSE whenever it is available and not just when it is 

necessary to complete a tour. While BLAST-V features the capability 

to limit public charging to an “as necessary” basis, the default in this 

analysis is to plug in and charge regardless of need (recall that 

Infr402c was designed using a simulation that employed “as 

necessary” charging and evaluated assuming vehicles are plugged in 

and charged whenever within the necessary proximity of a public 

station). In addition to making for a more direct analysis, it is unclear 

how public charging stations will price electricity going forward. 

Depending on the success of various business models, electricity at 

public charging stations may be priced at a premium to cover 

overhead expenses, priced lower than residential electricity to 

strategically entice parking, or some variant thereof.

As a final contrast between existing public charging infrastructure 

and our synthetic rollouts, consider the map of Seattle shown in 

Figure 11. This view of western Washington State shows the 

locations of 281 existing public Level 2 EVSE (green) alongside the 

281 highest priority locations from Infr402c (blue). This figure shows 

that Infr402c was able to offer our simulated BEVs greater utility not 

by densely populating Seattle’s urban center, but rather by providing 

access to charging along Seattle’s perimeter, north and southbound

along Interstate 5, throughout the Puget Sound, and into the 

mountainous rural eastern areas. While a large amount of vehicle 

dwell time does occur in Seattle’s urban center, tours through 

metropolitan Seattle are often within the single-charge range of our 

simulated BEV. By providing charging access in some of the more 

rural areas of Washington State, public charging could potentially 

enable a significant number of long-distance tours without the need 

for travel behavior modification.
9

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 11. Google street map overlaid with locations of 281 existing public Level 2 charging stations and 281 highest priority stations from method Infr402c. (Google 

Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas)

Summary

This paper has documented recent enhancements to the National 

Renewable Energy Laboratory’s BLAST-V software that enable

detailed spatial analysis of public charging benefits. Study of the 

Seattle metropolitan area using travel data from the PSRC has 

revealed that use of spatially resolved travel data in conjunction with 

advanced vehicle simulation can offer insights on how to locate 

public charging infrastructure to achieve specific objectives (such as 

BEV utility and station throughput). Exploration of a few simple 

EVSE deployment strategies has shown that significant gains in the 

amount of BEV utility provided by a discrete number of public 

chargers can be had when these strategies consider the interplay of 

BEV energy management with consumer travel patterns, rather than 

just vehicle dwell time at specific locations. It has also raised 

questions on metrics for quantifying the benefit of EVSE 

deployments to BEV drivers. In particular, how should the BEV 

community evaluate the relative importance of absolute increases in 

vehicle utility versus proximity to 100% utility?

In addition to showing the effects of different EVSE deployments on 

vehicle utility, this study has illustrated their effects on electricity 

throughput. It has highlighted a possible conflict between the 

motivations of BEV drivers and EVSE operators, as the EVSE 

deployment that appears to best enhance vehicle utility is not the 

same as that which maximizes electricity throughput, which may 

correlate with EVSE operator profits. Accordingly, development of 

incentives and business strategies for EVSE operators that align their 

motives with that of BEV drivers is encouraged.

Beyond these topics, however, these studies suggest that public 

charging availability alone may not be enough to allow average realworld drivers to approach the utility of their CV in an approximate 

75-mile BEV without some level of travel behavior modification (i.e., 

extending public parked times, mid-trip stops for charging, BEVspecific tour planning). While BLAST-V is capable of addressing this 

topic in part (i.e., rerouting travel to available chargers), it is not 

capable of intelligently moving trips from one tour to another, 

altering the destinations of trips, or adjusting travel times, as realworld drivers may be prone to do when operating a range-limited 

BEV. Analyzing the effects of such behavior will require a greater 

understanding of both the nature of individual trips and human 

behavior.

References

1. The EV Project. Accessed October 31, 2014. 

http://www.theevproject.com/

2. Smart, J. “Latest Insights from The EV Project and ChargePoint 

America PEV Infrastructure Demos.” GITT meeting at Idaho 

National Laboratory, August 12, 2014. 

http://avt.inel.gov/pdf/EVProj/GITTEVProjectAug2014.pdf

3. Nicholas, Michael A., Gil Tal, Jamie Davies-Shawhyde, and 

Justin Woodjack. 2012. “DC Fast as the Only Public Charging 

Option? Scenario Testing From GPS Tracked Vehicles.” TRB

91st Annual Meeting Compendium of Papers DVD.

Transportation Research Board Annual Meeting 2012. Paper 

#12-2997, 18 pages. 

http://www.its.ucdavis.edu/research/publications/publicationdetail/?pub_id=1827
10

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4. Lawrence Berkeley National Laboratory “V2G-Sim.” Accessed 

October 31, 2014. http://v2gsim.lbl.gov/

5. Dong, Jing, and Z. Lin. 2012. “Within-Day Recharge of Plug-In 

Hybrid Electric Vehicles: Energy Impact of Public Charging 

Infrastructure.” Transportation Research Part D: Transport and 

Environment 17:405–412.

http://cta.ornl.gov/TRBenergy/trb_documents/2012_presentation

s/629_Dong%20TRB12_Session629_PHEVChargingStation.pdf

6. Neubauer, J., and E. Wood. 2014. “The Impact of Range 

Anxiety and Home, Workplace, and Public Charging 

Infrastructure on Simulated Battery Electric Vehicle Lifetime 

Utility.” Journal of Power Sources 257:12–20. 

http://www.sciencedirect.com/science/article/pii/S03787753140

00998

7. Neubauer, J., and E. Wood. 2014. “Thru-Life Impacts of Driver 

Aggression, Climate, Cabin Thermal Management, and Battery 

Thermal Management on Battery Electric Vehicle Utility.”

Journal of Power Sources 259:262–275. 

http://www.sciencedirect.com/science/article/pii/S03787753140

02766

8. “Transportation Secure Data Center.” (2014). National 

Renewable Energy Laboratory. Accessed October 31, 

2014: www.nrel.gov/tsdc.

9. Neubauer, J., and E. Wood. “Accounting for the Variation of 

Driver Aggression in the Simulation of Conventional and 

Advanced Vehicles,” Presented at SAE World Congress, April 

2013. http://www.nrel.gov/docs/fy13osti/58609.pdf.

10. National Renewable Energy Laboratory. Future Automotive 

Systems Technology Simulator. Accessed October 31 2014, 

www.nrel.gov/fastsim.

11. U.S. Environmental Protection Agency. Final Technical Support 

Document; Fuel Economy Labeling of Motor Vehicle Revisions 

to Improve Calculation of Fuel Economy Estimates. Published 

December 2006. Accessed October 1, 2014. 

http://www.epa.gov/carlabel/documents/420r06017.pdf

12. National Renewable Energy Laboratory. National Solar 

Radiation Database, Typical Meteorological Year Database 3.

Golden, CO. Accessed July 2011. 

http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/

13. U.S. Department of Energy. Alternative Fuels Data Center, 

Alternative Fueling Station Locator. Accessed January 29, 2014. 

http://www.afdc.energy.gov/locator/stations/

14. Wood, E., and J. Neubauer. “Quantifying the Effect of Fast 

Charger Deployments on Electric Vehicle Utility and Travel 

Patterns via Advanced Simulation” in draft for 2015 SAE World 

Congress, SAE 15SDP-0018.

15. Neubauer, J., and E. Wood. “Will Your Battery Survive a World 

with Fast Chargers?” in draft for 2015 SAE World Congress, 

SAE 15PFL-0466.

16. HERE, 2011 NAVTEQ North America SteeetMap Premium.

Contact Information

Transportation and Hydrogen Systems Center

National Renewable Energy Laboratory

15013 Denver West Pkwy MS 1634, Golden, CO 80401

Eric Wood (303) 275-3290 eric.wood@nrel.gov

Jeremy Neubauer (303) 275-3084 jeremy.neubauer@nrel.gov

Evan Burton (303) 275-3664 evan.burton@nrel.gov

Acknowledgments

This study was supported by the U.S. Department of Energy’s 

Vehicle Technologies Office. The authors would specifically like to 

thank David Howell and Brian Cunningham for their guidance.

Definitions/Abbreviations

BEV battery electric vehicle

BLAST-V Battery Lifetime Analysis 

and Simulation Tool for 

Vehicles

CV conventional vehicle

DRIVE Drive-Cycle Rapid 

Investigation, Visualization, 

and Evaluation Analysis

EVSE electric vehicle Supply 

equipment

FASTSim Future Automotive Systems 

Technology Simulator

PSRC Puget Sound Regional 

Council

SOC state of charge

TNT tour not taken

VMT vehicle miles travelled