Office of Transportation and Air Quality EPA420-P-05-001 February 2005



# Fuel Consumption Modeling of Conventional and Advanced Technology Vehicles in the Physical Emission Rate Estimator (PERE)



# DraftEPA420-P-05-001 February 2005



## Fuel Consumption Modeling of Conventional and

Advanced Technology Vehicles in the Physical Emission Rate Estimator (PERE)

Draft

Edward K. Nam Robert Giannelli

Assessment and Standards Division Office of Transportation and Air Quality U.S. Environmental Protection Agency

NOTICE This Technical Report does not necessarily represent final EPA decisions or positions. It is intended to present technical analysis of issues using data that are currently available. The purpose in the release of such reports is to facilitate an exchange of technical information and to inform the public of technical developments.1 Table of Contents

EXECUTIVE SUMMARY .............................................................................................................. ...........................3

INTRODUCTION ................................................................................................................... ....................................6

SECTION I: ROAD LOAD BASICS.................................................................................................... .....................7

V EHICLE M ODEL

............................................................................................................................... ........................7

SECTION II: CONVENTIONAL GASOLINE INTERNAL COMBUSTION ENGINES...................................9

G ASOLINE S PARK I GNITED I NTERNAL C OMBUSTION E NGINE ....................................................................................9

SECTION III – TRANSMISSIONS AND FUEL RATE ...................................................................................... .14

T RANSMISSIONS

............................................................................................................................... .......................14 Efficie ncy..................................................................................................................... .......................................15 L IGHT D UTY T RANSMISSIONS

............................................................................................................................... ..16 F UEL R ATE

............................................................................................................................... ...............................17

SECTION IV – MOTORC YCLES ....................................................................................................... ...................17

M OTORCYCLE R OAD L OAD

............................................................................................................................... ......17 M OTORCYCLE E NGINES

............................................................................................................................... ............20 M OTORCYCLE T RANSMISSIONS

............................................................................................................................... 22 M OTORCYCLE V ALIDATION

............................................................................................................................... ......22

SECTION V – LIGHT DU TY DIESEL VEHICLES......................................................................................... ....23

L IGHT D UTY D IESEL V EHICLE R OAD L OADS ...........................................................................................................23 L IGHT D UTY D IESEL E NGINES

............................................................................................................................... ..24 D IESEL L IGHT D UTY T RANSMISSIONS .....................................................................................................................26

SECTION VI – HEAVY DUTY DIESEL VEHICLES ........................................................................................ ..26

D ATA

............................................................................................................................... ........................................26 H EAVY D UTY V EHICLES R OAD L OAD .....................................................................................................................28 R OAD L OAD C OEFFICIENTS FOR H EAVY D UTY T RUCKS AND B USES .......................................................................31 H EAVY D UTY D IESEL E NGINES

............................................................................................................................... 32 H EAVY D UTY T RANSMISSIONS

............................................................................................................................... .39 V ALIDATION

............................................................................................................................... .............................42

SECTION VII: ADVANCED ENGINES AND HYBRID VEHICLES ................................................................43

A DVANCED I NTERNAL C OMBUSTION E NGINE (ICE)................................................................................................44 H YBRID V EHICLES

............................................................................................................................... ....................46 Strategy ....................................................................................................................... .......................................47 Motor/Generato r/Inver ter....................................................................................................... ...........................48 Energy Storage Devices – Batteries , Ultracapacitors and Hydraulics............................................................. .50 Vehicle Weight and ot her Specifications........................................................................................ ....................51 Accesso ries.................................................................................................................... .....................................52 Model Calib ration.............................................................................................................. ................................53 Fuel Economy Va lidation Results ................................................................................................ ......................54 Modal Valida tion Re sults....................................................................................................... ............................57 Electric Ve hicles .............................................................................................................. ..................................61

SECTION VIII – HYDROGEN INTERNAL COMBUSTION ENGINE VEHICLES.......................................61

SECTION IX – HYDROGEN FUEL CELL VE HICLES .....................................................................................62

F UEL C ELL AND H YBRID F UEL C ELL V EHICLES ......................................................................................................62 Validation..................................................................................................................... ......................................652 SECTION X - SE NSITIVITY ........................................................................................................ ..........................67

R OAD L OAD AND T RACK C OEFFICIENTS .................................................................................................................69 C OLD S TART

............................................................................................................................... .............................71

SECTION XI - APPLICATION TO MOVES .............................................................................................. ..........73

H OLE F ILLING U SING PERE........................................................................................................................... .........73 Driving Cycles and Running PERE ................................................................................................ ...................78 Filling Advanced Technology Using PERE ......................................................................................... ..............81 Advanced Technology Hole Filli ng Results ....................................................................................... ................86

SECTION XII - ACKNOWLEDGMENTS.................................................................................................. ...........90

REFERENCES ..................................................................................................................... .....................................91

APPENDIX A: PERE ALGORITHMS.................................................................................................... ...............97

APPENDIX B: TABLES OF PARAMETERS FOR VALIDATION VEHICLES..............................................99

APPENDIX C- COMMENTS FROM ALLIANCE OF AUTOMOBILE MANUFACTURERS, INCLUDING RESPONSES. .................................................................................................................... ......................................100

APPENDIX D- COMMENTS FROM INDEPENDENT REVIEWER: PROFESSOR MARC ROSS, INCLUDING RESPONSES............................................................................................................ ........................1173 Executive Summary

This study proposes a modeling methodology fo r conventional and advanced technology vehicles including:

• Conventional motorcycles

• Conventional gasoline and dies el (light and heavy duty)

• Advanced gasoline internal combustion engine

• Advanced diesel internal combustion engines

• Hybrid electric and gasoline/diesel

• Hydrogen fuel cell and hybrid

• Hydrogen internal combustion engine

The model called PERE (Physical Emission Rate Estimator) is designed to support the new EPA energy and emissions inventory model, MOVES (M Otor Vehicle Emissions Simulator). PERE is a series of stand-alone spreadsheets, which can be run and modified by an informed user. It outputs Pump-to-Wheel (PTW) fuel consumption rates. The purpose of PERE is to fill data gaps in MOVES and to provide a tool to extrapolate to future projections of energy and emissions. The current version of PERE will model the fu el consumption from many of the advanced technologies in MOVES in addition to conventiona l vehicles. Criteria pollutant emissions will be modeled in a later version. If th e user is knowledgeable of the vehicle parameters required in PERE, other technologies can be modeled that ar e not described in this report (e.g. alternative fuels, minimum/mild hybrids, etc.).

MOVES is currently being integrated with GR EET (developed at Argon ne Laboratory), which produces upstream Well-to-Pump (WTP) energy and emissions rates. The combination of PERE, MOVES, and GREET will provide a powerful comprehensive modeling suite for anyone requiring emissions inventory proj ections or life cycle (energy/ emissions) studies for mobile sources into the future.

As the name implies, PERE uses physical princi ples to model propulsion systems in the vehicle. It is therefore built on a relati vely strong foundation. It is base d on a sound, yet elegantly simple model for the internal combus tion engine. Modeling of hybrid s is achieved by inserting a secondary power source (usually a battery/motor combination). Fuel cell vehicles are simulated by replacing the engine with an aggregate fuel cell system. PERE takes vehicle input parameters, then runs the vehicle through dr iving cycles defined by the user and outputs second-by-second fuel consumption rates. All of the processes ar e transparent to the user, which allows for easy updates.

The model is validated to four production conventi onal gasoline (it has been validated to more in previous publications), a number of motorcycles, buses and hea vy-duty trucks. Also validations were performed on five advanced (and hybrid) ve hicles by comparing the PERE output to the rated fuel economy figures. The model performs we ll, in most cases the predictions are within 10%, as the figures below show. This demonstrat es the relative robustness of PERE to model a wide range of technologies.4 0 1 2 3 4 5 6 7palmsprings, 14L,400hp@1800rpm(truck a)palmsprings, 14L,400hp@1800rpm(truck b)palmsprings, 12.7liters,380hp@1800rpmpalmsprings,10.8L,330hp@1800rpmpalmsprings,14.6L,475hp@2100rpmbus 1bus 2bus 3bus 4bus 5fuel economy (mile/gallon) PERE
measured

Figure ES1. Validation of PERE to heavy-dut y trucks and buses as measured on-road.

0 10 20 30 40 50 60 70 80 90

PERE MIT Toyota Camry VW Jetta
gas VW Jetta Diesel Honda Civic DX Honda Civic HX Honda Civic Hybrid Honda Insight Toyota Prius '01 Toyota Prius '04FTP fuel economy (mpg) PERE EPA unadjusted

Figure ES2. Combined fuel economy (city/highway) estimates from PERE compared to vehicle rated values.5 The fuel cell model describes a direct hydrogen PEM fuel cell and hybrid. It is validated to the fuel economy results from the Honda FCX vehicl e. The model captures the fuel economy to within 5% of the measured value.

The report also includes a sensitiv ity analysis, which will help guide users to determine which parameters require more accuracy.

Finally, the report describes how PERE might be used to support MOVES. It provides guidance for how parameters may be estimated in order to perform future projections. It also describes a methodology by which the PERE output is integrated into MOVES.6 Introduction

The EPA is tasked to develop models, which can calculate the inventory of emissions from mobile sources as well as estimate future proj ections. The model presently used for this purpose is MOBILE6. MOVES (MOtor Vehicle Emission Si mulator) is the next generation model and will eventually replace MOBILE. MOVES is designe d to be a data-driven model, so that the emissions rates are derived from direct measuremen ts. However, there are substantial portions of the fleet where data is sparse or non-existent. A separate model is needed to fill these “data holes” in MOVES. Moreover, a model is required that can capture the behavior of advanced technology vehicles, which behave very di fferently from conventional vehicles.

The goal of this project is to develop a model, which can capture the behavior of a variety of conventional as well as future or advanced te chnology vehicles for the purpose of estimating emissions inventories. The Physical Emissions Rate Estimator (or PERE) is expected to generate fuel (or energy) consumption rates for MOVES. So, in its present form, the “E” in PERE should stand for “energy” more than “emissions”. Th e model will eventually characterize criteria pollutants [Nam, 2003], but fuel consumption is seen as the first step toward validation of the model. Modeling this “pump-to-wh eel” fuel consumption also allo ws for a life cycle, “well-towheel”, model if PERE output is combined with GREET, which models upstream (“well-topump”) emissions [Wang, 2001].

The primary users of PERE are the MOVES devel opers and users. However, PERE will be made available to the public, when others may fi nd it useful for modeling and comparing fuel consumption from conventional or advanced technology vehicles.

The current report describes the modeling of fuel (and energy) consumption of conventional and advanced technology vehicles usi ng physical principles. PERE is based on a number of models that exist in the l iterature (the references are peppered throughout this document). The model is developed for the following conventional technol ogy types (light and heavy duty): gasoline, diesel, and motorcycle. Previous publications have already presen ted results for many light duty gasoline vehicles. PERE also models advanced te chnologies such as: advanced gasoline/diesel, “moderate” parallel hybrid, “full” parallel hybrid, hydrogen internal combustion engine, electric only, and fuel cell vehicles. Validations are presented where available. These are the technologies, which seem to have near or long term possibiliti es for market penetration. The number of other advanced technology combina tions that could exist are enormous. Such examples are hydraulic hybrids, series hybrids, pl ug-in hybrids, etc. This report does not model all of the possible combinations, however the hope is to demonstrate that PERE is sufficiently robust and/or “generic” such that it can accommod ate many of these technologies if the need arises.

Though the model is based on mathematical and physical principles, it is intended to be aggregate, and is not appropriate for engineerin g or product design. Thus it is designed to model a “typical” vehicle of technology ty pe, rather than a specific vehi cle. However, the validations are conducted on specific vehicles for a general comparison. Due to the approximations made in some of the parameters of the model, it is not ex pected to accurately capture fuel consumption of specific vehicles better than with in 10% of measured values.7 The basic mechanism of PERE involves calculati ng the road load energy required to move the vehicle mass along a driving trace, then distribu ting that energy demand to the various vehicle components (engine, electric motor, fuel cell, etc). The energy components are modeled using overall systems efficiencies. The validations in this report are conducted mainly with certification fuel economy data (though there are two modal test results presented). Where appropriate, simplifications and approximations ar e made using physical constraints, or based on publications in the literature. The model currently only models hot running operation. Cold start factors are handled in MOVES separately, and will only briefly be described in this report.

For the purposes of modeling the future fleet, ou r goal is to allow as many of the significant assumptions as possible to be under the control of the user. However, default values will be presented in this paper. An attempt will be ma de to justify the assumptions in each case.

The report begins by describing conventional vehicl es, both gasoline and dies el (light and heavy duty). It then goes on to briefly examine advan ced engines. Hybrid vehicles are modeled and validated, followed by fuel cell vehicles. The repo rt caps off with a sensitivity analysis and describes how PERE rates might feed into MOVES. Each section is broken up into subsections representing the primary parts of the model: Vehicle, engine (o r fuel cell), transmission, and

motor.

For simplicity, PERE is currently in a spreadsheet format and should be usable for most users, who have a nominal amount of background inform ation on hybrids and fuel cells (as well as spreadsheet skills). The final form that the model takes for the integration may be different from what is presented in this report.

Section I: Road Load Basics

Vehicle Model As Figure 1 shows, the model is basically a “back wards-looking” model in that it takes a driving cycle (second-by-second speed vs. time) input and outputs the energy consumption required to follow the drive trace. The power demand is ba sed on overcoming inertia, road grade, tire friction, and aerodynamic loss. This road-load methodology was mainly introduced by Sovran and Bohn [1981]. This is essentially the V SP equation shown in numerous publications.

ACTIVITY INPUT V,A, Road grade

Vehicle Param:

M, Cr, CdA VSP or Power Demand Internal Combustion Engine 

# F uel C

o

nsum ptio n

Figure 1. Conventional internal combus tion engine vehicle model flow.8 The power demand (in Watts) is the brake (or tractive) power or, VSP*m:

Equation 1 P b = VSP*m = mv[a(1+ g ) + g*grade + gC R ] + 0.5 ρ C D A F v 3
where: v: is vehicle speed (assumi ng no headwind) in m/s (or mps) a: is vehicle acceleration in m/s 2

g : is mass factor accounting for the rotational masses (~0.1) g: is acceleration due to gravity (9.8 m/s 2 ) grade: is road grade C R : is rolling resistance (~0.009)
ρ : is air density (~1.2 kg/m 3 ) C D : is aerodynamic drag coefficient (~0.3) A F : is the frontal area in meters 2 (~2 m 2 ) m: is vehicle mass in metric tonnes.

The rotating mass term, g , is assumed to be 0.1 [Jimenez, 1999]. However it increases at lower gears [Gillespie, 1992]. Other publications have a smaller effect [Thomas and Ross, 1997], however the results are not very sensitive to th is term. The rolling resistance for radial tires, C R , can range from 0.008 – 0.013 for a majority of the on- road passenger car tires. This value can be larger depending on state of inflation, temper ature, ground surface type, and speed (though this effect is minor) [Bosch, 2000; Gillespie, 1992]. H eavy-duty trucks tend to have lower resistance. The aerodynamic drag, C D , varies according to the body shape, and is sometimes provided by the manufacturers. The frontal area, A F , of the vehicle is also sometimes supplied. Where it is unknown, PERE uses 93% of the box frontal area based on a number of vehicles examined by the author. This is calculated using the dimensions available for the vehicles:

Equation 2 A F = (H-GC)*W*0.93 where: H is the vehicle height GC is the ground clearance W is the width.

Alternatively to Equation 1 , is the dynamometer load equation:

Equation 3 P b = Av + Bv 2 + Cv 3 + mv[a + g*grade]

where the coastdown coefficients: A, B, C, are rolling, rotating and aerodynamic resistive coefficients

, respectively. These are determined from track coast-down tests and are available from the EPA certification database for certified light duty vehicles. The “A” coefficient roughly corresponds to the tire ro lling resistance terms in Equation 1 . “B” tends to be small, and describes higher order rolling resist ance factors in addition to mech anical rotating friction losses. The “C” term represents the air drag coefficien t terms. These two equations are not necessarily identical. When available, the coastdown (track ) coefficients should be used in Equation 3 since this more accurately represents the test conditions. This will be di scussed in greater detail in the sensitivity section, where PERE is r un using both equations for comparison.9 Older vehicles (pre-2000) only have a single Tractive Road Load HorsePower (TRLHP) rating provided by manufacturers [Sierra, 2000]. For the purposes of Inspection and Maintenance (IM) testing, A, B, and C coefficients have been estimated from TRLHP using the following equations:

Equation 4 A = (0.35/50)*TRLHP(hp/mph) B = (0.10/50)*TRLHP(hp/mph 2 ) C = (0.55/50)*TRLHP(hp/mph 3 )

The power is distributed 35%, 10% and 55% into each of the 3 load categories [USEPA, 1994].

The power (or energy) determined from the roadload can then be distributed, on a second-bysecond basis, throughout the powertrain. After en gine/transmission losses are included, this energy consumption forms the basis for PERE. For conventional vehicles, the primary energy conversion device is the internal combustion engine, which is the subject of the next section.

Section II: Conventional Gasoline Internal Combustion Engines

Gasoline Spark Ignited Internal Combustion Engine The internal combustion engines (ICE) convert s chemical into mechanical energy. This combustion process, as well as the losses inherent to it, is a critical el ement to the modeling of vehicles. Modern vehicles powered with gasoline have been the subject of several models in the literature. This paper remains consistent with the approach developed by Ross and An [1993]; Barth et al. [1999]; and Weiss et al. [2000] as well as other author s. The formalism is described well in Ross [1997], which will be briefly reviewed here.

The basis for the engine model lies in the lin ear relationship between brake power and fuel consumption, both represented in mean effectiv e pressure (mep in bar) units. The power equivalent of fuel is:

Equation 5 P f = FR*LHV

where FR is the fuel consumption rate in grams per second LHV is the lower heating value of the fuel (approximately 43.7 kJ/g for gasoline)

In mep [kPa], this becomes:

Equation 6 fuel mep = 2000*P f /(VN)

where V is the engine displacement in liters N is the engine speed (rps)

One can model it as follows:10 Equation 7 Fuel mep = k + bmep/ η

or Equation 8 fuel mep = (fmep + bmep)/ η

where k is the engine friction related term (in friction mep terms, k = fmep/ η

) and η is a measure of the indicated or thermal e fficiency of the engine, i.e.

, it is the fraction of the energy in the fuel which is converted to useful work. bmep is the brake mean effective pressure. This efficiency describes engine properties such as compre ssion ratio, fuel mixing, fuel type, cylinder temperature, valve timing, spark timing, combus tion chamber geometry, etc. (though some of these affect friction as well) [Muranaka, 1987; Ross, 1997]. Ideally, this is limited by engine heat cycle and thermodynamic 2 nd law losses at the upper limit.

The indicated thermal efficiency is not to be confused with “mechanical” efficiency, or even with “overall” engine efficiency (discussed belo w). k and fmep reflect the mechanical losses in the engine mainly from rubbing, pumping, and a uxiliary load losses. Specifically, the rubbing losses can originate from valves, gears, piston ri ngs, lubricant, piston, crankshaft, etc [Milington & Hartles, 1968]. The pumping losses are deri ved from the difference between the intake cylinder and ambient pressures. The throttle pl ate, and intake, exhaust and valve geometry on gasoline engines cause this loss in “volumetric efficiency” [Pa tton, et al., 1989]. As an engine “breathes” better, the pumping losses decrease a nd its volumetric efficiency increases. Thus an engine with 4 valves per cylinder is typically more fuel efficien t than one with 2 valves. Also diesel engines lack throttle pl ates, so they have improved vol umetric efficiency over gasoline engines. Throttling results in about 25% of the friction losses [Ross, 1997]. The product of mechanical efficiency (which we have not quant ified yet) and thermal efficiency is the overall engine efficiency, which will be discussed in more detail below.

Given the fuel mep, to obtain a mass fuel rate, one only needs engine displacement and speed. The bmep, is equivalent to brake power and can be determined from road load using the ubiquitous road load Equation 3. Equation 8 is e quivalent to the one used by Weiss , et al. [2000]:

Equation 9 imep = fmep + bmep

where imep is the indicated mep equivalent to fuel mep* η

.
This relationship has been plotted on Figure 2 be low for a series of 10 different engines at many different operating conditions, where the wide-o pen throttle points have been omitted [Nam & Sorab, 2004]. According to the figure the indicate d or thermal efficiency (inverse slope) is η ~ 0.40, while k ~ 4.24 bar and fmep ~ 1.72 bar for this diverse family of engines. This procedure for estimating the engine friction analytically is referred to as the “Willans Line” method [Millington & Hartles, 1968]. It is similar to experimentally motoring the engine down. The largest uncertainty associated with this methodology lies in the slight curvature of the lines especially at high load. This is why the high lo ad points were omitted, though there is still some curvature [Pachernegg, 1969]. Despite the limitations , the method is quite robust as the lack of11 scatter in Figure 2 demonstrates. For the scale of modeling in this report, this estimate is quite sufficient.

It has been found that the indica ted efficiency has not changed significantly over the preceding 3 decades [Nam & Sorab, 2004; Weiss, et al. 2000]. It should be noted that this holds for engines operating at stoichiometry, where there is exactly the right quantity of air to combust the fuel completely. Older vehicles can run rich under various high power drivi ng conditions, and those need to be modeled separately [Goodwin, 1996]. It is assumed that present and future vehicles (which have to meet SFTP standards) will not undergo significant periods of enrichment. However future vehicles may have improved effici ency if lean burn engines (or other advanced technologies) become more prevalent.

y = 2.47x + 4.24
R 2 = 0.99

0 5 10 15 20 25 30

-2 0 2 4 6 8 10 bmep (bar)fuel mep (bar)

Figure 2. Fuel mean effective pressure as a fu nction of brake mep for 10 engines from 4 different manufacturers, omitting wide-o pen throttle points [Nam & Sorab 2004].

It was mentioned earlier that “indi cated thermal” efficiency is not the same as “overall” engine efficiency. The latter includes the effect of mechanical or frictional losses. This can be demonstrated most dramatically in Figure 2. The overall (or “brake”) efficiency of the engine at a given operating (load) point, is the x-axis value (output ener gy) divided by the y-axis value (input energy). For example, at bmep = 2 bar, th e fuel mep = 10 bar, meaning the overall engine efficiency is 20%. Also at bmep = 8 bar, fuel mep = 24 bar, meaning the engine efficiency is 33%. It is clear that the overall efficiency incr eases with the load (omitting wide-open throttle and fuel enrichment) while the indicated thermal efficiency (inverse slope) remains constant at roughly 40%. Thus the indicated e fficiency is the thermodynamic ef ficiency limit of the engine – here the peak total efficiency is 36%. The product of the indicate d and mechanical efficiency is the overall engine efficiency. Thus the mechanical efficiency is depende nt on load and engine speed, and the most efficient operating points ar e those with low engine speed and high load. This highlights the advantage th at hybrids have over conventiona l vehicles: the engine operates more at higher loads and thus higher efficiencies, while the battery (or other energy storage medium) operates during low engine efficiency modes (allowing it to shut down). To calculate a12 final overall vehicle (or “pump- to-wheel” efficiency, it is al so necessary to factor in transmissions and final drive losses. Accessories can also play a minor role. In general the overall efficiency of the vehicle is defined as the amount of fuel ener gy converted to useful work:

Equation 10 LHV ed fuelconsum rk tractivewo Pb EnergyIn EnergyOut Efficiency
* ) (

= =

Though the scatter in Figure 2 is sm all, there is some systematic relationship, which can explain some of the variation. It is we ll known that the friction (or offs et) is dependent on the engine speed. This is demonstrated in Figure 3. The base friction term can thus be modeled

Equation 11 k = k 0 + k 1 N

where: k 0 = 3.283 bar and k 1 = 0.000515 bar/rpm for the present gasoline engines (multiply by 100 to get kPa, then divide by 2000 to get the proper units for the fuel rate equation). Trends for other years are reported in a late r section. The friction at idle is significantly higher (about 50%). Alternatively, one can interpret the efficiency be ing lower at idle - the end result for fuel consumption in the model is identical, though th is may not be the case if studying and isolating the effect of new engine technologi es at low engine speeds. There is a slight curvature evident in the figure and some references include higher orde r terms, but these are probably more necessary for higher revving engines [Yagi, et al., 1990].

Figure 3. Engine friction as a function of engi ne speed for 10 engines. [Nam & Sorab 2004].

Though the indicated efficiency has changed little over the years, the friction has decreased over the past 30 years at a rate of roughly 10% per decade [Nam & Sorab, 2004; Sandoval & Heywood, 2003]. Various technologies have he lped accomplish this: overhead cam, multiple valves per cylinder, improved lubricants, vari able valve timing, etc. Additionally, engine efficiency can be improved with “advanced” te chnologies such as lean burn gasoline (e.g., y = 0.00051x + 3.28232

R

2 = 0.68500

0 1 2 3 4 5 6 7 8

0 1000 2000 3000 4000 5000 6000

rpmfuel mep at 0 load (bar)13 Honda HX), Atkinson cycle (as in Toyota Prius) , Direct Injection Sp ark Ignition (DISI – no current model in US), Homogeneous Charge Co mpression Ignition (HCCI), or with variable compression ratio. Fuel economy can also be improved with variable displacement, cylinder deactivation, or adding a turbocharger with engi ne downsizing in the future. There may come a day when engines will be “variable everything”.

Despite the many different models and manufacturer s of gasoline engines, there is remarkable homogeneity in their performance ch aracteristics (neglecting advanced engines). As a result, it is possible to develop “rules of thumb”, which give rough estimates, of engi ne efficiency, friction, peak torque and peak power, given only its displacement (and model year). Figure 4 shows generic peak torque and power curves for a 2.0 l iter gasoline engine based on the bmep curves ( Error! Reference source not found. ) in Weiss et al. [2000]. The curves can be scaled to different engine sizes using standard tor que/bmep relationships in Sandoval and Heywood [2000]. The equation for f our stroke engines is:

Equation 12. mep(kPa) = 4 πτ /V d
Where: τ is torque in Newton meters V d is engine displacement in Liters

The relation may also shift over time (described in the section: Advanced Engines and Hybrid Vehicles). While not all 2.0 liter engines will have the same shape and peak values, the general trends appear in most engines and this relatio nship is sufficient for our modeling needs. These peak curves are mainly used to determine transmission downshift poi nts and for cut-point determination for hybrids (where engines may be significantly downsized), therefore the PERE output is typically not highly sens itive to the shape of the curves . In a future version of PERE, these peak torque curves may also define enrich ment strategy, which is modeled in a different fashion for older vehicles [Nam, 2003].

In PERE, the bmep curve is modeled with a simple 7 th order polynomial:

Equation 13. bmep = a 0 + a 1 N + a 2 N 2 + a 3 N 3 + a 4 N 4 + a 5 N 5 + a 6 N 6 + a 7 N 7

Where N is engine speed in rps

a 0 = -1200.51

a 1 = 298.934

a 2 = -17.5860

a 3 = 0.563420

a 4 = -.0104629

a 5 = 0.000113228

a 6 = -6.64513e-7

a 7 = 1.63097e-914 0 50 100 150 200 250

0 1000 2000 3000 4000 5000 6000 7000 rpmtorque

0 20 40 60 80 100 120Power

torque (N-m) power (kW)

bmep

0 200 400 600 800 1000 1200 1400

0 1000 2000 3000 4000 5000 6000 7000
rpmbmep (Pa)

bmep

Figure 4. Engine peak torque and power for a “generic” 2.0L gasoline engine (top) based on bmep curve (bottom).

Section III – Transmissions and Fuel Rate

Transmissions Another element required to calcu late total vehicle efficiency is transmission and final drive efficiency. All vehicle powerplants must conne ct to the wheels via a transmission. Sometimes this is as simple as a single gear reduction (as in some fuel cell and electri c vehicles). More often though, there are multiple gears to take advantag e of the engine’s limited operating range. The multi-gear transmission model used for PERE is based on Thomas and Ross [1997]. While transmissions and their shift strategies vary tremendously between vehicles, the overall fuel consumption rate is not highly se nsitive to this sub-model as long as ‘sensible’ parameters are employed.

The engine speed is:

Equation 14 N = (N/v) top *(60rps/rpm)*(g/g top )*v15 where N is engine speed in rpm (N/v) top is the rpm to speed ratio in top gear v is vehicle speed in mph (g/g top ) is the gear ratio at the various gears

(N/v) top is assumed to be a constant for light duty vehicles, but a future version of PERE may make it dependent on engine size. Smaller engine s tend to rev higher than larger engines. One could also calculate engine speed fr om final drive ratio and tire radius.

In order to meet the power dema nd of some driving cycles, it is necessary to downshift. The algorithm for downshifting is as follows:

If TorqueDemand > MaxTorque Then Downshift

The algorithm is repeated until the Torque demand is met, or gear 1, whichever comes first. There is no consideration fo r shift or ride quality in this simplified model.

Efficiency Transmission efficiency directly affects fuel consumption. In PERE, the efficiency simply depends on what gear the vehicl e is in. The efficiencies used in MOVES are shown on Table 1 based on the published work on Bishop and Kluge r [1996]. These values were based on an average over the combined EPA city/highway driv ing cycles (excluding idle). The efficiencies are lower in 1 st gear due to “slippage”, and better in the higher gears due to the gears “lockingup” in the torque converter.

Table 1

. Transmission efficiencies of a composite automatic transmission (based on Bishop & Kluger, 1996)
Gear Efficiency

1 72.2 2 80.9 3-5 87

More realistically, the efficienci es also vary with engine speed as well as torque. Due to the current spreadsheet nature of PERE (where it is difficult to do loops), these models were deemed impractical. If PERE is coded in the future, th ey could be easily applied. Efficiency can be a modeled as a linear function of engine speed [Par k et al, 1996], or as a linear function of both speed and torque [Greenbaum et al., 1994].

Manual transmissions range in efficiency from 87-99%, and fixed value of 95% has been assumed in this report (independent of gear). An overall 1.5% improvement in transmission efficiency could correspond to a 0.1 km/L increase in fuel economy [Greenbaum, et al., 1994; Kluger, et al., 1995; Bishop, et al ., 1996]. Continuously variable tr ansmissions (CVT), have been estimated to have cycle efficiencies of approximately 88% [Weiss et al, 2000].16 Vehicles whose drivetrains run off of motors do not necessarily require complex transmissions employing gear shifts. These motor driven vehicl es only require a single gear due to the large operating range of motors. Single ge ar transmissions naturally tend to be very efficient and are assumed to be 95% efficient in this report. Though multi-gear transmissions for motor-driven vehicles may be advantageous for some applicati ons, the hybrids and fuel cell vehicles in this version of PERE use only single gear transmissions.

Light Duty Transmissions

It has already been shown that due to the nature of the engine model, PERE (for light duty vehicles) is less sensitive to the specifics of a transmission model, than other models can be. Many other engine models are based on maps of torque, rpm and efficiency (or brake specific fuel consumption). The fuel consumption points are selected off of the map by the transmission shift strategy, which can make t hose models much more sensitive to transmission parameters. In PERE, the fuel consumption is defined by road lo ad power and engine friction (which are more or less independent). Therefore th e losses due to the transmission are primarily captured with the transmission efficiencies. However, an estimate of the engine speed is still required for the friction calculation.

While PERE users can adjust powertrain parameters as they see fit, when PERE is used to calculate fuel consumption rates for the fleet, it is necessary to make so me generalizations. For MOVES, PERE assumes all light duty vehicles to have 4 speed automatic transmission with overdrive (or a 5 speed automatic transmission). Th e vast majority of light duty vehicles on the road (in the United States) have automatic transmissions, and many modern vehicles are equipped with an overdrive for higher speed driv ing. The shift points a nd gear ratios are shown in Table 2. Older vehicles are assumed to have 4 speed automatic transmissions.

Table 2. Shift point and gear ratios for light duty vehicles (5 speed) [ Thomas & Ross, 1997 ].
Speed (mph) Gear g/gtop 0-18 1 4.04 18-25 2 2.22 25-40 3 1.44 40-50 4 1.0 50+ 5 0.9

Due to the downshifting model (in the previous s ection), vehicle speed is not the only variable defining gear.

For gasoline vehicles in the fleet some other generalizations were made:

- the engine idle speed is fixed at 700 rpm

- the (N/v)top (engine speed to vehicle speed ratio at gear ratio 1) is fixed at 35.6

- the cruising top gear ratio is us ually 1 by our definition of N/v17 Fuel Rate

If transmission speed and efficiency is added to Equation 5 through Equa tion 8, we would obtain the fuel rate equation used in PERE [Nam, 2003]:

Equation 15 FR = φ [kNV + (P b / η t + P acc )/ η ] / LHV

or Equation 16 FR = φ [fmepNV + P b / η t + P acc ] / η LHV

where
φ : is the fuel air equivalence ratio (mostly =1 for gasoline) k: is the engine friction (can depend on engine speed). fmep: is the friction mean effective pressure. N: is the engine speed, depending mostly on vehicle speed V: is the engine displacement volume
η t : is a transmission and final drive efficiency
η : is a measure of the engi ne indicated efficiency P acc : is the power draw of accessories such as air conditioning, pumps electrical loads etc. Without AC, it is some nominal value ~ 0.75kW. LHV: is the factor lower heating value of the fuel (~43.7 kJ/g for gasoline, 41.7 for diesel).

The only times (during hot running operation) when the fuel air eq uivalence ratio does not equal 1, is when the engine is in command enrichment (for more power and for catalyst cooling). This modal regime is modeled separately for older ve hicles, which tend to go more into enrichment than newer ones [Nam, 2003]. Lean burn is mo deled separately (as described below).

Section IV – Motorcycles

In principle, Motorcycles are modeled the same as passenger cars in PERE. However different parameters are required for the ro ad-load, engine, and transmission.

Motorcycle Road Load Motorcycle road load forces are typically parameterized [USEPA, 2000 and UN, 2003] with mass dependent A and C terms which take into acc ount rolling resistance and aerodynamic drag,

Equation 17. 2 Cv A F + =

where F is the total road load force in Newtons, A is the rolling resistance in Newtons and C is related to the aerodynamic drag, C d A F , and has units of Newtons/(meters/second) 2 . A and C are further parameterized in terms of the inertia l mass, M, of the vehicle and driver, i.e.,18 Equation 18.

M c c A C C M a a A

F d air 1 0 1 0

2 1 + =      

= + =

ρ

where a 0 , a 1 , c 0 , and c 1 are constants and ρ air is the density of air (~1.2 kilograms/meters 3 ), C d is the aerodynamic drag coefficient, and Α F is the frontal area of the motorcycle in meters 2 . The EPA uses a driver mass of 80kg, whereas the UN uses a driver mass of 75kg.

Both the EPA and the UN have similar paramete rization of the mass dependence of motorcycle tire rolling resistance, but differing pa rameters. The UN’s parameterization is

Equation 19. A UN = (0.088m/s 2 )M

which corresponds to a rolling resistance coeffici ent similar to that of passenger cars:

Equation 20. C r (=A/Mg) = 0.009.

The mass, M, corresponds to th e curb weight of the vehicle plus a 75 kg driver. The EPA’s parameterization uses

Equation 21. A EPA = (0.0874m/s 2 )M - (8.79Newtons)

which yields a static rolli ng resistance coefficient, C r , of

Equation 22. C r (= A EPA /Mg) = (0.0874m/s 2 )/(9.8m/s 2 ) - (8.79Newtons)/(M9.8m/s 2 )

≅ 0.009 – (0.9kg/M)

The mass, M, in the USEPA’s parameterization assumes the vehicle mass plus an 80kg driver. Table 3and Figure 5 summarizes the parameteri zed approaches as a function of mass.

Table 3. USEPA, UN, and Gillespie’s passenger car mass parameterization of the rolling loss road load coefficient, A(=MgC r ).

Rolling loss force parameterizations, A (= MgC
r ) = a 0 + a 1 M

a 0 a 1 C r (or A/Mg) Organization / Emissions Certification Procedure Newtons meters/second 2

- EPA Certification Procedure, CFR 40 part 86.529 -8.79 0.0874 0.0089– (0.90kg/M)
UN Report, “Worldwide Harmonised Motorcycle Emissions Certification Procedure” 0 0.088 0.0090

T.D.Gillespie, 1992 - - 0.01(1 + v/100mph)19 Motorcycle aerodynamic drag is governed by bo th its relatively small frontal area and its relatively large aerodynamic drag coefficient, which is due to its non-continuous shape / nonaerodynamic features of the motorcycle and the driver. Drag coefficients, C d , for motorcycles can vary between 0.4 to 0.6. Frontal areas, A F , of the motorcycle and driver range between 0.4 to 1 m 2 [Arakai et al., 2001]. Hence, the major ity of motorcycles have aerodynamic loss coefficients, C d A F , which lie between about 0.2 m 2 and 0.6 m 2

.

Both the EPA and the UN have established aero dynamic losses for different motorcycles based on the mass of the vehicle and the driver. The UN parameterization is

Equation 23. C UN = 0.26 N/(m/s) 2 + 1.94x10 -4 M [N/kg(m/s) 2 ]

which corresponds to an aerodynamic drag coefficient of C d A F { 0.50m 2 when M=227kg( { 500lbs). The EPA’s parameterization is sim ilar except it has two mass ranges, i.e.,

Equation 24. kg M for s m kg N M s m N C and kg M for s m kg N M s m N C

EPA EPA

450 ] ) / ( / [ 10 804

. 0 ) / /( 376

. 0 450 ] ) / ( / [ 10 35

. 0 ) / /( 254

. 0

2 4 2 2 4 2

>
⋅ × × + = ≤

⋅ × × + =

− −

In Figure 5, the graphs of the aerodynamic loss co efficient as a function of mass for both the EPA and UN parameters are displayed.

Figure 5. USEPA and UN A and C r oad load coefficients as a function of the mass of the motorcycle and driver.20 Figure 6 shows the total road load force as a function of speed comparing the UN and EPA methodologies. The vehicle plus driver mass for these plots is 700lbs or about 318kg. The largest differences (absolute differences greater than 10% ) between the two road load parameterizations occurs at speeds below 30km/hr (18 mph) and above 60km/hr (36mph). At about 38km/hr (24mph) the UN road load force becomes less than the EPA road load values. Below speeds of about 38km/hr the UN road load force is greater than the EPA values.

Figure 6. USEPA and UN total road load force as a function of speed along with the fractional differences between the two road load forces at each value of motorcycle speed.

In PERE (and MOVES), the UN methodology for moto rcycle road load is employed. However, the US standard driver weight of 80kg is assumed.

Motorcycle Engines Current literature states that th e typical (4-stroke) motorcycle engine thermal efficiency ranges between 32% to 40% [Yamamoto, 1999]. PERE will us e the same efficiency that has been used for other SI gasoline en gines, i.e., 40%.

Motorcycle engine friction for engine sizes ranging between 0.125 to 1.0 L has been studied extensively over the past 15 years [Yagi, et al., 1990; Yagi, et al., 1991; Tsuchida & Tsuzuku, 1991, 1995, 1999; Boretti, 1996]. In these studies, moto rcycle engine friction has typically been parameterized as a second order function of engi ne speed dependent on the engine bore, stroke, and mean equivalent crank shaft diameter. Exp licitly, the functional form in terms of the frictional mean effective pressure or fmep, is

Equation 25. 

## ( ) 

# ( ) 2 ' 2 ' 0 2 1 N C C B D S fmep

⋅ +
⋅ ⋅

=21 where S is the stroke, B is the bore and D is th e mean equivalent crank shaft diameter (average diameter of the crankshaft j ournals and crankshaft pins), '
0 C is a constant proportional to the lubricating oil viscosity, and '

2 C is a constant due to engine pumping losses.

Equation 26 

### ( ) ( )

'

2 2 1

2 '
0 2 1

0 C B D S C and C B D S C

⋅ ⋅

= ⋅ ⋅

=

From the literature cited above average coefficien ts, for over 20 motorcycle engines are listed in Table 4. Also listed are the standa rd deviations representing vehicl e-to-vehicle variability (not measurement errors). Figure 7 illustrates th e motorcycle engine friction mep dependence on engine speed compared to light duty spark igni tion engines determined earlier (which do not usually operate at such high speeds).

Table 4. Parameters found for motorcycle engine friction from the literature. The engines ranged in size from 0.125 liter to 1 liter.

C 0 (MPa) C 0 standard deviation (MPa / rpm 2 ) C 2 (MPa/rpm 2 ) C 2 standard deviation (MPa / rpm 2 ) 0.152 0.031 1.52E-09 6.23E-10

Figure 7. Comparison of engine speed depe ndence of motorcycle and recent light duty passenger vehicle engine friction.22 Finally, the accessory loading for motorcyc les in PERE was dropped down to 0.25 kW (compared to 0.75 kW for passenger cars).

Motorcycle Transmissions Due to the high RPM range of motorcycle engines, the transmission model is somewhat different than that of light duty manual transmissions. Tr ansmission modeling parame ters for motorcycles were derived from a number of sources. Th e N/v is determined from the UN Document: “Worldwide Harmonised Motorcycle Emissions Ce rtification Procedure,” [2003] (Figure 2).

The N/v at top gear is 115 rpm/mph. The 6 gear ratios are 3.1, 2.34, 1.75, 1.4, 1.17, and 1.0. In PERE, these are spaced out among 5 gears: 3.1, 2.3, 1.7, 1.4, and 1.0. N/v is normalized to be 1 in top gear, so the actual gear ratios may differ on vehicles.

The shift point is based on engine speed, rather than vehicle speed. This is because the shifting occurred at excessively high RPM when fixed speeds were used. The shift point in PERE is set at ~5,000 rpm, though it is surely lower for the heav ier bikes. The transmission efficiency was assumed to be the same as light duty manual transmissions (~0.95).

Motorcycle Validation The validation data is the EPA certification da tabase. The EPA maintains a database (The Certification and Fuel Economy Information System or CFEIS, e.g., http://www.epa.gov/otaq/crttst.htm) which contai ns (among other vehicle types) motorcycle emissions certification in formation. It covers, as of this wr iting, motorcycles with model years ranging from 1995 to 2004. Included in the database are the vehicle model name, model year, weight, engine displacement, maxi mum torque and horsepower, the ratio of engine speed, N, to vehicle speed, v, at the highest gear (N/v), and emission certification levels for HC and CO. This includes 55 motorcycles of model year 2004-2005, ranging in weight from 240-580kg and engine displacement from 0.5–2.3L. The fuel economy is calculated from the CO 2 and emissions reported by the manufacturers, however these figures were not confirmed by the EPA.

Figure 8 shows the scatter plot of the fuel ec onomy comparison, with the 1:1 line included. On average, the lighter motorcycles seem to be captu red well. However, it is evident from the plot, that there is a systematic underp rediction of fuel economy for th e heavier (lower fuel economy) motorcycles, i.e. PERE seems to be less efficient than expected. This is due to the fact that a limited transmission model was used. Since motorcyc les have significantly lo wer road loads than passenger cars, their fuel consumption is more sensitive to transmission (and engine friction losses). For example, the Harley Davidson moto rcycles tend to have lower N/v values, lower peak RPM, and lower shift point s, thus changing their fuel consumption significantly. Using more appropriate transmission values, PERE cap tured the 39 mpg rating of a heavier Harley Davidson (2004 FLHR Road King, 723lb, 1.44L, port fu el injected, N/v = 29, top gear ratio = 3.76, assuming a shift point ~3500RPM) [Harle y-Davidson, 2004]. The heavier bikes have transmission parameters that are more similar to passenger cars than the lighter motorcycles. Using more specific values for the transmissions of these motorcycles increases the likelihood that the results from PERE will be more accurate. This can be changed in a future version of the model.23 0 10 20 30 40 50 60 70 80

0 10 20 30 40 50 60 70 80 meas (mpg)model (mpg)

Figure 8. Fuel Economy model compared to measured for 55 motorcycles (2004-2005) in the EPA certification database. The 1:1 line is included.

Section V – Light Duty Diesel Vehicles

This section describes the model parameters for light-duty diesel vehicles. The model validation is lumped together with the advanced technology section.

Light Duty Diesel Vehicle Road Loads Apart for the powertrain, light duty diesel vehicles are very simila r to their gasoline counterparts in PERE. The main exception to this is in th e vehicle weight. Diesel vehicles tend to be somewhat heavier, mainly due to their heavier engines.

It is useful to conduct a comparison of gasoline and diesel light duty vehicles currently sold. Comparing the current technologies can help us to extrapolate how they will compare in the future. Table 5 shows the power and weight of va rious vehicles in the American and European markets.

Based on the table, we note that diesel engines te nd to be heavier than th eir gasoline counterparts (to accommodate higher pressures), thus they often need to be downs ized in order to fit into the same frame. According to the ra tios of these same vehicles, th e turbocharged diesel engines should be approximately 5% smaller. Due to th e many differences between gasoline and diesel engines, it is impossible to do a perfect apples -to-apples comparison. However, based on these vehicles, the equivalent vehicle we ight of a diesel is assumed to be 4% higher than the gasoline vehicle when PERE fills rates for MOVES.

Table 5. Diesel vehicles used for comparison24 model max torque max power displace
ment ( l ) curb weight ( k g) Ford Focus Diesel 200N-m@ 2000r p m 66kW@ 4000r p m 1.753 1258

Ford Focus

Gasoline 160Nm@ 4400r p m 85kW@ 5500r p m 1.8 1200

Ford Mondeo Diesel 245N-m@ 1900r p m 85kW@ 4000r p m 1.998 1498

Ford Mondeo Gasoline 190Nm@ 4500r p m 107kW@ 6000r p m 1.999 1376 peugot 607 diesel 250N-m@ 1750r p m 79kW@ 4000r p m 1.997 1500 peugot 607

g asoline 217Nm@ 3900r p m 116kW@ 5650r p m 2.231 1455

VW Beetle Diesel 210N-m@ 1900r p m 66kW@ 3750r p m 1.896 1270

VW Beetle Gasoline 172Nm@ 3200r p m 85kW@ 5400r p m 1.984 1222
VW J e tt a

Diesel 240Nm@ 1800rpm 75kW@ 4000rpm 1.9 1347 VW Jetta Gasoline 165Nm@ 2600rpm 86kW@ 5200rpm 2.0 1331 * All sources are either from manufactur er website, Car&Driver, or Road&Track

Light Duty Diesel Engines Diesel engines are different from gasoline engine s in several important ways. They run on diesel fuel, which has higher density, energy density, and carbon content. The fuel is self-ignited by compression rather than with a spark (hence it is also known as compression ignition engine). The increase in compression ratio required fo r compression ignition significantly improves the thermal efficiency compared to a gasoline spar k ignition engine. The engines can also combust fuel in an environment that is lean of stoichio metry. A stoichiometric fuel air mixture has just enough air to completely combust (or oxidize) all of the fuel, so a lean mixture has more air, and rich mixture has more fuel. Naturally, lean mixt ures result in higher fuel efficiency. Modern diesel engines inject the fuel di rectly in the chamber with electronically controlled bursts, which increases the pumping efficiency (d ecreases friction) by doing away with the losses inherent to a throttle. Unfortunately, diesel engines tend to have higher NOx and particulate matter (PM) emissions. The excess NOx emissions is due to th e lack of aftertreatment system, and the PM emissions is due to the combus tion mechanism with fuel droplet s. Advanced diesel engines complying with federal Tier 2 emission standards will require aftertreatment technologies, which may require the fuel combustion to run at stoichiometry on occasion so that NOx and PM can be stored, then treated. This w ill in turn, have a small negative effect on fuel economy.

In PERE, Diesel indicated effici ency is assumed to be 0.48. This is 20% more efficient than current gasoline engines [Wu & Ross 1997]. Weiss et al. [2000] employ an indicated efficiency of 0.51 for advanced diesel engines.

The friction in diesel engines is typically lower than their gasoline counterparts [Heywood, 1988, Weiss, et al., 2000, Wu&Ross 1997] – though this is mostly in the speed independent term (k 0 in Eq. 8). However, Ball et al. [ 1986] measured a diesel engine to have similar (total) friction compared to an equivalent gasoline engine at higher operating conditions. In a diesel engine, the25 pumping losses are significantly lo wer, but the piston/crank assemb ly as well as auxiliary load (mainly from high pressure fuel injector) losses are higher. This hints that the speed dependent term (k 1 ) should be higher for diesels, or that higher order terms in Equation 11 may be necessary. For the efficiency and friction parame ters in PERE, we use the heavy-duty friction results presented in the next section. These resu lts are consistent with Wu and Ross’ average measurements from 4 light duty diesel engines [1997].

The peak torque and power curves are generically determined from analysis from engine map data from the EPA database. This is discussed further in the heavy-duty transmission section of this report. The results are roughl y consistent with bmep curves from Weiss et al. [2000]. Figure 9 shows the relationship for bmep (from which torque and power can be calculated). The torque curves end up flatter than that of gasoline engine s. This high low-end torque gives diesel engines the advantage in towing as well as decent 0-30 mph acceleration compared to their naturally aspirated gasoline equivalents (o f same displacement). However the peak power tends to be lower, thus the overall 0-60 mph acceleration may be lower depending on the vehicle.

bmep

0 200 400 600 800 1000 1200 1400 1600 1800

0 1000 2000 3000 4000 5000 6000 7000
rpmbmep (Pa)

bmep

Figure 9. The peak bemp curve for a “ge neric” turbo-charged diesel engine.

In PERE, this generic bmep cu rve is modeled by a simple 7 th order polynomial fit:
Equation 27. bmep = a 0 + a 1 N + a 2 N 2 + a 3 N 3 + a 4 N 4 + a 5 N 5 + a 6 N 6 + a 7 N 7

Where N is engine speed in rps

a 0 = -19950.8

a 1 = 3479.90

a 2 = -231.809

a 3 = 8.25775

a 4 = -0.169919

a 5 = 0.00202259

a 6 = -1.2921e-5

a 7 = 3.42208e-826 Diesel engines are ideally suited for turbo charging largely due to their unthrottled operation. A turbo charger employs the exhaust heat energy to spin a turbine, which pumps more air charge into the engine cylinders. This efficient use of energy generates more power with a smaller engine. As a result virtually all light and heavy duty di esels employ turbo chargers. Unfortunately, the variety of turbocharger sizes makes generating peak torque and power curves, given only engine displacement, a difficult exer cise. This would matter more if we were constructing the model using a performance ba sis (e.g. 0-60 mph time), rather than a fuel economy basis.

Diesel Light Duty Transmissions The transmission model for light duty diesel vehicl es is identical to that of gasoline, with the exception that diesel engines tend to rev lower. The (N/v) top is estimated to be 75% that of gasoline. This figure is obtained by comparing peak rpm power va lues from an assortment of vehicles, which have a diesel option (US and Eur opean). E.g. VW Jetta, VW Beetle, Ford Focus, Ford Mondeo, & Peugeot 607. See Table 5.

Section VI – Heavy Duty Diesel Vehicles

This section describes the method for determining a complete set of model parameters for heavy duty diesel vehicles, including in-use city buses , heavy duty diesel truc ks, as well as some nonroad diesel engines. The model validation is also presented.

Data Emissions, global positioning (GPS) , fuel, and engine control m odule data were taken from 15 in-use Ann Arbor, MI city tr ansit buses [Ensfield, 2002] meas ured by SENSORS and EPA, as well as from 12 heavy duty diesel tractor-trail ers measured on-road by the University of California Riverside (CE-CERT) [Barth et al., 20 04]. The 17 non-road diesel engine data were taken from two EPA studies [Fritz and Starr, 1998; and Starr, 2003] contracted through Southwest Research Institute. The non-road dies el engines varied from a small 0.2 liter electric generator diesel engine to a 34.5 liter diesel locomotive engine. The data for these non-road engines were collected from engine dynamomete rs. Table 6 below summarizes the vehicle and engine specifics from which emissions were meas ured and parameters for road load and engine characteristics were determined. More details about the vehicles and engines can be found in the references listed in the text.

Table 6. Abridgement of the vehicle and engine speci fications which were used in this study.

source # of vehicles

or engines model
year range vehicle weight
range odometer
range rated torque@RPM engine displacement # of
gears

- - - kilogram kilometer Newtonmeter@RPM liters -

CECERT (in-use/ on road) 12 1997 to 2001 26,535 to 28,145 12,875 to 83,864,682 1830@1200
to 2373@1200 10.8 to 14.6 9 to 13

EPA 15 1995 to 12,020 320,260 to 1207@1200 8.5 627 AATAbus (inuse/ on road) 1996 457,055

EPA nonroad engines (dynam ometer) 17 1988 to 1999 - - 230@2200 to 3586@1400 0.2 to 34.5 -

Both the CE-CERT trucks and the Ann Arbor bus emissions testing measured emissions, vehicle speed, position, fuel use and engine parameters during on-road driving. The CE-CERT trucks were driven on approximately 20 highway and e ngineered trips includi ng vehicle coast downs. This study used four of these trips, the UDDS, th e Riverside to Palm Springs, the creep, and the ARB cruise trips. Examples of the coast down and Riverside, CA to Palm Springs, CA speedtime traces for one of the trucks are illustrated in Figure 10.

Figure 10. Speed-time trace of a complete CE-C ERT heavy duty diesel tractor-trailer trip between Riverside, CA and Palm Springs, CA.

Measurements from the AATA buses were coll ected on bus routes used by the Ann Arbor Transit Authority. An example of a speed-time tr ace for one of the bus trips is shown in Figure 11.28 Figure 11. Speed time trace fo r the first 1000 seconds of AAT A bus 1 in-use emissions testing. The trips in the on-board measurem ents were from a typical AATA bus route whereby the bus did not carry any passengers.

EPA non-road engines were tested on engine dynamometers. The e ngines were run at different loads and RPM points throughout the operating range of the test engine. Ta ble 7 lists the test schedule used for the 2003 on-road engines. In particular, each engine was operated at 39 different RPM, engine load pairs, which are listed in the table as percent of the highest engine RPM and percent of maximum load. More details of these test cycles and the measurements taken can be found from th e respective references.

Table 7. The non-road engine tests. The p ercentages are the percent of the highest rated engine speed and the maximum rated engine load. These tests included an additional operating mode at the engine’s low idle speed, with no load applied, which is not listed in this table.
Percent of the Maximum Rated Engine Load

Percent of Highest Engine Speed 100% 85% 82% 75% 63% 50% 40% 25% 10%

100% 1 2 3 4 5 6 91% 37 80% 38 75% 7 8 9 10 11 12 63% 39 60% 13 14 15 16 17 18 50% 19 20 21 22 23 24 25% 25 26 27 28 29 30 10% 31 32 33 34 35 36

Heavy Duty Vehicles Road Load Tractive road-load power coefficients for h eavy-duty vehicles include tire rolling and aerodynamic losses. Coefficients for both of th ese effects are developed from a relatively29 comprehensive study in terms of the range of vehi cles that are considered [Petrushov, 1997]. In particular, the vehicles range from passenger cars to heavy-duty tractor-tr ailers pulling two cargo trailers. Although the vehicles in this study are all from the Euro -Russian fleet, comparisons of the parameters developed are mostly consistent with general studies done in the United States on vehicles and tires used in the U.S. fleet [e.g., Yong, 2001; Gillespie, 1992; USEPA, 2002; Andrei, 2001; McCallen, et al.,1999; USDO T, 1977; Younglove, 2003]. However, the aerodynamic loss coefficients (C d A F ) are approximately 35% lower and will be updated in a future version of MOVES.

Dynamometer based emissions testing use empirical A, B, and C tractive road-load parameters which can be related to the rolling resistan ce and aerodynamic drag coefficients through the speed dependence of the road load equation. Beca use they are empirically determined they may include additional factors such as axle rotational inertia which can account for approximately 6% of an 18 wheel tractor-trailer’s kinetic energy. (This can be approximated by a simple kinematic analysis of the rotational ener gy of the truck’s wheels, tires , and axles). Also, additional aerodynamic factors from transverse, or cross, winds are not accounted for with the aerodynamic parameters presented here.

Aerodynamic drag coefficients from Petrushov are developed for heavy-duty delivery trucks and for buses. The truck aerodynamic drag coefficients are divided into three classes of delivery trucks. Single unit delivery trucks are divided in to two classes according to length and the third group included all long-haul tract or-trailers. The two classes of single unit trucks have lengths between 5 to 6 meters or lengths between 7 to 8 meters. Buses vary in length between 9 to 12 meters. Table 8 below lists the values of these dr ag coefficients along with a standard deviation, which includes the inter-vehicle differences and the reported m easurement errors. Also included in the table for comparison with the U.S. fleet, ar e values of aerodynamic drag coefficients from recent literature [USEPA, 2002; Andrei, 2001] and va lues determined in this work from the CECERT truck data. Figure 12 below illustrate s the speed dependence of the aerodynamic drag coefficients listed in the table. These comparis ons reveal that the tractor-trailer numbers are about 35% smaller than those typical values reporte d in the U.S. fleet (Table 8). The frontal areas of the tractor-trailers in the Petrus hov work are on average about 9 m 2 whereas in the U.S. the frontal areas are 10 to 12m 2 . Also, values of C D from the U.S. fleet can vary between 0.5 and 0.8.

Table 8. Comparison of aerodynamic drag coefficients.

source vehicle description C D A (m 2 ) standard deviation (m 2 ) CE-CERT from coast downs tractor-trailer combo 7.2 4.5 (63%)
Petrushov, SAE 970408 tractor-tra iler combo 4.8 1.6 (33%) Paul Andrei, WVU thesis, 2001 buses 4.2 -
Petrushov, SAE 970408 buses 5.4 0.8 (14%)
Petrushov, SAE 970408 box trucks and vans, length ~5 to 6 m 2.4 0.6 (24%)

Petrushov, SAE 970408 box trucks and vans, length ~7 to 8 m 3.2 0.7 (20%)
Petrushov, SAE 970408 cars 1.2 0.6 (48%)30 source Aerodynamic loss reduction feature C D A (m 2 ) (frontal area A=10m 2 ) None, 40” trailer gap 7.5

Roof deflector 6.4
Full aerodynamic package 5.8

Reduced trailer gap, 18” 5.6 “Industry options for

Improving Ground Freight Fuel Efficiency”, ICF Consulting, prepared for USEPA, 2002/ Detroit Diesel Spec Manager Software Cab over engine 5.1

Heavy duty truck tire rolling loss coefficients ar e developed from the Petr ushov work. In contrast to the light duty tire rolling lo ss, Petrushov includes the second or der (in speed) rolling resistance force term (3 rd order in power) which is accounted for in the “C” term of the road load Equation 3. Thus, the following relationship can be determin ed between the physical and the A, B, C terms from Equation 1 and Equation 3:

Equation 28.

mg C A C C B mg C A

R air r d R

2

0

2 0

. 0

+ = = =

ρ

Where the C R0 and the C R2 are the zero and second order (i n speed) rolling resistance force terms. Figure 12 displays the speed depe ndence of the aerodynamic force term.

Figure 12. Comparison of aerodynamic forces from existing literature and from values determined using speed-time traces of CE-CERT truck coast downs.

Figure 13 displays the speed depe ndence of the rolling force term. For comparison, earlier work done at the University of Michigan (e.g., Yong and Gillespie) are also displayed. The aerodynamic force coefficient determined from the CE-CERT truck coast downs is also included. Note that at a certain speed, the aero dynamic effects overcome the rolling resistance31 effects. The agreement with University of Mich igan [1984] work is with in the 20% measurement and vehicle-to-vehicle variabil ity. The uncertainty bars on the Petrushov tractor-trailer points illustrate these uncertainties. One exception out of the range of the uncertainties is the 1974 radial ply tires which are an additional 15% (a total of a bout 35%) lower than the rolling resistance losses developed from the Petrushov work.

Figure 13. Comparison of rollin g resistance force coefficients from existing literature and from values determined using speed-time tr aces of CE-CERT truck coast downs.

Finally, Table 9 lists the form of the coefficients that are deve loped from Petrushov as a function of vehicle mass and according to the three h eavy duty truck and the si ngle bus categories described above. The three parameters are listed as the typical road load A, B, and C coefficients. The four different categories ar e two single unit delivery categorie s, the tractor-trailer category, and the bus category. The two single unit deliver y categories correspond to the two smallest weight categories.

Table 9. Road load parameters developed from V.A.Petrushov. (Note: Although the frontal area of the trucks in the study were relat ively smaller than the CE-CERT trucks, the aerodynamic drag terms were similar and with in statistical and measurement errors.)
Road Load Coefficients for He avy Duty Trucks and Buses

8500 to 14000 lbs (3.855 to 6.350 tonne) 14000 to 33000 lbs (6.350 to 14.968 tonne) >33000 lbs (>14.968 tonne) Buses

A (kW*s/m) 6

. 2204 0996

. 0 M

6

. 2204 0875

. 0 M

6

. 2204 0661

. 0 M

6

. 2204 0643

. 0 M

B (kW*s 2 /m 2 ) 0 0 0 0

C (kW*s 3 /m 3 ) 6

. 2204 5

10 22

. 5 47

. 1 M − × + 6

. 2204 5

10 90

. 5 93

. 1 M − × + 6

. 2204 5

10 21

. 4 89

. 2 M − × + 6

. 2204 5

10 06

. 5 22

. 3 M − × +32 In summary, these tire rolling loss road load para meters are in reasonable agreement with values from recent literature, but the aerodynamic loss coefficients are lower than what might be expected in the U.S. fleet. The tire rolling losses lie within the variabil ity range of tire rolling losses determined within the last 30 years. The agreement is better for the latter U.S. fleet measurements. The uncertainty in the rolling losses is within 20%. Petrushov aerodynamic loss coefficients were found to be lower than the report ed values from the U.S. fleet. For example, the values for an 80,000lb tractor-trailers are about 35% lower, with a C D of 0.6 and a frontal area of 12.5 m 2 [McCallen, et al., 1999]. The Petrushov aerodyna mic losses have an uncertainty of about 25% which includes a 5% measurement error along with about a 17% vehicle–to-vehicle variability. MOVES aerodynamic heavy- duty road load coefficients will be updated to reflect the higher values of aerodynamic losses more typical to the U.S. fleet.

Heavy Duty Diesel Engines As with light duty vehicles, the Willans line methodology is used to determine both the engine efficiency and the engine friction of the heavy-du ty diesel engines studie d in this report. To construct the Willans line for these engines, instantaneous values of fuel mep and bmep were determined from measurements of fuel flow, e ngine speed, and engine load. The non-road engine measurements were made on an engine dynamomete r [Fritz & Starr, 1998] , the in-use / on-road measurements for the CE-CERT trucks, and the AAT A buses were taken from the engine control module (ECM) with portable emissions measurem ent systems described elsewhere [Ensfield, 2002; Miller, et al., 2002; Barth, et al.,2004]. This section describes a new method of estimating engine efficiency and friction from on-road data.

Data from the non-road engine study, AATA bus es and the CE-CERT trucks had fuel flow measurements from which the fuel mean effective pressure c ould be determined:

Equation 29. fuel mep = n R *(1000/100)[bar/MPa]*FR*LHV/V d N

where the fuel rate, FR, has units of kg/s, the diesel fuel lower heating value ( { 43MJ/kg), engine speed, N, is in units of rev/ s, the engine displacement, V d , is in units of m 3 , and n R is 2 for four stroke engines. The factor of 1000/100 is a conv ersion factor from MPa to bar as indicated within the boxed parentheses.

Brake mean effective pressure or bmep is determ ined from engine torque maps and engine speed for the non-road engines. For both the buses and the CE-CERT trucks, the bmep is determined from engine maximum load curves, engine speed (N), and engine percent load measurements taken from the engine control module (ECM ). From above quantities and the engine displacement, the bmep is calculated as follows,

Equation 30. N V N P load bmep

d ) (

100 %

max ×      

=

where %load is the measured percent of the maximum engine load, V d is the engine displacement, N is the measured engine speed, and P max (N) is the maximum engine load [and33 includes volumetric efficiency (Ens field, 2002)] at the cu rrent engine speed, N, determined from manufacturer specified engi ne maximum load curves.

Figure 14 and Figure 15 illustrate this methodology for all of the non-road engine dynamometer based measurements and for the CE-CERT truc k 1 on-road measurements. The CE-CERT truck plot includes only those points where the vehicle accleration is greater th an zero. Clearly, the onroad measurements illustrate the scatter associated with operation of the engine at many of the possible points of the engine map and indicate that a further refinement or data reduction will be needed to determine an accurate engine speed-e ngine friction relationship. The non-road engines, operated on a dynamometer, are restricted to the constant load operating points without transmissions (Table 7).34 Table 10 lists the individual non-road engine resu lts for indicated engine efficiency and the fit statistics. The non-road engine efficiencies ra nge between 39% and 46% with an average value of 43% and a standard deviation of about 2%. Th ese engines range in size from 0.2 liters to 34.5 liters (and were from 7 different engine manufact urers). The efficiencies show no indication of a dependence on engine size. Beyond 5L, there also appears to be little dependence of engine friction on engine size.

Figure 14. Willans line for nonroad dynamo meter based measurements of fuel consumption and brake me an effective pressure.35 Table 10. Engine efficiency results along with th e linear fit statistics fo r the diesel non-road engines.
engine displacement (liters) k (bar) k standard error (bar) 1/ η 1/ η standard

error R 2 η

0.2 8.7 0.9 2.43 0.1 0.93 0.421 0.92 5.2 0.4 2.37 0.09 0.83 0.406 2.2 3.4 0.2 2.46 0.05 0.95 0.444 3.9 2.5 0.2 2.25 0.04 0.99 0.462 3.9 3.1 0.5 2.16 0.09 0.95 0.451 4 3.3 0.3 2.22 0.04 0.99 0.413 6 2.3 1.0 2.42 0.1 0.92 0.392 7.4 2.0 0.4 2.55 0.05 0.98 0.446 7.6 3.6 0.3 2.24 0.05 0.99 0.424 8.3 2.4 0.5 2.36 0.06 0.98 0.437 8.8 2.4 0.6 2.29 0.05 0.98 0.430 10 2.3 0.2 2.33 0.02 0.99 0.410 10.1 3.1 0.3 2.44 0.04 0.99 0.458 10.2 2.2 0.4 2.18 0.03 0.998 0.419 10.5 2.5 0.5 2.39 0.06 0.98 0.461 12.7 1.8 0.2 2.17 0.02 0.99 0.441 34.5 2.3 0.2 2.27 0.03 0.99 0.412

Figure 15. Willans line for CE-CERT truck 1 on road based measurements of fuel consumption and brake mean ef fective pressure determined from engine maps and engine % load measurements. The green line is a fit to the data. The points were limited to those with accelerations greater than zero.

Average engine efficiencies over all the on-road tractor trailer diesel powered vehicles is approximately 0.48 (with standard deviation of about 0.02) and the AATA buses have a slightly lower average of 0.46. Based on this, PERE us es 0.48 for all heavy-duty on-road diesel applications. Table 11 below lists the values fo r each of the individual trucks and a single number for all of the buses. Some of the lin ear fit statistics are included in the table.36 A summary of the results are show n in Table 12. The on-road diesel trucks are in agreement with the smaller passenger car resu lts of Wu and Ross [1997], who looked at four light duty cars with engine sizes ranging from 1 to 2.5 liters. R 2 of the fits are typically at or better than 0.9 and the standard errors in the slope parameter of the li near fits are around 20% ofthe fit value. The nonroad diesel engine efficiencies are typically a bout 5% lower than the on- road vehicles and the buses are approximatel 2% less efficient than the diesel tractor-trailers which may be due to how the vehicles were driven (includi ng significantly more transients).

Table 11. Engine efficiency results along with the linear fit statistics for the on-road CECERT tractor trailer and the AATA bus engines.

engine displacement (liters) vehicle k (bar) 1/ η standard error in 1/ η R 2 η

8.5 buses 0.926 2.17 0.01 0.95 0.461 10.8 truck 2 1.81 2.14 0.01 0.92 0.468 11.9 truck 9 1.39 2.21 0.02 0.85 0.452 12.7 truck 6 1.90 1.97 0.03 0.63 0.507 12.7 truck 7 -0.043 2.15 0.02 0.86 0.466 12.7 truck 8 0.611 2.10 0.02 0.83 0.475 12.7 truck 3 -1.10 2.13 0.02 0.83 0.469

14 truck 4 3.91 2.05 0.01 0.95 0.488 14.6 truck 1 4.27 2.10 0.01 0.95 0.476

Table 12. Diesel engine indi cated efficiencies for the engines in this study.

source average efficiency standard deviation(vehicle-tovehicle differences) engine displacement
range fuel delivery # of vehicles or engines

- - - (liters) - -
CE-CERT

trucks 48% 2%* 10.8 to 14.6 Turbo- EUI 8

AATA buses 46% - 8.5 TDI 15

Nonroad engines 43% 2%* 0.2 to 34.5 varied 17

Wu & Ross, 1997 47% to 49% 1.08 to 2.46 TDI 4

*R 2 of the fits are typically at or better than 0.9 an d the standard errors in the slope parameter of the linear fits are about 20% of the fit value. So the total percent error in these engine efficiencies is ~ 25%.

In order to determine engine friction coefficients, it is necessary to further filter out the effect of transients - especially if our goal is to dete rmine the relationship betw een engine friction and engine speed (Equation 11). A novel method of doi ng this is to isolate only low acceleration operating modes. Dynamometer based measurements of fuel rate and brake mean effective pressure are done at a single value of engine load and engine spee d. On-road/in-use based measurements don’t yield such ideal conditions. Th erefore, different ranges of accelerations are used to limit the range of engine load points for a set of different engine speeds.37 The methodology to find the frictiona l losses from in-use data is: (1) construct a family of fuel mep – bmep graphs for different values (average values of intervals) of engine speed; the range of bm ep’s were limited by using positive vehicle accelerations near zero, e.g., large non-zero posi tive accelerations may be where transmission shifts typically occur an d may add step-wise changes in engi ne speed; also negative values of accelerations where braking occurs will also not represent true engine map values of bmep and engine speed. (2) curve fit each of the different graphs, yielding a set of k’s and 1/ η ’s for the range of engine speed. (3) from these sets of coefficients, the slopes or e ngine efficiencies should be relatively constant. However, the fuel mep-intercepts should yiel d an engine speed dependence, which defines the engine friction. (4) finally, fit the fuel mep intercepts (k) to a line dependent on engine speed

Figure 16 and Table 13 illustrate this procedure and show the relativel y constant values of engine efficiency as well as the engine speed dependence of engine fricti on. When these values are fit to the (average value of the inte rval) engine speed, it yields the complete fuel mep – bmep dependence for the engine under consideration.

Figure 16. A series of figures, showing fuel me p vs bmep for engine speed values between 1100 and 2099 rpm, and accelerations between 0 and 0.007 mph/s. The linear fit to these data is the solid line.38 Table 13. Example of the series of fuel mep vs bmep curves at different engine speeds, N, required to determine the speed dependence of en gine friction. The usable data are from (9 of 12 usable) trucks on four drive cycles. (Note th at the units of k(N) in this table are in bar and the units of engine speed are in rpm where as in Table 14 the units are in kPa and rps.)

0 mph/s < acceleration < 0.07 mph/s

k(N) (bar) k(N) standard error (bar) Mean N +/- Std. Deviation (RPM) R 2

3.54 0.13 1045 +/- 26 0.21 2.53 0.19 1154 +/- 27 0.95 2.94 0.27 1260 +/- 28 0.88 2.05 0.20 1352 +/- 28 0.89 3.58 0.19 1448 +/- 28 0.89 1.69 0.27 1549 +/- 30 0.89 linear fit results for k(N) = k
1 N + k 0

0.54 0.32 1647 +/- 28 0.89 k 0 (bar) 1.09 3.08 0.33 1747 +/- 29 0.85 2.55 0.46 1842 +/- 28 0.86 k 0 standard error (bar) 1.6

3.7 1.1 1936 +/- 30 0.63 k
1 (bar/rpm) 0.00116 4.94 1.7 2050 +/- 30 0.83 5.27 1.4 2135 +/- 21 0.84 k 1 standard error (bar/rpm) 0.0009

2.68 1.1 2228 +/- 14 0.84 R 2 0.04

The results of this study are shown on Table 14 and in Figure 17. A comparison is provided to both Millington and Hartles [1968] and a more recent work by Wu and Ross [1997] (averaging the four of their light duty e ngines). The comparison of this st udy’s heavy-duty engine friction to the light duty in Wu and Ross is good, however, the comparison is between the former study’s light duty to the pres ent study’s heavy duty engines. The fm ep formula in Wu and Ross may not extrapolate accurately to large engi ne sizes (due to their limited da ta set). However, neither this work nor the Wu and Ross work are able to disc riminate the second order effects of mean piston speed as was done by Millington and Hartles. Th ese results also demonstrate the improvements in engine friction, which have occu rred in the last thirty years.

Table 14. CE-CERT truck engine friction det ermination from on-road measurements [of 9 trucks]. *Values are standard deviations in the averages. The actual uncertainties are larger.

Acceleration range (mph/s) k 0 (kPa) k 1 (kPa/rps) k 2 (kPa / (m/s))
(1) 0.001<accel<0.25 -9.32 12.6 - (2) 0.001<accel<0.2 13.7 11.4 - (3) 0.001<accel<0.15 96.1 7.85 - (4) 0.001<accel<0.1 157 5.17 - (5) 0.001<accel<0.07 109 6.98 - Ave. of ranges 3, 4, and 5 121 (+/-32*) 6.66 (+/-1.37*) - Wu and Ross, 1997 135 5.4 -

Millington and Hartles 179 6.1 0.8339 Figure 17. Engine friction results of this work co mpared with previous compression ignition studies from the comprehensive work of Millington and Hartles (and Heywood, 1988) and from a more recent work by Wu and Ross (1997).

Heavy Duty Transmissions Heavy duty transmissions in PERE are assumed to work similarly to the light duty transmissions described earlier. The number of gears and shift points would certainly di ffer, but the algorithms and efficiencies are assumed to be identical.

The on-road measurements of engine and vehi cle speed are measured simultaneously, thus allowing for the empirical determination of gear ratios, shift points, and N/v based on the methodology presented earlier. Because these sp eed-gear distributions will be inherently different for different vehicles classes, a re lationship between transm ission shift points and vehicle speeds are deduced for different vehicl e transmissions classes in this section. Two transmissions are modeled; one for medium duty (based on the AATA buses, which were automatic) and the second for the heavy duty- diesel tractor-trailers (manual). These are supplemented with vehicle downs hifting (during accelerations).

Figure 18 shows an example of the speed dist ributions for one of the CE-CERT heavy duty diesel tractor-trailers. Each of the 12 gears has a distinct distribution of speeds which tails off toward the lower speeds in the distribution. Alth ough the distributions for the lower gears seem to be smeared, if that portion of the graph is expanded, the lower gear distributions appear distinct.40 Figure 18. CE-CERT truck 1 vehicl e speed to engine speed ra tios vs vehicle speed to determine average speeds for a given gear.

The CE-CERT trucks have between 10 and 13 total forward gears. An average speed from each of the distributions is determined along with a st andard deviation in that average speed. This was also done for the AATA buses. Figure 19 displays the results of those averages along with a polynomial fit to the two different vehicle speed -(engine speed/vehicle speed) ratio relations. From these, the current gear and engine speed are determined.41 Figure 19. Final engine speed - vehicle speed relationships for buses (red) and the heavy duty trucks (blue). The error bars are sta ndard deviations determined from the distributions depicted in vehicle speed - engine speed ratio graph.

The transmission parameters chosen for the me dium duty 6-speed automatic, and the heavy-duty 12 speed manual transmissions modeled in PERE are displayed in th e following table.

Table 15. Transmission parameters for medi um duty and heavy duty vehicles in PERE.

The last component of the transmission model is the (power) downshifting. Vehicle power must be checked against the actual maximum manufactu rer specified engine po wer for a given engine speed. A downshift occurs whenever the vehicl e tractive power is greater than the maximum engine power at a given engine speed. Because PERE’s primary purpose is to model a class of vehicles, rather than individual engine/vehic le combinations, an average engine maximum torque/power curve is used. A s calable torque (or bmep) curve is determined from a number of different diesel engines ranging in size from 8 to 15 liters and is shown in Figure 20. The map is scaled depending on the maximum power or engine displacement. The level of turbo-charging cause some variability in these scaling factors. (Note: Actual validati on of these heavy duty algorithms used the manufacturer listed maximum e ngine power). This figure is consistent with Figure 9. Vehicle Type HD MD Transmission Type Manual 12 Auto 6 N/v (rpm/mph) 26.7 26.7 Nidle (rpm) 700 700 trans eff 0.95 0.85 Shift point 1-2 (mph) 2.48 6.2 Shift point 2-3 4.75 15.6 Shift point 3-4 7.75 22.9 Shift point 4-5 13.5 30.3 Shift point 5-6 17.5 37.7 Shift point 6-7 23 Shift point 7-8 34 Shift point 8-9 50 Shift point 9-10 56 Shift point 10-11 57 Shift point 11-12 64 g/gtop 1 13 14.1 g/gtop 2 9.4 4.46 g/gtop 3 6.9 2.66 g/gtop 4 5 1.91 g/gtop 5 3.7 1.34 g/gtop 6 2.7 1.11 g/gtop 7 2 g/gtop 8 1.5 g/gtop 9 1.2 g/gtop 10 1.1 g/gtop 11 0.93 g/gtop 12 0.8642 Figure 20. Scalable torque det ermined from comparison of 13 individual diesel engine maps from 4 different engine manufacturers. This scalable map is based on a 380 hp, 12 liter diesel engine extrapolated to 500 rpm and 2350 rpm.

Another method employed is to check the vehicl e maximum bmep against a single highest value of bmep based on an average maximum engine torque. From the USEPA’s CFEIS database an average maximum engine torque from over 638 diesel engines ranging in size from 1.8 to 27 liters is calculated. It is used to find an average maximum bmep value and a standard deviation,

m N bmep V n bmep R

⋅ ± = =

*

max max

max

440 1648 2 τ

π

This method is not used for MOVES rates, bu t was used for an earli er version of model development.

Validation Using the fuel rate equation along with the other elements of the PERE model described above, calculated values of fuel consumption are comp ared with the measured fuel rate for an independent vehicle trip. Both bus and truck calculated and measured emissions and fuel consumption numbers are compared on Table 16. All calculated values are within 10% of the measurements. For all cases consid ered here the values are neither systematically high nor low. However, the heavy-duty trucks had relatively large discrepancies for driving cycles with average speeds less than 40mph. At the time of this writing, drivi ng cycles with average speeds43 less than 40 mph had calculated va lues of fuel consumption more than 20% lower than actual measured values. These discrepancies are currently being addressed and will be adjusted in the next version of PERE.

Table 16. Percent differences between calculated and measured value of fuel consumption for 5 buses and two CE-CERT truck 1 tr ips. All differences are within 10%.
Vehicle/ trip fuel % difference between measured and calculated mass values AATA bus 1 9%
AATA bus 2 -2% AATA bus 3 4% AATA bus 4 -5% AATA bus 5 -6% CE-CERT truck 1, Victorsville 9% CE-CERT truck 1, Palm Springs -1% CE-CERT truck 2, Victorsville 3% CE-CERT truck 2, Palm Springs 9% CE-CERT truck 4, Palm Springs 1% CE-CERT truck 4, Palm Springs 5% CE-CERT truck 5, Palm Springs trip 1 -1% CE-CERT truck 5, Palm Springs trip 2 8% Calculated fuel consumption is typically 20% or more under predicted for drive cycles with average speeds less than 40mph (see text for explanation)

Section VII: Advanced Engines and Hybrid Vehicles

From reading manufacturer press releases, and seeing the new vehicles being offered, it is becoming evident that advanced technology vehi cles, such as hybrids, will become more commonplace in the near future. A hybrid vehicle combines two forms of propulsion in order to optimize efficiency and fuel economy. The incremen tal cost of hybrid vehicles is expected to decrease as volumes increase, and as public acce ptance increases. There ar e also other types of advanced vehicles, such as clea n diesel, and fuel cell vehicles, which may claim a portion of the market in the future. It is important to understand the possible effects that these vehicles will have on the fleet and the environment.

The following sections describe the assumptions that went into modeling advanced internal combustion engines (gas and diesel ), hybrid and fuel cell vehicles. Validation results are also be presented.44 Advanced Internal Combustion Engine (ICE)

In this report, “advanced” techno logy is defined as a vehicle (or component) that is improved over those in most vehicles currently. Advan ced ICE vehicles might employ one or more nonstandard engine or driveline technologies with su perior thermal, and/or driveline efficiencies, which lead to better fuel ec onomy and performance. Exampl es include: lean-burn gasoline engines, variable displacement, direct injecti on gasoline, and continuously variable transmission (CVT).

In order to model future vehicles, it is educatio nal to study current techno logies comparing them with their predecessors. Two areas that have seen marked improvement in the past, are engine friction and power. The former lik ely leading to the latter. Typi cally, improvements in (overall) engine efficiency can either improve fuel economy, or increase engine size in order to increase power performance (thus maintaining the same fuel economy). The latte r has been the main trend over the past few decades, though there is strong evidence to show that specific power (power per unit displacement) has al so improved [Chon and Heywood, 2000].

Based on the research of Chon and Heywood, the relationship between engine power and model year has been established for the past 15 years. This trend has been projected forward by Weiss et al. [2000] and is shown in Figure 21. The fric tion reduction projections have also been added to the figure, based on the research of Sandova l and Heywood [2003] and Nam and Sorab [2004] at a rate of approximately 10% per decade. Advan ced diesel trends are assumed to be the same, though their starting values may be different.

Figure 21. Projected gasoline engine trends over time [Chon & Heywood, 2000; Nam & Sorab, 2004].

In PERE, this trend is modeled with a quadratic fit on the friction term k 0 (Equation 11) in the following equation. k 1 is assumed to remain constant over time. The trends for diesel are less well known, but it is assumed to follow th e same progression as gasoline. 0 50 100 150 200 250

1990 2000 2010 2020 2030 Model Yearfmep (kPa),peakTorque (Nm)

0 10 20 30 40 50 60 70peak Power (kW) fmep (kPa) peak torque (Nm) peak power (kW)45 Equation 31. k 0 (kJ/L) = a 0 + a 1 Y + a 2 Y 2
Where Y is year and

The current version of PERE does not currently ch ange peak torque (or bmep) curves with model year, due to the relative lack of sensitivity of the model to this te rm. Future versions of the model may include this option.

Beyond incremental improvements in engine friction, it is difficult to predict how engines will change in the next 30 years. It is concei vable that production gasoline engines can have improved indicated efficiencies. This could be accomplished with th e trend of “variable everything”. The Atkinson cycle en gine (as in the current Toyot a Prius) is a good example of variable valve timing being used to increase effi ciency at the cost of power. The lean burn engines of Honda (currently in stalled in the Civic HX, Hybri d, and Insight models) improve efficiency by burning gasoline under lean conditions at low loads.

The Honda Lean burn engine parameters are dete rmined from Ogawa et al. [2003]. The engine improves the best bsfc (best brake specific fuel consumption, or “sweet spot”) over conventional engines by approximately 20%, and decreases fr iction by 8%. PERE models this with an indicated efficiency of ~0.48, with an 8% fric tion improvement. During higher engine speeds, the engine reverts to stoichiometric operation, wh ere it is still more e fficient than conventional engines (0.44). The engine speed breakpoint is not provided in the reference, so in PERE it is assumed to be 50% of the peak power rpm (6100 rpm*0.5). These are probably optimistic estimates, since the vehicle must still meet stringent NOx emissions standards. Also, improvement in peak efficiency alr eady includes improvements in friction.

There is less information on the Toyota Atkinson engine. PERE assumes that the efficiency is 15% greater than that of conventional engine s, until the rpm cut point is reached [Heywood, 1988]. The peak torque values are decreased co rrespondingly. Beyond that point, it behaves as a conventional engine.

Gasoline Direct Injection (GDI) or Homoge neously Charged Compression Ignition (HCCI) engines are also a promising alte rnative, though these are still mainly laboratory engines. In the near future, there may be engines with vari able displacement, variable compression ratio, cylinder deactivation, etc. coming on to the s cene. Moreover the current move toward 42 Volt power systems on the vehicle will allow for Integrat ed Starter Generator systems to start and stop the car during idle. This has been refe rred to as “minimal” or “mild” hybrid.

The specific technologies descri bed above are for PERE vali dation purposes only. For the “generic” advanced internal combustion (AIC) en gine vehicle in MOVES, PERE uses target coefficients, rather than choosing a suite of sp ecific technologies. These target values are Gas Diesel

a 0 7.1505E+01 3.5205E+01

a 1 -6.8667E-02 -3.3833E-02

a 2 1.6498E-05 8.1286E-0646 assumed to be a 10% improvement in indicated e fficiency (0.44), and engi ne friction equivalent of 2015 (Figure 21). The diesel AIC engine assumes slightly less ambitious targets ( η = 0.50), and friction improvement of 15%.

In addition to technical advances in engine desi gn, it is possible that ti re rolling resistance and vehicle aerodynamics can improve in the future . Currently passenger cars have tire rolling resistance of approximately 0.009. It is unlikely that these will improve significantly without sacrificing other performance measures. The aero dynamic resistance and vehi cle frontal area of vehicles have room for improvement, but here, fo rm tends to follow function, or design. Current cars have coefficients of drag a pproximately 0.30 - 0.35 (Toyota Camry C d = 0.30). Vans and SUVs have Cd ~ 0.35 – 0.40 and pickups ra nge from 0.40 – 0.45 [Gillespie, 1992]. The exceptional 2004 Toyota Prius has a C d of 0.26, and the Honda Insight has the best in class coefficient of 0.25.

Hybrid Vehicles

Currently the most successful method implem ented commercially to improve the overall efficiency of vehicles is to hybridize their driv etrains, by adding an electric propulsion path to the wheels.

There are a number of electric hybr id vehicles on the road today: The Honda Insight was the first to be introduced in the United States in 1999. It was followed by the Toyota Prius and the Honda Civic (the Prius was first sold in Japan in 1997) . Other manufacturers have also announced that they will release hybrid models of vari ous kinds within the next few years.

There are several different kinds of hybrid ve hicles under development. All share the common trait that there are two power sources that dr ive the vehicle forward during different operating modes. Most methods hybridize an internal co mbustion engine with an energy storage device such as a battery, ultracapacitor, or even hydrau lic pump (fuel cell hybrids will be discussed in the next section). The hybrid configu r ation is usually either series, parallel, or some combination of the two. Series hybrids run off electric pow er only, and the batteries are recharged by the engine, which can run (continuously) in an e fficient mode. Series hybrids suffer from the disadvantage that both a large battery and motor are required, while losses are incurred in both charging and discharging th e battery. Parallel hybrids run th e drivetrain alternately with the engine, battery or both. This allows for a smaller battery/motor than the series hybrid and for the engine to run efficiently. The disadvantage compar ed to the series configuration is that the system requires more sophisticated strategy, pack aging and transmission. So me models require a separate generator. The Honda hybrids are parallel, while the Toyota Prius is a combination series/parallel.

The spectrum of hybrids soon to be on the road is broad. General Motors will soon sell a “minimal” or “mild” hybrid version of its Sierra /Silverado truck, which is expected to improve fuel economy by 12%. By 2007, they announced a displacement on demand (cylinder deactivation) hybrid system for the Tahoe a nd Yukon line [GM, 2004]. Ford released a full hybrid version of the Escape compact SUV in 2004. Honda is selling a h ybrid version of the47 Accord in 2004, which will have Variable Cy linder Management (VCM) in its V6 engine [Honda, 2004]. Toyota announced the Highlander hybr id in 2005, while the Lexus 400 hybrid is expected in 2004 [Toyota, 2004]. All of the hybrid s mentioned above are in (mostly) parallel configuration and store seconda ry energy in a battery. They also use “advanced” gasoline engines. All of these hybrids recharge the batte ries by employing the motor to recapture energy from the brakes, however, they do no t all use the motor for traction.

With the promise of hybrids hitting many of the markets segments in th e near future, it is imperative that any modeling of future vehicles in clude this class of vehicle. There have been a number of models developed, which capture s the behavior of hybrid vehicles. ADVISOR (developed at NREL) is widely used in the re search community [Kelly, 2001]. PSAT is another model developed at Argonne National Laborator ies [Rousseau et al., 2004]. They both model conventional as well as hybrid and fuel cell ve hicles. They are also relatively sophisticated models that run on the MATLAB  platform. Unfortunately, the models could not easily be integrated into MOVES for several reasons. Namel y, the intensive data and effort required to calibrate the model is prohibitive for trying to model broad sectio ns of the fleet required. It was thus necessary to adjust the approach so thata model could be run on the PERE spreadsheet. From there, it may either be run in spreadshee t mode, or programmed directly to link with MOVES in the future. The basic framework fo r PERE is similar to ADVISOR though: starting with a driving cycle input, calcula te the road loads, then distri bute the power and losses to the various energy conversion and storage media. Ma ny other models exist as well, but the hybrid architecture in PERE is roughly based on the si mple control logic described in Weiss et al., [2000] at MIT.

Strategy Figure 22 shows the PERE flow chart for the para llel hybrid design. The battery can be any energy storage device (ultracapacitor or hydraulic). The logic control is relatively simple [Weiss et al., 2000 (MIT)]. When power demand is less th an the hybrid threshold (2kW in the MIT case), the car runs on battery alone. Therefore, th e engine shuts off during decelerations in the model. Beyond the threshold, the car only runs on the gasoline engine. When power demand is greater than the peak engine power, then the ba ttery assists or provides the needed boost. When braking, part of the energy is recaptured into th e batteries. Each of the power paths described has its unique losses. This strategy is modified slightly in PERE to require that the hybrid threshold be set such that the state of char ge on the battery is sustained over the course of the driving cycle input, i.e. the battery ends the te st with the same charge with which it started. In the validation, charge is conserved over the Federal Test Pro cedure driving cycle (city and highway). Other differences to the MIT model will be described below. The para llel power and state of charge algorithms are shown in Appendix A.48 Power assist ACTIVITY INPUT V,A, Road grade
Logic Control

Vehicle Param:

M, Cr, CdA VSP or Power Demand

Regen braking Motor

Battery/ UltraCap Fuel or Energy Consumption ICE Transmission/

Gearbox

Figure 22. Parallel hybrid (gaso line/battery) flow chart.

Actual hybrid strategies can get much more sophi sticated, where the battery can get recharged by the engine directly, during operation (as is the case for the Toyota Prius THS - Toyota, 2003). This system is a series/parallel architecture a nd is especially appropria te for stop and go driving. However, the PERE methodology takes the basic a pproach of distributing energy in the system defined by the strategy above, and taking into account losses. Thus , whether the energy is stored in the battery, or converted by the engine, the same overall ener gy is required, therefore the overall predicted fuel economy should be similar to what is measured (within 10%). The main omissions of PERE at this time is that it does no t include a cold-start m odule. Cold start factors will be inserted by MOVES, not PERE. Hybrid ve hicle fuel consumption can be significantly larger during cold start to heat the catalyst to light-off temperatures and also to charge up the batteries or capacitors (if needed ). Cold start factors are disc ussed in greater detail in the Sensitivity section.

Hybrid threshold was chosen in order to give cl ose to net zero state of charge over the driving cycle input (FTP city and HWY). This loosely simulates a “charge sust aining” strategy (‘pseudocharge sustaining strategy’). For the hybrids modeled in this report, this th reshold fell between 2 and 4 kW. Values are in Appendix B. This strate gy dictates that the batteries are only being recharged through regenerative braking, and not th e engine. This can still result in a charge sustaining strategy if the batteries ar e not used excessively for traction.

Unfortunately, MOVES cannot at this time accommoda te hybrid vehicles with a series element. This is due to the design of V SP (vehicle specific power) based emissions in the model, which relates road load causally to fuel consumption. Se ries hybrids are not stri ctly “load-following” in this manner, therefore, the fuel consumption can follow some time after the road-load transient. This “history-effect” is impossible to model in the current MOVES framework.

Motor/Generator/Inverter The conversion between electrical an d mechanical energy is done with a motor. In reverse this is called a generator. For hybrids, we will use the term “motor” to indicate both devices. In PERE49 the motor operates both forwards and backward s at different times depending on the driving, with different losses. Weiss et al. [2000] estim ates the motor system (including converters and inverters) average efficiency to be 76% with an additional 15% loss during regenerative braking. This estimate is likely low given the recent impr ovements in motor system efficiencies. The peak motor efficiency (alone) is higher, greater th an 90% for a permanent magnet brushless DC motor, though it depends on the si ze of the motor [Laramie & Dicks, p. 279 2000, Ogawa, et al., 2003]. However, the MIT is a cycle average and value includes othe r system losses (such as from an inverter, which can have efficiencies of ~ 94%). Motor efficiency depends somewhat on the operating mode (speed and torque), but PERE also assumes a fixed value for simplicity.

The motor power and torque curve is shown in Fi gure 23. It is scalable with peak power [Weiss et al., 2000]. In the present version of PERE, the mo tor in the model is connected directly to the wheels (with a single gear step ), bypassing the multi-step tran smission (and thus the losses). However, it is more likely that real-world hybrid motors will requi re a multi-step transmission to maintain efficiency and torque performance. A future version of PERE may shift the motor to connect to the engine output shaft. At the level of modeling in PE RE, however, the overall system losses would likely be similar.

Figure 23. Motor peak torque and po wer from Weiss et al., [2000].

In PERE, the peak motor torque is scaled by po wer rating (calibrated to 60kW) according to the following polynomial:

Equation 32. T (Nm) = P m (a 0 + a 1 N + a 2 N 2 + a 3 N 3 + a 4 N 4 + a 5 N 5 + a 6 N 6 + a 7 N 7 )/60

Where P m is motor power rating (kW)

a 0 = 196.26

a 1 = -1.2542

a 2 = 0.085004

a 3 = -5.1976e-4

a 4 = -4.4345e-5

60 kW Motor

0 40 80 120 160 200 240

0 2000 4000 6000 8000 10000 RPMTorque (Nm)

-10 5 20 35 50 65 80Power (kW) Torque (Nm) Power (kW)50 a 5 = 8.1616e-7

a 6 = -5.2169e-9

a 7 = 1.1566e-11

For the sake of nomenclature, we define the terms “moderate” and “full” hybrid. There is no standard definition in the liter ature since there are no clear demarcations, only a continuous spectrum of possibilities. In one reference, the separating valu e is defined by the ratio of peak motor power to peak motor + engine power [An & Santini, 2004]. So that a conventional vehicle powered with an engine has a ratio of 0, and a “pure” battery electric ve hicle has a ratio of 1. This report remains consistent with this approach and defines moderate hybrid as having a ratio < 0.25. An example is the Honda Civic Hybrid (~0.14). The full hybrid has a ratio =>0.25. The 2004 Toyota Prius has a rating of ~ 0.47. This de finition does not necessarily imply that moderate hybrids are worse, or less efficient than full hybrids. In reality, however, the size of the motor is meaningless without a ba ttery (or ultrac apacitor) that can manage the current flows in and out of it. The effect of hybridization natura lly depends on the hardware, and what functions are built in, more so than th e ratio of any two numbers.

Energy Storage Devices – Batteri es, Ultracapacitors and Hydraulics Batteries on hybrid vehicles tend to be of the nickel metal hydride (NiMH) variety. They have relatively high energy densities and can withstan d many charge/discharge cycles, while being relatively affordable. Compared to a conventional ve hicle, however, batterie s add significant cost and weight. PERE does not model battery weight independently, but wraps the electrical system weight to an aggregate added weight to the vehi cle (described below). In the future (when the prices may drop), lithium ion batteries may si gnificantly reduce the weight of electrified vehicles.

In PERE, each second the power draw (discharge) from the battery is adjusted by the discharge efficiency (95%) as well as the motor efficienc y. To be more accurate, this efficiency really depends many factors, including the state of charge of the battery, temperatur e, history etc. but is approximated as constant. During brake rechar ge, the rate includes a regenerative brake efficiency of 85% [Weiss et al., 2000]. There is added loss due to fact that front wheel drive hybrids only can recapture braking power from th e front wheels (not the rear). Thus up to approximately 75% of the braking energy is av ailable for recapture [An & Santini, 2004]. However, the new Prius brake by wire system can recapture significantly more [Toyota, 2003]. After the losses are included, the power is integr ated over each second of the driving cycle, and then the discharge (positive) and recharge (nega tive) totals are added to give a final state of charge (SOC), as a fraction of the kWhr rating. Currently, PERE is designed so that the user adjusts the hybrid power threshol d (manually) until the state of ch arge is close to zero for the given driving cycle. A more sophisticated charge sustaining strategy woul d require coding of the strategy and is considered for a future versi on of PERE. A more accura te battery model would include voltage/SOC/temperature (or similar) plot s, and the spreadsheet would need to ensure that the battery can handle transients. Howeve r, due to the relative complexity of battery systems, there are many possible levels of modeling.

The battery is assumed to have a rating of 0.936 kWhr on the moderate hybrid, the nominal rating for the Honda Insight (the Civic is 0.864). Fo r the full hybrid, the battery is assumed to be51 similar to the ’04 Prius, or 1.31kWhr. However, because we have a ‘pseudo-charge sustaining strategy’ and the vehicle is not assumed to drive on any extremely aggressive driving cycles, this number does not affect the model. In reality it is more important to define the limitations of the hybrid based on the total electrical power gene rated from the battery/motor system. PERE simplifies this, by placing emphasis on the motor system only. This assumes that manufacturers will integrate a “properly” sized battery for the motor. If PERE is required for high performance (0-60mph) runs, this, and other, portions of the model may require revision.

Replacing a battery with an ultracapacitor change s the model only slightly. The discharge and recharge efficiencies may be diffe rent, as would its ability to recapture energy from higher power braking events.

Hydraulic hybrids combine internal combustion e ngines with an hydraulic energy storage device. This storage device stores the energy in pressuri zed gases. The hydraulics would respond similar to an ultracapacitor except that the link would be mechanical, rath er than electrical. This would increase recapture efficiency. The EPA is developing this te chnology with industry partners through cooperative research and development agreements, including implementation of the technology on a number of protot ype vehicles [Alson et al., 2004]. Since hydraulic hybrids do not require large batteries (whi ch are expensive), they lend themselves well to a series configuration.

Vehicle Weight and other Specifications Hybrid vehicles are usually able to downsize th e engine in order to save weight and fuel, however the added components of the motor, inve rter, battery, and othe r connecting components more than compensate for the engine weight lo ss. Previous studies have summed the weights of the components based on power density in order to estimate a vehicle weight. This method has limitations in that other structural and compone nt weights may be omitted. This report uses a different approach. The weights of existing hybrids are compared with their conventional vehicle counterparts. Table 17 shows the weights of vari ous hybrid vehicles. In particular the Honda Civic Hybrid is compared with the Civic DX, and the Dodge Durango hybrid is compared with the Durango 4WD. While the Durango is no long er planned for production, it is still useful to compare specifications. The Prius and Insight obvi ously have no direct pa rtner, so no direct weight comparison could be done. The weight ra tio of hybrid to non-hybrid is found to be 1.07, or a 7% increase. This is based on the ratio of test weights, wh ich is curb weight + 300 lbs. Though not shown, the Silverado “mild” hybrid (4WD) is 5% heavier than its conventional truck counterpart. The diesel hybrid is assumed to have a 4% in crease over its gasoline hybrid counterpart. As more hybrid versions of vehicles are released, this mass increase ratio estimate can be improved.

Table 17: Hybrid Vehicle Specification in Co mparison to Conventional (if exists). All sources are from EPA CFEIS database, Ma nufacturer, Car&Driver or Road&Track52 websites.

model max torque max power displace ment (l) curb weight (kg) Motor Power Battery Capacity (kWhr)

Honda Insight 89.5N-m@ 2000rpm 50kW@ 5700rpm 0.995 853 10.4kW@ 3000rpm 0.936 Honda Civic Hybrid 118Nm@ 43300rpm 63.4kW@ 5700rpm 1.339 1213 10.kW@ 3000rpm 0.864 2003 Toyota Prius 111N-m@ 4200rpm 52kW@ 4500rpm 1.497 1254 33kW@ 1540rpm 1.31 2004 Toyota Prius 111Nm@ 4200rpm 57kW@ 5000rpm 1.497 1311 50kW@ 1540rpm 1.31 Dodge Durango 305N-m 130.6kW 3.9 2389 66.4kW ? Honda Civic
DX 149Nm@ 4500rpm 86kW@ 6100rpm 1.668 1111

Honda Civic
HX 151Nm@ 4500rpm 87.3kW@ 6100rpm 1.668 1111
Toyota Camry SE 220Nm@ 4000rpm 117kW@ 5600rpm 2.4 1425
Dodge Duran g o 4wd 475Nm@ 3000rpm 186kW@ 4200rpm 5.9 2251HybridConventional

Many of the other vehicle attributes that ar en’t published had to be approximated. Often coefficients of drag and weight s are available, but rolling resi stance is not. Frontal area is approximated from the published dimensions (Equation 2) if a direct measurement is not provided.

The table of hybrid coefficients are also in A ppendix B. Many of the variables may have a model year relationship into the future. E.g. compone nt weights, Cr, Cd, Pacc, motor efficiency, regeneration braking efficiency, discharge/recharge efficiency, et c. However, most of these are expected to drop somewhat and level off in the near future. The accessory term (Pacc) is expected to rise in the next few years with th e addition of more electri cal loads (and luxuries).

Accessories Currently, PERE does not model air conditioning or supplemental cabin heat in hybrid (or fuel cell vehicles). Therefore the accessory loads ar e a relatively small portion of the power required (~0.75 kW). However it is not insignificant, if the accessories were run completely on the batteries, there would be less power remaining for accelerations. In addition it is expected that accessory power will increase in the future.

In PERE, the accessories are run off the batter y when the battery is on a discharge cycle. Otherwise it runs off the generator (engine). Th e algorithms are listed in the Appendix A.

Air conditioning and heating are potentially larg e loads for hybrid vehicles. From a fuel economy standpoint, the accessory loading can be large. This is due in large part to the fact that the engine might have to c ontinue running under conditions wh en it would normally shut off (idle and deceleration). In MOVES, the effect of air conditioning will be applied to the base ACoff energy consumption rates developed by PERE. A direct air conditioning model for PERE is considered for the future.53 Model Calibration Since the PERE hybrid strategy model is based on Weiss, et al. [2000], the model is ‘calibrated’ to the model “MIT hybrid” vehicle. The MIT re port gives most of the vehicle parameters required for the model. Figure 24 compares the fuel economy results of PERE with the MIT hybrid. Figure 25 compares the stat e of charge of the batteries following the FTP test. Since the models are not identical, differences are expected . However, the fuel economies are an excellent match. This indicates that the modeli ng methodologies are quite similar.

0 10 20 30 40 50 60 70 80 90

Combined FTP HWYFuel Economy (mpg) PERE
MIT

Figure 24. Fuel economy of a mo del year 2020 hybrid car compared to MIT study [Weiss et al., 2000].

-0.006 -0.004 -0.002 0 0.002 0.004 0.006 0.008

FTP HWYBattery Status (MJ/km) PERE
MIT

recharging discharging

Figure 25. State of Battery comp ared to MIT study [2000].54 The differences between the MIT and PERE models are listed below: 1- PERE does not include bag 1 cold start. The MIT report does not mention if cold start effects are included in their model. 2- MIT transmission model is unknown. 3- Hybrid threshold is determined differently 4- Battery recharge model pr obably is not identical. 5- PERE adds additional loss fr om front wheel drive rege nerative braking only. 6- Engine friction model is slightly different.

The actual parameters that MOVES and PERE will employ will differ significantly from the MIT model vehicles.

Fuel Economy Validation Results The initial validations are conducted in comp arison to unadjusted EPA certification fuel economy numbers. The second set of validations ar e done on two hybrid vehi cles tested at the EPA laboratories. It is difficult to conduct validation results with real hybrid vehicles, when the model is only an approximation of a hybrid vehicl e. Actual hybrid vehicles have much more sophisticated control strategies, as well as efficiencies that depe nd on various operating conditions. However, the energy flows still fo llow a similar physical and logical progression. Given a power demand the fuel consumption ra te should be similar if the loss terms are characterized sufficiently well.

The fuel economy validation is conducted on a number of vehicles (hybrid and non-hybrid), whose specifications could be found on either th e manufacturer, or a trade magazine website (such as Car and Driver and Road and Track). Al l weights are test wei ghts (curb weight plus 300lbs). The list of vehicles is on Table 18. The Honda Civic HX is the lean burn engine version of the Civic series. This repor t does not attempt to validate PERE to a larger variety of conventional vehicles (light dut y vehicles and trucks), since that has already have been conducted successfully in the literature.

Table 18. Validation vehicles – all model years are 2004 unless otherwise specified.

Mfr Model Toyota Camry

VW Jetta gas VW Jetta Diesel Honda Civic DX Honda Civic HX Honda Civic Hybrid Honda Insight Toyota Prius '01 Toyota Prius '04

The rated fuel economy numbers for production ve hicles are adjusted based on real world driving correction factors: adjusted FTP = una djusted FTP*0.9 and adjusted HWY = unadjusted HWY*0.78. PERE output should be compared w ith the dynamometer measured fuel economy, which are the unadjusted numbers, since PERE is modeling the physical loads on the vehicle as imposed by the dynamometer directly. The combin ed fuel economy (miles per gallon) is a55 weighted average 0.45 (FTP) and 0.55 (HWY), calcu lated in the following standard fashion [Schaefer, 1994]:

Equation 33 Combined F.E. = 1/(0.55/FTP + 0.45/HWY).

Figure 26, Figure 27, and Figure 28 show the validations for the vehicles (city, highway and combined respectively). The model performs esp ecially well for conventional vehicles. There were several other conventional vehicles (light cars and trucks) validated, the Camry, Civic, and Jetta are the only ones sh own. All of the conventional vehicles matched rated fuel economy to within 5% (on city and highway ) with the exception of the Volk swagen Jetta (gasoline). There were also several conventional ve hicles validated to dynamometer te sts in previous studies, using a similar, but older version of PERE [Nam, 2003]. Although the Jetta (1.9L TDI) results were within 5%, the reason for the discrepancy with the Jetta (gas) is unknown. For a small fraction of vehicles, the fuel economy is not accurately cap tured by PERE. This is most likely due to many different factors unique to each make and model. It is also interesting to note that PERE slightly overestimates fuel economy from the city for ma ny of the vehicles. This is not too surprising since cold start factors are not included.

The “advanced vehicle” validations begin with the Honda Civic HX, with the lean burn engine. The predictions for this vehicl e is very good (within 5%). This suggests that the powertrain model is correctly modeled, based on the descri ption of the lean burn engine by Ogawa et al. [2003]. All of the advanced vehicl es are accurately cap ture by PERE for hi ghway driving (within 5%). With the exception of the Toyota Prius, th e city estimates are also all within 10%. PERE overpredicts the fuel economy on the Honda Civi c Hybrid on the city cycle by around 9%. This is likely due to two reasons: PE RE shuts the engine down during deceleration events and idle (running off only battery) but the Honda does not shut down all the time. This is more evident in the next section of this paper. Also, there is no cold start module in the present version of PERE. Also, the Honda engines may not shut off duri ng short idles as PERE models it (see modal results below). Hybrids are especially expected to have significantly higher fuel consumption during the cold start first phase (bag 1) of the FTP. This is due to the fact that the engine has to stay on in order to ensure that the catalyst light s off in a timely fashion (after which it can start breaking down the criteria pollutants). Many conv entional engines also run slightly rich during the start up period. Presently PERE shuts the en gine down during decelerations and idle, which would tend to significantly under predict fuel co nsumption (over predict fuel economy) during start-up for hybrids, and to a lesser extent conv entional engines. Modifying PERE so that the engine remains on for the first two minutes would be relatively simple. When this is done, PERE still underpredicts city fuel economy for the Prius by 12% and 18% for the 2001 and 2004 models respectively. This dem onstrates how different the stra tegy of the Prius is from the rudimentary strategy employed in PERE. The Toyot a Hybrid System (THS) is a series/parallel hybrid design. A powersplit device routes some of the power from the engine to recharge the batteries, thus the motor is used much more fre quently than in parallel hybrid [Toyota, 2003] to supplement power. The hybrid system can be fi ne-tuned to give optimal fuel economy. The brake-by-wire system on the 2004 Prius also help s to regain maximal energy from regenerative braking. In this way, the Prius can and does get better fuel economy in the city than on the highway (a difficult condition for PERE to duplicat e). Moreover, the specifications (efficiency improvements) for the Prius Atkinson cycle engine are unknown.56 0 10 20 30 40 50 60 70 80 90

PERE MIT Toyota Camry VW Jetta
gas VW Jetta Diesel Honda Civic DX Honda Civic HX Honda Civic Hybrid Honda Insight Toyota Prius '01 Toyota Prius '04FTP fuel economy (mpg) PERE EPA unadjusted

Figure 26. Fuel Economy validation for the FT P city (UDDS) compared to unadjusted EPA values.

0 10 20 30 40 50 60 70 80 90

PERE MIT Toyota Camry VW Jetta
gas VW Jetta Diesel Honda Civic DX Honda Civic HX Honda Civic Hybrid Honda Insight Toyota Prius '01 Toyota Prius '04HWY Fuel Economy (mpg) PERE EPA unadjusted

Figure 27. Fuel Economy validation for the FTP (HWY) compared to unadjusted EPA values.57 0 10 20 30 40 50 60 70 80 90

PERE MIT Toyota Camry VW Jetta
gas VW Jetta Diesel Honda Civic DX Honda Civic HX Honda Civic Hybrid Honda Insight Toyota Prius '01 Toyota Prius '04FTP fuel economy (mpg) PERE EPA unadjusted

Figure 28. Fuel Economy validation for the FT P/HWY Combined compared to unadjusted EPA values. It has been demonstrated that PERE can capture the fuel consumption behavior of gasoline, diesel, and “generic” parallel hybrid vehicles. The vehicle parameters required from the user are model year, weight, body type (r esistive coefficients and size , which can be approximated), engine displacement, motor power, and fuel type . With the exception of body type, these are the primary “source” inputs for MOVES.

This simplification of hybrid co ntrol strategy modeling could resu lt in under-predicting fuel economy. A more optimized control strategy would be to run the ba ttery at low load and allow the engine to recharge the battery at higher load s, where the engine can operate more efficiently (as the Prius does). This was not done because it wa s difficult to put in spreadsheet format. If PERE is coded, then a more sophisticat ed control algorithm could be added.

Modal Validation Results PERE is based on a quasi-steady state powertrain model, designed to model “generic” vehicles (conventional and advanced). The hybrid vehicl e modeling is especia lly simplified since it distributed energy loads using relatively stra ightforward algorithms. It can be somewhat misleading to compare to actual second-by-seco nd data taken on a dynamometer. We should not be surprised if the results vary significantly. However, explor ing these discrepancies are of interest.

Two in-use hybrid vehicles were tested at the National Vehicle a nd Fuel Emissions Laboratory in 2003 as part of an in-use compliance surve y. The 2001 Honda Insight (9,400 miles) and 2001 Toyota Prius (58,100 miles) were tested on a chassis dynamometer on a 4 bag FTP test (no highway). Both vehicles were certified to federa l ULEV standards. Some of the tests included bench modal data (second-by-second) and some included measurements from an portable on-58 board emissions analyzer. The SEMTECH-G manuf actured by SENSORS Inc. was incorporated on the instrumented vehicle tests. The SEMTEC H-G measures real-time concentrations of CO 2 , CO, HC and NOx. It also measures the exhaust volume flow independently using a hot wire anemometer. Combining concentration and flow (after time alignment) gives second-by-second mass emissions measurement. The experiments and results are described in greater detail in [Nam et al., 2005]

Of the 12 tests performed on these two vehicles, 1 representative sample from each is compared to the model. The Prius data is dynamometer mo dal data, whereas the Insight only has on-board emissions data. The fuel consumption is calculated from mass CO 2 (and emissions) measured.

Figure 29 shows the bag 3 results of the Honda In sight. The hot wire anemometer was incapable of accurately measuring flow below 10cfm. Due to the small engine on the Insight, the flow can approach this value during low load operation. Ther efore, the low flow bag 2 measurements were not used. Both the instantaneous and cumulativ e emissions are displayed in the figure for comparison. One can see from the figure that the meas ured fuel rate is higher in the peaks than PERE. However PERE shuts off the engine during deceleration and idle, thus giving lower fuel rate than measured during these modes of driving. The cumulative fuel rate plot shows that the overall fuel rate mode for PERE is quite accura te for this type of vehicle. Though not shown, the total fuel consumption between model and measur ement compare within the bounds of the test to test variability.

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0

0 100 200 300 400 500 tim espeed

0 0 .2 0 .4 0 .6 0 .8 1 1 .2 1 .4 1 .6 1 .8 2Fuel (g/s) speed

PERE FR
SEMTECH FR59 Figure 29. Bag 3 second by second fuel rate and cumulative fuel rate comparing PERE with SEMTECH on a 2001 Honda Insight.

Figure 30 shows the bag 3 and 4 results fo r the Toyota Prius. It appear s at first glance that there may be a timing shift error. The effect is real. B ecause of the series-parallel nature of the Prius hybrid system, the engine does not deliver power until a few seconds after it is demanded. Thus the battery is “launching” this vehicle during a moderate load acc eleration. The engine turns on later in order to recharge the recently depleted battery.

0 0.5

1 1.5

2 2.5

3

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

timefuel (g/s)

0 10 20 30 40 50 60speed dynomometer
PERE speed Insight SEMTECH vs PERE

0 10 20 30 40 50 60

0 100 200 300 400 500

timespeed

0 20 40 60 80 100 120 140 160 180Cumulative fuel rate (g) speed

PERE FR
SEMTECH FR60 0 50 100 150 200 250 300 350 400 450

0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

timefuel

0 10 20 30 40 50 60speed dynomometer
PERE speed

Figure 30. Bag 3 and 4 fuel rate and cumulati ve fuel consumption modeled and measured (on a chassis roll) fo r the 2001 Toyota Prius.

The difficulty with using the Prius data in MOVES is that it is not directly “load-following”, i.e. the emissions event does not immediately follow the driving, which caused it. This “problem” may apply for all hybrid vehicles that have a se ries element (where the engine recharges the battery rather than drive the wheels). Since MOVES and PERE are modal or load-based models, it may be necessary to adjust the data so that th ey are more aligned with the models. If the timing on the Prius data were shifted 12 seconds to align with the spee ds and accelerations, we would observe more of a load-following behavior. This was conducted on the FTP measurements, however, other driving traces may have different or no engine lag. The Prius data was shifted and then was binned by VSP (this binni ng procedure is described in gr eater detail in a later section and in other MOVES documents) in Figure 31. Th e straight binning (without time alignment for load correction – indicated by squares) gives poor results. This is because the VSP event coincident to the fuel consumption is non-causa l. Instantaneous emissions rates clearly cannot be based on this method. However, after time shifting the data, the comparison between the model and the data is good. Any more rigorous comparison is beyond the abilities of PERE at this time. The difficulty of using this load co rrecting time alignment procedure is that the time shift will vary depending on a number of conditions : magnitude of acceleration, road grade, state of charge of battery, air cond itioning, and others. Unfortunately, this poses potential problems when including the data from any hybrid vehicle with a series ( power storage) element into the MOVES database.61 0 0.2 0.4 0.6 0.8

1 1.2 1.4 1.6 1.8

0 5 10 15 20 25 30 35

vspfuel rate (g/s) PERE bag3,2 fr (DYNO bag3,2) fr (prius dyno bag3,2, 12 sec lead)

Figure 31. Prius fuel rate as a function of Ve hicle Specific Power Bin with and without time alignment of the data.

Electric Vehicles At this time, MOVES does not put strong empha sis on electric (only) vehicles (EV). EVs are modeled in PERE simply as hybrid vehicles with the internal combustion engine removed. Most of the major components are already included (ba ttery, traction motor, re generative braking, etc). In a future version, there may be further atte mpts to develop and validate this model.

Section VIII – Hydrogen Internal Combustion Engine Vehicles

As of the date of the publicati on of this report, hydrogen ICE ve hicles are not yet in production. However, there are a number of prototypes cons tructed by various manufacturers, notably Ford and BMW. Due to the relative advanced stage of development of Ford’s vehicle, we obtain PERE parameters primarily from a series of Fo rd SAE papers [Tang et al., 2002, Natkin et al., 2003].

The hydrogen vehicle is approximately 25% heav ier than its conventi onal ICE counterpart [Natkin, 2004]. Much of this is due to the a dded weight of the hydrogen tanks and the added structures required for holding the tank securely. Fuel Cell vehicles have the same weight additions (see Table 19). The Ford vehicle also has hybrid electric elements. Moreover, this is a prototype vehicle, so weight reductions are inevitable if this vehicle were taken to full production.

The indicated efficiency of the (boosted) H 2 ICE is taken to be 45% (though it may well be somewhat higher). However, the specific pow er of the naturally aspirated engines are significantly lower than their gasoline counterparts. In orde r to achieve similar power performance it is necessary to boost the intake air. PERE assumes that the H 2 ICE is supercharged engine (like the Ford engine). Beca use of this, the friction is also increased. The62 friction is determined from the motoring torque cu rves in Natkin et al. [2003] and is shown in Figure 32. The friction losses from s upercharging are significant.

In PERE, the transmission for the H2 ICE vehicl e is assumed to the id entical to conventional transmissions.

y = 0.0967x + 15.8365
R 2 = 0.9681

y = 0.0339x + 42.595
R 2 = 0.917

0 100 200 300 400 500 600

0 1000 2000 3000 4000 5000 rpmfmep (kPa) fmep supercharged (kPa) fmep normal asipirated

Figure 32. Friction as a function of engine speed for the Ford 2.3L supercharged and naturally aspirated H 2 ICE [Natkin et al., 2003].

Unfortunately, a validation could not be performed on the PERE H 2 ICE vehicle at this time since there is no comparison vehicle. This will be attempted in the future.

Section IX – Hydrogen Fuel Cell Vehicles

Fuel Cell and Hybrid Fuel Cell Vehicles While, there are several types of fuel cells, the hydrogen Proton Exchange Membrane (PEM) fuel cell is the fuel cell of choi ce for vehicle traction applications . A fuel cell is a power source that runs on hydrogen to make current across a Proton Exchange Membrane. Each cell produces a small amount of current and ( like a battery) these are stacked together to produce a relatively efficient power pack. There are other types of fuel cells, but PEM cells are the most commonly used for automotive applications today. Most pr ototype vehicles are equipped with Hydrogen PEM stack fuel cells. Hydrogen is the primary energy source, which produces energy when combined with oxygen. This reaction produces water, which is the only tailpipe emissions. Some fuel cell systems use a reformer to break hydr ocarbons from liquid fuel s into gaseous hydrogen, but this technology appears to be falling out of favor for light-dut y vehicle use due to its size and relative inefficiency.

In PERE, fuel cell vehicles are modeled as “serie s hybrid” vehicles. This means that there is only one mechanical path. However, there is still a pa rallel electrical power path, in that either the63 battery or fuel cell can drive the motor. This model is similar to the one proposed by Weiss et al. [2000 & revised in 2003] (there is still a hybrid threshold requir ed). The power flow chart is shown on Figure 33. It is essentially the same as the hybrid model above, but the engine is replaced with a fuel cell, and there is no tr ansmission (only a single gear driven by the high speed motor). The fuel cell system efficiency (from the same reference) is shown on Figure 34. The fuel cell efficiency curve is a projection and reflects an optimistic estimate. The figure shows the efficiency points (scaled to a 72 kW cell) including a 7 th order polynomial fit for PERE. The equation is shown below (calibrated to a 80kW stack):

Equation 34. FCeff = a 0 + a 1 P frac + a 2 P frac 2 + a 3 P frac 3 + a 4 P frac 4 + a 5 P frac 5 + a 6 P frac 6 + a 7 P frac 7

Where P frac = 80*P d /P fc (scaled) P fc is fuel cell power rating (kW) P d is power demand (kW)

a 0 = 0.067284

a 1 = 0.166336

a 2 = -0.018361

a 3 = 9.6832e-4

a 4 = -2.7509e-5

a 5 = 4.2972e-7

a 6 = -3.4719e-9

a 7 = 1.1328e-11

Again in this version of PERE, there is no co ld start modeled, the effects of which may be significant. We also use the batter y, similar to a parallel hybrid, to ‘launch’ the vehicle below the hybrid threshold (initial acceleration). This avoids the very low fuel cell system efficiencies at low load (see Figure 34). The model is similar in structure to the hybrid presented above. The “calibration” comparison to the MIT results is shown in Figure 35. The fuel economy figures are calculated in miles per gallon ga soline equivalent by converting the work equivalent hydrogen into gasoline mass (with their resp ective lower heating values). Th is vehicle is found to have a tank to wheel efficiency of 50% (this is also what Toyota claims for their fuel cell vehicle efficiency). The tank to wheel efficiency is si mply defined here as the useful energy out/fuel energy in. The useful energy out is only the (pos itive) energy required for the car to follow the driving cycle. It would be slightly lo wer if the accessories were included.64 ACTIVITY INPUT V,A, Road grade
Logic Control

Vehicle Param:

M, Cr, CdA VSP or Power Demand

Regen braking Motor

Battery/ UltraCap

Fuel (H 2 ) Consumption

Fuel Cell

Figure 33. Power flow chart for the hy drogen hybrid fuel cell vehicle.

72kW Fuel Cell

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0 10 20 30 40 50 60 70 80 Net Power Output (kW)efficiency

MIT

fit

Figure 34. Fuel cell integrated system effi ciency [Weiss et al, 2000, 2003] including a 7 th order polynomial fit.65 0 20 40 60 80 100 120 140 160

Combined FTP HWYFuel Economy (mpgge) PERE
MIT

Figure 35. Fuel economy comparis on between PERE and Weiss et al [2003]. The units are in miles per gallon gasoline equivalent.

Validation It is difficult to conduct a valid ation of the fuel cell model since there isn’t a tremendous amount of data available for the prototyp e vehicles that exist. Some specifications have been collected for a number of manufacturer prototypes. They are listed on Table 19 along with the conventional vehicle body on which the vehicle is built (the Daimler Ch rysler F-Cell vehicle weight is unknown). The Honda FCX has a uni que body, so it does not have a conventional vehicle equivalent (though it does have a similar electric vehi cle). The average increase in weight of the first 3 vehicles compared to its equivalent conven tional vehicle is 23% (interestingly this is consistent with th e increase in the weight of the Ford H 2 ICE). This increase may be less for larger (medium and heavy-duty) vehicles.

The Ford, DaimlerChrysler, and Honda vehicles all use the Ballard  Mark 902 fuel cell stack. The GM Hydrogen 3 and Toyota FCHV use different stacks. The former is not a hybrid. As the fuel cell energy densities increase in the future, the weight increase is expected to be reduced from the current estimate of 23%. The estimated system efficiency for the 80kW fuel cell is different from the one on Figure 34 (which can be used for future projections). The more conservative estimates are shown on Figure 36. Th is is used for the validation below.

Table 19. List of some fuel cell vehicles. (References are all from information sheets distributed during ride & drives by manufac turer representatives, or from website)66 model Eng
power ( kW ) curb weight

( k g) Motor Power (kW) FC power (kW)

Ford Focus 1600 65 80
Toyota FCHV 1860 80 90
GM HydroGen3 1700 60 94

DC FCELL N/A 65 80

Honda FCX 1684 60 80

Ford Focus 97 1213

Toyota
Hi g hlander 119 1597

GM Opel

Zafira 74 1393

DC Mercedes A

Class 76 1115Fuel CellConventional

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 10 20 30 40 50 60 70 80 Net Power Output (kW)Fuel Cell System Efficiency

Figure 36. Fuel Cell System efficiency for an 80kW stack [Nelson, 2003].

Validation can only be conducted on the Honda FCX, since this is the only vehicle whose fuel economy equivalent numbers are known.

In order to model the Honda FCX, some of the energy storage parameters had to be adjusted since the vehicle is equipped with an ultracapacitor, rather than a battery and does not have a DC-DC converter. The motor syst em, and recharge efficiencies are higher. The Honda FCX has an adjusted EPA fuel economy rating of 51/46 miles per kilogram hydrogen (city/highway). A67 kg of hydrogen is roughly equal to a gallon of gasoline in energy. In comparison, PERE predictions are shown on Figure 37. Th e discrepancy is within 5%.

0 10 20 30 40 50 60 70

Combined FTP HWYFuel Economy (mpgge) PERE
Honda FCX

Figure 37. Fuel economy (in miles per gallon gasoline equivalent) for the Honda FCX.

Section X - Sensitivity

This section is devoted to a sensitivity analysis of PERE. The analysis determines which parameters are most important to characterize a ccurately. A static sensi tivity is performed in relation to a change in city vs highway fuel cons umption. It is necessary to do a separate analysis since the parameters affect the re sults from two different driving types in a disparate fashion. The simple static sensitivity test assumes a 10% variation in each parameter (though some of the parameters are correlated). The change in the fu el consumption result is ranked and compared. In order to perform a more complete uncertainty anal ysis, it is necessary to quantify the variation in the parameters. This is a potentially difficult undertaking, since some of the parameters are estimates (and not measurements). Also, it is quite probable that different al ternate driving cycles will give different sensitivity results. Futher more, some of the parameters may vary by significantly more than 10% (some by an order of magnitude). A more complete analysis may be performed in a future report.

Table 20 shows the sensitivity results, in % di fference, for a typical modern passenger car modeled using PERE. To get the relative sensit ivity, divide by 10. TRLHP is the Track Road Load Horsepower. It is a single quantity on which the (track) road load coefficients (A,B,C) are based in MOVES. PERE does not typically us e TRLHP, but MOVES does. As expected, the parameters most affecting fuel consumption tend to be different for city vs highway driving. It is not surprising though, that the e fficiency, weight, and engine displacement all dominate the top ranking. Fortunately, these quantities are typically known inputs for th e model, or in the case of68 indicated efficiency, should not vary much. For those advanced engines where the efficiency is unknown, it clearly poses a problem for the m odel. The high ranking of the transmission efficiency might support the need for a speed or load based transmissions model. Aside from efficiency and N/v, however, PERE seems to be relatively insensitive to the other transmission parameters. However, it is important to make sure that the gear ratios are not systematically off from the actual gear ratios. It is interesting also to note that th e model is quite insensitive the peak torque curves. It would be more crucia l if high acceleration (fu ll throttle) performance modeling was being performed. Since the activity in MOVES describes representative “realworld” driving, these driving mode s are not stressed in the model, nor is it in PERE. Though it is not included the table, the model was not se nsitive (<1%) to the rotational term in Equation 1 ( g ).

Table 20. Static sensitivity te st for the PERE parameters used to model a typical conventional passenger car. Parameters are adjusted 10%

RAN K Parameter Error City 1 P/T indicated eff (eta) 4.93 2 Engine Displ (L) 4.57 3 trans eff 4.46 4 Vehicle wgt (kg) 3.98 5 k0 (N indep friction kJ/ L 3.72 6 N/v (rpm/mph) 3.31 7 TRLHP 1.95 8 Shift point 3-4 1.31 9 g/gtop 4 1.26 10 Shift point 2-3 1.24 11 Shift point 4-5 1.11 12 Cr0 (rolling resistance) 1.05 13 g/gtop 5 0.96 14 Cd (drag coeff) 0.93 15 A (frontal area m^2) 0.93 16 Nidle (rpm) 0.90 17 k1 (N dependent fric) 0.85 18 Pacc (accessory - kW) 0.52 19 g/gtop 1 0.38 20 g/gtop 2 0.22 21 g/gtop 3 0.16 22 Shift point 1-2 (mph) 0.04 23 torque curve up 10% 0.03 RAN K Parameter Error Hwy 1 P/T indicated eff (eta) 5.92 2 trans eff 5.55 3 TRLHP 5.14 4 Engine Displ (L) 3.49 5 Cd (drag coeff) 3.11 6 A (frontal area m^2) 3.11 7 Vehicle wgt (kg) 3.01 8 k0 (N indep friction kJ/ L 2.75 9 Nidle (rpm) 2.27 10 g/gtop 2 2.25 11 g/gtop 1 2.25 12 Shift point 2-3 2.22 13 Shift point 1-2 (mph) 2.21 14 N/v (rpm/mph) 1.89 15 g/gtop 3 1.88 16 Cr0 (rolling resistance) 1.88 17 Shift point 4-5 1.46 18 Shift point 3-4 0.91 19 k1 (N dependent fric) 0.74 20 g/gtop 4 0.67 21 Pacc (accessory - kW) 0.40 22 g/gtop 5 0.23 23 torque curve up 10% 0.00

The sensitivity results for a “typical” full hybrid are presented in Table 21. Here the motor is power split such that it can bypass the transmission. This improves the efficiency of the vehicle. This is a fictional hybrid vehicle designed using PERE only, but assuming the same body and overall power as the previous vehicle. With the exception of efficiency, the transmission parameters have been omitted from this analysis . The hybrid threshold was adjusted to maintain the pseudo-charge sustaining strategy with each run. It is clear from this table that the engine parameters are still more important for fuel economy modeling compared to the electric parameters. Since the small engine is operating at much higher loads, the model is much more69 sensitive to changes than the c onventional vehicle. Again, since th e vehicle is able to meet the drive cycle (sometimes with the help of the moto r), the battery limit parameters are not important for the model.

A sensitivity analysis is not conducted on the fu el cell hybrid, however, it is obvious from the above two studies that the fuel cel l system efficiency curve is th e most important parameter. The fuel cell peak power also being of moderate importance.

Table 21. Static sensitivity test for the PERE parameters us ed to model a fictional full electric hybrid passenger car.

RANK Parameter Error City 1 P/T indicated eff (eta) 8.11 2 Vehicle wgt (kg) 7.18 3 Engine Displ (L) 5.42 4 overall motor efficiency 3.38 5 Cr0 (rolling resistance) 3.32 6 Cd (drag coeff) 3.12 7 A (frontal area m^2) 3.12 8 torq curve up 10% 2.96 9 Regen Brake Eff 1.99 10 FWD power frac 1.99 11 Motor peak power (kW) 1.08 12 fmep0 (N indep friction k J 1.01 13 Pacc (accessory - kW) 0.73 14 fmep1 (N dependent fric) 0.39 RANK Parameter Error Hwy 1 P/T indicated eff (eta) 8.52 2 Cd (drag coeff) 3.47 3 A (frontal area m^2) 3.47 4 Vehicle wgt (kg) 2.35 5 Cr0 (rolling resistance) 2.21 6 Engine Displ (L) 2.08 7 fmep0 (N indep friction k J 1.81 8 overall motor efficiency 0.61 9 fmep1 (N dependent fric) 0.49 10 Pacc (accessory - kW) 0.44 11 Regen Brake Eff 0.30 12 FWD power frac 0.30 13 torq curve up 10% 0.24 14 Motor peak power (kW) 0.05

Road Load and Track Coefficients Finally a brief comparison was run between us ing the road load coefficients Cr, Cd, A F (frontal area), etc, vs using the track (or target) coast-down co efficients (A, B, C). The fuel consumption was compared using both techniqu es for the conventional and hybrid vehicles. This is essentially a sensitiv ity analysis of the B term. It was found that the choice of methods made little difference for city fuel consumption results, but were significant for highway. For the city, the fuel consumption wa s underpredicted by about 2% (or less). For the highway cycle, the fuel consumption was underpredicted by 5% for conventional vehicles and 8% for hybrids on average. This implies that at high sp eed, the load curves from the estimated coefficients tend to be lower than those measured on the track. This could be due to the lack of a first order speed dependent term (B in force units), which tends to be quite small (or sometimes negative) on tr ack tests. Additionally, there could be higher order rolling resistance terms [Gillespie, 1992]. It may be further aggravated in hybrids because of motor drag and their lack of a true neutral gear.

With production vehicles, the issue of choosi ng the method of calcula ting road loads is simple, since most of the track A, B, C coe fficients are publicly available. Unfortunately, this cannot be done with vehicles that have not yet been produced. It is impractical to combine Equation 1 with Equation 3 in order to back-d erive physical quantities from track70 A, B, and C coefficients, since the values are determined from empirical fits to coastdown data. Thus, it is necessary to have a me thodology for estimating the effect of the B coefficients for future vehicles. Two methods of estimating its magnitude are compared here. The first method involves fitting to the known parameters.

The following coast-down force e quations are based on Equation 3:

Equation 35 F RL = mgC R + 0.5 ρ C D A F v 2

Equation 36 F T = A + Bv + Cv 2

Where the subscripts “RL” and “T” stand fo r “Road load” and “Track” respectively. The equations are compared for the validation vehicles in the study. Then a B “correction” term is added into Equation 35 and empirically fitted to Equation 36.

Equation 37 F RL = mgC R + Bv + 0.5 ρ C D A F v 2

The second method is to use the higher order rolling resistance term from Gillespie [1992] modified slightly here:

Equation 38 C R = C R0 *(1+v/44.7)

Where C R0 is the base rolling resistance (typic ally ~.009). An estimate of B is

Equation 39 B’ = MgC R0 /44.7 [N/mps]

The speed dependent parameter in the denominator is not precise. It is estimated to double the rolling resistance factor at 100 mph. In ot her references, the speed dependent rolling resistance is embedded in the C parameter [Petrushov, 1997]. Unfortunately, this approach does not take other vehicle (non-tire) frictional factors into account.

Table 22 shows the fitted B coefficients along w ith their 1 standard deviation uncertainties. The third column displays the percentage underp rediction in fuel cons umption from the use of the road load Equation 1 compared to using the track Equation 3. The last column (B’) shows the estimated value of B using Gillespie’s method. First it is clear that the B term has a significant effect on fuel consumption. In all but the Camry, the difference is over 5% on the highway cycle. Comparing the fitted B with the estimated B’, there is some agreement, but the trends break down for certain vehicles (e.g. Camry). Unfortunately, the tires (and rolling resistance) used for testing these vehicles were unknown. Moreover, the lack of correlation with some vehicles only supports the notion that other factor s are inherent to the B term, such as rotating friction in bearings , transmission, and fina l drive, tire windage, motor drag etc. Because of the motor drag (a nd other hybrid differences), the official fuel71 economy numbers for certain hybrids may not nece ssarily match what one would achieve in the real world [Rechtin, 2003].

Table 22. Fitted B coefficients and 1 standard deviation. CV = Conven tional Vehicle, HEV = Hybrid Electric Vehicle.

B fit (N/mps) sigma HWY Fuel Cons Effect % B' Civic DX 2.01 0.19 8.60 2.44 Camry 0.67 0.07 1.48 3.09 Jetta 2.85 0.11 8.87 2.89 Civic hybrid 2.32 0.11 8.18 2.37 Insight 1.37 0.15 6.27 1.73 Prius 01 3.11 0.35 10.46 2.13 avg CV 1.84 0.13 6.31 2.81 avgHEV 2.27 0.20 8.30 2.08 avg ttl 2.06 0.16 7.31 2.44

This subject clearly requires further researc h. However, if a vehicle’s A, B, C track coefficients are unknown, PERE will assume Gillespie’s (or Petrushov’s) methodology, until an improved methodology is presented.

The subjects of dynamometer, track, and road load coefficients are complex. This brief study, only begins to scratch at the surface. A more full discussion of this topic is beyond the scope of this report.

Cold Start

Modern vehicles are capable of eliminating ne arly all of the criter ia pollutants during hot running periods. However, during co ld starts, the engine and the catalytic converter are cold, thus preventing the breakdown of emissions . For the vehicles meeting the cleanest standards, the cold start emissions account fo r a large fraction of the vehicle’s operating emissions. Though it is significant for the se nsitivity of PERE, the impact on fuel consumption is not as dramatic as it would be for criteria pollutants.

In order for criteria pollutants to be treated in the catalytic converter, the catalyst must first obtain a sufficiently high temperature to break down the pollutants. Before this can happen, the engine must warm up to a point where its exhaust is hot enough to heat the catalyst. When this temperature is reached, this condition is referred to as “light-off”. In order for the catalyst to light-off, the heat from the engine exhaust must heat the catalyst, therefore the engine must be running until this point. At th is time, PERE cannot predict at what time light-off will occur, this must be done empirica lly. Typical gasoline vehicles light-off within 2 minutes into the first portion (bag 1) of the FTP. There are advanced technologies, which help minimize this light-off time, but they usually come at a cost. For example, the engine may run rich briefly, or electrica l power may be needed to pre-he at the catalyst. In either of72 these cases, energy is being consumed. Additiona lly, the engine runs less efficiently while it is cold.

Kim and Lee [2004] measured the cold vs hot fuel economy for the (pre-2004) Toyota Prius. Their report indicates that the city cold start fuel economy wa s 12.4% worse than a hot start

test. However, their test vehicle may have had a different calibration than one in the United States. In the EPA testing, the cold start fuel consumption (defined here as bag 1 – bag 3) was 53g for the 1.5L Prius. This is an average of 5 tests and correspond s to an increase of 27% compared to bag 3. For the 1.0L Insight, th e cold start fuel consumption was 20g. This is an average of 7 tests and corresponds to an increase of 12% over bag 3. The fuel consumption at the beginning of bag 3, did not a ppear unusually high for either vehicle. It is interesting to compare these figures to those obtained from the displacement dependent cold start factors described in Koupal et al., [2004]. The MOVES equation describing gasoline cold start factors is included below and the table compares the results.

Equation 40. Cold-Start Fuel Consumptio n (grams) = 31.867 + 10.863 * Displacement (L)

Table 23. Cold start rates compared for the Insight and Prius.

Measured MOVES model 1.0L Insight 20g 43g 1.5L Prius 53g 48g

The model is adequate for the Prius but ove rpredicts for the Insight. Due to the large uncertainties involved in cold start modeling, it is decided to use th e conventional vehicle cold start factors in MOVES for hybrids until the model can be improved in the future.

An attempt is also made to model cold start modally in PERE. The fuel strategy on a 1.1L full hybrid vehicle (used in the sensitivity study ) is adjusted so that the engine remains on for the first 2 minutes of the test. This time was chosen based on estimating the cold duration on numerous conventi onal vehicles. The battery po wer is set to zero for the duration, so that vehicle is ac ting much like a conventional vehi cle, with the exception that the regenerative brakes are still active. The fuel never goes to zero in this scenario, but is minimal at engine idle. The additional energy (fue l) consumed in this period is assumed to balance any alternative technologies such as an electrically heated catalyst.

The fuel consumption from this modified hybr id vehicle increases by 11% on bag 1 vs bag 3 (10% lower fuel economy). By subtracting the hot running bag 3 from the bag 1, this corresponds to a cold start f actor of 22.3 grams of fuel per start, which is curiously consistent with the measured Honda Insi ght results (which ha s a similar engine displacement). The model would naturally underp redict the Prius cold start however. This start factor will depend on the t ype of hybrid, size of engine, and other vehicle parameters. This sub-model is not yet used due to its developmental nature.

There is as yet, no cold start mode l for fuel cell vehicles in PERE.73 Section XI - Application to MOVES

PERE has been developed fundamentally to produce energy consumption and emission rates for MOVES for vehicle categories (known as “source bins” in MOVES terminology) where in-use data is insufficient to populate these rates directly. Thus PERE will produce these rates for most of the advanced technology vehicles (the focus of this report) and some of the conventional vehicles where data is lack ing (“holes”). The potential for PERE to compliment MOVES in this way is discussed in the USEPA report [ 2002 -MOVES GHG emission analysis report ].

Presently PERE is a standalone spreadsheet m odel capable of modeling on a finer scale than MOVES requires. PERE also has the potential to model a wider assortment of technologies than MOVES currently has. This flexibility allo ws for more source bins to be filled in the future as the need arises. However, the development time for each technology type using PERE is significant, so MOVES will model some advanced technol ogies directly using energy and emissions rates measured from ot her studies such as GREET (e.g. alternative fueled vehicles).

The process of how PERE result s could be integrated into M OVES is the subject of this section.

Hole Filling Using PERE

The original intent of MOVES is to derive a ll energy and emissions ra tes from existing data. However, it is not possible to ob tain data from all mobile sour ces in the vast fleet. The MSOD (the database upon which MOVES obtains its em issions rates – [Koupal et al., 2004]) currently accounts for approximately 95% of the on-road fleet. Therefore, it is necessary to come up with a method of “filling these holes”. It was demonstrated in a series of reports, that PERE provides a fairly robust fuel consumption model for conven tional vehicles [Nam, 2003; Nam & Giannelli, 2004]. The model also serves the purpose of allowing for the study of physical effects on vehicle fuel consumption and emissions by isolating certai n factors. As it currently stands, PERE will be employed to fill some of the more “common” source bin holes in MOVES. “Common” is defined as the source bins that are most frequently represented in the fleet. The following table describes a parsed source bin ID along with th e assumed values used by PERE for conventional vehicles. This bin happens to be th e highest ranked hole, i.e. of all the holes, this bin represents the largest fraction of the fleet (~0.78%).

Table 24: Source bin nomenclature using the highest ranked hole in MOVES: Heavy duty diesel (70,000lb).

Source Bin: 1020100045099080000 1: leading digit (no physical meaning) 02: Fuel type: diesel 01: Engine Technology: Conventional Internal Combustion 00: Regulatory Class: N/A 04: Model Year: 1991-1999 (assumed 1995)74 5099: Engine Size: >5.0L (assumed 6.9L – see Nam, Giannelli, 2004) 0800: Weight: 60,000-80,000 lbs (Heavy Duty – assumed 70,000) 00: Trailing zeroes (no physical meaning)

Some of the common source bin holes (ranked t op 45 or so) are listed in the table below (grouped by model year). Also in cluded are the central values used in PERE for model year, weight and engine size. With these filled by PERE, > 97% of the fleet will be included.

Table 25. List of the top source bins so rted by fuel type and model year.

rank Source bin ID fuel ID Fuel MY ID MY engsz Wt ID

15 1010100032025006000 a 1 Gas 3 86-90 2025 60 32 1010100044050002500 a 1 Gas 4 91-99 4050 25 37 1010100044050007000 a 1 Gas 4 91-99 4050 70 36 1010100044050009000 a 1 Gas 4 91-99 4050 90 4 1010100045099016000 a 1 Gas 4 91-99 5099 160 7 1010100045099019500 a 1 Gas 4 91-99 5099 195 18 1010100045099026000 a 1 Gas 4 91-99 5099 260
38 1010100045099033000 a 1 Gas 4 91-99 5099 330
35 1010100053035014000 a 1 Gas 5 01-10 3035 140 43 1010100055099002500 a 1 Gas 5 01-10 5099 25 44 1010100055099003000 a 1 Gas 5 01-10 5099 30 42 1010100055099003500 a 1 Gas 5 01-10 5099 35 40 1010100055099004000 a 1 Gas 5 01-10 5099 40 5 1010100055099016000 a 1 Gas 5 01-10 5099 160 20 1010100055099019500 a 1 Gas 5 01-10 5099 195 14 1010100055099026000 a 1 Gas 5 01-10 5099 260 25 1010100055099033000 a 1 Gas 5 01-10 5099 330
65 1020100043540007000 a 2 Diesel 4 91-99 3540 70 16 1020100043540014000 a 2 Diesel 4 91-99 3540 140
62 1020100043540019500 a 2 Diesel 4 91-99 3540 195 19 1020100045099003500 a 2 Diesel 4 91-99 5099 35 21 1020100045099004000 a 2 Diesel 4 91-99 5099 40 11 1020100045099004500 a 2 Diesel 4 91-99 5099 45 12 1020100045099005000 a 2 Diesel 4 91-99 5099 50 13 1020100045099006000 a 2 Diesel 4 91-99 5099 60 10 1020100045099016000 a 2 Diesel 4 91-99 5099 160 1 1020100045099080000 a 2 Diesel 4 91-99 5099 800 2 1020100045099100000 a 2 Diesel 4 91-99 5099 1000
27 1020100055099007000 a 2 Diesel 5 <90 5099 70 26 1020100055099008000 a 2 Diesel 5 <90 5099 80 17 1020100055099009000 a 2 Diesel 5 <90 5099 90 29 1020100055099010000 a 2 Diesel 5 <90 5099 100
8 1020100055099014000 a 2 Diesel 5 <90 5099 140 9 1020100055099016000 a 2 Diesel 5 <90 5099 160
3 1020100055099080000 a 2 Diesel 5 <90 5099 800 6 1020100055099100000 a 2 Diesel 5 <90 5099 1000 28 1020100045099130000 a 2 Diesel 4 91-99 5099 130075 1010100012025003500 1 Gas 1 <80 2025 35 1010100013035003500 1 Gas 1 <80 3035 35 1010100013540003500 1 Gas 1 <80 3540 35 1010100013540004000 1 Gas 1 <80 3540 40 1010100013540004500 1 Gas 1 <80 3540 45 1010100014050004500 1 Gas 1 <80 4050 45 1010100015099006000 1 Gas 1 <80 5099 60
1010100020020002000 1 Gas 2 81-85 20 20 1010100022025004500 1 Gas 2 81-85 2025 45 1010100023035004500 1 Gas 2 81-85 3035 45 1010100024050003000 1 Gas 2 81-85 4050 30 1010100024050005000 1 Gas 2 81-85 4050 50
1010100024050006000 1 Gas 2 81-85 4050 60 1010100025099002500 1 Gas 2 81-85 5099 25
1010100030020002000 1 Gas 3 86-90 20 20 1010100032530002500 1 Gas 3 86-90 2530 25 1010100033540003000 1 Gas 3 86-90 3540 30 1010100034050006000 1 Gas 3 86-90 4050 60 1010100035099007000 1 Gas 3 86-90 5099 70
1010100043035002500 1 Gas 4 91-00 3035 25 1010100043540002500 1 Gas 4 91-00 3540 25 1010100043540003500 1 Gas 4 91-00 3540 35 1010100044050002000 1 Gas 4 91-00 4050 20
1010100045099003000 1 Gas 4 91-00 5099 30 1010100995099004500 1 Gas 4 91-00 5099 45 1010100045099009000 1 Gas 4 91-00 5099 90
1010100052530004000 1 Gas 5 01-10 2530 40 1010100053540004000 1 Gas 5 01-10 3540 40 1010100984050005000 1 Gas 5 01-10 4050 50 1010100985099004500 1 Gas 5 01-10 5099 45 The parameter selection for PERE is a somewhat involved process. The decision tree is outlined below. The parent variables (underlined) are abov e the dependent variables. The parent variables define the decision source (tree) of the paramete rs. The dependent variables are the parameters that are determined from the parent variable. Some dependent parameters vary with multiple parents (e.g. engine friction). A fe w other parameters are fixed.

SOURCE BIN

Weight - LD/MD/HD - Road load: C r , C d ,

A F , or A, B, C - Enrichment

Threshold: FRth Model Year - Engine Friction:

k0, k1 - peak bmep fit - Enrichment

Threshold: FRth Engine Size - Peak torq fit - Enrichment

Threshold: FRth Gas/Diesel - Ind Efficiency: η - Engine Friction: k0, k1 - peak bmep fit - fuel: LHV, ρ f

LD/MD/HD/Motorcycle - Driving cycle, Transmission: N/v, shift poi nts, gear ratio, manual/auto, accessory: Pacc

FIXED PARAMETERS Auto transmission efficiency ( by gear), manual transmissions efficiency, density of air, fuel enrichment sl ope (gasoline only), inertial rotational mass term.76 The determination of general vehicle weight classifica tion is the very first step in the parsing process. Since MOVES does not have explicit splits for light , medium and heavy duty, PERE assumes that the light to medium split occurs at weighclassID=80 (7,000-8,000 lbs), where above this weight bin is medium duty (single unit delivery trucks, buses, et c). The medium to heavy duty split occurs at weightclassID=400 (33,000-40,000 lbs). This in turn de fines whether the transmission will be a 5 speed automatic (LD), 6 speed automatic (MD) , 12 speed manual (HD), or 5 spd manual (motorcycle). Motorcycles have their own weight a nd engine size categories as well as a different accessory loading (0.25 vs 0.75kW).

The weight determines the road-l oad coefficients for the vehicles directly. For model years 2000 to present, A, B, and C track coefficients are provi ded by manufacturers. Olde r vehicles only have the single Tractive Road Load Horsepower (TRLHP) fi gure. The TRLHP for a t ypical light-duty vehicle (and truck) in the source bin can be approximated from weight using the relation in the MOVES Documentation [2004]. For heavy duty vehicles, the coefficients are determined from the equations presented earlier in this report.

The weight class also defines the driving cycles that PERE will run. Before model year 1999, weight also is a term in the enrichment equation. After 1 999, the vehicles are assume d to go into enrichment so rarely that it is ignored by PERE.

The model year also affects the performance of the engine, both in terms of friction and peak bmep (which mainly influences power downshifting). Finally, the engine/fuel type determines, the efficiency and friction characterist ics of the engine. All of these relationships can be found in this report. Finally, the model year also defines the fu el parameters since gasoline and diesel have different physical properties.

The following table shows the result parameters from the truck source bin described above.77 Table 26. Parameters for the PERE 70,000lb heavy duty truck.

Vehicle Model Year 1995 Vehicle wgt (kg) 31752 Cr0 (rolling resistance) N/A Cd (drag coeff) N/A A (frontal area m^2) N/A A (N) 2098.81 B (N/mps) 0.000 C (N/mps^2) 4.2268 Pacc (accessory - kW) 0.75 Engine Engine Displ (L) 6.9 k0 (N indep friction kJ/Lrev) 0.0605 k1 (N dependent fric) 0.00333 P/T indicated eff (eta) 0.48
Transmission N/v (rpm/mph) 26.7 Nidle (rpm) 700 trans eff 0.95 Shift point 1-2 (mph) 2.48 Shift point 2-3 4.75 Shift point 3-4 7.75 Shift point 4-5 13.5 Shift point 5-6 17.5 Shift point 6-7 23 Shift point 7-8 34 Shift point 8-9 50 Shift point 9-10 56 Shift point 10-11 57 Shift point 11-12 64 Shift point 12-13 64 g/gtop 1 13 g/gtop 2 9.4 g/gtop 3 6.9 g/gtop 4 5 g/gtop 5 3.7 g/gtop 6 2.7 g/gtop 7 2 g/gtop 8 1.5 g/gtop 9 1.2 g/gtop 10 1.1 g/gtop 11 0.93 g/gtop 12 0.86 g/gtop 13 0.86
Fuel LHV (kJ/g) 43.2 density gas (kg/L) 0.8114

The motorcycle engine size and wei ght classifications fall under disc rete bins as well. The divisions are marked in the following figure and are based on the fleet mix.78 populated (non-zero sizeweightfractions) motorcycle engine size - weight bins (from the MOVES sizeweight table)

1- 0.05 to 0.169 liters 2 - 0.170 to 0.279 liters 9 - 0.280 liters and larger 350 350 600 600 700

(1, 350) (2, 350) (2, 600) (9, 700)

(9, 600)

0 100 200 300 400 500 600 700 800

0 1 2 3 4 5 6 7 8 9 10
engSizeIDvehicle midweight (lbs)

Figure 38. Engine size-weight categoriz ation of motorcycles in MOVES.

Driving Cycles and Running PERE The output to PERE is second-by-second energy cons umption, therefore the ne xt step is to input the driving cycles. The driving cycles input into PERE help determine the VSP “binned” energy consumption rates for MOVES.

It is important to capture a re presentative sampling of real-world driving. For PERE, the driving cycle input is dependent on th e weight of the vehicle. 14 driving cycles for light duty applications have been selected for this pur pose from MOVES. These cycles represent a broad spectrum of driving, from very low to very high speeds. The cycles and their average speeds are shown on the following table. When merged, the cycl es run 6,981 seconds. It is also important to artificially set the accelerations to zero in between the cycles since they do not all start (and end) at rest. It is likely th at similar binned rates c ould be obtained from a smaller sample of driving cycles, thus making the model easier to manage.

Table 27. The 14 MOVES light-duty driving cycles used as input to PERE.79 For medium and heavy-duty applic ations, the driving cycles wher e drawn from the medium and heavy-duty schedules used to popul ate the MOVES default database [Beardsley et al., 2004]. The medium duty drive cycles ar e listed in the table below and totaled 6,050 seconds.

Table 28. MOVES medium-duty driving cy cles used as input to PERE.

c yc le ID Na me a vg spd 201 MD 5m p h No n-Fre e wa y 1.8 202 MD 10m p h No n-Fre e wa y 10.5 203 MD 15m p h No n-Fre e wa y 15.6 204 MD 20m p h No n-Fre e wa y 20.4 205 MD 25m p h No n-Fre e wa y 24.4 206 MD 30m p h No n-Fre e wa y 30.8 251 MD 30m p h Fre e wa y 37.4 252 MD 40m p h Fre e wa y 45.3 253 MD 50m p h Fre e wa y 55.5 254 MD 60m p h Fre e wa y 60.1 The heavy-duty cycles (in the table below) are also a subset from Beardsley et al. [2004]. Together it totals 28,313 seconds of driving weighted mainly w ith freeway driving. The length of this file makes PERE cumbersome, and in the future, th e number of cycles should be reduced.

Table 29. MOVES heavy-duty driving cy cles used as input to PERE. CYCLE avg spd FWYHI1 63.2 FWYAC 59.7 FWYD 52.9 FWYE 30.5 FWYF 18.6 FWYG 13.1 RAMP n/a ARTAB 24.8 ARTCD 19.2 ARTEF 11.6 NYCC 7.1 FWYHI2 68.2 FWYHI3 76.0 LOWSPEED1 2.580 ID Na me a vg spd 301 HD 5m p h No n-Fre e wa y 1.2 302 HD 10m p h No n-Fre e wa y 10.8 303 HD 15m p h No n-Fre e wa y 15.2 304 HD 20m p h No n-Fre e wa y 19.8 305 HD 25m p h No n-Fre e wa y 24.9 306 HD 30m p h No n-Fre e wa y 30.8 351 HD 30m p h Fre e wa y 34.9 352 HD 40m p h Fre e wa y 46.9 353 HD 50m p h Fre e wa y 54.3 354 HD 60m p h Fre e wa y 59.5

The motorcycles are run only on the FTP and HWY dr iving cycles. The small engine scooters, are run only on the reduced speed FTP scooter cycle.

With the driving cycles entered, the binner macro (on the spreadsheet) is run in order to get the VSP binned fuel consumption rates, which are in turn converted to MOVES energy rates.

The holes in MOVES not filled by PERE are then filled using interpolation and extrapolation techniques described in the next section and in Koupal et al. [ 2004]. Finally after the process of interpolation and extrapolation is complete, ther e were a few bins remaining, which PERE filled. These are listed in the following table:

Table 30. “Leftover” holes after interpolation/extrapolation.

1020100010020002500 1020100012025003500 1020100020020002500 1020100020020003000 1020100022025003000 1020100030020002500 1020100030020003000 1020100032025003000 1020100054050002000 1020100054050002500
1020100054050003000

Finally, the motorcycle source bins are in the table below and includes model years into the future (the rates are unchanged).

Table 31. Motorcycle source bins filled by PERE.

1010110010001000500' 1010110020001000500' 1010110030001000500' 1010110040001000500' 1010110050001000500' 1010110060001000500'81 1010110070001000500'
1010110010002000500' 1010110020002000500' 1010110030002000500' 1010110040002000500' 1010110050002000500' 1010110060002000500'
1010110070002000500'
1010110010002000700' 1010110020002000700' 1010110030002000700' 1010110040002000700' 1010110050002000700' 1010110060002000700' 1010110070002000700'
1010110010009000700' 1010110020009000700' 1010110030009000700' 1010110040009000700' 1010110050009000700' 1010110060009000700' 1010110070009000700'
1010110010009000900' 1010110020009000900' 1010110030009000900' 1010110040009000900' 1010110050009000900' 1010110060009000900'
1010110070009000900'

Filling Advanced Technology Using PERE Step 1: Choose the Source Bin The first step is to determine what kind of vehicle PERE will model. In MOVES, these are "source bins” defined by vehicl e characteristics which differentiate energy consumption and emissions. For energy consumption, source bins have 5 dimensions: fuel type, engine technology, model year group, engine size, and loaded weight [ Koupal et al., 2004 ]. The list of the combined fuel types and engine technologies to be included in MOVES are in Table 32. PERE is used to fill a majority of these technologies. The categories not filled by PERE are marked with an asterix (*). In these cases, energy rates are derive d from already-published analyses.

Table 32. Examples of advanced tec hnologies, as defined by fuel types and engine technologies, to be included in MOVES. “C” is convention al, “A” is advanced, and “IC” is internal combustion. This list is not final.

• Gasoline conventional (IC)

• Gasoline Advanced IC82 • Gasoline Hybrid -CIC Moderate

• Gasoline Hybrid -CIC Full

• Gasoline Hybrid -AIC Moderate

• Gasoline Hybrid -AIC Full

• Diesel Fuel conventional (IC)

• Diesel Fuel Advanced IC

• Diesel Fuel Hybrid -CIC Moderate

• Diesel Fuel Hybrid -CIC Full

• Diesel Fuel Hybrid -AIC Moderate

• Diesel Fuel Hybrid -AIC Full

• Compressed Natural Gas (CNG) conventional (IC) *

• Liquid Propane Gas (LPG) conventional (IC) *

• Ethanol (E85 or E95) conventional (IC) *

• Methanol (M85 or M95) conventional (IC) *

• Gaseous Hydrogen Advanced IC

• Hydrogen -Fuel Cell

• Hydrogen Hybrid -Fuel Cell

• Electricity electric only

Not all of the engine technologi es are represented fully across the other 5 dimensions, for example, it is highly unlikely that long haul truc ks will be hybridized in the future, due to the relatively minor effect it has on highway fuel econo my compared to the already efficient diesels. However, hybridization is quite promising for urban buses. Due to the sheer number of tests required, simplifications are necessary. Examples of these are described in greater detail below.

Step 2: Define the Vehicle Specifications Based on the engine technology sour ce bin, the vehicle parameters su ch as weight, engine size, vehicle shape etc, can be defined. The weight and engine size values are simply the central value of the bin. Thus PERE effectively models an “a verage” vehicle in the s ource bin. For the source bins on the “edge” of the matrix (<2000 lbs; > 130,000lbs; <2.0L; and >5.0L), their results will be extrapolated - see Step 6, or average valu es are determined from the fleet mix data.

Since engine size, has limited meaning for hybrid s and no meaning for fuel cells and electric vehicles, it is necessary to define a power su rrogate for engine size. This has already been mentioned in this report [Chon & Heywood, 2000]. T hough it is not perfectly equivalent, we will take the convention that motor power + engine peak power = total peak power. The fuel cell definition of power is simpler since there is only one mechanical power device (motor) and the battery and fuel cell should be sized optimally within the motor limitations.

Because the source bins do not have a dimension for body type, the road load coefficients are estimated based on the weight. Lighter light duty weights tend to be (compact) passenger cars, then midsize cars, luxury, compact pickups, SUVs, minivans on up to medium-duty trucks. Advanced technology heavy-duty trucks are no t included in MOVES. The variation in body types will certainly lead to variation (or uncertainty) in the emission rates.83 Step 3: Run PERE on the FTP if Light duty fuel economy required. For the light-duty vehicles, once the vehicle specifications have been finalized, the next step is to run the model over the FTP driving cycle (city an d highway). This is to determine the fuel economy of the vehicle. If fuel economy is not required, this step can be skipped.

Step 4: Define the Driving Cycles The output to PERE is second-by-second energy cons umption, therefore the ne xt step is to input the driving cycles. The driving cycles input in to PERE help determine the “binned” energy consumption rates for MOVES (see Figure 1). Hencef orth “binned” refers to operating mode bin and not source bin, which has already been defined.

The binned energy consumption and emissions rates are dependent on the driv ing cycle input. It is important to capture a repres entative sampling of real-world driving. Three driving cycles were chosen for this purpose: FTP (urban), FT P (highway), and LA92. Th e latter is a selfweighted cycle based on more recent real-world driving, and includes ha rder accelerations and higher speeds. For hybrids, the state-of-charge was maintained over the tota l of the three cycles. For conventional vehicles, more driving cycles ar e used to get a better representation within a VSP bin.

For medium-duty applications, 3 urban bus plus the UDDS cycles were employed for a total of 3,260 seconds. The following table lists them with their average speeds.

Table 33. Medium duty drive cycles for advanced technologies.

Na me a vg spd
C BD 12.7
NYBUS 3.7
NYC C T 8.8
UDDS 18.8

Step 5: Run PERE and Bin Output After PERE is run, the rates must be binned in to the respective operati ng modes. These are the 17 VSP and speed operating mode bins as defined in Koupal [2003]. The binning procedure is straightforward and can be programmed in any script.

Figure 39 shows a sample of predicted fuel cons umption as a function of VSP for a fictional hybrid passenger car. This is an indication of how an output from PERE would be translated into an emissions rate in MOVES. The uncertainty bars are from PERE generate d variations within a bin, and do not adequately reflect the true uncertainty of the emission rates, which will be added in future versions of PERE.84 34 301 173 46 62 144 281 283 151 15 48 85 121 171 111 357 258 N =

VSPBIN3 36.00

35.00 33.00

26.00 25.00

24.00 23.00

22.00 21.00

16.00 15.00

14.00 13.00

12.00 11.00

1.00 .0095% CI FUELCONS 2.0

1.5

1.0

.5

0.0

Figure 39. Hybrid fuel consumption as a function of VSP bin.

Step 6: Repeat for the Rest of the Source Bins Rather than run PERE for every operating mode bin within every adva nced technology source bin (there are hundreds), it is more efficient to extrapolate from one bin to another along a dimension. A dimension could be weight or power (engine displa cement), so the rates could be interpolated between the endpoint s (e.g. lightest and heaviest wei ght class. To demonstrate the validity of the linear extrapola tion technique, PERE was run on a conventional gasoline powered passenger car for a variety of weights and engi ne displacements. The dr iving cycles employed were the city and highway portions of the FTP. The base car is a 3500lb, 2.5L passenger car. For the different engine sizes, the VSP values are iden tical, however, this is no t true along the weight axis. Here the second-by-second fuel consumpti on as well as the VSP values are binned.

Figure 40 shows the fuel consumption rates as a function of VSP bin. Each color (shade) represents a separate engine disp lacement, as labeled along the right. It is evident that given the minimum and maximum points, that the other could be linearly interpolated.85 76 302 130 64 78 161 274 238 152 36 51 72 19 162 111 357 258 76 302 130 64 78 161 274 238 152 36 51 72 19 162 11 357 258 76 302 130 64 78 161 274 238 152 36 51 72 119 162 1 357 258 76 302 130 64 78 161 274 238 152 36 51 72 119 162 1 357 258 76 302 130 64 78 161 274 238 152 36 51 72 19 162 111 357 258 76 302 130 64 78 161 274 238 152 36 51 72 19 162 111 357 258 76 302 130 64 78 161 274 238 152 36 51 72 119 162 111 357 258 76 302 130 64 78 161 274 238 152 36 51 72 119 162 111 357 258 N =

VSPBIN25 36

35 33

26 25

24 23

22 21

16 15

14 13

12 11

1 095% CI 4.0

3.0

2.0

1.0

0.0 V2

. 0L

V2.5L

V3.0L

V3.5L

V4.0L

V4.5L

V5.0L

Figure 40. Fuel Rate (g/s) as a function of VSP bin for engine sizes 1.5 – 5.0L.

Figure 41 shows the same relationship but with vehicle weight varied instead of engine displacement. Despite the curvatur e between the VSP bins, within a bin, an interpolation is quite reasonable.

22 166 30 40 64 129 260 286 188 35 51 71 16 17 1 357 258 23 179 306 44 64 131 266 276 186 35 51 71 17 16 1 358 258 23 223 262 47 65 14 272 272 17 35 51 71 17 166 111 356 258 36 254 218 8 71 134 280 263 171 35 51 72 16 166 111 357 258 51 285 172 5 70 143 271 61 167 36 50 73 19 162 1 357 258 76 302 130 64 78 161 274 238 152 36 51 72 119 162 111 357 258 121 303 84 75 95 193 255 222 127 38 49 74 119 160 111 357 258 N =

VSPBIN 36

35 33

26 25

24 23

22 21

16 15

14 13

12 11

1 095% CI FUEL 4

3

2

1

0 2000

2500

3000

3500

4000

4500

500086 Figure 41. Fuel Rate (g/s) as a function of VS P bin for weights ranging from 2000 – 5000 lbs.

Given the rates in any two cells near the edge of the matrix, the rest of the emission rates can be interpolated or extrapolated. Figure 42 shows a matrix and defines an example bin filling strategy. The red bins are possibl e engine size (power) and weight combinations. The green bins are filled using PERE and the black arrows represent the direction of interpolation or extrapolation. The rest of the bins can be filled interpolating from the edge bins. In this case, PERE needs to be run a minimal of four times. No te that each of the bins below has 17 operating mode bins to fill.

Figure 42. A sample source bin matrix hole filling strategy.

When choosing bins to fill, the absolute edge bins (<2000 lbs; >130,000lbs; <2.0L; and >5.0L) should not be run since the masses and or engine sizes are not well defined. This is the reason for choosing the “outer” bins in Figur e 42, but not the “edge” bins.

If it is easier to fill each bin with PERE, rather than interpolating, this can also be done.

Step 7: Insert Estimated Cold Start Energy/Emission Factors As discussed before, cold start factors will be approximated using the MOVES approach. The rates are dependent on engine disp lacement. A more detailed model is a subject of future study.

Advanced Technology Hole Filling Results While the method of filling holes (step 6 above) is st ill being considered, due to time constraints, it was decided to take an even simpler approach for this first version of MOVES. For each of the advanced technology categories in Table 10, a representative ve hicle was modeled using PERE. The rates from these vehicles were then ratioe d to their corresponding co nventional vehicle rates (gasoline equivalent). These ra tios were then used to populate all of the advanced technology source bins using a simple computer program.87 Due to its relative frequency in the fleet, the “representative” ve hicle was chosen to lie in a MOVES source bin with weight ranging from 3,500 – 4,000 lbs (test weight), and engine size ranging 3.0 – 3.5 Liters (automatic transmission) . This corresponds to a PERE conventional vehicle of 3,750 lbs, 3.25L, and 155kW (peak power). It also corresponds to a 1.7L engine full hybrid with a 72kW motor, or a 2.8L engine mo derate hybrid with a 22kW motor (gasoline and diesel).

The ratio of energy (or fuel consumption) for so me of the advanced tech nologies to conventional is shown in the series of figures. Note that it is possible for the ratios to exceed 1 in some operating mode bins. This is due to the fact th at the hybrids are heavier vehicles, driven with smaller engines, yet still follow the same drivi ng trace. Thus, it is quit e possible for the fuel consumption to be higher than the conventi onal vehicle during certa in modes of driving. However, over a given driving cycle, the fuel cons umption is significantly lower. In most driving cycles, the time spent in modes where the ratio is > 1 is usually quite small (high acceleration events). These results su pport the well known fact that the advant age of hybrids is best seen in stop-and-go driving.

Gasoline Hybrid Ratios

0 0.2 0.4 0.6 0.8

1 1.2 1.4

0 1 11 12 13 14 15 16 21 22 23 24 25 26 33 35 36 Operating Mode binRunning Energy Ratio to ConventionalGasoline Conv Mod Hybrid Conv Full Hybrid

>1 ratio due to higher rpm/smaller engine size combination

Figure 43. The ratio of energy (or fuel consumpt ion) for gasoline moderate and full hybrid vehicles to conventional by operating mode.88 Diesel Hybrid Ratios

0 0.2 0.4 0.6 0.8

1 1.2

0 1 11 12 13 14 15 16 21 22 23 24 25 26 33 35 36 Operating Mode binRunning Energy Ratio to ConventionalGasoline Diesel Diesel Mod Hybrid Diesel Full Hybrid

Diesel ratio is set to constant value (gasoline equivalent)

Figure 44. The ratio of fuel consumption for dies el, moderate, and full hybrid vehicles with respect to gasoline conventional by operating mode (using gasoline equivalent numbers).

Fuel Cell Ratios

0 0.2 0.4 0.6 0.8

1 1.2

0 1 11 12 13 14 15 16 21 22 23 24 25 26 33 35 36 Operating Mode binRunning Energy Ratio to ConventionalGasoline Fuel Cell Fuel Cell hybrid

H2 ICE av g ( AIC )

Figure 45. The ratio of energy for H 2 fuel cell, H 2 hybrid fuel cell, and H 2 ICE vehicles with respect to gasoline conventional (u sing gasoline equivalent numbers).

The ratios are simplified to a single constant value (for all operating modes) when the ratio of energy consumption is less dependent on the type of driving (mainly for non-hybrids). These ratios are shown as bars on the above figures. The ratio of H 2 ICE is nearly identical to that of Advanced ICE (~0.9).

This method for determining MOVES rates for hybrid s is clearly superior to having a single fixed fuel economy ratio (over an FTP cycle for example) as other studies have proposed. This is due to the fact that hybrid fuel economy depends in large part on the type of driving, idle time, decelerations etc., which are washed out in a single cycle av erage number. However, aside from being more complex, the methodology also has limita tions, which are presently explored.89 The ratios are based on a single (though common) re presentative vehicle cl ass. We might expect vehicles of different weight but sa me power-to-weight ratio to give similar rates. However, vehicles with lower power-to-weight ratios would (for example) require more assistance from the engine, both during launch and assist (power boos t). This would necessarily change the shape of the curves. An example of this is shown in Figure 46. The vary ing power-to-weight ratios having different shapes. The effect is mainly pronounced in the “2” bins, i.e. bins 12, a nd 22. These correspond to moderate acceleration from low to medium speed (launch).

LDG Moderate Hybrids

0 0.2 0.4 0.6 0.8

1 1.2 1.4

0 5 10 15 20 25 30 35 40
vsp binratio of hybrid toconventional

0.043
0.05
0.061
0.072
0.08
0.091
0.1
0.14
0.21

Figure 46. Fuel consumption ratios for a series of power-to-weight ratio (kW/kg) light-duty gasoline hybrid vehicles. The shape difference is even more pronounced in medium-duty vehicles where the power-toweight ratio can fall below 0.01. This is shown on Figure 47 for a series of medium-duty diesel hybrid vehicles. Note that the fuel consumpti on drops at higher loads for the underpowered vehicles. This is due to the fact that the engine cannot keep up with the cycle by itself and more of the total power is supplied by the battery/motor. It is also likely that these vehicles are unable to follow the driving cycles. These hybrids we re run on the following medium-duty driving cycles: CBD (Central Business Dist rict), NYBUS, NYCCT, and UDDS-D.

Moderate MDD Hybrids

0 0.2 0.4 0.6 0.8

1 1.2

0 5 10 15 20 25 30 35 40
vsp binratio of hybrid to conventional

0.0113
0.0214
0.0302
0.0411
0.0494
0.0757
0.067790 Figure 47. Fuel consumption ratios for a series of power-to-weight ratio (kW/kg) medium-duty diesel hybrid vehicles.

For future versions of MOVES, the rates may likely be separated by power-to-weight (P/Wt) ratio bins, rather than engine di splacement or weight separately in order to differentiate the energy ratios for the different vehicle classes. An example of such a split is shown in Table 34. The bands of color represent possible P/Wt ratios to group together into bi ns. The boxed cells are modeled explicitly using PERE to obtain the results in the previous figure.

Table 34. Possible Power-to-weight ratio bins (kW/kg) for turbo diesel hybrids.

Engine displacement or avg pwr lo wt bin avg wt in bin 2 2.5 3 3.5 4 5 lbs kg 95.2 116.3 137.5 158.6 190.3 291.9 2000 1021 0.0933 0.1140 0.1347 0.1554 0.1865 0.2860 2500 1247 0.0763 0.0932 0.1102 0.1271 0.1526 0.2340 3000 1474 0.0646 0.0789 0.0933 0.1076 0.1291 0.1980 3500 1701 0.0560 0.0684 0.0808 0.0932 0.1119 0.1716 4000 1928 0.0494 0.0603 0.0713 0.0823 0.0987 0.1514 4500 2155 0.0442 0.0540 0.0638 0.0736 0.0883 0.1355 5000 2495 0.0382 0.0466 0.0551 0.0636 0.0763 0.1170 6000 2948 0.0323 0.0394 0.0466 0.0538 0.0645 0.0990 7000 3402 0.0280 0.0342 0.0404 0.0466 0.0559 0.0858 8000 3856 0.0247 0.0302 0.0357 0.0411 0.0494 0.0757 9000 4309 0.0221 0.0270 0.0319 0.0368 0.0442 0.0677 10000 5443 0.0175 0.0214 0.0253 0.0291 0.0350 0.0536 14000 6804 0.0140 0.0171 0.0202 0.0233 0.0280 0.0429 16000 8051 0.0118 0.0144 0.0171 0.0197 0.0236 0.0363 19500 10319 0.0092 0.0113 0.0133 0.0154 0.0184 0.0283 26000 13381 0.0071 0.0087 0.0103 0.0119 0.0142 0.0218

Section XII - Acknowledgments

The authors would like to thank the following st aff at EPA for their help and support: Megan Beardsley, Emily Chen, Kent Helmer, John Koupal, Larry Landman, Joe McDonald, Arvon Mitcham, Gene Tierney, James Warila, and Ta d Wysor. Feng An and Dan Santini from Argonne National Laboratory provided valuable feedback . We would also like to acknowledge Robert Natkin from Ford Motor Company for his techni cal advice on H2 ICE. Matthew Barth, George Scora, and Ted Younglove offered significant as sistance in the heavy- duty diesel modeling section.

Finally, we acknowledge the contributions of the reviewers who provi ded valuable comments about the report. Some of these comments are in the following appendices. Comments from three SAE (Society of Automotive Engineers) reviewer s, however, are not included. These comments were from a paper formed from portions of the hybr id sections (both ICE and fuel cell) of this report, and published in 2005.91 References

Andrei, P., “Real World Heavy-Duty Vehicle Emissi ons Modeling,” West Virginia University thesis, 2001.

Alson, J., D. Barba, J. Bryson, M. Doorlag, D. Haugen, J. Kargul, J. McDonald, K. Newman, L. Platte, M. Wolcott, “Progress Report on Clean and Effective Automotive Technologies Under Development at EPA,” EPA document number: EPA420-R-04-002, 2004.

An, F., and M. Ross, “The Use of Fuel by Spark Ignition Engines,” SAE 930329, 1993.

An, F., D.,J. Santini, “Mass Impacts on Fuel Ec onomies of Conventional vs. Hybrid Electric Vehicles,” SAE 2004-01-0572, 2004.

Arakai, Y., Kazuhiro, G., “Development of the Aerodynamic Characteristics for Motorcycles Using Scale Model Wind Tunnel”, SAE 2001-01-1851, 2001.

Barth, M., G. Scora, T.Younglove, “A Modal Model for Heavy Duty Diesel Vehicles”, Transportation Research Board, Jan. 2004.

Barth, M., F. An, T. Younglove, G. Scora, C. Le vine, M. Ross, T. Wenzel, “Comprehensive Modal Emission Model (CMEM), vers ion 2.0 User’s Guide,” 1999.

Beardsley, M., D. Brzezinski, R. Gianelli, J. Ko upal, S. Srivastava, “MOVES 2004: Highway Vehicle Population and Activity Data,” EP A Document to be published, 2004.

Bishop, J.S., M.A. Kluger, “Proposed Efficiency Rating for Automatic Transmissions,” SAE 960425, 1996

.
Boretti, A.A., “Experimental and Computationa l Analysis of a High Performance Motorcycle Engine,” SAE 962526, 1996.

Bosch, “Automotive Handbook – 5 th Edition,” Stuttgart, Robert Bosch GmbH, 2000.

Car and Driver Website: www.caranddriver.com

Chon, D.M., J.B. Heywood, “Performance Scaling of Spark-Ignition Engines: Correlation and Historical Analysis of Producti on Engine Data,” SAE 2000-01-0565, 2000.

DaimlerChrysler publication: “F-C ell, Driving the Future,” Oct ober, 2003. (http://www.mercedesbenz.com/com/e/home/innovation/laboratory/fue lcell/impraxistest/aclassfcell/index2.html)

Ensfield, C., “On-Road Emissions Testing of 18 Ti er 1 Passenger Cars and 17 Diesel Powered Public Transport Buses,” USEPA Statement of work, ref. # QT-MI-01-000659, Oct. 22 2002.

Ford Motor Company pamphlet, “Ford Focus Hydrogen Fuel Cell Elec tric Vehicle,” 200392 Fritz, S.G., M.E. Starr, “Emission Factors for Compression Ignition Nonroa d Engines Operated on No. 2 Highway and Nonroad Diesel Fuel,” Southwest Research Institute, 1998.

General Motors pamphlet, “Hydrogen 3, GM Fuel Cell Technology,” 2003.

General Motors News Websit e: “GM's Hybrid Timeline,” http://www.gm.com/company/gmability /adv_tech/300_hybrids/hyb_timeline.html , 2004

Gillespie, T., “Fundamentals of Vehicle Dynami cs,” Society of Automotive Engineers, 1992.

Goodwin, R.W., M. Ross, “Off-Cycle Exhaust Em issions from Modern Passenger Cars with Properly-Functioning Emissi ons Controls,” SAE 960064, 1996.

Greenbaum, J.J., M.A. Kluger, B.E. Westmorela nd, “Manual Transmission Efficiency Trends and Characteristics,” SAE 942274, 1994.

Harley-Davidson website: http://www.harley-davidson.com/en/homef.asp?bmLocale=en_US

Heywood, J.B., “Internal combustion engine fundamentals,” McGraw-Hill, New York, 1988.

Honda Motor Company publication (pam phlet): “FCX Fuel Cell Power,” 2003.

Honda Motor Company website: “Remarks by Take o Fukui, President and CEO, Honda Motor Co., Ltd. -- North American Inte rnational Auto Show,” http://www.hondacars.com/info/news/article.a sp?ArticleID=2004010562646&Category=currenthond

a , 2004

Jimenez-Palacios, J., “Understanding and Quan tifying Motor Vehicle Emissions with Vehicle Specific Power and TILDAS Remote Sensing,” PhD Thesis, Massac husetts Institut e of Technology, Cambridge, MA, 1999.

Kelly, K.J., M. Zolot, G. Glinsky, A. Hieronymous, “Test Results and Modeli ng of the Honda Insight using ADVISOR,” SAE 2001-01-2537.

Kim, G.C., Y.J. Lee, “The Influence of Drivi ng Conditions on Fuel Economy and Exhaust Emissions of the Toyota PRIUS HEV,” The 20 th International Electric Vehicle Symposium and Exposition, Nov, 2003.

Kluger, M.A., J.J. Greenbaum, D.R. Mairet, “Proposed Efficiency Guidelines for Manual Transmissions for the Year 2000,” SAE 950892, 1995.

Koupal, J., L. Landman, E. Nam, J. Warila, E. Glover, B. Giannelli, “MOVES2004 Energy and Emissions Inputs,” EPA document number: TBD, Dec, 2004.93 Koupal, J., “Emission Rate Development,” Presenta tion at FACA technical review meeting Dec 2, 2003, Ann Arbor, MI.

Laramie, J., A. Dicks, “Fuel Cell Systems E xplained,” John Wiley & Sons Ltd., West Sussex, England, 2000.

McCallen, R, R. Couch, A. Leonard, M. Brady, K. Salari, W. Rutledge, J. Ross, B. Storms, J.T. Heineck, D. Driver, J. Bell, G. Zilliac, “P rogress in Reducing Aer odynamic Drag for Higher Efficiency of Heavy Duty Trucks (Class 7-8)”, SAE 1999-01-2238.

Milington, B.W., E.R. Hartles, “Frictiona l Losses in Diesel Engines,” SAE 680590, 1968.

Miller, W., Cocker, D., Johnson, K., Norbeck, J.M., Park, C.S., Welch, B., "Use of a Mobile OnRoad Laboratory to Measure HDD 'Real World' Emissions from Standard and Non-Standard Operating Cycles", 12th Coordi nating Research Council On-Road Emissions Workshop in San Diego in April 2002.

Muranaka, S., Y. Takagi, T. Ishida, “Factors Limiti ng the Improvement in Thermal Efficiency of S.I. Engine at Higher Compression Ratio,” SAE 870548, 1987

Nam, E.K., J. Sorab, “Friction Reduction Tr ends in Modern Engines,” SAE 2004-01-1456, 2004.

Nam, E.K., “Proof of Concept In vestigation for the Physical Emission Rate Estimator (PERE) for MOVES,” EPA document number: 420-R-03-005, February, 2003.

Nam, E.K., A. Mitcham, C. Ensfield, “Evaluatio n of In-Use Hybrid Vehicles Using a Portable Emissions Measurement System,” SAE to be published, 2005.

Natkin, R.J., X. Tang, B. Boyer, B. Oltmans, A. Denlinger, “Hydrogen IC Engine Boosting Performance and NOx Study,” SAE 2003-01-0631, 2003.

Natkin, R.J. Conversation with Bob Natkin.

Nelson, D.J., “Automotive Fuel Cell Systems,” L ecture Slides, Society of Automotive Engineers, 2003.

Ogawa, H., M. Matsuki, T. Eguc hi, “Development of a Power Train for the Hybrid Automobile – The Civic Hybrid,” SAE 2003-01-0083.

Pachernegg, S.J., “A Closer Look at the Willans-Line,” SAE 690182, 1969.

Park, D.H., T.S. Seo, D.G. Lim, H.B. Cho, “The oretical Investigation on Automatic Transmission Efficiency,” SAE 960426, 1996.

Patton, K.J., R.G. Nitschke, J.B. Heywood, “Devel opment and Evaluation of a Friction Model for Spark-Ignition Engines,” SAE 890836, 1989.94 Petrushov, V.A., “Coast Down Method in Time-Distance Variables,” SAE 970408, 1997.

Rechtin, M., “EPA mpg test doesn’t work for hybrids,” Au tomotive News, November 24, 2003.

Road and Track website: www.roadandtrack.com

Ross, M., “Fuel Efficiency and the Physics of Automobiles,” Contemporary Physics, vol. 38, number 6, pp 381-394, 1997.

Rousseau,.A., Sharer, P., Besnier, F., "Feasibility of Reusable Vehicle Modeling: Application to Hybrid Vehicles ", SAE paper 04P-454, SAE World Congress, Detroit(March 2004)

Sandoval, D., J.B. Heywood, “An Improved Friction Model for Spark Ignition Engines,” SAE 2003- 01-0725, 2003.

Schaefer, R.M., “Testing of an Electric Vehicle on a Clayton Water-Brake Chassis Dynamometer,” EPA document: EPA/AA/TDG/94-01, 1994.

Sierra Research, Inc. I/M Lookup Table Update . Report No. SR00-10-01. Sacramento, California. October 3, 2000.

Sovran, G., M.S. Bohn, “Formulation for the Tractiv e Energy Requirement of Vehicles During the EPA Schedules, “SAE810184, 1981.

Starr, M.E., “Transient and Steady State Emissions Testing of Ten Different Nonroad Diesel Engines Using Four Fuels,” Southwest Research Institute, 2003.

Tang, X., D.M. Kabat, R.J. Natkin, W.F. St okhausen, “FordP2000 Hydrogen Engine Dynamometer Development,” SAE 2002-02-0242, 2002.

Thomas, M., M. Ross, “Development of Second-by- second Fuel Use and Emissions Models Based on an Early 1990s Composite Car,” SAE 971010, 1997.

Toyota Motor Coporation, “Toyota FCHV Book,” Tokyo, Japan. March, 2003.

Toyota Motor Coporation, Public Affairs Divisi on, “Toyota Hybrid System, THS II,” Tokyo, Japan, May 2003.

Toyota Motor Coporation Press re lease: “Toyota Stages World Pr emiere of All-new Highlander Hybrid SUV at 2004 North American In ternational Auto Show,” website: http://www.toyota.co.jp/en/even t/auto_shows/2004/naias/toyota_suv.html , 2004

Tsuchida, N., H.Tsuzuku, “Reduction of Friction Loss through the Use of Rolling Big-End Bearings,” SAE 951783, 1995.95 Tsuzuku H., N. Tsuchida, “Output and Fuel Consum ption of Motorcycle Mu lti-Valve Engines,” SAE 1999-01-0283, 1999.

Tsuchida, N., H.Tsuzuku, “Piston Friction Lo sses in High-Speed Engines,” SAE 911230, 1991.

United Nations (UN) “Worldwide Harmonised Moto rcycle Emissions Cert ification Procedure,” Informal document No. 15, 46th GR PE, agenda item 3, 19-23 May 2003.

U.S. Department of Energy, U.S. Environmenta l Protection Agency, “Fuel Economy Guide,” 2004. (www.fueleconomy.gov)

U.S. Department of Transportation, ”Rolling Re sistance of Truck Tires as Measured Under Equilibrium Conditions and Transient Conditions on CALSPAN’s Tire Research Facility”, USDOT report number DOT-TST-78-1, 1977.

U.S. Environmental Protection Agency, “Indus try Options for Improving Ground Freight Fuel Efficiency – Technical Report”, prepar ed by ICF Consulting for the USEPA, 2002

U.S. EPA, High-Tech I/M Test Pr ocedures, Emissions Standards, Quality Control Requirements, and Equipment Specifications: Final Technical Guidance , EPA-AA-EPSD-IM-93-1, USEPA, 1994.

U.S. EPA, Code of Federal Regul ations (CFR) 40 section 86.529-78, 1998.

U. S. Environmental Protection Agency. Mobile Source Observation Data: User Guide and Reference . EPA420-B-04-004. Office of Transportation and Air Quality, Ann Arbor, Michigan. February, 2004.

Wang, M.Q, “Development and Use of GREET 1.6 Fu el-Cycle Model for Transportation Fuels and Vehicle Technologies”, Argonne National Laboratory Publicati on number: ANL/ESD/TM-163, 2001.

Weiss, M.A., J.B. Heywood, E.M. Drake, A. Schafer, F.F. AuYeung, “On the Road in 2020 – A Life Cycle Analysis of New Automob ile Technologies,” Massachusetts Institute of Technology Energy Laboratory Report # MI T EL 00-003. October, 2000.

Weiss, M.A., J.B. Heywood, A. Schafer, V.K. Natarajan, “Comparitive Assessment of Fuel Cell Cars,” Massachusetts Institute of Technol ogy Energy Laboratory Report # MIT LFE 2003-001 RP. February, 2003.

Wu, W., M. Ross, “Modeling of Di rect Injection Diesel Engine Fuel Consumption,” SAE 971142, 1997.

H. Yamamoto, "Motorcycle Engines : Past, Pr esent and Future," SAE 1999-01-3347; JSAE 9938102, 1999.

Yagi, S., Y. Ishibasi, H. Sono, “Experimental Analysis of Total Engine Friction in Four Stroke S.I. Engines,” SAE 900223, 1990.96 Yagi, S., K. Fujiwara, N. Kuroki, Y. Maeda, “Est imate of Total Engine Loss and Engine Output in Four Stroke S.I. Engines”, SAE 910347, 1991.

Yong, J.Y., “Theory of Ground Vehicles”, 3 rd edition, Wiley, NY, 2001.97 Appendix A: PERE Algorithms

Key: FR Fuel rate (g/s) KNV Friction term LHV Lowere heating value of fuel P acc Accessory power P batt Battery power P d Power demand P eng Engine power P FC Fuel Cell power P engmax Maximum power of engine P fcmax Maximum power of fuel cell P motmax Maximum power of motor P min Minimum regeneration power P mot Motor power P th Hybrid threshold power
η Engine indicated efficiency
η

disch Battery discharge efficiency
η

FC Fuel cell efficiency
η

FD Final drive efficiency (for motor only)
η

FWD Front wheel drive power fraction
η

mot Motor efficiency
η

tran Transmission efficiency
η

regen Regenerative braking recharge efficiency

Parallel Hybrid Algorithm

Engine Power (nested if statements) IF OR(v < 2, P d < P th ) THEN P eng = 0 ‘idle & decel engine shut-off IF P d > P engmax THEN P eng = P engmax ‘can’t exceed max ELSE P eng = P d Fuel Rate IF P d = 0 THEN FR = 0 ‘fuel shut-off
IF P d > P engmax THEN FR = [KNV + P eng /ηη tran ]/LHV ‘no P acc – slightly overpower ELSE FR = [KNV + (P eng / η tran + P acc )/η ]/LHV Battery discharge (P batt > 0) IF AND(P d <= P th , P d > 0) THEN P batt = (P d /η mot η FD + P acc )/η disch ‘launch IF P d > P engmax + P motmax THEN P batt = P motmax ‘can’t exceed max
IF P d > P engmax THEN P batt = [(P d – P engmax )/η mot η FD + P acc ]/ η disch ‘assist ELSE P batt = 0 Battery recharge (P batt < 0)
IF P d <= - P motmax /η FWD THEN P batt = -P motmax η mot η regen η FD ‘can’t exceed max If P d < - P min /η FWD THEN P batt = P d η FWD η mot η regen η FD ‘can’t fall below min ELSE P batt = 098 Fuel Cell Hybrid Algorithm

Fuel Cell Powerplant IF OR(v < 2, P d < P th ) THEN P FC = 0 ‘idle & decel fc shut-off IF P d > P motmax THEN P FC = P fcmax ‘can’t exceed max
ELSE P P FC = [P d /( η

mot η

FD ) + P acc ]/ η FC Battery (ultracapacitor) discharge IF AND(P d <= P th , P d > 0) THEN P batt = (P d / η

mot η

FD + P acc )/ η

disch ‘launch IF AND (P d > P fcmax , P d < P motmax ) THEN P batt = [(P d – P fcmax )/ η

mot η

FD + P acc ]/ η

disch IF P d <= 0 THEN P batt = P acc / η

disch ELSE P batt = 0 Battery recharge Same as for hybrid (above)99 Appendix B: Tables of paramete rs for validation vehicles.

Vehicle MIT Camry Jetta Jetta TDI Civic DX Civic HX C ivic Hybri d Insight Prius '01 Prius '04
Model Year 2020 2004 2004 2004 2004 2004 2004 2004 2001 2004 Vehicle wgt (kg) 1154 1565 1467 1483 1239 1224.2664 1354 989 1390 1447 Cr0 (rolling resistance) 0.006 0.009 0.009 0.009 0.009 0.009 0.008 0.008 0.009 0.007 Cd (drag coeff) 0.22 0.3 0.3 0.3 0.3 0.3 0.28 0.26 0.26 0.26 A (frontal area m^2) 1.8 2.4 2.11 2.11 2.14 2.14 2.14 1.92 2.11 2.33 Pacc (accessory - kW) 1 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 0.75 A (N) 127.36 111.25 111.25 105.47 105.47 125.58 53.76 86.33 88.63 B (N/mps) 0.9578 3.6834 3.6834 5.4276 5.4276 -0.9000 2.2837 2.2355 1.3849 C (N/mps^2) 0.4374 0.3764 0.3786 0.2670 0.2670 0.4474 0.3013 0.4144 0.3645 Engine Engine Displ (L) 1.11 2.4 2 1.9 1.7 1.7 1.35 1 1.5 1.5 k0 (N indep friction kJ/Lrev) 0.153 0.164 0.164 0.123 0.164 0.15088 0.15088 0.15088 0.164 0.164 k1 (N dependent fric) 0.00155 0.00155 0.00155 0.00215 0.00155 0.00155 0.00155 0.00155 0.00155 0.00155 P/T indicated eff (eta) 0.405 0.405 0.405 0.45 0.405 0.48 0.48 0.48 0.46575 0.46575
Transmission N/v (rpm/mph) 35.6 35.6 35.6 26.7 35.6 35.6 35.6 35.6 35.6 35.6 Nidle (rpm) 700 700 700 700 700 700 700 700 700 700 trans eff 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 0.88 Shift point 1-2 (mph) 18 18 18 18 18 15 18 18 18 18 Shift point 2-3 25 25 25 25 25 25 25 25 25 25 Shift point 3-4 40 40 40 40 40 40 40 40 40 40 Shift point 4-5 50 50 50 50 50 50 50 50 50 50 g/gtop 1 4.04 4.04 4.04 4.04 4.04 4.04 4.04 3.461 4.04 4.04 g/gtop 2 2.22 2.22 2.22 2.22 2.22 2.22 2.22 1.75 2.22 2.22 g/gtop 3 1.44 1.44 1.44 1.44 1.44 1.44 1.44 1.096 1.44 1.44 g/gtop 4 1.00 1.00 1.00 1.00 1.00 1 1.00 0.86 1.00 1.00 g/gtop 5 0.90 0.90 0.90 0.90 0.90 0.9 0.90 0.71 0.90 0.90
Fuel LHV (kJ/g) 43.7 43.7 43.7 41.7 43.7 43.7 43.7 43.7 43.7 43.7 density gas (kg/L) 0.737 0.737 0.737 0.856 0.737 0.737 0.737 0.737 0.737 0.737
Motor overall efficiency 0.76 0.76 0.76 0.76 0.76 Regen Brake Eff 0.85 0.85 0.85 0.85 0.85 FWD power frac 0.7 0.75 0.75 0.7 0.95 Motor peak power (kW) 30 10 10 33 50 min regen (kW) 2.8 2.8 2.8 2.8 2.8 Motor Energy (kWhr) 1.8 1.8 1.8 1.8 1.8 Battery Initial SOC 0.56 0.56 0.56 0.56 0.56 Batt Energy (kWh) 1.8 0.936 0.936 1.8 1.3104 min SOC 0.2 0.2 0.2 0.4 0.4 max SOC 0.8 0.8 0.8 0.8 0.8 discharge eff 0.95 0.95 0.95 0.95 0.95 Hybrid hybrid threshold (kW) 1.75 2.2 1.5 2.18 2.9100 Appendix C- Comments from Alliance of Auto mobile Manufacturers, including responses.
1

1 Comments were also submitted from several SAE reviewers when the following paper was submitted: E.K. Nam, “Fuel Consumption Modeling of Hybrid Vehicles in PERE.,” SAE2005-01-0627, 2005. This SAE paper describes the hybrid modeling portions of PERE. While these comments are not in cluded here, the reviews were generally positive. The relevant corrections were also applied to this draft. These comments were made regarding the previ ous version of this paper “Advanced Technology Vehicle Modeling in PERE” EPA # EPA420-D-04-002, March, 2004. The response to comments will be separated into general and specific. The specific responses are below the comments, which are copiedover from the original text.

General Response : We appreciate the time and effort that AAM memb ers put into commenting on the report: “Advanced Technology Vehicle Modeling in PE RE.” The depth and breadth of the comments indicate that careful thought and consid eration went into many of the specific comments.

However, from the nature of the more technical comments, it is possible that the report failed to communicate the limitations and use of the model. While, it is stated more clearly in the revised report, it will be clarified here. The PERE m odel will NOT be used in MOVES to capture the behavior of a specific vehicle, but rather a fleet of vehicles. Therefore, the accurate representation of its components (transmission, motor, etc) was no t the goal. If users are aware of improved coefficients or algorithms, the model will be availa ble for changes so that fuel consumption from individual vehicles can be more closely estimat ed. The validation to individual vehicle (make and model) is an extreme validation when the model is pus hed to its limits. This will be clarified in some of the response to comments below.

The original report (dated March, 20 04) was released as a draft vers ion. As a result, this new report supercedes the previous version, and addresses some of the commen ts. Two sections were added to the report (Heavy-duty and motorcyc le modeling). However, the funda mental structure of the model (PERE), remains unchanged. The present report also benefits from the support and cross referencing of other MOVES documentation, which help describe how PERE will supply rates to MOVES.

Comments on EPA "Advanced Technology Modeling in PERE" Alliance of Automobile Manufacturers April 19, 2004

Executive Summary On March 11, 2004, EPA released a report entitl ed Advanced Technology Vehicle Modeling in PERE (Physical Emission Rate Estimator). The re port describes a model created by EPA to estimate101 fuel consumption over any input driving cycle from different types of vehicl es, including advanced gasoline internal combustion engines, advanced dies el internal combustion e ngines, hybrid electric and gasoline/diesel powertrains, and hydrogen fuel cell hybrids. Basi cally, PERE takes vehicle input parameters, then runs the vehicl e through driving cycles defined by the user and outputs second-bysecond fuel consumption rates. The purpose of the m odel is to “fill data gaps in MOVES and to help it extrapolate to future projections of energy and emissions.”

The report states that the model is validated to four production conventional gasoline and five advanced (and hybrid) vehicles by comparing the PERE fuel consumption for these vehicles to the rated fuel economy figures. The repo rt further states that the predic tions are within 10% of the rated values.

Alliance members reviewed the PERE report. However, in order to do a thorough review, we would need to evaluate the actual PERE model. The model was not provided with the report, and we hope that in time EPA will provide the actual PERE model to enable a more thorough review. Since we were only able to eval uate the report, our comments s hould be viewed as preliminary. Further, given the technical issues raised be low, we recommend EPA redraft the report with additional description of how it will be implemented into the MOVES model (as well as other projected uses) and re-release the re port for external peer review.

A summary of our major comments is outlined below. Together, these comments raise concerns with the technical content of the report as well as over-arching issues related to the use and purpose of the model.

Model Design and Validation

Comment:

• The design of the model does not inco rporate vehicle perf ormance requirements, which can have a significant effect on predicted fuel economy.

Response: There are a number of reasons that power was the only perfor mance requirement applied to PERE. 1- Power is a surrogate variable (f or engine displacement) that is capable of being transferred directly into the MOVES framework. MOVES is incapable of distingu ishing vehicles with varying 0-60 acceleration, gradeabili ty, or trailer tow capability. 2- The report mentions that the PERE model is meant to fill holes in MOVES. This also implies that the activity patterns (driving cycl es) employed to run the models are typical driving patterns. The extreme driving pa tterns are captured by PERE (e.g., full out acceleration, or steep grades), but its accuracy has not yet been determined. 3- As a spreadsheet model, PERE could not eas ily capture 0-60 (acceleration) performance criteria. PERE may need to be coded (due to multiple feedback loops required). This may be done in the future.

Comment:102 • The model sensitivity test resu lts raise technical concerns. The static sensitivity tests performed by EPA for conventional vehicles demonstrate weakne sses of PERE. For example, PERE's sensitivity to idle speed is greater on the federal Highway cy cle than in the lower speed FTP cycle. The term 'powertrain indicated efficiency' has no physical meaning; indicated efficiency only has meaning in the context of the heat engine al one. That said, the se nsitivity to powertrain indicated efficiency reported in Table 5 is about half of the expected 10% for engine indicated e fficiency. The sensitivities to aero and rolling resistance differ between conventional and hybrid vehicles.

Response: -The highway fuel consumption is sensitive to idle speed because in PERE, the engine defaults to the idle state when there is no power demanded of the engine, such as during decelerations. By this definition, ther e is actually significant engine “idle” operation, though it is not “modally” idle. -“powertrain indicated efficiency” has been changed in the paper to “engine indicated efficiency”. -Due to the approach that PE RE takes (equation 11), the indicat ed efficiency affects fuel consumption in the brake term, and not th e friction term (directly). This may be slightly counter-intuitive, a nd would probably be more correct if an “fmep” value of friction were used instead of “K”, which al ready has the indicated efficiency inherent to it. “K” was used to remain consistent w ith past modeling efforts. However, as the model stands, this correction would make little difference (except in the sensitivity study or when indicated efficiency change s, but K does not). This will probably be changed in a future version of PERE. -The sensitivities to road coefficients for hybrids do indeed vary between hybrids and conventional vehicles. The degree to which the sensitivity differs is difficult to predict due to the intermix of power demand, dr iving cycle, and regenerative braking.

Comment

• The report does not demonstrate the ra tionale for a 10% va lidation criterion. The report indicates that the model predicts fuel economy for several validation vehicles within 10%. However, it is not clear how EPA ha s determined that 10% is an acceptab le level. Depending on the use of the model, predictability within 10% over the FTP an d Highway Test may not be acceptable. EPA should explain in more detail in the report the various uses it intends for th e model so that the reviewers can decide whether 10% is acceptable. Further, EPA should determine whether PERE makes random errors or systematic errors, such as consistently over-predicting or under-predicting FE for all or particular powertrains.

Response: The 10% criterion was determined by combination of the coarseness of following factors: the fleet and vehicle source bins in MOVES, the (VSP) operating mode definitions (including cycle sensitivities), and PERE. It would not be practical for PERE to predict fuel economy to within 2% given the limitations of MOVES. Othe r models are more suited to such accuracy levels. It is expected that mo st of the uncertainties are “ra ndom”, reflecting the uncontrolled variability in the parameters due to operating region or fleet characte ristics, rather than “systematic” effects.103 Comment:

• The report does not account for cold starts: The report compares FTP and Highway tests with cold start effects to the PERE model without cold st art effects. Cold start CO 2 should be 3% to 5% higher than fully warmed up CO 2

.
Response: Cold start is modeled separately and desc ribed in a separate document. However, a supplemental discussion of hybrid cold start ra tes is described in the revised document. This is preliminary and only based on 2 hybrid vehicles tested.

Comment:

• The report does not demonstr ate overall robust validation. In spite of claims in the PERE report and the apparent agreement between PERE predicti ons and unadjusted vehicle FE, the PERE model has not been validated. There are a number of reasons for this, as follows: - In validating the model, only a narrow range of vehicles was considered. The model should be validated on a wider range of vehi cle weights and vehicle classes. - Nearly all of the vehicles used for validation were assumed to have the same transmission and shift point pa rameters, instead of those of the actual vehicles in the study. Meaningful validation can only be conducte d when the input parameters to the model are identical to the characteri stics of the vehicles being studied. - Only the FTP and Highway test cycles were considered during validation. However, the MOVES model is based on 14 other test cycles that have higher speeds and accelerations than the FTP and Highway tests, and no validation is performed on these cycles. - Benchmarking PERE against the MIT studies is not sufficient to demonstrate the quality of the predictions. There must be a comp arison to actual vehicle data.

Response: -The PERE model has been validated against 41 “conventional” passenger vehicles driven on the FTP as well as US06 cycles in a prev iously published repor t: (Nam, EPA document number:420-R-03-005, Table 2). In the same document, analyses were conducted against another 17 vehicles driven on a series of 8 cycles developed by CARB (UCC cycles). Moreover, the same modeling methodology ha s been proven robust by a number of researchers in several different (peer-reviewed ) publications. These authors include (Feng An, Matt Barth, Marc Ross, among others...). Some of these publications include truck, diesel, and even advanced technology vehicle modeli ng over several different driving cycles. -PERE does not actually incorporat e a detailed transmission model. Since the engine model is based on power (and not torque), transmission takes on secondary importance. [This also reduces the importance of proper transmissi on/engine matching – a comment below]. The transmission parameter that is of primary importa nce is the efficiency, which will discussed in more detail below (and in the new version of th e paper). The shift point s, gear ratios, etc., only affect the friction term of the fuel rate equation 11. If PERE was based on Torque/RPM/bsfc engine maps (as are many othe r more detailed models), transmission would play a more significant role. Due to the “coarseness” of the PERE powertrain model (and the coarseness of the MOVES vehicle source bin de finitions) precise transmission models were not deemed necessary. To further demonstrate th is point, the validation in this report (and in104 other references mentioned above) is proven to be quite robust, despite the use of the same “average transmission” for many of the vehicles. -PERE is compared to the MIT report merely to demonstrate that when the same vehicle is modeled, PERE gives the same result. This does not imply that all of the same assumptions made in the MIT report (especially about fu ture technologies) were made in PERE or MOVES. The hybrid results from PERE are quite different from the re sults obtained in the MIT report due to these difference in assumptions . Naturally, comparisons to data will be conducted as data is received.

Comment: Model Uses and Purpose

• The report does not describe the da ta gaps to be filled by PERE. Complete information regarding the applicatio n of the PERE model for use in MO VES is central to determining the sufficiency of modeling assumptions, accuracy and/ or validation. Without this key information the reviewer does not have adequate in formation to fully evaluate the adequacy of the model as described in the report.

Response: The “hole-filling” report is described in a separate document to be released concurrently with this one. Also, the section has been expanded in the revised report.

Comment:

• The report does not explain need for new modeling tool. EPA has not fully explained why it had to create a new model for the purpose of examini ng fuel consumption from advanced technologies, rather than using other available models such as the Argonne vehicle model utilized in GREET.

Response: Models such as ADVISOR, PSAT, etc. were considered, but deemed impractical for integration with MOVES. ADVISOR (for exam ple) is more detailed and complex (than PERE) and is designed to model individual ve hicles, rather than fleets of vehicles. Additionally, ADVISOR is coded in the MATLA B/SIMULINK environment, which the users of MOVES are not assumed to possess. ADVISOR is also soon to be commercialized and not freely available to users. It is possible that in the near future, PERE might be bechmarked with PSAT (developed at Argonne National Laborat ory), however, it will be important to remember that there are many differences between the models. It was necessary to provide a model that was more accessible to MOVES users, who may want to adjust the PERE rates. The specific user and purposes of PERE adjustments are not yet known. Finally, the management of EPA consid ers it important to ke ep the expertise in this type of modeling in- house, rather than relyi ng on contractor support.

Comment: PERE does not address powertrain ma tching considerations (that is, se lection of engine displacement, transmission gear ratios and fina l drive) explicitly. Performan ce metrics are not specified or105 calculated. This is especially critical in extending PERE to the analysis of other vehicle classes; particularly trucks where the trai ler tow requirements can be significan t. How is the user assured that the powertrain match is ‘ballpark’ correct? For exam ple, if only 0-to-60 time is matched, SI engines are undersized, fuel economy is overstated and o ff-cycle vehicle performance is generally not acceptable. In industry vehicle simulations, vehicle performance requirements for passenger cars and trucks are specified a priori and include real wo rld requirements such as performance times, passing time in top gear, vehicle launch acceleration, t op vehicle speed, gradeabili ty and trailer tow.

Response: This point is addressed above.

Comment: • In spite of claims in the PERE report and the apparent agreement between PERE predictions and unadjusted vehicle FE, the PERE model has not b een validated. There are a number of reasons for this, as follows: 1. In the PERE validation effort, only a narrow range of vehicles was considered. 2. All of the vehicles used for validation we re assumed to have the same transmission and shift point parameters, instead of thos e of the actual vehicl es in the study. 3. Only the FTP and Highway test cycles were considered. 4. The report compares FTP and Highway tests with cold start effects to the PERE model without cold start effects. 5. Benchmarking PERE against the MIT studies is not sufficient to demonstrate the quality of the predictions. These reasons are discussed further below. 1. In validating the model, only a narrow range of vehicles was considered. The report compared unadjusted fuel economy to the fuel economy as pr edicted by the model fo r the following vehicles: Toyota Camry, VW Jetta (gas and diesel), Honda Civic DX, Honda Civic HX, Honda Civic hybrid, Honda Insight, Toyota Prius (01), and Toyota Prius (04). The conventional gaso line vehicles are the Camry, Jetta, and Civic DX. None of these vehicles is larger than EPA mid-size car class. The comparison included no heavier light duty passenger cars, no SUVs, no miniva ns, and no light duty trucks. The report indicates the model is capable of estimating fuel economy for all conventional gasoline vehicles. It is not clea r why the validation on conventional gasoline vehicles was so limited. A sample of representative vehicles from each of the EPA vehicle categories should be used to demonstrate that the model is valid for the range of vehicles that will be studied using PERE. 2. All of the vehicles used for validation were assu med to have the same transmission characteristics, instead of their own individual characteristics. The vehicle descriptions in Appe ndix B show that the same transmission, gear ratios, N/V and shift points were used in making the PERE estimates for the MIT study, Toyota Camry, VW Jetta, Honda Civic DX, Honda Civic HX, H onda Civic Hybrid, Honda Insi ght, Toyota 2001 Prius and Toyota 2004 Prius. Meaningful validation ca n only be conducted when the input parameters to the model are identical to the characteristics of the vehicles being studied. 3. Only the FTP and Highway test cycles were considered. EPA has only shown a demonstration of PERE fo r regulatory drive cycles where peak vehicle acceleration is below 0.165 G and peak vehicle speed is below 60 mph. Nothing was shown for customer driving where the vehicle speeds and accel erations are significantly higher. Extrapolating106 speeds and loads from regulatory cycles to simula te customer driving is not appropriate. The FE benefits of advanced technologies depend on the drive cycle (such as, the hybrid FE benefit on the federal city cycle being greater than that on federal highway cycl e). The ability to use PERE to estimate corresponding FE benefits on unspecified customer cycles has not been demonstrated. Figure ES1 and Figures 11-13 in the report compare the PERE model pr edictions to the reported fuel economy values for both the FTP and the highway test. However, Table 9 of the report indicates that neither the FTP nor the highway test will be used to establish vehicle activity and emissions in MOVES; this vehicle activity and the emissions will be based on 14 other light duty driving cycles. These 14 driving cycles generally have higher accelerations and speed s than either the FTP or the highway test, and these cycles were also used in MOBILE6. The report has no comparisons of fuel economy for the PERE model and these cycles. In addi tion, the report states that while the model is calibrated so that the state of charge for hybrids is cl ose to zero after the FTP, this may not be true for other driving cycles, like a ll of the driving cycles that will be used in MOVES. EPA has determined that these 14 cycles are more representative of overall in-use driving than either the FTP or the highway test. EPA should therefore check the accur acy of the PERE model on some of the cycles intended to be used in MOVES. 4. The report compares FTP with cold start effects to the PERE model without cold start effects. The PERE model currently does not model cold start eff ects on fuel consumption, nor is there any plan to add cold start effects to PERE. EPA plans to add cold start effects in MOVES. However, the FTP tests do have cold start effects included. Therefore, EPA compares PERE predictions of fuel economy without co ld start effects to test data that includes cold start effects. This is an “apples to oranges” comparison. EPA should either add cold start effects to PERE and compare the model predicti ons to test data with cold st art effects, or EPA should obtain test data on the FTP without cold start, and compare that to the PE RE model predictions. 5. Benchmarking PERE against the MIT studies is not sufficient to demonstrate the quality of the predictions.

o Figures ES1, 11, 12 and 13 (that show agreement between PERE and the MIT study for a near-70 mpg vehicle) should clarify that the model results shown are for an advanced SI engine hybrid for which there is no experimental data. This does not m ean either model is right or wrong; merely that the models seem to make similar predictions. o Benchmarking PERE against the MIT "on the Road in 2020" and "Competitive Assessment of Fuel Cell Cars" is not sufficient to demonstrate the quality of the predictions. PERE ha s used several assumptions from the MIT study that are questionable, specifically: Th e energy density of gasolin e per liter is 1.5% too large. For similar vehicles, the MIT estimate of FE would be 1.5% higher, everything else being equal. The 91 RON BMEP of the naturally aspirated advanced SI engine in the MIT report is too optimistic. Specifically, peak BMEP exceeds 13.3 bar and that at 1500 rpm is 11.0 bar, compared to observed levels of ~12.2 bar and ~9.8 bar, respectively. The MIT report equates equal power-to-weight to equal performance ( On the Road in 2020 , p. 3-5) which is the same as setting only full throttle acceleration, e.g., 0-60 time, equal. Other characteristics of vehicle performance, such as contin uous gradeability, are not strongly related to the peak power-to-weight ratio. The equal power-to-weight107 assumption for SI engines results in undersized engines and overstated fuel economy. Conversely, this assump tion is likely to result in oversized diesel engines. The MIT reports assume very aggr essive engine downsizing in hybrid vehicles that would produce compro mised off-cycle driveability and overly optimistic projected FE bene fits; for example, >40% projected highway cycle FE benefit. The 2003 MIT report is based on very optimistic fuel cell vehicle assumptions: high fuel cell system performance, significantly reduced ancillary losses and high H 2 utilization.

Finally, there are several other studi es published based on more realistic technology assumptions than the MIT st udy which could better serve for the purpose of calibrating the PERE mo del. Among these studies are the following: “Well to Wheels Analysis of Future Automotive Fuels and Powertrains in the European Context”, EUCA R, CONCAWE, and European Commission, Joint Research Ce nter, Version 1b, January 2004. “Potential of IC-Engines as Mi nimum Emission Propulsion System”, Eichlseder and Wimmer, Atmospheri c Environment, 37 (2003) 5227-5236 “A Comparison of Hydrogen, Metha nol, and Gasoline as Fuels for Fuel Cell Vehicles: Implications for Ve hicle design and Infrastructure Development”, Ogden, Steinburgler, Kr eutz”, Journal of Power Sources 79 (1999) 143-168.

Response: Most of these comments are addressed above. There are some specific additional responses: 3- As mentioned earlier, other studies have validated PERE modeling conventional technologies on other (more aggressive driving cycles. However, the reviewer is correct that PERE for hybrids has not been validated to other driving cycles. This is difficult due to the lack of data. The model will be improved as more data on hybrid vehicles becomes available. Some discussion was added based to published data from NREL, which shows some results from the US06 driving cycle for the Honda Insight. The hope is that at the level MOVES models energy rates, the fuel rates by operating mode bin (VSP) will be relatively independent of (most) drive cycles effects. Any added complexities would take the model details beyond the level of deta il that MOVES is capable of capturing. However, to address some of the concerns from this comment, the modeled advanced technology vehicles were run on an additional cycle: the LA92. 4- Cold start will indeed have an affect on measured fuel economy. This was not quantified in the original report. Some measurements taken at the EPA now supplement the data. However, cold start in MOVES is modeled by other means (mainly as a function of engine displacement), and can be found among the MOVES documentation. 5- We appreciate the comments on the MIT re port. Again, PERE only uses the same parameters as the MIT model does when compar ing the same vehicle (the MIT vehicles). When modeling MOVES or existing vehicles, a different set of parameters are employed. For example, note that the fuel cell system ef ficiency curves are significantly lower than108 the ones used by MIT. Also, PERE is not iden tical to the MIT model since some of the details of the MIT model are not published. PERE is more like the models in other publications (An, Bart h, Ross, et al).

Specific Technical Comments: The following comments are organize d in order of page number.

Page 6 (second paragraph): The text states that the PERE Vehicle Specific Powe r estimate is lower than that obtained from the coastdown equation and that FE would be overest imated. No estimate of this error was provided.

Response: This is discussed in a little more detail pp 33-35 (original report).

Page 7: At part load, a four-valve SI e ngine is more efficient than a tw o-valve engine because improvements in indicated efficiency from the higher compre ssion ratio and lower combus tion chamber surface area more than offset the increase in valve train a nd cam drive friction. Differences in pumping are minimal.

Response: The friction term (fmep) includes the improveme nt in “breathing” that accompanies the increase in area of the intake (and exha ust) openings. PERE does not assume a significant increase in indicated efficiency due to additional valves.

Page 9 and Page 15: The opportunities for FE gains from lean operation ar e not likely to materialize because of the high levels of NOx conversion efficien cy required at Tier 2 Bin 5 and PZEV tailpipe NOx standards. Major breakthroughs in lean after tr eatment systems are required to realize significant FE gains from lean cruise and stratified charge DI operation. GDI is already in production in Europe where the NOx emissions standards are more lenient. Any estimate of benefits from lean ope ration must also include an assessment of compliance with tailpipe NOx and HC standards and estimate s of other factors such as vehicle weight and performance.

Response: The modeling of the lean burn engines was of current vehicles (even though their lean operation may be very limited), which are not su bject to Tier 2 standa rds. It is certainly possible that lean burn vehicles meeting Tier 2 will have fuel economy disbenefits to meet emissions standards. This will likely be incorporated into the model.

Page 11:109 The efficiencies of the transmissions seem to be too simplistic and need to be checked. If average efficiencies are used then these should be obt ained from experts and should reflect average efficiencies on the duty cycles. We believe it is poor argument that th e efficiency of a manual is the similar to a continuously variable transmission (CVT). CVTs improve engine operating efficiency but are not more efficient on their own than discrete step transmissions (they have higher pumping losses, especially when designed to deliver ratio change responses to meet disc rete step transmission performance requirements).

The shift logic implemented as described on p.11 is too simplistic and does not agree with data shown in the table on p.48. The loss in automatic transmission efficiency duri ng gearshifts due to slippage in the torque converter is not a signif icant loss to worry about relative to other losses represented in the transmission models (this is usually combined into the average efficiency on the driving cycle already).

Drive quality or other real world performance requi rements (launch acceleration, 0-60 time, and top vehicle speed) have a significant impact on fuel economy and are ignored by PERE. Tradeoffs between fuel economy and drive quality vary signif icantly between conventional and hybrid vehicles and some basic drive quality criteria can be easily incorporated into the overall analysis methodology.

Response: The transmissions efficiency discussion has b een fleshed out after some discussion with transmission experts. Transmission models (e specially automatic) can be extremely complex. As mentioned earlier, the transmission model was never meant to be detailed since PERE models a “typical” vehicle, rath er than a specific one. As with other components, when run for MOVES, PERE may not specify the actual physical transmission, it will merely give a “target value” for efficiency. This is what was mean t by advanced transmissions approaching manual transmission efficiency. It is agreed that CVT e fficiency is not presently that efficient yet.

Page 12: It is not clear if the efficiencies used in the PERE model are average duty cycle or maximum brake thermal efficiencies. This should be clearly stated.

The assumed 45% diesel engine indicated efficiency appears to represent the peak indicated efficiency and is too high for current engines. Applying this efficiency uniformly over the entire range of speeds and loads of a dr ive cycle is not appropriate. What size engine is assumed and what engine or aftertreatment technologi es have been considered? It appe ars that PERE assumes for diesel engines that U.S. emission sta ndards can be met with aftertre atment that has no fuel economy penalty. Diesel engine compliance with U.S. tailpipe NOx standa rds is a significant challenge; estimates of the fuel economy penalty for emissions co mpliance associated with lean NOx trap and catalyzed diesel particulate filt er regeneration should be incorpor ated into PERE. Although diesels are used widely in Europe, the NOx standard is ab out 8 times more lenient there than in the U.S.

EPA estimates that vehicles with diesel engines weigh approximately 4% mo re than their gasoline counterparts. However, this assessment is based on current diesel vehicl es that do not include aftertreatment and do not meet mo re stringent Tier 2 standards. The Tier 2 standards will require110 extensive aftertreatment, which will add to the weight of diesel vehicles relative to gasoline vehicles. EPA should include expected Tier 2 aftertreatment impacts in its asse ssment of the relative weight of gasoline and diesel vehicles.

Response: The efficiencies used in PERE are average indi cated thermal efficiencies according to engine map data (excluding idle and wide open thro ttle operation). The paper (and one reference) gives a detailed description of what this indi cated efficiency is. The use of this method has been proven to be quite robust in estimating fu el consumption in various papers (see above responses and references). The fuel econom y penalty of diesels (and other lean burn technologies) was also discussed above. EPA will likely include a penalty for future model years as this beco mes better understood.

Page 13: The argument for engine sizing not being critical b ecause this model is to be used for fuel economy and not for performance analysis di sregards the most critical asp ect of assessing and comparing the fuel economy potential of advanced powertrain tec hnologies. This intentional oversight ignores the tradeoff between fuel economy and vehicle perf ormance requirements; fuel economy comparisons must be made on an equal performance basis.

Response: As mentioned earlier, the performance metric used in PERE (and MOVES) are power and weight, (or perhaps power to we ight ratio). This is really the only practical performance metric MOVES can accommodate.

Page 15: EPA lists many reasons why diesel engines are more efficient than gasoline engines. EPA concludes that diesel engine indicated effici ency is assumed to be 45% and ga soline engine indicated efficiency is 40%. Therefore, the 48% indicated efficiency of the Honda lean burn engine is better than a diesel and should be revised.

There is no justification for assuming that the At kinson cycle engine is 15 % more efficient than current engines. Peak torque of Atkinson cycle e ngines are typically close to those of conventional engines, peak torque speed is higher because of the late intake valve closing. Also, Atkinson low speed torque is degraded. Consequently, Atkinson cycle engines are best used in hybrid powertrains and not with conventional transmissions.

Engine models for advanced technologies such as HCCI, SIDI must be descri bed in more detail and demonstrated to be valid. The model should be reviewed by the engine experts for current and advanced technologies.

Response: The efficiency of the Honda lean burn is probably too high for PERE. However, it was obtained from their SAE paper [SAE 2003-01-0083] . Even if it is appr opriate, the operating111 regime for lean burn operation might be quite sm all, especially in the future (due to NOx requirements). Since PERE used generic coeffici ents for transmissions (see above), it should also have used generic coefficients to describe these advanced engines, rather than attempting to model them explicitly. This would be more in line with inputs to MOVES. However, since MOVES models “generic” hybrid vehi cles, the parameters of this sp ecific engine are not used.

Pages 17, 23-29, Appendix A. The controls models are too si mplistic. Control strategies ar e important for propulsion system modeling because hybrid c ontrol strategy has a strong impact on the fuel economy estimate. In the PERE model, incorporation of a variety of control ar chitectures and optimiz ation of the control strategy are difficult, if not impossible. There is no assurance that the assu med control strategy would result in appropriate vehicle performance off-cycle.

Response: This is certainly true. However (as mentioned earlier), PERE is not meant to be a detailed model. Energy (or power) demand is distributed to engine, motor, etc. accounting for losses. The target for PERE is to be within 10% fuel consumption (more or less random effects), so a detailed model is not deemed necessary at this scale. The model will be continually validated as more data is collected.

Page 17: PERE is a spreadsheet tool and should not be referred to as a ‘simulation’ tool

Response: This is a matter of semantics, however, all refere nces to “simulation” have been replaced with “model”.

Page 18: The charge-sustaining strategy in PERE’s HEVs a ssumptions must be more thoroughly reviewed, as there appears to be a contradiction in description; i.e., there is a st atement that batteries are only being charged through regenerative braking a nd not the engine, whereas in a pr ior section it was stated that the batteries are being char ged during highway driving

.
Response: The batteries are regenerated also during some coasting operation. During the highway cycle, there are coasting as well as braking ev ents. Thus, the assumptions are sound.

Page 20: The purpose of mentioning hydraulic hybrids and ultracap acitors is not clear. The report discusses methods of modeling hybrid s that utilize hydraulics and ultracap acitors, but does not discuss how so called “minimal” hybrids would be modeled with PERE.112 Response: Thus far, PERE has no plans to model “minimal” hybrids (ISG with idle shut-off and minimal regenerative braking). This may change in th e future. The energy storage device is treated rather generically in PERE, rega rdless of whether that storage device is a battery, ultracap, or hydraulic in nature. The user can adju st the coefficients appropriately.

Page 21: Vehicle mass estimation is very critical in the co mparison of advanced engine and hybrid vehicle technologies impacting vehicle performance and fuel economy and thus the model input data should be carefully reviewed.

Page 22: Description of handling the accessory loads for conve ntional and hybrid vehicles are not clear. Also, there is no description of the elect ric motor performance. An AC induc tion motor is used in the Ford Focus and DCX F-Cell while permanent magnet moto rs are used in the Toyota FCHV and Honda FCX. These motors have different perfor mance and efficiency characteristics.

Response: Once again, the components are generic in natu re (since the model is for fleet modeling). Accessory loads (air conditioning) are modeled separately as a temperature correction in MOVES. If it is necessary to m odel it explicitly in PERE, this may be done in the future.

Page 23: Figure 10 does not demonstrate that there is no change in battery stat e of charge for hybrid vehicles across the city and highway drive cycles. How doe s PERE assure that th e battery state across customer driving and other off-regul atory cycles does not change? Th is is a critical input to the estimate of fuel use in the model.

Response: Figure 10 merely demonstrates that the state of charge algorithm is di fferent between PERE and the MIT model. The algorithm that MIT uses was not published. For the vehicles modeled in PERE, charge is conserved over the driving cycle. When PERE supplies rates to MOVES, charge will be conserved over the drive cycles (when it can). We realize that an optimized hybrid model is beyond the scope of this project.

Page 27-29: Fuel cell vehicle modeling where the propulsion system characteristics are obtained from a spreadsheet produces rough estimates of fuel cell and fuel cell hybrid fuel economy (within ~10%). More accurate fuel economy estimates require a dynamic model that simulates the detailed performance of individual components interacting w ith the vehicle and complex control strategies.113 Page 29: There is insufficient data to validate the model pred ictions for fuel cell hybrid vehicles, as the author of this report pointed out. Only comparisons coul d be made with the Honda FCX with its unique hybrid system architecture and control strategy.

Response: As more data is obtained, the model will be updated.

Page 30 Figure 17. Fuel cell system performance seems to represen t static performance. Efficiency during dynamic behaviors and at real world operati ng conditions (operating temperature is not stable in the real world usage) will be worse. The issue is how to inco rporate these adjustments into the model. A dynamic model would include these characteristics.

Page 30: The maximum efficiency at 60% for fuel cell vehicles seems high. One member company has modeled this at 55%.

Page 32-33: The static sensitivity tests performed by EPA for conventional vehicles demonstrate weaknesses of PERE. For example, PERE's sensitivity to idle speed is greater on the federal Highway cycle than in the lower speed FTP cycle. The term 'powertrain indicated effi ciency' has no physical meaning; indicated efficiency only has meaning in the cont ext of the heat engine alone. That said, the sensitivity to powertrain indicated ef ficiency reported in Table 5 is a bout half of the expected 10% for engine indicated efficiency. Why is the sensitiv ity to aero and rolling resistance differ between conventional and hybrid vehicles?

Response: See above.

Page 33: For the road load and track coefficients – it is not very significant what method is used as long as this method is used consistently for all vehicle concepts modeled; however, using vehicle aerodynamic and tire rolling resistance coefficients may add less uncertainty to the predictions than assuming vehicle coast down data which combines too many unknown losses.

Response: The point of this analysis was to demonstrate that a higher order rolli ng resistance term is necessary in modeling most vehicles, or else the loads (and hence fuel consumption) will be underestimated significantly. Most models in th e literature either us e a higher order rolling resistance term, or a “B” term explicitl y. The MIT model (for example) does not.114 Page 37: The report indicates that the source bins do not ha ve a dimension for body type, and that the estimates will be based on vehicle weight. Within a given we ight range, however, there can be considerable variation in body type that can re sult in wide variation in aer odynamic drag coefficient. The uncertainty in each of the vehicle specific power bins could be very high because of this factor. To reduce this uncertainty, EPA may want to consider di viding each weight range into different levels by general body type or aerodynamic drag coefficient range.

Response: This will be taken under consider ation. However, this consideration must be evaluated against the need to avoid excessive numbers of vehicle “bins” in MOVES. It is likely that this will remain one of the uncertainties (hopefully quantified).

Page 41. The observation that emissions compliance is dete rmined entirely on perf ormance during the cold start period is based on experience with engine systems that operate at stoichiometry with a TWC. This is not true for advanced gasoline and diesel engine technologies where the engines operate lean throughout the drive cycles because the lean NOx conversion efficienci es are substantially lower than those of a TWC. For compliance with PZEV emi ssion standards, NOx throughout the entire drive cycle is critical. Additionally, a statement about the tailpipe crit eria emission standard must be made for all fuel economy estimates.

Response: This subject will have to be revisited a nd will likely be corrected by the time criteria pollutants are modeled in MOVES.

Page 48 The table of parameters for vehicle validation need s to be reviewed and explained. For example, the shift points presented for Prius are suspect.

Comments Regarding Modeling Purpose and Use EPA states that the purpose of PERE is to “fill data gaps in MOVES and to help it extrapolate to future projections of energy and emissions.” The following are our comments on the
purpose.

• EPA is not specific about the “data gaps” EPA anti cipates PERE will fill. There is also little or no information out these data gap s in the report, or how the model will be used for such a
purpose. The MOVES model is being developed from second-by-second data to estimate fuel consumption and emissions from user-inputted driving cycles. Similarly, PERE has been developed to estimate fuel consumption from us er-inputted driving cycl es, on both current and advanced technology vehicles. There appears to be an overlap between the two models, since both115 models estimate fuel consumption for current conve ntional vehicles. EPA plans to integrate PERE into MOVES. What are the data gaps that EPA hope s to fill with PERE? Wh ere will PERE be used instead of MOVES? Will this be a specific model year for each vehicle class, and if so, which one? Since the model has been developed to fill certai n “data gaps”, the report should be clearer about which data gaps the model will fill. Complete information regarding the application of the PERE model for use in MOVES is central to determining the sufficiency of modeli ng assumptions, accuracy and/or validation. Without this key information the reviewer does not have adequate information to fully evaluate the adequacy of the model as described in the report.

Response: The filling of data gaps is a complicated subject to be handled in more detail in a separate document. There has also been some added te sts to the new version of this report.

• EPA has also not completely explained why it had to create a new model for the purpose of examining fuel consum ption from advanced technologies. The PERE report indicates that EPA consid ered using the ADVISOR model, but decided against this approach because ADVISOR could not be readily integrated into MOVES. However, there are other models that estimate fuel consumption from advanced technology vehicles. For example, there is the Argonne National Lab ( ANL) vehicle model used in GREET that also perform these functions. Was this mo del considered, and if not, why not?

Further, assuming that a creation of PERE is ju stified, how do the results of PERE compare with those of the other models (that is, ADVISOR, PSAT, GREET)? What is being done to reconcile differences, if they exist?

Response: These comments have been addressed above. PERE has been validated to the some of the same vehicles that ADVISOR has been validated with.

• It is not clear from the report who the “u sers” of PERE will be, or what level of information EPA will supply to the users. The executive summary of the EPA PERE report specifi es that an "informed user" can run the model. An informed user is not defined. We are concerne d about how much and what type of experience simulating the behaviors of advanced powertrains is required to use the model correctly. States and local governments doing emission inventory anal yses will run the MOVES model. Does EPA anticipate that states and local gov ernments will also be running PERE, and if so, what information will EPA provide to these users?

Response: EPA does not expect state or local governments to run PERE. Nevertheless, the model will be readily available to all users to evaluate and us e as they see fit. Suppor t will be provided at some level.116 • Finally, the Alliance reiterates previo us comments to the EPA to defer CO 2 modeling and fuel economy to the Department of Energy, DOT, and NHSTA. We are concerned with EPA's efforts to model fuel and energy consumption. We continue to recommend EPA defer CO 2 modeling and fuel economy to the Department of Energy, DOT, and NHSTA. (see previous comments to the EPA in March 4, 2003 and December 20, 2002 letters from Mr. Casimer Andary and Ms. Ellen Shapiro to Jo hn Koupal). We note that EPA has stated in its September 8, 2003 decision that the Clean Air Act doe s not authorize the regul ation of emissions of CO 2 and other greenhouse gase s from motor vehicles.

Response: As stated in previous oral and written communications to the Alliance, MOVES, like MOBILE6, is designed to complement rather than compete with the national estimates prepared by the Department of Energy (DOE) of fuel consumption. Bottom up estimates of fuel consumption are essential not only fo r validation of MOVES2004 fleet and activity estimates, but also for the pr oper integration of GREET, for generating regional and local inventories, and for assessing the impacts on a ll pollutants in various changes in future technology mix. These are important functions that cannot be met with top-down modeling.117 Appendix D- Comments from independent reviewer: Professor Marc Ross, including
responses.

PERE Review 10/13/04 Marc Ross. These comments were made regarding the previous version of this paper “Advanced Technology Vehicl e Modeling in PERE” EPA # EPA420-D-04-002, March, 2004.

OVERVIEW In my opinion, PERE is an excellent motor-vehic le fuel consumption model, which should prove practical and powerful in use. (I may be somewh at biased in this because in the 1990s I was involved – not for EPA - in several aspects of this general approach to modeling of motor vehicle fuel consumption and emissions.) The model is re latively simple, requiring relatively few inputs, and yet rather accurate over a wide range of vehi cles and driving patterns. The major strength of the model is its basis in physical principles , which are responsible for its generality and simpledescription. These principles are ade quately approximated, over many vehicles and driving patterns, by simple algebr aic expressions. Most of the pa rameters in these expressions are known for any type of light-d uty vehicle, or are essentiall y constant for a given technology. This report clearly presents the model, and disc usses the assumptions made, and relates them to many other referenced studies and measurements.

Of course the model requires appropriate validati on in terms of measurements. But this is not primarily a statistical model. While the a pproximations and their parameterization need justification, it is essentially a matter of physics rather than of complete coverage. Wherever an application involves different physics, th en appropriate measurements are needed . That means that since it and similar models have been valida ted for lighter light-duty vehicles, extensive data on the heavier light-duty vehicles is almost certa inly not needed. However, more measurements may be needed on very heavy vehicles. (I have no t carefully reviewed the heavy-duty, section VI - pp24 to 40.) Although the success of this type of modeling is encouraging, it does not mean PERE will necessarily be satisfact ory in all applications in su pport of MOVES, unless there are additional measurements and analysis, as I briefly discuss further below.

In addition, there are some par ticular conditions and new technol ogies which are important, and which one knows are not adequately described by the model in its present form, i.e. in the absence of further measurements and analysis. Cold start, already addressed in part in the report under review, is, as stated, one such area. A seri ous beginning has been ma de on hybrid vehicles, but hybrids are likely to be a diverse class, and too few vehi cles have been available for measurement to adequately implement PERE in th at respect. Fuel cell vehicles represent another new technology where even fewer relevant specificat ions and measurements are available. But it is likely, if not certain, that this general m odeling approach in its simple form will prove adequate in that case as well. In addition, criter ia pollutants have been successfully addressed elsewhere using this kind of modeling, so I would be hopeful that PERE can contribute effectively to the MOVES program in that area as well. (It is not clear, however, whether particulate emissions, especially distributions in size and composition - whic h might in the future become an important regulatory concern, can be modeled in this simple a fashion.)118 I applaud the position taken here th at 5% variability, or even 10%, can be a very useful level of accuracy. Elaborate models with many more para meters, and elaborate measurement programs, often prove to be no more reliable in practice

.

SIGNIFICANT SPECIFIC COMMENTS/QUESTIONS 1) Fill data holes (p5, p71): MOVES empirical ba sis, on-board measurements in random driving, may prove inappropriate in particular cases becau se: driving or weather conditions different from those sampled are (or become) of interest in applying MOVES. Comparison of predictions by PERE with the statistical results in MOVES in MOVES may also prove useful in unanticipated ways. In addition, PERE could offer alternative ways, as time goes on, to up-date information for MOVES. RESPONSE : Good point. PERE could be used to isolat e certain physical effe cts to study their impact on emissions and fuel consumption separa tely. Such a statements is added in this section.

2) I agree that applica tion of PERE to advanced technologies (p5, 77ff) may not, in most cases, require a large accompanying measurement program.

3) Engine friction dependence on N (p11, moto rcycle comparison p19, and Fig. 32 p59): In the figure, p11, N appears to go to 6000 rpm. Ignoring the details of low-N dependence, is there a significant difference between lin ear and quadratic to 6000 rpm? Indeed, a casual look suggests the data might fit a quadratic (a + bN^2) better than the linear dependence used, and have the further advantage of being simila r to the motorcycle dependence. Also the statement: "end result in the model (of assuming increas ing friction vs assuming poorer indicated efficiency as N decrea ses) is identical" needs a brie f discussion. The two assumptions are "identical", as stated, unless one considers applying the model to an alysis of technologies focused on saving fuel at low N. RESPONSE : Yes, a quadratic fit may also work. If th is is advantages, it may be changed in the future. Regarding the 2 nd comment, the sentence has been cl arified. It is true that if a more detailed study is require d for new engine technologies in this operating regime, the user would have to understa nd this fine distinction.

4) N/v)top is assumed to be a constant (p13). In my modeling work, I found N/v)top to be an important variable for predicting fuel rate. See Fig. 3 of my 1 993 paper with Feng An (A Model of Fuel Economy and Driving Patterns, SAE 930328) where we used mass, displacement and N/v)top to predict the fuel inte nsity of many cars. On the other hand, since N/v is related to displacement, if somewhat roughly, the fuel ra te may be insensitive to N/v, as such, in an empirical approach. [I fit N/ v)top roughly to SQRT(N).] RESPONSE : The sensitivity study in the paper confirms that N/v is an important variable for fuel consumption modeling. Bob Giannelli came up with some relation of N/v with displacement, which we used more for heavy duty applications.

5) "PERE is less sensitive to the specifics of the transmission model" aside from the transmission’s efficiency. (See also bottom p99.) Good. This important insight had escaped me when I was working in this area. Of course th e fuel rate still depe nds on N via the engine friction term.119 RESPONSE : Of course, it is also sens itive to N/v as mentioned above. I added the parenthetical, that this generalization is for light duty. We found motorcycle s (for example) to be quite sensitive to transmissions parameters (shift points, gear ratios, etc).

6) Top two paragraphs p 42: Is it really possible, as claimed, to improve efficiency substantially at low load by going lean without producing mu ch NOx? I once estimated the improvement in fuel intensity by going lean (E qn. 4b of my Contemporary P hysics paper). That estimate suggests a smaller benefit; e.g. a reduction of 14% in second-by-second fuel rate if the fuel-air ratio is reduced by a highly lean one-half. But the dependence on fuel-air ratio in my expression may be greatly over-simplified. See Thomas & Ross for an analysis of an effec tive threshold for engine-out NOx emissions. We found that threshold to be a fuel rate of 0.5 g/s for an early 1990s composite car

. That implies, in the moderate driving of bag 2, most of the time very little NOx is emitted engine-out. In turn that suggests going lean in the bag 2 driving regime would not result in much engine-out NOx. However in more vigorous driving there is much engine-out NOx. So going lean will save fuel and not cause high NOx, but amount of low-power driving may be small, so the efficiency improvement may be small in practice. RESPONSE : NOx control is definitely more of an i ssue with Tier2 regs. There is some concern over the lean engine model using parameters st raight from the Honda paper. For example, improvement in peak efficiency already in cludes improvements in friction. This comment has been added to the paper. However, even with these liberal assumptions, the Honda Insight tests show that the system is even more efficient than the model is predicting during engine transients! This is like ly due to hybrid strategy differences.

7) Hybrid vehicles (pp45, 46): If th e "hybrid threshold" is taken to be higher (i.e. the control calls for somewhat more all-electric driving and some charging of the battery by the engine beyond that associated with regenerati on), I believe the cycle fuel use would be further reduced. By how much would depend on regeneration efficiency and other characteristics, in line with the comment at the top of p47. One is likely to want a more detailed model for hybrids than we have here, as more-diverse examples of the techno logy become available. See my comment (10). RESPONSE : True, but as the model becomes more and more complex, it evolves closer to a “design” model, and loses the relative simp licity that gives PERE its advantages.

8) Battery loss (long paragraph p47) and battery ratings paragraph p 47: Does this mean PERE tracks SOC over the cycle? On p 47, it says "this number does not affect the model" (for the cycles considered?). How great a variation in SOC occurs for the two vehicles and cycles considered here? Would the lowest SOC reached significantly affect battery efficiency? Would adding a moderate hill kill the SOC? Or are such issues not addressed in the current version of PERE? RESPONSE : Yes, one can track SOC on the spreadshee t, but since the battery model is so limited, the results have room for improvement . A simple battery model could be added in the future if needed.

9) Accessories, p49: As stated, big accessory lo ads, especially A/C, would make a lot of difference. Does EPA have a dynamometer test fo r the mpg of hybrid cars which are expected to have A/C? If so, how is it done?120 RESPONSE : This is beyond the scope of this repor t, though it is an important topic.

10) Big paragraph p52: There’s a significant difference with PERE over-predicting Civic Hybrid city mpg by 9% and under-predicting Prius city mpg by 12 or 18%. Can you speculate as to a possible specific explanation? See my comment (7). RESPONSE : As mentioned in the paper, and earlier, the Prius strategy is VERY different from the PERE hybrid strategy. The Pr ius is a series parallel hybri d and regenerates the engine during operation, PERE cannot do this currentl y. So the comparisons, should be taken with a grain of salt since these are essentially two different vehicles . The only things they have in common, are vehicle characteristics (weight, etc) and engine, motor sizing. The fact that the results differ should be of no great surprise . The Honda hybrids, however, are parallel hybrids. Even stil l, there are differences in th e strategies, since the PERE strategy is extremely simplified. Also, note in th e results that the Insi ght does not shut off the engine completely during most short idle s. The Civic is likely to have a similar strategy. This would account for some of the overprediction of fuel economy. This comment has been added to the paper.

11) bottom p64, top p65, sensitivity analysis, also p 98: This sensitivity analysis is valuable for anyone who studies the model with at least some care; but it is likel y to be misleading for others; because a few variables commonly differ from the assumed values by much more than 10%, while most others will differ by much less. Exam ples of highly variable variables are (1 – epsilon) where epsilon is transmi ssion efficiency, N/v, and perhaps k0 . RESPONSE : Good point, another statement of more vari ability has been added to the section.

12) pp 75-76 Has the accuracy of PERE been checked against measurement for both high and low speed/power driving, or only primarily for the standard city and highway cycle totals ? I would expect PERE to be adequately accurate for moderate and high speed/power, but of uncertain accuracy at low speed/power, where the modeled fuel rate is lower relative to that in the standard cycles and the physic al basis for the model, as is, is poorer. As shown here (and is known), the model, as is, is not r ealistic in coasting/brak ing modes; so one must be concerned if there are MOVES applications where coasting/braki ng is an important part of the whole of fuel consumption. RESPONSE : Aside from the two modal tests presented in the paper, there is little data for a systematic study during different modes of driv ing. As more data becomes available, the model can be improved.

MINOR ISSUES - Rotating mass term (p7). Jimenez' number seem s high for high gears. Again the issue is not important for general driving cycles, but may be significant for special cycles RESPONSE : Jimenez provides two references for th is in his thesis: Bauer, 1996 (Bosch Handbook) and Emmelman and Hucho, “Perfo rmance of Cars and Light Trucks. Aerodynamics of Road Vehicles ,” SAE,1998). However this value is not entirely consistent with other works I’ve referenced in the paper. Even when I changed this value significantly (more than 10%) the effect on overall fuel consumption was minimal.

- Frontal area approximation (p7). I ha ve used 0.83*height*width for cars.121 RESPONSE : I also subtract the clearance height, could that explain the discrepancy?

- Units for figures 4 and 9 (p12 & 23). I assume the units are the same as on p46. RESPONSE : The units are in the key box.

- It’s important to be clear w ith terminology on diesel efficiency (pp21-23). We are talking about fuel energy, not fuel volume. RESPONSE : I only deal with volumes when calculating fuel economy (at the end of all the calculations).

- Table 12, p33, * and ** are not defined. RESPONSE : This has been clarified.

- Fig. 16 caption says "red line" . What does that refer to? RESPONSE : This has been clarified.

- 1st paragraph p 44: I th ink it can be claimed that all the hyb rids listed use some regenerative braking. I think they don ’t all use electric motors for traction. RESPONSE : This has been clarified.

- p54: Does "body type" simply refer to determina tion of CDA, or are there other uses of this info? RESPONSE : This has been clarified.

- 11) Fig. 29, p56 (Insight). I must have missed wher e it says fuel is turned off during decels in PERE. The measured fuel rate in decels in the example here is substantial. RESPONSE : This has been clarified in the section describing hybrid strategy (fig 22). Indeed for the Insight, the vehicle is even more e fficient during accelerati ons than PERE would predict (even using a relatively gene rous lean-burn engine model).

- p57: What do you think is the reason for the diversion between calculation and measurement beyond 400s for Prius? RESPONSE: As the paper mentions, the model is not en tirely appropriate to use to model the Prius. This is because the Prius architectur e is significantly different from the PERE parallel hybrid architecture. For example, th e Prius recharges during driving, which is a strategy that PERE is unable to handle at the present. However, given these vast differences, the model seems to perform r easonably well on an aggregate level. Of course, further validations shoul d be performed on more drivi ng schedules (which will be done as more data is available).

- page 58: Ford gaseous H2 and probably soon also BMW liquid H2. RESPONSE : This has been added.

- p59 bottom: Say: vehicle traction applications. RESPONSE : This has been clarified.122 - p50: Fuel cell efficiencies ar e relatively high at low load (a lthough not at zero load), like a battery. What is the shape of the system effi ciency between 0 and 4 kW in Fig. 34? Nelson's estimate (p63) indeed shows "very low" efficiency up to 4 kW, but I wonder if that is general, or the result of some very particular assumptions Nelson made. RESPONSE : These low values are approximate.

Additional Marked up Comments (on paper) - mid p16 "curb" weight - mid p 42 1.20x0.40 = 0.48? - top p 44 You say Many of the hybrids.. Doesn't that mean all those mentioned? - p42 & 43 figure is repeated - mid p 52 Honda "Civic" Hybrid - fig 29 bot (p 56) not clear where SEMTECH line is in my copy - bot p 59 veh "traction" app - mid p 60 Fuel cell inefficiencies at low load are not obvious in Fig 34, but are in Fig 36 - 2nd line from bottom p64: What about transmision efficiency? - mid p 67 vehicles which have not yet been produced omit

comma - p 75 I may not have commented clearly enough in my review of
PERE that the model has not been adequately evaluated in the low speed cycles. - pp87&88 I appreciate the citations, which I did not kmow, for the Bishop and Kluger, and also for a couple of other papers. - p89 I dont think the Heywood book has this date. My copy is
1988. - p89 Nitkin is spelled wrong once. - p96 3rd line above the solid line Omit the redundant

"However".

RESPONSE: All of these comments have been addressed in the appropriate section.