an evaluation of nitrogen oxide emission from a light-duty hybrid-electric vehicle to meet usepa
TRANSCRIPT
AN EVALUATION OF NITROGEN OXIDE EMISSION FROM A
LIGHT-DUTY HYBRID-ELECTRIC VEHICLE
TO MEET U.S.E.P.A. REQUIREMENTS USING A DIESEL ENGINE
A Thesis
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
Robert Neil Paciotti
August, 2007
ii
AN EVALUATION OF NITROGEN OXIDE EMISSION FROM A
LIGHT-DUTY HYBRID-ELECTRIC VEHICLE
TO MEET U.S.E.P.A. REQUIREMENTS USING A DIESEL ENGINE
Robert Neil Paciotti
Thesis
Approved: Accepted:
Co-Advisor Department Chair
Dr. Richard Gross Dr. Celal Batur
Co-Advisor Dean of the College
Dr. Iqbal Husain Dr. George K. Haritos
Faculty Reader Dean of the Graduate School
Dr. Scott Sawyer Dr. George R. Newkome
Date
iii
ABSTRACT
With the availability of petroleum in shorter supply and the demand for a cleaner
environment more prevalent than ever, a recent trend in the automotive industry is to
produce more fuel efficient and lower emission vehicles. A current effort for reduction of
petroleum usage in the auto industry is centered on the development and production of
hybrid-electric vehicles. By the addition of an electric powertrain, hybrid vehicles are
able to consume less fuel by allowing the vehicle’s engine to operate under more efficient
conditions more often than a conventional vehicle. Furthermore, petroleum usage can be
further reduced by utilization of a more efficient diesel fueled engine rather than the
conventional gasoline engines that power the majority of passenger vehicles in the United
States.
The downside to hybrid-electric operation is that in forcing the engine to operate
more efficiently, higher levels of nitrogen oxides (NOx) are generated. Gasoline powered
engines operate with a fuel-rich combustion mixture; thus rendering the exhaust stream
hot and containing little oxygen which leads to effective catalytic promotion of NOx
treatment. On the other hand, diesel fueled engines have the distinct disadvantage of
operating in an oxygen-rich combustion environment that produces lower combustion
temperatures; both factors rendering typical catalytic converters impractical.
The focus of this study aims to evaluate a small displacement, four cylinder,
turbo-diesel engine for nitrogen oxide emission intended for use in a hybrid vehicle. The
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ultimate goal is to determine how the level of NOx emission can be reduced by targeting
different engine operating scenarios via the hybrid control strategy and examine its
effects on fuel economy.
A diesel engine was tested in a laboratory setting over the range that it is expected
to operate in a hybrid vehicle. An efficient experiment design was created to minimize
both the amount of required data and error introduced into the final results. Through
combustion modeling, collected data for the engine’s intake air and fuel mass flow as
well as volumetric exhaust content data was used to determine levels of engine-out mass
flow of NOx over the engine’s operating domain. Several fuel consumption and NOx
emission parameters were calculated and regression models were developed to produce
baseline engine maps. Based on the baseline maps, targeted engine operation points were
selected to examine how the vehicle’s hybrid control strategy might be tuned towards
engine operation that provides lowered NOx emission at the cost of fuel economy.
Results show that quite significant levels of NOx reduction can be had at a small
cost in increased fuel use. However, even at reduced engine-out levels, NOx emission is
still relatively considerable in terms of meeting standards set for by the United States
Environmental Protection Agency. The use and effectiveness of selective catalyst
reduction by injection of urea into the exhaust stream to treat engine-out NOx is also
explored in this thesis.
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ACKNOWLEDGEMENTS
The author thanks the following for their contributions:
• Co-advisors Dr. Richard Gross and Dr. Iqbal Husain, and faculty reader Dr. Scott
Sawyer of the College of Engineering at The University of Akron for their
guidance and suggestions.
• The Lubrizol Corporation for donating time and allowing use of their facilities to
conduct the testing for this research. Specifically, engineer Ed Akucewich for
taking his time to answer questions and operate the equipment.
• The entire ChallengeX team including administration, faculty, and students for
their support of the ChallengeX program that has provided inspiration for this
thesis.
• Nathan Picot, graduate student in electrical engineering at The University of
Akron and ChallengeX team member, for running the vehicle simulation models
used within this thesis.
• University of Akron lab technicians Steve Gerbetz and Rick Nemer of the
Mechanical and Biomedical Engineering departments respectively for their
insights and support.
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TABLE OF CONTENTS
Page
LIST OF TABLES ix
LIST OF FIGURES x
CHAPTER
I. INTRODUCTION 1
1.1 Background of the Study 3
1.2 Efficiency of Diesel vs. Gasoline Engines 7
1.3 Emission Components of Combustion Engines 7
1.4 USEPA Emissions Regulations for Light Duty Vehicles 8
1.5 Treatment of Diesel Exhaust Emissions 9
1.5.1 HC and CO Reduction Using Diesel Oxidation Catalysts 10
1.5.2 Particulate Filters for Soot Control 10
1.5.3 Methods of Nitrogen Oxide Reduction 13
1.6 Research Focus 14
II. NOx GENERATION AND CONTROL BY EXHAUST AFTERTREATMENT 16
2.1 Diesel Combustion and NOx Formation 16
2.2 Effects of Engine Operation on Efficiency and NOx Emission Level 18
2.3 Aftertreatment Methods for NOx Control 22
2.3.1 Lean NOx Traps 22
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2.3.2 Treating Nitrogen Oxides with Ammonia 23
2.3.3 Catalytic Converters for SCR Systems 24
2.3.4 SCR Systems and Control 25
2.3.5 Results of Previous Studies 27
III. EXPERIMENT DESIGN AND SETUP 29
3.1 Experiment Overview 29
3.2 The Test Engine 30
3.3 Required Data 30
3.4 Experiment Design 32
3.4.1 Statistical Theory 33
3.4.2 Domain Analysis 36
3.4.3 Experiment Optimization 40
3.5 Experimental Setup 45
IV. DATA AND ANALYSIS 47
4.1 Data Treatment 47
4.1.1 Fuel Use Analysis 48
4.1.2 NOx Emission Analysis 50
4.1.3 Comparison Data 63
4.2 Experimental Uncertainty Analysis 64
4.3 Regression Model Development 66
4.4 Computational Drive Cycle Modeling with Regression Data 68
V. RESULTS AND DISCUSSION 73
5.1 Baseline Engine Mapping 73
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5.1.1 Fuel Use Mapping 74
5.1.2 Nitrogen Oxide Emission Mapping 77
5.1.3 Fuel Consumption and NOx Emission Comparison Mapping 80
5.2 Determination of Target Series Mode Engine Operation for Simulation 81
5.3 Drive Cycle Simulation Results 82
5.4 Validity of Regression Models 83
VI. CONCLUSIONS AND RECOMMENDATIONS 86
6.1 Research Conclusions 86
6.2 Recommendations for Future Work 87
REFERENCES 89
APPENDICES 92
APPENDIX A. CALCULATION OF PREDICTION ERROR VARIANCE 93
APPENDIX B. UNCERTAINTY ANALYSIS 95
APPENDIX C. DRIVE CYCLE SIMULATION RESULTS 98
APPENDIX D. DATA SUMMARY AND SAMPLE CALCUALTION 101
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LIST OF TABLES
Table Page
1.1 USEPA legislation for tier 2 classified vehicles 9
3.1 Optimum data collection points 42
4.1 Recorded experimental data for steady-state engine operation 47
4.2 Calculated values for fuel consumption 49
4.3 Calculated values for NOx emission 63
4.4 Relative uncertainty computation for computed parameters 65
4.5 Regression results summary 68
5.1 UDDS drive cycle simulation results 82
5.2 Required NOx reduction via aftertreatment to meet USEPA standards 83
x
LIST OF FIGURES
Figure Page
1.1 Typical hybrid vehicle architectures 3
(a) series architecture
(b) parallel architecture
(c) split architecture
1.2 The University of Akron ChallengeX hybrid vehicle architecture 4
2.1 Example efficiency map 20
2.2 Example Plot of NOx Emission as a Function of bmep 21
3.1 VW 1.9L TDI peak performance curves 36
3.2 Siemens ACW-80-4 PM motor performance curves 37
3.3 Possible engine operation for electrical power generation 39
3.4 Graphical representation of data collection points 42
3.5 PEV Response surface for experiment design 44
3.6 Test setup schematic 45
3.7 Test setup 45
4.1 USEPA UDDS drive cycle 70
5.1 Engine mapping of fuel mass flow rate, units in lbm/hr 74
5.2 Engine mapping of brake specific fuel consumption, units in lbm/(hp*hr) 75
5.3 Engine mapping of fuel efficiency, units in percent 75
5.4 Volumetric NOx emission content as a function of bmep 77
5.5 Engine mapping of NOx mass flow rate, units in lbm/hr 78
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5.6 Engine mapping of brake specific NOx emission, units in lbm/(hp*hr) 79
5.7 Engine mapping of NOx emission index, units in lbm/lbm 80
5.8 Error evaluation of response models 84
(a) fuel mass flow rate - lbm/hr
(b) bsfc - lbm/(hp*hr)
(c) fuel efficiency - %
(d) NOx mass flow rate - lbm/hr
(e) bsNOx - lbm/(hp*hr)
(f) NOx emission index – lbm/lbm
1
CHAPTER I
INTRODUCTION
As a source of transportation, current production passenger motor vehicles utilize
internal combustion engines; primarily gasoline fueled, spark ignition or diesel fueled,
compression ignition engines. Not only are combustion engines fueled by non-renewable
petroleum, but are also significant contributors to atmospheric pollution in the United
States as well as world wide. One promising alternative to the U.S. popular spark ignition
engine is the use of hydrogen fuel cells as an automobile power source. Fuel cells can
offer a high level of performance and produce zero harmful tailpipe emissions while
operating on renewable resources. Unfortunately, the production of clean hydrogen for
fuel cell use is usually associated with some emissions and only a primarily nuclear based
economy would be capable of hydrogen production without significant fossil fuel
consumption [1]. Furthermore, current technology is far from reaching a level of mass
production for the auto market. While fuel cells may have a plausible outlook for the
future, current efforts must focus on reduction of petroleum usage and pollution control
using available technology.
A recent trend in the automobile industry is centered on the production of fuel
efficient hybrid-electric vehicles. The basic theory behind hybrid operation is simple;
utilize the addition of electrical drive components to allow a combustion engine to be
operated more efficiently. An electric generator, coupled to the crankshaft of an engine, is
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capable of taking mechanical power from the engine and converting it to electrical power.
The generator can be operated so that its power demand forces the engine to operate
under conditions that provide higher efficiency than could be had with a mechanical-only
powertrain. The produced electrical energy can then be stored via an onboard battery
pack for later usage where it is used to power an electric drive motor (typically more
efficient than a combustion engine). There are many different types of configurations for
hybrid vehicle architectures; the most common in today’s mass production market
include the following descriptions and illustrations in Figure 1.1:
1. Series Hybrids: a configuration in which power is transferred from an internal
combustion engine through a generator to a battery. The stored energy in the
battery is then used to power the vehicle via an electric drive motor. The drive
motor is the only source of propulsion for the vehicle.
2. Parallel Hybrids: a configuration in which a motor/generator is composed as one
unit. The motor/generator is coupled to the engine’s crankshaft and is capable of
generating electrical energy for storage in a battery pack or acting as a motor,
using stored electrical energy to provide assistance to the engine when needed.
3. Split Hybrids: a combination of series and parallel configurations. Because of
mechanical coupling constraints, typical current production split hybrids have a
fixed ratio at which power from series or parallel operation can deliver to the
drive wheels.
3
(a) series architecture
(b) parallel architecture
(c) split architecture
Figure 1.1: Typical hybrid vehicle architectures
While many variations of the different hybrid powertrain configurations listed
above exist both in production and prototype, the focus of this research is not on hybrid
vehicle architecture and they will not be discussed in detail.
While any variation of hybrid architecture is capable of generating higher levels
of fuel economy in comparison to their conventional powertrain counterparts, the effects
of engine operation at highly efficient scenarios generates high levels of emission of
oxides of nitrogen which forms the basis for this study.
1.1 Background of the Study
Inspiration for this research came about as a result of The University of Akron’s
participation in an engineering student design competition called ChallengeX. The
Engine
Generator Battery
Transmission Drive Motor
Engine
Motor/Generator Battery
Transmission
Engine Generator Drive Motor
Battery
4
ChallengeX competition challenges engineering students of all disciplines to take a
production vehicle, in this case the Chevrolet Equinox, and arrange its powertrain into a
hybrid configuration that meets the goals of increased fuel economy and reduced
emissions while maintaining consumer expected performance and acceptability. To gain
a full understanding of the research to be presented, the remainder of this section will
provide a brief overview of The University of Akron’s vehicle architecture and operation.
The selected architecture for the competition vehicle is unique in comparison to
current production hybrid vehicles. The design team has termed its architecture a series-
parallel 2x2. Its series-parallel designation suggests that the vehicle can operate in a
purely series or purely parallel mode as well as any combination between; the ratio is not
fixed as with typical split hybrids. The 2x2 designation refers to the fact that the front
wheels are primarily driven by means of a standard engine/transmission combination
while the rear wheels are driven by an electric drive motor. Moving the drive motor to the
rear of the vehicle and separating it mechanically from the wheels that are driven by the
engine is what allows the vehicle to be operated at an infinite ratio between series and
parallel. A generator coupled to the engine’s crankshaft provides energy conversion for
the rear drive motor and/or to a battery pack where it can be stored for later use. The
architecture is illustrated in Figure 1.2.
Figure 1.2: University of Akron ChallengeX hybrid vehicle architecture
Front Drive Rear Drive
Engine
Motor/Generator Ultracapacitors
Transmission Drive Motor
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The uniqueness of The University of Akron design has many advantages. In city
driving, at low speeds usually stop and go, the vehicle can operate in a purely series mode.
In series mode, if the state of charge of the energy storage system (battery pack) is high,
the vehicle can operate with the engine off, basically as an electric vehicle. When the
state of charge becomes low, the control system allows the engine to start itself and the
generator is used to load the engine such that it can operate steady-state at a highly
efficient operating point. When the state of charge in the energy storage system becomes
high enough, the engine is shut down to reduce fuel consumption. This cycle is repeated
as long as the driver demand is city-type driving.
When the driver of the vehicle demands higher speeds, such as that seen in
highway transportation, the front drive wheels take over for the rear. This is
advantageous due to a properly sized engine in which the mechanical powertrain will
have the engine operate at or near its most efficient point at highway cruise speed.
Typical highway operation involves a vehicle traveling at steady speed, the only
significant variances being slowing down or speeding up at the courtesy of other drivers,
so again operation is primarily steady-state.
Low and high speed operation have been discussed and the only factor that
remains is moving from low to high speeds. This is where the advantage of the series-
parallel vehicle comes into play. When the driver demands significant acceleration, both
the front and rear wheels can be driven at a variant power ratio providing the necessary
power to accelerate the vehicle. If the vehicle were purely series, the electric motor would
be insufficient to operate the vehicle under high power demand. Because the engine is
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undersized for fuel economy reasons, it also would be insufficient if the drivetrain
arrangement were purely mechanical.
Because the vehicle can be operated as a series hybrid in city driving, the engine
runs at a constant speed and load; highway driving being predominantly constant speed,
analysis of fuel efficiency and emissions is vastly simplified assuming that the majority
of driving is done with steady-state engine operation. The transient engine operating
conditions accelerating the vehicle to highway speed or slightly speeding up or slowing
down on the highway can be assumed negligent in comparison to the other driving
scenarios.
Specific component selections for The University of Akron ChallengeX vehicle
are as follows:
• Engine: 1.9L Volkswagen Turbo Diesel
• Transmission: Volkswagen 6-speed Direct Shift Gearbox
• Generator: Siemens, model ACW-80-4
• Drive Motor: Ballard, model A 168 440 00 88 Integrated Powertrain
• Energy Storage: 143 NessCap Ultracapacitors, model ESHSP-3500C0-002R7
The Volkswagen diesel engine was selected for fuel efficiency reasons which will be
discussed in detail later. The Volkswagen direct shift gearbox was chosen for its
efficiency and ease of operation, basically set up like an efficient manual transmission
that is automatically shifted. The generator and drive motor were chosen for their high
power output and high power density. Ultracapacitors, rather than conventional nickel-
metal-hydride or lithium-ion batteries provide much higher charge and discharge rates so
that full advantage of the power capabilities of the generator and drive motor can be had.
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1.2 Efficiency of Diesel vs. Gasoline Engines
Already popular in Europe is the use of compression ignition (diesel) engines for
passenger car transportation. More than 40% of Europeans drive diesel powered
automobiles in comparison to 4.5% of Americans [2]. An attractive alternative to
gasoline, compression ignition engines can deliver a higher level of fuel economy. Some
of the latest technology diesel engines utilizing high speed direct injection for fuel
atomization can achieve up to 35% lower volumetric fuel consumption than equivalent
performance spark ignition engines [3]. The use of a diesel engine as the primary power
source for a hybrid vehicle can lead to a significant improvement in fuel economy when
compared to common U.S. gasoline fueled vehicles.
1.3 Emission Components of Combustion Engines
Both spark and compression ignited engines are known to be significant sources
of environmental pollution. Internal combustion exhaust gas is partially, yet significantly
responsible for contribution to global warming, acid rain, and smog. It also contains
known carcinogens and can lead to asphyxiation in humans. The following provides a
brief discussion of compounds classified by the United States Environmental Protection
Agency (USEPA) as pollutants within the exhaust stream.
Gasoline fueled, spark ignition engine exhaust gases contain oxides of nitrogen
(NOx), carbon monoxide (CO), and un-burnt hydrocarbons (HC). Diesel exhaust
emissions contain lower levels of CO and HC with NOx levels being similar [4]. Both
gasoline and diesel fuels contain some amount of sulfur, diesel fuel having a much
greater content at 0.1-0.3% weight vs. <0.06% weight for gasoline [4]. Also prevalent in
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addition to the exhaust components discussed above, combustion of diesel fuel is also a
source of particulate emissions. About 0.1-0.5% of the fuel is emitted as small
particulates, which consist primarily of soot with some additional adsorbed hydrocarbon
material [4]. High levels of sulfur in diesel fuel are not of particular concern because its
content can be controlled in the manufacture of the fuel. The USEPA has mandated that
all diesel fuel produced for highway use shall contain less than 15ppm (0.015% weight)
after June 30, 2006 [5]. Diesel fuel with a sulfur content of less than 0.010% weight has
been available in Germany since 2003 [6].
1.4 USEPA Emissions Regulations for Light Duty Vehicles
Legislation of vehicle exhaust emission is governed by the USEPA. Light duty
vehicles must meet USEPA tier 2 emissions requirements and are classified as those that
exhibit a maximum 6000 lb curb weight and a maximum 8500 lb gross vehicle weight
rating. The tier 2 emissions requirement is broken up into 10 bins having different
limitations of exhaust emissions for different compounds classified as harmful;
legislation is irrespective of fuel type. Two of the bins were part of a phase-in policy and
have since been deleted at the end of the 2006 model year. Table 1.1 shows the allowable
emission limits for tier 2 vehicles per bin.
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Table 1.1: USEPA legislation for tier 2 classified vehicles
Bin USEPA Allowable Emission Limits Per Bin - g/mi (lbm/mi)
# NMHC CO NOx PM HCHO
Temporary Bins (phased out at conclusion of 2006 model year)
10 0.156 (0.00034) 4.2 (0.0093) 0.60 (0.00132) 0.08 (0.00018) 0.018 (0.000040)
9 0.090 (0.00020) 4.2 (0.0093) 0.30 (0.00066) 0.06 (0.00013) 0.018 (0.000040)
Permanent Bins
8 0.125 (0.00028) 4.2 (0.0093) 0.20 (0.00044) 0.02 (0.00004) 0.018 (0.000040)
7 0.090 (0.00020) 4.2 (0.0093) 0.15 (0.00033) 0.02 (0.00004) 0.018 (0.000040)
6 0.090 (0.00020) 4.2 (0.0093) 0.10 (0.00022) 0.01 (0.00002) 0.018 (0.000040)
5 0.090 (0.00020) 4.2 (0.0093) 0.07 (0.00015) 0.01 (0.00002) 0.018 (0.000040)
4 0.070 (0.00015) 2.1 (0.0046) 0.04 (0.00009) 0.01 (0.00002) 0.011 (0.000024)
3 0.055 (0.00012) 2.1 (0.0046) 0.03 (0.00007) 0.01 (0.00002) 0.011 (0.000024)
2 0.010 (0.00002) 2.1 (0.0046) 0.02 (0.00004) 0.01 (0.00002) 0.004 (0.000009)
1 0.000 (0.00000) 0.0 (0.0000) 0.00 (0.00000) 0.00 (0.00000) 0.000 (0.000000) Abbreviations:
NMHC - non-methane hydrocarbons CO - carbon monoxide NOx - nitrogen oxides
PM - particulate matter HCHO - formaldehyde
The USEPA specifies that vehicle manufacturers are required to meet a light duty
vehicle fleet average to the tier 2, bin 5 specification. While production vehicles are
allowed to be sold to the bin 8 specification, their sales must be offset by an equal sales
volume of bins lower than 5.
1.5 Treatment of Diesel Exhaust Emissions
While it is possible to reduce emissions output in diesel engine exhaust by
varying engine operating parameters, achievements are typically at the cost of fuel
efficiency. Since this discussion is focused on hybrid vehicles, where high levels of fuel
economy are of prime concern, discussion of variant engine operation will be omitted.
The focus will instead be shifted primarily to aftertreatment of diesel exhaust;
aftertreatment meaning methods of emission reduction in the exhaust stream, post
combustion. The following sections will discuss formation and possible treatment of
diesel emissions with specific reference and insight to their significance in passenger
hybrid vehicle operation using diesel fuel.
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1.5.1 HC and CO Reduction Using Diesel Oxidation Catalysts
Significant reduction of unburnt hydrocarbons (HC) and carbon monoxide (CO)
can be achieved by use of oxidation catalysts in the exhaust stream. Through precious
metal catalytic promotion, HC and CO in the exhaust stream are oxidized as follows [7]:
O]H[]CO[]O[[HC] 222 +→+ (1.1)
]CO[]O[[CO] 22 →+ (1.2)
The resulting water (H2O) and carbon dioxide (CO2) are not considered contributors to
pollution by the USEPA, although CO2 is classified as a greenhouse gas. Oxidation
catalysts for diesel engines are similar in operation to those of gasoline engines, however
their design differs greatly as performance must be directed toward operation at much
lower exhaust temperatures seen in diesel exhaust [8].
Formation of both HC and CO in the combustion process is a result of combusting
a rich mixture of fuel, meaning that the air/fuel ratio is low and insignificant oxygen is
present for complete combustion. Although no engine is capable of 100% complete
combustion, diesel engines do always operate with excess air and the small amounts of
HC and CO generated are not of prime concern.
1.5.2 Particulate Filters for Soot Control
Soot emission is composed of small pieces of solid carbon matter emitted from
the combustion of fuel in highly rich conditions, meaning that the availability of oxygen
present to fully combust the fuel is highly insufficient. While it was stated previously that
diesel engines always operate with excess air whereas a spark ignition engine does not, it
should follow that particulate matter is more of a problem with gasoline engines over
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diesel; however, quite the opposite is true. Because the fuel and air for a spark ignited
engine are premixed before entering the combustion chamber, the fuel content is almost
fully vaporized and only small portions of the mixture contains liquid fuel. This portion
does contribute some effect to particulate matter production in gasoline engines, but they
are so small they are considered negligent. Since the fuel in a diesel engine is injected
into the combustion chamber after the air has been compressed, a brief period of time
elapses where a high concentration of atomized (but not vaporized) fuel is present in the
injection region creating significant amounts of soot emission. Actual formation of soot
concentration is largely dependent on the design of the injection system and combustion
chamber as well as engine operating speed and load, more on soot formation can be
found in [9].
While the formation of soot is a problem in itself, another implication lies in that
the previously discussed unburnt hydrocarbons tend to be adsorbed by the soot particles
and add to the mass content. Collectively the combination of soot, HC, and other
compounds adsorbed in trace amounts is what makes up particulate emissions.
Fortunately, particulate mass can be reduced by treating the HC via a diesel oxidation
catalyst discussed above. Currently, oxidation catalysts are the only type of particulate
control used in production light-duty diesel vehicles. Recent studies show that total
particulate matter mass can be reduced by 20-35% using this method [10,11]; however
the solid carbon soot still remains a problem.
Soot can be controlled and reduced to miniscule quantities post combustion by the
use of a diesel particulate filter (DPF). Several designs for DPFs exist, but the most
common and effective designs are a wall-flow type. The filter provides channels for
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exhaust flow that force the gases through blocked ends requiring it to flow through a clay
derived material that traps the particles. Filters of this design are reported to have
trapping efficiency of almost 100% [11]. The problem in DPF use lies in that the filter is
limited as to how much particulate it can actually contain. Rapid clogging and blockage
of DPF filters occur much more rapidly than would be acceptable for consumer usage.
The method for clearing particle mass from DPFs is known as regeneration of the
filter and simply involves combusting the soot particles. Regeneration can occur either
passively or actively. Passive regeneration is the ideal situation and is accomplished by
maintaining a high enough exhaust temperature so that the particles can be combusted
during normal vehicle operation. Unfortunately the combustion of particulate matter in
oxygen occurs at 1020-1120ºF which is unreasonable for diesel exhaust temperatures.
However, nitrogen dioxide (NO2) is much more reactive with carbon than oxygen and
particulate matter combustion occurs at much lower temperatures of 480-570ºC. Both
light and heavy duty diesel engines running at high load have been shown to produce the
amount of NO2 and exhaust temperatures necessary to facilitate regeneration [11]. Heavy
duty diesel vehicles used in the commercial industry have engines sized for fuel economy
and high load operation is common as most driving is done on the highway, making DPF
usage ideal for this situation. Light duty passenger vehicles operate more frequently in
city type driving where high load generating high exhaust temperatures is not frequent
enough for regeneration, hence the lack of DPF usage in typical production diesel
passenger vehicles. If passive regeneration is possible and suitable for application, high
NO2 levels required for regeneration are a problem in themselves and will be discussed in
detail later.
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If passive regeneration cannot be employed, an active regeneration strategy can be
implemented in which catalyst additives can be added to the fuel to reduce necessary
particulate combustion temperature and/or a complex engine management strategy can be
developed in which fuel injection rates are altered to provide higher exhaust temperature.
A fuel borne catalyst would have to be added to the fuel either at fill up or already be
contained within the fuel from the manufacturer. An engine management strategy for
active regeneration requires the use of special sensors to determine when regeneration is
needed and control the regeneration as well as expensive development time. Promising
research has been conducted on implementation of such a strategy for light duty vehicles
[9-11].
An advantage is had by a light duty diesel hybrid vehicle vs. a conventional diesel
powered vehicle. If the hybrid’s generator is capable of pulling enough load on the
engine, higher exhaust temperatures can be had in city type driving. If the temperature is
high enough for regeneration of a DPF, it can be had passively. In the particular case of
The University of Akron’s ChallengeX vehicle, preliminary engine/generator evaluation
has shown that passive regeneration temperatures will be had in the range that the
generator could be operated for high fuel efficiency. In development of a vehicle of this
type, proper engine selection (or development) should consider the criteria for passive
regeneration which can save much time and money down the road.
1.5.3 Methods of Nitrogen Oxide Reduction
Relevant levels of oxides of nitrogen that result from combustion of hydrocarbon
fuels with air are nitric oxide (NO) and nitrogen dioxide (NO2). Collectively these
14
compounds are commonly referred to as simply NOx. Both compounds are considered by
the EPA to be hazardous to the environment and their emission levels regulated. Light
duty gasoline and diesel vehicles exhibit similar levels of NOx generation when utilized
in conventional vehicle powertrains. Reduction of NOx in gasoline engines is quite
effective by use of typical three-way catalytic converters. This type of converter requires
that temperatures be higher than those seen in diesel exhaust and that the exhaust stream
not be oxygen rich, which is the case with compression ignition. Because chemical
reactions always occur in the simplest way possible, excess oxygen (O2) in the diesel
exhaust stream will bond before promoting dissociation of NOx in the catalyst.
Implication that chemical conversion of NOx compounds in diesel exhaust is not
possible by catalytic promotion is further complicated when the operating scenario of a
hybrid vehicle is considered. As previously mentioned, high efficiency from an engine is
accomplished by running at high load for a given engine speed, a situation in which high
NOx is also generated; this concept will be explained fully in Chapter 2. If the low load
conditions normally seen in city type driving are taken away from the vehicle, NOx levels
over a drive cycle will increase dramatically. A few modern technologies that may be
feasible are starting to break the surface for diesel NOx control including lean traps that
are capable of storing NOx at low operating temperatures until higher loads are seen and a
process called selective catalyst reduction in which ammonia is used to treat engine
exhaust for NOx reduction. Both methods will be discussed fully in Chapter 2.
1.6 Research Focus
It has been discussed and shown that diesel engines have much to offer over
gasoline fueled engines in terms of fuel economy, especially in hybrid vehicles.
15
Emissions of carbon monoxide and unburnt hydrocarbons in diesel exhaust are low in
comparison to gasoline and what little produced can be dealt with easily and effectively
with oxidation-type catalytic treatment. A particulate trap to treat soot can be near 100%
effective in reduction and can easily be regenerated passively in a hybrid vehicle. The
problem remains in NOx emission reduction without increased fuel consumption.
Diesel engine emission of nitrogen oxides will be explored in the remainder of
this thesis. NOx generation and management via exhaust aftertreatment systems will be
discussed. An efficient experiment design will be developed to evaluate both NOx content
and fuel economy over the operating range of a diesel engine intended for hybrid vehicle
usage. The response will not only provide means for engine mapping, but also allow for
the construction of a vehicle simulation model that provides comparison of the tradeoff
that can be had in fuel economy vs. NOx levels. Evaluating the model, judgment can be
made as to how varying the hybrid vehicle’s hybrid control strategy to target different
engine operations might reduce the amount of NOx emission to meet USEPA tier 2
standards. The results of lowered NOx emission will be weighed against the resulting
decrease in fuel economy.
16
CHAPTER II
NOx GENERATION AND CONTROL BY EXHAUST AFTERTREATMENT
In this chapter the processes of combustion of diesel fuel and nitrogen oxide
formation will be examined. The causes for different levels of NOx generation under
different engine operation will be discussed in detail with specific reference to engine
efficiency and hybrid vehicle operation. Theory and operation of lean NOx traps and
selective catalyst reduction for aftertreatment will be explained along with results from
previous studies.
2.1 Diesel Combustion and NOx Formation
Diesel fuel is made up of combustible hydrocarbons; chemical compounds that
are made of only carbon and hydrogen atoms. The average chemical formula for common
diesel is C12H26, but diesel hydrocarbon compounds range from C10H22 to C15H32 [15].
Complete combustion of hydrocarbons with excess oxygen results in the formation of
only water and carbon dioxide as follows:
]CO[O]H[]O[]HC[ 222yx +→+ (2.1)
Due to the constraints placed on internal combustion engine operation, complete
combustion of hydrocarbons is not possible even though diesel engines will always
operate with excess oxygen. It was mentioned in the previous chapter that due to
incomplete combustion, formation of carbon monoxide and hydrocarbons are present in
17
the exhaust stream, but the small amounts generated in diesel engines is easily and
effectively treated with typical catalytic converters. Of more concern is the fact that the
atmosphere is composed of mostly nitrogen (approximately 78%); its presence along with
excess oxygen and high temperature seen from combustion is what produces the
undesirable and highly toxic NOx compounds. At combustion temperatures, molecular
nitrogen (N2) and oxygen (O2) in the combustion air disassociate and bond with each
other; a process commonly referred to as thermal NOx generation.
Most of the NOx generated in combustion is of the form nitric oxide (NO) and is
governed by the following reactions.
NNONO 2 +→+ (2.2)
ONOON 2 +→+ (2.3)
HNOOHN +→+ (2.4)
In much less quantities, but still a significant pollution source is the generation of
nitrogen dioxide (NO2), which is produced in the following manner from nitric oxide and
oxygen.
22 2NOO2NO →+ (2.5)
The amount of NOx compounds resulting from thermal generation is largely dependent
on temperature in the combustion chamber and the rate at which gases are flowing in and
out of the engine. Therefore, engine operation characteristics play the primary role in
engine-out NOx emission levels.
18
2.2 Effects of Engine Operation on Efficiency and NOx Emission Levels
A number of variables affect performance, efficiency, and emissions of
compression ignition engines such as load, speed, fuel injection parameters, and
combustion chamber and piston design amongst many others. Because this paper is not
devoted to engine design and directed more toward engine operation in hybrid vehicles,
only the parameters that can be controlled by the hybrid control strategy (engine load and
speed) will be discussed and the others omitted.
Fuel consumption in engine testing is measured as a flow rate; usually mass flow
of fuel per unit time, fm& . This data can be used to calculate fuel flow rate per unit output
power, called the specific fuel consumption (sfc); a measurement of how efficiently an
engine is using fuel. Typically when referring to reciprocating engines this value is called
the brake specific fuel consumption (bsfc), indicating that power, bP , was measured
using a dynamometer’s brake, and does not take into account any mechanical losses
associated with a particular drivetrain for a particular vehicle. The value is calculated as
follows.
b
f
P
m&=bsfc (2.6)
The typical evaluation tool for analyzing engine efficiency is to test the engine at
a variety of speed/load combinations and plot a contour map of bsfc under the engine’s
peak torque curve. Because torque is a measure of a particular engine’s ability to do work,
a more useful value is one that allows standardized comparison of several different
engines. Mean effective pressure (mep) or again for reciprocating engines, brake mean
effective pressure (bmep), is a measure of an engine’s work per cycle per cylinder
19
displaced volume. The result of calculated bmep gives a value for a theoretical constant
pressure exerted in the combustion chamber as a result of combustion. While it does not
describe the actual combustion pressure, it is a good standard for comparison.
To develop an equation for bmep, first start with a definition for work per cycle
using the engine’s output power:
N
nP Rb=cycleperWork (2.7)
where bP is engine power, Rn is the number of crankshaft revolutions per power stroke,
and N is the engine speed. Recognize that engine power is the power output for the
entire engine rather than just one cylinder. Therefore, dividing by the engine’s entire
displacement volume, dV , will yield bmep.
NV
nP
d
Rb=bmep (2.8)
To simplify the computation, power can be expressed as the product of brake torque, bT ,
and engine speed. Note that to be unit consistent with equation (2.8), engine speed will
have to be expressed in terms of revolutions per unit time, thus multiplication by 2π is
necessary. Also note that for any 4-stroke engine, Rn is 2. Subbing these values into
equation (2.8) gives the form of equation (2.9); an example of proper unit conversion is
given.
( )2
3
*22
*4bmep
in
lb
cyclein
inlbrev
rad
cycle
rev
V
T
d
b ⇒
⇒=
ππ
(2.9)
20
An example of what is termed a power map for a diesel engine is shown in the
following figure, brake specific fuel consumption contours are plotted in g/kW*h.
Respective scales are shown for engine torque and corresponding brake mean effective
pressure.
Figure 2.1: Example efficiency map [16]
The contour plot presented is specific to a 6.5 liter, 8-cylinder engine, but
recognize that all turbo-diesel engines will present a similar style profile. In terms of fuel
economy, the best region to operate the engine is where bsfc is lowest thus maximizing
fuel efficiency. This defines the area at which a hybrid vehicle’s generator would be most
Peak Torque
Curve
21
effective in terms of reduced fuel usage. The question that remains is what happens to
NOx levels in the area of high efficiency?
It has been shown that fuel efficiency is a strong function of engine speed and
load and it can also be said that high fuel efficiency is a result of high thermal efficiency.
It follows that NOx generation would be expected to be high in these areas as well due to
higher temperatures. Effects of engine operation and NOx generation are well
documented [16] and trends show that NOx levels increase with bmep. The following
Figure 2.2 shows a typical NOx/bmep relationship for a diesel test engine. Differences are
shown for indirect fuel injection vs. the more fuel efficient direct injection. Note the
reduction in NOx content as a result of retarded injection timing, a huge penalty in fuel
efficiency as less burn time is allowed per combustion stroke.
Figure 2.2: Example plot of NOx emission as a function of bmep [16]
22
In hybrid vehicle operation, where obtainment of a high level of fuel economy is
of the utmost concern, engine out NOx is definitely the worst enemy. It has been
demonstrated that conditions for engine operation that provide the best fuel economy also
provide for significant levels of NOx generation. If diesel engines are to be used in hybrid
vehicles, an aftertreatment system must be employed to meet EPA requirements.
2.3 Aftertreatment Methods for NOx Control
It was mentioned in Chapter 1 that gasoline and diesel fueled engines exhibit
similar NOx emission characteristics; however, due to the low temperature of diesel
exhaust in comparison and the presence of excess oxygen, typical three-way catalytic
converters used on gasoline engines are ineffective for diesel exhaust. Instead, current
research efforts have focused on more complex technologies to treat NOx.
2.3.1 Lean NOx Traps
Until recently, mass produced diesel passenger vehicles have not contained any
means for NOx control. New for the 2007 model year, Mercedes Benz was the first auto
maker to introduce a model equipped with a lean NOx trap, a promising technology that is
capable of NOx adsorption under lean (oxygen rich) conditions. A system as such
operates by use of a catalyst that promotes adsorption and storage of NOx in a lean
environment by forming a new compound and then later decomposing into non-harmful
water and nitrogen in a fuel rich environment. The actual chemical principles behind
these reactions are quite complex and beyond the scope of this study, but are well
documented [12]. The problem imposed is that in order to create the fuel rich
environment, excess fuel must be injected into the exhaust stream frequently for
23
regeneration, of course reducing fuel economy. Studies have shown that NOx reduction of
up to almost 80% have been achieved using this strategy, but at the cost of an
approximate 3% loss in fuel efficiency in high load conditions [4,12,13]. Fuel penalties of
7-9% have been observed in low load conditions [12]. Possibly a better solution to post-
combustion NOx conversion without the fuel consumption penalty is the use of selective
catalyst reduction discussed in the following sections.
2.3.2 Treating Nitrogen Oxides with Ammonia
The process of selective catalyst reduction (SCR) begins with injection of a
reducing agent into the exhaust capable of bonding with NOx compounds to form non-
harmful ones. While several reducing agents have been studied with limited success,
injection of ammonia (NH3) has proven to be quite effective. Possible reactions of
ammonia with nitrogen oxides are presented below. The term “selective” is demonstrated
in ammonia’s unique ability to selectively react with NOx compounds rather than be
oxidized to form N2, N2O, and NO [17].
O6H4NO4NONH4 2223 +→++ (2.10)
O3H2NNONO2NH 2223 +→++ (2.11)
O12H7N6NO8NH 2223 +→+ (2.12)
Greater than 90% of NOx from diesel emissions is composed of NO and thus
reaction (2.10) accounts for most of the reduction as it occurs with NO and NH3 at a 1:1
ratio in excess oxygen. Reaction (2.11) is most desirable because it occurs at a lower
temperature than the others, but requires a 1:1 ratio of NO and NO2. Reaction (2.12) takes
care of the remaining NO2 that cannot be reduced by (2.11) due to insufficient NO.
24
While ammonia can be an effective NOx reduction agent, storage and
transportation becomes an issue as ammonia itself is a toxic chemical regulated by the
EPA. Instead, injection of an aqueous urea solution into the hot exhaust stream will
eventually decompose into ammonia and carbon dioxide. Urea (CON2H4) is a solid,
crystalline structure at room temperature, but easily dissolvable in water. For SCR
systems it is usually mixed at 32.5% by weight, creating a eutectic mixture (one that
exhibits the lowest freezing point possible for the solution). Decomposition of urea is a
complex process and explained in detail by various literature [18,19,20]. Let it be said
that the basic mechanism occurs as follows: Injection of the aqueous solution into hot
exhaust gas first produces the following result via thermolysis (thermal decomposition of
a chemical compound).
HNCONHHCON 342 +→ (2.13)
The HNCO compound is then further reduced by hydrolysis (reaction with water), which
is plentifully available from the aqueous solution.
232 CONHOHHNCO +→+ (2.14)
2.3.3 Catalytic Converters for SCR Systems
While the decomposition of urea into usable ammonia for SCR is a process that
occurs quickly enough for use by injection into hot exhaust gas [21], efficient bonding of
ammonia and NOx compounds is only possible with the aid of a specially designed
catalytic converter. Three types of catalysts have been developed for commercial use:
noble metals, metal oxides, and zeolites.
25
Noble (precious) metal catalysts have proven highly active in NOx reduction, but
also effectively oxidize NH3 rendering it almost useless as a reducing agent. For this
reason, slightly less effective metal oxide (compounds composed of metals and oxygen)
catalysts are the most common for conventional SCR applications. Composed of minerals
that have micro-porous structures, zeolite catalysts have been proposed for SCR systems;
however, their use is more suited to gas-fired cogeneration plants rather than diesel
engines. While most commercially available SCR catalysts are of the metal oxide design,
their actual chemical makeup is considered proprietary by most suppliers. The chemistry
of how these catalysts work is beyond the scope of this paper, but detailed information on
the subject is available [17].
2.3.4 SCR Systems and Control
Injection of urea into the exhaust stream is accomplished quite easily using an
injector unit comparable to common fuel injectors found on gasoline engines. The
injection rate can be controlled by sending the correct injection frequency and pulse
width signals to the injector. The required injection pressure is usually maintained by a
pressure regulator fitted to an air reservoir. Commercial systems intended for retro-fit
application typically have a compressor and motor with built in control that re-pressurizes
the air tank as needed.
Initial thoughts of controlling a urea-SCR system may be posed as an easy task:
Inject enough urea upstream of the catalyst to facilitate NOx reduction. However, the
dissociation of excess urea yielding ammonia leads to a discharge of raw ammonia out
the tail pipe termed slippage. In addition to ammonia slip, it is desired that only the
26
correct amount of urea be added to the exhaust stream so that the supply be conserved,
thus maximizing the time before refill of urea is necessary. Control systems for dosing
urea range from fairly simplistic, retro-fit applications to systems integrated into the
engine control system.
The most basic form of controlling urea dosage lies in recognition that the NOx
content as well as the exhaust temperature are functions of engine speed and load.
Therefore, monitoring of exhaust temperature by the urea dosage controller provides an
adequate means for injection rate. The simplest systems intended for retrofit use two
temperature sensors at some distance apart in the exhaust for feedback. Monitoring two
exhaust temperature points helps to discern between separate engine operating scenarios.
More sophisticated control systems include interfacing with the engine electronic
control unit (ECU). If ECU communication is possible with the urea dosage system, other
parameters can be had such as engine speed and fuel injection rate. The most complex is
a system that incorporates ECU interface as well as a sensor for ammonia slip
downstream of the catalyst, however sensors for determining ammonia content are
currently in the prototype phase.
While knowledge of ECU operation and programming is usually reserved for
OEM development only, some manufacturers of retro-fit SCR systems have attempted to
bridge the gap for more accurate control. If analog output of OEM sensors such as engine
speed, mass air-flow, or fuel injection can be had, retro-fit controllers can be programmed
to utilize the data.
Now that knowledge of the systems’ basic operations are understood, specific
application can be explored. Each make and model engine has its own unique operating
27
characteristics and thus calibration to each specific system is necessary. Mathematical
modeling techniques utilizing chemical equations for urea decomposition and NOx
bonding along with geometry-descriptive computational fluid dynamics have been
developed and can provide a good starting point [21,22]. As modeling techniques are
only capable of providing a prediction, final calibration must be carried out via engine
evaluation. Testing an engine at a variety of steady-state speed/load combinations while
monitoring ammonia slip can be effective. If the engine is to be used in conventional
powertrain application, transient evaluation should also be considered. If application is
intended for use such as The University of Akron’s ChallengeX hybrid vehicle, transients
may be ignored if the vast majority operation is steady-state. Because SCR can provide
NOx reduction without sacrificing fuel use, The University of Akron has selected SCR for
use in its ChallengeX vehicle.
2.3.5 Results of Previous Studies
Previous research indicates that urea-injected SCR systems for NOx control have
proven to be quite effective; some general trends will be discussed. Studies conducted on
conventional powertrain, heavy-duty vehicles [14,22-24] over various drive cycles that
encompass both city and highway transportation show average NOx reduction rates of 70-
85% depending on particular drive cycles. NOx reduction rates in all of the previous
mentioned research reached as high as 90% and in some cases well above for high load
conditions, a definite indicator that SCR catalyst performance is highly influenced by
temperature. In the hybrid vehicle case, it would be expected that average NOx
conversion rates be even higher as it will always operate at high load, assuming the light-
28
duty engine can produce the same exhaust temperatures as a heavy-duty engine. As SCR
systems are generally intended for commercial vehicle and stationary use, little literature
is available on its performance in light duty vehicles. However, accurate mathematical
modeling of instantaneous exhaust gas temperature and velocity for diesel engines shows
that temperature is primarily a function of combustion chamber design and not cylinder
volume [25]. The conclusion, light and heavy-duty engines will be capable of the same
temperatures, the heavy duty engine will simply move a larger volume of gases.
29
CHAPTER III
EXPERIMENT DESIGN AND SETUP
3.1 Experiment Overview
The focus of this research is to evaluate NOx emission from a diesel engine
intended for use in a hybrid-electric vehicle with the following goals in mind:
• Produce an efficient experiment design that will minimize data collection efforts
yet provide a high level of accuracy.
• Development of engine maps to predict NOx levels in the exhaust stream and fuel
economy under all steady-state operating conditions anticipated for the vehicle.
• Development of a model for vehicle simulation to predict fuel used and NOx
emission per distance traveled.
• Evaluate the effects of aiming the hybrid control strategy to operate the engine for
reduced NOx generation at the cost of fuel economy.
• Evaluate the level of NOx aftertreatment required to meet USEPA standards.
While this study is intended for the use of vehicle modeling for The University of
Akron’s ChallengeX hybrid vehicle, discussion will be kept general enough so that the
methods used can be applied in other applications where a comparison of the tradeoff of
some emission component can be weighed against fuel efficiency. The remainder of this
chapter will cover the test engine in detail, required data, data collection methods,
experiment design, and experiment setup.
30
3.2 The Test Engine
The engine used for evaluation was of the same make and similar model that is to
be used in The University of Akron’s competition vehicle: a 1.9L, 4-cylinder
Volkswagen diesel, manufacturer engine model code ALH. Rather cutting edge in terms
of small displacement production diesel engines, this model is turbocharged with variable
vane technology and utilizes highly efficient direct injection for fuel atomization. Its
emission control is typical of current production diesels and consists of exhaust gas
recirculation for lower temperature NOx control and a conventional diesel oxidation
catalyst. The engine was tested in its stock form with OEM engine controls. The
oxidation catalyst was removed for testing.
3.3 Required Data
It has been mentioned that the high levels of NOx of interest are generated from
running a diesel engine under some of its most efficient operating conditions to achieve
high levels of fuel economy in a hybrid vehicle, thus it makes sense that levels of NOx be
studied in comparison to fuel use. The following will discuss the form in which data is
needed to complete this type of study. Various methods of obtaining the data will also be
discussed.
All commercial exhaust emission analysis systems operate in a similar manner;
during operation, samples of the flowing exhaust gas are evaluated for content of a
particular emission component. The data is expressed as a percentage of that emission
component, usually reported in parts per million (ppm) of dry exhaust. Data from this
type of system is relatively useless on its own in this type of analysis since a percentage
31
of emission content will not provide an accurate portrayal of different engine operation
scenarios. Some of the newer, more sophisticated, and also more expensive equipment
intended for on-road testing also contains means for flow rate measurement and
accelerometers which allows expression of emission components in terms of mass per
distance traveled, the standard for the USEPA’s guidelines for evaluation of production
vehicles. If this type of system is not available, useable data can still be achieved by
implementing a flow rate measurement system in the exhaust stream such as a flowmeter
to collect exhaust flow rate data. Distance data can be obtained from counting wheel
revolutions via wheel position sensors which are standard equipment on most current
production vehicles as antilock braking systems and traction control are increasing in
popularity. Both of the systems discussed above, stand alone units and the more
primitive units combined with exhaust flow rate and distance measurement, are capable
of producing high levels of accuracy for in-vehicle emissions evaluation. However, these
means of data collection are insufficient for stationary laboratory testing where engine
data is desired for vehicle modeling. In order to be able to predict emission components
in terms of mass per distance traveled through modeling, some correlation must be made
between the emission content and volume of fuel used. Not only is fuel measurement
essential to treatment of emission data for modeling, it will also allow for simple fuel
efficiency calculations so that tradeoffs between fuel economy and NOx emission rates
can be examined.
Fuel usage data can be gathered in a variety of ways with varying degrees of
accuracy. The most accurate means of fuel consumption evaluation is to use two
flowmeters on the engine dynamometer setup; one on the feed line to the fuel injectors
32
and the other on the return line. The difference in volumetric flow rates of the feed line
and return line results in the volumetric usage rate of fuel in the engine. Another more
indirect form of fuel use measurement is, if available for a particular model engine, to
utilize engine diagnostics software that reads fuel use rate. This will work for steady-state
testing where data can be observed and recorded, however will prove difficult for
transient data as OEM engine diagnostics software does not typically allow for output
channels where data can be exported and logged aligned in real time with the other
parameters of interest.
The last bits of data that need be collected for the evaluation are the control
parameters, specifically engine load and speed. The engine test stand or dynamometer
can be programmed to hold a constant engine speed by varying the amount of load
(torque) applied at the flywheel. Varying the throttle pedal position input signal will
allow adjustment to the desired flywheel load. Engine speed and load data will allow
steady-state engine maps to be developed for both fuel efficiency and NOx output.
3.4 Experiment Design
Before the design of an experiment can be approached, a brief discussion of the
desired results and treatment of data is necessary. The engine maps of interest can be
developed through evaluation a dependent variable (NOx emission or fuel use parameters
in this case) at a finite number of control test points within the engine’s operating range.
A regression technique can then be employed to create a response surface that covers the
entire operating range of the engine. The response surface can be expressed visually by
plotting the data for evaluation or mathematical modeling of a drive cycle can be
33
approached by utilization of the regression equations or formulating a look-up table for
numerical analysis.
Because of the growing complexity of modern engines, evaluation can prove to be
quite a cumbersome task without appropriate strategy. The traditional method for
evaluation of engine output parameters is to test the engine at many different operating
points so that minimum error is introduced in the final product. Collection of this many
data points can prove both extremely time consuming and expensive in the laboratory and
thus is nearly impossible in a setting other than the engine development industry. For this
reason an efficient experimental design using statistical methods was constructed using
computer aided design to minimize the amount of data that needs be collected while
maintaining accuracy, a process termed response surface methodology.
3.4.1 Statistical Theory
Because it is not possible to collect the hundreds of data points that are desirable
for a minimal error response, the statistical theory to be presented was used to reduce the
possibility of error generation using a reasonable amount of data. The goal is to be able to
predict variability in the regression model and then choose a combination of points for
engine operation testing that will minimize it.
Statistical variance is a typical tool for evaluating the validity and accuracy of a
model. For regression models, the variance can be thought of as a value that states
variability between the observed data (actual data) and a regression line or curve. A large
value dictates that observed data are quite spread out about the regression curve while a
small value provides that observed data falls close to the regression curve. It is obvious
34
that minimization of the variance will lead to minimum variability and thus a more
accurate model is implied. If a known set of data is had, the variance can be quite easily
calculated by first determining the residual sum of squares of the variances. Let the
residual, ei, be defined as the difference between an observed piece of data, yi, and its
corresponding point that lies on the regression curve, iy for a given ix . The residual sum
of squares is then determined as the sum of squares of all of residuals in the data set and
is denoted RSS:
( )∑∑ −== 22 ˆRSS iii yye (3.1)
The variance, s2, can then be obtained by dividing the residual sum of squares by the
number of degrees of freedom. Let n denote the number of total data points and k the
order of the regression model.
( )
( )( )1
ˆ
1
RSS2
2
+−
−=
+−= ∑
kn
yy
kns
ii (3.2)
It can be inferred from equation (3.2) that reduction of variance in the model can be
accomplished by minimization of the residual sum of squares (RSS). The problem now is
that while RSS can be tabulated for a known set of data, a set of data collection points
must be determined that will yield minimized RSS when calculated. This is possible
through a series of iterations that examines many different data points based on a
prediction model for RSS.
The impact that one particular data point has on RSS for experimental data can be
forecasted using a statistic termed the predicted residual sum of squares (PRESS). The
concept of PRESS is quite simple; a data point of interest is removed from the model, the
curve is then refit in order to examine the impact of that observation on the model. Let
35
)(ˆiy be a regression estimator of the same type as iy except for the fact that the
regression is performed without the point ),( ii yx , xi being the independent variable. Now
the residual for an observation yi and regression estimator )(ˆiy can be defined as follows:
)()(ˆˆiii yye −= (3.3)
Summing the squares of the residuals of the form of equation (3.3) results in the PRESS
statistic for an observation i from a data set consisting of n values:
2
1
)(
2
)(ˆˆPRESS ∑ ∑
=
−==
n
i
iii yye (3.4)
If an experiment can be designed such that the selected observation points will provide
minimization of the PRESS statistic, then it follows that the design should provide a
minimized RSS when the data is collected and regression implemented. It would appear
from equation (3.4) that minimization of PRESS is dependent on knowing the actual data
observations. However, if the observations are independent, then )(ˆiy is independent of yi;
thus a weighting factor for PRESS can be calculated for data that is not yet collected [26].
Actual optimization of an experiment design by minimization of PRESS is quite a
complex task and the computation beyond the scope of this thesis. First a set of plausible
control variables must be determined and a weighting factor determined for the PRESS
statistic for each control variable. Based on the results of this evaluation, new
independent variables can be chosen and the PRESS weighting factor calculated again.
This process of iteration continues until the PRESS is minimized thus ultimately reducing
the expected variance in the final data. Fortunately, much commercial software that is
capable of choosing independent variables by iteration for experiments exists. Because
36
the nature of research for this paper involves choosing independent variables constructed
from engine operating speed/load combination defined by an abstract domain (the
engine’s peak torque curve), the Design of Experiments (DOE) suite on MATLAB was
chosen for its flexibility in allowing the user to define domanial constraints.
3.4.2 Domain Analysis
Design optimization for the experiment consists of strategically choosing a
number of engine speed/load operating points that will reduce variance in the regression
models to be developed for NOx content and fuel economy. Because the number of data
points for evaluation that can be collected is limited, the analysis domain will be limited
to the operating conditions that the engine will experience in operation, further increasing
accuracy of the model. For this particular research, steady-state engine operation for the
vehicle’s series mode in city driving is used to provide the lower bound for the domain.
From manufacturer’s data, the following Figure 3.1 shows the engine’s peak performance.
Figure 3.1: VW 1.9L TDI peak performance curves [27]
37
Examining the peak torque curve in the previous Figure 3.1, it can be said that the
engine is capable of operating at any speed/load combination below the curve and for a
typical vehicle evaluation, taking the area under the curve as the domain would be
appropriate. However, due the uniqueness of The University of Akron’s ChallengeX
vehicle architecture, evaluation of this entire area is not necessary. During intervals of
city driving (low speed, stop-and-go), the vehicle is driven as a purely electric vehicle
with the engine intermittently running only to recharge the energy storage system, via the
generator. Since the engine will never idle and only operate at a power demand greater
than that of the generator during highway speeds or heavy acceleration, the lower bound
for engine operation can be developed from generator operation. In order to provide a
rapid charge time to see that the engine runs as little as possible, the generator should run
at its maximum continuous power generating capability, 21kW (28.2hp). From
manufacturer’s data, performance curves over the operating range of the generator are
plotted in Figure 3.2.
0
10
20
30
40
50
0 2000 4000 6000 8000
Speed (rpm)
Torque (ft*lb)
0
10
20
30
40
50Power (hp)
Peak Torque Peak Continuous Torque
Peak Power Peak Continuous Power
Figure 3.2: Siemens ACW-80-4 PM motor performance curves
38
The generator is capable of a speed of 12,500 rpm without damage. For this
reason, gearing between the engine and generator was chosen to match the maximum
speed of both. With a maximum anticipated engine operating speed of 4,300rpm under
heavy acceleration, the ideal gear ratio would yield 2.91; a ratio of 2.85 was chosen based
on part availability for gearing. Power generation can be expressed in terms of engine
parameters as a product of torque and speed for engine operation:
engineenginegen NTP = (3.5)
where Pgen represents the generator’s power take-off and Tengine and Nengine represents the
corresponding engine’s load and speed respectively. Solving equation (3.5) for engine
torque, possible engine speed/load combinations can be determined.
engine
gen
engineN
PT = (3.6)
Plotted in Figure (3.3) is the resulting evaluation of equation (3.6) with proper unit
conversion. Shown is a representation of possible engine operating points that will yield
maximum continuous and peak power generation in comparison to the peak engine
torque.
39
0
40
80
120
160
200
0 1000 2000 3000 4000 5000
Engine Speed (rpm)
Torque (ft*lb)
Engine Peak Torque
Torque For Max. Power Generation
Torque for Cont. Power Generation
Figure 3.3: Possible engine operation for electrical power generation
Examining the plot presented in Figure 3.3, some conclusions can be drawn as to
limiting the domain for testing. From the previous general discussion of fuel efficiency in
Chapter 2, maximum fuel efficiency for maximum generator power should occur
somewhere around 2,000 engine rpm (refer to Figure 2.1). Therefore, the torque region of
the domain will be limited to loads above 60 lb*ft as maximum power generation occurs
there at about 2,500 rpm. Because maximum continuous power generation cannot occur
below approximately 2,000 engine rpm and the engine will never be allowed to idle, it
makes sense that taking the domain as low as the engine’s idle speed is not beneficial.
Instead a lower bound for engine speed is chosen at 1,250 rpm. Also note that lower
engine speeds will result in lower exhaust temperatures, possibly limiting the
regeneration capability of the DPF.
40
For operating scenarios that require a power demand greater than the maximum
possible generator power such as moderate to heavy acceleration, higher cruise speeds, or
trailer towing, the engine will be required to operate at points above the continuous
power generation curve shown in Figure 3.4. Accordingly, the remaining area under the
torque curve should be taken as the domain; however, to avoid running the engine at its
maximum speed and load continuously which can damage an engine quickly, the
maximum engine speed will be capped at 4,000 rpm.
3.4.3 Experiment Optimization
Having determined the appropriate domain for analysis, experiment design was
carried out using MATLAB’s DOE suite. A one stage model was constructed for a third-
order polynomial regression for fuel use and NOx emission that uses engine torque and
speed as independent variables. The regression model will allow construction of a
response map for the parameters of interest that can be plotted over the domain of the
engine’s operating range. Independent variables, engine speed and torque, are not
independent of each other and thus interaction of the two must be considered. For a third-
order regression, the maximum interaction order of three was chosen. The theory for
constructing the regression model will be discussed further in the analysis portion of this
thesis (Chapter 4), for now let it be stated that the model for an output parameter η will
be of the following form having regression coefficients βi.
2
211222
2
1112
3
2222
3
1111
2112
2
222
2
11122110
XXXXXX
XXXXXX
ββββ
ββββββη
++++
+++++= (3.7)
41
In the particular case of the research being presented, the equation response η will
represent either fuel economy or NOx emission parameters while engine speed and load
will be characterized by independent variables X1 and X2.
The presented form of a third order polynomial regression having third order
interaction, equation (3.7), contains ten total terms. It follows that in order to perform the
regression model, at least ten observations or data points would be needed. Adding more
data points than the minimum for a particular regression model will yield higher accuracy
in the final result. For data to be taken in this evaluation, experiment design for limited
laboratory time was carried out using fifteen observations.
MATLAB’s DOE suite operates by iterating over the analysis domain to
determine a specified number of optimum observation points that will provide minimum
error according to some specified criteria. In this case, the criterion for error
minimization in the response models that will be developed is choosing data observation
points that provide minimization of the PRESS statistic over the domain discussed in the
previous section. Methods for iteration and determination of the optimum data collection
points are beyond the scope of this paper, but more information about idealized variable
selection by PRESS minimization can be found in [26,28,29]. The optimum experiment
design over the discussed domain yields the following fifteen data collection points
presented in Table 3.1.
42
Table 3.1: Optimum data collection points Engine Speed Torque Load
rpm ft*lb
2625.0 150.3 3312.5 83.8 4000.0 60.0 2075.0 126.5 1525.0 150.3 3862.5 136.0 1250.0 60.0 1250.0 107.5 2212.5 126.5 3175.0 121.8 1800.0 83.8 3312.5 88.5 2625.0 60.0 1937.5 79.0 4000.0 102.8
Plotting the data collection points over the peak torque curve yields a graphical
representation of the speed/load operating points to be tested (figure 3.4).
1000 1500 2000 2500 3000 3500 4000 45000
20
40
60
80
100
120
140
160
Engine Speed, rpm
Torque, ft*lb
Data Collection Points
Maximum Torque Profile
Figure 3.4: Graphical representation of data collection points
43
The engine operation points discussed previously will provide the least amount of
variance in the final results for fifteen observations. It must now be determined whether
fifteen data points will be sufficient to provide accuracy in the final results. Statistical
evaluation of the design was performed using the DOE suite, which allows the designer
to examine the variance of predicted error across the domain of the designed experiment
as a percentage. Prediction error variance (PEV) can be thought of as an evaluation of
how the magnitude of error will vary across the entire error spectrum in the completed
model once regression from the collected data is implemented. A value of less than one
indicates that error will be reduced at a particular point of interest. Conversely, a value
greater than one indicates that error will be magnified. It is common for experiment
designs to contain PEV greater than one at some points and it should not be treated with
the thought that 100% error will be introduced [30]. While PEV can be used with great
accuracy in the comparison of several experiment designs, no steadfast rule exists in
defining a threshold for effectiveness when the experimenter has no leads as to how the
data may be distributed. However, it can be said that if PEV is kept on average to within
one, reasonable accuracy will generally be had as long as the researcher is confident in
the smoothness characteristics of the anticipated response [30]. For the evaluation of both
fuel use and NOx emission, intuition from examination of plots from previous studies of
the topics on diesel engines tells that the anticipated response surface is fairly smooth and
no spikes in either parameter’s response are expected for steady-state operation. A
summary of computation of PEV is given in appendix A; more information on statistical
theory of predicted residual error variance can be found in [26,28,29]. The following
44
response surface in Figure 3.5 shows the results of the statistical evaluation for the design
in question over the relevant domain.
Figure 3.5: PEV response surface for experiment design
The mean PEV across the domain is 0.484, which is well within the less than 1.0 criterion
previously discussed. Also note that the average is driven up by the large spikes seen at
the boundary, regions that are not of particular interest. It can be concluded that the third-
order polynomial regression model having third-order interaction should provide an
accurate response to the gathered data. Statistical evaluation with the collected data will
validate this conclusion.
Engine Speed - RPM Engine Load – lb*ft
Pre
dic
ted E
rror
Var
iance
45
3.5 Experimental Setup
While section 3.3 outlined the necessary data and possible means of collecting it,
the following presents specifics of the test setup used for analysis in this evaluation.
Steady-state engine evaluation was carried out on the test engine at The Lubrizol
Corporation, Wickliffe, OH. The test setup is illustrated in the following Figure 3.6 and a
photo of the actual test bed is included in Figure 3.7.
Figure 3.6: Test setup schematic
Figure 3.7: Test setup
Dynamometer
Test
Engine
Measured Data:
Engine Speed/Load
Fuel Use
Dyno
Controller
Fuel
Supply
Flowmeters
Emissions
Sampling
Measured Data:
Volume comp. of
O2, CO, NO, NO2, HC
Engine
Diagnostics
Measured Data:
Mass Air Flow --- Communication
Physical Flow
Dynamometer
Engine
Exhaust
Sampling
Port
Flowmeters
Dyno
Controller
MAF
Sensor
46
The experiment was run using ultra-low sulfur diesel fuel and the engine warmed.
The fuel is typical of common diesel available at the pump, but its manufacture has acted
to control sulfur content to a minimum. Torque load and speed of the engine was
modulated and read by the dynamometer controller. Fuel use was measured by the
flowmeters, then calculated and read off the dynamometer controller as well. The mass
air flow rate was read from the MAF sensor by Volkswagen engine diagnostics software
(VAG-COM R704 from Ross-Tech) via the engine’s OBDII port. A sample of exhaust
gas was taken just downstream of the turbocharger and its volumetric content analyzed
by a flue-gas analyzer system (model ECOM KL, manufactured by ECOM America).
47
CHAPTER IV
DATA AND ANALYSIS
4.1 Data Treatment
This section will explore the data obtained during experimentation and proper
treatment so that regression can be implemented in a useful form. It is intended that the
developed regression models provide a baseline evaluation of the engine’s fuel use and
NOx emission characteristics as well as offer equations that can be applied to vehicle
drive cycle modeling. Table 4.1 below depicts a summary of the experimental data
collected that will be used in evaluation. Flow rate data for both air and fuel was
collected in readings of kg/hr and conversions to lbm/hr are given.
Table 4.1: Recorded experimental data for steady-state engine operation
Engine Control Intake Air & Fuel Data Measured Dry Exhaust Emission Data
Speed Torque Air Flow Fuel Flow O2 CO NO NO2 NOx CxHy rpm lb*ft kg/hr lbm/hr kg/hr lbm/hr vol % ppm ppm ppm ppm ppm
1250 60 138.0 304.2 2.31 5.09 11.5 243 554 33 587 1105
1250 108 142.5 314.2 3.92 8.65 6.6 321 1101 36 1137 966
1525 150 179.3 395.4 6.74 14.86 4.7 346 1403 34 1437 746
1800 83 235.4 519.1 4.42 9.75 10.3 197 593 26 619 1026
1938 79 251.2 553.7 4.56 10.05 11.3 224 472 43 515 1422
2075 127 288.8 636.8 7.32 16.13 6.2 157 1282 44 1326 1095
2213 127 305.4 673.3 7.93 17.49 6.1 193 1308 55 1363 1028
2625 60 340.2 750.0 5.47 12.06 11.4 275 311 33 344 1853
2625 150 368.6 812.5 11.59 25.55 4.5 181 1392 71 1463 1256
3175 122 422.9 932.4 11.66 25.70 6.0 226 1174 69 1243 1305
3313 84 433.3 955.4 8.83 19.47 10.5 176 838 52 890 1597
3313 89 409.5 902.8 9.20 20.28 10.1 188 879 67 946 1370
3863 136 389.4 858.5 16.66 36.73 5.1 187 1296 88 1384 1086
4000 60 412.8 910.1 9.19 20.27 12.5 146 720 40 760 1250
4000 103 412.8 910.1 13.49 29.73 10.0 159 1015 59 1074 1159
48
Before discussion of desired output from regression, a short discussion of the
control (independent) variables and their treatment is necessary. It is desired that fuel use
parameters or NOx output be expressed as functions of engine speed and load. Note from
the raw data that the control variables do not need further treatment and the given values
can be used directly in regression.
4.1.1 Fuel Use Analysis
Analysis of fuel usage by the diesel engine will be presented in three forms over
the relevant domain of operation: an evaluation of the actual rate of fuel consumed
(already present in raw data), evaluation of fuel consumption relative to power output,
and an assessment of engine efficiency.
Discussed in chapter 2, the standard for comparison of fuel usage for internal
combustion engines is evaluation of the brake specific fuel consumption (bsfc); a ratio of
the fuel’s mass flow rate, fuelm& , per measured output power, bP . The engine’s output
power at the flywheel can be expressed as the product of the measured engine brake
torque and speed. The resulting equation for bsfc is as follows,
NT
m
P
m
b
fuel
b
fuel&&
==bsfc (4.1)
where bT is the measured brake torque and N the engine speed.
While bsfc can be useful in standardized examination of fuel usage relative to
power output, evaluation of efficiency will tell what percentage of fuel being put into the
engine is being used for mechanical work output. Efficiency for a combustion engine can
be tabulated by dividing the measured brake power by the potential power of the injected
49
fuel. Power potential of a particular fuel is dependent on both its energy content and the
rate that it is injected. Energy content can be expressed in terms of a fuel’s higher heating
value (HHV); the amount of heat released per quantity when combusted and allowed to
cool to its initial temperature. The product of HHV and the fuel mass flow rate gives the
power potential of the fuel. The following expression is used to calculate efficiency for
an internal combustion engine.
HHVHHVPotentialPower Fuel
Power MeasuredEfficiency Fuel
fuel
b
fuel
b
m
NT
m
P
&&=== (4.2)
Using the collected data for fuel usage and assuming the fuel as common diesel
having HHV of 19,733 BTU/lbm, the following values of bsfc and fuel efficiency were
calculated using the respective engine speed and brake torque.
Table 4.2: Calculated values for fuel consumption
Eng. Speed Eng. Torque. Fuel Flow bsfc Fuel Efficiency
rpm lb*ft lbm/hr lbm/(hp*hr) %
1250 60 5.09 0.356 36.17
1250 108 8.65 0.338 38.14
1525 150 14.86 0.341 37.87
1800 83 9.75 0.342 37.76
1938 79 10.05 0.345 37.40
2075 127 16.13 0.323 39.95
2213 127 17.49 0.328 39.30
2625 60 12.06 0.402 32.06
2625 150 25.55 0.340 37.91
3175 122 25.70 0.349 36.94
3313 84 19.47 0.368 35.01
3313 89 20.28 0.363 35.49
3863 136 36.73 0.367 35.12
4000 60 20.27 0.444 29.07
4000 103 29.73 0.380 33.96
50
4.1.2 NOx Emission Analysis
Evaluation of NOx emission from the recorded data is more complex than the fuel
usage analysis. It was mentioned in chapter 3 that laboratory evaluation of NOx emission
should be expressed in terms of a mass flow rate or in comparison to fuel used so that it
can be applied to vehicle modeling. Because means for NOx detection are only capable of
measuring its content on a volume basis, its mass quantity must be calculated from the
available data. If means for flow rate and temperature measurement were available at the
point in the exhaust stream where the emission data was collected, computation of
emission component mass flow rate could be calculated by applying the ideal gas law.
Unfortunately, means for exhaust temperature and flow rate data were not available for
this particular evaluation. Instead, NOx mass flow rate was computed using air and fuel
input data as well as volumetric emission measurement by the procedure to follow.
Starting with a mass balance for the nitrogen flow into the engine’s intake and
then out through the exhaust system, the following equation can be written. Let aNm ,&
represent the mass flow rate of nitrogen from some gas component a .
exhNONNONNN
intNN mmmm
++=
222 ,,,,&&&& (4.3)
The mass flow rate of nitrogen from molecular nitrogen (N2) taken into the combustion
chamber from the atmosphere can be calculated from the intake mass air flow rate data.
Also, content by volume (expressed as parts per million) of NO and NO2 within the total
dry exhaust volume are known and therefore their content can be expressed relative to
one another. If the volumetric content of N2 in a dry sample of the exhaust stream were
known, its content could be expressed in relation to NO and NO2 as well; facilitating the
51
computation of NOx mass flow rate from nitrogen-containing compounds of interest in
the exhaust. Volumetric content data for exhausted N2 is not available from the emissions
analysis equipment used. Its content was calculated via examination of the combustion
process and the known data for both exhaust and intake parameters.
Beginning with the assumption that the intake air and vaporized fuel as well as the
gaseous exhaust compounds behave as ideal gases, it can be said that the values obtained
for volumetric content in the dry exhaust stream are also the molar content values (mole
fractions). Based on this assumption, there is some correlation between the air and fuel
intake mass flow rate data and the exhaust volume composition data. If the engine’s
combustion were stoichiometric, the intake mixture would contain just enough oxygen
for complete combustion of the hydrocarbon fuel. The resulting gaseous exhaust would
be composed of only carbon dioxide (CO2), water (H2O), and the same amount of
atmospheric nitrogen (N2) present in the intake. However, complete combustion of fuel is
never possible; and in addition because diesel engines always operate above a
stoichiometric ratio (having excess air), more components of the exhaust stream must be
considered. The following combustion process describes the actual combustion case
accurately for a hydrocarbon fuel (CxHy) in air. Note the equation is not balanced; simply
be aware of the reactants and products.
exhint
+++++++→
++ 22222yx22yx NONOCOOHCONOHCNOHC (4.4)
Examining the exhaust emission products, the anticipated N2, CO2, and H2O are present.
In addition, some unburnt fuel (CxHy) as well as carbon monoxide (CO), nitrogen oxide
(NO), and nitrogen dioxide (NO2) are the result of incomplete combustion and thermal
52
oxidation. Neglected in equation (4.4) is the presence of sulfur content contained within
the intake fuel and thus a lack of sulfur dioxide (thermally oxidized sulfur) in the exhaust.
It is assumed that these compounds are small in comparison to the rest of the composition.
Also assumed small and neglected from the exhaust side of the combustion equation is
the presence of pure carbon that makes up the diesel particulate matter. The engine
testing was performed using dehumidified intake air and the readings taken from the
emission analysis equipment measure on a dry basis. Thus, it is assumed all of the H2O
content on the product side of the equation is a result of combustion.
The unknown concentrations of exhaust gas components can be obtained by
applying the known parameters and then balancing equation (4.4). While all of the
unknown compositions can be determined, the particular one of interest is the N2
composition of the exhaust stream which will facilitate NOx mass flow rate computation.
Balance with the existence of so many components can be tricky, so the following
equations are used to satisfy the combustion equation. Presented element balances for
compounds containing oxygen, nitrogen, hydrogen, and carbon respectively.
exh
NONOCOOHCOOintO nnnnnnn
,
, 22222 2
1
2
1
2
1
+++++= (4.5a)
exh
NONONintN nnnn
,
, 222 2
1
2
1
++= (4.5b)
exh
OHHCint
HC ny
nnyxyx
,
, 2
2
+= (4.5c)
exh
COCOHCint
HC nx
nx
nnyxyx
,
,
112
++= (4.5d)
53
The intake parameters presented in equations (4.5a-d) can be determined from the
intake air and fuel data collected and expressed as a molar flow rate. However, because
only the dry exhaust volumetric concentration of N2 is desired at this point, there is no
reason to calculate these parameters. Instead the system is analyzed per mole oxygen
allowing expression of the intake parameters in terms of the atmospheric nitrogen/oxygen
ratio and the intake fuel/air ratio. Looking at just the reactants of the combustion equation:
intNOyxfuel NnOnHCnreactants
++= 22 22
(4.6)
For the analysis of combustion per mole intake oxygen, equation (4.6) can simply be
divided by the molar oxygen content.
intO
N
yx
O
fuel
O
Nn
nOHC
n
n
n
reactants
++= 22
2
2
22
(4.7)
The earth’s atmosphere by volume is made up of approximately 78.084% nitrogen,
20.946% oxygen, and the rest composed of trace amounts of other gases (mostly inert).
For combustion engine analysis, it is typical to consider an engine’s air intake from the
atmosphere as a composition by volume of 20.946% oxygen and assume the rest nitrogen
[16], yielding a volume (molar) ratio of 3.774 nitrogen/oxygen assuming ideal gases; this
value can be substituted directly into equation (4.7). The fuel/oxygen ratio in equation
(4.7) is better expressed as a form of the molar fuel/air ratio. Expression of the intake air
as only the oxygen content and a constant will facilitate this.
22222774.4774.3 OOOONair nnnnnn =+=+= (4.8)
air
fuel
O
fuel
n
n
n
n774.4
2
= (4.9)
54
Now the molar fuel/air ratio is calculated from the recorded fuel and air mass flow rates
and the molar mass of each component.
airair
fuelfuel
air
fuel
Mm
Mm
n
n
&
&= (4.10)
Applying the previous expressions for atmospheric nitrogen/oxygen ratio and
intake fuel/air ratio to equation (4.7), the following equation for combustion analysis per
mole intake oxygen is obtained.
int
yx
airair
fuelfuel
O
NOHCMm
Mm
n
reactants
++= 22 774.3774.4
2&
& (4.11)
These reactants (intake parameters) can be used to facilitate the solving of the system of
equations (4.5a-d). Specifically, the following expressions can be used in solving the
system per mole intake 2O .
airair
fuelfuel
HCMm
Mmn
yx &
&774.4= (4.12a)
12=On (4.12b)
774.32=Nn (4.12c)
Now that the intake parameters have been expressed from the gathered data, a
solution to the system of equations (4.5a-d) can be developed. Recall that the exhaust
sample was taken dry, thus the water content of the exhaust composition will be taken to
the left side of the equations where appropriate as follows.
exh
NONOCOCOOexhOHintO nnnnnnn
,
,, 22222 2
1
2
1
2
1
++++=− (4.13a)
55
exh
NONONintN nnnn
,
, 222 2
1
2
1
++= (4.13b)
exh
HCexhOHint
HC yxyxnn
yn
,,
, 2
2=− (4.13c)
exh
COCOHCint
HC nx
nx
nnyxyx
,
,
112
++= (4.13d)
The molar quantities of the exhaust gas compounds cannot be solved for directly as only
the dry exhaust volume compositions of fuel, carbon monoxide, and nitrogen oxides are
known along with the intake molar content. However, the ideal gas assumption allows for
the molar ratio of two compounds as well as the volumetric ratio of the same compounds
to be analogous. Letting α designate the measured volumetric composition (mole
fraction) of two separate compounds a and b , the following is true.
b
a
b
a
n
n
αα
≡ (4.14)
For expression of equations (4.13a-d) in terms of volumetric exhaust content, division by
the molar quantity of any compound that has known volumetric data would suffice.
Because the system is being analyzed per mole oxygen, oxygen was chosen in this case.
Division of equations (4.13a-d) by 2O
n and application of equation (4.14) yields the
following:
++++=
−
222
22
22
2
1
2
112
1
,
,,
NONOCOCOO
OexhO
exhOHintO
n
nn
αααααα
(4.15a)
++=
22
22
2
2
1
2
11
,
,
NONON
OexhO
intN
n
nααα
α (4.15b)
56
22
2
,
,,
2
O
HC
exhO
exhOHint
HCyx
yx
n
ny
n
α
α=
− (4.15c)
++= COCOHC
OexhO
intHC
xxn
n
yx
yx αααα
1112
22 ,
,
(4.15d)
Unknowns in the above system of equations (4.15a-d) are the molar exhaust quantities of
water and oxygen, exhOHn ,2 and exhOn ,2
respectively, as well as the dry exhaust mole
fractions of carbon dioxide and nitrogen, 2COα and
2Nα respectively. The mole fraction
of exhausted nitrogen has now become the parameter of interest.
Determination of the volume composition of exhausted nitrogen is accomplished
by simultaneous solving of equations (4.15a-d). Division of (4.15a) by (4.15b) and (4.15c)
by (4.15d) gives the following two expressions:
22
222
2
22
2
1
2
12
1
2
1
2
1
,
,,
NONON
NONOCOCOO
intN
exhOHintO
n
nn
ααα
ααααα
++
++++=
− (4.16a)
COCOHC
HC
intHC
exhOHint
HC
xx
n
ny
n
yx
yx
yx
yx
ααα
α
11
2
2
2
,
,,
++=
− (4.16b)
Now solving both equations (4.16a,b) for the exhausted water content, exhOHn ,2, the
following equations are formed:
++
++++−=
22
222
222
2
1
2
12
1
2
1
2 ,,,
NONON
NONOCOCOO
intNintOexhOH nnn
ααα
ααααα (4.17a)
57
++−=
COCOHC
HC
intHCexhOH
xx
ny
n
yx
yx
yx
ααα
α
111
22
2 ,, (4.17b)
The system of two equations (4.17a,b) still contains three unknown parameters, exhOHn ,2,
2COα and 2N
α . Recognition that the mole fraction values for dry exhaust (α terms) must
equal unity allows solution of the system.
∑ =++++++= 12222, NONOCOCONOHCdryexh yx
αααααααα (4.18)
Equations (4.17a,b) and (4.18) could now be combined to solve the parameter of interest,
2Nα . However, the resulting equation form would be rather complex and difficult to work
with. Instead, equation (4.18) is solved for 2N
α .
22221 NONOCOCOOHCN yx
ααααααα −−−−−−= (4.19)
Now a value for 2COα can be guessed and a corresponding
2Nα calculated via equation
(4.19). The 2COα and
2Nα can then be used to solve equations (4.17a,b) for exhOHn ,2
and
compared. Iteration will continue until equations (4.17a,b) both yield the same result in
which case a solution has been obtained for the volumetric nitrogen exhaust content
(nitrogen mole fraction for dry, exhausted gases).
Now that the volumetric exhaust content of nitrogen can be determined,
calculation of NOx mass flow rate is possible. Recall equation (4.3) for the mass flow of
nitrogen which states that the mass of nitrogen that enters the engine through the intake
must equal the mass that exits through the exhaust. The equation is reiterated as follows.
exhNONNONNN
intNN mmmm
++=
222 ,,,,&&&& (4.20)
58
Because the data and calculations presented to this point deal only with volume fraction
concentrations and in turn molar concentrations when the ideal gas law is applied,
equation (4.20) must be expressed in molar terms so that the knowns can be applied.
Each term in equation (4.20) can be expressed as the product of the molar flow rate of a
compound and respective nitrogen-content molar mass.
exhNONNONNN
intNN nMnMnMnM
++=
22222&&&& (4.21)
Because only mole fractions of exhausted N2, NO, and NO2 are known and not values for
the actual molar flow rate, the mole fractions can be expressed relative to one another to
obtain the desired results. Let γ be a ratio of the molar flow rate of a specific compound
a contained within the exhaust stream to the molar quantity of nitrogen at the intake such
that the following is true.
2Naa nn && γ= (4.22)
Applying equation (4.22) to equation (4.21) and also noting that the molar mass of N is
half the molar mass of N2, equation (4.21) can be rewritten in the following manner.
intNNNO
intNNNO
intNNN
intNN nMnMnMnM
+
+
=
2222222222 2
1
2
1&&&& γγγ (4.23)
Recognizing that the product of 2N
M and 2N
n& yields the mass flow rate of nitrogen,
equation (4.23) is rewritten on an intake nitrogen mass flow rate basis.
intNNNO
intNNNO
intNNN
intNN mmmm
+
+
=
222222 ,,,,2
1
2
1&&&& γγγ (4.24)
The nitrogen mass balance presented in equation (4.24) is analogous to the mass balance
presented in equation (4.20), thus the following expressions can be written for the mass
flow rate of nitrogen for specific nitrogen-containing exhaust compounds.
59
intNNN
exhNN mm
=
222 ,,&& γ (4.25a)
int
NNNOexh
NON mm
=
2,,2
1&& γ (4.25b)
intNNNO
exhNON mm
=
222 ,,2
1&& γ (4.25c)
Now the mass flow rate of nitrogen contained within nitrogen-containing compounds
partially making up the global exhaust composition have been expressed in terms that
satisfy the molar flow rate of intake nitrogen through a parameter aγ . In order to apply
exhaust mole fraction data, aγ must be related to the exhaust data.
Reverting back to the nitrogen mass flow balance equation (4.24), further
simplification allowing examination of aγ values as exhaust content parameters is
possible.
22 2
1
2
11 NONON γγγ ++= (4.26)
Equation (4.26) represents a molar balance that must be satisfied to relate only the
nitrogen containing compounds in both the intake and exhaust. In order to satisfy the
equation in terms of the global exhaust concentration, the following equation is written
for the mole fractions of a nitrogen-containing exhaust compound a :
22 2
1
2
1NONON
a
a
ααα
αγ
++= (4.27)
where a represents N2, NO, or NO2 for dry exhaust gas.
Application of equation (4.27) to equations (4.25a-c) will allow computation of
nitrogen mass flow rate of nitrogen-containing compounds in the exhaust gas from the
60
nitrogen mass flow rate of the intake air. However, note that the mass flow rates of NO
and NO2 as a whole are of interest, not simply the nitrogen component of the compounds.
The mass flow rate of either NO or NO2 can be expressed as the product of the respective
molar flow rate and the molar mass of a particular compound. Since the nitrogen mass
flow rate is known for each compound, the molar flow rate of nitrogen can be obtained
by division of the molar mass of N. The resulting expression for mass flow rates of NO
and NO2 are as follows.
exhNON
N
NO
exhNO mM
Mm
= ,,&& (4.28a)
exhNON
N
NO
exhNO mM
Mm
=
2
2
2 ,,&& (4.28b)
Calculation of the intake nitrogen mass flow rate can be accomplished in a similar
manner. Data for the mass flow rate of air is known and a relation to the nitrogen mass
flow rate can be had through molar ratios. The molar flow rate of air can be expressed in
terms of the mass flow rate by division of air’s molar mass. Also note the molar flow rate
of air is accurately assumed the sum of nitrogen and oxygen molar flow rates.
22 ON
air
air
air nnM
mn &&
&& +== (4.29)
Previously stated, the assumption for combustion engine analysis that air is composed of
only oxygen and nitrogen gives a volumetric (molar) ratio of 3.774 N2/O2. This
assumption can be directly applied to equation (4.29).
774.3
774.4
774.3
22
222
NN
NON
nnnnn
&&&&& =+=+ (4.30)
61
Combining expressions (4.29) and (4.30), the molar flow rate of intake nitrogen can be
solved for in terms of the mass air flow rate.
air
air
NM
mn
&&
774.4
774.32= (4.31)
The mass flow rate of nitrogen from intake air can now be solved by multiplication of the
molar mass of N2. Equation (4.31) becomes the following.
intair
air
N
intNint
NN mM
Mmm ,,,
774.4
774.32
22&&& ==
(4.32)
The presented equations for NOx evaluation to this point can now be combined to
yield two equations that will facilitate calculation of the mass flow rates of NO and NO2.
Specifically, beginning with equations (4.28a,b) for exhausted NOx mass flow rates as
functions of the exhausted nitrogen mass flow rate, equations (4.25b,c) are substituted to
yield functions of the intake mass nitrogen flow rate and the aγ parameters.
intNNNO
N
NO
exhNO mM
Mm
=
2,,2
1&& γ (4.33a)
intNNNO
N
NO
exhNO mM
Mm
=
22
2
2 ,,2
1&& γ (4.33b)
Now equation (4.27) is applied to the aγ values and equation (4.32) is applied to the
mass flow rate of intake nitrogen in equations (4.33a,b). The resulting equations express
the exhaust mass flow rates of NO and NO2 as functions of the global volumetric content
of nitrogen-containing compounds in the exhaust stream and the intake mass air flow rate.
Also, applying the fact that the molar mass of N2 is twice the molar mass of N, the
following simplified expressions are quite easy to work with.
62
intair
NONON
NO
air
NO
exhNO mM
Mm ,,
22 2
1
2
1774.4
774.3&&
ααα
α
++= (4.34a)
intair
NONON
NO
air
NO
exhNO mM
Mm ,,
22
22
2
2
1
2
1774.4
774.3&&
ααα
α
++= (4.34b)
The global exhaust volumetric concentrations (mole fractions) NOα and 2NOα as well as
the intake mass air flow rate intairm ,& are contained within the collected data. The
volumetric content of exhausted nitrogen, 2N
α , must still be solved for by iteration to
satisfy equations (4.17a,b) and (4.19). Solving of 2N
α is reliant on all of the measured
dry exhaust concentrations including CxHy, O2, CO, NO, and NO2 as well as the molar
fuel/air ratio which is easily obtained as previously described utilizing the measured mass
air and fuel flow rates. The total mass flow rate of exhausted NOx is of course the sum of
the mass flow rates of exhausted NO and NO2.
exhNOexhNOexhNO mmmx ,,, 2
&&& += (4.35)
In terms of engine mapping, nitrogen oxide mass flow rate can be presented as
simply the NOx mass flow rate values obtained from equation (4.35) over the relevant
domain. This will well facilitate computational modeling of a vehicle’s NOx output per
mile over a particular drive cycle. However, expression of NOx mass flow rate relative to
power output allows performance mapping of an engine that can be used in standardized
comparison to brake specific fuel consumption as well as other engines. As with fuel use
analysis, brake specific values are again calculated. Brake specific NOx emission (bsNOx)
is simply the NOx mass flow rate divided by the engine’s measured brake power.
63
NT
m
P
mbs
b
exhNO
b
exhNO xx ,,NOx
&&
== (4.36)
The analysis discussed in this section was performed using the collected
experimental data. The ultra-low sulfur diesel fuel used for evaluation was assumed
having constant chemical formula C12H26, the average for common diesel. The molar
mass of air was taken as 28.97 g/mol. Contained in the following table are the results for
computation of NOx mass flow rate and bsNOx.
Table 4.3: Calculated values for NOx emission
Eng. Speed Eng. Torque Mass Flow Rates (lbm/hr) bsNOx
rpm lb*ft NO NO2 NOx lbm/(hp*hr)
1250 60 0.170 0.0156 0.186 0.0130
1250 108 0.349 0.0177 0.367 0.0143
1525 150 0.553 0.0206 0.574 0.0132
1800 83 0.314 0.0211 0.335 0.0117
1938 79 0.267 0.0373 0.304 0.0104
2075 127 0.826 0.0432 0.869 0.0174
2213 127 0.889 0.0576 0.947 0.0178
2625 60 0.239 0.0388 0.278 0.0093
2625 150 1.138 0.0886 1.227 0.0163
3175 122 1.105 0.0991 1.204 0.0163
3313 84 0.814 0.0773 0.891 0.0169
3313 89 0.805 0.0945 0.900 0.0161
3863 136 1.104 0.1152 1.220 0.0122
4000 60 0.666 0.0573 0.723 0.0158
4000 103 0.928 0.0833 1.011 0.0129
4.1.3 Comparison Data
The emissions index (EI), a ratio of emission mass flow rate to mass fuel use rate,
is a useful normalized comparison in engine mapping to explore an engine’s emission
characteristics at different speed/load combinations. Computation of EI allows
standardized comparison of multiple engines as well as examination of how a change to
an engine’s air and fuel induction system, such as injection timing or turbocharger boost
pressure, has affected harmful emission generation. An EI evaluation of the ratio of NOx
64
emission to fuel use by mass is presented in this thesis for baseline engine mapping
purposes. The quantity is calculated by simply dividing the NOx mass flow rate by the
fuel mass flow rate.
fuel
exhNO
m
mx
&
&,
NOxEI = (4.37)
4.2 Experimental Uncertainty Analysis
Error in experimentation can be developed through calibration of equipment, data
acquisition, or data reduction. Prior to experimentation, calibration sequences were run
on the engine dynamometer controller from which data for engine speed and torque as
well as fuel flow rate was read. The dynamometer and fuel flow meters are kept in
calibration per a set schedule at The Lubrizol Corporation. The exhaust emission
sampling equipment used runs a self calibration at startup. In addition to controlling load
on the engine and reading or recording data of interest, the dynamometer controller
constantly monitors a probe placed in the intake air stream that analyzes the intake air
and makes corrections to data relative to standard temperature and pressure.
Typically, if the experiment is well controlled, the most significant amount of
error is introduced via data reduction. Using the collected data to compute other
parameters of interest can present additional uncertainty as a result of the measurement
device’s resolution. The following method of relative uncertainty computation was used
to calculate error for the computed values of brake specific fuel consumption, fuel
efficiency, NOx mass flow rate, brake specific NOx emission, and NOx emission index:
65
∑
=
2
1ix
i
y Udx
dy
ye (4.38)
where y represents an equation governing the computation of a parameter of interest, ix
is a variable with uncertainty, and ix
U is half the precision of the measurement device.
Specifics of the error analysis for each computed value are detailed in Appendix B, the
results are include as follows in Table 4.4.
Table 4.4: Relative uncertainty computation for computed parameters
Eng. Speed Eng Load Uncertainty (percent)
RPM lb*ft bsfc efficiency xNOm& * bsNOx**
xNOEI **
1250 60 3.64 3.64 6.17 27.13 26.92
1250 108 2.04 2.04 6.15 13.80 13.67
1525 150 1.19 1.19 6.08 8.80 8.73
1800 83 1.82 1.82 6.22 15.05 14.96
1938 79 1.79 1.79 6.23 16.54 16.45
2075 127 1.05 1.05 6.18 5.84 5.76
2213 127 0.98 0.98 6.17 5.36 5.29
2625 60 1.72 1.72 6.25 18.09 18.02
2625 150 0.69 0.69 6.14 4.13 4.08
3175 122 0.71 0.71 6.16 4.21 4.16
3313 84 0.98 0.98 6.21 5.69 5.62
3313 89 0.93 0.93 6.20 5.63 5.56
3863 136 0.52 0.52 6.06 4.13 4.10
4000 60 1.12 1.12 6.21 7.00 6.92
4000 103 0.66 0.66 6.14 4.98 4.95
* estimated, see Appendix B for details
** determined from a function of NOx mass flow rate
Examination of the uncertainty analysis shows minimal error introduction from
computation of bsfc and fuel efficiency and acceptable levels in xNOm& . Higher relative
uncertainties displayed for bsNOx and xNOEI are a result of the NOx mass flow rate,
relatively small in magnitude, only being able to be computed accurately to one
significant digit. Calculated bsNOx and xNOEI were only used for baseline engine
66
mapping and not used in further computation or as selection criteria for vehicle drive
cycle modeling like the other parameters. Note in Table 4.4 the general trend of
decreasing error as engine speed and load increase making the measurement resolution
less significant.
4.3 Regression Model Development
Mentioned in the experiment design portion of this thesis (Chapter 3), optimum
data observation points were chosen based on a third-order regression model having
third-order interaction. Thus, the implemented regression method will be the same. A
model as such, having desired output η , is of the following form having control variables
1X and 2X with regression coefficients iβ .
εββββ
ββββββη
+++++
+++++=2
211222
2
1112
3
2222
3
1111
2112
2
222
2
11122110
XXXXXX
XXXXXX (4.39)
In the particular cases of interest for this research, the output variable η is representative
of fuel use parameters or NOx emission output in any one of the forms presented
previously in this chapter. The control variables 1X and 2X can be termed engine speed
and brake torque output for engine performance mapping and computational modeling.
Because the regression is said to be a function that is an estimate of the true response, the
variable ε accounts for the error in the model.
The method of least squared regression was used to fit the third order model,
equation (4.39), to the data. This method acts to determine the iβ coefficients so that the
global error, ε , is minimized through minimization of the sum of the squares of residuals,
or the difference in the observed data and the response model to be determined. While
67
the method of least squared regression is quite simple, application to a non-linear model
with interaction can become quite complex and analysis via a statistical package is almost
essential. In this case, regression was implemented via the statistical evaluation suite on
MATLAB.
Regression models were determined for all of the fuel use and NOx output
parameters previously determined. As discussed, the regression models are of the
following third-order form where engine speed, N , has units of rpm and measured brake
torque, bT , has units of lb*ft.
2
122
2
112
3
222
3
111
12
2
22
2
11210
bbb
bbb
NTTNTN
NTTNTN
ββββ
ββββββη
++++
+++++= (4.40)
The summary of the collection of iβ coefficients from each of the regressions developed
from the collected data and their respective units is shown in table 4.5. The parameter η
is represented respectively as follows as the fuel mass flow rate ( fuelm& ), brake specific
fuel consumption (bsfc), fuel efficiency (eff), NOx emission mass flow rate (xNOm& ), brake
specific NOx emission ( xNObs ), and NOx emission index (xNOEI ). Equation (4.40) can
be used by direct application of the iβ coefficients. However, input of N and bT values
have been linearly mapped to a scale of [-1:1] over the domain; details displayed in table
4.5. In cases where minimization of the error has driven a particular iβ coefficient to
zero, its value has been omitted in table 4.5. Regression results were used in both engine
mapping and vehicle drive modeling, the results of which are presented in Chapter 5.
Validity of the regression models is also explored in Chapter 5.
68
Table 4.5: Regression results summary
η : fuelm& bsfc eff
xNOm& xNObs
xNOEI
Units: lbm/hr lbm/(hp*hr) % lbm/hr lbm/(hp*hr) lbm/lbm
0β 18.0573 0.33433 38.3700 0.9152 0.015370 0.04850
1β 11.0545 0.01997 -2.4402 0.5451 0.003321
2β 7.2055 -0.02579 2.6234 0.5095 0.003758 0.01427
11β 1.6193 0.01915 -1.7731 -0.1967 -0.00923
22β 1.1238 0.03527 -3.0548 -0.1301 -0.002924 -0.01077
12β 4.0680 -0.00970 0.9258 0.0504 -0.002469
111β -0.2142 -0.004149
222β
112β 0.8746 0.01410 -1.6231 -0.3643 -0.005931 -0.02065
122β 0.5631 0.01414 Domain mapping of independent variables for regression equation use:
2122
2112
3222
311112
222
211210 bbbbbb NTTNTNNTTNTN ββββββββββη +++++++++=
Engine Speed - N (rpm): [1250:4000] � [-1:1]
Engine Torque - bT (lb*ft): [60:155] � [-1:1]
4.4 Computational Drive Cycle Modeling with Regression Data
Application of the derived regression models over the appropriate analysis
domain and plotting of the results yield a good visual description of how the engine’s
output parameters of interest behave under different engine speed/load combinations.
While the brake specific values for fuel use and NOx out as well as engine efficiency and
NOx emission index provide good baseline models for the tested engine, the regression
models that simply describe mass fuel use and mass NOx out as functions of engine speed
and load will be of most use in this study.
The concepts of vehicle drive cycle simulation start with the basics of linear
vehicle dynamics. The goal is to, over a drive cycle (a schedule of varying vehicle
velocity over a time period), look at the vehicle’s velocity at a particular point in time and
69
using known vehicle parameters determine the engine speed/load operation so that the
previously discussed regression models can be applied. While a simple dynamic model of
a conventional vehicle can be had quite easily; one that is truly accurate, especially for a
complex hybrid powertrain, is beyond the scope of this study and discussion will be
omitted. Focus will be shifted to how to treat the engine speed/load operation data
obtained from simulation and model fuel consumption and NOx emission output.
Drive cycle analysis was run on a complex vehicle model developed at The
University of Akron for the ChallengeX Chevrolet Equinox. The model is based on the
Powertrain Systems Analysis Toolkit (PSAT) developed by Argonne National Labs to
interface with MATLAB computational software and simulate vehicle operation over a
drive cycle. The particular drive cycle simulated for this study is termed the USEPA
urban dynamometer drive schedule (UDDS), a test that consists of the schedule for
certification of vehicle fuel economy in city driving and also certification of emissions.
The drive cycle is for light vehicle evaluation only and covers a total distance of 7.5
miles in 22.5 minutes with a series of starts and stops. Note that the drive schedule
presented is intended to be run on a chassis dynamometer that does not take into the
aerodynamics of the vehicle while the simulation does. Therefore, values obtained for
fuel economy simulation should not be used in comparison to fuel economy values seen
on new vehicle window stickers even though they were determined from running the
same drive cycle. Vehicle velocity versus time is presented for the UDDS drive cycle in
the following figure 4.1.
70
0 200 400 600 800 1000 1200 14000
20
40
60
Time - seconds
Vehicle Velocity - m
iles/hour
Figure 4.1: USEPA UDDS drive cycle
The UDDS cycle was run in the vehicle model using a time interval 0.1 seconds
for three separate scenarios: one with the hybrid control strategy aimed at maximum fuel
economy and the other two sacrificing some fuel economy and targeting lowered levels
of NOx emission. The input parameters for tuning of the control strategy were
determinant on the engine mapping analysis presented previously in this chapter for
which the results will be presented in Chapter 5. Therefore, details of parameter selection
for the two cases will be saved for the results chapter to follow.
Even though the drive schedule shows vast fluctuations in vehicle velocity
consisting of many accelerations and decelerations, recall that the implemented hybrid
powertrain has the advantage of not running the engine when the energy storage state of
charge is high and the generator providing constant torque take-off from the engine while
operating, thus most of the engine operation is steady-state. Furthermore, The University
of Akron vehicle is capable of running the entire UDDS cycle in series mode; thus the
collected data and regression models for steady-state engine evaluation are assumed
accurate.
71
While the computer simulation of the vehicle drive cycle is capable of outputting
a multitude of parameters, the ones of interest are the engine speed and torque load at
each time interval. From this data, the values can be applied directly to the regression
equations obtained previously for fuel mass flow rate and NOx mass flow rate as
functions of engine speed (rpm) and load (lb*ft) to determine instantaneous fuel and NOx
flow rates at a given simulation data point. Let these parameters determined from
regression models be referred to as i
bfuel TNm
33 ,& and
ibNO TNm
x
33 ,& respectively for a
given time step i . Once these values have been determined for each time step in the drive
cycle, fuel economy and NOx output can be calculated as an average over the drive cycle.
In order to determine average fuel economy, total fuel use must first be computed
for the drive cycle and then compared to the total distance traveled. Fuel use over the
drive cycle is calculated as the sum of the products of instantaneous mass fuel flow rate
and the taken time step, tδ , for n total time steps in the drive cycle.
∑∑==
=
=
n
i ibfuel
n
i
ii
bfuel TNmttTNmusefuel1
33
1
33 ,, && δδ (4.41)
The average combined fuel economy over the drive cycle is obtained by division of the
total distance traveled, cycled , by the fuel use as follows. Multiplication by the fuel’s
density, fuelρ , allows fuel economy to be expressed in terms of distance traveled per
volume fuel consumed (mpg).
fuel
ibfuel
cycle
fuel
cycle
TNmt
d
usefuel
dFE ρ
δρ
∑
==33 ,&
(4.42)
72
Like fuel use, the total mass of NOx emission can be calculated over the drive
cycle. A sum of the product of instantaneous mass NOx emission rate and the taken time
step, tδ , for n total time steps in the drive cycle provides this value.
∑∑==
=
=
n
i ibNO
n
i
ii
bNOx TNmttTNmemissionNOxx
1
33
1
33 ,, && δδ (4.43)
Average NOx emission is determined by division of the total NOx emission over the drive
cycle total distance, cycled .
cycle
n
i ibNO
cycle
x
NOd
TNmt
d
emissionNOm
x
x
∑=
== 1
33 ,&
&
δ (4.44)
The computations for drive cycle analysis of fuel economy and NOx emission
were run over the USEPA UDDS drive cycle for three separate scenarios of vehicle
control strategy tuning to examine the impact of running the engine for reduced NOx at
the cost of increased fuel usage. The results of which are detailed in Chapter 5.
73
CHAPTER V
RESULTS AND DISCUSSION
This chapter will present and interpret the results of the study. First, the outcomes
of the developed regression models for fuel use and NOx emission response will be
displayed for purposes of baseline engine mapping over the relevant domain. Some of the
response mapping was used in determination of the target engine operation for the hybrid
control strategy and thus the logic for those criteria will be presented. The results from
drive cycle simulation will be shown in a form that allows comparison between the
tradeoff in fuel economy and lowered NOx emission and determine what level of NOx
reduction will be needed to meet USEPA standards. Lastly, validity of the regression
models will be explored.
5.1 Baseline Engine Mapping
The results from the regression equations developed in Chapter 4 for several
calculated fuel use and NOx emission parameters as functions of engine speed and load
are plotted over the relevant domain of 1250-4000 rpm and 60-155 lb*ft respectively. All
of the baseline engine maps to follow are presented in the same manner: contour plots of
the response variables over the relevant domain with superimposed curves for peak
engine torque and thresholds for both continuous and maximum power generation that
the generator is capable of. The power generation thresholds are displayed as torque
74
curves that the generator is capable of operating to with respect to engine operation; i.e.,
the torque values are not the actual generator’s generating torque, but rather the torque
placed on the engine’s crankshaft through the 2.85:1 coupling gear ratio.
5.1.1 Fuel Use Mapping
Fuel consumption is displayed in three different forms: plotting of the raw data
for fuel mass flow rate, brake-specific fuel consumption (bsfc) allowing comparison of
fuel use to power output, and engine fuel efficiency.
6.52914
7.88761
9.24607
10.6045
10.6045
11.963
11.963
13.3215
13.3215
14.6799
14.6799
16.0384
16.0384
17.3969
17.3969
18.7553
18.7553
20.1138
20.1138
21.4723
21.4723
22.8307
22.8307
24.1892
24.1892
25.5477
25.5477
26.9061
26.9061
28.2646
28.2646
29.623
29.623
30.9815
32.34
33.698435.0569
36.4154
37.7738
39.1323
Engine Speed - RPM
Engine Torque - lb
*ft
1500 2000 2500 3000 3500 400060
70
80
90
100
110
120
130
140
150
fuel use - lbm/hr Peak Eng. Trq. Cont. Pw r. Gen. Max. Pw r. Gen.
Figure 5.1: Engine mapping of fuel mass flow rate, units in lbm/hr
75
0.33025
0.33025
0.330
25
0.33432
0.334
32
0.33432
0.33432
0.33839
0.33839
0.33839
0.33839
0.34246
0.342
46
0.34246
0.342
46
0.34653
0.34653
0.34653
0.3506
0.3506
0.3506
0.35467
0.35467
0.35467
0.3587
4
0.35874
0.35874
0.3628
1
0.36281
0.36281
0.36688
0.366
88
0.36
688
0.37095
0.370
95
0.37095
0.3750
2
0.37502
0.37502
0.3791
0.3791
0.3791
0.383
17
0.38317
0.38317
0.387
24
0.38724
0.391
31
0.39538 0.39
945
0.403520.40
759
0.41166
0.41573
0.4198
0.42387
Engine Speed - RPM
Engine Torque - lb*ft
1500 2000 2500 3000 3500 400060
70
80
90
100
110
120
130
140
150
bsfc - lbm/(hp*hr) Peak Eng. Trq. Cont. Pw r. Gen. Max. Pw r. Gen.
Figure 5.2: Engine mapping of brake specific fuel consumption, units in lbm/(hp*hr)
30.4537
30.879631.3
05531.731532.1
574
32.58
3433.00
9333.43
52
33.4352
33.86
12
33.8612
34.2871
34.2871
34.2871
34.713
34.713
34.713
35.139
35.139
35.139
35.5649
35.564
9
35.5649
35.9908
35.99
08
35.9908
36.4168
36.41
68
36.4168
36.8427
36.842
7
36.8427
37.2686
37.268
6
37.2686
37.26
86
37.6946
37.69
46
37.6946
37.69
46
38.1205
38.12
05
38.120538.12
05
38.5464
38.5464
38.546
4
38.9724
38.9724
38.9724
Engine Speed - RPM
Engine Torque - lb*ft
1500 2000 2500 3000 3500 400060
70
80
90
100
110
120
130
140
150
fuel eff iciency - % Peak Eng. Trq. Cont. Pw r. Gen. Max. Pw r. Gen.
Figure 5.3: Engine mapping of fuel efficiency, units in percent
76
The response plot presented in figure 5.1 is not of particular interest for mapping
purposes, only telling how much fuel is used and nothing about the engine’s potential to
do work. It does however provide a good description of how the fuel mass flow rate
response function is distributed over the domain; a parameter that will prove extremely
useful in drive cycle modeling.
Plots presented in figures 5.2 & 5.3 are of most use in terms of engine fuel use
mapping, allowing examination of the quantity of fuel used in comparison to the engine’s
output. Recall from the computation of bsfc and fuel efficiency that they are essentially
reciprocals of each other and thus the plots should, in theory, be identical. However, since
the two parameters were calculated from the raw data before performing the regression,
their appearance does vary slightly.
In provision of targeting high fuel economy, it is obvious that the hybrid control
strategy should aim to force the engine to operate in a region of low brake-specific fuel
consumption and in turn high efficiency. For the most part, generator operation is limited
to the continuous power threshold and can only withstand a short duration of operation
beyond; it is assumed that operation of the generator is not able to be focused outside the
continuous generation region. Examining figures 5.2 & 5.3, it may seem that the best
place to operate in terms of fuel economy is below 1500 rpm along the horizontal portion
of the continuous power generation threshold as efficiency is highest there. However,
recognize that in this horizontal region, the generator is current (torque) limited and not
capable of its maximum possible continuous power generation; thus increasing the time
that the engine must run to fully charge the energy storage system in series mode before it
is shut down. To minimize the time that the engine must be running in series mode, the
77
generator can be aimed at operation at its maximum power generation. The machine is
power limited beyond a corresponding engine speed of approximately 1900 rpm, the
region in which the continuous power generation threshold goes from horizontal-linear to
non-linear. Operating anywhere along the continuous power generation threshold above
1900 rpm will ensure that engine operation is kept to a minimum. Inspection of the plots
leads to the assumption that target operation at the point where the generator goes from
torque to power limited along the continuous generation threshold should provide good
fuel economy.
5.1.2 Nitrogen Oxide Emission Mapping
Volumetric exhaust content of nitrogen oxide emission is said to be a strong
function of engine load or brake mean effective pressure (directly calculated from engine
load) [16]. The recorded data from experimentation for volumetric NOx content of dry
exhaust is plotted vs. brake mean effective pressure in figure 5.4.
volumetric NOx = 8.4353*bmep - 117.77
0
200
400
600
800
1000
1200
1400
1600
1800
0 50 100 150 200 250
Brake mean effective pressure - PSI
Volumetric NOx content of dry exhaust - ppmv Measured Data
Linear (Measured
Figure 5.4: Volumetric NOx emission content as a function of bmep
78
The plot and linear regression model displayed in figure 5.4 can prove useful for
modeling and simulating NOx emission. For a desired engine load, bmep can be
calculated and volumetric NOx emission estimated from the linear regression equation. If
means for temperature and exhaust flow rate prediction at a common point are also
available, mass NOx emission can easily be predicted by application of the ideal gas law;
thus avoiding a complex, non-linear regression model. If the means for temperature and
flow rate prediction are not available, the complex and iterative equations requiring all of
the exhaust data and fuel/air intake parameters presented in Chapter 4 must be used to
calculate mass NOx out; a situation in which an interactive, multi-order equation of the
form used to create the following figures 5.5 & 5.6 is preferred. Subsequent plots show
response contour maps for calculated NOx mass flow rate and brake specific NOx
emission.
0.15573
0.22649
0.29725
0.36801
0.36801
0.43877
0.43877
0.43877
0.50953
0.50953
0.50953
0.58029
0.58029
0.58029
0.65104
0.65104
0.65104
0.7218
0.7218
0.7218
0.79256
0.79256
0.79256
0.86332
0.86332
0.86332
0.93408
0.93408
1.0048
1.0048
1.0756
1.075
6
1.1464
1.1464
1.1464
1.2171
1.2171 1.2879
1.2879
1.3586
Engine Speed - RPM
Engine Torque - lb
*ft
1500 2000 2500 3000 3500 400060
70
80
90
100
110
120
130
140
150
mass NOx out - lb
m/hr Peak Eng. Trq. Cont. Pw r. Gen. Max. Pw r. Gen.
Figure 5.5: Engine mapping of NOx mass flow rate, units in lbm/hr
79
0.00800960.0085257
0.0090418
0.009558
0.010074
0.01059
0.010590.01059
0.011106
0.011106
0.011
106
0.011623
0.011623
0.011623
0.012139
0.012139
0.012139
0.012655
0.0126550.0126550.013
171
0.013171
0.013171
0.013687
0.013687
0.013687
0.014203
0.014203
0.014203
0.014203
0.014719
0.014719
0.014719
0.014719
0.014719
0.015236
0.015236
0.015236
0.015236
0.015236
0.015752
0.015752
0.015752
0.015752
0.016268
0.0162680.016268
Engine Speed - RPM
Engine Torque - lb*ft
1500 2000 2500 3000 3500 400060
70
80
90
100
110
120
130
140
150
bsNOx - lb
m/(hp*hr) Peak Eng. Trq. Cont. Pw r. Gen. Max. Pw r. Gen.
Figure 5.6: Engine mapping of brake specific NOx emission, units in lbm/(hp*hr)
In addition to the ability to predict mass nitrogen oxide emission for drive cycle
simulation, figure 5.5 also proves useful in determination of targeted engine operation for
reduced NOx. While looking at simply the mass flow rate of fuel was not of much help in
targeting such values for fuel use, mass flow rate of NOx alone is of course a good
predictor of its emission. Examination of figure 5.5 yields that mass NOx emission rate
definitely increases with increased engine speed and load. Specifically in the realm of the
generator’s continuous power generation region, the trend seems to be dominated by an
engine load dependency; thus targeting engine operation for series mode at a lower load
than was discussed for prime fuel economy in the previous section should yield reduced
NOx emission. Figure 5.5 also suggests that, within the continuous power generation
region, NOx reduction can be had through decreased engine speed. However, note that
gains will be small and the generator will be forced to operate with even less power
80
generation, increasing the time that the engine runs and in turn providing more time for
NOx emission.
Evaluation of figure 5.6 for brake specific NOx emission simply validates the
assumption that regions of operation that yield low fuel consumption also yield high NOx
generation. Because figure 5.6 is a brake-specific evaluation, standardized comparison to
figure 5.2 for brake-specific fuel consumption is possible.
5.1.3 Fuel Consumption and NOx Emission Comparison Mapping
Termed the emission index (EI), the ratio of mass emission of a harmful exhaust
component in comparison to mass fuel used provides an accurate description of how an
engine’s emission characteristics compare to its fuel use over the operation domain.
While it is not of much use for the drive cycle analysis to come, EI is a useful baseline
evaluation that allows a quick comparison of several engines. Plotted in the following
figure 5.7 is the NOx emission index for the tested engine over the relevant domain.
0.0240660.026009
0.0279520.027952
0.029896
0.0298960.031839
0.03183
9
0.0337820
.033782
0.033782
0.033782
0.035725
0.035725
0.035
725
0.035725
0.035725
0.037668
0.037668
0.0376
68
0.037668
0.037668
0.0396120.039612
0.039612
0.039612 0
.041555
0.0415550.041555
0.041555
0.043498
0.043498
0.043498
0.043498
0.045441
0.045441
0.045441
0.047384
0.047384
0.047384
0.049328
0.049328
0.051271
0.051271
Engine Speed - RPM
Engine Torque - lb
*ft
1500 2000 2500 3000 3500 400060
70
80
90
100
110
120
130
140
150
EI-NOx - lb
m/lb
m Peak Eng. Trq. Cont. Pw r. Gen. Max. Pw r. Gen.
Figure 5.7: Engine mapping of NOx emission index, units in lbm/lbm
81
5.2 Determination of Target Series Mode Engine Operation for Simulation
As discussed in the previous sections of this chapter, series mode engine
operation can be targeted at an engine operation point within the realm of the generator’s
continuous power generation capability. Being able to predict average fuel economy and
NOx emission, drive cycle analysis allows comparison to be made in the tradeoff of fuel
efficiency vs. engine-out emissions for variant engine operating scenarios. From the
engine out NOx results, it is also possible to determine the amount of emission reduction
necessary via aftertreatment to meet USEPA standards.
It was mentioned that operation along the generator’s continuous power
generation threshold at the transition from torque to power limited will provide engine
operation be kept to a minimum and produce good fuel efficiency while it is in operation.
This scenario occurs at 1900 rpm engine speed and 78 lb*ft engine torque and is one of
the points that were targeted for series mode operation in drive cycle simulation. To
examine the effects of a lowered target engine torque aimed at NOx reduction at the cost
of some fuel economy, another speed/load combination of 1900 rpm and 65 lb*ft torque
was chosen. The 65 lb*ft criterion was selected in that it is the lowest engine load at 1900
rpm capable of keeping exhaust temperature high enough for passive particulate filter
regeneration; determined from a preliminary study of engine exhaust temperature on the
test engine at The University of Akron. Note that reduction of torque not only causes a
small drop in engine efficiency, but also does not allow the generator to operate at its full
power generation capability. At 65 lb*ft engine load, maximum power generation can
had at 2250 rpm. While levels of NOx formation at this point should still produce a
lowered value and the engine will run as little as possible, fuel efficiency is again slightly
82
compromised. A drive cycle evaluation for targeted series mode operation at the 2250
rpm, 65 lb*ft scenario was run as well to examine whether this operation point’s
provision to reduce engine run time could outweigh the lower fuel efficiency seen there;
and also examine its NOx emission characteristics in comparison.
5.3 Drive Cycle Simulation Results
Drive cycle simulation was run using the PSAT model for The University of
Akron’s ChallengeX hybrid Chevrolet Equinox over the UDDS drive cycle targeting
series mode operation at the points discussed in the previous section 5.2. Initial condition
for the vehicle’s energy storage system was assumed worst-case scenario, having its
lowest possible state-of-charge. The simulation results for engine speed and torque
demand were applied to the regression models developed for mass fuel use and mass NOx
emission. Average values were computed for fuel economy and engine-out NOx over the
drive cycle as summarized in the following table 5.1. An evaluation is presented in terms
of fuel economy sacrifice as well as NOx emission reduction for the two scenarios aimed
at lowered NOx compared to the scenario aimed at high fuel economy. Complete results
of the simulation over the drive cycle time duration can be found in Appendix C.
Table 5.1: UDDS drive cycle simulation results
Target Series Mode Avg. Fuel Avg. NOx Fuel Economy NOx
Engine Operation Economy Emission Reduction Reduction
Speed, RPM Load, lb*ft mi/gal lbm/mi g/mi % %
1900 78 29.937 0.0082934 3.762 - -
1900 65 28.48 0.0062777 2.848 4.87 24.30
2250 65 24.592 0.0076974 3.491 17.85 7.19
The levels of NOx reduction via aftertreatment required to meet USEPA Tier 2
requirements per bin are given in table 5.2.
83
Table 5.2: Required NOx reduction via aftertreatment to meet USEPA standards Target Series Mode Required NOx Emission Reduction to Meet
Engine Operation USEPA Tier 2 Requirement per Bin - %
Speed, RPM Load, lb*ft 8 7 6 5 4 3 2 1
1900 78 94.7 96.0 97.3 98.1 98.9 99.2 99.5 100
1900 65 93.0 94.7 96.5 97.5 98.6 98.9 99.3 100
2250 65 94.3 95.7 97.1 98.0 98.9 99.1 99.4 100
USPEA allowed NOx
emission per bin - g/mi 0.20 0.15 0.10 0.07 0.04 0.03 0.02 0.00
The results presented in table 5.1 show that quite desirable results can be achieved
in engine-out NOx reduction by varying the targeted series mode engine operation of the
hybrid vehicle. In comparison to the scenario aimed at high fuel economy (1900 rpm, 78
lb*ft), targeting a lower torque value of 65 lb*ft at the same engine speed shows an
almost 25% reduction in NOx generation at a cost of just under 5% fuel economy.
However, note in examining table 5.2 that even at 25% NOx reduction, quite significant
levels of aftertreatment efficiency are needed to meet even bin 8 requirements.
In regard to evaluation of the series targeted 2250 rpm, 65 lb*ft intended to
examine the effects of running the engine less at a lower efficiency, results are less than
desirable. A large penalty in fuel economy is observed at a low level of NOx reduction.
5.4 Validity of Regression Models
The intent of the experiment design presented in Chapter 3 was to produce an
experimental observation strategy such that after data collection and regression
implementation, the developed model would induce an optimally minimum amount of
error. This was done so by defining data collection points within the domain based on the
criteria of minimizing the predicted residual sum of squares. Now that the data has been
collected and models built, legitimacy of the developed regression models used for
84
analysis are validated simply by examination of the residuals. A good visual method for
evaluating the residuals for a given model is to plot predicted values from the model vs.
the observed data at control points. The following figure 5.8 shows this evaluation for the
models used in this analysis; error bars show the 95% confidence interval.
0 5 10 15 20 25 30 35 400
5
10
15
20
25
30
35
40
Observed
Predicted
(a) fuel mass flow rate - lbm/hr
28 30 32 34 36 38 40 4226
28
30
32
34
36
38
40
42
Observed
Predicted
(c) fuel efficiency - %
0.008 0.01 0.012 0.014 0.016 0.018 0.020.005
0.01
0.015
0.02
Observed
Predicted
(e) bsNOx - lbm/(hp*hr)
0.32 0.34 0.36 0.38 0.4 0.42 0.44 0.460.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
Observed
Predicted
(b) bsfc - lbm/(hp*hr)
0 0.2 0.4 0.6 0.8 1 1.2 1.40
0.2
0.4
0.6
0.8
1
1.2
1.4
Observed
Predicted
(d) NOx mass flow rate - lbm/hr
0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.0550.01
0.02
0.03
0.04
0.05
0.06
0.07
Observed
Predicted
(f) NOx emission index – lbm/lbm
Figure 5.8: Error evaluation of response models
85
The presented error evaluation plots illustrate the accuracy of the models for fuel
mass flow rate, bsfc, fuel efficincy, and NOx mass flow rate to be quite good predictors of
the actual response. Evaluations of bsNOx and the NOx emission index are somewhat
questionable. However, realize that evaluation of bsNOx was simply to provide
standardized evaluation of NOx emission relative to fuel use by comparison to bsfc. Also,
the NOx emission index was produced only for baseline engine mapping. Most
importantly, the regression models that were used in drive cycle simulation, flow rates for
fuel and NOx, appear to be excellent predictors.
86
CHAPTER VI
CONCLUSIONS AND RECOMMENDATIONS
6.1 Research Conclusions
It has been shown that while a diesel engine can be an excellent choice for a
propulsion source in a hybrid vehicle in terms of fuel economy, the resulting high levels
of NOx emission as a result of running the engine at high load to drive up efficiency are a
definite problem to be dealt with. This study has shown that aiming the control strategy to
operate a hybrid vehicle’s generator at a lighter torque load can have significant impact
on the thermal generation of NOx at a small sacrifice in fuel economy. However, even
with the observed 25% reduction of engine-out NOx, comparison to USEPA standards
shows a large NOx conversion efficiency rate must still be had via aftertreatment methods
to meet even tier 2, bin 8 standards, let alone the target fleet average tier 2, bin 5; 93%
and 97.5% respectively.
While high levels of NOx conversion have been achieved by the use of selective
catalyst reduction, 93% to meet bin 8 status will be a definitive challenge in itself. The
simple solution to NOx reduction is to further lessen the load the generator places on the
engine; however in addition to a higher penalty in fuel economy, the uniqueness of The
University of Akron’s design is compromised in that such a high level of power
generation is possible through the use of rapid-charging ultracapacitors. Furthermore,
running at a load less than the lowest evaluated in this study would result in exhaust
87
temperatures insignificant in terms of passive particulate filter regeneration. Some
thoughts on how these issues might be addressed and recommendations for future
research are presented in the next section.
6.2 Recommendations for Future Work
Now that baseline engine mapping has been performed along with a study of how
variant targeted engine operation strategies affect NOx emission without sacrifice of DPF
regeneration, some suggestions for future research in this area can be made. The target
NOx reduction levels via aftertreatment have been established and the next step should be
to evaluate the effectiveness of a well-tuned aftertreatment system. The current
ChallengeX vehicle at The University of Akron uses open-loop control SCR for NOx
control with two exhaust temperature sensors as well as engine speed input. It is doubtful
that the current system will be able to achieve the high levels of NOx conversion needed
and thus moving to a closed-loop control can be very beneficial. If an ammonia sensor
can be obtained for the system, feedback will allow precise control a sufficient amount of
urea injection while avoiding ammonia slip. Ammonia sensors are currently
manufactured but are still in the prototype phase, models are expected to be available for
consumers by the year 2010.
Further NOx reduction via aftertreatment can also be had by addition of a lean
NOx trap downstream of the SCR system, capable of storing a percentage of the NOx left
untreated. Note that a lean trap’s storage capacity is limited and would have to be
periodically regenerated by injection of diesel fuel into the exhaust upstream of the trap,
thus sacrificing some fuel economy. However, since the lean trap would be located
88
downstream of the SCR, the NOx collection rate would be slower than conventional
application in turn maximizing the time needed before regeneration.
Additional study can also be conducted in the area of engine-out NOx reduction.
Previously mentioned, further reduction of load placed on the engine via the generator
will result in unsatisfactory passive DPF regeneration. However an active regeneration
strategy can easily be had. If the hybrid control strategy were to allow the generator to
cycle operation between low and high load in series mode, a level of reduced NOx could
be had intermittently while still allowing periodic DPF regeneration. Another option to
reduce engine-out NOx is alteration of the engine’s injection timing. Retarding the start of
injection results in lower combustion temperatures and thus lower thermal NOx
generation levels; although some of the engine’s power output potential is sacrificed.
Both lowered electrical power generation and modified injection timing do come with
fuel penalties. If it is shown that the required NOx conversion rate cannot be had via SCR,
evaluation of the results of using lean traps, targeted lower torque values for electrical
power generation, and modified timing should be weighed against one another to
determine an optimum design.
89
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6. Various Authors, DIESEL ENGINE MANAGEMENT, 3rd
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GmbH, 2004. Translation SAE Society of Automotive Engineers.
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90
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Antolini, P. G. Blakeman, M. Lauenius, B. Magnusson, A. P. Walker, and H. Y.
Chen, “The Application of a NOx Absorber Catalyst System on a Heavy-Duty
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K. Hughes, L. Simons, L. Berrimann, J. Zabsky, T. Gomulka, F. Rinaldi, M.
Tynan, J. Salem, and J. Joyner, “A NOx Reduction Solution of Retrofit
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Catalysis A: General 222 (2001) 221-236.
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Hurley, and R. H. Hammerli, “An Urea NOx Catalyst System for Light Duty
Diesel Vehicles”, SAE International 952493.
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Advantages of Urea SCR for Light-Duty and Heavy-Duty Diesel Applications”,
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CONTROL COMMERCIAL TECHNOLOGY, 2nd
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Inc., 2002.
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24. M. Block, N. Clark, S. Wayne, R. Nine, and W. Miller, “An Investigation into the
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92
APPENDICES
93
APPENDIX A
CALCULATION OF PREDICTION ERROR VARIANCE
The following details the computation for predicted error variance (PEV) over an
analysis domain for data that is yet to be collected. PEV is a useful means to evaluate the
predictive capability of a regression model. In this case, a third order regression model
having third order interaction is examined.
Start with the regression (or design) matrix, where iX ,1 and iX ,2 represent
designation of two independent variables for n total data points.
=
2,2,1,2
2,1
3,2
3,1,2,1
2,2
2,1,2,1
22,22,12,2
22,1
32,2
32,12,21,1
22,2
22,12,22,1
21,21,11,2
21,1
31,2
31,11,21,1
21,2
21,11,21,1
1
..............................
1
1
nnnnnnnnnnnn XXXXXXXXXXXX
XXXXXXXXXXXX
XXXXXXXXXXXX
X (A.1)
If the actual model was known, the β regression coefficients would be known
and the observations would be presented by the following matrix equation:
εβη += X (A.2)
where η is the actual observation data and ε is the measurement error having its
variance determined using the mean squared error (MSE). Note MSE is an estimator of
the expected value of the square of the error. Its computation requires knowing the actual
data, most texts on statistics should cover its calculation.
( ) ( ) MSEvar1−
= XX Tε (A.3)
94
Because the actual model can not be known, only predicted coefficients can be
determined. They can be computed using the observed experimental data.
( ) ηβ TT XXX1ˆ −
= (A.4)
Variance for the prediction model can be calculated in the same manner as the actual
model could if it were known.
( ) ( ) MSEˆvar1−
= XX Tβ (A.5)
Now consider a point in the domain defined by independent variables pX ,1 and
pX ,2 that the regression model can predict. Define the regression matrix for this point of
interest as follows.
= 2212
21
32
3121
22
21211 ,p,p,p,p,p,p,p,p,p,p,p,p XXXXXXXXXXXXx (A.6)
The prediction model for this point of interest is found in the following manner.
( ) ηβη TT XXXxx1ˆˆ−
== (A.7)
Calculation of variance at the predicted point from the regression model yields the
predicted error variance for that point in the domain.
( ) ( ) ( )( ) ( )( )MSEˆvarPEV11 TTTT xXXXXXXxx−−
== η (A.8)
The PEV calculation can be simplified as follows.
( ) ( ) MSEPEV1 TT xXXxx−
= (A.9)
Note in the above equation that the only dependence is on the variance of the
measurement error (MSE). Thus, computation of PEV without MSE will give a scaling
factor of how the error in the regression model at that point will be reduced or amplified.
A value of less that one will reduce error, while a value greater than one will amplify.
95
APPENDIX B
UNCERTAINTY ANALYSIS
Presented is the theory and equations used in relative uncertainty analysis for the
collected data beginning with the general form for the error analysis presented below;
description given in Chapter 4.
∑
=
2
1ix
i
y Udx
dy
ye (A.10)
Displayed in the following table A.1 are the equations used for computation by means of
collected data and the measurement resolution of the parameters of interest. Note the
equation for mass flow rate of NOx is excluded as its uncertainty analysis is more
complex and will addressed after the rest.
Table A.1: Summary of uncertainty parameters
Expressions of Interest Measurement Resolution
N 1 rpm
b
fuel
P
m&=bsfc
bT 1 lb-ft
bP 1 hp*
HHVefficiency
fuel
b
m
P
&=
fuelm& 0.01 lbm/hr
int,airm& 0.1 lbm/hr**
b
NO
P
mbs x
&
=NOx
xNOm& 0.1 lbm/hr
* Computed from N and Tb
fuel
NO
m
mx
&
&
=xNOEI
** Computed to 1 sig. fig.
96
Applying the equations and resolutions listed in table A.1, the following uncertainty
evaluations are obtained.
2
2
2
bsfc
5.005.
+
=
b
fuel
bfuel
b
P
m
Pm
Pe
&
& (A.11)
2
2
2
effHHV
005.
HHV
5.
+
=
fuel
b
fuelb
fuel
m
P
mP
me
&&
& (A.12)
2
2
2
NO
5.05.x
+
=
b
NO
bNO
b
bsP
m
Pm
Pe x
x
&
& (A.13)
2
2
2
_
005.05.
+
=
fuel
NO
fuelNO
fuel
NOEIm
m
mm
me x
x
x&
&
&&
& (A.14)
Evaluation of uncertainty for the rate of NOx mass flow is more sophisticated as
the governing equation contains variables that were obtained by iteration and thus
equation (A.10) cannot be directly applied; estimation was made in the following manner.
Reverting to equations (4.35a,b) and (4.36), an expression for the mass flow rate of NOx
can be written as follows.
+
++=
22
22 2
1
2
1774.4
774.3 int,
NONONONO
NONON
air
air
NO MMm
Mm
xαα
ααα& (A.15)
The value for 2N
α was a calculated value itself derived from iteration of multiple
equations for which relative uncertainty cannot be computed. The variable having the
least measured resolution used in the computation of 2N
α was accurate to one significant
97
digit, thus the calculated values for 2N
α are used in error analysis to a precision of 0.1.
To further simplify the computation, recognize that mole fractions NOα and 2NOα were
recorded to an accuracy of 10-6
. It is assumed that these values contribute negligible error
in this case; in turn, applying equation (A.10) yields the following.
22
int, 2
05.05.
+
=
N
NO
NOair
NO
NO
md
md
mmd
md
me x
x
x
x
NOx α
&
&&
&
&&
(A.16)
Evaluation and simplification leads to the following form which is able to be applied.
21
2
2
int,22 2
1
2
1
05.05.
+++
=
NONONair
mm
eNOx
ααα&&
(A.17)
98
APPENDIX C
DRIVE CYCLE SIMULATION RESULTS
0 200 400 600 800 1000 1200 14000
50
100Vehicle Speed - MPH
0 200 400 600 800 1000 1200 14000
10002000
Engine Speed - RPM
0 200 400 600 800 1000 1200 14000
100
200Enging Torque - lb*ft
0 200 400 600 800 1000 1200 14000
1020
Mass Fuel Use - lbm/hr
0 200 400 600 800 1000 1200 14000
0.51
1.5
Mass NOx Emission - lb
m/hr
Elapsed Time - seconds
Figure A.1: Simulation results targeting 1900 rpm, 78 lb*ft
99
0 200 400 600 800 1000 1200 14000
50
100Vehicle Speed - MPH
0 200 400 600 800 1000 1200 14000
10002000
Engine Speed - RPM
0 200 400 600 800 1000 1200 14000
100
200Enging Torque - lb*ft
0 200 400 600 800 1000 1200 14000
1020
Mass Fuel Use - lbm/hr
0 200 400 600 800 1000 1200 14000
0.51
1.5
Mass NOx Emission - lb
m/hr
Elapsed Time - seconds
Figure A.2: Simulation results targeting 1900 rpm, 65 lb*ft
100
0 200 400 600 800 1000 1200 14000
50
100Vehicle Speed - MPH
0 200 400 600 800 1000 1200 14000
10002000
Engine Speed - RPM
0 200 400 600 800 1000 1200 14000
100
200Enging Torque - lb*ft
0 200 400 600 800 1000 1200 14000
2040
Mass Fuel Use - lbm/hr
0 200 400 600 800 1000 1200 14000
0.51
1.5
Mass NOx Emission - lb
m/hr
Elapsed Time - seconds
Figure A.3: Simulation results targeting 2250 rpm, 65 lb*ft
101
APPENDIX D
DATA SUMMARY AND SAMPLE CALCULATION
Table A.2: Recorded experimental data
Engine Control Intake Air & Fuel Data Measured Dry Exhaust Emission Data
Speed Torque Air Flow Fuel Flow O2 CO NO NO2 NOx CxHy rpm lb*ft kg/hr lbm/hr kg/hr lbm/hr vol % ppm ppm ppm ppm ppm
1250 60 138.0 304.2 2.31 5.09 11.5 243 554 33 587 1105
1250 108 142.5 314.2 3.92 8.65 6.6 321 1101 36 1137 966
1525 150 179.3 395.4 6.74 14.86 4.7 346 1403 34 1437 746
1800 83 235.4 519.1 4.42 9.75 10.3 197 593 26 619 1026
1938 79 251.2 553.7 4.56 10.05 11.3 224 472 43 515 1422
2075 127 288.8 636.8 7.32 16.13 6.2 157 1282 44 1326 1095
2213 127 305.4 673.3 7.93 17.49 6.1 193 1308 55 1363 1028
2625 60 340.2 750.0 5.47 12.06 11.4 275 311 33 344 1853
2625 150 368.6 812.5 11.59 25.55 4.5 181 1392 71 1463 1256
3175 122 422.9 932.4 11.66 25.70 6.0 226 1174 69 1243 1305
3313 84 433.3 955.4 8.83 19.47 10.5 176 838 52 890 1597
3313 89 409.5 902.8 9.20 20.28 10.1 188 879 67 946 1370
3863 136 389.4 858.5 16.66 36.73 5.1 187 1296 88 1384 1086
4000 60 412.8 910.1 9.19 20.27 12.5 146 720 40 760 1250
4000 103 412.8 910.1 13.49 29.73 10.0 159 1015 59 1074 1159
102
Table A.3: Calculated values for fuel consumption
Eng. Speed Eng. Torque. Fuel Flow bsfc Fuel Efficiency
rpm lb*ft lbm/hr lbm/(hp*hr) %
1250 60 5.09 0.356 36.17
1250 108 8.65 0.338 38.14
1525 150 14.86 0.341 37.87
1800 83 9.75 0.342 37.76
1938 79 10.05 0.345 37.40
2075 127 16.13 0.323 39.95
2213 127 17.49 0.328 39.30
2625 60 12.06 0.402 32.06
2625 150 25.55 0.340 37.91
3175 122 25.70 0.349 36.94
3313 84 19.47 0.368 35.01
3313 89 20.28 0.363 35.49
3863 136 36.73 0.367 35.12
4000 60 20.27 0.444 29.07
4000 103 29.73 0.380 33.96
Table A.4: Calculated valued for NOx emission
Eng. Speed Eng.
Torque Alpha_N2 Mass Flow Rates (lbm/hr) bsNOx
rpm lb*ft vol. NO NO2 NOx lbm/(hp*hr)
1250 60 0.802 0.170 0.0156 0.186 0.0130
1250 108 0.812 0.349 0.0177 0.367 0.0143
1525 150 0.821 0.553 0.0206 0.574 0.0132
1800 83 0.804 0.314 0.0211 0.335 0.0117
1938 79 0.802 0.267 0.0373 0.304 0.0104
2075 127 0.809 0.826 0.0432 0.869 0.0174
2213 127 0.810 0.889 0.0576 0.947 0.0178
2625 60 0.800 0.239 0.0388 0.278 0.0093
2625 150 0.814 1.138 0.0886 1.227 0.0163
3175 122 0.811 1.105 0.0991 1.204 0.0163
3313 84 0.805 0.814 0.0773 0.891 0.0169
3313 89 0.806 0.805 0.0945 0.900 0.0161
3863 136 0.824 1.104 0.1152 1.220 0.0122
4000 60 0.805 0.666 0.0573 0.723 0.0158
4000 103 0.814 0.928 0.0833 1.011 0.0129
103
SAMPLE CALCULATION:
The following depicts a sample calculation of one observation points from the data
collected for this thesis. In some cases, proper unit conversion is required to achieve the
results displayed.
Engine Control Intake Air & Fuel Data Measured Dry Exhaust Emission Data
Speed Torque Air Flow Fuel Flow O2 CO NO NO2 NOx CxHy rpm lb*ft kg/hr lbm/hr kg/hr lbm/hr vol % ppm ppm ppm ppm ppm
1250 60 138.0 304.2 2.31 5.09 11.5 243 554 33 587 1105
The following parameters are observed:
N 1250 rpm
bT 60 lb*ft
airm& 304.2 lbm/hr
fuelm& 5.09 lbm/hr
2O
α 0.115
COα 0.000243
NOα 0.000554
2NOα 0.000033
yxHC
α 0.001105
Fuel use parameters are calculated as follows: (after proper unit conversion)
hrhp
lbm
rpmftlb
hrlbm
NT
m
b
fuel
*356.0
1250*60
/09.5bsfc ===
&
===lbmBTUhrlbm
rpmftlb
m
NT
fuel
b
/19733*/09.5
1250*60
HHVEfficiency Fuel
&36.17 %
104
To be able to calculate the mass flow rate of nitrogen oxide emissions, the dry gas
volumetric composition of nitrogen in the exhaust stream must first be determined. The
following three equations must be satisfied by iteration.
++
++++−=
22
222
222
2
1
2
12
1
2
1
2 ,,,
NONON
NONOCOCOO
intNintOexhOH nnn
ααα
ααααα
++−=
COCOHC
HC
intHCexhOH
xx
ny
n
yx
yx
yx
ααα
α
111
22
2 ,,
22221 NONOCOCOOHCN yx
ααααααα −−−−−−=
Substituting for the molar intake parameters yields the following for an analysis per mole
oxygen of the engine’s intake air:
++
++++−=
22
222
2
2
1
2
12
1
2
1
774.312,
NONON
NONOCOCOO
exhOHn
ααα
ααααα
++−=
COCOHC
HC
airair
fuelfuel
exhOH
yx
yx
Mm
Mmn
ααα
α
12
1
12
11774.4
2
26
2
2 ,&
&
2222
1 NONOCOCOOHCN yxααααααα −−−−−−=
Now the known data can be substituted. It will be shown for this particular case a value
of 802.02=Nα and 081.0
2=COα satisfies the iterative solution.
151967.0
000033.02
1000554.0
2
1802.0
000033.0000554.02
1000243.0
2
1081.0115.0
774.312,2=
++
++++−=exhOHn
151900.0
000243.012
1081.0
12
1001105.0
001105.01
97.28138
33.17031.2774.4
2
26,2
=
++−=exhOHn
802.0802065.0000033.0000554.0000243.0081.0115.0001105.012
≈=−−−−−−=Nα
105
Now that the volumetric concentration of N2 is known, the mass flow rates of nitrogen
oxides in the exhaust stream are calculated as follows:
hrlbmhrlbm
mM
Mm intair
NONON
NO
air
NOexhNO
/170.0/2.304
000033.02
1000554.0
2
1802.0
000554.0
07.28*774.4
01.30*774.3
2
1
2
1774.4
774.3,,
22
=
++=
++= &&
ααα
α
hrlbmhrlbm
mM
Mm intair
NONON
NO
air
NO
exhNO
/0156.0/2.304
000033.02
1000554.0
2
1802.0
000033.0
07.28*774.4
01.46*774.3
2
1
2
1774.4
774.3,,
22
22
=
++=
++= &&
ααα
α
hrlbmmmm NONONOx/186.00156.0170.0
2=+=+= &&&
The NOx emission index is calculated from the fuel and NOx mass flow rates:
0365.009.5
186.0EI
,
NOx===
fuel
exhNO
m
mx
&
&