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.

Duty Cycle Analysis & Tools: 

Maximizing Vehicle Performance

High Efficiency Advanced 

Trucks Session

HTUF 2009 – Atlanta, GA

October 28, 2009

Kevin Walkowicz – NREL 

Advanced Vehicle Testing 

Activity

NREL/PR-540-46972
National Renewable Energy Laboratory Innovation for Our Energy Future

Duty Cycle Affects MPG and Emissions!

0 10 20 30 40 50 60

WVUINTER

CBDTRUCK

COMMUTER

WVUSUB

Transit Bus

Local

ARTERIAL

CILCC

CSHVR

WVUCITY

Drive Cycle

Percent Improvement in Fuel Economy by HEV

Example of HEV improvement in fuel economy over various duty cycles:
National Renewable Energy Laboratory Innovation for Our Energy Future

It gets more complicated with PHEV’s!

Add in another factor: distance driven on a given drive cycle –

MPG is not linear

You’ll now need to know: 

1) What type of cycle do I have? and

2) How long do I drive on that cycle in order to calculate mpg? 

% MPG Improvement

Distance

0

PHEV

HEV

Baseline

SOC of Battery

initial

CS

This transition point 

varies based on duty 

cycle
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So, Who Should Care and Why? 

Top 3 Questions in Each Area

Fleets: 

• When considering a large purchase of advanced technology vehicles: 

1. What benefit will this technology have in ‘my’ fleet?

2. What’s the payback?

3. Where should I place the vehicles in my fleet?

OEM’s:

• When designing a system:

1. What is the range of performance observed for the vehicle type ?

2. What should we target our design for? (component sizing, control, etc)

3. How should we test the vehicle?

Regulators/Funding Agencies:

• When considering funding implementation:

1. What is ‘real’ benefit in a fleet?

2. How to assign vehicle HEV credits?

3. Do we need to target specific locations or routes?
National Renewable Energy Laboratory Innovation for Our Energy Future

All These Important Questions...

Fleets: 

• When considering a large purchase of advanced technology vehicles: 

1. What benefit will this technology have in ‘my’ fleet?

2. What’s the payback?

3. Where should I place the vehicles in my fleet?

4. Will the performance of the vehicle in my fleet match that of others? 

OEM’s:

• When designing a system:

1. What is the range of performance observed for the vehicle type ?

2. What should we target our design for? (component sizing, control, etc)

3. How should we test the vehicle?

Regulators:

• When considering funding implementation:

1. What is ‘real’ benefit in a fleet?

2. Is the benefit claimed legitimate?

3. Do we need to target specific locations or routes?
National Renewable Energy Laboratory Innovation for Our Energy Future

All These Important Questions...

Fleets: 

• When considering a large purchase of advanced technology vehicles: 

1. What benefit will this technology have in ‘my’ fleet?

2. What’s the payback?

3. Where should I place the vehicles in my fleet?

4. Will the performance of the vehicle in my fleet match that of others? 

OEM’s:

• When designing a system:

1. What is the range of performance observed for the vehicle type ?

2. What should we target our design for? (component sizing, control, etc)

3. How should we test the vehicle?

Regulators:

• When considering funding implementation:

1. What is ‘real’ benefit in a fleet?

2. Is the benefit claimed legitimate?

3. Do we need to target specific locations or routes?

The answer to all these 

questions: It depends on the 

Duty Cycle
National Renewable Energy Laboratory Innovation for Our Energy Future

Why is this important to Fleets?

Example: What is the payback?

Typical Example: Purchase a ‘traditional’ truck or an ‘HEV’? The ‘HEV’ costs $25k 

more. It’s driven 20,000 miles per year and it currently gets 10 mpg. Fuel 

=$3.00/gal. 

(Cost = $6,000/vehicle/yr)

0 10 20 30 40 50 60

WVUINTER

CBDTRUCK

COMMUTER

WVUSUB

Transit Bus

Local

ARTERIAL

CILCC

CSHVR

WVUCITY

Drive Cycle

Percent Improvement in Fuel Economy by HEV

• If the HEV gets a +10% improvement (11 

mpg) the payback through fuel reduction is 

roughly 46 years ($5400/vehicle/year)

• If the HEV gets a +50% improvement (15 

mpg) my payback (through fuel reduction) is 

roughly 12 1⁄2 years ($4000/vehicle/year)

Bottom Line: you’d better know where you’ll fall 

on fuel economy before you invest!

EPA Window Sticker: ‘Actual mileage may vary’!
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Why is this Important to Fleets?

Example: Where should I place the vehicles in my fleet?

Example: One fleet might have a large variation in drive characteristics and it 

might not make sense to place vehicles on certain routes.

0

10

20

30

40

50

60

MANHATTAN BUS

NYCC

4400 Truck Low

WVU CITY

NYC COMP

HTUF Class 6 PDDS

Automated Side Loader

ECE Segment

HTUF Class 4 PDDS

ORANGE COUNTY BUS

CBD -ReFuel version

CBD - Dieselnet version

CILCC

Braunschweig

CSHVC

HHDDT Transient 

...

4400 Truck Average

JP_JE05

BAC Cycle

UDDS SCHEDULE D

Dray Truck Cycle -...

FTP 72

WVU 5 PEAK

ECE + EUDC Low 

...

ECE +EUDC Cycle

FTP 75

SFTP SC03

RUCSBC

4400 Truck High

CA UNIFIED CYCLE

CARB HHDDT

BAC Arterial Segment

DALups

IM240

EUDC Low Power 

...

EUDC Segment

HHDDT Cruise Segment

BAC Commuter 

...

HWFET

SFTP US06

Average Speed Over Cycle (mph) of Select Cycles
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Why is this Important to OEMs?

Example: 

1. What is the range of performance observed for the vehicle type ?

2. What should we target our design for?

3. How should we test the vehicle?

74.6% 50.6% 48.3% 35.9% 30.3% 31.4% 25.0%

6.8 mph 12.3 mph 12.6 mph 23.4 mph

Average Cycle Speeds:
National Renewable Energy Laboratory Innovation for Our Energy Future

Why is this important to funding agencies?

Example:

• When considering funding implementation:

1. What is ‘real’ benefit in a fleet?

2. How to assign vehicle HEV credits?

3. Do we need to target specific locations or routes?

38.7% 28.6% 26.6% 17.0% 17.8% 16.9% 10.1%

Example: If you justify based on emissions, consider cost per ton of NOx Reduction:
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What else can you do with DC info?

If you know a few measured data points, you can now predict % improvement 

for other routes/duty cycles fall based on a few key metrics: Be Smart With 

Your Testing

• Kinetic Intensity is an attempt to provide a measure of acceleration in cycle
National Renewable Energy Laboratory Innovation for Our Energy Future

Doesn’t the industry study this already?

Some do - traditional attempts to determine appropriate duty 

cycle have included: 

1) Using previously published data for a vocation (MAN, CILCC, OCTA, etc)

• Not always accurate for specific location

2) Using limited or basic metrics like ‘stops per mile’ or ‘average speed’ 

based on overall fleet averages or ‘desired’ routes

• Not specific to tell you enough

3) Measuring actual vehicles and large numbers or vehicles 

• Takes a long time to design and complete this process without a 

process or tool
National Renewable Energy Laboratory Innovation for Our Energy Future

There could be an easier way....

1. Method to help users generate and better understand their specific drive cycle (fleet wide, region 

or depot/local level)

2. Generate custom test cycles (~30 mins) from their large set of on-road experimental data.

3. Compare their own user supplied data to known and common industry test cycles – answers the 

question: what cycle should I use to evaluate this technology?

NREL and others have developed ‘tools’ for industry to utilize: 

WVU: A tool that builds new driving cycles from standard cycles – Emissions Focus 

Oak Ridge National Lab: A tool that can generate duty cycles based on data collected by ORNL.

NREL’s Drive Cycle Tool: Created mainly for fuel economy analysis, it provides a simple, 

accessible method to help industry users easily capture their data, analyze it, create and compare it to 

fully understand what is happening in their own fleet: 

Tell Me What I Have in My Fleet!
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Specifics – What Does the NREL Tool Do?

1. It Provides for User Directed 

Analysis of User Supplied Data

• Extracts:

• Combines or Splits data:

• Filters: ~10 filters

• Calculates all known stats (55 and 

counting)

User Data: Multiple 

Days of Route Data 

(split or combined)
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Specifics – What Does the NREL Tool Do?

1. It Provides for User Directed 

Analysis of User Supplied Data

• Extracts:

• Combines or Splits data:

• Filters: ~10 filters

• Calculates all known stats (55 and 

counting)

Quickly processes and 

analyzes data in a 

consistent manner
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Specifics – What Does the NREL Tool Do?

2. Cycle Generation – matches fuel economy • Crunch any amount of user supplied data and output a user cycle (speed vs. time) for dynamometer testing or modeling based on raw data inputted

• Goal = creates a short, statistically representative cycle (within 5% accuracy statistically and fuel economy) for dyno testing or modeling

• We’ve validated to show same mpg for short or long cycle within 5%

Original Data (~8000 seconds)

Filtered and Shortened Data 

(~1800 seconds)
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Specifics – What Does the NREL Tool Do?

3. Compare and Select Best Available 

Industry Cycle

• Matched based on user selected statistics 

(mph, stops per mile, kinetic intensity, etc). 

This will tell the user the best cycle to quantify 

MPG. Original data vs ‘best selected’ data 

showed modeled mpg results within 3%

Multiple metric selection criteria 

for ‘best match’ 

Original data to 

compressed
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Summary

Simple GPS 

loggers to 

acquire data

set of daily GPS 

route data

Full understanding of supplied data: 

daily variation info, stats for original, 

filtered and shortened data

Tool filters and sorts 

data in desired set

0

10

20

30

40

50

60

MANHATTAN BUS

NYCC

4400 Truck Low

WVU CITY

NYC COMP

HTUF Class 6 PDDS

Automated Side ...

ECE Segment

HTUF Class 4 PDDS

ORANGE COUNTY BUS

CBD -ReFuel version

CBD - Dieselnet version

CILCC

Braunschweig

CSHVC

HHDDT Transient ...

4400 Truck Average

JP_JE05

BAC Cycle

UDDS SCHEDULE D

Dray Truck Cycle -...

FTP 72

WVU 5 PEAK

ECE + EUDC Low ...

ECE +EUDC Cycle

FTP 75

SFTP SC03

RUCSBC

4400 Truck High

CA UNIFIED CYCLE

CARB HHDDT

BAC Arterial Segment

DALups

IM240

EUDC Low Power ...

EUDC Segment

HHDDT Cruise ...

BAC Commuter ...

HWFET

SFTP US06

Average Speed Over Cycle (mph) of Select Cycles

Closest ‘Standard’ Cycle Match

User Specific Test Cycle Generated

cycles with 

similar mpg
National Renewable Energy Laboratory Innovation for Our Energy Future

Future – Tool Moving Forward

1. DOE Clean Cities / NREL will be implement a web 

based version of tool and make this available to the 

general public

• Allow users (fleets or individuals) to upload their own 

data, generate a custom drive cycle that represents 

their daily driving habits, and finds ‘best fit’ standard 

cycle –

• useful to see if ‘actual’ driving does not match the 

industry standard test cycle

• Will be user-friendly and ‘secure’

2. Improve Visualization and interaction capabilities

• Graphically select individual sections of source data 

from which to generate test cycles

3. Modify tool to analyze duty cycle characteristics for 

other parameters (battery duty cycle, temperatures, 

etc)

4. Tie this tool and others into accessible data bases for 

industry to utilize
National Renewable Energy Laboratory Innovation for Our Energy Future

Future – Tool Moving Forward

1. DOE Clean Cities / NREL will be implement a web 

based version of tool and make this available to the 

general public

• Allow users (fleets or individuals) to upload their own 

data, generate a custom drive cycle that represents 

their daily driving habits, and finds ‘best fit’ standard 

cycle –

• useful to see if ‘actual’ driving does not match the 

industry standard test cycle

• Will be user-friendly and ‘secure’

2. Improve Visualization and interaction capabilities

• Graphically select individual sections of source data 

from which to generate test cycles

3. Modify tool to analyze duty cycle characteristics for 

other parameters (battery duty cycle, temperatures, 

etc)

4. Tie this tool and others into accessible data bases for 

industry to utilize
National Renewable Energy Laboratory Innovation for Our Energy Future

Future – Tool Moving Forward

1. DOE Clean Cities / NREL will be implement a web 

based version of tool and make this available to the 

general public

• Allow users (fleets or individuals) to upload their own 

data, generate a custom drive cycle that represents 

their daily driving habits, and finds ‘best fit’ standard 

cycle –

• useful to see if ‘actual’ driving does not match the 

industry standard test cycle

• Will be user-friendly and ‘secure’

2. Improve Visualization and interaction capabilities

• Graphically select individual sections of source data 

from which to generate test cycles

3. Modify tool to analyze duty cycle characteristics for 

other parameters (battery duty cycle, temperatures, 

etc)

4. Tie this tool and others into accessible data bases for 

industry to utilize
National Renewable Energy Laboratory Innovation for Our Energy Future

Thank You!

Take Aways:

1. Duty Cycle Matters – has a large effect on fuel economy

2. Easy to Use, Fleet Focused Tool Now Available For Use to More 

Fully Understand This

– For More Info:

• NREL Tool and NREL Fleet Activities:

– Kevin Walkowicz – NREL’s Advanced Vehicle Testing Activity

– Kevin.walkowicz@nrel.gov

– 303-275-4492

– Acknowledgements:

• Work Funded by DOE’s Vehicle Technologies Program:

– Lee Slezak – Advanced Vehicle Program Manager for Vehicle and Systems 

Simulation and Testing
National Renewable Energy Laboratory Innovation for Our Energy Future

WVU Tool Info: An interactrive design tool has been developed in Matlab that allows building new driving cycles through the concatenation of

individual microtrips obtained by segmentation of second by second measurements from standard cycles. The generated new cycles have prescribed 

characteristics in terms of relevant parameters such as average speed, stops per mile, percentage idle, speed standard deviation, and kinetic intensity. 

The selection of microtrips to achieve the desired cycle characteristics is performed using a customized genetic algorithm. The generated cycles are 

used to increase the available database for regression-based modeling of fuel efficiency and emissions of CO2, CO, NOx, HC, and PM. The validation 

of the approach is currently in process at WVU.

Tu J., Wayne W. S., Perhinschi M. G., “Correlation Analysis of Duty Cycle Effects on Exhaust Emissions and Fuel Economy”, submitted to the 

Journal of the Transportation Research Forum, January 2009

Tu J., Perhinschi M. G., Wayne W. S., Marlowe C., Tamayo S., Clark, N. N., “Development of Duty Cycle Generator Based on Genetic

Algorithm for Emissions and Fuel Economy Modeling”, 19th Coordinating Research Council (CRC) On-Road Vehicle Emissions Workshop,

Poster Session, March 23-25, 2009, Hyatt Regency Mission Bay, San Diego, CA

Marlowe C., “Development of Computational Tools for Modeling Engine Fuel Economy and Emissions”, MS Thesis in Mechanical Engineering, 

Department of Mechanical and Aerospace Engineering, West Virginia University, 2009

Mario G. Perhinschi, PhD

Assistant Professor, West Virginia University, Morgantown, WV 

(304) 293-3301

Mario.Perhinschi@mail.wvu.edu

ORNL Tool Info: “The Oak Ridge National Laboratory has developed a Duty Cycle Generation Tool (DCGenT) that can generate duty cycles of user 

specified duration and user specified characteristics (e.g., metro/urban/rural, good/poor weather conditions, road grade, etc.) based on data collected 

from real-world driving environments. The data base for Class-8 long-haul operations contains more than 750,000 miles of driving data. Contact Bill 

Knee 865.946.1300, kneehe@ornl.gov for additional information, and download the Class-8 Final Report that discusses the data collection effort, the 

collected data and the duty cycle generation tool from: http://cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2008-122.pdf. ORNL is currently 

engaged in collecting medium truck performance data on two-of four vocations in the Class-6/-7 domain.”

Bill Knee 865.946.1300, kneehe@ornl.gov

Additional Information