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Contract No. DE-AC36-08GO28308

Accounting for the Variation of 

Driver Aggression in the 

Simulation of Conventional and 

Advanced Vehicles 

Preprint

J. Neubauer and E. Wood

National Renewable Energy Laboratory

To be presented at the SAE World Congress and Exhibition

Detroit, Michigan

April 16-18, 2013

Conference Paper

NREL/CP-5400-57503

March 2013
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1 

Accounting for the Variation of Driver Aggression in the Simulation of 

Conventional and Advanced Vehicles

Jeremy Neubauer and Eric Wood

National Renewable Energy Laboratory

ABSTRACT

Hybrid electric vehicles, plug-in hybrid electric vehicles, and 

battery electric vehicles offer the potential to reduce both oil 

imports and greenhouse gases, as well as to offer a financial 

benefit to the driver. However, assessing these potential 

benefits is complicated by several factors, including the 

driving habits of the operator. We focus on driver aggression, 

i.e., the level of acceleration and velocity characteristic of 

travel, to (1) assess its variation within large, real-world drive 

datasets, (2) quantify its effect on both vehicle efficiency and 

economics for multiple vehicle types, (3) compare these 

results to those of standard drive cycles commonly used in the 

industry, and (4) create a representative drive cycle for future 

analyses where standard drive cycles are lacking.

INTRODUCTION

Hybrid electric vehicles (HEVs), plug-in hybrid electric 

vehicles (PHEVs), and battery electric vehicles (BEVs) offer 

the potential to reduce both oil imports and greenhouse gases, 

as well as to offer a financial benefit to the driver. However, 

assessing these potential benefits is complicated by several 

factors, including the local climate, the cleanliness of the grid 

supplying electricity (for PHEVs and BEVs), and the driving 

habits of the operator, among many other things. Driving 

habits can be divided into two topics for consideration: (1) trip 

patterns, i.e., the distribution of trip times and distances, and 

(2) aggression, i.e., the level of acceleration and the velocity 

characteristics of travel. Investigation of trip patterns in [1] 

and [2] found that the variation in the distribution of daily 

miles traveled observed in real-world, multi-day drive data [3] 

produces significant variation in gasoline savings and total 

cost of ownership. 

Herein, we focus on driver aggression to (1) assess its 

variation within large, real-world drive datasets, (2) quantify 

its effect on both vehicle efficiency and economics for 

multiple vehicle types, (3) compare these results to those of 

standard drive cycles commonly used in the industry, and (4) 

create a representative drive cycle for future analyses where 

standard drive cycles are lacking. By doing so, we aim to 

supply an approach for vehicle performance simulation and 

testing that accurately captures the variation between different 

drivers, in particular for high-level techno-economic analyses 

performed using the National Renewable Energy Laboratory’s 

(NREL’s) Battery Ownership Model. This work is supported 

by the U.S. Department of Energy’s Vehicle Technologies 

Program.

ANALYSIS

In this study, we apply a high-resolution vehicle simulator to 

real-world drive data to calculate a distribution of vehicle 

efficiencies for multiple vehicle types and operational modes. 

We then analyze these distributions and compare them to 

standard drive cycles commonly employed in the industry. 

From there, we synthesize and validate an artificial drive cycle 

that characterizes the average of the complete set of real-world 

drive data. We also describe in detail a set of associated 

scaling factors valid for computing the observed variation in 

vehicle efficiency. 

Real-World Drive Data

Real-world, high-accuracy, and high-resolution vehicular 

velocity histories are needed to predict the actual on-road 

variation in vehicle efficiencies of different driver and 

powertrain combinations. For this purpose, 2,154 unique 

vehicle records (spanning 1–2 days each) were sourced from 

the NREL Secure Transportation Data Center: a composite of 

data from Los Angeles, CA; Austin, TX; San Antonio, TX; 

and Houston, TX travel studies [4]. The data were recorded 

using on-board global positioning system data acquisition 

systems filtered down to second-by-second acceleration and 

velocity histories per the methods described in [5].

The box charts in Figures 1–3 show a statistical summary of 

this composite vehicle record data set broken out by city for 

vehicular speed, acceleration, and kinetic intensity [6]. Note 

that both speed and acceleration are averaged over time for 

each individual vehicle record. For reference, these same 

statistics are included for four industry-standard drive cycles: 

(1) Highway Fuel Economy Driving Schedule (HWFET), (2) 

Urban Dynamometer Driving Schedule (UDDS), (3) Air 

Resources Board Dynamometer Driving Schedule (LA92), 

and (4) Supplemental Federal Test Procedure (US06). The 

median, 25th percentile, and 75th percentile values for speed 

and kinetic intensity are fairly consistent among all four cities. 

Acceleration statistics are visibly larger for Los Angeles, but 

they are consistent for the three Texas cities.
2 

Vehicle Simulation

Vehicle simulation is conducted to achieve two goals: (1) to 

generate powertrain specifications for models of comparable 

conventional vehicles (CVs), HEVs, PHEVs, and BEVs; and 

(2) to simulate the fuel consumption of these different 

powertrains when subjected to the driving requirements of 

real-world operators (as defined by the real-world drive data 

discussed previously). 

For both tasks, we employ the NREL-developed ADVISOR 

vehicle simulator to calculate the energy consumption of 

different powertrains under both industry-standard and realworld drive cycles [7]. Operating in the MATLAB/Simulink 

environment, ADVISOR employs a hybrid backward/forwardfacing approach to evaluate system interactions and 

performance relative to individual component limitations. 

To calculate the powertrain specifications of comparable CVs, 

HEVs, PHEVs, and BEVs, we simulate each architecture 

iteratively with different combustion engines, electric motors, 

and battery sizes until a 0–60 mph acceleration time of 9 

seconds and a 40- or 75-mile all-electric range (AER) for the 

PHEV and BEV, respectively, are achieved simultaneously. 

Note that the range is determined via the usable energy of the 

battery and the calculated vehicle efficiency of the UDDS and

HWFET cycles combined and adjusted in a manner 

representative of the two-cycle approximation to the U.S. 

Environmental Protection Agency’s combined city and 

highway window-sticker rating per [8]. A midsize sedan with 

a coefficient of drag of 0.3 and a frontal area of 2.27 m2 is 

assumed in all cases, as is a 136 kg cargo mass. The HEV and 

PHEV are modeled with a parallel engine/motor 

configuration, and are held to a 40% degree of hybridization. 

The HEV battery was sized to approximate existing 

commercial HEVs [9]. Additional inputs and results are 

presented in Table 1.

Subsequently, we apply these models to calculate the vehicle 

efficiency of each powertrain for four industry-standard drive 

cycles (HWFET, UDDS, LA92, and US06), as well as each of 

the processed real-world drive days discussed above. For 

vehicle record simulations, we lock the PHEV in charge 

depleting (CD) and charge sustaining (CS) modes separately; 

in doing so, we ignore the AER limitations of the PHEV and 

allow it to operate in CD mode indefinitely. 

Figure 1. Speed statistics for four real-world drive data sets

Figure 2. Acceleration statistics for four real-world drive 

data sets

Figure 3. Kinetic intensity statistics for four real-world drive 

data sets
3 

We approach the BEV the same by ignoring AER limitations. 

This approach is used so that the entire vehicle record may be 

simulated regardless of distance travelled. We further assume 

that initial engine coolant is at operational temperature at the 

beginning of each trip. We recognize that such assumptions 

are not realistic of actual vehicle use, but they are important to 

ensure the impact of local climate and that the timing of trips 

may be separated from kinetic intensity. The effect of AER, 

climate, and cold starts will be addressed in a future 

publication as the appropriate level of consideration of multiday driving patterns, thermal effects, and battery/engine 

control strategy is beyond the scope of this paper. The effects 

of grade, wind, and cornering are also neglected.

It is worth noting that the driving data employed herein were

collected from an array of vehicle makes, models, years, and 

body styles from the existing local fleet of personal-use 

vehicles. The authors acknowledge that some drive data 

evaluated herein were likely collected from vehicles with 

greater performance capabilities than the employed models 

(namely maximum acceleration, where our simulated vehicles 

are capable of a 9-second 0–60-mph sprint). Accordingly, we 

checked all simulated trips to evaluate the occurrence of speed 

trace errors where our simulated vehicle was incapable of 

matching the original recorded speed history. We found that of 

the 13,622 real-world trips simulated across five powertrains, 

95.9% of all trips exhibited a maximum error of less than 1

mph, while 99.5% achieved a maximum error of less than 5

mph. As the occurrence of speed trace error is small, we 

conclude that the discrepancies between the performance 

capability of our simulated vehicle and that of the vehicles 

behind the data will have a negligible impact on our results. 

Thus, we do not down-select vehicle records for analysis 

based on performance criteria. 

Effect of Driver and Vehicle

Variation of vehicle efficiency across individual vehicle 

records by powertrain (and operational mode, for the PHEV) 

is presented in Figure 4. Values on the y-axis have been 

nondimensionalized relative to the average vehicle efficiency 

for each case, such that values less than one indicate superior

efficiency relative to the mean and vice-versa. PHEV 

efficiency in CD mode is captured by converting any fuel use 

by the internal combustion engine (occurring in approximately 

half the data) to an equivalent amount of electrical energy, 

using the lower heating value for gasoline, and summing with 

electric motor energy consumption on a per-distance basis. 

Several important points become clearly evident from this 

graph. First, we see that the median vehicle efficiency from 

the data set is relatively close to the mean. Second, the 

distribution is asymmetric around the mean—variations in 

aggression have a significantly greater capability to increase 

fuel consumption from the mean than to decrease it.

Finally, we find that the powertrain type has minimal 

influence on the distribution of vehicle efficiency as a function 

of population percentile. It is important to highlight, though, 

that this may not be true for different vehicle platforms (e.g., 

compact cars or large SUVs), those with different 0–60 mph 

acceleration targets, or hybrids of different architectures (e.g., 

serial hybrids).

Comparison to Standard Drive Cycles

Figures 5–9 compare the distribution of vehicle efficiency 

computed from real-world vehicle records to that calculated 

for four industry-standard drive cycles (HWFET, UDDS, 

LA92, and US06) for four vehicle types (CV, HEV, PHEV, 

and BEV). Two plots are shown for the PHEV, one for CS

operation and one for CD, split for the reasons noted above. 

These plots show distinct differences between operational 

modes (combustion-only, hybrid operation, and electricityonly) with respect to how the standard drive cycles compare to 

the distribution of vehicle record results. When operating only 

under the power of combustion (CV), we find that simulation 

of the HWFET cycle significantly underpredicts vehicle 

record fuel consumption calculations, whereas the UDDS is 

representative of the mean, and the US06 and LA92 cycles

slightly overpredict average vehicle record fuel consumption.

Table 1. Vehicle Specifications

Powertrain CV HEV PHEV40 BEV75

Accessory Load (W) 700 300 300 300

Vehicle Mass (kg) 1,679 1,613 1,794 1,611

Internal Combustion Engine 

Power (kW) 121 62

69 n/a

Electric Motor Power (kW) n/a 41 46 95

Battery Total Energy (kWh) n/a 1.67 17.6 25.5

Battery Maximum State of 

Charge n/a 80% 95% 95%

Battery Minimum State of 

Charge n/a 60% 25% 5%

Vehicle Efficiency 27.0 mpg 38.6 mpg 34.2 mpg (CS)

308 Wh/mi (CD) 305 Wh/mi
4 

Figure 4. Normalized vehicle efficiency vs. percentile of 

vehicle records for different powertrains

Figure 5. Comparison of vehicle record and standard cycle 

CV efficiency

Figure 6. Comparison of vehicle record and standard cycle 

HEV efficiency

Figure 7. Comparison of vehicle record and standard cycle 

PHEV efficiency in charge sustaining mode

Figure 8. Comparison of vehicle record and standard cycle 

PHEV efficiency in charge depleting mode

Figure 9. Comparison of vehicle record and standard cycle 

BEV efficiency
5 

For the CV, LA92 predicts the highest fuel consumption, but it 

is relatively close to that predicted by US06. When operating 

in hybrid mode (HEV, PHEV-CS), we find that the HWFET 

still predicts the lowest fuel consumption, but it is now within 

the tail end of the vehicle record data. The UDDS now slightly 

underestimates average vehicle record simulations, but it 

represents reasonably well the mode thereof. Further, the 

differences between UDDS, LA92, and US06 predictions are 

more exaggerated than in the combustion-only case, and US06 

becomes the least fuel-efficient standard drive cycle. These 

trends continue when advancing to all-electric mode (PHEVCD, BEV). The UDDS cycle falls further down in relative fuel 

consumption when compared to the mode value of vehicle 

record data, and the difference between UDDS, LA92, and 

US06 predictions increases even further. Here we find that the 

LA92 cycle is the best indicator of average vehicle record 

predictions among the four standard cycles we examined.

It is important to note that this latter trend of increasing 

variation in efficiency predictions between standard drive 

cycles as the degree of electrification increases is not 

indicative of increasing sensitivity to real-world drivers. 

Recall that the results in the previous section showed that the 

variation in fuel efficiency across real-world drive data is 

fairly consistent across powertrains (see Figure 3). Rather, 

these data imply that the response of these powertrains to 

different drive cycles is varied and complex, as will be 

discussed below. 

Creating a Representative Drive Cycle

A set of drive cycles representative of specific percentiles of 

the recorded real-world drive data is desired for future 

simulation. Doing so will greatly speed computational time by 

reducing the information of thousands of 24-hour or longer 

drive cycles to a single ~15-minute drive cycle that can be 

quickly simulated. However, the complex interplay of 

powertrain and drive cycle makes it impractical to create such 

a set, as discussed below. However, we do find it possible to 

design one drive cycle indicative of the average of all 

computed vehicle record efficiencies, which then allows the 

application of scaling factors from Figure 3 to estimate that of 

a specific population percentile. Herein, we apply the NRELdeveloped DRIVE [10] software package to synthesize this 

single drive cycle.

DRIVE employs a deterministic multivariate hierarchical 

clustering method to generate representative drive cycles from 

source data. It first concatenates our 2,154 vehicle records into 

a single “super” cycle, which is characterized for more than 

170 drive-cycle metrics. The tool then decomposes the cycle 

into its component microtrips, which are individually analyzed 

over the same set of operational metrics. This set of statistics 

includes well-known metrics such as average driving speed, 

stops per mile, and zero-speed time as a percentage of cycle 

operation. Other specialized metrics include kinetic intensity, 

aerodynamic speed, and characteristic acceleration, which are 

used to characterize energy consumption [6]. Afterwards, the 

individual microtrips are grouped into clusters and ranked 

based on a set of performance metrics. Upon ranking, the ideal 

microtrip from each cluster is selected and concatenated to 

form a representative cycle. This clustering process is iterated 

over the number of clusters chosen for the data as well as the 

performance metrics chosen for ranking. It is based on a 

maximum number of clusters, which is calculated as the 

product of the desired representative cycle duration, the 

number of stops per mile for the “super” cycle, and the 

average speed over the “super” cycle. As a final step in the 

generation of a representative drive cycle, zero-speed time is

either added or removed from the final drive cycle output to 

match the percentage found in the original data “super” cycle.

The resultant drive pattern is shown in Figure 10. It has an 

average speed of 30.5 mph, an average positive acceleration of 

0.960 mph/s, and a kinetic intensity of 0.577 mi-1. Simulation 

of this drive cycle in ADVISOR for all four powertrains 

shows a good agreement of average predicted vehicle 

efficiency when compared to that observed from the 

simulation of all 2,154 vehicles, as shown in Table 2. 

Accordingly, this drive cycle can be used to quickly compute 

average vehicle efficiency, which can then be expanded to 

represent different population percentile vehicle efficiencies 

using the correction factors of Table 3 (computed from the 

dataset used to prepare Figure 4).

Figure 10. Representative drive cycle produced from 2,154 

vehicles using DRIVE

Table 2. Comparison of Average Predicted Vehicle 

Efficiency Between Synthesized Drive Cycle and Vehicle 

Record Simulations

Error of mean vehicle record and 

synthesized drive-cycle efficiency

CV HEV PHEV40 

(CS) 

PHEV40 

(CD) 

BEV75

-0.37% -0.70% 0.07% 0.23% -0.03%
6 

Table 3. Scaling Factors

Vehicle 

Day 

Percentile

CV HEV PHEV40 

(CS) 

PHEV40 

(CD) BEV75

5th 0.810 0.828 0.847 0.826 0.841

25th 0.883 0.896 0.920 0.900 0.915

50th 0.962 0.963 0.977 0.969 0.980

75th 1.077 1.074 1.062 1.072 1.068

95th 1.325 1.290 1.220 1.266 1.223

Optimal Drive-Cycle Characteristics

Creation of a suite of drive cycles representative of arbitrary 

percentile drivers was deemed infeasible. This outcome is the 

result of individual drivetrains being particularly well-suited to

specific drive-cycle characteristics. This point is illustrated in 

Figure 11, where the relative efficiency of the simulated CV 

and HEV are plotted in 4D space. In this plot, the 2,154 

vehicle records are segmented into 13,622 trips and plotted 

with marker x-y coordinates relative to cycle speed and 

acceleration statistics, marker size relative to trip length, and 

marker color relative to percentile energy consumption rate 

per the specified powertrain.

In terms of cycle statistics (speed, acceleration, distance), the 

CV and HEV plots represent the same 13,622 data points. This 

“point cloud” alone reveals interesting information concerning 

real-world drive data. We see that low-speed trips 

(approximately less than 15 mph) coincide with short-distance 

travel, and thus account for relatively small amounts of energy 

consumption. As travel speed increases, we see a tendency for 

trip distances to increase while acceleration metrics decrease. 

The observed inverse relationship between driving speed and 

acceleration can be linked to both behavioral norms (lack of 

driver-requested high-acceleration events during high speed 

travel) and technical realities (inability of most vehicles to 

deliver high acceleration at high speed).

Concerning relative energy consumption rates, we see that the 

optimal drive cycle for our CV occupies the intersection of 

high-speed and low-acceleration travel. The industry-standard 

HWFET cycle is an exemplar of such a drive cycle, yielding 

energy consumption rates close to the lower bound. 

Alternatively, the HEV exhibits optimal energy consumption 

rates when driven over low speed, low acceleration cycles 

(near 20 mph, 0.5 mph/s). The differences in gradients 

between these two maps can be primarily attributed to the

effects of regenerative braking made possible through electric 

hybridization. By recouping energy traditionally lost during 

braking events, our HEV is not as heavily penalized by the 

stop-and-go patterns characteristic of low-speed travel. 

Instead, the HEV achieves its highest fuel consumption values 

over cycles with high speed/acceleration metrics, presumably 

as a result of the aerodynamic, rolling, and inertial losses of 

the classical road load equation.

These maps illustrate the challenge of synthesizing drive 

cycles capable of capturing arbitrary percentile energy 

consumption rates for disparate powertrains and underscore 

the utility of the synthesized drive cycle (presented in the 

previous section) in its ability to capture mean energy

consumption rate regardless of powertrain configuration.

Figure 11. CV and HEV efficiency gradients: marker 

location indicates the average speed and acceleration of each 

vehicle record; marker size indicates distance traveled in 

each vehicle record; and marker color indicates the relative 

energy consumption rate for each record. The locations of 

standard test cycles in this 2D space (including the 

synthesized cycle built using DRIVE) are also indicated. 
7 

Relative Efficiency across Powertrains

Having highlighted the discrepancy between optimal drivecycle characteristics for our CV and HEV, we now examine 

the relative efficiency of each drive cycle to understand how 

vehicle efficiency translates across powertrains.

The data on CV and HEV efficiency from Figure 11 are 

repurposed for Figure 12 with percentile HEV fuel rate plotted 

against percentile CV fuel rate. This representation

underscores the discrepancy in optimal drive-cycle 

characteristics between the simulated CV and HEV 

powertrains. For example, drive cycles falling in the 60th

percentile fuel rate when simulated under a CV powertrain

result in HEV fuel rates between the 10th and 90th percentiles.

Figure 12. Comparison of HEV and CV relative fuel 

consumption rate for 13,622 simulated drive cycles

Figure 13. Comparison of BEV and HEV relative fuel 

consumption rate for 13,622 simulated drive cycles

Coloring the data by average trip speed provides insight 

concerning the nature of this discrepancy. This added 

dimension shows that high-speed trips achieve lower relative 

vehicle efficiencies in an HEV when compared to a CV, 

presumably as a result of the diminishing returns encountered 

in an HEV in the absence of stop-and-go conditions indicative 

of low-speed travel. Alternatively, medium-to-low speed trips 

achieve high relative vehicle efficiency under an HEV 

powertrain when compared to a CV. We note, however, that a 

subset of very low-speed trips exhibits a high relative fuel rate 

(low relative efficiency). The result is due to a large 

percentage of vehicle idle time relative to the number of miles 

traveled. 

We take this opportunity to underscore the difference between 

absolute and relative vehicle efficiency. The HEV powertrain 

resulted in greater absolute vehicle efficiency than the CV for 

each simulated trip. However, specific drive-cycle 

characteristics are best suited to individual powertrains (e.g.,

high-speed travel in CVs and low-speed travel in HEVs) 

resulting in a discontinuity in the comparison of relative

efficiency.

Fortunately, this discontinuity in relative efficiency appears 

unique to comparisons involving the CV. Figure 13 shows a 

comparison of the HEV and BEV relative efficiencies 

exhibiting a strong linear relationship between percentile 

rankings of drive cycles when simulated under distinct 

powertrains. This linear correlation is representative of the 

relationship between all combinations of the HEV, PHEV, and 

BEV powertrains. The CV is believed to be the outlier in this 

analysis due to its lack of regenerative braking, favoring 

cycles where high average speed is presumably an indicator of 

infrequent stop-and-go conditions.

CONCLUSIONS

In this study, we investigated the variation of driver 

aggression to assess its variation within large, real-world drive 

datasets, and quantify its effect on vehicle efficiency for 

multiple vehicle types. We have found that aggression 

variation between drivers can decrease fuel efficiency by more 

than 50% or increase it by more than 20% from average. 

Interestingly, across the population we investigated, the 

normalized efficiency deviation from average as a function of 

population percentile was found to be largely insensitive to 

powertrain. However, the specific high-efficiency drivers did 

vary across the CV, HEV, PHEV, and BEV powertrains, 

implying that ideal driving behavior varies with respect to 

powertrain.

Accordingly, the relation of efficiency predicted from realworld drive data to that predicted by the industry-standard 

HWFET, UDDS, LA92, and US06 drive cycles was not 

consistent across powertrains, either. Thus, we applied 

NREL’s DRIVE tool to synthesize a drive cycle that is 

capable of reproducing the average vehicle efficiency 

predicted from our sampling of real-world drive data. Our 

results showed that the predicted vehicle efficiency from 
8 

simulation of this drive cycle agrees with that of simulations 

of real-world data to within 0.7% for the four vehicles (five 

operational modes) addressed herein. Along with the supplied 

scaling factors, this drive cycle is, therefore, capable of 

accurately representing the total variation in vehicle efficiency 

we have observed in the employed vehicle records.

It is important to note that none of the simulated drive cycles 

account for the effects of grade, wind, large vehicle auxiliary 

loads (e.g., heating, ventilation, and air-conditioning) or other 

more complex effects. Accounting for such factors will be the 

subject of future work.

REFERENCES

[1] Neubauer, J.; Wood, E.; and Brooker, A. “Sensitivity 

of battery electric vehicle economics to drive patterns, vehicle 

range, and charge strategies,” Journal of Power Sources, 

2012; 209, 269-277.

[2] Neubauer, J.; Wood, E.; and Brooker, A. “Sensitivity 

of plug-in hybrid electric vehicle economics to drive patterns, 

electric range, energy management, and charge strategies,” 

Journal of Power Sources, 2013 (in press). 

[3] Traffic Choices Study – Summary Report, Puget 

Sound Regional Council, April 2008.

[4] “Secure Transportation Data Project.” National 

Renewable Energy Laboratory. 

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

ata.html [Accessed: August 28, 2012]

[5] Duran, A. and Earleywine, M. ”GPS Data Filtration 

Method for Drive Cycle Analysis Applications.” SAE 2012 

World Congress & Exhibition, April 24, 2012, Detroit, 

Michigan2012-01-0743, 2012, doi:10.4271/2012-01-0743.

http://papers.sae.org/2012-01-0743 [Accessed December 

2012].

[6] O’Keefe, M.; Simpson, A.; Kelly, K. and Pedersen, 

D. "Duty Cycle Characterization and Evaluation Towards 

Heavy Hybrid Vehicle Applications." SAE World Congress &

Exhibition, April 16, 2007, Detroit, Michigan 2007-01-0302, 

2007, doi:10.4271/2007-01-0302. http://papers.sae.org/2007-

01-0302 [Accessed December 2012].

[7] Markel, T.; Brooker, A.; Hendricks, T.; Johnson, V.; 

Kelly, K.; Kramer, B.; O’Keefe, M.; Sprik, S.; and Wipke, K. 

“ADVISOR: A Systems Analysis Tool for Advanced Vehicle 

Modeling,” Journal of Power Sources, 2002, 110, pp. 255-

266. 

[8] Revisions and Additions to Motor Vehicle Fuel 

Economy Label; Final Rule. Washington, D.C.: Federal 

Register. http://www.gpo.gov/fdsys/pkg/FR-2011-07-

06/html/2011-14291.htm [Accessed: Aug 29, 2012]

[9] “Hybrid Electric Vehicles.” Idaho National 

Laboratory. http://avt.inl.gov/hev.shtml [Accessed: Sept 5, 

2012]

[10] “Analysis Tool Generates Custom Vehicle Drive 

Cycles Based on Real-World Data.” National Renewable 

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http://www.nrel.gov/vehiclesandfuels/news/2011/1593.html 

[Accessed: Sept 5, 2012]

ACKNOWLEDGMENTS

This study was supported by the Energy Storage, Vehicle 

Technologies Program, Office of Energy Efficiency and 

Renewable Energy, U.S. Department of Energy. The authors 

would specifically like to thank Dave Howell and Brian 

Cunningham of the U.S. Department of Energy for their 

continued guidance and support. The use of the ADVISOR 

and DRIVE computational tools, both developed at NREL

under funding from the U.S. Department of Energy’s Vehicle 

Technologies Program, was critical to the completion of this 

study. Special thanks to Adam Duran for supporting our use of 

DRIVE; Jeff Gonder for supporting the use and analysis of the 

drive data sources from NREL’s Transportation Secure Data 

Center; and Terry Penney and Ahmad Pesaran for their 

continual guidance. 

DEFINITIONS

AER all-electric range

BEV battery electric vehicle

CD charge depleting

CS charge sustaining

CV conventional vehicle

HEV hybrid electric vehicle

HWFET Highway Fuel Economy Driving Schedule

LA92 Air Resources Board Dynamometer Driving 

Schedule

NREL National Renewable Energy Laboratory

PHEV plug-in hybrid electric vehicle

UDDS Urban Dynamometer Driving Schedule

US06 Supplemental Federal Test Procedure