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Contract No. DE-AC36-08GO28308

Quantifying the Effect of Fast 

Charger Deployments on 

Electric Vehicle Utility and 

Travel Patterns via Advanced 

Simulation

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-5400-63423

February 2015
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Quantifying the Effect of Fast Charger Deployments on Electric Vehicle Utility and 

Travel Patterns via Advanced Simulation

Eric Wood, Jeremy Neubauer, and Evan Burton

National Renewable Energy Laboratory

Abstract

The disparate characteristics between conventional (CVs) and battery

electric vehicles (BEVs) in terms of driving range, refill/recharge 

time, and availability of refuel/recharge infrastructure inherently limit 

the relative utility of BEVs when benchmarked against traditional 

driver travel patterns. However, given a high penetration of highpower public charging combined with driver tolerance for rerouting 

travel to facilitate charging on long-distance trips, the difference in 

utility between CVs and BEVs could be marginalized. We quantify 

the relationships between BEV utility, the deployment of fast 

chargers, and driver tolerance for rerouting travel and extending 

travel durations by simulating BEVs operated over real-world travel 

patterns using the National Renewable Energy Laboratory’s Battery 

Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V). 

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

Technologies Office, BLAST-V has been developed to include 

algorithms for estimating the available range of BEVs prior to the 

start of trips, for rerouting baseline travel to utilize public charging

infrastructure when necessary, and for making driver travel decisions 

for those trips in the presence of available public charging 

infrastructure, all while conducting advanced vehicle simulations that 

account for battery electrical, thermal, and degradation response.

Results from BLAST-V simulations on vehicle utility, frequency of 

inserted stops, duration of charging events, and additional time and 

distance necessary for rerouting travel are presented to illustrate how 

BEV utility and travel patterns can be affected by various fast charge 

deployments.

Introduction

As automotive manufacturers continue to develop and market 

advanced technologies to satisfy consumer demand and government 

requirements for increasingly efficient vehicles, battery electric 

vehicles (BEVs) become an increasingly attractive option. By 

sourcing 100% of energy from an electric grid that becomes cleaner 

every year, BEVs are an effective option for reducing petroleum 

consumption, decreasing greenhouse gases, and improving air 

quality. While range and recharge time limitations make BEVs a 

difficult sell to many consumers, increased availability of high-power 

direct current fast charging (DCFC) is seen as one pathway to 

improving BEV utility and accelerating market adoption (utility is 

used in this paper to express the percent of travel accomplished with 

a BEV relative to a conventional vehicle or CV).

However, DCFC presents a myriad of technical challenges to battery 

manufactures and electric utility operators. Increased cycling and 

elevated temperatures during fast charge events are cited as 

contributing factors to premature battery degradation. Battery packs 

unable to meet warranty requirements because of high DCFC 

utilization would prove mutually problematic for consumer, 

automotive, and battery stakeholders. Meanwhile, electric utilities are 

closely monitoring the impacts of residential Level 1 and 2 charging 

on distribution networks in neighborhoods with high BEV 

concentrations. Increased residential concentrations coupled with an 

evolving network of public DCFC stations leaves electric utilities 

with a high degree of uncertainty regarding future load profile 

projections.

Recent efforts by Idaho National Laboratory as part of the EV Project

[1] have provided real-world DCFC usage statistics for early BEV 

adopters. The data reveal that 1% to 21% of all charge events from a

subset of Nissan Leafs occurred at DCFC stations (with usage 

varying among vehicles and through time) [2]. While illuminating, 

this data does not lend to future projections of DCFC impacts under 

various infrastructure deployment and vehicle penetration scenarios.

Quantification of DCFC utilization relative to arbitrary combinations 

of infrastructure and travel behavior 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 electric vehicle support equipment (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 

(NREL) has developed BLAST-V—the Battery Lifetime Analysis 

and Simulation Tool for Vehicles. BLAST-V has been 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, this 

analysis was constrained to travel behavior data collected in 
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conventional vehicles (CVs) with driving range and refuel time 

characteristics drastically different than those allowed by current 

BEV technology. The static nature of travel data in previous BLASTV studies did not allow for rerouting or impromptu stops to utilize 

fast charging and extend driving range mid-trip.

This paper discusses updated spatial capabilities within BLAST-V for 

evaluating utilization of and incremental utility afforded by various 

public DCFC deployment scenarios. The analysis focuses on 

quantifying impacts of several distinct rollouts of publically available 

DCFC stations in the Seattle metropolitan area. Publically available 

data on existing DCFC stations are also used as an input to BLAST-V 

with the resulting vehicle utility compared to a number of mock 

rollout scenarios. The discussion focuses on the estimated number of 

DCFC stations necessary to substantially increase vehicle utility and 

how stations can be strategically sited to maximize their potential 

benefit to prospective BEV owners.

BLAST-V for BEV Utility Estimation

Nominal Capabilities

BLAST-V is an electric vehicle simulator focused on computing the 

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. Figure 1 illustrates an approximate graphical 

representation of the key elements and flow of data within BLASTV. Further details on the methods employed in this simulation are 

described in [7].

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

Determination of which trips to take with a BEV and which to forgo

is a key to BLAST-V. As input driving patterns 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 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 

availability of work/public EVSE to estimate battery SOC throughout 

the potential tour. This estimation 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 

the SOC is estimated to become 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 dismissed, 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 
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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 their 

BEV is suitable for a particular tour.

Upgrades to Consider Rerouting and DCFC Stops

In situations where estimated battery SOC is not predicted to be 

maintained above the driver’s required threshold, BLAST-V now 

includes the capability to consider alternate paths of travel and stops 

at available DCFC stations. Tours requiring rerouting leverage the

Google Maps API [8] (application programming interface) to 

generate one to three potential paths of travel for each trip in the 

prospective tour. BLAST-V then considers combinations of potential 

trip paths, DCFC station availability, and estimated battery SOC to 

satisfy the constraint of maintaining the minimum SOC above the 

required threshold while minimizing the number of DCFC stops. The 

rerouting algorithm employs several rules regarding driver behavior

at DCFC stations. The following rules are employed:

• Stations requiring more than a 1-mile excursion from the path of 

travel are not considered.

• Drivers only dwell at stations long enough to charge their 

batteries to 95% of full capacity.

• Dwell time at stations must be at least 5 minutes and no more 

than 60 minutes.

• Vehicle must arrive at the station before the driver’s minimum 

allowable SOC constraint is exceeded.

• Each rerouted trip, which may imply additional travel time and 

includes a stop at a DCFC up to 60 minutes long, must be 

completed prior to the start time of the subsequent trip from the

original travel data.

Of these rules, the most restrictive in terms of limiting achievable 

BEV miles is the requirement that the original trip start times remain 

intact. For example, consider a tour consisting of two 100-mile trips

with a 75-mile BEV and DCFC stations available at tour miles 50, 

100, and 150. In theory, this tour could be rerouted into four 50-mile 

trips with DCFC stations at the end of the first three trips and home 

charging available after the fourth (home charging is available at the 

end of every tour by definition). However, if the original data 

specified a dwell time of only one minute after 100 miles of driving, 

that would only allow for one minute of dwell at the first DCFC 

station after 50 miles of driving. Not only does a one-minute DCFC 

station dwell violate another rerouting rule, but the vehicle would not 

be able to recoup enough battery SOC in one minute of fast charging

to reach its next destination.

The assumption that trip departure times are inelastic is likely 

inaccurate in many instances. In reality, some trips have inelastic 

departure times, arrival times, both, or neither. In the absence of 

additional travel data describing the nature of each trip requirements, 

the trip start time constraint is applied as a reasonable compromise.

While limiting BEV utility in some scenarios, the “start on time” 

requirement is implemented to prevent egregious manipulations of 

the original travel data.

If BLAST-V’s rerouting algorithm is able to successfully identify a 

revised travel plan that maintains estimated battery SOC above the 

driver’s minimum requirement, statistics on the rerouted tour are 

recorded (e.g., number of DCFC stops, duration of DCFC stops, 

incremental distance relative to original tour) and the rerouted tour is 

simulated in greater detail considering electrical, thermal, and life 

models of the battery pack. However, if an adequate alternate tour is 

not identified, 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.

Rerouting Example 1

For illustrative purposes, consider details of the rerouted tour shown 

in Figures 2–4. Figure 2 shows the rerouted tour path of travel 

consisting of four separate trips for a total of 106 miles. When 

simulated with a 75-mile BEV, the estimated battery SOC falls below 

a driver tolerance of 15% midway through Trip 3 (see the data for the 

original tour in Figure 4). Because the estimated battery SOC does 

not stay above the driver’s required threshold, BLAST-V considers 

potential mid-trip stops at DCFC stations for charging. Figure 3

summarizes the rerouting output by showing the rerouted path of 

travel, a one-mile buffer around the path of travel (only stations 

within this buffer are considered), all available DCFC stations in the

simulation, and DCFC stations selected for mid-trip charging stops.

Travel itineraries and estimated battery SOC for the original tour and 

the rerouted tour with one DCFC stop are shown in Figure 4. By 

inserting a 17-minute DCFC stop midway through Trip 3, the 

estimated battery SOC is maintained above the driver’s required 

threshold for the entire tour.
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Figure 2. Rerouting example 1: Rerouted tour mapped by trip. Actual trip destinations in all maps have been modified as a privacy precaution.

Figure 3. Rerouting example 1: Rerouted tour overlaid with available and utilized DCFC stations. Actual trip destinations in all maps have been modified as a privacy 

precaution.
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Figure 4. Rerouting example 1: Original and rerouted travel itineraries and estimated battery SOC.

Rerouting Example 2

BLAST-V’s rerouting algorithm also supports tours requiring 

multiple DCFC stops. Figure 5 shows a rerouted path of travel, 

available DCFC stations, and selected DCFC stops for a 289-mile 

tour between Tacoma, Washington, and Portland, Oregon. The 

estimated battery SOC from the rerouted tour with DCFC stops is 

shown in Figure 6. This example highlights the potential of fast 

charging both to 1) enable long-distance travel in otherwise rangelimited BEVs, and 2) accelerate battery degradation via aggressive 

cycling profiles and the resultant heat generation. A detailed analysis 

of simulated impacts of DCFC on battery life is available in a parallel 

publication [9].

Figure 5. Rerouting example 2: Rerouted tour overlaid with available and 

utilized DCFC stations. Actual trip destinations in all maps have been 

modified as a privacy precaution.

Figure 6. Estimated battery SOC for rerouting Example 2 with DCFC stops.

Analysis

Simulation Parameters

Having established a methodology for estimating BEV utility that is 

sensitive to user-defined rollouts of public DCFC stations and 

dynamic tour rerouting, we now wish to investigate various 

deployments of high-power 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.
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Travel Profiles

For the travel profiles, we used historical travel data from the Puget 

Sound Regional Council’s (PSRC’s) Traffic Choices Study [10], 

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 (including codes such as home, 

work, and public in addition to precise latitude/longitude 

coordinates). Relevant statistics for the 317 vehicle sample are shown 

in Figure 7.

Figure 7. Trip distance, daily distance, annual distance, and trip average speed 

distributions for all 317 PSRC vehicle histories.

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

more over this one-year period for simulation to focus on highermileage drivers. In Figure 8, we plot all 317 histories to show the 

utility factor and the annual mileage they would achieve driving a 75-

mile BEV without fast 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 and were therefore excluded from this study. 

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. Accordingly, they were not

simulated. The 91 drivers boxed in the upper right corner of the plot 

represent those that both completed more than 8,000 miles and 

achieved a utility factor greater than 80% in the 75-mile BEV.

Arguably, these drivers are well suited to such a BEV without fast 

charging. The remaining 89 drivers are high-mileage drivers that 

achieve low utility factors with a 75-mile BEV. Because Profile Set 

A has the potential to reach 100% utility with the addition of fast 

charging infrastructure, and Profile Set B has the potential for large 

absolute gains in vehicle utility, both sets will be examined. 

Figure 8. Simulated utility and achieved vehicle miles traveled (VMT) for 

PSRC travel histories in a 75-mile BEV.

Driver Behavior

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

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

previous BLAST-V studies [7, 11].

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 range tolerance of 

15% battery SOC (or about 11 miles) per the previous discussion of 

BLAST-V’s “Nominal Capabilities,” which is to say that drivers will 

only elect to travel with 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 employ a mid-size sedan with technology and performance levels 

anticipated for a 2020 model year vehicle. We utilize FASTSim

(Future Automotive Systems Technology Simulator) [12] 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 [13] to approximate the U.S. 

Environmental Protection Agency-rated range. We further employ 

FASTSim to simulate the vehicle’s response to NREL’s DRIVE 

cycle to calculate the vehicle’s real-world efficiency [11]. 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. The vehicle parameters are given in Table 1.
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Table 1. Vehicle Parameters.

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, which 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 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 data set. While battery degradation calculations are 

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

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

on results. A detailed investigation of the thermal impacts of fast 

charging in more demanding climates, as well as the degradation 

response thereof, is performed in a parallel study [9]. For more 

extensive documentation on BLAST-V pack modeling approaches, 

please refer to [7].

Environmental Conditions

Seattle was selected because it has ambient temperature and solar 

irradiation input data available, and it has coincident PSRC travel 

data and represents a relatively moderate climate. Typical 

meteorological year data for Seattle is taken from [14] and is 

illustrated in Figure 9.

Figure 9. Ambient temperature data from Seattle, Washington.

Charging Infrastructure

For vehicle charging, we assume 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).

All public charging networks in this study employ 50-kW DCFC 

stations. A public charging network representative of existing DCFC 

locations is shown in Figure 10 with three synthetic rollout scenarios. 

Location of existing DCFC stations references the U.S. Department 

of Energy’s Alternative Fuels Data Center [15] (sourced Jan 2014). 

The existing DCFC network includes 34 DCFC stations in 

Washington State in addition to 306 DCFC stations outside of 

Washington State. Synthetic infrastructure rollouts are labeled as 

NREL Methods 1–3 and have been generated using combinations of 

heuristics, systems optimization, and operations research techniques.

Each synthetic method offers the ability to prioritize station 

deployments in terms of estimated incremental BEV utility enabled 

(only one iteration of each synthetic method is shown for mapping 

purposes).

Figure 10. Existing DCFC locations shown alongside three synthetic rollout 

scenarios.

Simulation Results

Although some parameter uncertainties exist in the underlying 

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

source of uncertainty is structural. The principal structural 

uncertainties include our approach to modeling human tour decisions, 

our 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.
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The second factor, computation of vehicle energy consumption, is 

applied consistently across all scenarios. Thus, while it is expected to 

affect the absolute vehicle utilities calculated herein, 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 [9]. Despite these uncertainties, however, the 

following findings are telling as to the relative impact of public 

charging impact on overall BEV utility.

Existing DCFC Infrastructure

Simulation results using existing DCFC infrastructure reveal that the 

average BEV utilized DCFC stations fewer than 10 times per year. 

Figure 11 shows a relatively linear trend between DCFC utilization 

and incremental utility (represented in addition to vehicle miles 

traveled (VMT)). The trend line of this plot reveals that 

approximately 75 miles of additional travel are enabled by each fast 

charge event. Note that this does not imply that each DCFC event 

charged the battery from 0% to 100% SOC. For example, brief use of 

a fast charger to partially charge a battery after 50 miles of travel 

could have enabled a 75-mile tour.

Figure 11. Incremental VMT afforded by existing DCFC for simulated 75-

mile BEV vs. the count of fast charge events per year for each simulated 

vehicle.

In Figure 12, we see that it is most common for vehicles utilizing 

DCFC stations to require only one or two additional stops for 

charging per tour. This result is significant in light of a University of 

California, Davis consumer survey in which 100% of respondents 

reported finding one DCFC stop per day reasonable on an occasional 

basis (51% found two stops per day reasonable) [16].

Figure 12. Distribution of average fast charge events per tour by vehicle (for 

tours with at least one fast charge event).

Validation to EV Project

While identifying appropriate validation data for BLAST-V’s 

rerouting and behavior modification algorithms is difficult, some 

confidence can be gained by evaluating BLAST-V outputs from runs 

with existing DCFC infrastructure and data reported by the EV 

Project. For instance, Figure 13 shows average dwell times at DCFC 

stations as a function of average arrival battery SOC. The correlation 

between these outputs is a byproduct of BLAST-V’s tour rerouting 

algorithm determining DCFC dwell times based on estimated time 

required to charge to a specified SOC (95% in this analysis). BLASTV’s simulated dwell times of 11 to 22 minutes agrees well with EV 

Project data showing real-world charger connection times of 14 to 24 

minutes [17].

Figure 13. Average dwell time at DCFC station versus average arrival SOC 

(by simulated vehicle).

Another metric comparing BLAST-V outputs and EV Project 

reported data is percent of energy sourced from DCFC stations. 

Figure 14 shows a distribution of simulated vehicles with the average 

vehicle sourcing 7.6% of total energy from fast charging. This value 

falls well within EV Project reported bounds of 1% to 21% of realworld BEV energy originating from DCFC stations [2].
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Figure 14. Distribution of percent of total energy sourced from DCFC by 

vehicle.

Synthetic DCFC Rollout Scenarios

BEV utility with respect to existing DCFC infrastructure is now 

compared to multiple iterations of the three NREL-developed 

deployment methods. Figure 15 shows average achieved VMT in a 

75-mile BEV from 180 year-long driving profiles (the union of 

Profile Sets A and B) in the Seattle metropolitan area. Average utility 

is plotted against the number of available DCFC stations in each 

simulation (i.e., NREL Method 3 at the 40-station level resulted in an 

average annual VMT of 10,000 miles achieved). Considering the 

incremental utility afforded by existing DCFC stations alongside that 

of multiple distinct synthetic rollouts provides context for the 

effectiveness of existing infrastructure. The 34 existing stations in 

Washington State resulted in a simulated average utility of 10,090 

miles per year, which compares favorably with synthetic rollouts of 

similar volume. While simulated utility from various segmentations 

of travel histories (e.g., all profiles, set A, set B, set A+B) was found 

to impact baseline utility values, relative benefit of DCFC availability 

was largely insensitive to travel history segmentation.

Also included in Figure 15 are reference lines for utility afforded by 

no DCFC stations (9,310 miles per year) and VMT of original CV 

travel profiles (11,830 miles per year). The reference line at 10,500 

miles per year reflects average annual VMT from a simulation where 

DCFC stations are made available at every gasoline refueling station 

in the United States (123,703 unique locations [18]). The “existing 

gas station” scenario provides an upper bound for the potential utility 

of our simulated 75-mile BEV. By densely populating a BLAST-V 

simulation with DCFC stations, we ensure that unachieved tours are 

not a result of public charging availability. Instead, unachieved tours 

in the “existing gas station” scenario are attributed to the 

aforementioned original trip start time constraint. By enforcing the 

condition that all trips start “on time” relative to the original travel 

data, we find that the final increment of BEV utility is potentially 

constrained by human behavioral norms.

Figure 15. Average achieved VMT in a 75-mile BEV from 180 year-long 

driving profiles in the Seattle metropolitan area relative to various DCFC 

deployments.

Additional Battery Sizes

It is also of interest to explore the effects of installed battery size on 

utility when fast charging is available. To study this, we employed 

the existing DCFC deployment for simulation of six vehicles with 

different size batteries. The baseline data of Table 1 were applied in 

all cases, with the exception that the battery energy was changed 

from a low of 16 kWh (corresponding to approximately 58 miles of 

range) to a high of 60 kWh (approximately 218 miles of range). 

Vehicle efficiency was not altered to accommodate the changes in 

mass expected from the different battery sizes. The 60-kWh case 

would add 380 kg of battery mass (assuming 100 Wh/kg technology) 

to our baseline vehicle, which is anticipated to affect energy 

consumption by less than 10%, which was judged to be acceptable 

for the purposes of this investigation.

Looking first at results for the largest range vehicle, we see that 

increasing the battery size encourages BEVs to arrive at DCFCs with

lower SOC and dwell there for a longer period of time. This can be 

observed by comparing Figures 16 and 13. The longer vehicle range 

effectively presents more options for fast charging within a given 

tour, enabling drivers to wait to charge until the battery is further 

depleted, ultimately making fewer additional stops.

Figure 17 compares the simulated utility across all travel histories 

(driver sets A and B), battery sizes (16 to 60 kWh), and charging 

options (with and without fast charging). The box plot for each 

combination of battery size and charging option provides the 

maximum, 75th percentile, median, 25th percentile, and minimum 

utility simulated across the set A and B travel histories. The data 

show that the added utility of fast charging diminishes as battery size 

is increased. It also enables comparison of the merits of additional 

battery capacity and additional infrastructure. For example, the utility 

provided by adding accesses to the elected DCFC infrastructure 

deployment to our baseline 22-kWh BEV is approximately equivalent 

to increasing the battery size to 38 kWh in the absence of DCFC 

infrastructure. Similarly, operation of a 60-kWh BEV without fast 

charging is approximately equivalent to operating a 30-kWh BEV 

with fast charging. In all cases, however, the median and 25th 

percentile drivers achieve significantly less than 100% utility.
10

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

Figure 16. Average dwell time at DCFC station versus average arrival SOC 

(by simulated vehicle). Data from simulation of 60kWh battery size.

Figure 17. Simulated utility factor distributions with and without fast charging 

for a range of battery sizes.

Figure 18 shows the absolute annual mileage enabled by the addition 

of fast charging. Again, the benefits of adding fast charging are seen 

to diminish as installed battery size increases. This plot better 

illustrates the immense value that fast charging can have for select 

drivers: for our smallest battery case, fast charging enables more than 

11,000 miles of additional BEV travel each year for the most 

benefitted driving profile. As battery capacity increases, however, 

this benefit falls sharply. It also shows that the benefit of fast 

charging to our median driver becomes largely insensitive to installed 

battery capacity at and above 38 kWh, at which point 25% of our 

investigated travel patterns achieve no benefit from fast charging.

Figure 18. Simulated VMT enabled by fast charging for a range of battery 

sizes.

Summary

A novel method for estimating real-world utilization of DCFC has 

been developed in BLAST-V, allowing rerouting of original travel 

data to facilitate mid-trip stops for charging at predetermined DCFC 

locations as necessary. Simulated utilization rates of DCFCs have 

been shown to agree well with real-world data from the EV Project, 

suggesting the employed rerouting algorithms adequately reflect realworld driver behavior. This tool has been applied to study both the 

effect of different fast charger deployments and the additional vehicle 

utility afforded thereby for BEVs of different ranges.

We found that Seattle’s existing public DCFC infrastructure was 

shown to compare favorably with various synthetic rollouts of DCFC 

stations. Under Seattle’s existing DCFC deployment, we have found 

that use of fast charging can greatly increase the achieved annual 

mileage of select drivers operating BEVs. However, the vast majority 

of travel patterns we studied observed a benefit of less than 1,000 

DCFC-enabled miles per year, decreasing as the installed range of the 

vehicle is increased. 

Where much larger deployments of DCFCs are available (e.g., 

approaching the prevalence of gas stations), we find that our imposed 

constraints on adjusting travel timing becomes the limiting factor for 

utility improvements. This implies that more flexible adjustments to 

travel profiles (e.g., moving trips between tours, altering the 

destinations of trips, or adjusting travel times, as real drivers may be 

prone to do when operating a range-limited BEV) could reveal 

greater benefits of fast charging. Analyzing the effects of such 

behavior will require a greater understanding of both the nature of 

individual trips and human travel behavior.

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

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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

DCFC direct current fast charging

DRIVE Drive-Cycle Rapid 

Investigation, Visualization, 

and Evaluation Analysis

EVSE electric vehicle support 

equipment

FASTSim Future Automotive Systems 

Technology Simulator

FC fast charge

NREL National Renewable Energy 

Laboratory

PSRC Puget Sound Regional 

Council

SOC state of charge

VMT vehicle miles traveled