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Contract No. DE-AC36-08GO28308

Impact of Paint Color on Rest 

Period Climate Control Loads 

in Long-Haul Trucks

Preprint

J. Lustbader, C. Kreutzer, and M. Jeffers

National Renewable Energy Laboratory

S. Adelman and S. Yeakel

Volvo Group Trucks Technology

P. Brontz, K. Olson, and J. Ohlinger 

PPG Industries

To be presented at SAE 2014 World Congress and Exhibition

Detroit, Michigan

April 7 – 11, 2014

Conference Paper

NREL/CP-5400-61084

February 2014
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Impact of Paint Color on Rest Period Climate Control Loads in Long-Haul 

Trucks

J. Lustbadera, C. Kreutzera, M. Jeffersa, S. Adelmanb, S. Yeakelb, P. Brontzc, K. Olsonc, and J.

Ohlingerc

a National Renewable Energy Laboratory b Volvo Group Trucks Technology c PPG Industries

Abstract

Cab climate conditioning is one of the primary reasons for 

operating the main engine in a long-haul truck during driver 

rest periods. In the United States, sleeper cab trucks use 

approximately 667 million gallons of fuel annually for rest 

period idling. The U.S. Department of Energy’s National 

Renewable Energy Laboratory’s (NREL) CoolCab Project 

works closely with industry to design efficient thermal 

management systems for long-haul trucks that minimize 

engine idling and fuel use while maintaining occupant comfort.

Heat transfer to the vehicle interior from opaque exterior 

surfaces is one of the major heat pathways that contribute to 

air conditioning loads during long-haul truck daytime rest 

period idling. To quantify the impact of paint color and the 

opportunity for advanced paints, NREL collaborated with Volvo 

Group North America, PPG Industries, and Dometic 

Environmental Corporation. Initial screening simulations using 

CoolCalc, NREL’s rapid HVAC load estimation tool, showed 

promising air-conditioning load reductions due to paint color 

selection. Tests conducted at NREL’s Vehicle Testing and 

Integration Facility using long-haul truck cab sections, “test 

bucks,” showed a 31.1% of maximum possible reduction in rise 

over ambient temperature and a 20.8% reduction in daily 

electric air conditioning energy use by switching from black to 

white paint. Additionally, changing from blue to an advanced 

color-matched solar reflective blue paint resulted in a 7.3% 

reduction in daily electric air conditioning energy use for 

weather conditions tested in Colorado. National-level modeling 

results using weather data from major U.S. cities indicated that 

the increase in heating loads due to lighter paint colors is much 

smaller than the reduction in cooling loads.

Introduction

Cab climate conditioning is one of the primary reasons for 

operating the main engine in a long-haul truck during driver 

rest periods. In the United States, sleeper cab trucks use an 

estimated 667 million gallons of fuel annually for rest period 

idling [1]. This represents 6.8% of the total estimated long-haul 

truck fuel use. An additional 2.4 billion gallons of fuel are used 

annually for commercial truck workday idling [1]. Idling 

represents a zero freight efficiency operating condition for the 

truck. With the recent high prices of diesel, fuel is one of the 

largest trucking costs per mile at 35% of the total [2]. 

Therefore, the increasing cost and cost volatility of fuel

provides a significant financial incentive to reduce fuel use. 

Recent federal, state, and city anti-idling regulations [3] are 

providing further incentive to reduce truck idling. One example 

is the idle reduction technology credit in the Heavy-Duty 

Greenhouse Gas Emissions Standards, which are set to begin 

in 2014 [4]. 

An opportunity exists to reduce fuel use and emissions 

associated with idling by reducing thermal loads and improving 

the efficiency of climate control systems. Enhancing the 

thermal performance of cab/sleepers will enable smaller, 

lighter, and more cost-effective idle reduction solutions. In 

addition, if the fuel savings from new technologies provide a 

one- to three-year payback period [5], fleet owners will be 

economically motivated to incorporate the new technologies. 

Therefore, financial incentive provides a pathway to rapid 

adoption of effective thermal load and idle reduction solutions.

The U.S. Department of Energy’s National Renewable Energy 

Laboratory’s (NREL’s) CoolCab project is researching efficient 

thermal management systems to maintain cab occupant 

comfort without the need for engine idling. The CoolCab 

project uses a system-level approach that addresses thermal 

loads, designs for occupant thermal comfort, and maximizes 

equipment efficiency. In order to advance the goals of the 

CoolCab project and the broader goals of increased national 

energy security and sustainability, the CoolCab team works 

closely with industry partners to develop and apply 

commercially viable solutions to reduce national fuel use and 

industry costs. In order to reduce thermal and resulting idle 

loads in long-haul trucks, NREL has identified air volume 

management, conductive pathways, and solar envelope as 

focus areas for potential technologies. A previous study 

published by the group focused on the effect of insulation 

packages on heating and cooling idle loads [6]. The present 

study focuses on reducing the solar envelope loads through 

the application of production and advanced paint colors. Prior 

studies on light-duty vehicles showed opportunity for reducing 

cooling load by changing vehicle exterior paint color. Rugh et 

al. showed a 28% of theoretical maximum breath air 

temperature reduction by applying aluminum foil to exterior 

opaque surfaces, quantifying the maximum potential

improvement [7]. Levinson et al. showed a 4°–6°C reduction in 

cabin air temperature with a silver car compared to a black car 

[8]. The larger opaque-to-glazing surface ratio of long-haul 

sleeper cabs compared to light-duty vehicles suggests a 

potentially higher sensitivity of thermal loads to opaque surface 

treatment. This study investigates the thermal loads of a longhaul sleeper cab in engine-off rest period operation using 

black, white, blue, and an advanced color-matched solar
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reflective paint. This advanced paint is designed to have a very 

similar color as traditional paint in the visible spectrum while 

having an increased reflectivity in the infrared region. Solar 

reflective paint could provide a means to reduce loads while 

maintaining paint color selection for branding and aesthetic 

purposes.

Approach

NREL collaborates with original equipment manufacturers and 

suppliers to develop and implement a strategic three-phase 

approach capable of producing commercially viable solutions 

to enable idle reduction systems. This three-phased approach 

was developed to evaluate commercially available and 

advanced vehicle thermal management and idle reduction 

technologies. The three phases are Baseline Testing and 

Model Development, Thermal Load Reduction, and Idle 

Reduction. Each phase features application of NREL’s suite of 

thermal testing and analysis tools.

In Phase I, Baseline Testing and Model Development, thermal 

data is collected on a test and control vehicle simultaneously. 

Several days of data are collected for each test procedure 

under varying weather conditions. The data is used to calibrate 

the control vehicle to represent an unmodified baseline test 

vehicle. Once the control vehicle is calibrated to predict the 

performance of the test vehicle, validation tests are conducted. 

Validation data is collected with the control and test vehicles 

under unmodified baseline conditions. Calibration coefficients 

are applied to the control vehicle validation data, and the 

results are used to confirm the accuracy of the calibration. 

Baseline performance data of the test vehicles is also used for 

development and validation of CoolCalc models [9]. 

In Phase II, Thermal Load Reduction, CoolCalc parametric 

studies are used as a screening tool for potential thermal load 

reduction technologies. Reductions in cab/sleeper thermal 

loads are quantified through experimental investigation of 

selected commercial and advanced technologies identified 

from CoolCalc modeling. 

In Phase III, Idle Reduction, the most promising of the 

evaluated technologies are researched further by closely 

collaborating with industry partners and their suppliers to 

design and evaluate cab thermal packages that improve 

thermal performance, reduce climate control loads, and enable 

market penetration of idle reduction systems. In this phase, 

vehicles are equipped with commercial and advanced cab 

thermal management packages coupled with an idle reduction 

system. NREL experimentally characterizes the impact of 

these technologies on idle loads. CoolCalc analysis and 

vehicle simulations are also used to characterize the reduction 

in idle loads and fuel consumption over a wide range of use 

and environmental conditions.

The test program was conducted at NREL’s Vehicle Testing 

and Integration Facility (VTIF), shown in Figure 1, during the 

months of May through September. The facility is located in 

Golden, Colorado, at an elevation of 5,997 feet at latitude 39.7 

N and longitude 105.1 W. The experimental setup included an 

NREL-owned test truck and two cab test “bucks.” Both bucks 

were the cab section from a representative truck in current 

production provided by Volvo Trucks North America. One buck 

was utilized as the control buck while the other was 

experimentally modified.

For the experimental setup, the truck, test buck, and control 

buck were oriented facing south and separated by a distance 

of 25 feet to maximize solar loading and minimize shadowing 

effects. To keep the buck firewalls from receiving direct solar 

loads, a firewall shade cloth was implemented on both the 

control and test bucks. In each vehicle, five curtains were 

available for use depending on the test being conducted. The 

curtains available were the privacy, cab skylight, sleeper, and 

two bunk window curtains.

Figure 1: NREL’s Vehicle Testing and Integration Facility

A National Instruments SCXI data acquisition system was used 

to record measurements at a sampling frequency of 1.0 Hz, 

which was averaged over one-minute intervals. Among the 

three vehicles, over 140 calibrated type K thermocouples were 

used. An isothermal bath and reference probe were used for 

thermocouple calibration, achieving a U95 uncertainty of 

±0.32°C in accordance with ASME standards [10]. Air 

temperature sensors were equipped with a double concentric 

cylindrical radiation shield to prevent errors due to direct solar 

radiation.

Weather data was collected from both NREL’s Solar Radiation 

Research Laboratory and NREL’s VTIF weather station [11], 

which together feature more than 160 instruments dedicated to 

high-quality measurements of solar radiation and other 

meteorological parameters.

Thermal soak tests were conducted to evaluate the impact of 

paint in an engine-off solar loading condition on interior air 

temperatures in a test truck or buck (T�modified) compared to 

interior air temperatures in the baseline buck (T�baseline). During 

summer operation with passive vehicle thermal load reduction 

technologies, the best possible steady-state performance is to 

reduce the interior temperature to ambient temperature. The 

percent of maximum possible temperature reduction (β) was 

developed to describe this maximum possible reduction in 
3 

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interior air temperature rise above ambient (T�ambient), as 

described in Equation 1. A β value of 0% indicates that the 

technology under evaluation did not change the rise over 

ambient temperature, while a β value of 100% indicates the 

technology reduced the interior air temperature in the modified 

vehicle to equal the temperature of ambient air in the 

environment. 

%100

baseline ambient

baseline modified ⋅ −

− = TTTT β (1) 

For the evaluation of β, the interior air temperature was 

determined as a volume weighted average of the combined 

sleeper and cab air temperatures. The average interior cab air 

temperature was calculated by averaging six thermocouples 

with four located in accordance with the American Trucking 

Association Technology Maintenance Council’s recommended 

practice RP422A [12] as shown in Figure 2A. Similarly, 

average sleeper air temperature was calculated by averaging 

eight thermocouples with six located in accordance with 

RP422A, illustrated in Figure 2B. The addition of two 

thermocouples located in both the cab and sleeper air spaces 

improved the accuracy of the average air temperature by more 

accurately capturing the air temperature distribution. During 

testing, it was determined that the cab footwell air temperature 

measurements were exposed to occasional direct solar 

radiation. Due to the increased variability that would occur in 

the calculation of average interior air temperature, these two 

measurements were omitted from the calculation.

For the thermal soak testing, data was collected for a time 

interval from 5:00 a.m. to 3:00 p.m. MST. During baseline 

thermal soak measurements, all privacy curtains were 

removed. The thermal soak performance of the bucks in their 

baseline conditions was used to characterize and calibrate the 

inherent differences between the two bucks. Calibration was 

accomplished by collecting four days of baseline data and 

generating a time-of-day dependent correction factor between 

the control buck and test buck. Solar load intensity peaked at 

approximately 12:00 p.m. daily during thermal soak testing. In 

addition, peak differential temperatures were found to occur 

within the 11:00 a.m. to 1:00 p.m. MST time interval 

corresponding to this peak solar load. Therefore, interior air 

and ambient temperatures from 11:00 a.m. to 1:00 p.m. MST 

were used for the calculation of β. 

For quantifying idle load reduction of paint, daytime rest period 

air conditioning (A/C) tests were conducted on an electric noidle A/C system. A 2,050 W (7,000 Btu/hr) Dometic electric A/C 

system [13] was installed in the sleeper compartment of each 

vehicle. For A/C experimentation, all five curtains were utilized 

on the control and test buck. All curtains were employed to 

match the expected standard configuration during a rest period 

operation. The test period was defined as A/C system first-on 

to last-off to quantify the daily A/C energy consumption. A 

standard battery-powered A/C system containing four 1,500 

W·h lead acid batteries and weighing a total of 132 kg (291 lb) 

was used for the calculation of system improvements in the 

results section. A/C electrical power consumption was 

measured using a Load Controls Incorporated model UPC 

adjustable capacity power sensor. The power sensor was 

calibrated to ±15 W. Air conditioning systems were controlled 

to a target sleeper air temperature of 22.2°C (72°F). Calibration 

of the modified buck A/C system was performed by collecting 

multiple days of baseline data. A clear solar day with 

insignificant cloud cover was required for data to qualify as a 

baseline test day.

NREL’s CoolCalc software was used to model the impact of 

paint on vehicle temperature and heating, ventilation, and air 

conditioning (HVAC) loads. CoolCalc is an easy-to-use, 

simplified, physics-based HVAC load estimation tool that 

requires no meshing, has flexible geometry, excludes 

unnecessary detail, and is less time-intensive than more 

detailed computer-aided engineering modeling approaches. 

For these reasons, it is ideally suited for performing rapid 

trade-off studies, estimating technology impacts, and sizing 

preliminary HVAC designs. CoolCalc complements more 

detailed and expensive computer-aided engineering tools by 

first exploring the design space to identify promising 

technologies and specific parameters that require deeper 

investigation.

CoolCalc, described in more detail in [9], was built on NREL’s 

original OpenStudio platform as a plug-in extension for the

SketchUp three-dimensional design software (now owned by 

Trimble), and has been adapted to better suit the 

transportation industry. DOE’s EnergyPlus software 

(developed for building energy modeling) is used as the heat 

transfer solver for CoolCalc.

For the evaluation of paint color, black, blue, a color-matched 

solar reflective blue, and white paints were selected. The paint 

was applied with a primer, basecoat, and clear coat 

process. Both the blue and solar reflective blue basecoats 

were applied to a white basecoat adding an additional paint 

layer for those configurations. While there are different 

approaches to formulating coatings for increased IR reflection, 

the blue color used was made with pigmentation that would 

impart a very similar color in the visible spectrum but has

increased transparency to wavelengths in the IR region. This 

prevents the energy in the IR spectrum from being absorbed in 

the blue color layer and it is reflected by the white layer 

underneath. Both test and control bucks were initially painted 

a standard white color. For evaluation of black, blue, and solar

reflective blue paints, the test buck was painted and compared 

to the white control buck. In order to compare blue and solar 

reflective blue test results, a calibration correction was first 

applied to the control buck. The ratio of corrected control buck 

results obtained during blue and solar reflective blue tests were 

then used to scale the blue test results to that of the solar 

reflective blue test results. 
4 

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Figure 2: Cab (A) and sleeper (B) thermocouple locations, blue – 

Technology and Maintenance Council’s standard [12], red – NREL 

added, dimensions A = 12,” B = 6,” C = 18”

Results

Phase I: Baseline Testing and Model 

Development

Phase I research focused on the installation, instrumentation, 

and baseline testing of the two bucks supplied by Volvo Trucks 

and the NREL-owned test truck. To confirm the bucks were 

accurate representations of a complete truck, average sleeper 

and cab air temperatures were compared between the control 

buck and test truck baseline data. The average air temperature 

between the control buck and test truck differed by less than 

7°C for the cab air space and 5°C for the sleeper air space. 

The temperature differences observed may be largely 

explained by differences in manufacturer, geometry, and 

components. The temperature difference between the buck 

and truck prior to calibration was highly repeatable with a 

standard error of less than ± 0.17°C. For the test buck and 

control buck, cab air temperature agreed to within 1.6°C and 

sleeper air temperature was within 0.9°C prior to calibration. 

After calibrating the modified buck and test truck with the 

control buck, calibration accuracy was checked using 

validation test data. Thermal soak calibration was shown to be 

within ±0.4°C for the test cab and within ± 0.6°C for the test 

truck between the peak solar loading time of 11:00 a.m. and 

1:00 p.m. MST. The results of the calibration applied to a 

validation dataset for the test truck sleeper air temperature are 

shown in Figure 3. For the validation dataset, sleeper air 

temperature prediction agreed to within ±0.4°C for the test 

truck and ±0.2°C for the test buck.

Figure 3: Average sleeper interior air temperature validation day

Baseline A/C testing of the test and control bucks showed 

repeatable differences between the two configurations. The 

calibration data for A/C baseline testing is shown in Figure 4, 

which includes both calibration days and additional test days. 

The additional test days were collected but are excluded from 

the calibration dataset because the solar load throughout these 

days was not consistent due to partially cloudy weather. The 

additional test days confirm the strong linear correlation 

between the two test configurations. The additional test data 

also indicate that the correlation between test and control buck 

A/C power consumption is somewhat insensitive to minor solar 

load variations. 

Figure 4: Daily A/C energy calibration data for test and control bucks

After completing baseline testing, a CoolCalc model of a Volvo 

test “buck” (shown in Figure 5) was built from computer-aided 

design files of the vehicle geometry and other vehicle 
5 

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information supplied by Volvo, as well as information collected 

at NREL. When information was not available, model 

parameters were estimated to most closely match the 

configuration of the actual Volvo test bucks (Figure 10) 

undergoing thermal testing at the VTIF. Test bucks were used 

in place of complete vehicles to reduce cost and improve 

adaptability. 

Figure 5: CoolCalc model of a Volvo test buck, white baseline and 

black paint configuration shown

A custom weather file was created from data collected with the

weather stations during testing. The model used the same 

south-facing orientation, thermal soak configuration, and 

weather conditions experienced by the test bucks. The model 

was then validated against experimental thermal soak test data 

to verify its accuracy.

Comparison of the model and experimental results for three 

consecutive days (Figure 6) shows close agreement in trends 

and peak air temperatures for a variety of weather conditions. 

The maximum difference between experimental and model 

average sleeper air temperature during the hours of peak solar 

load (11 a.m. –1 p.m. MST) was 0.89°C. Exterior surface 

temperature comparisons, shown in Figure 7, between model 

and test results demonstrate that the model accurately 

captures the effect of solar position and vehicle orientation.

Figure 6: Volvo test buck CoolCalc model validation – sleeper 

compartment mean air temperatures

Figure 7: Volvo test buck CoolCalc model validation – exterior side wall 

temperatures for both driver and passenger sides

After validating the model with test data, the CoolCalc model 

was used to help guide testing efforts by identifying

opportunities to reduce long-haul truck thermal loads.

Phase II: Thermal Load Reduction

Phase II research investigated the significance of vehicle color 

on thermal loads, using interior air temperature as an indicator 

of trends. The validated CoolCalc model was used to quantify 

the impact of paint colors on vehicle air temperatures. Figure 8

shows the predicted interior air temperatures from each of the 

simulations using estimated paint properties. The percentage 

of maximum possible temperature reduction, β, was calculated 

from the average air temperatures during the peak solar time 

of day (11 a.m. –1 p.m. MST). The model predicts β = 35.4% 

changing from black to white paint, and β = 14.6% from blue to 

solar reflective blue paint. Based on the promising modeling 

results obtained, NREL worked with industry partners to 

experimentally measure the potential impact of paint properties 

on engine-off thermal soak air temperature reduction for 

heavy-duty trucks.

Figure 8: CoolCalc surface absorptivity screening study with estimated 

solar reflective blue properties
6 

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The radiative properties of the paints provided by PPG for 

experimental testing were measured at NREL. Figure 9 shows 

the reflectance spectra in the ultraviolet, visible, and infrared 

regions. The reflectance spectrum of blue and solar reflective 

blue show very similar behavior throughout the visible 

spectrum (380–750 nm) followed by a sharp increase in 

reflectance for solar reflective blue in the infrared region. Table 

1 summarizes the measured solar-weighted radiative 

properties. The initial estimated properties for solar reflective 

blue were based on exploratory information, and the actual 

solar reflective blue properties showed less but still significant 

change from the reference blue paint.

Figure 9: Measured reflectance spectrum for paint colors used in 

testing

Table 1: Solar-weighted radiative properties of paint test samples

Paint Color Absorptance [%] Emissivity

White 37.2 0.953

Black 95.3 0.951

Blue 88.0 0.951

SR Blue 74.2 0.948

SR Blue 

Estimated 65.0* 0.950*

*Estimated values used in CoolCalc prediction study

The test bucks were painted and thermal soak tests were 

conducted, Figure 10. In addition, the measured weather data 

was used to simulate the same conditions in the CoolCalc 

model. Figure 11 shows the experimental and CoolCalc model 

average interior air temperatures over the daytime thermal 

soak test for both black and white paint. The figure also shows 

both experimental and model results for the percent of 

maximum possible reduction in interior air temperature rise 

above ambient, β, at peak solar load between 11:00 a.m. and 

1:00 p.m. MST. The CoolCalc model accurately captures air 

temperature with time for both black- and white-painted cabs 

and shows close agreement with experimental results obtained 

for the percent of maximum possible reduction in interior air 

temperature rise above ambient, with β = 31.1% for experiment 

and β = 32.5% for the CoolCalc model. 

Figure 10: Cab experimental configurations: Test buck painted white 

(A), black (B), blue (C), and solar reflective blue (D)

Figure 11: Thermal soak results with black and white opaque surfaces

Thermal soak testing of blue and solar reflective blue exterior 

surfaces showed a percent of maximum possible temperature 

reduction of β = 6.0% during peak solar loading from 11:00 

a.m. – 1:00 p.m. MST. The average interior air temperature for 

blue and solar reflective blue during thermal soak conditions is

shown in Figure 12A. In addition to experimental testing, 

CoolCalc modeling was performed using the test day’s weather

and measured paint properties as input. Model predicted 

average interior air temperatures are provided in Figure 12B. 

The model predicted a maximum possible temperature 

reduction of β = 6.2%, which closely matched experimental 

results.
7 

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Figure 12: Thermal soak results with blue and solar reflective blue 

opaque surfaces. A: Experimental results, B: CoolCalc model results

Phase III: Idle Load Reduction 

Phase III focused on the quantification of the impact of paint 

color on the A/C system electric power use in the test cab 

sleeper air space during engine-off daytime test conditions. 

During evaluation of the black test buck, the A/C target 

temperature was increased to 26.7°C (80°F) for the test buck

to accommodate the increased load for this configuration. 

Hourly average A/C power consumption (Figure 13) shows 

consistent reduction in A/C electrical energy loads throughout 

daytime operation. The average daily A/C power consumption 

decreased 20.8% switching from black to white paint. The 

decrease corresponds to a 1,001 W·h battery energy savings 

over the daytime test period. A 1,001 W·h daily energy savings 

would correspond to a 16.7% reduction in battery capacity and 

22 kg (48 lb) reduction in weight from the standard battery as 

described in the Approach. 

Figure 13: Hourly average test cab A/C power consumption for black 

and white opaque exterior surfaces

During idle load reduction testing of the blue and solar 

reflective blue test bucks, the A/C target temperature was

maintained at the recommended 22.2°C (72°F). The average 

daily A/C electrical load was reduced by 563 W·h, representing 

a 7.3% savings going from blue to solar reflective blue paint. A 

563 W·h reduction in daily electrical energy would equate to a 

9.4% reduction in battery capacity and 12.4 kg (27.3 lb) 

reduction in battery weight.

To understand the impact of exterior paint color on long-haul 

truck HVAC thermal loads over a wider range of conditions 

than those that were experimentally measured at the NREL 

test facility, a national-level CoolCalc analysis was conducted. 

To capture the weather conditions throughout the United 

States, the three most populous cities in each of the 

contiguous U.S. states were selected. For the simulation, 

typical meteorological year (TMY) weather data [15] for each of 

the 144 cities was used. TMY weather data consist of actual 

hourly weather information representative of typical local 

climatic conditions on a monthly basis concatenated into an 

entire year. For the evaluation, paint color was defined as a 

parametric variable, and the colors used for the model 

matched those used for experimental outdoor testing provided 

in Table 1 above. For the simulation, the sleeper compartment 

was heated when the interior air temperature dropped to 

18.3°C (65°F) and cooled when the indoor air temperature 

increased to 23.9°C (75°F), leaving a 5.6°C (10°F) deadband 

between the two temperatures. The model was configured with 

all curtains closed and the front of the cab facing directly south. 

To increase the speed of simulation for large parallel runs, 

NREL coupled CoolCalc with a high performance computer.

Figure 14 shows national maps of normalized cooling and 

heating thermal loads simulated at each of the major U.S. 

cities as indicated for the four paint colors in the study. The 

data represent the 95th percentile for heating and cooling 

loads, meaning that 95% of the weather days evaluated have a 

lower thermal load. For the figure, cooling and heating loads 

were independently normalized to cooling maximum and 
8 

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heating maximum loads; therefore, their scales do not indicate 

the same magnitude. As shown in the figure, changing from 

black to white paint significantly reduces the cooling thermal 

loads and has a minimal increase in heating loads. In addition, 

cooling loads with black paint show a strong sensitivity to 

regional climate while white paint shows a much smaller 

sensitivity. Similarly, changing from blue to solar reflective blue 

paint has a measurable reduction in thermal load with no 

significant change in heating loads. The insensitivity of winter 

heating loads to paint color is due to the short days and low 

solar intensity in the winter, particularly in the colder northern 

latitudes. Unlike cooling loads, peak heating loads also occur 

at night and are unaffected by the solar radiative paint 

properties. 

Figure 14: National daily cooling (left) and heating (right) sleeper 

HVAC thermal loads for the four cab paint colors of interest. Data 

represent 95th percentile thermal loads and are normalized based on 

peak load.

Summary/Conclusions

Long-haul truck rest period idling, a zero freight efficiency 

operating condition, consumes an estimated 6.8% of long-haul 

truck fuel use in the United States. Much of this idling is done 

to provide climate control to the cab/sleeper. To reduce loads, 

NREL is investigating the solar envelope, conductive 

pathways, and conditioned air volume management. For this 

study, NREL has partnered with Volvo Trucks, PPG Industries, 

and Dometic Environmental Corporation to investigate the 

impact of paint on long-haul truck climate control loads. 

By switching from black to white paint, long-haul sleeper daily 

electrical A/C loads were reduced by as much as 20.8%. An 

electrical energy saving of 1,001 W·h was achieved during a 

daytime rest period while operating an A/C system under 

ambient conditions in Golden, Colorado. The electrical energy 

savings would correspond to a 16.7% reduction in A/C battery 

capacity and 22 kg (48 lb) weight reduction from the standard 

battery size. Selecting white paint on a new vehicle adds no 

additional cost and would have an immediate payback. When 

the choice of paint color is critical for brand recognition and/or

aesthetic purposes, load reductions are obtainable through the 

use of advanced color-matched solar reflective paints. 

Switching from a blue to an advanced color-matched solar 

reflective blue paint resulted in a 7.3% reduction in daily 

electrical A/C load under the conditions tested. This 563 W·h 

reduction in daily electrical energy would equate to a 9.4% 

reduction in battery capacity and 12.4 kg (27.3 lb) reduction in 

battery weight.

A CoolCalc model of a Volvo long-haul truck sleeper cab was 

developed and validated, showing good agreement with test 

data. Over the three validation days, the maximum difference 

between experimental and model average sleeper air 

temperature during the hours of peak solar load (11 a.m. –

1 p.m. MST) was 0.89°C. The model also predicted the impact 

of paint colors well, agreeing with both trends and maximum 

possible reduction in rise over ambient temperature 

estimations. The validated model was then used to conduct a

national paint impact study using TMY weather data for 144 

major U.S. cities which confirmed that paints with higher solar 

reflectivity reduce thermal loads significantly in cooling 

conditions and have little to no detrimental impact on heating 

loads. The lower cooling loads show the potential to reduce 

A/C loads through the selection of paint color. 

Working closely with industry partners, NREL has applied 

experimental and modeling tools to show that systematically 

combining vehicle thermal management and idle reduction 

technologies can reduce climate control loads needed for longhaul truck rest period idling. This can reduce cost, weight, and 

volume of idle reduction systems, improving payback period 

and increasing economic motivation for fleet owners and 

operators to consider idle reduction systems. Increasing idle 

reduction system effectiveness and adoption rates will help 

reduce the 667 million gallons of diesel used annually in the 

United States for long-haul truck rest period idling and 

potentially reduce truck operation costs.

The results and methods developed for this study will be used 

to conduct a more detailed national-level impact study. This 

future study will be used to understand and develop effective 

full vehicle thermal load reduction approaches that combine 

solutions for solar envelope, conductive pathway, and 

conditioned air volume management that reduce HVAC loads 

and resulting fuel use. 

References

1. Gaines, L., Vyas, A., and Anderson, J., “Estimation of Fuel 

Use by Idling Commercial Trucks,” 85th Annual Meeting of 

the Transportation Research Board, Washington, D.C., 

Paper No. 06-2567, January 22–26, 2006. 

2. K. Fender and D. Pierce, “ATRI Operational Costs of 

Trucking 2012,” American Transportation Research 

Institute, Atlanta, GA, 2012.
9 

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

3. “Idling Regulations Compendium, American 

Transportation Research Institute,” http://atrionline.org/2013/02/20/idling-regulations-compendium/ 

Accessed on 9/16/2013. 

4. “Greenhouse Gas Emissions Standards and Fuel 

Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles, Final Rule,” Federal Register 76 

(15 September, 2011): 57106–57513.

5. Roeth, M., Kircher, D., Smith, J., and Swim, R., “Barriers 

to the Increased Adoption of Fuel Efficiency Technologies 

in the North American On-Road Freight Sector,” Report for 

the International Council for Clean Transportation. 

NACFE. July 2013.

6. Lustbader, J., Venson, T., Adelman, S., Dehart, C., Yeakel 

S., Sanchez, M., “Application of Sleeper Cab Thermal 

Management Technologies to Reduce Idle Climate Control 

Loads in Long-Haul Trucks,” SAE Commercial Vehicle 

Engineering Congress, 2012-01-2052, October 1, 2012.

7. Rugh, J., Farrington, R. Vehicle Ancillary Load Reduction 

Project Close-Out Report, National Renewable Energy 

Laboratory, NREL/TP-540-42454, January 2008.

8. Levinson, R., Pan, H., Ban-Weiss, G., Rosado, P., Paolini, 

R., Akbari, H. “Potential benefits of solar reflective car 

shells: Cooler cabins, fuel savings and emissions,” Applied 

Energy, 2011, 88, 4343-4357.

9. Lustbader, J., Rugh, J., Rister, B., and Venson, T., 

“CoolCalc: A Long-Haul Tuck Thermal Load Estimation 

Tool,” SAE Technical Paper 2011-01-0656, SAE World 

Congress, Detroit, MI, April 12–14, 2011.

10. Dieck, R.H., Steele, W.G., and Osolsobe, G., Test 

Uncertainty. ASME PTC 19.1-2005. New York, NY. 

American Society of Mechanical Engineers, 2005.

11. http://www.nrel.gov/midc/vtif_rsr/ 

12. “Cab Insulation Testing Methodology,” RP422A-1-9, in 

Technology and Maintenance Council’s Recommended 

Maintenance Practices Manual, 2010–2011 edition, 

Arlington, VA: American Trucking Association, 

p. RP422A-1.

13. http://www.dometic.com/enus/Americas/USA/Truck/ 

Accessed on 10/18/2013.

14. “Vehicle and Systems Simulation and Testing 2012 

Annual Progress Report,” DOE/EE-0834, Section IV.N. 

CoolCab Truck Thermal Load & Idle Reduction, pg. 284–

291.

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

2005/tmy3/. Accessed 9/1/2013.

Contact Information

Jason A. Lustbader

Heavy Vehicle Thermal Management Team Lead

National Renewable Energy Laboratory

Jason.Lustbader@nrel.gov

303-275-4443

Acknowledgments

The authors would like to thank Chase Kopetz, Stephen 

Joseph, and Stuart Hellring at PPG Industries and Charlie 

Barefoot at Dometic Environmental Corporation for their 

important contributions to this project.

The authors would also like to thank David Anderson and Lee 

Slezak, Technology Managers for the U.S. Department of 

Energy’s Advanced Vehicle Technology Analysis and 

Evaluation for sponsoring this work.

For their contributions to this project, thank you to the following 

NREL employees: Jon Cosgrove, Jeff Tomerlin, Henry Horsey, 

John Rugh, and Rob Farrington.

Photo Credits: Figure 1– Top, Dennis Schroeder, Bottom, Cory 

Kreutzer, NREL; Figure 10– Cory Kreutzer, NREL.

Definitions/Abbreviations

A/C air conditioning

HVAC Heating, Ventilation, and Air Conditioning

NREL National Renewable Energy Laboratory

VTIF Vehicle Testing and Integration Facility