NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by th e 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 idling 1

o 6.8% of total long

- haul fuel use 1



### • Increased idling regulation at the local, state, and national level 2



### • 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 Transporta tio n 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 21 st Century Truck and SuperTruck Goals THE OPPORTUNITY3 

# 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 technologies5 Reductions in load have a larger impact on fuel use due to

equipment and delivery losses. 

# System Level Approach

Reduce Load
Efficient Delivery
Efficient Equipment6 

# 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 Idle
Reduction Systems Opaque Surface Treatment Insulation & Advanced Materials

Comfort
- Based Design Efficient HVAC & Controls Advanced Glazings Curtains & Shades

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[2]

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# 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 T racks the sun and captures diurnal solar impacts



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

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

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

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

7:00 pm 9:00 pm11 

# Experimental Setup

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

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 TMC

recommended practice with additional locations



### • U 95 

### = ± 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 Conductive Pathways

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

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# Spectral Property Results Measured properties to be used for modeling15 

# Advanced Paints, Phase II – Solar Soak Experiment and CoolCalc agreement, blue  solar reflective blue Solar Envelope 

### 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 energy17 

## 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 Management20 

## 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 p atent 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 baseline21 

### 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 i

Node 2 Individual Tasks Completed Tasks Impact23 

## 95% Heating and Cooling Loads Summary Significant cooling load reduction, insignificant heating load change

Normalized Cooling Thermal Loads

Normalized Heating Thermal Loads Impact24 

# 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 Evaporator Heat Transfer Rate avg. error: 2.2%25 95% (2 σ ) 99.7% (3 σ ) 

## 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 assumptions26 

# 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

Autonomie27 

# Collaboration Summary

• 21 st 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

428 

# 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 sleeper

curtain design Microclimate Configuration N/A 23.8% Condition occupant rather than vehicle interior

• Added CoolCalc features

– Parallel run capability, large

- scale analysis tool, process

driven 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 vehicles29 

# 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

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