NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Reducing Long-Haul Truck Idle Loads and Resulting 

Fuel Use 

Presenter and P.I.:

Jason A. Lustbader

National Renewable Energy Laboratory

Team: 

Cory Kreutzer

Matthew Jeffers

Jeff Tomerlin

Tibor Kiss

Ryan Langewisch

Kameron Kincade

EPA NREL Vist

Tuesday, July 29, 2014

[1]
2

THE CHALLENGE

Motivation

• 667 million gallons of diesel fuel 

used annually for long-haul truck 

rest period idling1

o 6.8% of total long-haul fuel use1

• Increased idling regulation at the 

local, state, and national level2

• Large uncertainty with technology 

payback period and effectiveness 

• Solutions must be effective over 

seasons and modes of operation

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. 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.

• Reducing idling loads will enable 

idle-reduction technologies

• Fleets are economically 

motivated by a 3-year or better 

payback period

• Effective solutions needed to 

meet regulations

o Anti-idling products on the market supply loads, not 

reduce them

• Fuel use and payback period 

quantification aid in overcoming 

barriers

• Support 21st Century Truck and 

SuperTruck Goals 

THE OPPORTUNITY
3

CoolCab Smart Goal

Demonstrate at least a 30%

reduction in long-haul truck idle 

climate control loads with a 3-year 

or better payback period by 2015

 Work with industry partners to develop effective, market-viable 

solutions using a system-level approach to research, development and 

design

 Design efficient thermal management systems that keep the occupants 

comfortable without the need for engine idling

 Develop analytical models and test methods to reduce uncertainties and 

improve performance in idle reduction technologies
5

Reductions in load have a larger impact on fuel use due to 

equipment and delivery losses.

System Level Approach

Reduce Load

Efficient Delivery

Efficient Equipment
6

Overall Strategy

Technology Focus Areas

Solar 

Envelope

Volume 

Management

Conductive 

Pathways

Efficient

Equipment

[1]
7

Overall Strategy

Solar 

Envelope

Volume 

Management

Conductive 

Pathways

Efficient

Equipment

Modeling

Industry Collaboration

Testing

Impact

[1]
8

Approach – Advanced Technologies

Conductive 

Pathways

Solar 

Envelope

Volume 

Management

Efficient

Equipment

Advanced IdleReduction Systems

Opaque Surface 

Treatment

Insulation & 

Advanced Materials

Comfort-Based 

Design Efficient HVAC & Controls

Advanced 

Glazings

Curtains & Shades

[1]

[2]

[3]
9

CoolCalc Model Validation

Model results closely match test data

Blue bands represent periods of peak solar loading (11 a.m.-1 p.m. MST)
10

CoolCalc Model Validation

Tracks the sun and captures diurnal solar impacts

12:00 pm 3:00 pm 7:00 pm 9:00 pm 7:00 am 5:00 am 9:00 am 5:00 pm

5:00 am 7:00 am 9:00 am

12:00 pm 3:00 pm 5:00 pm

7:00 pm 9:00 pm
11

Experimental Setup

• Test truck, test “buck” cab, control “buck” cab

o South-facing vehicles

o Buck firewall shade cloths

• Local weather station at test site

o Solar, wind, ambient temperature, pressure, and RH

• Dometic A/C Systems: 2,050 W (7,000 BTU/hr)

o Set points of 22.2°C (72°F) and 26.7°C (80°F)

Vehicle Testing and Integration Facility, Golden, CO

(1) Cab and (2) Sleeper thermocouple locations, dimension A = 12", B = 6”, C = 18”, 

blue – TMC standard [5], red – NREL added

• 40 thermocouples per 

vehicle

o Air and surface locations, 

following TMCrecommended practice 

with additional locations

• U95 = ± 0.3°C

• A/C Power = ± 15 W

1 2

Testing

[1]
12

Previous Work Highlights - Insulation

Insulation Package Evaluations

E-A-RTM Thermal Acoustic Systems

• Heating Testing: 26%–36% reduction 

in heat loss

• A/C Testing: 20%–34% reduction in 

A/C energy use

0

200

400

600

800

1000

0

5

10

15

20

25

30

35

40

12:00 AM 1:00 AM 2:00 AM 3:00 AM 4:00 AM 5:00 AM 6:00 AM Heater Power [Watts] Average Interior Air Temperature, Sleeper [°C]

Time [MST]

Baseline truck

Insulated Truck

Heater

Conductive 

Pathways

10-Hour Idle A/C Test Overall Heat Transfer Test (UA)

[1]
13

Previous Work Highlights – White Paint

Paint Evaluation, Phase I: Black to White Evaluation

• A/C Testing: 20.8% reduction in daily A/C system energy

• Thermal Soak Testing: 31.1% of maximum possible interior air temperature reduction

Solar 

Envelope

[1]
14

Spectral Property Results

Measured properties to be used for modeling
15

Advanced Paints, Phase II – Solar Soak

Experiment and CoolCalc agreement, blue  solar reflective blue

Solar 

Envelope

100%







baseline ambient

baseline modified

T T

T T



Thermal Soak Testing

CoolCalc Model Experiment

[1]
16

Evaluation of Advanced Paints, Phase II – A/C loads

7.3% reduction in daily A/C energy from blue to reflective blue

Blue Paint

Emittance 0.950

Solar-weighted 

Reflectivity

0.120

Solar-weighted 

Absorptivity

0.880

Solar Reflective Blue Paint

Emittance 0.948

Solar-weighted 

Reflectivity

0.258

Solar-weighted 

Absorptivity

0.742

• 563-Wh battery 

energy savings

• 9.4% reduction in 

battery capacity

• 12-kg reduction in 

battery weight

Solar 

Envelope

A/C Testing

7.3% reduction in 

daily A/C energy 
17

Evaluation of Load Through Glazings

13.3% reduction in daily A/C energy with film over glazings

Baseline Test Configuration – All curtains closed

White Film Test Configuration – Privacy curtains open, sleeper curtain closed

Potential Areas of Impact: 

Improved glazings and privacy 

curtains

• 604 Wh battery energy savings

• 10.1% reduction in battery capacity

• 13-kg reduction in battery weight

Solar 

Envelope

[1]
18

Opportunities for Improved Sleeper Curtain

12.7% reduction in daily A/C energy with idealized sleeper curtain

Volume 

Management

Idealized Sleeper Curtain

Radiant barrier, foam insulation, no air gaps

Baseline Test Configuration – All curtains closed, standard sleeper curtain in use

• 1,153 Wh battery energy savings

• 19.2% reduction in battery capacity

• 25-kg reduction in battery weight

CoolCalc analysis identified potential for sleeper curtain improvements

[1]
19

Manikin Baseline Testing

Baseline characterization for typical resting cooling conditions 

Baseline manikin A/C test conditions

• Standard A/C test configuration (curtains closed)

• Climate control of entire sleeper air volume

• 72°F set point

Very Hot

Hot

Warm

Slightly Warm

Neutral

Slightly Cool

Cool

Cold

Very Cold

Very Comfortable

Just Comfortable

Just Uncomfortable

Very Uncomfortable

Comfort Sensation

Volume 

Management
20

Sleeper Microclimate Evaluation

23.8% reduction in daily A/C energy with microclimate configuration

• Increased control temperature from 72°F to 76°F to reduce overcooling

• Submitted a provisional patent application

Volume 

Management

Comfort Difference Scale 

Positive Values = More comfortable than baseline

Negative Values = Less comfortable than baseline

Sensation Difference Scale 

Positive Values = Warmer sensation than baseline

Negative Values = Colder sensation than baseline
21

Experimental Test Capabilities Development

Emulators provide controllable boundary conditions to a vehicle

HVAC Emulators

• Direct measurement of thermal 

load

• Heating or cooling

• Prescribed boundary condition 

at air inlets to vehicle

• Variable control strategies

Efficient

Equipment

[1]
22

Paint Impact Model Study

Leveraging high performance computing

CoolCalc with high-performance computing system

CoolCalc / HPC 

Interface

High-performance 

Computing (HPC) Server 

and Job Manager

Node 1 Node 2 Node i

Individual Tasks

Completed Tasks

Impact
23

95% Heating and Cooling Loads Summary

Significant cooling load reduction, insignificant heating load change

Normalized Cooling Thermal Loads

Normalized Heating Thermal Loads

Impact
24

A/C Model Overview

A/C system model framework for coefficient of performance estimation

• A/C system model 

– Finite volume formulation of energy, 

momentum, and continuity

– Matlab/Simulink environment, allows for 

easy modificiation

• Model results validated to within 

3% of 22 steady-state test data 

points

• Allows for addition of detailed 

models one component at a time

• Stand-alone and integrated with 

Autonomie vehicle model 

software version

24 0 5 10 15 20

0

1

2

3

4

5

6

7

8

Test Point

Heat Exchange Rate [kW]

measured

simulated

Evaporator Heat Transfer Rate

avg. error: 2.2%

200 250 300 350 400 450 500

103 Pressure [kPa]

Specific Enthalpy [kJ/kg]

Thermodynamic Cycle on the P-h Diagram

mix boundary

measured

simulated
25

95%

(2σ)

99.7%

(3σ)

Auxiliary AC System Battery Sizing

National-level analysis applied to guide system design Impact

Percent of Cooling Days, Combined U.S. Locations

95% 99.7% 100%

Black 15.6 kWh 21.8 kWh 29.7 kWh

White 10.5 kWh 15.1 kWh 21.4 kWh

Improvement 32.7% 30.7% 27.9%

Example Results – Auxiliary AC System Battery Sizing 

Dependent on A/C System Performance, Inverter Efficiency, Climate Control Settings

Example results based on 

preliminary assumptions
26

Fuel-use Estimation Methodology

Long-Haul Truck 

Vehicle Parameters

Model 

Inputs

Thermal Load (t)

Ambient Temp (t)

HVAC 

Load

Engine 

Speed

Fuel Use (t)

Vehicle Fuel Use Map

HVAC System Map

Autonomie 
27

Collaboration Summary

• 21st Century Truck Partnership

• Kenworth

– Fully instrumented and tested for thermal-load measurements

– Developed, validated, and released CoolCalc model

• Volvo Trucks

– Completed thermal testing

– Developed and validated CoolCalc model, 

– CoolCalc model application

• Daimler Truck, Super Truck Program 

– Completed thermal testing of Super Truck

– Developed and validated CoolCalc model

• Oshkosh Truck 

–Modeled vehicles with CoolCalc

• PPG Industries

– Evaluated advanced paint technology

• 3M Renewable Energy Laboratory 

– Evaluated solar reflective film

• Aearo Technologies / E-A-RTM Thermal Acoustic Systems

– Evaluated insulation packages

• Dometic Environmental Corporation

– Evaluated electric A/C system

• Additional Partners: Bayer, Sekisui, and MTNW

1

2

3

4
28

Summary/Conclusions

Test Configuration Beta Cooling Reduction 

[% of A/C] Potential Impact

Black to White (Previous result) 31.1% 20.8% No cost

immediate payback

Blue to Solar Reflective Blue 6.0% 7.3% Benefit while maintaining 

branding and aesthetics

Film over Glazings N/A 13.3% Advanced glazings

Improved privacy curtains

Idealized Sleeper Curtain N/A 12.7% Improved sleepercurtain design

Microclimate Configuration N/A 23.8% Condition occupant rather 

than vehicle interior

• Added CoolCalc features – Parallel run capability, large-scale analysis tool, processdriven tool, convection model GUI, weather-viewer tool

• Applied CoolCalc to guide outdoor testing – Solar-reflective paint and sleeper curtain

• CoolCalc model prediction of beta for solar soak testing of paints was within 4.5% of 

experimental results

• National-level paint analysis confirmed strong sensitivity of cooling loads and showed 

insensitivity for heating loads to paint color

• Developed HVAC emulators for direct measurement of thermal load in vehicles
29

Contacts

Special thanks to:

• David Anderson and Lee Slezak

Advanced Vehicle Technology 

Analysis and Evaluation Vehicle 

Technologies Program

For more information:

Principal Investigator: 

Jason A. Lustbader

National Renewable Energy 

Laboratory

Jason.Lustbader@nrel.gov

303-275-4443

[1]