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)

Draft
EPA420-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

VEHICLE MODEL .......................................................................................................................................................7

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

GASOLINE SPARK IGNITED INTERNAL COMBUSTION ENGINE ....................................................................................9

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

TRANSMISSIONS ......................................................................................................................................................14

Efficiency............................................................................................................................................................15

LIGHT DUTY TRANSMISSIONS .................................................................................................................................16

FUEL RATE ..............................................................................................................................................................17

SECTION IV – MOTORCYCLES ..........................................................................................................................17

MOTORCYCLE ROAD LOAD .....................................................................................................................................17

MOTORCYCLE ENGINES...........................................................................................................................................20

MOTORCYCLE TRANSMISSIONS ...............................................................................................................................22

MOTORCYCLE VALIDATION.....................................................................................................................................22

SECTION V – LIGHT DUTY DIESEL VEHICLES.............................................................................................23

LIGHT DUTY DIESEL VEHICLE ROAD LOADS...........................................................................................................23

LIGHT DUTY DIESEL ENGINES.................................................................................................................................24

DIESEL LIGHT DUTY TRANSMISSIONS .....................................................................................................................26

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

DATA.......................................................................................................................................................................26

HEAVY DUTY VEHICLES ROAD LOAD .....................................................................................................................28

ROAD LOAD COEFFICIENTS FOR HEAVY DUTY TRUCKS AND BUSES.......................................................................31

HEAVY DUTY DIESEL ENGINES ...............................................................................................................................32

HEAVY DUTY TRANSMISSIONS................................................................................................................................39

VALIDATION............................................................................................................................................................42

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

ADVANCED INTERNAL COMBUSTION ENGINE (ICE)................................................................................................44

HYBRID VEHICLES...................................................................................................................................................46

Strategy ..............................................................................................................................................................47

Motor/Generator/Inverter..................................................................................................................................48

Energy Storage Devices – Batteries, Ultracapacitors and Hydraulics..............................................................50

Vehicle Weight and other Specifications............................................................................................................51

Accessories.........................................................................................................................................................52

Model Calibration..............................................................................................................................................53

Fuel Economy Validation Results ......................................................................................................................54

Modal Validation Results...................................................................................................................................57

Electric Vehicles ................................................................................................................................................61

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

SECTION IX – HYDROGEN FUEL CELL VEHICLES .....................................................................................62

FUEL CELL AND HYBRID FUEL CELL VEHICLES ......................................................................................................62

Validation...........................................................................................................................................................65
2 

SECTION X - SENSITIVITY ..................................................................................................................................67

ROAD LOAD AND TRACK COEFFICIENTS .................................................................................................................69

COLD START............................................................................................................................................................71

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

HOLE FILLING USING PERE....................................................................................................................................73

Driving Cycles and Running PERE ...................................................................................................................78

Filling Advanced Technology Using PERE .......................................................................................................81

Advanced Technology Hole Filling 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....................................................................................................................................117
3 

Executive Summary 

This study proposes a modeling methodology for conventional and advanced technology 

vehicles including: 

• Conventional motorcycles 

• Conventional gasoline and diesel (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 (MOtor 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 fuel consumption from many of the advanced 

technologies in MOVES in addition to conventional vehicles. Criteria pollutant emissions will be 

modeled in a later version. If the user is knowledgeable of the vehicle parameters required in 

PERE, other technologies can be modeled that are not described in this report (e.g. alternative 

fuels, minimum/mild hybrids, etc.). 

MOVES is currently being integrated with GREET (developed at Argonne 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 projections or life cycle (energy/emissions) studies for mobile 

sources into the future. 

As the name implies, PERE uses physical principles to model propulsion systems in the vehicle. 

It is therefore built on a relatively strong foundation. It is based on a sound, yet elegantly simple 

model for the internal combustion engine. Modeling of hybrids 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 driving cycles defined by the user and outputs second-by-second 

fuel consumption rates. All of the processes are transparent to the user, which allows for easy 

updates. 

The model is validated to four production conventional gasoline (it has been validated to more in 

previous publications), a number of motorcycles, buses and heavy-duty trucks. Also validations 

were performed on five advanced (and hybrid) vehicles by comparing the PERE output to the 

rated fuel economy figures. The model performs well, in most cases the predictions are within 

10%, as the figures below show. This demonstrates the relative robustness of PERE to model a 

wide range of technologies. 
4 

0

1

2

3

4

5

6

7

palmsprings, 14L,

400hp@1800rpm

(truck a)

palmsprings, 14L,

400hp@1800rpm

(truck b)

palmsprings, 12.7

liters,

380hp@1800rpm

palmsprings,

10.8L,

330hp@1800rpm

palmsprings,

14.6L,

475hp@2100rpm

bus 1bus 2bus 3bus 4bus 5

fuel economy (mile/gallon)

PERE

measured

Figure ES1. Validation of PERE to heavy-duty 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 '04

FTP 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 vehicle. The model captures the fuel economy to 

within 5% of the measured value. 

The report also includes a sensitivity 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 projections. The model presently used for this purpose 

is MOBILE6. MOVES (MOtor Vehicle Emission Simulator) is the next generation model and 

will eventually replace MOBILE. MOVES is designed to be a data-driven model, so that the 

emissions rates are derived from direct measurements. 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 differently 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 technology 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”. The 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-wheel” fuel consumption also allows 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 developers and users. However, PERE will be made 

available to the public, when others may find 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 using physical principles. PERE is based on a number of models 

that exist in the literature (the references are peppered throughout this document). The model is 

developed for the following conventional technology types (light and heavy duty): gasoline, 

diesel, and motorcycle. Previous publications have already presented results for many light duty 

gasoline vehicles. PERE also models advanced technologies 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 possibilities for market penetration. The 

number of other advanced technology combinations that could exist are enormous. Such 

examples are hydraulic hybrids, series hybrids, plug-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 accommodate 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 engineering or product design. Thus it is designed to model 

a “typical” vehicle of technology type, rather than a specific vehicle. 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 expected to accurately capture fuel consumption of 

specific vehicles better than within 10% of measured values. 
7 

The basic mechanism of PERE involves calculating the road load energy required to move the 

vehicle mass along a driving trace, then distributing 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 are 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, our 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 made to justify the assumptions in each case. 

The report begins by describing conventional vehicles, both gasoline and diesel (light and heavy 

duty). It then goes on to briefly examine advanced engines. Hybrid vehicles are modeled and 

validated, followed by fuel cell vehicles. The report 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 (or 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 information 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 “backwards-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 based 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 VSP equation shown in numerous publications. 

ACTIVITY

INPUT V,A,

Road grade

Vehicle Param:

M, Cr, CdA

VSP or

Power Demand

Internal

Combustion Engine Fuel Consumption

Figure 1. Conventional internal combustion engine vehicle model flow. 
8 

The power demand (in Watts) is the brake (or tractive) power or, VSP*m: 

Equation 1 Pb = VSP*m = mv[a(1+g) + g*grade + gCR] + 0.5ρCDAFv3 

where: 

 v: is vehicle speed (assuming no headwind) in m/s (or mps) 

 a: is vehicle acceleration in m/s2

 g: is mass factor accounting for the rotational masses (~0.1) 

 g: is acceleration due to gravity (9.8 m/s2) 

 grade: is road grade 

 CR: is rolling resistance (~0.009) 

 ρ: is air density (~1.2 kg/m3) 

 CD: is aerodynamic drag coefficient (~0.3) 

 AF: is the frontal area in meters2 (~2 m2) 

 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 this term. The rolling resistance for radial tires, CR, 

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, temperature, ground surface type, and speed (though this 

effect is minor) [Bosch, 2000; Gillespie, 1992]. Heavy-duty trucks tend to have lower resistance. 

The aerodynamic drag, CD, varies according to the body shape, and is sometimes provided by the 

manufacturers. The frontal area, AF, 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 AF = (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 Pb = Av + Bv2

 + Cv3 + 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 rolling resistance terms in Equation 1. “B” tends to be small, and 

describes higher order rolling resistance factors in addition to mechanical rotating friction losses. 

The “C” term represents the air drag coefficient 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 discussed in greater detail in the 

sensitivity section, where PERE is run 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/mph2

) 

C = (0.55/50)*TRLHP(hp/mph3)

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

The power (or energy) determined from the road load can then be distributed, on a second-bysecond basis, throughout the powertrain. After engine/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) converts chemical into mechanical energy. This 

combustion process, as well as the losses inherent to it, is a critical element 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 authors. The formalism is described 

well in Ross [1997], which will be briefly reviewed here. 

The basis for the engine model lies in the linear relationship between brake power and fuel 

consumption, both represented in mean effective pressure (mep in bar) units. The power 

equivalent of fuel is: 

Equation 5 Pf = 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*Pf/(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 efficiency 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 compression ratio, fuel mixing, fuel type, cylinder 

temperature, valve timing, spark timing, combustion 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 2nd 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 below). k and fmep reflect the mechanical losses in 

the engine mainly from rubbing, pumping, and auxiliary load losses. Specifically, the rubbing 

losses can originate from valves, gears, piston rings, lubricant, piston, crankshaft, etc [Milington 

& Hartles, 1968]. The pumping losses are derived from the difference between the intake 

cylinder and ambient pressures. The throttle plate, and intake, exhaust and valve geometry on 

gasoline engines cause this loss in “volumetric efficiency” [Patton, et al., 1989]. As an engine 

“breathes” better, the pumping losses decrease and its volumetric efficiency increases. Thus an 

engine with 4 valves per cylinder is typically more fuel efficient than one with 2 valves. Also 

diesel engines lack throttle plates, so they have improved volumetric efficiency over gasoline 

engines. Throttling results in about 25% of the friction losses [Ross, 1997]. The product of 

mechanical efficiency (which we have not quantified 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 equivalent 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 below for a series of 10 different engines at many 

different operating conditions, where the wide-open throttle points have been omitted [Nam & 

Sorab, 2004]. According to the figure the indicated 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 load points were omitted, though there is still some 

curvature [Pachernegg, 1969]. Despite the limitations, the method is quite robust as the lack of 
11 

scatter in Figure 2 demonstrates. For the scale of modeling in this report, this estimate is quite 

sufficient. 

It has been found that the indicated 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 driving 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 efficiency if lean burn engines (or other advanced 

technologies) become more prevalent. 

y = 2.47x + 4.24

R2

 = 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 function of brake mep for 10 engines from 4 

different manufacturers, omitting wide-open throttle points [Nam & Sorab 2004]. 

It was mentioned earlier that “indicated 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 energy) divided by the y-axis value 

(input energy). For example, at bmep = 2 bar, the 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 increases 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 efficiency is the thermodynamic efficiency limit of the engine – 

here the peak total efficiency is 36%. The product of the indicated and mechanical efficiency is 

the overall engine efficiency. Thus the mechanical efficiency is dependent on load and engine 

speed, and the most efficient operating points are those with low engine speed and high load. 

This highlights the advantage that hybrids have over conventional 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 a 
12 

final overall vehicle (or “pump-to-wheel” efficiency, it is also 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 energy converted to useful 

work: 

Equation 10 fuelconsumed LHV

Pb tractivework

EnergyIn

EnergyOut Efficiency *( ) = = 

Though the scatter in Figure 2 is small, there is some systematic relationship, which can explain 

some of the variation. It is well known that the friction (or offset) is dependent on the engine 

speed. This is demonstrated in Figure 3. The base friction term can thus be modeled 

Equation 11 k = k0 + k1N 

where: k0 = 3.283 bar and k1 = 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 later section. The friction at idle is significantly higher (about 50%). 

Alternatively, one can interpret the efficiency being lower at idle - the end result for fuel 

consumption in the model is identical, though this may not be the case if studying and isolating 

the effect of new engine technologies at low engine speeds. There is a slight curvature evident in 

the figure and some references include higher order terms, but these are probably more necessary 

for higher revving engines [Yagi, et al., 1990]. 

Figure 3. Engine friction as a function of engine 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 helped accomplish this: overhead cam, multiple 

valves per cylinder, improved lubricants, variable valve timing, etc. Additionally, engine 

efficiency can be improved with “advanced” technologies such as lean burn gasoline (e.g., 

y = 0.00051x + 3.28232

R2

 = 0.68500

0

1

2

3

4

5

6

7

8

0 1000 2000 3000 4000 5000 6000

rpm

fuel mep at 0 load (bar)
13 

Honda HX), Atkinson cycle (as in Toyota Prius), Direct Injection Spark Ignition (DISI – no 

current model in US), Homogeneous Charge Compression Ignition (HCCI), or with variable 

compression ratio. Fuel economy can also be improved with variable displacement, cylinder 

deactivation, or adding a turbocharger with engine downsizing in the future. There may come a 

day when engines will be “variable everything”. 

Despite the many different models and manufacturers of gasoline engines, there is remarkable 

homogeneity in their performance characteristics (neglecting advanced engines). As a result, it is 

possible to develop “rules of thumb”, which give rough estimates, of engine 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 liter 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 torque/bmep relationships in Sandoval and Heywood 

[2000]. The equation for four stroke engines is: 

Equation 12. mep(kPa) = 4πτ/Vd

 Where: τ is torque in Newton meters 

 Vd 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 relationship is sufficient for our modeling needs. These 

peak curves are mainly used to determine transmission downshift points and for cut-point 

determination for hybrids (where engines may be significantly downsized), therefore the PERE 

output is typically not highly sensitive to the shape of the curves. In a future version of PERE, 

these peak torque curves may also define enrichment strategy, which is modeled in a different 

fashion for older vehicles [Nam, 2003]. 

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

Equation 13. bmep = a0 + a1N + a2N2

 + a3N3 + a4N4 + a5N5 + a6N6 + a7N7 

 Where 

 N is engine speed in rps 

a0 = -1200.51 

a1 = 298.934 

a2 = -17.5860 

a3 = 0.563420 

a4 = -.0104629 

a5 = 0.000113228 

a6 = -6.64513e-7 

a7 = 1.63097e-9 
14 

0

50

100

150

200

250

0 1000 2000 3000 4000 5000 6000 7000

rpm

torque

0

20

40

60

80

100

120

Power

torque (N-m)

power (kW)

bmep

0

200

400

600

800

1000

1200

1400

0 1000 2000 3000 4000 5000 6000 7000

rpm

bmep (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 calculate total vehicle efficiency is transmission and final drive 

efficiency. All vehicle powerplants must connect to the wheels via a transmission. Sometimes 

this is as simple as a single gear reduction (as in some fuel cell and electric vehicles). More often 

though, there are multiple gears to take advantage 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 sensitive to this sub-model as long as ‘sensible’ parameters are 

employed. 

 

The engine speed is: 

Equation 14 N = (N/v)top*(60rps/rpm)*(g/gtop)*v 
15 

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/gtop) 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 engines tend to rev higher than larger engines. One 

could also calculate engine speed from final drive ratio and tire radius. 

In order to meet the power demand 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 for 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 vehicle is in. The efficiencies used in MOVES are shown on Table 1 

based on the published work on Bishop and Kluger [1996]. These values were based on an 

average over the combined EPA city/highway driving cycles (excluding idle). The efficiencies 

are lower in 1st 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 efficiencies 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, they could be easily applied. Efficiency can be a 

modeled as a linear function of engine speed [Park 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 transmissions (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 vehicles only require a single gear due to the large 

operating range of motors. Single gear 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 applications, 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 those models much more sensitive to transmission parameters. In 

PERE, the fuel consumption is defined by road load power and engine friction (which are more 

or less independent). Therefore the 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 some generalizations. For 

MOVES, PERE assumes all light duty vehicles to have 4 speed automatic transmission with 

overdrive (or a 5 speed automatic transmission). The 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 driving. The shift points and 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 section), 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 usually 1 by our definition of N/v 
17 

Fuel Rate 

If transmission speed and efficiency is added to Equation 5 through Equation 8, we would obtain 

the fuel rate equation used in PERE [Nam, 2003]: 

Equation 15 FR = φ [kNV + (Pb/ηt + Pacc)/η] / LHV 

or 

Equation 16 FR = φ [fmepNV + Pb/ηt + Pacc] / ηLHV 

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 

ηt: is a transmission and final drive efficiency 

η: is a measure of the engine indicated efficiency 

Pacc: 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 equivalence 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 vehicles, which tend to go more into enrichment 

than newer ones [Nam, 2003]. Lean burn is modeled 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 road-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 account rolling resistance and aerodynamic drag, 

Equation 17. 2 F = A + Cv

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, CdAF, and has units of Newtons/(meters/second)2. A and C are 

further parameterized in terms of the inertial mass, M, of the vehicle and driver, i.e., 
18 

Equation 18. 

C C A c c M

A a a M

air d F 0 1

0 1

2

1  = + 

 





=

= +

ρ

where a0, a1, c0, and c1 are constants and ρair is the density of air (~1.2 kilograms/meters3), Cd is 

the aerodynamic drag coefficient, and ΑF is the frontal area of the motorcycle in meters2. 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 parameterization of the mass dependence of motorcycle 

tire rolling resistance, but differing parameters. The UN’s parameterization is 

Equation 19. AUN = (0.088m/s2)M 

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

Equation 20. Cr (=A/Mg) = 0.009. 

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

parameterization uses 

Equation 21. AEPA = (0.0874m/s2)M - (8.79Newtons) 

which yields a static rolling resistance coefficient, Cr, of 

Equation 22. Cr (= AEPA/Mg) = (0.0874m/s2)/(9.8m/s2) - (8.79Newtons)/(M9.8m/s2) 

 ≅ 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 parameterized 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(=MgCr). 

Rolling loss force parameterizations, A (= MgCr) = a0 + a1M 

Organization / Emissions Certification a0 a1 Cr (or A/Mg) 

Procedure Newtons meters/second2

 - 

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 both 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, Cd, for motorcycles 

can vary between 0.4 to 0.6. Frontal areas, AF, of the motorcycle and driver range between 0.4 to 

1 m2 [Arakai et al., 2001]. Hence, the majority of motorcycles have aerodynamic loss 

coefficients, CdAF, which lie between about 0.2 m2

 and 0.6 m2. 

Both the EPA and the UN have established aerodynamic losses for different motorcycles based 

on the mass of the vehicle and the driver. The UN parameterization is 

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

which corresponds to an aerodynamic drag coefficient of CdAF { 0.50m2 when 

M=227kg({500lbs). The EPA’s parameterization is similar except it has two mass ranges, i.e., 

Equation 24. 

C N m s M N kg m s for M kg

and

C N m s M N kg m s for M kg

EPA

EPA

0.376 /( / ) 0.804 10 [ / ( / ) ] 450

0.254 /( / ) 0.35 10 [ / ( / ) ] 450

2 4 2

2 4 2

= + × × ⋅ >

= + × × ⋅ ≤

−

−

In Figure 5, the graphs of the aerodynamic loss coefficient as a function of mass for both the 

EPA and UN parameters are displayed. 

Figure 5. USEPA and UN A and C road 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 motorcycle road load is employed. However, 

the US standard driver weight of 80kg is assumed. 

Motorcycle Engines 

Current literature states that the typical (4-stroke) motorcycle engine thermal efficiency ranges 

between 32% to 40% [Yamamoto, 1999]. PERE will use the same efficiency that has been used 

for other SI gasoline engines, 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, motorcycle engine friction has typically been 

parameterized as a second order function of engine speed dependent on the engine bore, stroke, 

and mean equivalent crank shaft diameter. Explicitly, the functional form in terms of the 

frictional mean effective pressure or fmep, is 

Equation 25. ( ) ( ) ' 2

2

'

0

2

1

C C N

B

S D fmep ⋅ + ⋅ ⋅ =
21 

where S is the stroke, B is the bore and D is the mean equivalent crank shaft diameter (average 

diameter of the crankshaft journals and crankshaft pins), ' C0 is a constant proportional to the 

lubricating oil viscosity, and ' C2 is a constant due to engine pumping losses. 

Equation 26 ( ) ( ) '

2

2

1

2

'

0

2

1

0 C

B

S D C and C

B

S D C ⋅ ⋅ ⋅ = ⋅ =

From the literature cited above average coefficients, for over 20 motorcycle engines are listed in 

Table 4. Also listed are the standard deviations representing vehicle-to-vehicle variability (not 

measurement errors). Figure 7 illustrates the motorcycle engine friction mep dependence on 

engine speed compared to light duty spark ignition 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. 

C0

(MPa) 

C0 standard 

deviation 

(MPa / rpm2

) 

C2

(MPa/rpm2

) 

C2 standard 

deviation 

(MPa / rpm2

) 

0.152 0.031 1.52E-09 6.23E-10 

Figure 7. Comparison of engine speed dependence of motorcycle and recent light duty 

passenger vehicle engine friction. 
22 

Finally, the accessory loading for motorcycles 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. Transmission modeling parameters for motorcycles 

were derived from a number of sources. The N/v is determined from the UN Document: 

“Worldwide Harmonised Motorcycle Emissions Certification 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 heavier 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 database. The EPA maintains a database (The 

Certification and Fuel Economy Information System or CFEIS, e.g., 

http://www.epa.gov/otaq/crttst.htm) which contains (among other vehicle types) motorcycle 

emissions certification information. It covers, as of this writing, motorcycles with model years 

ranging from 1995 to 2004. Included in the database are the vehicle model name, model year, 

weight, engine displacement, maximum 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 CO2 and emissions 

reported by the manufacturers, however these figures were not confirmed by the EPA. 

Figure 8 shows the scatter plot of the fuel economy comparison, with the 1:1 line included. On 

average, the lighter motorcycles seem to be captured well. However, it is evident from the plot, 

that there is a systematic underprediction of fuel economy for the 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 motorcycles have significantly lower road loads than 

passenger cars, their fuel consumption is more sensitive to transmission (and engine friction 

losses). For example, the Harley Davidson motorcycles tend to have lower N/v values, lower 

peak RPM, and lower shift points, thus changing their fuel consumption significantly. Using 

more appropriate transmission values, PERE captured the 39 mpg rating of a heavier Harley 

Davidson (2004 FLHR Road King, 723lb, 1.44L, port fuel injected, N/v = 29, top gear ratio = 

3.76, assuming a shift point ~3500RPM) [Harley-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 similar to their gasoline counterparts 

in PERE. The main exception to this is in the 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 various vehicles in the American and European 

markets. 

Based on the table, we note that diesel engines tend to be heavier than their gasoline counterparts 

(to accommodate higher pressures), thus they often need to be downsized in order to fit into the 

same frame. According to the ratios of these same vehicles, the turbocharged diesel engines 

should be approximately 5% smaller. Due to the 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 weight 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 comparison 
24 

model max torque max power displace

ment (l)

curb weight 

(kg) Ford Focus 

Diesel

200N-m@ 

2000rpm

66kW@ 

4000rpm

1.753 1258

Ford Focus 

Gasoline

160Nm@ 

4400rpm

85kW@ 

5500rpm

1.8 1200

Ford Mondeo 

Diesel

245N-m@ 

1900rpm

85kW@ 

4000rpm

1.998 1498

Ford Mondeo 

Gasoline

190Nm@ 

4500rpm

107kW@ 

6000rpm

1.999 1376

peugot 607 

diesel

250N-m@ 

1750rpm

79kW@ 

4000rpm

1.997 1500

peugot 607 

gasoline

217Nm@ 

3900rpm

116kW@ 

5650rpm

2.231 1455

VW Beetle 

Diesel

210N-m@ 

1900rpm

66kW@ 

3750rpm

1.896 1270

VW Beetle 

Gasoline

172Nm@ 

3200rpm

85kW@ 

5400rpm

1.984 1222

VW Jetta 

Diesel

240Nm@ 

1800rpm

75kW@ 

4000rpm 1.9 1347

VW Jetta 

Gasoline

165Nm@ 

2600rpm

86kW@ 

5200rpm 2.0 1331

* All sources are either from manufacturer website, Car&Driver, or Road&Track 

Light Duty Diesel Engines 

Diesel engines are different from gasoline engines 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 for compression ignition significantly improves the 

thermal efficiency compared to a gasoline spark ignition engine. The engines can also combust 

fuel in an environment that is lean of stoichiometry. 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 mixtures result in higher fuel efficiency. Modern 

diesel engines inject the fuel directly in the chamber with electronically controlled bursts, which 

increases the pumping efficiency (decreases 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 the lack of aftertreatment system, and the PM 

emissions is due to the combustion mechanism with fuel droplets. 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 will in turn, have a small negative effect on fuel economy. 

In PERE, Diesel indicated efficiency 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 (k0 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, the 
25 

pumping losses are significantly lower, but the piston/crank assembly as well as auxiliary load 

(mainly from high pressure fuel injector) losses are higher. This hints that the speed dependent 

term (k1) should be higher for diesels, or that higher order terms in Equation 11 may be 

necessary. For the efficiency and friction parameters in PERE, we use the heavy-duty friction 

results presented in the next section. These results 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 roughly 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 engines. 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 (of 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

rpm

bmep (Pa)

bmep

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

In PERE, this generic bmep curve is modeled by a simple 7th order polynomial fit: 

Equation 27. bmep = a0 + a1N + a2N2 + a3N3 + a4N4 + a5N5 + a6N6 + a7N7 

 Where 

 N is engine speed in rps 

a0 = -19950.8 

a1 = 3479.90 

a2 = -231.809 

a3 = 8.25775 

a4 = -0.169919 

a5 = 0.00202259 

a6 = -1.2921e-5 

a7 = 3.42208e-8 
26 

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 diesels employ turbo chargers. 

Unfortunately, the variety of turbocharger sizes makes generating peak torque and power curves, 

given only engine displacement, a difficult exercise. This would matter more if we were 

constructing the model using a performance basis (e.g. 0-60 mph time), rather than a fuel 

economy basis. 

Diesel Light Duty Transmissions 

The transmission model for light duty diesel vehicles 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 values from an assortment of 

vehicles, which have a diesel option (US and European). 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 trucks, as well as some nonroad diesel engines. The model validation is also presented. 

Data 

Emissions, global positioning (GPS), fuel, and engine control module data were taken from 15 

in-use Ann Arbor, MI city transit buses [Ensfield, 2002] measured by SENSORS and EPA, as 

well as from 12 heavy duty diesel tractor-trailers measured on-road by the University of 

California Riverside (CE-CERT) [Barth et al., 2004]. 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 diesel 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 dynamometers. Table 6 below summarizes the vehicle and 

engine specifics from which emissions were measured 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 specifications 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 6 
27 

AATA 

bus (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 engineered trips including vehicle coast downs. 

This study used four of these trips, the UDDS, the 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-CERT heavy duty diesel tractor-trailer trip 

between Riverside, CA and Palm Springs, CA. 

Measurements from the AATA buses were collected on bus routes used by the Ann Arbor 

Transit Authority. An example of a speed-time trace for one of the bus trips is shown in Figure 

11. 
28 

Figure 11. Speed time trace for the first 1000 seconds of AATA bus 1 in-use emissions 

testing. The trips in the on-board measurements 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 engines were run at different 

loads and RPM points throughout the operating range of the test engine. Table 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 the respective references. 

Table 7. The non-road engine tests. The percentages 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 Percent of the Maximum Rated Engine Load 

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 heavy-duty vehicles include tire rolling and 

aerodynamic losses. Coefficients for both of these effects are developed from a relatively 
29 

comprehensive study in terms of the range of vehicles that are considered [Petrushov, 1997]. In 

particular, the vehicles range from passenger cars to heavy-duty tractor-trailers 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; USDOT, 1977; Younglove, 2003]. However, the 

aerodynamic loss coefficients (CdAF) 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 resistance and aerodynamic drag coefficients through the 

speed dependence of the road load equation. Because 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 energy 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 into two classes according to length and the third 

group included all long-haul tractor-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 drag coefficients along with a standard deviation, 

which includes the inter-vehicle differences and the reported measurement errors. Also included 

in the table for comparison with the U.S. fleet, are values of aerodynamic drag coefficients from 

recent literature [USEPA, 2002; Andrei, 2001] and values determined in this work from the CECERT truck data. Figure 12 below illustrates the speed dependence of the aerodynamic drag 

coefficients listed in the table. These comparisons reveal that the tractor-trailer numbers are 

about 35% smaller than those typical values reported in the U.S. fleet (Table 8). The frontal areas 

of the tractor-trailers in the Petrushov work are on average about 9 m2

 whereas in the U.S. the 

frontal areas are 10 to 12m2. Also, values of CD from the U.S. fleet can vary between 0.5 and 0.8. 

Table 8. Comparison of aerodynamic drag coefficients. 

source vehicle description CDA (m2) standard deviation (m2) 

CE-CERT from coast 

downs tractor-trailer combo 7.2 4.5 (63%) 

Petrushov, SAE 970408 tractor-trailer 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 CDA (m2

) 

(frontal area A=10m2

) 

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 are developed from the Petrushov work. In contrast 

to the light duty tire rolling loss, Petrushov includes the second order (in speed) rolling resistance 

force term (3rd order in power) which is accounted for in the “C” term of the road load Equation 

3. Thus, the following relationship can be determined between the physical and the A, B, C terms 

from Equation 1 and Equation 3: 

Equation 28. 

C mg

C A C

B

A C mg

R

d r air

R

2

0

2

0.0

= +

=

=

ρ

Where the CR0 and the CR2 are the zero and second order (in speed) rolling resistance force 

terms. Figure 12 displays the speed dependence 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 dependence 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 aerodynamic effects overcome the rolling resistance 
31 

effects. The agreement with University of Michigan [1984] work is within the 20% measurement 

and vehicle-to-vehicle variability. 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 about 35%) lower than the rolling 

resistance losses developed from the Petrushov work. 

Figure 13. Comparison of rolling resistance force coefficients from existing literature and 

from values determined using speed-time traces of CE-CERT truck coast downs. 

Finally, Table 9 lists the form of the coefficients that are developed from Petrushov as a function 

of vehicle mass and according to the three heavy duty truck and the single bus categories 

described above. The three parameters are listed as the typical road load A, B, and C coefficients. 

The four different categories are two single unit delivery categories, the tractor-trailer category, 

and the bus category. The two single unit delivery 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 relatively smaller than the CE-CERT trucks, the 

aerodynamic drag terms were similar and within statistical and measurement errors.) 

Road Load Coefficients for Heavy 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) 2204.6

0.0996M

2204.6

0.0875M

2204.6

0.0661M

2204.6

0.0643M

B 

(kW*s2

/m2) 0 0 0 0 

C 

(kW*s3/m3) 2204.6

5 5.22 10 1.47 M − × +

2204.6

5 5.90 10 1.93 M − × +

2204.6

5 4.21 10 2.89 M − × +

2204.6

5 5.06 10 3.22 M − × +
32 

In summary, these tire rolling loss road load parameters 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 variability 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 reported values from the U.S. fleet. For example, the 

values for an 80,000lb tractor-trailers are about 35% lower, with a CD of 0.6 and a frontal area of 

12.5 m2 [McCallen, et al., 1999]. The Petrushov aerodynamic 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-duty diesel engines studied 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, engine speed, and engine load. The non-road engine 

measurements were made on an engine dynamometer [Fritz & Starr, 1998], the in-use / on-road 

measurements for the CE-CERT trucks, and the AATA buses were taken from the engine control 

module (ECM) with portable emissions measurement 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 buses and the CE-CERT trucks had fuel flow 

measurements from which the fuel mean effective pressure could be determined: 

Equation 29. fuel mep = nR*(1000/100)[bar/MPa]*FR*LHV/VdN 

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, Vd, is in units of m3

, and nR is 2 for four 

stroke engines. The factor of 1000/100 is a conversion factor from MPa to bar as indicated 

within the boxed parentheses. 

Brake mean effective pressure or bmep is determined 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. V N

load P N bmep

d

( )

100

% max ×



 



 =

where %load is the measured percent of the maximum engine load, Vd is the engine 

displacement, N is the measured engine speed, and Pmax(N) is the maximum engine load [and 
33 

includes volumetric efficiency (Ensfield, 2002)] at the current engine speed, N, determined from 

manufacturer specified engine 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 truck 1 on-road measurements. The CE-CERT truck 

plot includes only those points where the vehicle accleration is greater than 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-engine 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 results for indicated engine efficiency and the fit 

statistics. The non-road engine efficiencies range between 39% and 46% with an average value 

of 43% and a standard deviation of about 2%. These engines range in size from 0.2 liters to 34.5 

liters (and were from 7 different engine manufacturers). 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 dynamometer based measurements of fuel 

consumption and brake mean effective pressure. 
35 

Table 10. Engine efficiency results along with the linear fit statistics for the diesel non-road 

engines. 

engine 

displacement 

(liters) 

k (bar) k standard 

error (bar) 1/η

1/η

standard 

error 

R2 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 effective 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 uses 0.48 for all heavy-duty on-road diesel 

applications. Table 11 below lists the values for each of the individual trucks and a single 

number for all of the buses. Some of the linear fit statistics are included in the table. 
36 

A summary of the results are shown in Table 12. The on-road diesel trucks are in agreement with 

the smaller passenger car results of Wu and Ross [1997], who looked at four light duty cars with 

engine sizes ranging from 1 to 2.5 liters. R2

 of the fits are typically at or better than 0.9 and the 

standard errors in the slope parameter of the linear fits are around 20% of the fit value. The nonroad diesel engine efficiencies are typically about 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 (including 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

R2 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 indicated 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 

*R2 of the fits are typically at or better than 0.9 and 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 determine the relationship between engine friction and 

engine speed (Equation 11). A novel method of doing 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 speed. On-road/in-use based 

measurements don’t yield such ideal conditions. Therefore, 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 frictional 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 bmep’s were limited by using positive vehicle 

accelerations near zero, e.g., large non-zero positive accelerations may be where transmission 

shifts typically occur and may add step-wise changes in engine 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 engine efficiencies should be relatively constant. 

However, the fuel mep-intercepts should yield 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 relatively constant values of engine 

efficiency as well as the engine speed dependence of engine friction. When these values are fit to 

the (average value of the interval) engine speed, it yields the complete fuel mep – bmep 

dependence for the engine under consideration. 

Figure 16. A series of figures, showing fuel mep 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 engine friction. The usable data are from (9 

of 12 usable) trucks on four drive cycles. (Note that the units of k(N) in this table are in bar 

and the units of engine speed are in rpm whereas 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) 

R2

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) = k1 N + k0

0.54 0.32 1647 +/- 28 0.89 k0 (bar) 1.09 

3.08 0.33 1747 +/- 29 0.85 

2.55 0.46 1842 +/- 28 0.86 

k0 standard error 

(bar) 1.6 

3.7 1.1 1936 +/- 30 0.63 k1 (bar/rpm) 0.00116 

4.94 1.7 2050 +/- 30 0.83 

5.27 1.4 2135 +/- 21 0.84 

k1 standard error 

(bar/rpm) 0.0009 

2.68 1.1 2228 +/- 14 0.84 R2 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 engines). The comparison of this study’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 present study’s heavy duty engines. The fmep formula in Wu and Ross may not 

extrapolate accurately to large engine sizes (due to their limited data set). However, neither this 

work nor the Wu and Ross work are able to discriminate the second order effects of mean piston 

speed as was done by Millington and Hartles. These results also demonstrate the improvements 

in engine friction, which have occurred in the last thirty years. 

Table 14. CE-CERT truck engine friction determination from on-road measurements [of 9 

trucks]. *Values are standard deviations in the averages. The actual uncertainties are 

larger. 

Acceleration range 

(mph/s) 

k0 

(kPa) 

k1 

(kPa/rps) 

k2 

(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 0.83 
39 

Figure 17. Engine friction results of this work compared 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 differ, but the algorithms 

and efficiencies are assumed to be identical. 

The on-road measurements of engine and vehicle 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 speed-gear distributions will be inherently 

different for different vehicles classes, a relationship between transmission shift points and 

vehicle speeds are deduced for different vehicle 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 downshifting (during accelerations). 

Figure 18 shows an example of the speed distributions 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. Although 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 vehicle speed to engine speed ratios 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 standard 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 standard deviations determined from the 

distributions depicted in vehicle speed - engine speed ratio graph. 

The transmission parameters chosen for the medium duty 6-speed automatic, and the heavy-duty 

12 speed manual transmissions modeled in PERE are displayed in the following table. 

Table 15. Transmission parameters for medium 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 manufacturer specified engine power for a given engine 

speed. A downshift occurs whenever the vehicle 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/vehicle combinations, an average engine maximum 

torque/power curve is used. A scalable 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 validation of these heavy duty 

algorithms used the manufacturer listed maximum engine 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 0.86
42 

Figure 20. Scalable torque determined 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 vehicle 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, 

bmep N m

V

n bmep R

= ± ⋅

=

*

max

max

max

1648 440

2π τ

This method is not used for MOVES rates, but was used for an earlier 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 compared 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 considered 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, driving cycles with average speeds 
43 

less than 40 mph had calculated values 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 trips. 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 vehicles, 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 incremental cost of hybrid vehicles is expected to 

decrease as volumes increase, and as public acceptance increases. There are also other types of 

advanced vehicles, such as clean 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” technology is defined as a vehicle (or component) that is improved 

over those in most vehicles currently. Advanced ICE vehicles might employ one or more nonstandard engine or driveline technologies with superior thermal, and/or driveline efficiencies, 

which lead to better fuel economy and performance. Examples include: lean-burn gasoline 

engines, variable displacement, direct injection gasoline, and continuously variable transmission 

(CVT). 

In order to model future vehicles, it is educational to study current technologies comparing them 

with their predecessors. Two areas that have seen marked improvement in the past, are engine 

friction and power. The former likely leading to the latter. Typically, 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 latter 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 also 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 friction reduction projections have also been added 

to the figure, based on the research of Sandoval and Heywood [2003] and Nam and Sorab [2004] 

at a rate of approximately 10% per decade. Advanced 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 k0 (Equation 11) in the 

following equation. k1 is assumed to remain constant over time. The trends for diesel are less 

well known, but it is assumed to follow the same progression as gasoline. 

0

50

100

150

200

250

1990 2000 2010 2020 2030

Model Year

fmep (kPa), peak 

Torque (Nm)

0

10

20

30

40

50

60

70

peak Power (kW)

fmep (kPa) peak torque (Nm) peak power (kW)
45 

Equation 31. k0 (kJ/L) = a0 + a1Y + a2Y2

 Where Y is year and 

 

 

The current version of PERE does not currently change peak torque (or bmep) curves with model 

year, due to the relative lack of sensitivity of the model to this term. 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 conceivable that production gasoline engines can have 

improved indicated efficiencies. This could be accomplished with the trend of “variable 

everything”. The Atkinson cycle engine (as in the current Toyota Prius) is a good example of 

variable valve timing being used to increase efficiency at the cost of power. The lean burn 

engines of Honda (currently installed in the Civic HX, Hybrid, and Insight models) improve 

efficiency by burning gasoline under lean conditions at low loads. 

The Honda Lean burn engine parameters are determined 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 friction by 8%. PERE models this with an 

indicated efficiency of ~0.48, with an 8% friction improvement. During higher engine speeds, 

the engine reverts to stoichiometric operation, where it is still more efficient 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 already 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 engines, until the rpm cut point is reached [Heywood, 

1988]. The peak torque values are decreased correspondingly. Beyond that point, it behaves as a 

conventional engine. 

Gasoline Direct Injection (GDI) or Homogeneously Charged Compression Ignition (HCCI) 

engines are also a promising alternative, though these are still mainly laboratory engines. In the 

near future, there may be engines with variable displacement, variable compression ratio, 

cylinder deactivation, etc. coming on to the scene. Moreover the current move toward 42 Volt 

power systems on the vehicle will allow for Integrated Starter Generator systems to start and stop 

the car during idle. This has been referred to as “minimal” or “mild” hybrid. 

The specific technologies described above are for PERE validation purposes only. For the 

“generic” advanced internal combustion (AIC) engine vehicle in MOVES, PERE uses target 

coefficients, rather than choosing a suite of specific technologies. These target values are 

 Gas Diesel 

a0 7.1505E+01 3.5205E+01 

a1 -6.8667E-02 -3.3833E-02 

a2 1.6498E-05 8.1286E-06 
46 

assumed to be a 10% improvement in indicated efficiency (0.44), and engine 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 design, it is possible that tire 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 aerodynamic resistance and vehicle frontal area of 

vehicles have room for improvement, but here, form tends to follow function, or design. Current 

cars have coefficients of drag approximately 0.30 - 0.35 (Toyota Camry Cd = 0.30). Vans and 

SUVs have Cd ~ 0.35 – 0.40 and pickups range from 0.40 – 0.45 [Gillespie, 1992]. The 

exceptional 2004 Toyota Prius has a Cd of 0.26, and the Honda Insight has the best in class 

coefficient of 0.25. 

Hybrid Vehicles 

Currently the most successful method implemented commercially to improve the overall 

efficiency of vehicles is to hybridize their drivetrains, by adding an electric propulsion path to 

the wheels. 

There are a number of electric hybrid 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 various kinds within the next few years. 

There are several different kinds of hybrid vehicles under development. All share the common 

trait that there are two power sources that drive the vehicle forward during different operating 

modes. Most methods hybridize an internal combustion engine with an energy storage device 

such as a battery, ultracapacitor, or even hydraulic pump (fuel cell hybrids will be discussed in 

the next section). The hybrid configuration is usually either series, parallel, or some combination 

of the two. Series hybrids run off electric power only, and the batteries are recharged by the 

engine, which can run (continuously) in an efficient 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 the battery. Parallel hybrids run the 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 compared to the series configuration is that the 

system requires more sophisticated strategy, packaging and transmission. Some 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 and Yukon line [GM, 2004]. Ford released a full 

hybrid version of the Escape compact SUV in 2004. Honda is selling a hybrid version of the 
47 

Accord in 2004, which will have Variable Cylinder Management (VCM) in its V6 engine 

[Honda, 2004]. Toyota announced the Highlander hybrid in 2005, while the Lexus 400 hybrid is 

expected in 2004 [Toyota, 2004]. All of the hybrids mentioned above are in (mostly) parallel 

configuration and store secondary energy in a battery. They also use “advanced” gasoline 

engines. All of these hybrids recharge the batteries by employing the motor to recapture energy 

from the brakes, however, they do not all use the motor for traction. 

With the promise of hybrids hitting many of the markets segments in the near future, it is 

imperative that any modeling of future vehicles include this class of vehicle. There have been a 

number of models developed, which captures the behavior of hybrid vehicles. ADVISOR 

(developed at NREL) is widely used in the research community [Kelly, 2001]. PSAT is another 

model developed at Argonne National Laboratories [Rousseau et al., 2004]. They both model 

conventional as well as hybrid and fuel cell vehicles. 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. Namely, the intensive data and effort required to 

calibrate the model is prohibitive for trying to model broad sections of the fleet required. It was 

thus necessary to adjust the approach so that a model could be run on the PERE spreadsheet. 

From there, it may either be run in spreadsheet mode, or programmed directly to link with 

MOVES in the future. The basic framework for PERE is similar to ADVISOR though: starting 

with a driving cycle input, calculate the road loads, then distribute the power and losses to the 

various energy conversion and storage media. Many other models exist as well, but the hybrid 

architecture in PERE is roughly based on the simple control logic described in Weiss et al., 

[2000] at MIT. 

Strategy 

Figure 22 shows the PERE flow chart for the parallel 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 than the hybrid threshold (2kW in the MIT 

case), the car runs on battery alone. Therefore, the 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 battery assists or provides the needed boost. When 

braking, part of the energy is recaptured into the 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 charge on the battery is sustained over the course of the driving cycle 

input, i.e. the battery ends the test with the same charge with which it started. In the validation, 

charge is conserved over the Federal Test Procedure driving cycle (city and highway). Other 

differences to the MIT model will be described below. The parallel 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 (gasoline/battery) flow chart. 

Actual hybrid strategies can get much more sophisticated, 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 and is especially appropriate for stop and go driving. 

However, the PERE methodology takes the basic approach 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 energy 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 not include a cold-start module. Cold start factors 

will be inserted by MOVES, not PERE. Hybrid vehicle 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 discussed in greater detail in the 

Sensitivity section. 

Hybrid threshold was chosen in order to give close to net zero state of charge over the driving 

cycle input (FTP city and HWY). This loosely simulates a “charge sustaining” strategy (‘pseudocharge sustaining strategy’). For the hybrids modeled in this report, this threshold fell between 2 

and 4 kW. Values are in Appendix B. This strategy dictates that the batteries are only being 

recharged through regenerative braking, and not the engine. This can still result in a charge 

sustaining strategy if the batteries are not used excessively for traction. 

Unfortunately, MOVES cannot at this time accommodate hybrid vehicles with a series element. 

This is due to the design of VSP (vehicle specific power) based emissions in the model, which 

relates road load causally to fuel consumption. Series hybrids are not strictly “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 and 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 PERE 
49 

the motor operates both forwards and backwards at different times depending on the driving, 

with different losses. Weiss et al. [2000] estimates 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 improvements in motor system efficiencies. The peak 

motor efficiency (alone) is higher, greater than 90% for a permanent magnet brushless DC 

motor, though it depends on the size of the motor [Laramie & Dicks, p. 279 2000, Ogawa, et al., 

2003]. However, the MIT is a cycle average and value includes other 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 Figure 23. It is scalable with peak power [Weiss 

et al., 2000]. In the present version of PERE, the motor in the model is connected directly to the 

wheels (with a single gear step), bypassing the multi-step transmission (and thus the losses). 

However, it is more likely that real-world hybrid motors will require 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 PERE, however, the overall 

system losses would likely be similar. 

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

In PERE, the peak motor torque is scaled by power rating (calibrated to 60kW) according to the 

following polynomial: 

Equation 32. T (Nm) = Pm(a0 + a1N + a2N2

 + a3N3 + a4N4 + a5N5 + a6N6 + a7N7)/60 

Where 

 Pm is motor power rating (kW) 

a0 = 196.26 

a1 = -1.2542 

a2 = 0.085004 

a3 = -5.1976e-4 

a4 = -4.4345e-5 

60 kW Motor

0

40

80

120

160

200

240

0 2000 4000 6000 8000 10000

RPM

Torque (Nm)

-10

5

20

35

50

65

80

Power (kW)

Torque (Nm)

Power (kW)
50 

a5 = 8.1616e-7 

a6 = -5.2169e-9 

a7 = 1.1566e-11 

For the sake of nomenclature, we define the terms “moderate” and “full” hybrid. There is no 

standard definition in the literature since there are no clear demarcations, only a continuous 

spectrum of possibilities. In one reference, the separating value 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 vehicle 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 definition 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 battery (or ultracapacitor) that can manage the current flows in 

and out of it. The effect of hybridization naturally depends on the hardware, and what functions 

are built in, more so than the ratio of any two numbers. 

Energy Storage Devices – Batteries, 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 withstand many charge/discharge cycles, while being 

relatively affordable. Compared to a conventional vehicle, however, batteries 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 vehicle (described below). In the future (when the 

prices may drop), lithium ion batteries may significantly 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 efficiency. To be more accurate, this efficiency really 

depends many factors, including the state of charge of the battery, temperature, history etc. but is 

approximated as constant. During brake recharge, 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 the front wheels (not the rear). Thus up to 

approximately 75% of the braking energy is available 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 integrated over each second of the driving cycle, and 

then the discharge (positive) and recharge (negative) 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 threshold (manually) until the state of charge is close to zero for the 

given driving cycle. A more sophisticated charge sustaining strategy would require coding of the 

strategy and is considered for a future version of PERE. A more accurate battery model would 

include voltage/SOC/temperature (or similar) plots, and the spreadsheet would need to ensure 

that the battery can handle transients. However, 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). For the full hybrid, the battery is assumed to be 
51 

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 generated 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 changes the model only slightly. The discharge and 

recharge efficiencies may be different, as would its ability to recapture energy from higher power 

braking events. 

Hydraulic hybrids combine internal combustion engines with an hydraulic energy storage device. 

This storage device stores the energy in pressurized gases. The hydraulics would respond similar 

to an ultracapacitor except that the link would be mechanical, rather than electrical. This would 

increase recapture efficiency. The EPA is developing this technology with industry partners 

through cooperative research and development agreements, including implementation of the 

technology on a number of prototype vehicles [Alson et al., 2004]. Since hydraulic hybrids do 

not require large batteries (which are expensive), they lend themselves well to a series 

configuration. 

 

Vehicle Weight and other Specifications 

Hybrid vehicles are usually able to downsize the engine in order to save weight and fuel, 

however the added components of the motor, inverter, battery, and other connecting components 

more than compensate for the engine weight loss. 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 component 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 various 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 longer planned for production, it is still useful to 

compare specifications. The Prius and Insight obviously have no direct partner, so no direct 

weight comparison could be done. The weight ratio of hybrid to non-hybrid is found to be 1.07, 

or a 7% increase. This is based on the ratio of test weights, which 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% increase 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 Comparison to Conventional (if exists). All 

sources are from EPA CFEIS database, Manufacturer, Car&Driver or Road&Track 
52 

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 

Durango 4wd

475Nm@ 

3000rpm

186kW@ 

4200rpm 5.9 2251

Hybrid Conventional

 

Many of the other vehicle attributes that aren’t published had to be approximated. Often 

coefficients of drag and weights are available, but rolling resistance 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 Appendix B. Many of the variables may have a model 

year relationship into the future. E.g. component weights, Cr, Cd, Pacc, motor efficiency, 

regeneration braking efficiency, discharge/recharge efficiency, etc. 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 the addition of more electrical 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 are 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 battery when the battery is on a discharge cycle. 

Otherwise it runs off the generator (engine). The algorithms are listed in the Appendix A. 

Air conditioning and heating are potentially large 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 continue running under conditions when 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 report 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 state 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 modeling methodologies are quite similar. 

0

10

20

30

40

50

60

70

80

90

Combined FTP HWY

Fuel Economy (mpg)

PERE

MIT

Figure 24. Fuel economy of a model 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 HWY

Battery Status (MJ/km)

PERE

MIT

recharging

discharging

Figure 25. State of Battery compared 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 probably is not identical. 

5- PERE adds additional loss from front wheel drive regenerative 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 comparison to unadjusted EPA certification fuel 

economy numbers. The second set of validations are done on two hybrid vehicles 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 vehicle. Actual hybrid vehicles have much more 

sophisticated control strategies, as well as efficiencies that depend on various operating 

conditions. However, the energy flows still follow a similar physical and logical progression. 

Given a power demand the fuel consumption rate 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 the manufacturer, or a trade magazine website 

(such as Car and Driver and Road and Track). All weights are test weights (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 report does not attempt to validate PERE to a larger variety of 

conventional vehicles (light duty 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 vehicles are adjusted based on real world 

driving correction factors: adjusted FTP = unadjusted FTP*0.9 and adjusted HWY = unadjusted 

HWY*0.78. PERE output should be compared with 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 combined fuel economy (miles per gallon) is a 
55 

weighted average 0.45 (FTP) and 0.55 (HWY), calculated 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 especially well for conventional vehicles. There 

were several other conventional vehicles (light cars and trucks) validated, the Camry, Civic, and 

Jetta are the only ones shown. All of the conventional vehicles matched rated fuel economy to 

within 5% (on city and highway) with the exception of the Volkswagen Jetta (gasoline). There 

were also several conventional vehicles validated to dynamometer tests 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 captured 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 many 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 vehicle is very good (within 5%). This suggests that the powertrain 

model is correctly modeled, based on the description of the lean burn engine by Ogawa et al. 

[2003]. All of the advanced vehicles are accurately capture by PERE for highway driving (within 

5%). With the exception of the Toyota Prius, the city estimates are also all within 10%. PERE 

overpredicts the fuel economy on the Honda Civic Hybrid on the city cycle by around 9%. This 

is likely due to two reasons: PERE 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 during 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 lights off in a timely fashion (after which it can start 

breaking down the criteria pollutants). Many conventional engines also run slightly rich during 

the start up period. Presently PERE shuts the engine down during decelerations and idle, which 

would tend to significantly under predict fuel consumption (over predict fuel economy) during 

start-up for hybrids, and to a lesser extent conventional 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 demonstrates how different the strategy of the Prius is from the 

rudimentary strategy employed in PERE. The Toyota 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 frequently than in parallel hybrid [Toyota, 2003] to 

supplement power. The hybrid system can be fine-tuned to give optimal fuel economy. The 

brake-by-wire system on the 2004 Prius also helps 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 duplicate). 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 '04

FTP fuel economy (mpg)

PERE

EPA unadjusted

Figure 26. Fuel Economy validation for the FTP 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 '04

HWY 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 '04

FTP fuel economy (mpg)

PERE

EPA unadjusted

Figure 28. Fuel Economy validation for the FTP/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 (resistive 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 control strategy modeling could result in under-predicting fuel 

economy. A more optimized control strategy would be to run the battery at low load and allow 

the engine to recharge the battery at higher loads, where the engine can operate more efficiently 

(as the Prius does). This was not done because it was difficult to put in spreadsheet format. If 

PERE is coded, then a more sophisticated 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 vehicle modeling is especially simplified since it 

distributed energy loads using relatively straightforward algorithms. It can be somewhat 

misleading to compare to actual second-by-second data taken on a dynamometer. We should not 

be surprised if the results vary significantly. However, exploring these discrepancies are of 

interest. 

Two in-use hybrid vehicles were tested at the National Vehicle and Fuel Emissions Laboratory in 

2003 as part of an in-use compliance survey. 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 federal 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 manufactured by SENSORS Inc. was incorporated 

on the instrumented vehicle tests. The SEMTECH-G measures real-time concentrations of CO2, 

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 modal data, whereas the Insight only has on-board 

emissions data. The fuel consumption is calculated from mass CO2 (and emissions) measured. 

Figure 29 shows the bag 3 results of the Honda Insight. 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. Therefore, the low flow bag 2 measurements were 

not used. Both the instantaneous and cumulative emissions are displayed in the figure for 

comparison. One can see from the figure that the measured 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 accurate for this type of vehicle. Though not shown, the 

total fuel consumption between model and measurement 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 e

speed

0

0 .2

0 .4

0 .6

0 .8

1

1 .2

1 .4

1 .6

1 .8

2

Fuel (g/s)

speed

PERE FR

SEMTECH FR
59 

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 for the Toyota Prius. It appears at first glance that there 

may be a timing shift error. The effect is real. Because 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 acceleration. 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

time

fuel (g/s)

0

10

20

30

40

50

60

speed

dynomometer

PERE

speed

Insight SEMTECH vs PERE

0

10

20

30

40

50

60

0 100 200 300 400 500

time

speed

0

20

40

60

80

100

120

140

160

180

Cumulative fuel rate (g)

speed

PERE FR

SEMTECH FR
60 

0

50

100

150

200

250

300

350

400

450

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

time

fuel

0

10

20

30

40

50

60

speed

dynomometer

PERE

speed

Figure 30. Bag 3 and 4 fuel rate and cumulative fuel consumption modeled and measured 

(on a chassis roll) for 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 series 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 they are more aligned with the models. If the timing 

on the Prius data were shifted 12 seconds to align with the speeds 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 binning procedure is described in greater detail in a later section 

and in other MOVES documents) in Figure 31. The 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-causal. 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 correcting 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 conditioning, 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

vsp

fuel 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 Vehicle Specific Power Bin with and without time 

alignment of the data. 

Electric Vehicles 

At this time, MOVES does not put strong emphasis 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 (battery, traction motor, regenerative braking, etc). 

In a future version, there may be further attempts to develop and validate this model. 

Section VIII – Hydrogen Internal Combustion Engine Vehicles 

As of the date of the publication of this report, hydrogen ICE vehicles are not yet in production. 

However, there are a number of prototypes constructed 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 Ford SAE papers [Tang et al., 2002, Natkin et al., 

2003]. 

The hydrogen vehicle is approximately 25% heavier than its conventional ICE counterpart 

[Natkin, 2004]. Much of this is due to the added 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) H2 ICE is taken to be 45% (though it may well be 

somewhat higher). However, the specific power of the naturally aspirated engines are 

significantly lower than their gasoline counterparts. In order to achieve similar power 

performance it is necessary to boost the intake air. PERE assumes that the H2 ICE is 

supercharged engine (like the Ford engine). Because of this, the friction is also increased. The 
62 

friction is determined from the motoring torque curves in Natkin et al. [2003] and is shown in 

Figure 32. The friction losses from supercharging are significant. 

In PERE, the transmission for the H2 ICE vehicle is assumed to the identical to conventional 

transmissions. 

y = 0.0967x + 15.8365

R2

 = 0.9681

y = 0.0339x + 42.595

R2

 = 0.917

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000

rpm

fmep (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 H2 ICE [Natkin et al., 2003]. 

Unfortunately, a validation could not be performed on the PERE H2 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 choice 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 prototype 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 hydrocarbons from liquid fuels into gaseous hydrogen, 

but this technology appears to be falling out of favor for light-duty vehicle use due to its size and 

relative inefficiency. 

In PERE, fuel cell vehicles are modeled as “series hybrid” vehicles. This means that there is only 

one mechanical path. However, there is still a parallel electrical power path, in that either the 
63 

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 required). 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 transmission (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 7th order polynomial fit for 

PERE. The equation is shown below (calibrated to a 80kW stack): 

Equation 34. FCeff = a0 + a1Pfrac+ a2Pfrac

2

 + a3Pfrac

3

 + a4Pfrac

4

 + a5Pfrac

 5 + a6Pfrac6

 + a7Pfrac

7

Where 

 Pfrac = 80*Pd/Pfc (scaled) 

Pfc is fuel cell power rating (kW) 

Pd is power demand (kW) 

a0 = 0.067284 

a1 = 0.166336 

a2 = -0.018361 

a3 = 9.6832e-4 

a4 = -2.7509e-5 

a5 = 4.2972e-7 

a6 = -3.4719e-9 

a7 = 1.1328e-11 

Again in this version of PERE, there is no cold start modeled, the effects of which may be 

significant. We also use the battery, 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 gasoline equivalent by converting the work equivalent hydrogen 

into gasoline mass (with their respective lower heating values). This 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 simply defined here as the useful energy out/fuel 

energy in. The useful energy out is only the (positive) energy required for the car to follow the 

driving cycle. It would be slightly lower 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 (H2)

Consumption

Fuel Cell

Figure 33. Power flow chart for the hydrogen 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 efficiency [Weiss et al, 2000, 2003] including a 7th

order polynomial fit. 
65 

0

20

40

60

80

100

120

140

160

Combined FTP HWY

Fuel Economy (mpgge)

PERE

MIT

Figure 35. Fuel economy comparison between PERE and Weiss et al [2003]. The units are 

in miles per gallon gasoline equivalent. 

Validation 

It is difficult to conduct a validation of the fuel cell model since there isn’t a tremendous amount 

of data available for the prototype 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 Chrysler F-Cell vehicle 

weight is unknown). The Honda FCX has a unique body, so it does not have a conventional 

vehicle equivalent (though it does have a similar electric vehicle). The average increase in 

weight of the first 3 vehicles compared to its equivalent conventional vehicle is 23% 

(interestingly this is consistent with the increase in the weight of the Ford H2ICE). 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. This 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 manufacturer representatives, or from website) 
66 

model

Eng 

power 

(kW)

curb 

weight 

(kg)

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 

Highlander 119 1597

GM Opel 

Zafira 74 1393

DC 

Mercedes AClass

76 1115

Fuel Cell Conventional

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 system, and recharge efficiencies are higher. The Honda FCX has 

an adjusted EPA fuel economy rating of 51/46 miles per kilogram hydrogen (city/highway). A 
67 

kg of hydrogen is roughly equal to a gallon of gasoline in energy. In comparison, PERE 

predictions are shown on Figure 37. The discrepancy is within 5%. 

0

10

20

30

40

50

60

70

Combined FTP HWY

Fuel 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 accurately. A static sensitivity is performed in 

relation to a change in city vs highway fuel consumption. It is necessary to do a separate analysis 

since the parameters affect the results 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 fuel consumption result is ranked and compared. In 

order to perform a more complete uncertainty analysis, 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 alternate driving cycles 

will give different sensitivity results. Futhermore, 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 % difference, for a typical modern passenger car 

modeled using PERE. To get the relative sensitivity, 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 use 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 efficiency, weight, and engine displacement all dominate the top 

ranking. Fortunately, these quantities are typically known inputs for the model, or in the case of 
68 

indicated efficiency, should not vary much. For those advanced engines where the efficiency is 

unknown, it clearly poses a problem for the model. 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 the model is quite insensitive the 

peak torque curves. It would be more crucial if high acceleration (full throttle) performance 

modeling was being performed. Since the activity in MOVES describes representative “realworld” driving, these driving modes are not stressed in the model, nor is it in PERE. Though it is 

not included the table, the model was not sensitive (<1%) to the rotational term in Equation 1 

(g). 

Table 20. Static sensitivity test for the PERE parameters used to model a typical 

conventional passenger car. Parameters are adjusted 10% 

RANK 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 

RANK 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 more 
69 

sensitive to changes than the conventional vehicle. Again, since the vehicle is able to meet the 

drive cycle (sometimes with the help of the motor), the battery limit parameters are not important 

for the model. 

A sensitivity analysis is not conducted on the fuel cell hybrid, however, it is obvious from the 

above two studies that the fuel cell system efficiency curve is the most important parameter. The 

fuel cell peak power also being of moderate importance. 

Table 21. Static sensitivity test for the PERE parameters used 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 kJ 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 kJ 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 using the road load coefficients Cr, Cd, AF

(frontal area), etc, vs using the track (or target) coast-down coefficients (A, B, C). The fuel 

consumption was compared using both techniques for the conventional and hybrid vehicles. 

This is essentially a sensitivity 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 was 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 speed, 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 track 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 choosing the method of calculating road loads is 

simple, since most of the track A, B, C coefficients 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-derive physical quantities from track 
70 

A, B, and C coefficients, since the values are determined from empirical fits to coastdown 

data. Thus, it is necessary to have a methodology 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 equations are based on Equation 3: 

Equation 35 FRL = mgCR + 0.5ρCDAFv

2

Equation 36 FT = A + Bv + Cv2

 

Where the subscripts “RL” and “T” stand for “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 FRL = mgCR + Bv + 0.5ρCDAFv

2

 

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

modified slightly here: 

Equation 38 CR = CR0*(1+v/44.7) 

Where CR0 is the base rolling resistance (typically ~.009). An estimate of B is 

Equation 39 B’ = MgCR0/44.7 [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 other 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 with their 1 standard deviation uncertainties. 

The third column displays the percentage underprediction in fuel consumption 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 factors are inherent to the 

B term, such as rotating friction in bearings, transmission, and final drive, tire windage, 

motor drag etc. Because of the motor drag (and other hybrid differences), the official fuel 
71 

economy numbers for certain hybrids may not necessarily match what one would achieve in 

the real world [Rechtin, 2003]. 

Table 22. Fitted B coefficients and 1 standard deviation. CV = Conventional 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 research. 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 nearly all of the criteria pollutants during hot 

running periods. However, during cold 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 for a large fraction of the vehicle’s operating 

emissions. Though it is significant for the sensitivity 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 this time, PERE cannot predict at what time 

light-off will occur, this must be done empirically. 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 electrical power may be needed to pre-heat the catalyst. In either of 
72 

these cases, energy is being consumed. Additionally, 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 was 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 corresponds to an increase of 

27% compared to bag 3. For the 1.0L Insight, the 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 appear 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 Consumption (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 overpredicts for the Insight. Due to the large 

uncertainties involved in cold start modeling, it is decided to use the 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 conventional vehicles. The battery power is set to zero for the 

duration, so that vehicle is acting much like a conventional vehicle, 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 (fuel) consumed in this period is assumed to 

balance any alternative technologies such as an electrically heated catalyst. 

The fuel consumption from this modified hybrid 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 factor of 22.3 grams of fuel per start, which is curiously 

consistent with the measured Honda Insight results (which has a similar engine 

displacement). The model would naturally underpredict the Prius cold start however. This 

start factor will depend on the type 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 model 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 lacking (“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 model 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 allows 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 technologies directly using 

energy and emissions rates measured from other studies such as GREET (e.g. alternative 

fueled vehicles). 

The process of how PERE results could be integrated into MOVES is the subject of this 

section. 

Hole Filling Using PERE 

The original intent of MOVES is to derive all energy and emissions rates from existing data. 

However, it is not possible to obtain data from all mobile sources in the vast fleet. The MSOD 

(the database upon which MOVES obtains its emissions 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 conventional 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 certain 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 the assumed values used by PERE for conventional 

vehicles. This bin happens to be the 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 top 45 or so) are listed in the table below 

(grouped by model year). Also included 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 sorted by fuel type and model year. 

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

15 1010100032025006000a 1 Gas 3 86-90 2025 60

32 1010100044050002500a 1 Gas 4 91-99 4050 25

37 1010100044050007000a 1 Gas 4 91-99 4050 70

36 1010100044050009000a 1 Gas 4 91-99 4050 90

4 1010100045099016000a 1 Gas 4 91-99 5099 160

7 1010100045099019500a 1 Gas 4 91-99 5099 195

18 1010100045099026000a 1 Gas 4 91-99 5099 260

38 1010100045099033000a 1 Gas 4 91-99 5099 330

35 1010100053035014000a 1 Gas 5 01-10 3035 140

43 1010100055099002500a 1 Gas 5 01-10 5099 25

44 1010100055099003000a 1 Gas 5 01-10 5099 30

42 1010100055099003500a 1 Gas 5 01-10 5099 35

40 1010100055099004000a 1 Gas 5 01-10 5099 40

5 1010100055099016000a 1 Gas 5 01-10 5099 160

20 1010100055099019500a 1 Gas 5 01-10 5099 195

14 1010100055099026000a 1 Gas 5 01-10 5099 260

25 1010100055099033000a 1 Gas 5 01-10 5099 330

65 1020100043540007000a 2 Diesel 4 91-99 3540 70

16 1020100043540014000a 2 Diesel 4 91-99 3540 140

62 1020100043540019500a 2 Diesel 4 91-99 3540 195

19 1020100045099003500a 2 Diesel 4 91-99 5099 35

21 1020100045099004000a 2 Diesel 4 91-99 5099 40

11 1020100045099004500a 2 Diesel 4 91-99 5099 45

12 1020100045099005000a 2 Diesel 4 91-99 5099 50

13 1020100045099006000a 2 Diesel 4 91-99 5099 60

10 1020100045099016000a 2 Diesel 4 91-99 5099 160

1 1020100045099080000a 2 Diesel 4 91-99 5099 800

2 1020100045099100000a 2 Diesel 4 91-99 5099 1000

27 1020100055099007000a 2 Diesel 5 <90 5099 70

26 1020100055099008000a 2 Diesel 5 <90 5099 80

17 1020100055099009000a 2 Diesel 5 <90 5099 90

29 1020100055099010000a 2 Diesel 5 <90 5099 100

8 1020100055099014000a 2 Diesel 5 <90 5099 140

9 1020100055099016000a 2 Diesel 5 <90 5099 160

3 1020100055099080000a 2 Diesel 5 <90 5099 800

6 1020100055099100000a 2 Diesel 5 <90 5099 1000

28 1020100045099130000a 2 Diesel 4 91-99 5099 1300
75 

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 above the dependent variables. The parent variables 

define the decision source (tree) of the parameters. 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 few other parameters are fixed. 

SOURCE BIN 

Weight 

- LD/MD/HD 

- Road load: Cr, Cd, 

AF, 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, ρf

LD/MD/HD/Motorcycle 

- Driving cycle, Transmission: N/v, shift points, gear ratio, manual/auto, accessory: Pacc 

FIXED PARAMETERS Auto transmission efficiency (by gear), manual transmissions 

efficiency, density of air, fuel enrichment slope (gasoline only), inertial rotational mass term. 
76 

The determination of general vehicle weight classification 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, etc). The medium to heavy duty split occurs at 

weightclassID=400 (33,000-40,000 lbs). This in turn defines 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 and engine size categories as well as a different 

accessory loading (0.25 vs 0.75kW). 

The weight determines the road-load coefficients for the vehicles directly. For model years 2000 to 

present, A, B, and C track coefficients are provided by manufacturers. Older vehicles only have the 

single Tractive Road Load Horsepower (TRLHP) figure. The TRLHP for a typical 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 1999, the vehicles are assumed 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 characteristics of the engine. All of these relationships can be found in this 

report. Finally, the model year also defines the fuel 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 weight classifications fall under discrete 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

engSizeID

vehicle midweight (lbs)

Figure 38. Engine size-weight categorization of motorcycles in MOVES. 

Driving Cycles and Running PERE 

The output to PERE is second-by-second energy consumption, therefore the next 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 representative sampling of real-world driving. For PERE, the driving 

cycle input is dependent on the weight of the vehicle. 14 driving cycles for light duty 

applications have been selected for this purpose 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 cycles 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 that similar binned rates could 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 applications, the driving cycles where drawn from the medium and 

heavy-duty schedules used to populate the MOVES default database [Beardsley et al., 2004]. The 

medium duty drive cycles are listed in the table below and totaled 6,050 seconds. 

Table 28. MOVES medium-duty driving cycles 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 with freeway driving. The length of this file makes 

PERE cumbersome, and in the future, the number of cycles should be reduced. 

Table 29. MOVES heavy-duty driving cycles 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 2.5
80 

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 driving 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, there 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 vehicle 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 derived from already-published 

analyses. 

Table 32. Examples of advanced technologies, as defined by fuel types and engine technologies, 

to be included in MOVES. “C” is conventional, “A” is advanced, and “IC” is internal 

combustion. This list is not final. 

• Gasoline conventional (IC) 

• Gasoline Advanced IC 
82 

• 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 technologies are represented fully across the other 5 dimensions, for 

example, it is highly unlikely that long haul trucks will be hybridized in the future, due to the 

relatively minor effect it has on highway fuel economy 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 source bin, the vehicle parameters such 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 “average” vehicle in the source 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 values are determined from the fleet mix data. 

Since engine size, has limited meaning for hybrids and no meaning for fuel cells and electric 

vehicles, it is necessary to define a power surrogate for engine size. This has already been 

mentioned in this report [Chon & Heywood, 2000]. Though 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 not 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 and 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 consumption, therefore the next step is to input 

the driving cycles. The driving cycles input into PERE help determine the “binned” energy 

consumption rates for MOVES (see Figure 1). Henceforth “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 driving cycle input. It 

is important to capture a representative sampling of real-world driving. Three driving cycles 

were chosen for this purpose: FTP (urban), FTP (highway), and LA92. The latter is a selfweighted cycle based on more recent real-world driving, and includes harder accelerations and 

higher speeds. For hybrids, the state-of-charge was maintained over the total of the three cycles. 

For conventional vehicles, more driving cycles are 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 into the respective operating 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 consumption 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 generated 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 

N = 258 357 111 171 121 85 48 15 151 283 281 144 62 46 173 301 34

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

.00

95% 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 advanced 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 displacement), so the rates could be 

interpolated between the endpoints (e.g. lightest and heaviest weight class. To demonstrate the 

validity of the linear extrapolation technique, PERE was run on a conventional gasoline powered 

passenger car for a variety of weights and engine displacements. The driving 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 identical, however, this is not true along the weight 

axis. Here the second-by-second fuel consumption 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 displacement, as labeled along the right. It is evident that given the 

minimum and maximum points, that the other could be linearly interpolated. 
85 

N = 258 258 258 258 258 258 258 258357 357 357 357 357 357 357 357111 111 111 11111 11 111162 162 162 162 162 162 162 162119 1191919 119 1191919727272727272727251515151515151513636363636363636152 152 152 152 152 152 152 152238 238 238 238 238 238 238 238274 274 274 274 274 274 274 274161 161 161 161 161 161 161 16178787878787878786464646464646464130 130 130 130 130 130 130 130302 302 302 302 302 302 302 3027676767676767676

VSPBIN25

36

35

33

26

25

24

23

22

21

16

15

14

13

12

11

1

0

95% 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 curvature between the VSP bins, within a bin, an interpolation is quite 

reasonable. 

N = 258 258 258 258 258 258 258357 357 357 357 356 358 357111 1111 111 11111160 162 162 166 166 16 17119 1191916171716747273727171714951505151515138363635353535127 152 167 171 17 186 188222 23861 263 272 276 286255 274 271 280 272 266 260193 161 143 134 14 131 1299578707165646475645847444084130 172 218 262 306 30303 302 285 254 223 179 166121765136232322

VSPBIN

36

35

33

26

25

24

23

22

21

16

15

14

13

12

11

1

0

95% CI FUEL

4

3

2

1

0

 2000

 2500

 3000

 3500

 4000

 4500

 5000
86 

Figure 41. Fuel Rate (g/s) as a function of VSP 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 possible 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. Note 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 Figure 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 displacement. 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 still 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 vehicle was modeled using PERE. 

The rates from these vehicles were then ratioed to their corresponding conventional vehicle rates 

(gasoline equivalent). These ratios 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” vehicle 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 moderate hybrid with a 22kW motor (gasoline and 

diesel). 

The ratio of energy (or fuel consumption) for some of the advanced technologies 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 that the hybrids are heavier vehicles, driven with 

smaller engines, yet still follow the same driving trace. Thus, it is quite possible for the fuel 

consumption to be higher than the conventional vehicle during certain modes of driving. 

However, over a given driving cycle, the fuel consumption 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 support the well known fact that the advantage 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 bin

Running Energy Ratio to Conventional 

Gasoline

Conv Mod Hybrid

Conv Full Hybrid

>1 ratio due to higher 

rpm/smaller engine size 

combination

Figure 43. The ratio of energy (or fuel consumption) 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 bin

Running Energy Ratio to Conventional 

Gasoline

Diesel

Diesel Mod Hybrid

Diesel Full Hybrid

Diesel ratio is set to 

constant value 

(gasoline equivalent)

Figure 44. The ratio of fuel consumption for diesel, 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 bin

Running Energy Ratio to Conventional 

Gasoline

Fuel Cell

Fuel Cell hybrid

H2 ICE avg (AIC)

Figure 45. The ratio of energy for H2 fuel cell, H2 hybrid fuel cell, and H2 ICE vehicles with 

respect to gasoline conventional (using 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 H2 ICE is nearly identical to that of Advanced ICE 

(~0.9). 

This method for determining MOVES rates for hybrids 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 average number. However, aside from being more 

complex, the methodology also has limitations, which are presently explored. 
89 

The ratios are based on a single (though common) representative vehicle class. We might expect 

vehicles of different weight but same 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 boost). This would necessarily change the shape of the curves. An 

example of this is shown in Figure 46. The varying power-to-weight ratios having different shapes. 

The effect is mainly pronounced in the “2” bins, i.e. bins 12, and 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 bin

ratio of hybrid to 

conventional

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 consumption 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 were run on the following medium-duty driving 

cycles: CBD (Central Business District), 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 bin

ratio of hybrid to conventional

0.0113

0.0214

0.0302

0.0411

0.0494

0.0757

0.0677
90 

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 displacement 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 bins. 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 staff 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 Tad 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 technical advice on H2 ICE. Matthew Barth, George 

Scora, and Ted Younglove offered significant assistance in the heavy-duty diesel modeling 

section. 

Finally, we acknowledge the contributions of the reviewers who provided valuable comments 

about the report. Some of these comments are in the following appendices. Comments from three 

SAE (Society of Automotive Engineers) reviewers, however, are not included. These comments 

were from a paper formed from portions of the hybrid sections (both ICE and fuel cell) of this 

report, and published in 2005. 
91 

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Appendix A: PERE Algorithms 

Key:

FR Fuel rate (g/s) 

KNV Friction term 

LHV Lowere heating value of fuel 

Pacc Accessory power 

Pbatt Battery power 

Pd Power demand 

Peng Engine power 

PFC Fuel Cell power 

Pengmax Maximum power of engine 

Pfcmax Maximum power of fuel cell 

Pmotmax Maximum power of motor 

Pmin Minimum regeneration power 

Pmot Motor power 

Pth Hybrid threshold power 

η Engine indicated efficiency 

ηdisch Battery discharge efficiency 

ηFC Fuel cell efficiency

ηFD Final drive efficiency (for motor only) 

ηFWD Front wheel drive power fraction 

ηmot Motor efficiency 

ηtran Transmission efficiency 

ηregen Regenerative braking recharge efficiency 

Parallel Hybrid Algorithm 

Engine Power (nested if statements) 

IF OR(v < 2, Pd < Pth) THEN Peng = 0 ‘idle & decel engine shut-off 

IF Pd > Pengmax THEN Peng = Pengmax ‘can’t exceed max 

 ELSE Peng = Pd 

Fuel Rate 

 IF Pd = 0 THEN FR = 0 ‘fuel shut-off 

 IF Pd > Pengmax THEN FR = [KNV + Peng /ηηtran]/LHV ‘no Pacc – slightly overpower 

ELSE FR = [KNV + (Peng /ηtran + Pacc)/η]/LHV 

Battery discharge (Pbatt > 0) 

 IF AND(Pd <= Pth, Pd > 0) THEN Pbatt = (Pd /ηmotηFD + Pacc)/ηdisch ‘launch 

IF Pd > Pengmax + Pmotmax THEN Pbatt = Pmotmax ‘can’t exceed max 

IF Pd > Pengmax THEN Pbatt = [(Pd – Pengmax)/ηmotηFD + Pacc]/ηdisch ‘assist 

 ELSE Pbatt = 0 

Battery recharge (Pbatt < 0) 

 IF Pd <= - Pmotmax/ηFWD THEN Pbatt = -PmotmaxηmotηregenηFD ‘can’t exceed max 

 If Pd < - Pmin/ηFWD THEN Pbatt = PdηFWDηmotηregenηFD ‘can’t fall below min 

 ELSE Pbatt = 0 
98 

Fuel Cell Hybrid Algorithm 

Fuel Cell Powerplant 

IF OR(v < 2, Pd < Pth) THEN PFC = 0 ‘idle & decel fc shut-off 

IF Pd > Pmotmax THEN PFC = Pfcmax ‘can’t exceed max 

 ELSE P PFC = [Pd/(ηmotηFD) + Pacc]/ ηFC 

Battery (ultracapacitor) discharge 

IF AND(Pd <= Pth, Pd > 0) THEN Pbatt = (Pd/ηmotηFD + Pacc)/ηdisch ‘launch 

 IF AND (Pd > Pfcmax, Pd < Pmotmax) THEN Pbatt = [(Pd – Pfcmax)/ηmotηFD + Pacc]/ηdisch 

IF Pd <= 0 THEN Pbatt = Pacc/ηdisch

 ELSE Pbatt = 0 

Battery recharge 

Same as for hybrid (above) 
99 

Appendix B: Tables of parameters for validation vehicles. 

Vehicle MIT Camry Jetta Jetta TDI Civic DX Civic HX Civic Hybrid 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 2.9
100 

Appendix C- Comments from Alliance of Automobile 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 included here, the reviews were generally positive. The 

relevant corrections were also applied to this draft. 

These comments were made regarding the previous 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 copied over from the original text. 

General Response: 

We appreciate the time and effort that AAM members put into commenting on the report: “Advanced 

Technology Vehicle Modeling in PERE.” The depth and breadth of the comments indicate that 

careful thought and consideration 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 model 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 not the goal. If users are aware of improved 

coefficients or algorithms, the model will be available for changes so that fuel consumption from 

individual vehicles can be more closely estimated. The validation to individual vehicle (make and 

model) is an extreme validation when the model is pushed to its limits. This will be clarified in some 

of the response to comments below. 

The original report (dated March, 2004) was released as a draft version. As a result, this new report 

supercedes the previous version, and addresses some of the comments. Two sections were added to 

the report (Heavy-duty and motorcycle modeling). However, the fundamental 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 entitled Advanced Technology Vehicle Modeling in 

PERE (Physical Emission Rate Estimator). The report describes a model created by EPA to estimate 
101 

fuel consumption over any input driving cycle from different types of vehicles, including advanced 

gasoline internal combustion engines, advanced diesel internal combustion engines, hybrid electric 

and gasoline/diesel powertrains, and hydrogen fuel cell hybrids. Basically, PERE takes vehicle input 

parameters, then runs the vehicle through driving cycles defined by the user and outputs second-bysecond fuel consumption rates. The purpose of the model 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 report further states that the predictions 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 evaluate the report, our comments should be viewed as preliminary. 

Further, given the technical issues raised below, 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 report 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 incorporate vehicle performance requirements, which can 

have a significant effect on predicted fuel economy. 

Response: 

There are a number of reasons that power was the only performance requirement applied to 

PERE. 

1- Power is a surrogate variable (for engine displacement) that is capable of being transferred 

directly into the MOVES framework. MOVES is incapable of distinguishing vehicles with 

varying 0-60 acceleration, gradeability, 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 cycles) employed to run the models are typical 

driving patterns. The extreme driving patterns 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 easily 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 results raise technical concerns. 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 efficiency' has no physical meaning; indicated efficiency only has meaning in 

the context of the heat engine alone. That said, the sensitivity to powertrain indicated efficiency 

reported in Table 5 is about 

half of the expected 10% for engine indicated efficiency. 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, there 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 PERE takes (equation 11), the indicated efficiency affects fuel 

consumption in the brake term, and not the friction term (directly). This may be 

slightly counter-intuitive, and would probably be more correct if an “fmep” value of 

friction were used instead of “K”, which already has the indicated efficiency inherent 

to it. “K” was used to remain consistent with past modeling efforts. However, as the 

model stands, this correction would make little difference (except in the sensitivity 

study or when indicated efficiency changes, 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, driving cycle, and regenerative braking. 

Comment 

• The report does not demonstrate the rationale for a 10% validation criterion. The report 

indicates that the model predicts fuel economy for several validation vehicles within 10%. However, 

it is not clear how EPA has determined that 10% is an acceptable level. Depending on the use of the 

model, predictability within 10% over the FTP and Highway Test may not be acceptable. EPA should 

explain in more detail in the report the various uses it intends for the 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. Other models are more suited to such accuracy 

levels. It is expected that most of the uncertainties are “random”, reflecting the uncontrolled 

variability in the parameters due to operating region or fleet characteristics, 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 start effects. Cold start CO2 should be 3% to 5% 

higher than fully warmed up CO2. 

Response: 

Cold start is modeled separately and described in a separate document. However, a 

supplemental discussion of hybrid cold start rates is described in the revised document. 

This is preliminary and only based on 2 hybrid vehicles tested. 

Comment: 

• The report does not demonstrate overall robust validation. In spite of claims in the PERE report 

and the apparent agreement between PERE predictions 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 vehicle weights and vehicle classes. 

- Nearly all of the vehicles used for validation were assumed to have the same 

transmission and shift point parameters, instead of those of the actual vehicles in the study. 

Meaningful validation can only be conducted when the input parameters to the 

model are identical to the characteristics 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 comparison 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 previously published report: (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 has 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 modeling over several different driving cycles. 

-PERE does not actually incorporate 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 transmission/engine matching – a comment below]. The 

transmission parameter that is of primary importance is the efficiency, which will discussed in 

more detail below (and in the new version of the paper). The shift points, 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 other 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 definitions) precise transmission models were 

not deemed necessary. To further demonstrate this point, the validation in this report (and in 
104 

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 future technologies) were made in PERE or 

MOVES. The hybrid results from PERE are quite different from the results 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 data gaps to be filled by PERE. Complete information 

regarding the application of the PERE model for use in MOVES is central to determining the 

sufficiency of modeling 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 “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 examining 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 example) is more detailed and complex (than 

PERE) and is designed to model individual vehicles, rather than fleets of vehicles. 

Additionally, ADVISOR is coded in the MATLAB/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 Laboratory), 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 considers it important to keep the expertise in 

this type of modeling in-house, rather than relying on contractor support. 

Comment: 

PERE does not address powertrain matching considerations (that is, selection of engine displacement, 

transmission gear ratios and final drive) explicitly. Performance metrics are not specified or 
105 

calculated. This is especially critical in extending PERE to the analysis of other vehicle classes; 

particularly trucks where the trailer tow requirements can be significant. How is the user assured that 

the powertrain match is ‘ballpark’ correct? For example, if only 0-to-60 time is matched, SI engines 

are undersized, fuel economy is overstated and off-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 world requirements such as performance times, passing 

time in top gear, vehicle launch acceleration, top vehicle speed, gradeability 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 been 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 were assumed to have the same transmission 

and shift point parameters, instead of those of the actual vehicles 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 predicted by the model for 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 gasoline 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 minivans, and no light duty 

trucks. The report indicates the model is capable of estimating fuel economy for all conventional 

gasoline vehicles. It is not clear 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 assumed to have the same transmission characteristics, 

instead of their own individual characteristics. 

The vehicle descriptions in Appendix 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, Honda Civic Hybrid, Honda Insight, Toyota 2001 Prius and Toyota 

2004 Prius. Meaningful validation can 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 for 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 accelerations are significantly higher. Extrapolating 
106 

speeds and loads from regulatory cycles to simulate 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 cycle). 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 predictions 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 speeds 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 addition, the report states that while the model is 

calibrated so that the state of charge for hybrids is close to zero after the FTP, this may not be true for 

other driving cycles, like all 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 accuracy 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 effects 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 cold 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 predictions to test data with cold start effects, or EPA should obtain 

test data on the FTP without cold start, and compare that to the PERE 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 mean 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 has used several assumptions from the 

MIT study that are questionable, specifically: The energy density of gasoline 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 continuous gradeability, are not strongly 

related to the peak power-to-weight ratio. The equal power-to-weight 
107 

assumption for SI engines results in undersized engines and overstated 

fuel economy. Conversely, this assumption is likely to result in 

oversized diesel engines. 

 The MIT reports assume very aggressive engine downsizing in hybrid 

vehicles that would produce compromised off-cycle driveability and 

overly optimistic projected FE benefits; 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 H2 utilization. 

Finally, there are several other studies published based on more realistic 

technology assumptions than the MIT study which could better serve for the 

purpose of calibrating the PERE model. Among these studies are the 

following: 

 “Well to Wheels Analysis of Future Automotive Fuels and Powertrains in 

the European Context”, EUCAR, CONCAWE, and European 

Commission, Joint Research Center, Version 1b, January 2004. 

 “Potential of IC-Engines as Minimum Emission Propulsion System”, 

Eichlseder and Wimmer, Atmospheric Environment, 37 (2003) 5227-5236 

 “A Comparison of Hydrogen, Methanol, and Gasoline as Fuels for Fuel 

Cell Vehicles: Implications for Vehicle design and Infrastructure 

Development”, Ogden, Steinburgler, Kreutz”, 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 detail 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 report. Again, PERE only uses the same 

parameters as the MIT model does when comparing 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 efficiency curves are significantly lower than 
108 

the ones used by MIT. Also, PERE is not identical 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, Barth, Ross, et al). 

Specific Technical Comments: 

The following comments are organized in order of page number. 

Page 6 (second paragraph): 

The text states that the PERE Vehicle Specific Power estimate is lower than that obtained from the 

coastdown equation and that FE would be overestimated. 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 engine is more efficient than a two-valve engine because improvements 

in indicated efficiency from the higher compression ratio and lower combustion chamber surface area 

more than offset the increase in valve train and cam drive friction. Differences in pumping are 

minimal. 

Response: 

The friction term (fmep) includes the improvement in “breathing” that accompanies the 

increase in area of the intake (and exhaust) 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 are not likely to materialize because of the high 

levels of NOx conversion efficiency required at Tier 2 Bin 5 and PZEV tailpipe NOx standards. 

Major breakthroughs in lean after treatment 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 operation must also include 

an assessment of compliance with tailpipe NOx and HC standards and estimates 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 subject to Tier 2 standards. 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 obtained from experts and should reflect average 

efficiencies on the duty cycles. We believe it is poor argument that the 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 discrete 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 during gearshifts due to slippage in 

the torque converter is not a significant 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 requirements (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 significantly 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 been fleshed out after some discussion with 

transmission experts. Transmission models (especially automatic) can be extremely complex. 

As mentioned earlier, the transmission model was never meant to be detailed since PERE 

models a “typical” vehicle, rather 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 meant by advanced transmissions approaching manual 

transmission efficiency. It is agreed that CVT efficiency 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 drive cycle is not appropriate. What size engine is assumed and what 

engine or aftertreatment technologies have been considered? It appears that PERE assumes for diesel 

engines that U.S. emission standards can be met with aftertreatment that has no fuel economy 

penalty. Diesel engine compliance with U.S. tailpipe NOx standards is a significant challenge; 

estimates of the fuel economy penalty for emissions compliance associated with lean NOx trap and 

catalyzed diesel particulate filter regeneration should be incorporated into PERE. Although diesels 

are used widely in Europe, the NOx standard is about 8 times more lenient there than in the U.S. 

EPA estimates that vehicles with diesel engines weigh approximately 4% more than their gasoline 

counterparts. However, this assessment is based on current diesel vehicles that do not include 

aftertreatment and do not meet more stringent Tier 2 standards. The Tier 2 standards will require 
110 

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 assessment of the relative weight of 

gasoline and diesel vehicles. 

Response: 

The efficiencies used in PERE are average indicated thermal efficiencies according to engine 

map data (excluding idle and wide open throttle operation). The paper (and one reference) 

gives a detailed description of what this indicated efficiency is. The use of this method has 

been proven to be quite robust in estimating fuel consumption in various papers (see above 

responses and references). The fuel economy penalty of diesels (and other lean burn 

technologies) was also discussed above. EPA will likely include a penalty for future model 

years as this becomes better understood. 

Page 13: 

The argument for engine sizing not being critical because this model is to be used for fuel economy 

and not for performance analysis disregards the most critical aspect of assessing and comparing the 

fuel economy potential of advanced powertrain technologies. This intentional oversight ignores the 

tradeoff between fuel economy and vehicle performance 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 weight 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 efficiency is assumed to be 45% and gasoline 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 Atkinson cycle engine is 15% more efficient than 

current engines. Peak torque of Atkinson cycle engines 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 described 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 appropriate, the operating 
111 

regime for lean burn operation might be quite small, especially in the future (due to NOx 

requirements). Since PERE used generic coefficients 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 vehicles, the parameters of this specific engine are not used. 

Pages 17, 23-29, Appendix A. 

The controls models are too simplistic. Control strategies are important for propulsion system 

modeling because hybrid control strategy has a strong impact on the fuel economy estimate. In the 

PERE model, incorporation of a variety of control architectures and optimization of the control 

strategy are difficult, if not impossible. There is no assurance that the assumed 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 references to “simulation” have been replaced with 

“model”. 

Page 18: 

The charge-sustaining strategy in PERE’s HEVs assumptions must be more thoroughly reviewed, as 

there appears to be a contradiction in description; i.e., there is a statement that batteries are only being 

charged through regenerative braking and not the engine, whereas in a prior section it was stated that 

the batteries are being charged during highway driving. 

Response: 

The batteries are regenerated also during some coasting operation. During the highway cycle, 

there are coasting as well as braking events. Thus, the assumptions are sound. 

Page 20: 

The purpose of mentioning hydraulic hybrids and ultracapacitors is not clear. The report discusses 

methods of modeling hybrids that utilize hydraulics and ultracapacitors, 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 the future. The energy storage device is treated 

rather generically in PERE, regardless of whether that storage device is a battery, ultracap, or 

hydraulic in nature. The user can adjust the coefficients appropriately. 

Page 21: 

Vehicle mass estimation is very critical in the comparison 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 conventional and hybrid vehicles are not clear. Also, 

there is no description of the electric motor performance. An AC induction motor is used in the Ford 

Focus and DCX F-Cell while permanent magnet motors are used in the Toyota FCHV and Honda 

FCX. These motors have different performance and efficiency characteristics. 

Response: 

Once again, the components are generic in nature (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 model 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 state of charge for hybrid vehicles 

across the city and highway drive cycles. How does PERE assure that the battery state across 

customer driving and other off-regulatory cycles does not change? This 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 different 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 with the vehicle and complex control strategies. 
113 

Page 29: 

There is insufficient data to validate the model predictions for fuel cell hybrid vehicles, as the author 

of this report pointed out. Only comparisons could 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 represent static performance. Efficiency during dynamic 

behaviors and at real world operating conditions (operating temperature is not stable in the real world 

usage) will be worse. The issue is how to incorporate 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 efficiency' has no physical meaning; 

indicated efficiency only has meaning in the context of the heat engine alone. That said, the 

sensitivity to powertrain indicated efficiency reported in Table 5 is about half of the expected 10% for 

engine indicated efficiency. Why is the sensitivity 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 rolling resistance term is 

necessary in modeling most vehicles, or else the loads (and hence fuel consumption) will be 

underestimated significantly. Most models in the literature either use a higher order rolling 

resistance term, or a “B” term explicitly. The MIT model (for example) does not. 
114 

Page 37: 

The report indicates that the source bins do not have a dimension for body type, and that the estimates 

will be based on vehicle weight. Within a given weight range, however, there can be considerable 

variation in body type that can result in wide variation in aerodynamic 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 dividing each weight range into different levels by 

general body type or aerodynamic drag coefficient range. 

Response: 

This will be taken under consideration. 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 determined entirely on performance 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 efficiencies are substantially lower than 

those of a TWC. For compliance with PZEV emission standards, NOx throughout the entire drive 

cycle is critical. Additionally, a statement about the tailpipe criteria emission standard must be 

made for all fuel economy estimates. 

Response: 

This subject will have to be revisited and will likely be corrected by the time criteria 

pollutants are modeled in MOVES. 

Page 48 

The table of parameters for vehicle validation needs 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 anticipates PERE will fill. There is also little or 

no information out these data gaps 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 user-inputted driving cycles, on both current and 

advanced technology vehicles. There appears to be an overlap between the two models, since both 
115 

models estimate fuel consumption for current conventional vehicles. EPA plans to integrate PERE 

into MOVES. What are the data gaps that EPA hopes to fill with PERE? Where 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 certain “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 modeling 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 tests 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 consumption from advanced technologies. 

The PERE report indicates that EPA considered 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 model considered, and if not, why not? 

Further, assuming that a creation of PERE is justified, 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 “users” of PERE will be, or what level of 

information EPA will supply to the users. 

The executive summary of the EPA PERE report specifies that an "informed user" can run the model. 

An informed user is not defined. We are concerned 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 analyses will run the MOVES model. Does EPA 

anticipate that states and local governments 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 use as they see fit. Support will be provided at 

some level. 
116 

• Finally, the Alliance reiterates previous comments to the EPA to defer CO2 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 CO2 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 John Koupal). We note that EPA has stated in its 

September 8, 2003 decision that the Clean Air Act does not authorize the regulation of emissions of 

CO2 and other greenhouse gases 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 for validation of MOVES2004 fleet and activity 

estimates, but also for the proper integration of GREET, for generating regional and local 

inventories, and for assessing the impacts on all 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 Vehicle Modeling in PERE” EPA # EPA420-D-04-002, 

March, 2004. 

OVERVIEW 

In my opinion, PERE is an excellent motor-vehicle fuel consumption model, which should prove 

practical and powerful in use. (I may be somewhat 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 relatively simple, requiring relatively few inputs, 

and yet rather accurate over a wide range of vehicles 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 adequately approximated, over many vehicles and 

driving patterns, by simple algebraic expressions. Most of the parameters in these expressions 

are known for any type of light-duty vehicle, or are essentially constant for a given technology. 

This report clearly presents the model, and discusses the assumptions made, and relates them to 

many other referenced studies and measurements. 

Of course the model requires appropriate validation in terms of measurements. But this is not 

primarily a statistical model. While the approximations and their parameterization need 

justification, it is essentially a matter of physics rather than of complete coverage. Wherever an

application involves different physics, then appropriate measurements are needed. That means 

that since it and similar models have been validated for lighter light-duty vehicles, extensive data 

on the heavier light-duty vehicles is almost certainly not needed. However, more measurements 

may be needed on very heavy vehicles. (I have not 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 satisfactory in all applications in support of MOVES, unless there are 

additional measurements and analysis, as I briefly discuss further below. 

In addition, there are some particular conditions and new technologies 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 serious beginning has been made on hybrid vehicles, 

but hybrids are likely to be a diverse class, and too few vehicles have been available for 

measurement to adequately implement PERE in that respect. Fuel cell vehicles represent another 

new technology where even fewer relevant specifications and measurements are available. But it 

is likely, if not certain, that this general modeling approach in its simple form will prove 

adequate in that case as well. In addition, criteria 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 - which might in the future 

become an important regulatory concern, can be modeled in this simple a fashion.) 
118 

I applaud the position taken here that 5% variability, or even 10%, can be a very useful level of 

accuracy. Elaborate models with many more parameters, 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 basis, on-board measurements in random driving, 

may prove inappropriate in particular cases because: 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 isolate certain physical effects to study their 

impact on emissions and fuel consumption separately. Such a statements is added in this 

section. 

2) I agree that application 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, motorcycle 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 linear 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 similar to the motorcycle dependence. 

Also the statement: "end result in the model (of assuming increasing friction vs assuming poorer 

indicated efficiency as N decreases) is identical" needs a brief discussion. The two assumptions 

are "identical", as stated, unless one considers applying the model to analysis of technologies 

focused on saving fuel at low N. 

RESPONSE: Yes, a quadratic fit may also work. If this is advantages, it may be changed in the 

future. Regarding the 2nd comment, the sentence has been clarified. It is true that if a 

more detailed study is required for new engine technologies in this operating regime, the 

user would have to understand 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 1993 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 intensity of many cars. On the other hand, since N/v is related to 

displacement, if somewhat roughly, the fuel rate 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 the fuel rate still depends on N via the engine 

friction term. 
119 

RESPONSE: Of course, it is also sensitive to N/v as mentioned above. I added the parenthetical, 

that this generalization is for light duty. We found motorcycles (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 much NOx? I once estimated the improvement in 

fuel intensity by going lean (Eqn. 4b of my Contemporary Physics 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 effective 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 issue with Tier2 regs. There is some concern 

over the lean engine model using parameters straight from the Honda paper. For example, 

improvement in peak efficiency already includes 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 likely due to hybrid strategy differences. 

7) Hybrid vehicles (pp45, 46): If the "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 regeneration), 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 technology 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 simplicity 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 spreadsheet, 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 loads, especially A/C, would make a lot of 

difference. Does EPA have a dynamometer test for 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 report, 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 Prius is a series parallel hybrid and regenerates the engine 

during operation, PERE cannot do this currently. 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 still, there are differences in the strategies, since the PERE 

strategy is extremely simplified. Also, note in the results that the Insight does not shut off 

the engine completely during most short idles. 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 likely 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. Examples of highly variable variables are (1 – 

epsilon) where epsilon is transmission efficiency, N/v, and perhaps k0 . 

RESPONSE: Good point, another statement of more variability 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 physical basis for the model, as is, is poorer. As shown here (and is 

known), the model, as is, is not realistic in coasting/braking modes; so one must be concerned if 

there are MOVES applications where coasting/braking 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 driving. As more data becomes available, the 

model can be improved. 

MINOR ISSUES 

- Rotating mass term (p7). Jimenez' number seems 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 this in his thesis: Bauer, 1996 (Bosch 

Handbook) and Emmelman and Hucho, “Performance 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 have 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 with 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 think it can be claimed that all the hybrids 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 determination of CDA, or are there other uses of this 

info? 

RESPONSE: This has been clarified. 

- 11) Fig. 29, p56 (Insight). I must have missed where 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 efficient during accelerations than PERE would 

predict (even using a relatively generous 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 entirely appropriate to use to model the 

Prius. This is because the Prius architecture is significantly different from the PERE 

parallel hybrid architecture. For example, the 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 reasonably well on an aggregate level. Of 

course, further validations should be performed on more driving 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 are relatively high at low load (although not at zero load), like a 

battery. What is the shape of the system efficiency 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.