# GEM Validation Greenhouse [Gas] Emission Model

Southwest Research Institute 11 December 2014

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# Outline

• Introduction • Test vehicles, test platforms, and drive cycles • Test configurations 

## • GEM validation • Understanding test variation • GEM use enhancements • Summary

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# Introduction

• 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

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# Role of SwRI in GEM Development

• In earlier Work Assignments, SwRI worked on adding certain component models to GEM

– Current version developed by the EPA 

## • In recent Work Assignments, SwRI role is as a test house validating GEM results by

– Comparing test data to GEM



### – Analyzing and understanding differences

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# 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)

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# 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) 

## • Characteristics later in the presentation

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# 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)
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# Criterion for GEM Validation

• GEM simulation is deterministic and produces a single MPG number for a given vehicle configuration over a given drive cycle • Testing is not so. A given vehicle configuration over a given drive 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
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# 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

– 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; see Appendix for details on box - plot as used here

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# Summarizing All Data Composite Plots – All Vehicles All Tests


## • A compact way to display MPG difference between mean test data and GEM results for all data 

## • More elaborate analysis of MPG difference possible via constructed kernel density

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# Understanding MPG Difference Drive Cycle Characteristics


### • See next two slides for time - chart and 2 - d density plot of vehicle speed “v” and acceleration “a” • CARB: Short duration, yet fairly transient • Parcel: Long duration; lower speeds; higher accelerations; heavy idle time • Utility: Long duration; lower speeds; lower accelerations; steady - state cruise at various speeds • WHVC: Medium duration; balanced mix of transient operation and highway cruise (higher speeds)

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# 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  Refuse truck (and 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 11 - Dec - 2014 2411 - Dec - 2014 2511 - Dec - 2014 26© Copyright Southwest Research Institute ® 2014 

# 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
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# 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

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# GEM: Use Enhancements

• Perform vehicle coast - down simulation in GEM and compare to on - road coast - down

– Adjust A, B, C parameters to match coast - down



### – GEM - set A, B, C akin to dynamometer - set A, B, C?

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# GEM as a Screening Tool

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# GEM as a Screening Tool Outside the Designed Use!


## • Parametric study on F650 HIL - stand about to begin for additional GEM validation • Parameters of interest: final drive ratio, drag 

## coefficient, vehicle mass • Conducted parametric study using GEM to get a sense of operating range and corners 

## • HIL testing will be conducted for the corner combinations marked A - I • Legend identifies final drive ratios 11 - Dec - 2014 3111 - Dec - 2014 3211 - Dec - 2014 33

# 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 11 - Dec - 2014 34© Copyright Southwest Research Institute ® 2014 © Copyright Southwest Research Institute ® 2014 

## Appendix

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# Visual Data Analysis: Box and Whisker Plot

• Thick line at median of the sample population (halfway mark) • Bottom edge of the box at first quartile Q1 (i.e., 25% below it) • Top edge of the box at third quartile Q3 (i.e., 25% above it) • Half of sample population within the box • Inter - Quartile Range = Q3 – Q1 • UF = third quartile + 1.5*IQR. UF stands for “upper fence” • Data above UF is labeled “outlier” (circle). Upward whisker ends at the largest value of the sample population below UF • LF = first quartile – 1.5*IQR. LF stands for “lower fence” • Data below LF is labeled “outlier” (circle). Downward whisker ends at the smallest value of the sample population above LF • Width of the box proportional to the sample size 11 - Dec - 2014 36

# Visual Data Analysis: Box and Whisker Plot

• Non - parametric: No assumed underlying distribution • Spread and skew: Spacing between different parts of the plot • Outliers: Uses a generally accepted criterion to mark outliers

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# Box and Whisker Plot: A Familiar Example

• For normal distribution ~ N(0,1):

– μ = 0; σ = 1

– Q1= - 0.6745; Q3 = 0.6745; IQR = 1.349;

– LF=Q1 - 1.5*IQR = - 2.698; UF=Q3+1.5*IQR = 2.698

– 99.3% population between fences [LF,UF]

– 99.73% population between ± 3; i.e., 3 σ

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