Road Grade Testing

November, 2014
Test Plan

• Class-8 vehicle was instrumented for real-time fuel consumption and realtime gaseous emission measurement

– Fuel system supply and return routed through a fuel totaling device capturing 

real-time fuel consumption information

– Raw gaseous emissions were continuously sampled by a portable emissions unit 

providing NO, NO2, NOx, THC, O2, CO and CO2 measurements 

• Vehicle was operated under normal traffic conditions while following a 

prescribed route

– The route was chosen to meet requirements of having a mixture of sustained 

gradation, intermittent light traffic and continuous highway speed operation

– Maximum highway speeds were limited to 75 mph (current speed limit for 

portions of the tested route)

– The route was reversed for half of the testing to mitigate wind direction 

influence on the collected data

• Data was collected over the designated route for a total of 5 valid tests 
Determination of Road Grade

• Approximation of road grade continues to be a 

hurdle for in-use emissions testing

• Possible methods:

– Highway Department road design schematics

– Light Detection and Ranging Measurement (aerial 

based LIDAR)

– Inclinometer

– Solid-State Gyroscope 

– Three axis GPS systems

3
Determination of Road Grade

• Real-time measurement of road grade 

through on-board inclinometer is not 

practical. Vehicle accelerations and 

decelerations interfere with single. Removal 

of these interferences through signal 

processing is difficult.

– Mathematical Morphology

– Neural Networking 

4
Determination of Road Grade

• Running Linear Regression of 

GPS Signal

– GPS based longitude, latitude 

and altitude are already part of 

the acquired data set

– Measurement of altitude 

through GPS signal typically is 

not as accurate as latitude and 

longitude

– Signal filtering and calculation 

window variation can be 

applied to help obtain a 

realistic road grade profile 

5
Determination of Road Grade

• Gyro Sensor Road Grade 

Measurement

– Solid state gyros typically used

 Laser based gyros

 Fiber optic based gyros

– Vehicle suspension can influence 

resulting road grade measurement

– Would require matching of 

collected data to previously 

acquired spatial data set

6
Collected Data

• Fuel flow

– Using fuel totalizer system (AVL KMA)

– Able to accurately capture dynamic fuel flow during transient operation

• Raw gaseous emissions

– Used AVL Portable Emissions Monitoring System (PEMS)

– Real-time particulate matter was not measured

– All emissions were sampled from a location after all aftertreatment

devices

• Engine operation parameters

– Captured through J1939 protocol broadcast through OBD system

• Environmental and Geolocation

– Ambient temperature, pressure and humidity were recorded over 

testing duration

– Geolocation was recoded using GPS receiver information (providing 

latitude, longitude, altitude)
Cycle Route

• Total mileage = 295 miles

• Total drive time ~ 5 hours

• Maximum legal speed = 75 mph
Cycle Elevation Profile
Engine Parameter Profile
Fuel Consumption Profile
Gaseous Emissions Profile
Engine Operating Map – NTE Analyis
Sample Test Summary
GEM Validation

November, 2014
Outline

• Introduction

• Test vehicles, test platforms, and drive cycles

• Test configurations

• GEM validation

• Understanding test variation

• GEM use enhancements

• Summary
Introduction

• EPA and NHTSA program to improve fuel economy of 

and reduce GHG from MD/HD trucks and buses

• GEM is a vehicle simulation tool

– Meant for showing compliance to US FE and GHG standards; 

not designed for research and development

– Used by all vehicle OEMs

• GEM capable of simulating

– Engine: All technologies tested on engine stand – fuel map, 

accessory load, friction, pumping, parasitic loss etc.

– Transmission: MT, AMT, AT

– Driveline: Axles, shafts

– Vehicle: Rolling resistance, aerodynamics
Test Vehicles and Test Platforms

• Test vehicles

– Ford F650 – Class-6 Vocational Tow Truck with AT

– Kenworth T270 – Class-6 Vocational Box Truck with AT

– Kenworth T700 – Class-8 Tractor with AMT

– Refuse Truck with AT

– Freightliner Cascadia – Class-8 Tractor with MT

• Test platforms

– Heavy-duty Chassis Dynamometer (HDC)

– Medium-duty hardware-in-the-loop stand (HIL or C22)
Drive Cycles

• Six drive cycles

– 55 MPH (with grade option)

– 65 MPH (with grade option)

– CARB HHDDT Transient (CARB)

– HTUF Class-6 Parcel (Parcel)

– NREL CILCC (Utility)

– UNECE WHVC (WHVC)

– Refuse Hauler 2 (in lieu of Parcel for Refuse Truck)

• Characteristics later in the presentation
Test Configurations

• Seven configurations

– Baseline (“as received” parameters)

– ± Cd

– ± Crr

– + Mass

– Optimized

• 6 cycles x 7 configurations = 42 cases for parametric 

study (F650 HIL, T270 HDC, and T700 HDC)

• 6 cycles x 1 baseline configuration = 6 cases (F650 

HDC, Refuse HDC, Cascadia HDC)
Criterion for GEM Validation

• GEM simulation is deterministic and produces a single 

MPG number for a given vehicle configuration over a 

given test cycle

• Testing is not so. A given vehicle configuration over a 

given test cycle likely produces a different MPG 

number for each run

• EPA has chosen to compare test mean to GEM result –

match within 5% considered good

• But, comparison to test mean should not overlook 

standard error of test mean and test variance
Examining Test Data and GEM Results

• More than 130 combinations tested

– Vehicles – F650, T270, T700, Cascadia, Refuse

– Drive cycles – 55 MPH, 65 MPH, CARB, Parcel, 

Utility, WHVC, Refuse Hauler 2

– Parameter configurations – Cd, Crr, mass

– Platforms – Chassis Dynamometer, HIL

• Time-traces useful for detailed comparison and 

understanding of HDC and GEM data

• Box-plot, density-plot useful to summarize
F650; Test Platform HIL; grouped by test cycle

mean(HIL) [MPG]

GEM [MPG]

8

10

12

14

8 10 12 14

SwRI 2014-12-07 12:06

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC
T700; Test Platform HDC; grouped by test cycle

mean(HDC) [MPG]

GEM [MPG]

4

6

8

10

4 6 8 10

SwRI 2014-12-07 12:10

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC
T270; Test Platform HDC; grouped by test cycle

mean(HDC) [MPG]

GEM [MPG]

6

8

10

12

6 8 10 12

SwRI 2014-12-07 12:09

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC
Refuse Truck; Test Platform HDC; grouped by test cycle

mean(HDC) [MPG]

GEM [MPG]

3

4

5

6

7

3 4 5 6 7

SwRI 2014-12-07 12:10

55 MPH

65 MPH

CARB

Refuse Hauler 2

Utility

WHVC
Test Data; grouped by vehicle

mean(Test Data) [MPG]

GEM [MPG]

4

6

8

10

12

14

4 6 8 10 12 14

SwRI 2014-12-07 12:02

F650

Refuse

T270

T700
Test Data; grouped by test cycle

mean(Test Data) [MPG]

GEM [MPG]

4

6

8

10

12

14

4 6 8 10 12 14

SwRI 2014-12-07 12:02

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

Refuse Hauler 2
Distribution of Test Statistic

Composite Plots – All Vehicles All Tests

• Need a compact way to assess quality of test 

data for all vehicles over all test cycles

– Constructed kernel density function of coefficient of 

variation of test data defined as cov=mean/std

 WHVC most repeatable of all transient drive cycles

 F650 most repeatable of all vehicles

– Constructed kernel density better than histogram

 Histogram: strong dependence on bin boundaries

 Kernel density: choose “bandwidth” carefully

 Area under constructed kernel density is 1
Kernel Density: HDC Coef of Variation by Cycle

cov=sigma/mu

Density

0

50

100

150

200

250

0.00 0.01 0.02 0.03

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

Refuse Hauler 2
Kernel Density: HDC Coef of Variation by Vehicle

cov=sigma/mu

Density

0

50

100

150

200

0.00 0.01 0.02 0.03

F650

Refuse

T270

T700
Understanding Test Variation

Dynamometer Control and Vehicle Operation

• F650: Mix of HDC and HIL data; HDC using roll 

torque meter; HIL using transmission output 

shaft torque meter; robotic driver; AT

• T270: HDC using roll torque meter; human 

driver; AT

• T700: HDC using roll torque meter; human 

driver; AMT

• Refuse Truck: HDC using driveshaft torque 

meter; robotic driver; AT
Understanding Test Variation

Additional Factors

• [Human] driver behavior

– Pedal aggressiveness

– Transmission shift schedule and shift duration

 Shift times affected by HDC speed behavior

• Transmission temperature

• Tire temperature

• After-treatment thermal management

• Accessory load
Summary

• Development version of GEM incorporates several 

engine, transmission, driveline, and vehicle 

technologies being considered to meet FE and 

GHG standards for MD/HD vehicles

• GEM validated against 130+ test cases (multiple 

runs) of different size vehicles

• Difference between GEM FE and test data 

generally less than 5%

– Mismatch explainable

• Sources of variation in test data must be 

understood and considered during GEM validation