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Contract No. DE-AC36-08GO28308

Simulated Real-World Energy 

Impacts of a Thermally Sensitive 

Powertrain Considering Viscous 

Losses and Enrichment

Preprint

Eric Wood, Jeff Gonder, and Sean Lopp

National Renewable Energy Laboratory

Forrest Jehlik

Argonne National Laboratory

To be presented at the SAE 2015 World Congress and Exhibition

Detroit, Michigan

April 21–23, 2015

Conference Paper

NREL/CP-5400-63255

February 2015
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Simulated Real-World Energy Impacts of a Thermally Sensitive Powertrain 

Considering Viscous Losses and Enrichment

Eric Wood, Jeff Gonder, and Sean Lopp

National Renewable Energy Laboratory

Forrest Jehlik

Argonne National Laboratory

Abstract

It is widely understood that cold ambient temperatures increase

vehicle fuel consumption due to heat transfer losses, increased 

friction (increased viscosity lubricants), and enrichment strategies 

(accelerated catalyst heating). However, relatively little effort has 

been dedicated to thoroughly quantifying these impacts across a large 

set of real world drive cycle data and ambient conditions. This work 

leverages experimental dynamometer vehicle data collected under

various drive cycles and ambient conditions to develop a simplified 

modeling framework for quantifying thermal effects on vehicle 

energy consumption. These models are applied over a wide array of 

real-world usage profiles and typical meteorological data to develop 

estimates of in-use fuel economy. The paper concludes with a 

discussion of how this integrated testing/modeling approach may be 

applied to quantify real-world, off-cycle fuel economy benefits of 

various technologies.

Introduction

A number of studies have demonstrated large negative impacts on 

fuel consumption and emissions due to colder ambient temperatures. 

Fuel enrichment and spark timing adjustments for catalyst light-off 

strategies, high rates of heat transfer, and non-linear viscosity of 

engine lubricants combine to negatively affect powertrain and drive 

cycle efficiency in cooler ambient conditions[1-6]

. These effects are 

also present in hybrid powertrains and may be magnified due to the 

powertrain operating at lower than optimal temperatures[7-9].

Additionally, regional drive cycle variability plays a large role in

overall vehicle efficiency [10]. Together, these factors represent 

important real-world considerations for powertrain design and 

efficiency. 

In 2012 new light-duty fuel economy standards were set for the North 

American market. The U.S. Environmental Protection Agency 

(EPA), National Highway Traffic Safety Administration (NHTSA), 

California Air Resources Board (CARB), original equipment 

manufacturers (OEMs), non-government entities, and other 

stakeholders collaborated to define 2012–2025 national fuel economy 

and greenhouse gas standards. The program called for a 4%–5% 

annual improvement in fuel economy with the final car and light-duty 

truck standard set to 54.5 miles per gallon (mpg) [11-12]

. Vehicle 

manufacturer fleet fuel economy certification for this Corporate 

Average Fuel Economy (CAFE) regulation is determined via a 

combination of on- and off-cycle methods. On-cycle certification is 

evaluated from weighted measured testing results of EPA’s test 

cycles [13]. On-cycle certification can be supplemented with off-cycle

credits representing estimated real-world vehicle efficiency not 

captured by on-cycle testing. The EPA and NHTSA currently 

recognize three pathways in which technologies can qualify for offcycle credit:

1) On-Table – An OEM gets a predefined credit value for 

technologies that are included in the credit table.

2) 5-Cycle – An OEM uses a predefined 5-cycle test 

methodology to determine credit value.

3) Alternative Method – An OEM may develop and justify a 

test methodology and credit value using real-world data.

The first two pathways are set and standardized. The third pathway, 

the alternative method, requires the use of real-world data in which 

OEMs and suppliers can demonstrate the viability of the technology 

and receive appropriate credit for its implementation. The work 

presented in this paper may serve as a framework to better understand 

and assess real-world fuel economy impacts for certain technologies. 

The approach outlined in this paper includes experimental vehicle 

chassis dynamometer tests conducted over a wide range of 

temperatures and loads, coupled with simplified models developed 

from the experimental data, that are used to demonstrate real-world 

economy over a broad range of drive cycles and ambient

temperatures. Experimental dynamometer tests were conducted at 

Argonne National Laboratory (ANL) on a 2011 Ford Fusion (2.5L,

6-speed automatic) over a broad range of temperatures. From this 

data, simplified response surface methodology [14] and lumped 

capacitance thermal models were developed to simulate vehicle

efficiency. These simplified models were integrated into the National 

Renewable Energy Laboratory’s (NREL) Transportation Secure Data 

Center (TSDC) [15] and coupled with U.S. typical meteorological data

[16]

. Goodness of fit statistics for the simplified modeling approach 

are presented with fleet fuel economy impacts quantified for multiple 

large combinations of real-world drive cycles and historical ambient 

conditions.
2 

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Approach and Test Setup

Tests were conducted at ANL’s Advanced Powertrain Research 

Facility (APRF) four-wheel-drive dynamometer test cell [17]. This 

dynamometer test facility is designed to handle light- to medium-duty 

sized (maximum 6,350 kg) vehicles capable of producing up to 373 

kW of wheel power. The test cell is EPA 5-cycle capable with 

ambient temperature capability from -7°C to +36°C. Additionally, the 

test cell can go to colder temperatures (for this work -17o

C). A 

vehicle fan located at the front of the test cell provides cooling 

airflow to the vehicle and its powertrain during testing. The 

simulation fan is a standard vehicle speed-matching fan that fulfills 

the test regulations for the SC03 air-conditioning (A/C) test. The cell 

also contains solar lamps simulating a multitude of solar loading 

conditions experienced in the real-world environment, with a typical 

target solar loading of 850 W/m2 at the base of the windshield and/or 

rear window. The vehicle is restrained to the dynamometer by a tiedown system. Two posts are bolted to T-slot rails on the floor, each 

post containing a height-adjustable system to restrain the vehicle and 

to remove vertical load from the wheels on the dynamometer roll. 

The test cell contains emission benches capable of bag measuring the 

criteria emissions total hydrocarbons, oxides of nitrogen, carbon 

monoxide, soot, as well as carbon dioxide for cycle fuel economy.

Additionally a turbine wheel fuel cart is used to accurately measure 

fuel. A data acquisition system is integrated that allows for a 

multitude of controller area network, analog, and digital signals to be 

collected. All data is collected and time aligned at 10 Hz frequency. 

The test vehicle and APRF test facility is shown in Figure 1. 

 

Figure 1. 2011 Ford Fusion test vehicle on APRF dynamometer for testing.

The vehicle was extensively instrumented to capture pertinent 

thermal and energy/power nodes. The engine was instrumented with 

K-type thermocouples to determine coolant, oil, inlet/exhaust 

temperature at various locations (exhaust port to pre-, mid-bed, and 

post-catalyst). Accuracy lies within 5oC below 1250oC of 

measurement, decreasing to 1.1o

C at 100oC. Additional K-type 

thermocouples were included in the transmission and vehicle interior. 

A strain based torque measurement system was installed on the 

engine flex plate and half shafts enabling measurement of engine out, 

transmission input, and transmission output torques and power levels. 

For the half shafts, full scale torque measurements were set at 3400 

Nm with a maximum static measured error of 0.2%. The flex plate 

full scale torque was set to 500 Nm with a maximum static measured 

error of 0.2%. Flow measurements for fuel are calibrated within 1.8% 

of measured error. Engine speed and transmission gear were recorded 

via CAN signals. Figure 2 represents the pertinent instrumentation 

and nodes completed for the work. 

Figure 2. Test vehicle instrumentation layout 

To minimize the number of tests completed while maximizing data 

fidelity, speed/load data from the vehicle was analyzed over the 

Urban Dynamometer Driving Schedule (UDDS), the Highway Fuel 

Economy Test (HWFET), and the aggressive US06 cycle. These tests 

were conducted at +20°C. Both the drive cycle velocities and engine 

speeds/loads are shown in Figure 4. By comparing the engine loading 

over the cycles it was determined that testing the vehicle using only 

the UDDS and US06 cycles would be sufficient in gathering a broad 

speed/load range, with the HWFET cycle speed/load ranges 

overlapping the other two cycles, therefore deemed unnecessary. 

Figure 3. 2011 Ford Fusion velocity profile over the UDDS, HWFET, and 

US06 cycles. 
3 

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Figure 4. 2011 Ford Fusion engine speed/load points over the UDDS, 

HWFET, and US06 cycles. 

In addition to a wide range of driving behavior, the scope of the work 

required a broad sweep of thermal conditions for assessment. Both 

the UDDS and US06 cycles were tested at ambient conditions 

ranging from -17°C to +35°C. Each of these cycles was tested from 

cold and hot start conditions. Here, a cold start is defined as the 

vehicle soaking overnight at the test cell temperature. This allowed 

for data at cooler powertrain operating states to be collected for both 

high and low power levels. For hotter tests, the vehicle air 

conditioner was not used so that the powertrain thermal effects would 

not be masked by the increased A/C compressor load. For cold tests, 

the vehicle heater was used and set on the median level. After the 

dynamometer test, cool-down data were recorded to determine the 

rate at which the coolant and lubricants cooled following peak 

operational temperature. Note that additional tests were conducted 

with the heater in the “on” and “off” positions, and no significant 

measurable load difference was recorded. Solar loading was not 

included in the testing as this has no impact on the component 

efficiency at a components given operational temperature (note: solar 

loading does, however, increases the rate of component warming thus 

impacts cycle efficiency). Table 1 lists the final testing matrix.

Table 1. Matrix of test conditions run at APRF (16 tests in total).

Drive Cycle UDDSx2, US06x2, non-operational cool down

Start Condition Hot Start, Cold Start

Test Cell Temperature -17°C, -7°C, +22°C, +35°C

Results

Figures 5 and 6 contrast measured engine oil temperature and 

integrated cycle fuel consumption over two repetitions of the UDDS 

drive cycle with the test cell ambient temperature ranging from -17°C 

to +35°C. As shown, the oil temperature rises from the initial ambient 

cold-start temperature during the cycle to the final steady state 

powertrain operational temperature. At -17°C, the engine oil steady 

state temperature is approximately 18°C cooler than the steady state 

temperature achieved at +35°C ambient. This is due to the increased 

convective heat transfer from the powertrain at cooler temperatures. 

These data are indicative of the results seen for the higher-load US06 

cycle (not shown for sake of brevity).

Figure 5. Engine oil warm-up temperatures (measured at dipstick) over two 

consecutive UDDS cycles following soak to four distinct ambient conditions. 

Comparing these results in Figure 6, a significant variation in fuel 

consumption relative to the temperatures is evident. The increase in 

fuel consumption for the cold start UDDS cycle at -17°C relative to 

the +35°C cold start is approximately 22%. Although considered 

relatively warm, a 3% fuel consumption decrease at +20°C relative to 

the +35°C UDDS test exists. These results further demonstrate the 

importance of understanding the real-world thermal effects on vehicle 

efficiency. This would assist in quantifying the effect that 

engineering solutions to reduce thermal losses could have on 

increasing vehicle efficiency. 

Figure 6. Cumulative fuel use over two consecutive UDDS cycles following 

soak to four distinct ambient conditions.
4

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Component Model Development

While many modeling approaches exist to estimate vehicle efficiency 

relative to road loads and powertrain component efficiency, less

effort has been expended characterizing the efficiency of internal 

combustion engines with respect to thermal state. The focus of this 

work centered on developing a simplified methodology to understand 

thermal effects in real-world driving conditions without using overly

complex models dependent upon accurate measurement of 

comprehensive component data. Not only are such data not generally 

available, but simulation of hundreds of thousands of real-world drive 

cycles from around the country using an unnecessarily complex 

model would currently be computationally intensive.

The following simplified component models provide a means for 

predicting component warm-up times and steady-state operating 

points sensitive to ambient conditions. The generalized approach 

involves response surface methodology models coupled with 

simplified lumped capacitance parameter-based models. Application 

of the Nelder-Mead non-linear optimization method solves for the 

unknown coefficients to minimize model error relative to vehicle 

measured results of fuel consumption over the drive cycles and 

thermal conditions listed in Table 1.

Engine Efficiency

A simplified model of the engine fueling rate as a function of engine 

power, engine oil temperature, and catalyst light-off was developed. 

The engine fueling rate is described by a third-order function 

response surface model with engine output power and engine oil 

temperature as model inputs[2]. The oil temperature in this case is a 

differential between the nominal operating temperature and the 

current oil temperature. The catalyst model is a simplified 

exponential decay equation that takes into consideration the catalyst 

temperature until light-off. This equation adds a decaying amount of 

fuel until a certain catalyst temperature is reached. The engine fueling 

equations are shown here for reference:

ffff = f1(Pooo, Tooo) + f2(Tcc ) (1)

f1(Pooo, Tooo) = a0 + a1,1Pooo3 + a1,2Pooo2 + a1,3Pooo +

a2,1d ooo3 + a2,2d ooo2 + a2,3d ooo + a3,1Pooo3 d ooo3 +

a3,2Pooo2 d ooo2 + a3,3Poood ooo (2)

f2(Tcc ) = m m(0, a1 ∗ ( 2∗(Tccc− 3) − 1)) (3)

d ooo = T0 − Tooo (4)

Following the Nelder-Mead solution of the coefficients, plots of the 

engine efficiency as a function of the engine oil temperature and 

power output were generated, as well as the time-based comparison 

between the model’s predicted fuel flow and the actual measured 

data. As an example of the time-based comparison, Figure 7 shows 

results for a 250-second UDDS cycle segment conducted at -17°C. 

As these results show, the model matches relatively well with the 

actual fuel flow measurements recorded during testing.

Figure 7. Example time series data of engine fueling rate (250-second section 

of UDDS). Measured test data from chassis dynamometer (solid black) 

overlaid with model estimate (dashed magenta).

Figure 8 contrasts the significant impact engine lubricant temperature 

has on efficiency. An island of optimal efficiency (approximately 

36%) exists at power levels between 80 kW and 110 kW and an oil 

temperature of 100°C. Yet at identical power levels, as engine oil 

temperature decreases to -17°C, engine efficiencies fall to between 

23%–26%, an approximate 33% decrease in overall efficiency for 

identical power outputs. This underscores the tremendous effect 

decreased ambient temperatures play on heat engine efficiency.

Figure 8. Optimized engine efficiency map as function of output mechanical 

power and engine oil temperature (considering operation over 16 test cycles).

These results may be best understood by noting the temperature 

effect on lubricant oil viscosity. Plotting the kinematic viscosity of 

unused engine oil versus temperature shown in Figure 9 [18], the 

strong non-linearity of viscosity relative to temperature indicates the 

excessive friction forces at lower temperatures for the engine. Similar 

results would be observed for transmission and gear oil as well. 
5

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Figure 9. Kinematic viscosity of engine oil as a function of temperature. Note 

logarithmic viscosity scale. Data taken from EES (engine-oil unused)[18]

. 

Finally, calculations were performed to determine the relative 

accuracy of the simplified models to predict overall drive cycle fuel 

consumption for the various drive cycles and temperatures used to 

develop the model. The results listed in Table 2 show that the

maximum predicted deviation from the measured results is 5.2%. The 

average cumulative error from the measured results of all the cycles 

is 2.2%, which falls within the range of experimental cycle-to-cycle 

dynamometer test uncertainty (typically within 3% for a given drive 

cycle [19]

).

Table 2. Cumulative fuel error between measured test data and model estimate

(positive error indicates model overestimation).

UDDS US06

Ambient Temp Cold Start Hot Start Cold Start Hot Start

-17°C -3.9% 1.5% -5.0% 2.5%

-7°C 0.5% 1.9% 2.5% 5.2%

+22°C -1.7% -1.3% -0.5% -0.9%

+35°C -4.1% -2.0% -0.5% -2.8%

Engine Oil Temperature

A simplified lumped capacitance model of engine oil temperature 

was developed, the parameters of which were fit to the experimental 

data. The model includes convective heat transfer from the oil to

environment, convective transfer between the oil and coolant, and the 

difference between the power in (fuel mass flow rate) and power out 

(engine brake torque and speed) of the engine. Additionally, the 

convective heat transfer term for the engine oil includes a vehicle 

velocity based function to reflect forced convection as the vehicle 

velocity increases. This methodology greatly simplifies the process 

while resulting in accurate estimations. The equations and relative 

predicted model accuracy are shown below and in Figure 10.

Ṫ

ooo = h1(T aa−Tooo)+h2(Tcooo−Tooo)+ α(Poo −P i)

mooo

(5)

h1 = a1vvvh + a2 (6)

Where:

ax = L L cc cc ccc f f

hx = L L ccc ccc h cc cc

mooo = M e

P i = P P e (ffff h e v )

Pooo = P P e (b b )

T a = A

Tc = C

Tooo = E

vvvh = V h v v

Figure 10. Example time series data of engine oil temperature (measured at 

dipstick). Measured test data from chassis dynamometer (solid black) overlaid 

with model estimate (dashed magenta).

Additionally, root mean square error analysis of the instantaneous 

model-predicted engine oil and actual measured temperature was 

completed. The results of this analysis (listed in Table 3) show that 

the average of the instantaneous root mean square error deviation 

from the actual temperature is 5.3°C. (For reference, the nominal 

engine oil operating temperature is approximately +100°C). These 

deviations do not last the entirety of the simulation, but rather for 

short durations of the simulated cycle. 

Table 3. Root mean square of instantaneous error between measured and 

model-estimated engine oil temperature. The nominal operating temperature 

of the engine oil is approximately 100°C.

UDDS US06

Ambient Temp Cold Start Hot Start Cold Start Hot Start

-17°C 5.2°C 7.2°C 5.4°C 2.0°C

-7°C 4.9°C 4.1°C 5.5°C 1.9°C

+22°C 5.7°C 6.1°C 8.5°C 2.5°C

+35°C 6.3°C 8.1°C 6.7°C 4.6°C
6

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

Engine Coolant Temperature

Similar to the engine oil model, a simplified lumped capacitance 

model of engine coolant temperature was developed. This model 

includes convective heat transfer from the coolant to environment, 

between the coolant and oil, and (similarly to the oil model) the 

difference between the power in and power out of the engine. 

Additionally, a logic operator is included that accounts for the 

thermostat opening which increases the heat transfer from the coolant 

to the ambient environment, and accounts for vehicle velocity and 

forced convective heat transfer. The equations and relative predicted 

model accuracy are shown below and in Figure 11 (see Equations 5 

and 6 for variable definitions). 

Ṫ

c = h1(T aa−Tc )+h2(Tooo−Tc )+ α(Poo −P i)

mc

(7)

Tc < Ts : h1 = a1vvvh + a2, : h1 = a3vvvh + a4 (8)

Figure 11. Example time series data of engine coolant temperature (measured 

at heater core inlet). Measured test data from chassis dynamometer (solid 

black) overlaid with model estimate (dashed magenta).

Similar to the oil, root mean square error analysis of the 

instantaneous model coolant and actual measured temperature was 

completed. The results of this analysis (listed in Table 4) show that 

the average of the instantaneous root mean square deviation from the 

actual coolant temperature is 6.9°C. (For reference, the nominal 

engine coolant operating temperature is approximately +90°C). As 

was the case for engine oil temperature, these deviations do not last 

the entirety of the simulation, but rather for short durations of the 

simulated cycle.

Table 4. Root mean square of instantaneous error between measured and 

model-estimated engine coolant temperature. The nominal operating 

temperature of the engine coolant is approximately 90°C.

UDDS US06

Ambient Temp Cold Start Hot Start Cold Start Hot Start

-17°C 7.0°C 9.2°C 7.4°C 3.9°C

-7°C 5.1°C 5.4°C 6.9°C 3.3°C

+22°C 6.8°C 4.3°C 12.5°C 5.9°C

+35°C 6.5°C 6.0°C 10.9°C 9.1°C

Exhaust Catalyst Temperature

The final simplified model is a catalyst thermal model that is used to 

account for fueling rate enrichment prior to catalyst light off. A

simplified lumped capacitance method was applied that included a 

convective term accounting for heat transfer away from the catalyst 

to the ambient environment, as well as a fraction of the difference in 

power between the energy into and out of the engine. As was the case 

with the coolant and oil, a vehicle velocity term is added to account 

for forced convection. The equations are listed below, as well as an 

example of the simultaneous modeled catalyst temperature versus 

measured results (see Equations 5 and 6 for variable definitions).

Ṫ

cc = h(T aa−Tccc)+ α(Poo −P i)

mccc

(9)

h = ah1vvvh + ah2 (10)

α = aα1Tcc + aα2 (11)

Figure 12. Example time series data of exhaust catalyst temperature 

(measured pre-catalyst). Measured test data from chassis dynamometer (solid 

black) overlaid with model estimate (dashed magenta).

As in the case for oil and coolant, instantaneous root mean square

error analysis was conducted to determine the relative accuracy of the 

modeled catalyst temperature against measured results. The results 

from Table 5 show that the average of the instantaneous root mean 

square error between the model-predicted and actual temperature is 

35.1°C. (For reference, the nominal exhaust catalyst operating 

temperature is approximately +600°C).

Table 5. Root mean square of instantaneous error between measured and 

model-estimated exhaust catalyst temperature. The nominal operating 

temperature of the exhaust catalyst is approximately 600°C.

UDDS US06

Ambient Temp Cold Start Hot Start Cold Start Hot Start

-17°C 69.5°C 26.9°C 70.3°C 24.4°C

-7°C 54.5°C 24.8°C 53.5°C 23.6°C

+22°C 37.5°C 19.8°C 50.1°C 24.6°C

+35°C 34.6°C 20.0°C 38.4°C 24.6°C
7

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Real-World Simulations

Calculation of vehicle road loads in this analysis was performed 

using NREL’s Future Automotive Systems Technology Simulator 

(FASTSim) [20]. FASTSim is a vehicle simulation tool used to 

evaluate the impact of various technologies on vehicle performance, 

cost, and utility in conventional and advanced technology 

powertrains. FASTSim calculates the power necessary to meet a 

given speed trace and overcome road loads (rolling, aerodynamic, 

kinetic, and potential) while considering component limitations, 

system losses, and auxiliary loads. Given the required engine output 

power at each time step the engine fuel use is calculated via the 

thermally sensitive efficiency map as previously detailed, while the

differential equations describing the thermal response of engine oil, 

engine coolant, and exhaust catalyst are evaluated.

Real-world drive cycle data for this study are sourced from the TSDC

[15]

. Specifically, 1-Hz travel histories one to seven days in duration 

are queried from vehicles across the United States. These data 

represent a composite of several data collection efforts from 

metropolitan planning organizations across the country as 

documented on the TSDC website (www.nrel.gov/tsdc). Figure 12

shows a geographic distribution of TSDC real-world drive cycle data.

Figure 13. Geographic coverage of 1Hz drive cycle data currently available in 

the Transportation Secure Data Center (TSDC).

Figure 14 and Figure 15 present real-world trip start and soak time 

data from the TSDC to inform the frequency of cold-start events and 

their confluence with daily swings in ambient temperature. A 

composite of approximately 146,000 trips (consisting of second-bysecond speed data) collected across the United States reveals a 

distribution of trip start times that coincides with traditional traffic 

patterns (low volumes overnight, a sharp spike in the morning rush 

hour, a smaller spike around the noon lunch hour, highest volumes 

around afternoon rush hour, and a slow decline into the evening).

Figure 14. TSDC Trip start-time distribution (by hour of day).

Figure 15 takes the same 146,000 TSDC trips and creates a 

distribution with respect to soak time (vehicle time in key-off state 

between trips). Approximately one-third of trips are observed to start 

following a soak time of greater than four hours; these are trips that 

can definitively be classified as cold starts. The remaining two-thirds 

of trips can be thought of as pseudo cold starts given that most engine 

compartments can be expected to retain some appreciable amount of 

heat during a park event of this duration. Notably, 20% of trips are 

observed to start following a soak time of less than 15 minutes. While 

cold-start effects are generally mitigated by short soak times, these 

trips represent the greatest potential for thermal retention 

technologies given the exponential response of passive engine 

cooling to ambient.

Figure 15. TSDC soak time distribution.

The trip counts presented in Table 6 as a convolution of the data from 

Figure 14 and Figure 15 reveal the correlation between trip start and 

soak times. The applied conditional formatting provides a visual 

sense of trip count magnitudes. For example, the most frequently 

observed trip starts from the data analysis occur in the 7:00 a.m. hour 

following a soak of greater than 4 hours (likely the first trip of the 

day following an overnight soak). Other noteworthy observations 

include the relatively normal distribution of trips with less than 15 

minutes of soak around the 3:00 p.m. hour (potentially characterized 

by a series of short errand-type trips).
8

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Table 6. TSDC trip counts binned by trip start time and vehicle soak time 

prior to departure. Conditional formatting applied to express cell values as a 

color gradient.

Ambient temperature data are assembled from NREL’s Typical 

Meteorological Year Database (TMY3) [16]

. The TMY3 database 

contains hourly ambient temperature and solar irradiation data for 

1,020 distinct U.S. weather stations. Typical meteorological patterns 

are synthesized from representative days across historical data from 

the 1991–2005 timeframe to form a 365-day history with hourly 

values for each site. Figure 16 shows a map of average yearly 

ambient temperatures across the continental United States, and Figure 

17 shows hourly ambient temperature data from three sample U.S. 

climates.

Figure 16. TMY map of average ambient temperatures for the contiguous 

United States (hot climates in red, cold in blue).

Figure 17. TMY hourly ambient temperature data for Phoenix, Baltimore, and 

Minneapolis.

By bringing global positioning system travel histories into a shared 

computing environment with the TMY3 data, it is possible to align 

real-world drive cycle data with representative ambient temperature 

data from any major U.S. city at any time of year. This flexibility 

enables realistic evaluations of simulated vehicle efficiency relative 

to large amounts of drive cycle and climate data.

Results

Introductory analysis was conducted to investigate the effect ambient

temperature has on vehicle fuel consumption when coupled with realworld drive cycle data. As an example, one vehicle from the TSDC 

data set was selected to demonstrate the effects. Figure 18 shows a 

time series for this vehicle driving a one-week period from January 2 

to January 9 as well as a one-hour magnified trip occurring on 

January 2.

This figure plots vehicle speed along with oil, coolant and ambient 

temperatures, and illustrates the modeled response of coolant and oil 

temperature rise relative to the power requirements of the trip—both 

the initial rise at the start of each trip segment and the convective 

cooling period after the vehicle is keyed off. In the modeled response, 

the coolant temperature rise leads the oil temperature rise (as is the 

case in the experimental data). Given the relationship between oil 

temperature and viscosity shown in Figure 9, this means that viscous 

friction losses would be underestimated during warm-up if engine 

coolant were instead used as the sole temperature indicator. 

Additionally, the connection between coolant and engine oil allows 

the model to capture the increased heat transfer losses as the 

thermostat opens, reducing the thermal load on the engine oil and 

thus more accurately predicting the engine thermal state. 
9

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Figure 18. Time series examples of thermal simulations predicting engine oil 

and coolant temperatures. Real-world drive cycle data overlaid with TMY 

climate data. Results are show at two horizontal zoom windows: one week 

(top), and one hour (bottom).

Fleet Statistics

Having established a modeling framework for quantifying real-world 

efficiency impacts of engine cold-start events, approximately 40 

million miles of driving were simulated given combinations of drive 

cycles (recorded from thousands of U.S. vehicles), local ambient 

temperature profiles (three representative climates), and weeks of the 

year (52 in total). Ambient temperature statistics were compiled for 

U.S. cities from the TMY3 database and climates representing 

extreme hot and cold scenarios were identified as Phoenix, Arizona

and Minneapolis, Minnesota respectively. Ambient temperature data 

from Baltimore, Maryland were selected as a nationally 

representative climate scenario.

Figure 19 shows simulation results with distance-weighted, fleet 

average fuel economy (in miles per gallon of gasoline) plotted by 

climate and week of year. The first trend identified is the intuitive 

fluctuation of fuel economy with season. Average fuel economy is 

shown to decrease by 1–3 mpg during cold winter months as a result 

of increased viscosity from cooler engine oil temperatures and more 

frequent fuel enrichment events to accelerate catalyst heating. These 

are likely conservative estimates of reduced fuel economy during 

cold weather months, as additional effects including increased 

transmission oil viscosity, decreased tire pressure, increased air 

density, and changes in road surface resistance (e.g., snow-covered 

roads) are not considered in this analysis.

Equally intuitive is the variation between generally hot and cold 

climates. Simulated Minneapolis fuel economy is shown to be 

approximately 8% lower during winter months relative to Phoenix, 

and 2% lower during summer months. In reality, fuel economy 

during summer months is potentially greater in Minneapolis than in 

Phoenix, as incremental mechanical loads associated with belt-driven 

cabin A/C systems have not been included in this analysis.

Figure 19. Distance weighted, fleet average miles per gallon by week of year 

for three U.S. climates.

To highlight variation in fuel economy between drivers, Figure 20

presents efficiency results for all vehicle histories simulated in the 

Baltimore climate, with individual box plots for each week of the 

year. In addition to the aforementioned seasonal fuel economy 

fluctuation, these box plots reveal significant variation between

different drive cycle histories with 25th and 75th percentile drivers 

differing by approximately 5 mpg (~19%). Drive cycle characteristics 

such as driving speeds, acceleration rates, and percent idle times have 

been shown to significantly impact simulated fuel economy in 

previous studies[10]. The featured modeling aspect of this work 

(engine efficiency sensitivity to thermal state) further exacerbates

driver-to-driver fuel economy variation. For example, a driving 

pattern with several short trips in a week would experience depressed 

fuel economy as a result of the engine spending a large percentage of 

operational time under “cold” conditions. These effects are magnified 

in driving patterns with extended dwell times between trips as 

components are afforded ample time to decay to ambient 

temperatures.
10

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Figure 20. Distribution of simulated vehicle history mpg by week of year 

(approximately 50% of the data is bounded between 23 and 28 mpg).

Simulated vehicle efficiency data were then aggregated into nine 

distinct combinations of drive cycle datasets and climate scenarios in 

Figure 21 to highlight the incremental energy impacts of modeling 

engine cold starts. The large composite drive cycle data set presented 

in Figure 19 and Figure 20 is now geographically grouped and 

anonymously named as drive cycle sets 1, 2, and 3 (statistics for the 

three aggregated drive cycle datasets can be found in Table 7). The 

relatively more aggressive drive cycle characteristics of set 3 result in 

the highest fleet fuel consumption, while set 1 on the whole features 

less aggressive driving and results in lower fuel consumption. Each 

of the drive cycle datasets is overlaid with ambient temperature data 

from the three selected climates with the familiar trends of increased 

fuel consumption with low average ambient temperature operation.

Figure 21 further shows that the incremental fuel consumption 

contributions from engine thermal state and enrichment effects 

account for 4.8% and 2.7% of total simulated fuel use respectively. 

Taken in aggregate, this is an increase of 7.5% on average and 

underscores the importance of considering thermal effects in analysis 

of real-world fuel economy.

Using FASTSim in combination with engine efficiency and thermal 

models specific to the 2011 Ford Fusion under test, the full EPA 5-

cycle test procedure is simulated (including cold FTP and SC03), 

weighted, and scaled per EPA documentation to determine the 

modeled vehicle’s certification fuel economy. Not surprisingly, the 

range of simulated real-world fuel consumption values generally falls 

short of the EPA estimate. This result is likely a byproduct of several 

real-world effects presently unaccounted for in the modeling 

environment, including cabin A/C loads and additional powertrain 

thermal sensitivities (transmission, rear differential, etc.). Accounting 

for these remaining real-world effects remains a goal of future 

research.

Figure 21. Relative fuel consumption model contributions shown for various 

combinations of drive cycle datasets and year-long TMY climate data. Results 

are compared with the simulated EPA 5-cycle test procedure.

Table 7. Distance-weighted drive cycle metrics for the three anonymized sets 

used in Figure 18.

DC Set 1 2 3

Vehicle Count 413 925 587

Total Miles 16,335 197,400 35,350

Average Driving Speed, mph 39.6 39.2 37.9

Average Positive Acceleration, mph/s 0.762 0.813 0.978

Conclusions

To better understand real-world thermal and driving effects on 

vehicle efficiency, this effort developed and implemented a novel 

approach of simplifying experimental data into a predictive model. 

The approach employed lumped capacitance and response surface 

modeling methodologies to predict vehicle fuel consumption as a 

function of velocity and thermal state. The effort focused on 

developing a simplified, yet accurate, means of predicting vehicle 

fuel usage over a broad range of drive cycles and ambient 

temperatures. On average, the simplified model proved able to 

predict UDDS and US06 fuel consumption in ambient temperatures 

from -17°C to +35°C to within 2.2% of the actual test measured 

values.

These simplified models were then run through a large data set of 

1-Hz travel histories ranging from one to seven days in duration from 

vehicles across the United States. Additionally, the models and travel 

histories were coupled with a typical meteorological year database in 

order to capture the interplay of real-world driving behavior (speeds, 

trip times, distances, etc.) with daily and seasonal ambient 

temperature variation. Using Phoenix, Baltimore, and Minneapolis as 

regional examples, the effort modeled three different fleet data sets to 

better understand seasonal and regional fuel consumption differences. 

The results show significantly higher fuel consumption in the colder 

Minneapolis climate relative to Phoenix. Average simulated realworld fuel use associated with viscous losses in the engine and fuel 

enrichment was found to be 7.5%.

As regulated fuel economy standards continue to climb, both in the 

United States and globally, the ability of certification test procedures

to accurately represent on-road vehicle efficiency continues to 
11

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increase in importance, especially in the case of emerging 

technologies. OEMs interested in receiving appropriate credit for 

technologies with significant off-cycle potential will be tasked with 

justifying fuel economy claims using large samples of statistically 

representative data. The integrated testing/modeling approach 

presented in this paper represents one such potential solution to 

quantifying real-world fuel economy.

Computational models of vehicle efficiency calibrated on highresolution laboratory data and evaluated over large databases of 

driving behavior and typical meteorological conditions provide a 

controlled process for generating repeatable estimates of on-road fuel 

economy with reasonable investments of time and effort. While 

modeling in this study focused on a baseline, conventional vehicle, 

the methodology can be easily applied to evaluate technologies with 

potential off-cycle savings (e.g., engine encapsulation, a highefficiency alternator, or engine start/stop). Analysis over equivalent 

drive cycle and ambient temperature data would enable direct

comparison with the baseline conventional vehicle.

Going forward, the authors intend to build upon the described

methods by:

• Incorporating additional model sensitivities to capture 

transmission thermal efficiency and cabin A/C loads.

• Validating the model’s real-world fuel economy estimates

to on-road data from an instrumented vehicle.

• Weighting results to generate fuel economy estimates

statistically representative of the national fleet based on 

distributions of available drive cycle data and the location 

of vehicle populations relative to meteorological data.

References

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Administration Finalizes Historic 54.5 mpg Fuel Efficiency 

Standards,” http://www.whitehouse.gov/the-pressoffice/2012/08/28/obama-administration-finalizes-historic-545-

mpg-fuel-efficiency-standard, accessed November 19, 2014.

12. U.S. Federal Register, Environmental Protection Agency, 

Department of Transportation, National Highway Traffic Safety 

Administration “2017 and Later Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and Corporate Average Fuel 

Economy Standards; Final Rule,” 

http://www.gpo.gov/fdsys/pkg/FR-2012-10-15/pdf/2012-

21972.pdf, accessed October 1, 2014.

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Agency “Fuel Economy Tests: Detailed Test Information,” 

http://www.fueleconomy.gov/feg/fe_test_schedules.shtml, 

accessed November 19, 2014.

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Methodology: Process and Product Optimization Using 

Designed Experiment,” Wiley-Interscience, 1995.

15. “Transportation Secure Data Center." (2013). National 

Renewable Energy Laboratory. Accessed October 1, 2013: 

www.nrel.gov/tsdc.

16. National Renewable Energy Laboratory, “National Solar 

Radiation Database, Typical Meteorological Year Database 3,”

Golden, CO, http://rredc.nrel.gov/solar/old_data/nsrdb/1991-

2005/tmy3/ , accessed July 2011.

17. Argonne National Laboratory, Transportation Technology R&D 

Center “Advanced Powertrain Research Facility (APRF),” 

http://www.transportation.anl.gov/facilities/aprf.html, accessed

October 1, 2014.

18. Data generated using Engineering Equation Solver (EES),

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properties of unused engine oil. http://www.fchart.com/ees/

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accessed October 1, 2013.
12

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

Contact Information

Advanced Powertrain Research Facility

Argonne National Laboratory

Energy Systems Division

9700 S. Cass Ave. 

Argonne, IL 60439

Forrest Jehlik

(630) 252-6403

fjehlik@anl.gov

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

Jeffrey Gonder

(303) 275-4462

jeff.gonder@nrel.gov

Sean Lopp

(303) 275-4549

sean.lopp@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 Lee Slezak and David Anderson for their guidance.

In addition to U.S. DOE support, data archiving and processing in the 

TSDC are also supported by the U.S. Department of Transportation’s 

Federal Highway Administration. The authors would specifically like 

to thank Elaine Murakami for her guidance and support.

Use of Argonne National Laboratory’s Advanced Powertrain 

Research Facility and the National Renewable Energy Laboratory’s 

FASTSim software (both developed under funding from the U.S. 

Department of Energy’s Vehicle Technologies Office) were critical 

to the completion of this study. Special thanks to Eric Rask (ANL), 

Mike Douba (ANL), and Aaron Brooker (NREL).
13

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Definitions/Abbreviations

°C Degrees centigrade

A/C Air Conditioning

ANL Argonne National Laboratory

APRF Advanced Powertrain Research Facility 

(ANL)

DOE

EPA

U.S. Department of Energy

U.S. Environmental Protection Agency

FASTSim Future Automotive Systems Technology 

Simulator

HWFET Highway Fuel Economy Test (EPA defined)

Hz Hertz frequency

mpg miles per gallon

NHTSA National Highway Traffic Safety 

Administration

NREL National Renewable Energy Laboratory

OEM Original Equipment Manufacturer

SC03 Supplemental air-conditioning dynamometer 

test procedure (EPA defined)

TMY Typical Meteorological Year 

TSDC Transportation Secure Data Center

UDDS Urban Dynamometer Driving Schedule 

(EPA defined)

UDDSx2 Back-to-back UDDS driving tests

US06 US06 dynamometer driving schedule (EPA 

defined)

US06x2 Back-to-back US06 dynamometer driving 

tests