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Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

CoolCalc: A Long-Haul Truck 

Thermal Load Estimation Tool 

Preprint

Jason A. Lustbader, John P. Rugh, 

Brianna R. Rister and Travis S. Venson

Presented at the SAE World Congress 2011 

Detroit, Michigan

April 12–14, 2011

Conference Paper

NREL/CP-5400-50308

May 2011
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1 

CoolCalc: A Long-Haul Truck Thermal Load Estimation Tool 

Jason A. Lustbader, John P. Rugh, Brianna R. Rister and Travis S. Venson

National Renewable Energy Laboratory

ABSTRACT

In the United States, intercity long-haul trucks idle 

approximately 1,800 hrs per year primarily for sleeper cab 

hotel loads, consuming 838 million gallons of diesel fuel [1]. 

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

Laboratory (NREL) is working on solutions to this challenge 

through the CoolCab project. The objective of the CoolCab 

project is to work closely with industry to design efficient 

thermal management systems for long-haul trucks that keep 

the cab comfortable with minimized engine idling. Truck 

engine idling is primarily done to heat or cool the cab/sleeper, 

keep the fuel warm in cold weather, and keep the engine warm 

for cold temperature startup. Reducing the thermal load on the 

cab/sleeper will decrease air conditioning system 

requirements, improve efficiency, and help reduce fuel use. To 

help assess and improve idle reduction solutions, the CoolCalc 

software tool was developed. CoolCalc is an easy-to-use, 

simplified, physics-based heating, ventilating and air 

conditioning (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 (CAE) modeling approaches. It is intended for 

rapid trade-off studies, technology impact estimation, and 

preliminary HVAC sizing design. It also complements more 

detailed and expensive CAE tools by exploring and 

identifying regions of interest in the design space. CoolCalc is 

built on NREL’s OpenStudio platform and is a plug-in 

extension of Google’s SketchUp software. This paper 

describes the CoolCalc tool, provides outdoor long-haul truck 

thermal testing results, shows validation using these test 

results, and discusses future applications of the tool. 

INTRODUCTION

Heating and air conditioning are two of the primary reasons 

for long-haul truck main engine operation when the vehicle is

parked. In the United States, trucks that travel more than 500 

miles per day use 838 million gallons of fuel annually for 

overnight idling [1]. Including workday idling, over 2 billion 

gallons of fuel are used annually for truck idling [2]. By 

reducing thermal loads and improving efficiency, there is a 

great opportunity to reduce the fuel used and emissions

created by idling. Reducing the thermal load for truck

cab/sleepers will enable cost-effective idle reduction solutions. 

If the fuel savings from new technologies can provide a 3- to 

5-year payback time, fleet owners will be economically 

motivated to incorporate them. This provides a pathway to 

rapid adoption of effective thermal load reduction solutions. 

The U.S. Department of Energy’s (DOE’s) National 

Renewable Energy Laboratory (NREL) CoolCab project is 

researching efficient thermal management systems that keep 

the cab occupants comfortable without the need for engine 

idling. The CoolCab research approach is to reduce thermal 

loads, concentrate on occupant thermal comfort, and maximize 

equipment efficiency. By working with industry partners to 

develop and apply commercially viable solutions that reduce 

idling fuel use, both national energy security and sustainability 

will be improved. To achieve this goal, NREL is developing 

tools and test methods to assess idle reduction technologies. 

The truck cab industry needs a high-level analysis tool to 

predict thermal loads that are used to evaluate load reduction 

technologies and their impact on climate-control fuel use.

To meet this need, NREL has developed CoolCalc, a software

tool to assist the industry in reducing climate control loads for 

heavy-duty vehicles. CoolCalc is a heating, ventilating and air 

conditioning (HVAC) load estimation tool that enables rapid 

exploration of idle reduction design options for a range of 

climates.

APPROACH

COOLCALC MODEL DEVELOPMENT

CoolCalc is an easy-to-use, simplified, physics-based HVAC 

load estimation software tool that requires no meshing, has 

flexible geometry, excludes unnecessary detail, and is less 

time-intensive than more detailed computer-aided engineering 

(CAE) modeling approaches. It is intended for rapid trade-off 

studies, technology impact estimation, and preliminary HVAC 

sizing design. It complements more detailed and expensive 

CAE tools.

CoolCalc is built on NREL’s OpenStudio platform. This was 

done to accelerate development and leverage previous and 

ongoing DOE investments. OpenStudio was developed at 

NREL and released in 2008. It is a plug-in extension of 

Google’s SketchUp software. DOE’s EnergyPlus is used as 

the heat transfer solver. EnergyPlus is a DOE-funded software

package designed for building efficiency analysis, which was 

found to be general enough to extend to truck cab thermal 

modeling.
2 

Unlike previous building thermal simulation programs, 

EnergyPlus provides a fully integrated simulation where the 

building HVAC zones, system, and plant (source) are solved 

together. This provides a more physically realistic solution and 

allows for more detailed control implementation. Heat transfer 

is described by a set of time-dependent energy and moisture 

balances, and the resulting ordinary differential equations are 

solved using a predictor-corrector approach. For solar loading, 

an anisotropic radiance model is used, allowing the 

superposition of three components: isotropic radiance, point 

source circumsolar brightening at the sun, and horizon 

brightening. The sun position is tracked as a function of 

geographic location, time of year, and time of day. A shading 

model is implemented that accounts for shadowing of surfaces 

by other surfaces. EnergyPlus’s window model is based on 

Lawrence Berkeley National Laboratory’s WINDOW program 

algorithms and uses solar transmittance, reflectance, and 

absorptance properties. Full spectral analysis is also possible. 

All surfaces are treated as gray bodies. The external surface 

heat transfer balance includes shortwave solar radiation, 

longwave thermal radiation, convection, and conduction 

through the walls. Conduction is one dimensional through the 

thickness of a wall. Walls are defined using a series stack-up 

of materials that allow for thermal storage. The interior 

surface heat transfer modes include shortwave solar radiation 

from windows, longwave radiation, and convection. The 

internal radiation view factors are approximated by a ratio of 

“seen” areas, then corrected for reciprocity and completeness. 

A detailed description of EnergyPlus’s modeling and solution 

methods is beyond the scope of this paper; for further details 

see the extensive documentation available [3]. 

While CoolCalc is flexible and does not dictate a specific 

process, a typical workflow (illustrated in Figure 1) might 

begin with the creation of geometry using the Parametric Cab 

creation tool (Figure 2). The Parametric Cab creation window 

has a series of tabs across the top, one for each air zone in the 

model. Each tab has a list of available parametric variables 

that will modify the geometry. These variables and the 

parametric geometry relationships are determined by a model 

definition file created in the geometry coding framework. The 

user can also switch between the available parametric cab 

models to find one that that best suits their needs. To the right 

of the units, the allowable variable range is displayed. To 

illustrate this parametric capability, the windscreen angle was 

changed from 60° to 80° (Figure 3). The cab model quickly 

updates, allowing for fast modification of the geometry. 

Once the cab geometry is established using the Parametric Cab 

tool, it can be manually modified by the user. Figure 4 shows 

an example of a user adding an additional sidelight to the 

sleeper cab. The sleeper cab sidewall was activated by double

clicking on the surface. Once activated, the SketchUp drawing 

tools can be used to modify the geometry. The dashed lines 

are construction lines that were created to help quickly draw 

the sidelight and can be easily hidden or deleted later. The 

pencil tool was then used to trace out the

Figure 1 - Typical CoolCalc workflow

Figure 2 - Parametric Cab geometry creation

Figure 3 - Parametric Cab with modified windscreen angle

construction lines. The pencil tooltip icon can be seen in the 

top left corner of the sidelight. Once the window shape is 

closed by the pencil tool, it is automatically recognized as a 

window and assigned default properties.
3 

Figure 4 - Manual modification of geometry: adding a 

sidelight

In EnergyPlus, every component of the model, e.g., walls, 

materials, location, and solver time step, is treated as an 

object. In CoolCalc, to modify or define new objects the 

Object Browser tool is opened (Figure 5). On the left side of 

the Object Browser window is the object tree, which shows all 

the objects that are available in the model and allows creation 

of new objects. Below the object tree is the library window. 

The library window allows the user to load and manipulate 

additional libraries of objects. These objects can then be added 

to the current model by dragging and dropping them into the 

object tree. Below the specific object window interface, to the 

right of the object tree, is the text editing window. This 

window allows for manual modification of the current object, 

giving full control to advanced users. For objects where no 

specific interface has been developed, the text editing window 

will comprise the entire right side of the split window. All 

object windows also provide a “Comment” option in the upper 

right corner to help users document their object assumptions. 

To modify or define new materials, a material object is 

selected in the object tree (Figure 5). Based on this object tree 

selection, the material definition window is displayed to the 

right of the object tree. The material definition window 

provides text boxes or pull-down menus for all the basic 

material thermal properties: name, roughness, thickness, 

conductivity, density, specific heat, and radiation absorptance. 

Each surface in CoolCalc is treated as multiple layer, onedimensional conduction, forming a “sandwich” type structure. 

To define this layered structure, a construction object is used. 

Once again navigating the object tree, a construction object is 

selected. This changes the current object window (right side)

to display the construction definition window (Figure 6). In

this window, the user selects the materials to include in the 

construction and can change their order. Materials assigned to 

the inner and outer layers will determine the solar radiation 

properties and the texture displayed in the texture-rendering 

mode.

Figure 5 - Object Browser and Material Definition Window

Figure 6 - Construction Definition Window

Figure 7 shows the model in construction rendering mode. In 

this mode, the inner and outer surfaces are colored by their 

respective construction’s material textures. The window to the 

left of the cab is the Construction Palette. It allows sorting and 

selection of constructions and their application to the model 

using a point-and-click paint can tooltip.

Before solving the model, a weather file is selected. There are

currently Typical Mean Year data available for 2,100 locations 

worldwide. Custom weather data can also be entered. A 

simulation period of one day to one year is also selected. Once 

the model is solved, the results can be displayed within the 

interface. Figure 8 shows the exterior temperatures displayed 

on the truck cab/sleeper.
4 

Figure 7 - Construction palette window and texture 

rendering mode

Figure 8 - Example temperature distribution

EXPERIMENTAL

To validate the CoolCalc modeling approach, outdoor thermal 

soak testing of a Kenworth Truck Company T660 Class 8 

sleeper cab truck was conducted at NREL’s Vehicle Thermal 

Soak Test Facility (Figure 9). The truck was parked on level 

ground at an elevation of 5,853 feet, 39.7 degrees latitude, 

-105.2 degrees longitude, facing due south. The test vehicle 

was instrumented with 14 exterior and 28 interior k-type 

thermocouples. The thermocouples were calibrated using a 

Hart Scientific 7103 Micro-Bath for an overall uncertainty of 

less than ±0.42°C. The thermocouples were strategically 

positioned to accurately characterize the vehicle thermal 

distribution and collect validation data. Surface thermocouples 

were attached using an Omega thermally conductive epoxy. 

Radiation shields were used for air thermocouples to minimize 

errors due to direct solar radiation. A pyranometer was placed 

on the instrument panel of each vehicle to confirm sunrise 

times, and humidity measurements were taken. An IOTech 

LogBook 360 data acquisition system was used and located in 

the truck’s toolbox. Data was collected every second and 

reduced to one minute averages over 24-hour periods. Solar 

radiation, wind speed, cloud coverage, relative humidity, and 

wind direction were also measured at NREL’s Solar Radiation 

Research Laboratory, which is located near the test site. 

Thermal soak tests were conducted from August through 

November 2009. The truck was exposed to daytime solar 

loads in an engine-off condition. An air exchange rate test was 

also conducted to help characterize thermal behavior. For this 

measurement, a small amount of sulfur hexafluoride (SF6) was

injected into the cab, and the concentration decay was

monitored. Samples were taken every 5 minutes for 3 hours, 

and the air exchange rate was calculated from the SF6

concentration decay rate. 

Figure 9 - Test trucks at NREL’s Vehicle Thermal Soak Test 

Facility 

VALIDATION

To validate the CoolCalc simulation approach, a Kenworth 

T660 thermal model was developed. When available, 

information from Kenworth or testing was used to define the 

CoolCalc model parameters; otherwise, engineering estimates 

were made. Since CoolCalc does not use meshing, all surfaces 

must be planar. Therefore, the computer-aided design model 

provided by Kenworth was simplified. Figure 10 shows the 

model geometry compared to a photograph of the vehicle. The 

red lines indicated the domain boundary used for the model. 

This simplification process inherently requires some 

approximation; however, an effort was made to accurately 

represent the geometry.

The vehicle was modeled with four air zones: Cab, Sleeper, 

Fairing, and Toolbox. The full EnergyPlus exterior radiation 

calculations were used; however, a simplified version of the 

interior radiation calculations was applied to increase 

geometry flexibility. The simple interior radiation model 

assumes that all beam radiation that passes through the 

windows falls on the floor of that zone and any reflected 

radiation is uniformly distributed on all interior surfaces. A 

previous case study was conducted that indicated that this 

results in increasing, but acceptable, differences during winter 
5 

months due to the changing sun angle. The detailed surface 

convection algorithms were used that account for both natural 

and forced convection. The model time step was set to 1 

minute intervals. 

Figure 10 - CoolCalc validation geometry

The glass properties for the cab and sleeper were obtained 

from Kenworth and its suppliers as were the materials and 

constructions for the vehicle walls. The overall resistivity of 

the walls was reduced to account for impacts of structural

members and other likely disruptions to insulation in a real 

vehicle. The paint properties were not available and were 

estimated based on previous experience with vehicle paints. 

Four internal masses were defined to represent interior objects. 

The first represents the seats in the cab. Specifications on 

vehicle seats were used to estimate the size, weight, and 

material properties. The next two internal masses represent the 

dashboard and the sleeper closets. Both were assumed to be 

polyurethane plastic, and the volume and surface area of each 

were estimated using truck geometry. Lastly, the two beds 

were modeled. Information on these was supplied by 

Kenworth. It was found that internal mass assumptions can 

have a significant impact on thermal results, and further work 

on internal mass modeling approaches is planned.

Since the chassis and hood would normally shade the 

underlying surfaces and were excluded from the solution 

domain, the solar load was removed from both surfaces. The 

firewall boundary condition was set to represent natural 

convection at 20% above ambient temperature. The exterior 

surfaces of the vehicle floor were exposed to wind at ambient 

temperature.

The measured air infiltration rate, as described in the 

experimental section, was applied to the cab and sleeper. Since 

this model does not include a fluid flow solution, the crossmixing between zones had to be estimated. Incorporating a 

simplified means for estimating this is another possible 

improvement for the future.

RESULTS

The CoolCalc concept was validated by comparing the

Kenworth T660 thermal model results with experimental

temperature and onsite weather data. Figure 11 shows the 

weather data used for the three consecutive validation days. 

The data set has been normalized at the request of industry 

partners. This data set captures a range of conditions. The first 

day was overcast, cooler, and had low wind. The second two 

days were both clear, but had different wind and temperature 

characteristics. 

Figure 11 - Normalized weather data for three validation 

days

The model solution time for the three-day simulation with 1-

minute intervals was 29.4 sec ± 0.1 sec over three repeated 

runs using a Dell Intel Core 2 Duo 3.16 GHz CPU with 3.25 

GB RAM desktop computer running the Windows XP 

operating system. Figure 12 and Figure 13 show a comparison 

between the model and the measured air temperatures. These 

graphs demonstrate good agreement between the model and 

the experimental data, both in the peak soak temperature and 

the overall trends. The experimental averages for the cab and 

sleeper are an average of six and eight distributed air 

temperatures, respectively. 

Figure 12 - Cab average air temperature comparison
6 

Figure 13 - Sleeper average air temperature comparison

Figure 14 summarizes the temperature difference between the 

model and the test data for the time average (2–4 p.m.) sleeper 

and cab air temperatures. The maximum interior air 

temperatures occurred during this time interval and therefore 

represent peak soak conditions. For the sunny days, the 

temperature difference was less than 0.4°C, which is less than 

the measurement uncertainty. On the cloudy day, the 

temperature difference between the data and the model was 

2°C. While the average peak soak temperature for day two 

compares very closely with the experimental data, the 

differences with time can be seen in Figures 12 and 13. 

Figure 14 - Comparison of test data and model, time average 

from 2–4 p.m. 

The predicted exterior surface temperatures were also 

compared to the experimental results. Figure 15 shows the 

results for the driver and passenger sleeper side wall surface 

temperatures. Because the truck is facing south, the driver-side 

surface temperatures rise in the morning, and then peak and 

decline as the sun passes over the vehicle. Likewise, the 

passenger-side surfaces rise in the afternoon, peak, and decline 

as the sun goes down. The higher frequency temporal 

variability seen in the afternoon temperatures on day one for 

both the experimental and the model results were caused by 

passing clouds. On day three, a larger error is seen between 

the model and the experimental data. Based on the high solar 

load and low wind speed, one possible cause for this could be 

that the natural convection portion of the heat transfer is 

underestimated in these conditions. However, further 

investigation would be needed to confirm this.

Figure 15 - Sleeper driver and passenger exterior side walls

The concept validation results also show good agreement 

between the model and the experimental data for the other 

surfaces that were compared. Figure 16 shows the inside 

surface of the sleeper side windows. The model tends to 

overestimate the temperature and respond faster. EnergyPlus 

does not currently have thermal mass for windows; therefore, 

this behavior is expected. This could also account for the 

higher sensitivity to transients, such as passing clouds on 

Day1. 

Figure 16 - Comparison of sleeper interior side windows

One of the larger differences between the model and the 

experimental results is the windshield, shown in Figure 17 and 

Figure 18. The temporal shift is more noticeable on the 
7 

windshield exterior. This might also be caused by the lack of 

window thermal mass.

Figure 17 - Comparison of windshield exterior

Figure 18 - Comparison of windshield interior

These validation results demonstrate that the CoolCalc 

modeling tool can quickly and accurately estimate vehicle 

temperature distributions for air, surfaces, and glass. While 

inherent errors are expected when simplifying the geometry, 

this comparison to experimental results suggests that sufficient 

accuracy is achieved for early design and rapid trade-off 

analysis. Air conditioning tests were outside the scope of this 

first validation study. Matching the interior temperatures well 

during a thermal soak provides some confidence that the 

predicted air conditioning evaporator load is realistic. Previous 

work on simulation of military-type vehicle geometry 

indicated that the evaporator thermal load calculations were 

reasonable. Future work will validate load calculations in 

more detail. By modifying exterior convection coefficients as 

a function of speed and adding engine heat loads, down the 

road HVAC loads could be investigated in future work. 

SUMMARY/CONCLUSIONS 

To help develop solutions that reduce the 838 million gallons 

of fuel used by long-haul trucks annually for overnight idling

in the United States, NREL’s CoolCab project has developed 

an HVAC load estimation software tool called CoolCalc. A 

typical workflow for modeling the thermal behavior of a 

tractor trailer sleeper cab using CoolCalc was demonstrated. 

Vehicle geometry is first created using parametric and manual 

tools. The user navigates and modifies solver objects using the 

Object Browser as needed to define model parameters. Once 

the model is set up, the weather file is selected and the model 

is solved. Results can be displayed within the software 

environment or post-processed in detail using output files.

Detailed experimental testing of a Kenworth T660 sleeper cab 

truck was conducted at NREL to validate CoolCalc. A model 

of this vehicle was developed using information provided by 

Kenworth Truck Company, testing, and engineering 

assumptions. Comparison between the model and 

experimental results collected over three days shows good 

agreement both in trends and peak temperature values for a 

variety of weather conditions. The difference between 

experimental and model peak soak air temperatures, an 

average from 2–4 p.m., was less than or equal to 0.4°C for the 

two sunny days and 2°C for the cloudy day. Surface 

temperature comparisons show that the effect of solar position 

was captured accurately. Experimental testing for a second 

validation case study has been completed, and modeling is 

planned for the near future. 

The ability of CoolCalc to quickly and accurately estimate 

vehicle thermal temperature distributions has been 

demonstrated. Further validation with other vehicles and 

weather conditions is being conducted. Methods and tools are 

currently being developed to link CoolCalc thermal load 

estimates to vehicle fuel use. The next step will be to apply 

CoolCalc to study the impact of thermal load reduction

technologies on idling and vehicle fuel use. This will fill an 

important role in the CoolCab project’s suite of experimental 

and analytical tools that are being used to develop 

commercially viable idle reduction technologies in 

collaboration with industry partners. 

REFERENCES

1. Stodolsky F., Gaines L., Vyas A. Analysis of Technology 

Options to Reduce the Fuel Consumption of Idling Trucks. 

Argonne National Laboratory, ANL/ESD-43, June 2000.

2. Gaines L., Vyas A., Anderson, J. 2006, “Estimation of Fuel 

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

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

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

3. EnergyPlus Engineering Reference. 

http://apps1.eere.energy.gov/buildings/energyplus/pdfs/engine

eringreference.pdf. October 11, 2010. Accessed on 

[10/25/2010] 
8 

CONTACT INFORMATION

Jason A. Lustbader

National Renewable Energy Laboratory

Jason.Lustbader@nrel.gov

303-275-4443

John P. Rugh

National Renewable Energy Laboratory

John.Rugh@nrel.gov

303-275-4413

ACKNOWLEDGMENTS

The authors would like to thank Lee Slezak and David 

Anderson, Technology Managers for DOE’s Advanced 

Vehicle Technology Analysis and Evaluation for sponsoring 

this work.

A special thanks to John Duffy and Stan DeLizo of Kenworth 

Truck Company for their participation in and support of this 

work.

For their contributions to this project, thank you to the 

following NREL employees: Rob Farrington, Charlie King, 

Mike Lammert, Ken Proc, Bob Rehn, and Brent Griffith. 

Additionally, thank you to Michael Birdsong and Peter Ellis 

for your contributions to the program.

DEFINITIONS/ABBREVIATIONS

CAE computer-aided engineering

DOE U.S. Department of Energy

HVAC heating, ventilation, and air conditioning

Kenworth Kenworth Truck Company

NREL National Renewable Energy Laboratory

SF6 sulfur hexafluoride