Simulated Fuel Economy and Performance of 

Advanced Hybrid Electric and Plug-in Hybrid 

Electric Vehicles Using In-Use Travel Profiles 

Matthew Earleywine, Jeffrey Gonder, Tony Markel, Matthew Thornton 

Center for Transportation Technologies and Systems 

National Renewable Energy Laboratory (NREL) 

Golden, CO, USA 

Matthew.Earleywine@nrel.gov; Jeff.Gonder@nrel.gov; Tony.Markel@nrel.gov; Matthew.Thornton@nrel.gov 

Abstract As vehicle powertrain efficiency increases through 

electrification, consumer travel and driving behavior have 

significantly more influence on the potential fuel consumption of 

these vehicles. Therefore, it is critical to have a good 

understanding of in-use or “real world” driving behavior if 

accurate fuel consumption estimates of electric drive vehicles are 

to be achieved. Regional travel surveys using Global Positioning 

System (GPS) equipment have been found to provide an excellent 

source of in-use driving profiles. In this study, a variety of vehicle 

powertrain options were developed and their performance was 

simulated over GPS-derived driving profiles for 783 vehicles 

operating in Texas. The results include statistical comparisons of 

the driving profiles versus national data sets, driving 

performance characteristics compared with standard drive 

cycles, and expected petroleum displacement benefits from the 

electrified vehicles given various vehicle charging scenarios. 

I. INTRODUCTION 

Plug-in hybrid electric vehicles (PHEVs) have received 

increasing attention due to their potential for high fuel 

economy and petroleum displacement. The U.S. 

Environmental Protection Agency (EPA) has historically 

measured vehicle fuel economy using standard driving cycles 

such as the Urban Dynamometer Driving Schedule (UDDS) 

and the Highway Fuel Economy Test (HWFET). However, as 

new technologies and designs allow vehicles to become more 

fuel efficient, consumer behavior and driving profiles become 

more influential on the variation in actual fuel economy 

observed. For instance, aggressive driving or off-cycle 

behavior not captured by these standard drive cycles can 

greatly influence the actual fuel economy experienced by a 

given driver. This effect is accentuated with advanced 

technology vehicles such as hybrid electric vehicles (HEVs) 

and PHEVs. In the case of PHEVs, the distance driven 

between opportunities to charge the vehicle creates an 

additional and significant impact on its fuel use. In-use driving 

profiles containing a distribution of real world driving 

aggressiveness and distance between recharge opportunities 

can help further evaluate the expected fuel savings from 

various advanced vehicle technologies and designs. 

II. ANALYSIS 

The real world drive cycle data for this study was obtained 

using Global Positioning System (GPS) technology. GPS 

devices use satellites to calculate second-by-second 

information about vehicle position, speed, and distance 

traveled. The GPS data used for this study was gathered by the 

Texas Department of Transportation and consisted of a total of 

783 vehicles in Austin and San Antonio, Texas [1]. This GPS 

data was filtered and processed, and then used to generate 24-

hour driving profiles for each vehicle in the study. The 

National Renewable Energy Laboratory’s (NREL) vehiclelevel simulation software, ADVISOR, was used to evaluate 

and compare the simulated performance of different types of 

vehicles and vehicle technologies on these in-use drive cycles 

[2]. 

Six different midsize platform vehicles were simulated on 

these cycles: a conventional vehicle (CV), an HEV, and four 

PHEVs. Of the four PHEVs, three had a parallel configuration 

with a blended control strategy, meaning that the internal 

combustion engine assisted the electric motor during times of 

high power demand. The three blended-strategy PHEVs are 

referred to as PHEV10, PHEV20, and PHEV40 because they 

were designed to travel approximately 10, 20, and 40 miles 

respectively on the UDDS before using any fuel. The fourth 

PHEV was a series configuration with a high-power battery 

energy storage system (ESS) and an electric motor capable of 

providing for all of the vehicle’s power demands. The internal 

combustion engine in the series PHEV was only used to 

sustain the charge of the batteries for longer distance driving. 

This vehicle is referred to as PHEV40s and was designed to 

travel approximately 40 miles on the UDDS cycle before 

using any fuel. Table I lists some of the attributes of the 

vehicle models in the simulation. The engine, electric motor, 

and batteries were sized using methods similar to previous 

NREL hybrid vehicle technology studies [3]. 

© 2011 IEEE. Reprinted, with permission, from M. Earleywine, J. Gonder, T. Markel, and M. Thornton, 

“Simulated Fuel Economy and Performance of Advanced Hybrid Electric and Plug-in Hybrid Electric 

Vehicles Using In-Use Travel Profiles,” Vehicle Power and Propulsion Conference (VPPC), 2010 IEEE.

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way 

imply IEEE endorsement of any of the National Renewable Energy Laboratory's products or services. 

Internal or personal use of this material is permitted. However, permission to reprint/republish this 

material for advertising or promotional purposes or for creating new collective works for resale or 

redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to 

view this document, you agree to all provisions of the copyright laws protecting it.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE I 

SIMULATED VEHICLE ATTRIBUTES 

Units CV HEV 10 20 40 40s 

Engine Power kW 123 77 77 78 80 85 

Motor Power kW n/a 36 40 41 43 130 

ESS Energy (total, DC) kWh n/a 1.7 4.5 8.2 16.4 16.4 

Curb Mass kg 1473 1552 1578 1614 1694 1789 

CS Consumption* L/100km 6.8 4.7 4.9 5.1 5.2 5.3 

CD Consumption (AC)* kWh/100km n/a n/a 18.2 18.2 18.4 18.9 

km n/a n/a 16.9 33.8 67.6 64.4 

mi n/a n/a 10.5 21 42 40 

PHEV 

Urban Electric-only Range 

*Values reflect unweighted composite urban/highway consumption. The CD 

values represent pure CD performance (when no fuel use occurs on the 

standard UDDS/HWFET cycles). 

Of the two cities in this survey, the Austin data set 

contained 228 vehicles while the San Antonio data set 

contained 555 vehicles. The average daily driving distance in 

the Austin data was 55.4 km (34.3 miles) and was 65.5 km 

(40.7 miles) in the San Antonio data, yielding an overall 

average of 62.5 km (37.4 miles). 

Fig. 1 shows the utility factor curves for the Austin and San 

Antonio data sets compared to the utility factor curve 

generated from the 2001 National Household Travel Survey 

(NHTS) data. The utility factor is used to estimate the 

percentage of vehicle miles traveled (VMT) covered for a 

specified charge-depleting (CD) range [4]. The Austin and 

San Antonio curves are significantly higher than the NHTS 

curve, which means that a higher percentage of vehicles in the 

NHTS travel longer distances relative to the vehicles in the 

Austin and San Antonio GPS samples. The discrepancy could 

simply result from an under-sampling of long distance driving 

in the survey methodology for the GPS samples, and/or a 

greater incidence of longer distance rural driving in the 

national data set. Either way, it should be noted that the larger 

percentage of CD operation in the GPS data sets will result in 

higher fuel savings for the PHEVs than those vehicles would 

experience in a similar vehicle fleet containing more long 

distance trips (and hence more charge-sustaining, or CS, 

operation). 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1 

Utility Factor (UF) 

NHTS 

Total (Austin + San Antonio) 

Austin 

San Antonio 

0 20 40 60 80 100 120 140 160 

Driving Distance (km) 

Fig. 1. Utility factor curves for Austin and San Antonio data sets compared to 

2001 NHTS data 

III. RESULTS 

Fig. 2 shows the average fuel and electricity consumption 

weighted by vehicle day from the vehicle simulations for all of 

the vehicles in both the Austin and San Antonio data sets. Fig. 

3 shows the distance- weighted average of fuel and electricity 

consumption for all vehicles. For the PHEVs, the graphs show 

the fuel and electricity consumption for both the base case and 

the opportunity charging (opchg) case. The base case assumes 

the PHEVs are recharged once per day (overnight). The 

opportunity charging case is a best case scenario that assumes 

that the vehicle has the opportunity to be plugged in every 

time the vehicle is stopped for more than two minutes. The 

vehicles are recharged at a rate of 1.56 kW AC with a charger 

efficiency of 90%. 

Since most public parking lots do not have outlets in every 

stall to plug vehicles into, the base case is more likely 

representative of the real world. However, some consumers 

may make many trips throughout the day, returning home 

between trips. One example would be a stay-at-home parent 

who shuttles their children from place to place and returns 

home between trips. For this type of situation, the opportunity 

charging case may be a better representation. If public parking 

lots were to have outlets available to plug vehicles into, the 

fuel savings would be very significant, as shown by the 

significant increase in fuel economy from the base case to the 

opportunity charging case. 

Note that the average fuel consumption of the PHEV40 

opportunity charging case is less than the PHEV40s base case 

when averaging across all kilometers driven, whereas the 

opposite is true when averaging the results by vehicle day. 

This is because in the vehicle-weighted average, the vehicles 

that are driven short distances only in CD mode are given 

equal weight to those that travel far enough to enter CS mode. 

The distance-weighted averages, however, give more weight 

to the vehicles traveling long distances, and therefore include 

more CS operation in the averages (which also leads to higher 

fuel consumption results for all PHEV cases with distance vs. 

vehicle based averaging). Opportunity charging between trips 

can enable much more CD operation for the longer driving 
 

 

 

 

 

 

 

vehicles, and hence shows a larger relative fuel savings benefit 

when weighting by daily kilometers driven. Since the vehicle 

weighted average in Fig. 2 is representative of the average fuel 

consumption per vehicle day, it is better representation of an 

average consumer’s fuel use, while the distance weighted 

average in Fig. 3 is a better representation of the average fuel 

consumption of the overall fleet (and the aggregate fuel 

displacement potential of each technology for this particular 

set of drivers). 

0 

5 

10 

15 

20 

25 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 Fuel Consumption L/100km 

Electricity Consumption kWh/100km 

L/100km AC kWh/100km 

CV HEV PHEV10 PHEV10 PHEV20 PHEV20 PHEV40 PHEV40 PHEV40-s PHEV40-s 

base opchg base opchg base opchg base opchg 

Fig. 2. Average fuel and electricity consumption weighted by vehicle day for 

Austin and San Antonio data sets 

L/100km AC kWh/100km 

0 

5 

10 

15 

20 

25 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 Fuel Consumption L/100km 

Electricity Consumption kWh/100km 

CV HEV PHEV10 PHEV10 PHEV20 PHEV20 PHEV40 PHEV40 PHEV40-s PHEV40-s 

base opchg base opchg base opchg base opchg 

Fig. 3. Average fuel and electricity consumption weighted by total miles 

traveled for Austin and San Antonio data sets 

The fuel consumption distribution for each vehicle variant 

over the Austin and San Antonio data sets is shown in Fig. 4 

and Fig 5. Note that the PHEV20 has a very wide distribution 

compared to the PHEV10 and PHEV40. Because a high 

percentage of daily driving is greater than 10 miles, the 

PHEV10 usually operates in CS mode. Likewise, because a 

high percentage of daily driving is less than 40 miles, the 

PHEV40 usually operates in CD mode. The PHEV20, 

however, doesn’t strongly favor either mode since a high 

percentage of vehicles drive between 10 and 40 miles per day. 

Also note that the PHEV40s has a very high peak at 0 

L/100km. Unlike the PHEV40, it does not use fuel for 

aggressive accelerations and therefore uses no fuel unless it 

travels more than 40 miles. Finally, Fig. 4 and Fig. 5 also 

demonstrate the noticeably large percentage variation in fuel 

consumption of the vehicles with increased electrification as 

compared to the CV. The distribution differences highlight the 

increased sensitivity of PHEV fuel consumption (particularly 

for blended-strategy PHEVs) to variations in driving patterns 

and conditions. 

0 2 4 6 8 10 12 0 

10 

20 

30 

40 

50 

60 

70 

Percent of Vehicles 

CV 

HEV 

PHEV10 Base 

PHEV20 Base 

PHEV40 Base 

PHEV40s Base 

Fuel Consumption (L/100km) 

Fig. 4. Fuel consumption distribution for Austin data set 

0 2 4 6 8 10 12 0 

10 

20 

30 

40 

50 

60 

70 

Percent of Vehicles 

CV 

HEV 

PHEV10 Base 

PHEV20 Base 

PHEV40 Base 

PHEV40s Base 

Fuel Consumption (L/100km) 

Fig. 5. Fuel consumption distribution in San Antonio data set 

Fig. 6 and Fig. 7 show the electricity consumption 

distribution for each vehicle in the Austin and San Antonio 

data sets. As expected, the electricity consumption follows 

somewhat of an inverse trend compared to the fuel 

consumption. The PHEV10 uses less electricity because of 

lower storage capacity, causing it to run in CS mode more 

often, while the PHEV40 consumes more due to its high 

capacity, allowing it run in CD mode more often. The 

PHEV20 spends similar amounts of time in both modes, 

giving it a wide distribution similar to the fuel consumption 

distribution. The PHEV40s consumes the most electricity 

since it does not rely on the internal combustion engine to 

assist it with aggressive accelerations. However, the electricity 

consumption differences with the blended-strategy PHEV40 

are small, suggesting that the PHEV40 makes just as good use 

of the energy stored in its batteries as the PHEV40s. (This 

observation is also supported by the near identical distance-
 

 

 

 

 

 

 

 

weighted average consumption characteristics between the 

two vehicles). 

0 5 10 15 20 25 30 35 40 0 

5 

10 

15 

20 

25 

Percent of Vehicles 

PHEV10 Base 

PHEV20 Base 

PHEV40 Base 

PHEV40s Base 

Electrical Energy Consumption (AC kWh/100km) 

Fig. 6. Electrical energy consumption (AC kWh/100km) distribution in Austin 

data set 

0 5 10 15 20 25 30 35 40 0 

5 

10 

15 

20 

25 

Percent of Vehicles 

PHEV10 Base 

PHEV20 Base 

PHEV40 Base 

PHEV40s Base 

Electrical Energy Consumption (AC kWh/100km) 

Fig. 7. Electrical energy consumption (AC kWh/100km) distribution in San 

Antonio data set 

In order to design a vehicle that has an all-electric range in 

the real world that approaches that on the UDDS cycle, the 

electric motor must be larger to accommodate the higher 

power demands that often occur in real world driving. This 

raises questions as to how much larger the electric motor 

should be, and how significant of an impact a larger motor 

will make. Some insights that are needed to begin 

understanding and answering these questions are found in the 

analysis of these high power demands to determine how often 

they occur, how much additional fuel is consumed during 

these periods, and if there are any noticeable patterns or trends 

related to other parameters. 

Although the PHEVs were designed to travel 10, 20, and 40 

miles respectively on the UDDS, approximately 90% of the 

daily travel profiles caused the parallel/blended PHEVs to use 

fuel during CD mode. This means that 90% of the time, the 

internal combustion engine initially began using fuel because 

of high power demand, not because the state of charge of the 

energy storage system was too low. This is due to higher 

accelerations observed in the real world data compared to the 

UDDS cycle. Over 45% of the time, such a high acceleration 

caused an engine turn-on in the parallel/blended PHEVs 

within the first mile of the driving profile. Fig. 8 and Fig. 9 

show the all-electric range for the PHEV40 in the Austin and 

San Antonio data sets, respectively. The red bars indicate the 

total distance traveled for vehicles that traveled allelectrically, while the blue bars indicate the distance traveled 

to the first occurrence of the internal combustion engine 

turning on. 

km 

0 8 16 24 32 40 48 56 64 72 80 

0 5 10 15 20 25 30 35 40 45 50 0 

10 

20 

30 

40 

50 

60 

Percentage of Fleet (%) 

Distance to First Engine On 

Total Distance for All Electric Trips 

All Electric Range (miles) 

Fig. 8. All-electric range for the PHEV40 in Austin data set 

km 

0 8 16 24 32 40 48 56 64 72 80 

0 5 10 15 20 25 30 35 40 45 50 0 

5 

10 

15 

20 

25 

30 

35 

40 

45 

50 

Percentage of Fleet (%) 

Distance to First Engine On 

Total Distance for All Electric Trips 

All Electric Range (miles) 

Fig. 9. All-electric range for the PHEV40 in San Antonio data set 

Fig. 10 shows the maximum power demand per vehicle day. 

Since the actual power needed for each vehicle also depends 

on the mass of the vehicle, the power displayed is in units of 

kW/kg in order to simplify the comparison of the vehicles 

since they each have a different mass. In the actual 

simulations, there were some occurrences of much higher 

power demands than those displayed in Fig. 10. However, 

these high power demands were most likely caused by sports 

cars or other vehicles with a much higher specific power than 

the standard mid-size sedan used in the model and are not 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

necessary for everyday driving. Therefore, maximum power 

demands that are higher than the maximum power of this 

study’s CV’s engine are not included in the results shown in 

Fig. 10 or Fig. 11. Frequency (%) 

30 

25 

IV. CONCLUSIONS 

PHEVs can consume significantly less fuel than CVs or 

HEVs and have a potential to play a key role in reducing U.S. 

petroleum consumption and carbon emissions in the future. 

Computer simulations using GPS data to generate in-use 

driving profiles are a convenient and useful way to evaluate 

vehicle performance, and fuel economy in the real world. 

These simulations can be used to size components such as the 

electric motors, internal combustion engines, and energy 

20 

15 

10 

5 

0 0	 0.02 0.04 0.06 0.08 

Power (kW/kg) 

Fig 10. Maximum power demand distribution for all vehicles in Austin and 

storage systems. They can also be useful in optimizing control 

strategies and other vehicle design choices. 

The historic UDDS and HWFET cycles by themselves 

provide a limited representation of real world driving, 

particularly when it comes to predicting fuel use for PHEVs 

and when used as a base for evaluating vehicle designs. This is 

largely due to aggressive driving and higher accelerations 

observed in the real world. The daily driving distance 

compared to the size of the energy storage system also plays a 

significant role in determining PHEV fuel use, whereas CV 

fuel economy is affected very little by daily driving distance. 

Increasing the size of the electric motor and energy storage San Antonio data sets 

The standard UDDS cycle has a maximum power demand 

of 0.019 kW/kg, which is well below the average of 0.052 

kW/kg shown in this study. In fact, only 1% of the vehicle 

days in this study had maximum power demand requirements 

that were less than the UDDS demand of 0.019 kW/kg. 

Therefore, if designing a vehicle to achieve similar all-electric 

range in the real world as it does on the UDDS cycle, the size 

of the electric motor must be increased significantly so that it 

requires assistance from the internal combustion engine less 

often. Fig. 11 shows the potential all-electric driving benefits 

of increasing the size of the electric motor based on the San 

Antonio and Austin data sets. Note, however, that in addition 

to increasing the electric motor size, the battery must be made 

more powerful at the same time to avoid turning on the 

engine. These changes will lead to a more expensive electric 

drive train. Frequency (%) 

100 

90 

80 

70 

system significantly beyond the power requirements of the 

UDDS and HWFET cycles is necessary to achieve substantial 

all-electric PHEV operation in the real world (i.e., driving 

where the engine never turns on). These changes would likely 

result in minimal measured performance difference on the 

UDDS and HWFET, and would increase the cost of the 

vehicle. The simulations in this study suggest that for once 

nightly charging the real-world fuel savings may also be 

minimal relative to a PHEV with comparable battery storage 

and an electric drive sized for the UDDS and HWFET. 

Opportunity charging is an effective way to increase the fuel 

savings of any PHEV variant; however, the higher electric 

power PHEV does seem to derive even greater benefit from 

opportunity charging than its lower-power blended-strategy 

counterpart. 

Future research efforts may include performing similar 

simulations using GPS data from other regions of the country 

and from multi-day data sets. Vehicle performance in different 

regions could be compared to determine if driving behavior 

varies based on region or geographic location. If there is a 

correlation, vehicles could be designed for optimal 

performance in the specific region in which they will be used. 

Demographic data could also help determine if different 

types of consumers have different driving habits. Again, if this 

is proven to be true, vehicles could be optimized for specific 

types of consumers. Preliminary studies by Argonne National 

Laboratory have begun to explore the relationship between 

population density and driving habits [5]. 

Future efforts should also incorporate changes in road grade 

into the simulations, as steep road grades will significantly 

impact vehicle power demands and in turn performance. This 

is especially important for very hilly regions, such as the 

mountain west. NREL is currently working to integrate this 

feature into its database of in-use driving profiles 

60 

50 

40 

30 

20 

10 

0

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 

Power (kW/kg) 

Fig. 11. Benefits of increasing motor-size 
 

 

 

 

 

 

ACKNOWLEDGMENT 

The authors gratefully acknowledge the programmatic 

support of the U.S. Department of Energy, Office of Energy 

Efficiency and Renewable Energy, Vehicle Technologies 

Program. Additional thanks go to Charlie Hall of the Texas 

Department of Transportation and David Pearson of the Texas 

Transportation Institute for providing access to the GPS travel 

survey data. 

REFERENCES 

[1]	 Ojah, M., and Pearson, D., 2006 Austin/San Antonio GPS-Enhanced 

Household Travel Survey, Texas Transportation Institute August 2008. 

[2]	 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, v. 110, pp. 255-266, 2002 

[3]	 Markel, T., Simpson, A., “Cost-Benefit Analysis of Plug-In Hybrid 

Electric Vehicle Technology”, WEVA Journal, Vol.1, 2006 

[4]	 SAE International, Surface Vehicle Information Report SAE J2841, 

Utility Factor Definitions for Plug-In Hybrid Electric Vehicles Using 

2001 U.S. DOT National Household Travel Survey Data, March 2009. 

[5]	 D. Santini, D., and Vyas, A., “Population Density, LDV Miles, Speed, 

Travel Time, and Fuel Use – Net Effects on Location Choice in the 

U.S.,” 88th Annual Meeting of Transportation Research Board, January 

2009.