## Road Grade Testing

November, 2014

## Test Plan • Class -8 vehicle was instrumented for real -time fuel consumption and real time 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, NO 2, NOx, THC, O 2, CO and CO 2 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

## 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

## 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